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Hao, ZiQi, ZhenJiang Zhang, and Han-Chieh Chao. "A Cluster-Based Fuzzy Fusion Algorithm for Event Detection in Heterogeneous Wireless Sensor Networks." Journal of Sensors 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/641235.
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As limited energy is one of the tough challenges in wireless sensor networks (WSN), energy saving becomes important in increasing the lifecycle of the network. Data fusion enables combining information from several sources thus to provide a unified scenario, which can significantly save sensor energy and enhance sensing data accuracy. In this paper, we propose a cluster-based data fusion algorithm for event detection. We usek-means algorithm to form the nodes into clusters, which can significantly reduce the energy consumption of intracluster communication. Distances between cluster heads and event and energy of clusters are fuzzified, thus to use a fuzzy logic to select the clusters that will participate in data uploading and fusion. Fuzzy logic method is also used by cluster heads for local decision, and then the local decision results are sent to the base station. Decision-level fusion for final decision of event is performed by base station according to the uploaded local decisions and fusion support degree of clusters calculated by fuzzy logic method. The effectiveness of this algorithm is demonstrated by simulation results.
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Anita, Mahato* Kailash Patidar. "A REVIWE ARTICLE OF COMPARITIVELY ANALYSIS OF DEEC PROTOCOL." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 6 (2017): 113–17. https://doi.org/10.5281/zenodo.805385.
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Energy Management can be improved by proficient clustering algorithms in heterogeneous wireless sensor networks. Coordination through cluster head selection provides efficient data aggregation that reduces communication overhead in the network. In this paper, we propose a fuzzy logic approach based DDEEC clustering algorithm which aims to prolong the lifetime of nodes in heterogeneous WSNs. We compare this algorithm with the PSO based DDEEC algorithm and original DDEEC algorithm according to the parameters of first node dies at different rounds and energy-efficiency metrics. The efficiency of proposed optimized fuzzy algorithm is proved by the Matlab experimental results. Simulation results exhibits that the proposed algorithm has higher energy efficiency and can improve life span of a node and data delivery at the base station over its comparatives.
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Rizwan, Muhammad, Muhammad S. Nisar, and Hongbo Jiang. "F-MEEP: Fuzzy Logic Based Multihop Energy Efficient Routing Protocol for HWSN." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 14 (2016): 7406–15. http://dx.doi.org/10.24297/ijct.v15i14.4796.
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Energy preservation is one of the most important research challenges in Wireless Senor Networks (WSNs). In recent research, topologies and architectures have investigated that allow energy efficiency in WSNs. Clustering is one of the most famous energy efficient techniques. In clustering, the selection of cluster head (CH) and short distance multi-hop energy efficient communication between CH and base station (BS) plays a vital role in order to achieve the desired energy efficiency in the sensor network. In this energy saving solution, we purpose and combine the idea of fuzzy logic based CH selection and multihop short distance communication between CH and base station in order to prolong the stable period and life span of network. Our proposed routing protocol, Fuzzy Logic based Multihop Energy Efficient Routing Protocol (FMEEP) for Heterogeneous WSN, which uses fuzzy logic inference system (FIS) in order to select a qualified CH in the cluster formation process and minimizes the overall energy dissipation in the sensor network. The simulation results have shown that purposed routing scheme outperforms in terms of stability period and network lifetime as compared to previous routing protocols.Â
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Al-Zabin, Lial Raja, Ola A. Al-Wesabi, Hamed Al Hajri, Nibras Abdullah, Baidaa Hamza Khudayer, and Hala Al Lawati. "Probabilistic Detection of Indoor Events Using a Wireless Sensor Network-Based Mechanism." Sensors 23, no. 15 (2023): 6918. http://dx.doi.org/10.3390/s23156918.
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Wireless sensor networks (WSNs) have been commonly utilized in event detection and environmental observation applications. The main aim of event detection is to define the presence or absence of an event. Various existing studies in the field of event detection depend on static or threshold values to reveal the occurrence of an event, which can result in imprecise sensor readings. Recently, many studies have utilized fuzzy logic to treat fluctuating sensor readings; as a result, they have decreased the number of false alarms created. However, there is some attention required when utilizing fuzzy logic. One aspect is that the efficiency and accuracy of the fuzzy membership function can be impacted by the utilization of heterogeneous sensors, which may increase the complexity of the fuzzy logic operation as the number of inputs rises. To address these issues, this paper proposes an approach named Probabilistic Collaborative Event Detection (PCED), which is a hybrid event detection technique that is based on a cluster WSN topology. The PCED approach utilizes a validated probabilistic technique for heterogeneous sensor nodes to transform sensing values into probability formulas and introduces a Cluster Head Decision Mechanism to make decisions based on the aggregated data from the sensors. The proposed approach employs fuzzy logic at the fusion center level to enhance the precision of event detection. The effectiveness of this method is thoroughly evaluated using MATLAB software, demonstrating an improvement in the probability of detection and a decrease in the probability of false alarms. PCED is compared to well-established event detection mechanisms such as the REFD mechanism. The results show that PCED reduces the occurrence of false alarms from 37 to 3 in certain scenarios, while improving detection accuracy by up to 19.4% over REDF and decreasing detection latency by up to 17.5%.
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Avinashjethi, Surender Singh and Yadwinder Singh. "Improved Hybrid Energy Aware Clustered Protocol for IoT Heterogeneous Network for WSN using Fuzzy Logic." International Journal for Modern Trends in Science and Technology 7, no. 07 (2022): 49–53. http://dx.doi.org/10.46501/ijmtst051009.
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The wireless sensor networks consist of numerous small battery-powered nodes. These networks provide a support to IoT applications related to agriculture, healthcare etc. Increasing the lifetime of the sensor nodes is a major issue. To increase the lifespan of the nodes in WSN, in the proposed protocol, clustering protocol have been presented where the cluster heads are selected according to fitness value of the nodes. The performance was evaluated based on number of dead nodes and throughput of the network. The better values of these parameters indicate the proposed protocol outperformed the existing one.
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Sarath kumar, A., M. Durga Kaveri, K. B.V Bhargavi, N. Naga Swetha, and K. Priyanka. "Efficient Routing In Wsn Using Enhanced Fuzzy Logic." International Journal of Engineering & Technology 7, no. 2.17 (2018): 108. http://dx.doi.org/10.14419/ijet.v7i2.17.11719.
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In order to gather info additional precisely, wireless detector networks (WSNs) square measure divided into clusters. The cluster provides a good merit to make longer the period of WSNs. Topical clump comes close to usually use 2 methods: choosing cluster heads with additional enduring energy, and turning cluster heads sporadically, to distribute the energy consumption among nodes in every cluster and extend the network period. However, most of the previous algorithms haven't thought of the expected residual energy, that is, that the predicated left behind energy for being hand-picked as a cluster head and running around. During this paper, a fuzzy-logic-based clump approach [22] with associate degree conservatory to the energy postulation has been planned to lengthen the period of WSNs by equally distributing the work. The simulation results show that the planned come close to is additional more economical than alternative distributed algorithms. It's believed that the practice given during this paper can be any applied to extensive wireless detector networks.
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Safana, H. Abbas, and M. Khanjar Israa. "Fuzzy Logic Approach for Cluster-Head Election in Wireless Sensor Network." International Journal of Engineering Research and Advanced Technology (IJERAT) 5, no. 7 (2019): 14–25. https://doi.org/10.31695/IJERAT.2019.3460.
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<em>Wireless Sensor Network (WSN) consists of small battery-powered sensor nodes with limited energy resources, with sensing, computation, and communications capabilities. One of the crucial issues in WSN is energy consumption thus, poor energy efficient routing. The lifetime of the entire network depending on the energy efficiency of the sensor network which finally requires an energy- efficient routing protocol. This paper provides energy-efficient routing by using a fuzzy logic approach in the cluster-head selection process that provides a completely non-probabilistic approach in order to balance the energy consumption of nodes and prolong the WSN lifetime. This approach uses three fuzzy variables: Residual energy of the nodes, distance to Base station and distance to the cluster-head. Simulations results show that the proposed algorithm improves energy consumption and significantly prolong the network lifetime in wireless sensor networks when compared with LEACH protocol.</em>
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Julie, E. Golden, and S. Tamil Selvi. "Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach." Scientific World Journal 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/5063261.
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Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes.
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Saadi, Amnah A., and Osama A. Awad. "LIFETIME MAXIMIZATION OF A MOBILE WSN USING ZRP-FUZZY CLUSTERING PROTOCOL BASED ON ANT-LION OPTIMIZER." Iraqi Journal of Information and Communications Technology 1, no. 1 (2021): 70–82. http://dx.doi.org/10.31987/ijict.1.1.171.
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Wireless Sensor Networks require energy-efficient protocols for communication and data fusion to integrate data and extend the lifetime of the network. An efficient clustering algorithm for sensor nodes will optimize the energy efficiency of WSNs. However, the clustering process requires additional overhead, such as selection of cluster head, cluster creation, and deployment. This paper prepared a modified ZRP for mobile WSN clustering scheme and optimization using ant-lion optimization algorithm and so far named as mobility cluster head fuzzy logic based on the zone routing protocol (ZRP-FMC-ALO). Which proposed fuzzy logic approach based on three descriptors node for the selection of the CH nodes such as, residual energy, the concentration, and the centrality of the node and also exploited the concept of the mobility of the Base Station (BS) to prolong the life span of a WSN. The performance of the proposed protocol compared with the famous protocol such as LEACH. Using the MATLAB simulator and the result shows that it outperforms in terms of the WSN network lifetime, the average remaining-consuming energy, and the number of a live node.
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Samara, Ghassan, Mohammad Hassan, and Yahya Zayed. "An Intelligent Vice Cluster Head Election Protocol in WSN." International Journal of Advances in Soft Computing and its Applications 13, no. 3 (2021): 202–22. http://dx.doi.org/10.15849/ijasca.211128.14.
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Wireless sensor networks (WSNs) has a practical ability to link a set of sensors to build a wireless network that can be accessed remotely; this technology has become increasingly popular in recent years. Wi-Fi-enabled sensor networks (WSNs) are used to gather information from the environment in which the network operates. Many obstacles prevent wireless sensor networks from being used in a wide range of fields. This includes maintaining network stability and extending network life. In a wireless network, sensors are the most essential component. Sensors are powered by a battery that has a finite amount of power. The battery is prone to power loss, and the sensor is therefore rendered inoperative as a result. In addition, the growing number of sensor nodes off-site affects the network's stability. The transmission and reception of information between the sensors and the base consumes the most energy in the sensor. An Intelligent Vice Cluster Head Selection Protocol is proposed in this study (IVC LEACH). In order to achieve the best performance with the least amount of energy consumption, the proposed hierarchical protocol relies on a fuzzy logic algorithm using four parameters to calculate the value of each node in the network and divides them into three hierarchical levels based on their value. This improves network efficiency and reliability while extending network life by 50 percent more than the original Low Energy Adaptive Clustering Hierarchy protocol. Keywords: Wireless Sensor Networks, Sensors, Communication Protocol, Fuzzy logic, Leach protocol.
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Mehra, Pawan Singh, Mohammad Najmud Doja, and Bashir Alam. "Enhanced Clustering Algorithm based on Fuzzy Logic (E-CAFL) for WSN." Scalable Computing: Practice and Experience 20, no. 1 (2019): 41–54. http://dx.doi.org/10.12694/scpe.v20i1.1443.
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Since longer lifetime of the network is utmost requirement of WSN, cluster formation can serve this purpose efficiently. In clustering, a node takes charge of the cluster to coordinate and receive information from the member nodes and transfer it to sink. With imbalance of energy dissipation by the sensor node, it may lead to premature failure of the network. Therefore, a robust balanced clustering algorithm can solve this issue in which a worthy candidate will play the cluster head role. In this paper, an enhanced clustering algorithm based on fuzzy logic E-CAFL is propound which is an improvement over CAFL protocol. E-CAFL takes account of the residual energy, node density in its locality and distance from sink and feed into fuzzy inference system. A rank of each node is computed for candidature of cluster coordinator. Experiments are performed for the designed protocol to validate its performance in adverse scenarios along with LEACH and CAFL protocol. The results illustrate better performance in stability period and protracted lifetime.
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Sang-Hyeok, Lim, and Cho Tae-Ho. "WSN Lifetime Extension Using GA Optimised Fuzzy Logic." International Journal of Computer Science & Information Technology (IJCSIT) 9, no. 5 (2017): 1–14. https://doi.org/10.5121/ijcsit.2017.9501.
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A wireless sensor network (WSN) consists of multiple sensor nodes and base stations that collect information from widely deployed sensors. However, the disadvantage is that WSNs are randomly distributed in an open environment, which makes them difficult to manage individually and more easily found and compromised by an attacker. An attacker can execute a false report insertion or invalid vote insertion attack through a compromised node. The Probabilistic Voting Filtering System (PVFS) is a system that prevents these two types of attacks. Before sending a report, the proposed method probabilistically selects a validation node, determines the validity of the report, and filters the report based on the thresholds that have been set. In this paper, the proposed scheme improves the lifetime, detection rate, and report delivery rate of the entire network by increasing the lifetime of the cluster head (CH) by selecting the numbers of message authentication codes (MACs) and verification nodes of the report. Using this system, the event detection rate and the network lifetime are improved by up to 18% and 6%, respectively, relative to the existing PVFS.
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Kaur, Amneet, and Harpreet Kaur. "HYBRID APPROACH USING MOBILE SINK AND FUZZY LOGIC FOR REGION BASED CLUSTERING IN WSN." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 16, no. 5 (2017): 6933–44. http://dx.doi.org/10.24297/ijct.v16i5.6264.
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Wireless sensor network has revolutionized the way computing and software services are delivered to the clients on demand. Wireless sensor network is very important to the mankind. It consist of number of sensor called nodes and a base station. Nodes collect data and send to the base station. There are number of nodes which send data at a time. So, number of problems are occurred. So, far this nodes are divided into cluster then a cluster head will be formed. WSN is a battery powered system. When the battery is died no data send or received. So when all nodes participate for sending and receiving data then system is died earlier. Our research work proposed a new method for cluster head selection having less computational complexity. It was also found that the modified approach has improved performance to that of the other clustering approaches. The network area is divided into same sized small–small regions. Sensor nodes are randomly deployed in each predefined sub-area. Each region will have its region head (RH) and multiple member nodes. The member nodes in a specific region will send the data to the RH. RH within the region will be elected by distributed mechanism and will be based on fuzzy variables. It was found that the proposed algorithm gives a much improved network lifetime as compared to existing work. Based on our model, transmission tuning algorithm for cluster-based WSNs has been proposed to balance the load among cluster heads that fall in different regions. This algorithm is applied prior to a cluster algorithm to improve the performance of the clustering algorithm without affecting the performance of individual sensor nodes.
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Hamzah, Abdulmughni, Mohammad Shurman, Omar Al-Jarrah, and Eyad Taqieddin. "Energy-Efficient Fuzzy-Logic-Based Clustering Technique for Hierarchical Routing Protocols in Wireless Sensor Networks." Sensors 19, no. 3 (2019): 561. http://dx.doi.org/10.3390/s19030561.
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In wireless sensor networks, the energy source is limited to the capacity of the sensor node’s battery. Clustering in WSN can help with reducing energy consumption because transmission energy is related to the distance between sender and receiver. In this paper, we propose a fuzzy logic model for cluster head election. The proposed model uses five descriptors to determine the opportunity for each node to become a CH. These descriptors are: residual energy, location suitability, density, compacting, and distance from the base station. We use this fuzzy logic model in proposing the Fuzzy Logic-based Energy-Efficient Clustering for WSN based on minimum separation Distance enforcement between CHs (FL-EEC/D). Furthermore, we adopt the Gini index to measure the clustering algorithms’ energy efficiency in terms of their ability to balance the distribution of energy through WSN sensor nodes. We compare the proposed technique FL-EEC/D with a fuzzy logic-based CH election approach, a k-means based clustering technique, and LEACH. Simulation results show enhancements in energy efficiency in terms of network lifetime and energy consumption balancing between sensor nodes for different network sizes and topologies. Results show an average improvement in terms of first node dead and half nodes dead.
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D.Viswanathan. "Improved Butterfly and Fuzzy Logic with Falcon Optimization Algorithm based Routing Protocol in WSN." Journal of Information Systems Engineering and Management 10, no. 27s (2025): 159–63. https://doi.org/10.52783/jisem.v10i27s.4388.
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Introduction: Wireless sensor networks (WSNs) are widely used in real-time applications, but optimizing node energy use and network lifetime are challenging. This issue is solved through clustering and cluster head (CH) selection, and efficient routing protocols enhance performance and decrease energy use. Objectives: To propose a hybrid optimization approach for minimizing energy consumption and increasing network lifeline. For efficient data transmission, it optimizes the selection of Cluster Head (CH). Methods: An optimization-based clustering and path selection method for data transmission in WSNs. Cluster heads (CHs) are selected using based on energy, distance, node degree, and centrality. Fuzzy Logic and F-FOA optimize data routing by evaluating node costs and determining the best path using pheromone-based probabilistic selection. Results: The proposed model exhibits better throughput, lower latency, packet delivery ratio, significantly lower energy consumption and packet loss compared to existing methods (ASFO, GJO and ESO). The results show that it works well to improve WSN. Conclusions: This study enhances WSN lifetime by reducing energy consumption. CH selection using IBO and routing via FFOA improve efficiency, while IBFFOA minimizes energy use and processing time. Simulations show a 28% reduction in processing time, 26% lower energy consumption, and an 8% increase in clustering accuracy compared to existing models.
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Joshi, Kamini, and Sandeep Singh Kang. "Improved LEACH protocol using cache nodes for wireless sensor network." International Journal of Engineering & Technology 7, no. 2.27 (2018): 138. http://dx.doi.org/10.14419/ijet.v7i2.27.13690.
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The wireless sensor network is the decentralized type of network which can sense information and pass it to base station. The energy consumption is the major issue of WSN due to small of sensor nodes and far deployment of the network. The clustering is the efficient approach to increase lifetime of the sensor network. In the approach of clustering cluster head are selected for the data aggregation. The fuzzy logic rules are derived based on node energy, distance to base station for the cluster head selection, which increase lifetime of sensor nodes in the existing system. In this research work, cache nodes are deployed in the network which reduce energy consumption of WSN. In the proposed approach cluster head send data to cache nodes and it will forward data to base station. The simulation is performed in MATLAB and proposed technique performs well in terms of number of packets transmitted, number of dead nodes, network lifetime, throughput and remaining energy.
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Vijayalakshmi, G., M. Anto Bennet, P. Shenbagavalli, M. Vijayalakshmi, and S. Saranya. "CLUSTER HEAD ELECTION MECHANISM-BASED ON FUZZY LOGIC (CHEF) WITH TDMA IN WSN." International Journal on Smart Sensing and Intelligent Systems 10, no. 4 (2017): 395–413. http://dx.doi.org/10.21307/ijssis-2017-259.
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Nivedhitha, V., P. Thirumurugan, A. Gopi Saminathan, and V. Eswaramoorthy. "Combination of improved Harris’s hawk optimization with fuzzy to improve clustering in wireless sensor network." Journal of Intelligent & Fuzzy Systems 41, no. 6 (2021): 5969–84. http://dx.doi.org/10.3233/jifs-202098.
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A Wireless Sensor Network (WSN) is divided into groups of sensor nodes for efficient transmission of data from the point of measuring to sink. By performing clustering, the network remains energy-efficient and stable. An intelligent mechanism is needed to cluster the sensors and find an organizer node, the cluster head. The organizer node assembles data from its constituent nodes called member nodes, finds an optimal route to the sink of the network, and transfers the same. The nomination of cluster head is crucial since energy utilization is a major challenge of sensor nodes deployed over a hostile environment. In this paper, a fuzzy-based Improved Harris’s Hawk Optimization Algorithm (IHHO) is proposed to select an able cluster head for data communication. The fuzzy inference model ponders balance energy, distance from self to sink node, and vicinity of nodes from cluster head as input factors and decides if a candidate node is eligible for becoming a cluster head. The IHHO tunes the logic into an energy-efficient network with less complexity and more ease. The novelty of the paper lies in applying the hawk-pack technique based on fuzzy rules. Simulations show that the combination of Fuzzy based IHHO reduces the death of nodes through which network lifetime is enhanced.
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.., Ankita, and P. K. Mishra. "Fuzzy Logic Based Load Balanced Clustering for Network Lifetime Enhancement in WSN." International Journal of Wireless and Ad Hoc Communication 7, no. 1 (2023): 08–17. http://dx.doi.org/10.54216/ijwac.070101.
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Large number of small sensor nodes exists in WSN’s for sensing and collecting information from the environment. In today’s time, these sensor nodes were applied in under water, military area, health care, earthquake sensing and in dedicated areas with recent technologies. Sensor nodes have limited life time and have supplementary network life. Network lifecycle depends on many factors such as connectivity, residual energy, topology types, single hop, multi hop, distance from base station, distance to cluster heads and much more. Among the various solutions given, clustering is considered to be good solution and optimal cluster head selection leads to efficient energy consumption. This paper proposes fuzzy based multi-attributes clustering that balances load among sensor nodes and also gives energy efficient clustering. Here we have used some attributes such as delay, residual energy, distance to CH, standard deviation to average network lifetime and standard deviation to residual energy. Results and experimental analysis validates that the proposed methods outperforms other compared algorithms.
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A., Vijayalakshmi, and Vanaja Ranjan P. "MULTI-CLUSTER MULTI-CHANNEL SCHEDULING (MMS) ALGORITHM FOR MAXIMUM DATA COLLECTION WITH DELAY MINIMIZATION IN WSN." International Journal of Computer Networks & Communications (IJCNC) 11, no. 6 (2020): 91–110. https://doi.org/10.5281/zenodo.3625611.
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Interference during data transmission can cause performance degradation like packet collisions in Wireless Sensor Networks (WSNs). While multi-channels available in IEEE 802.15.4 protocol standard WSN technology can be exploited to reduce interference, allocating channel and channel switching algorithms can have a major impact on the performance of multi-channel communication. This paper presents an improved Fuzzy Logic based Cluster Formation and Cluster Head (CH) Selection algorithm with enhanced network lifetime for multi-cluster topology. The Multi-Cluster Multi-Channel Scheduling (MMS) algorithm proposed in this paper improves the data collection by minimizing the maximum interference and collision. The presented work has developed Cluster formation and cluster head (CH) selection algorithm and Interference-free data communication by proper channel scheduled. The extensive simulation and experimental outcomes prove that the proposed algorithm not only provides an interference-free transmission but also provides delay minimization and longevity of the network lifetime, which makes the presented algorithm suitable for energy-constrained wireless sensor networks.
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Wan Din, Wan Isni Sofiah, Saadiah Yahya, Mohd Nasir Taib, Ahmad Ihsan Mohd Yassin, and Razulaimi Razali. "Developing Multi-Tier Network Design for Effective Energy Consumption of Cluster Head Selection in WSN." Scientific Research Journal 13, no. 1 (2016): 115. http://dx.doi.org/10.24191/srj.v13i1.5446.
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Clustering in Wireless Sensor Network (WSN) is one of the methods to minimize the energy usage of sensor network. The design of sensor network itself can prolong the lifetime of network. Cluster head in each cluster is an important part in clustering to ensure the lifetime of each sensor node can be preserved as it acts as an intermediary node between the other sensors. Sensor nodes have the limitation of its battery where the battery is impossible to be replaced once it has been deployed. Thus, this paper presents an improvement of clustering algorithm for two-tier network as we named it as Multi-Tier Algorithm (MAP). For the cluster head selection, fuzzy logic approach has been used which it can minimize the energy usage of sensor nodes hence maximize the network lifetime. MAP clustering approach used in this paper covers the average of 100Mx100M network and involves three parameters that worked together in order to select the cluster head which are residual energy, communication cost and centrality. It is concluded that, MAP dominant the lifetime of WSN compared to LEACH and SEP protocols. For the future work, the stability of this algorithm can be verified in detailed via different data and energy.
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Wan Din, Wan Isni Sofiah, Saadiah Yahya, Mohd Nasir Taib, Ahmad Ihsan Mohd Yassin, and Razulaimi Razali. "Developing Multi-Tier Network Design for Effective Energy Consumption of Cluster Head Selection in WSN." Scientific Research Journal 13, no. 1 (2016): 116. http://dx.doi.org/10.24191/srj.v13i1.9386.
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Clustering in Wireless Sensor Network (WSN) is one of the methods to minimize the energy usage of sensor network. The design of sensor network itself can prolong the lifetime of network. Cluster head in each cluster is an important part in clustering to ensure the lifetime of each sensor node can be preserved as it acts as an intermediary node between the other sensors. Sensor nodes have the limitation of its battery where the battery is impossible to be replaced once it has been deployed. Thus, this paper presents an improvement of clustering algorithm for two-tier network as we named it as Multi-Tier Algorithm (MAP). For the cluster head selection, fuzzy logic approach has been used which it can minimize the energy usage of sensor nodes hence maximize the network lifetime. MAP clustering approach used in this paper covers the average of 100Mx100M network and involves three parameters that worked together in order to select the cluster head which are residual energy, communication cost and centrality. It is concluded that, MAP dominant the lifetime of WSN compared to LEACH and SEP protocols. For the future work, the stability of this algorithm can be verified in detailed via different data and energy.
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Rohman, Miftahur, and Farid Baskoro. "The effect of modulation and pathloss coefficient on Estx optimum and ESB minimum wireless sensor networks." E3S Web of Conferences 513 (2024): 02002. http://dx.doi.org/10.1051/e3sconf/202451302002.
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Hardware devices in the Wireless Sensor Network (WSN) have several limitations, one of which is in terms of energy. Energy is needed so that activities running on the WSN keep running. WSN consists of many sensor nodes. Each sensor node has limited energy. In this study, the energy efficiency and bandwidth of the WSN were carried out using the Nelder Mead Simplex method, which uses the principle of differentiation of derivatives to simple forms. Nelder Mead Simplex has been widely used to manage energy efficiency in WSNs. The energy efficiency of this WSN is enhanced by the addition of the PSM-PC hybrid technique. The fuzzy algorithm is flexible in its use. Besides that, fuzzy logic can also save bandwidth by adjusting the waiting time in doing one data transmission from the sensor node to the cluster head. The analysis carried out aims to compare the optimization of energy savings in WSN with the Nelder Mead Simplex method. The research results we get are by using 2-QAM modulation with an Energy Bit used of 4.106×10-6J and an Energy transmitter of 7.1316×10-8J with the same parameter conditions, namely a distance of 125m, a path loss coefficient of 2, the number of bits 100 bits are used.
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Ranga, Virender, Mayank Dave, and Anil Kumar Verma. "A multi-criterion fuzzy logic intra-cluster and inter-cluster-based stable cluster head election approach in large scale WSN." International Journal of Communication Networks and Distributed Systems 17, no. 4 (2016): 433. http://dx.doi.org/10.1504/ijcnds.2016.080585.
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Verma, Anil Kumar, Virender Ranga, and Mayank Dave. "A multi-criterion fuzzy logic intra-cluster and inter-cluster-based stable cluster head election approach in large scale WSN." International Journal of Communication Networks and Distributed Systems 17, no. 4 (2016): 433. http://dx.doi.org/10.1504/ijcnds.2016.10001614.
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Al-Husain, Enaam, and Ghaida Al-Suhail. "E-FLEACH: An Improved Fuzzy Based Clustering Protocol for Wireless Sensor Network." Iraqi Journal for Electrical and Electronic Engineering 17, no. 2 (2021): 190–97. http://dx.doi.org/10.37917/ijeee.17.2.21.
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Clustering is one of the most energy-efficient techniques for extending the lifetime of wireless sensor networks (WSNs). In a clustered WSN, each sensor node transmits the data acquired from the sensing field to the leader node (cluster head). The cluster head (CH) is in charge of aggregating and routing the collected data to the Base station (BS) of the deployed network. Thereby, the selection of the optimum CH is still a crucial issue to reduce the consumed energy in each node and extend the network lifetime. To determine the optimal number of CHs, this paper proposes an Enhanced Fuzzy-based LEACH (E-FLEACH) protocol based on the Fuzzy Logic Controller (FLC). The FLC system relies on three inputs: the residual energy of each node, the distance of each node from the base station (sink node), as well as the node’s centrality. The proposed protocol is implemented using the Castalia simulator in conjunction with OMNET++, and simulation results indicate that the proposed protocol outperforms the traditional LEACH protocol in terms of network lifetime, energy consumption, and stability.
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Jungsub, Ahn, and Cho Taeho. "An Enhancement of Cluster-Based False Data Filtering Scheme Through Dynamic Security Selection in Wireless Sensor Networks." International Journal of Computer Networks & Communications (IJCNC) 11, no. 2 (2019): 83–94. https://doi.org/10.5281/zenodo.3233335.
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Today, wireless sensor networks (WSNs) are applied to various industries such as building automation, medical, security, intelligent agriculture, and disaster monitoring. A WSN consists of hundreds to thousands of tiny sensor nodes that perform monitoring tasks. A small sensor node has a limited amount of internal memory and energy resources. Sensor nodes are used to detect a variety of data in specific environmental areas. As a result, WSN should be energy efficient. Sensor nodes are vulnerable to false report injection attacks because they are deployed in an open environment. A false report injection attack consumes the limited energy of a node more quickly and confuses the user. CFFS has been proposed to prevent such an attack using a method of en-route filtering false reports by dividing nodes into clusters. However, the CFFS scheme is vulnerable for repeated false report injection attacks. In this paper, we propose an approach to prolong the WSN lifetime by adjusting the dynamic security threshold value and using a fuzzy logic-based key redistribution selection of cluster head nodes.
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AbdulAlim, Md Abdul, Yu Cheng Wu, and Wei Wang. "A Fuzzy Based Clustering Protocol for Energy-Efficient Wireless Sensor Networks." Advanced Materials Research 760-762 (September 2013): 685–90. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.685.
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Minimization of energy consumption is one of the most important research areas in Wireless Sensor Networks. Nowadays, the paradigms of computational intelligence (CI) are widely used in WSN, such as localization, clustering, energy aware routing, task scheduling, security, etc. Though many fuzzy based clustering techniques have been proposed earlier, many of them could not increase the total network life time in terms of LND (Last Node Dies) with comparing to LEACH. In this paper, a fuzzy logic based energy-aware dynamic clustering technique is proposed, which increases the network lifetime in terms of LND. Here, two inputs are given in the fuzzy inference system and a node is selected as a cluster head according to the fuzzy cost (output). The main advantage of this protocol is that the optimum number of cluster is formed in every round, which is almost impossible in LEACH (low-energy adaptive clustering hierarchy). Moreover, this protocol has less computational load and complexity. The simulation result demonstrates that this approach performs better than LEACH in terms of energy saving as well as network lifetime.
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Baradaran, Amir Abbas, and Keivan Navi. "HQCA-WSN: High-quality clustering algorithm and optimal cluster head selection using fuzzy logic in wireless sensor networks." Fuzzy Sets and Systems 389 (June 2020): 114–44. http://dx.doi.org/10.1016/j.fss.2019.11.015.
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Panbude, Shraddha, Brijesh Iyer, Anil B. Nandgaonkar, and Prachi S. Deshpande. "DFPC: Dynamic Fuzzy-based Primary User Aware clustering for Cognitive Radio Wireless Sensor Networks." Engineering, Technology & Applied Science Research 13, no. 6 (2023): 12058–67. http://dx.doi.org/10.48084/etasr.6279.
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Clustering-based routing solutions have proven to be efficient for wireless networks such as Wireless Sensor Networks (WSNs), Vehicular Ad Hoc Networks (VANETs), etc. Cognitive Radio WSN (CR-WSN) is a class of WSNs that consists of several resource-constrained Secondary Users (SUs), sink, and Primary Users (PUs). Compared to WSNs, there are several challenges in designing the clustering technique for CR-WSNs. As a result, one cannot directly apply the WSN clustering protocols to CR-WSNs. Developing a clustering protocol for CR-WSNs must address challenges such as ensuring PU protection, and SU connectivity, selecting the optimal Cluster Head (CH), and discovering the optimal cluster size. Present CR-WSN clustering solutions failed to resolve the trade-off among all essential clustering objectives. To address these challenges, this study presents a novel approach called Dynamic Fuzzy-based PU aware Clustering (DFPC) for CR-WSNs. DFPC uses a dynamic approach to discover the number of clusters, a fuzzy-based algorithm for optimal CH selection, and reliable multi-hop data transmission to ensure PU protection. To enhance the performance of CR-WSNs, an effective strategy was designed to define the optimal number of clusters using the network radius and live node. Fuzzy logic rules were formulated to assess the four CR-specific parameters for optimal CH selection. Finally, reliable intra- and intercluster data transmission routes are discovered to protect the PUs from malicious activities. The simulation results showed that the DFPC protocol achieved an improved average throughput of 48.04 and 46.49, a PDR of 93.36 and 84.37, and a reduced delay of 0.0271 and 0.0276 in static and dynamic topologies, respectively, which were better than those of ABCC, ATEEN, and LEACH protocols.
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Jayaram, Asokan, and Sanjoy Deb. "EA-MAC: A QoS Aware Emergency Adaptive MAC Protocol for Intelligent Scheduling of Packets in Smart Emergency Monitoring Applications." Journal of Circuits, Systems and Computers 29, no. 13 (2020): 2050205. http://dx.doi.org/10.1142/s0218126620502059.
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The evolution of the wireless sensor network (WSN) in recent years has reached its greatest heights and applications are increasing day by day, one such application is Smart Emergency Monitoring Systems (SMESs) which is in vision of implementation in every urban and rural areas. The implementation of WSN architecture in the Smart Monitoring Systems needs an intelligent scheduling mechanism that efficiently handles the high traffic load as well as the emergency traffic load without sacrificing the energy efficiency of the network. However, the traditional scheduling algorithms such as First Come First Served (FCFS), Round Robin, and Shortest Job First (SJF) cannot meet the requirements of high traffic load in SMESs. To address these shortcomings, this paper presents Emergency Adaptive Medium Access Control protocol (EA-MAC), a fuzzy priority scheduling based Quality-of-service (QoS)-aware medium access control (MAC) protocol for hierarchical WSNs. EA-MAC protocol employs the most powerful fuzzy logics to schedule the sensor nodes with both normal and emergency traffic load without any data congestion, and packet loss and maintaining the better QoS which is considered to be more important in SMESs applications. Moreover, a novel rank-based clustering mechanism in EA-MAC protocol prolongs the network lifetime by minimizing the distance between the Cluster Head (CH) and the Base Station (BS). Both analytical and simulation models demonstrate the superiority of the EA-MAC protocol in terms of energy consumption, transmission delay and data throughput when compared with the existing Time Division Multiple Access (TDMA) based MAC protocols such as LEACH protocol and Cluster Head Election Mechanism-Based On Fuzzy Logic (CHEF) protocol.
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Elmazi, Donald, Evjola Spaho, Keita Matsuo, Tetsuya Oda, Makoto Ikeda, and Leonard Barolli. "F3N." International Journal of Distributed Systems and Technologies 6, no. 2 (2015): 28–44. http://dx.doi.org/10.4018/ijdst.2015040103.
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Sensor networks supported by recent technological advances in low power wireless communications along with silicon integration of various functionalities are emerging as a critically important computer class that enable novel and low cost applications. There are many fundamental problems that Wireless Sensor Networks (WSNs) research will have to address in order to ensure a reasonable degree of cost and system quality. Cluster formation and cluster head selection are important problems in WSN applications and can drastically affect the net- work's communication energy dissipation. However, selecting of the cluster head is not easy in different environments which may have different characteristics. In this paper, in order to deal with this problem, the authors propose a power reduction algorithm for WSNs based on Fuzzy Logic (FL) and Number of Neighbour Nodes (3N). They call this system F3N. The authors evaluate F3N and LEACH by many simulation results. The performance of F3N system is evaluated for tree different parameters: Remaining Battery Power of Sensor (RPS); Degree of Number of Neighbour Nodes (D3N); and Distance from Cluster Centroid (DCC). From the simulation results, they found that the probability of a sensor node to be a cluster head is increased with increase of number of neighbour nodes and remained battery power and is decreased with the increase of distance from the cluster centroid.
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Kamble, Aparna Ashok, and Balaji Madhavrao Patil. "Performance study and critical review on energy aware routing protocols in mobile sink based WSNs." Energy Harvesting and Systems 8, no. 1 (2021): 37–54. http://dx.doi.org/10.1515/ehs-2021-0007.
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Abstract Wireless networks involve spatially extended independent sensor nodes, and it is associated with each other’s to preserve and identify physical and environmental conditions of the particular application. The sensor nodes batteries are equipped with restricted energy for working with an energy source. Consequently, efficient energy consumption is themain important challenge in wireless networks, and it is outfitted witharestricted power storage capacity battery. Therefore, routing protocol with energy efficiency is essential in wireless sensor network (WSN) to offer data transmission and connectivity with less energy consumption. As a result, the routing scheme is the main factor for decreasing energy consumption and the network's lifetime. The energy-aware routing model is mainly devised for WSN with high network performance when transmitting data to a sink node. Hence, in this paper, the effectiveness of energy-aware routing protocols in mobile sink-based WSNs is analyzed and justified. Some energy-aware routing systems in mobile sink-based WSN techniques, such as optimizing low-energy adaptive clustering hierarchy (LEACH) clustering approach, hybrid model using fuzzy logic, and mobile sink. The fuzzy TOPSIS-based cluster head selection (CHS) technique, mobile sink-based energy-efficient CHS model, and hybrid Harris Hawk-Salp Swarm (HH-SS) optimization approach are taken for the simulation process. Additionally, the analytical study is executed using various conditions, like simulation, cluster size, nodes, mobile sink speed, and rounds. Moreover, the performance of existing methods is evaluated using various parameters, namely alive node, residual energy, delay, and packet delivery ratio (PDR).
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Muñoz-Exposito, Jose-Enrique, Antonio-Jesus Yuste-Delgado, Alicia Triviño-Cabrera, and Juan-Carlos Cuevas-Martinez. "Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks." Sensors 24, no. 17 (2024): 5548. http://dx.doi.org/10.3390/s24175548.
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Wireless sensor networks (WSNs) are usually composed of tens or hundreds of nodes powered by batteries that need efficient resource management to achieve the WSN’s goals. One of the techniques used to manage WSN resources is clustering, where nodes are grouped into clusters around a cluster head (CH), which must be chosen carefully. In this article, a new centralized clustering algorithm is presented based on a Type-1 fuzzy logic controller that infers the probability of each node becoming a CH. The main novelty presented is that the fuzzy logic controller employs three different knowledge bases (KBs) during the lifetime of the WSN. The first KB is used from the beginning to the instant when the first node depletes its battery, the second KB is then applied from that moment to the instant when half of the nodes are dead, and the last KB is loaded from that point until the last node runs out of power. These three KBs are obtained from the original KB designed by the authors after an optimization process. It is based on a particle swarm optimization algorithm that maximizes the lifetime of the WSN in the three periods by adjusting each rule in the KBs through the assignment of a weight value ranging from 0 to 1. This optimization process is used to obtain better results in complex systems where the number of variables or rules could make them unaffordable. The results of the presented optimized approach significantly improved upon those from other authors with similar methods. Finally, the paper presents an analysis of why some rule weights change more than others, in order to design more suitable controllers in the future.
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Jebur, Tuka Kareem. "Fuzzy logic Based Seagull Optimization Algorithm for Efficiency and Security in Wireless Sensor Networks." April-May 2024, no. 43 (April 1, 2024): 34–48. http://dx.doi.org/10.55529/jecnam.43.34.48.
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Wireless sensor networks (WSN) find applications in diverse fields such as environmental monitoring, healthcare, and industrial control systems. The pivotal components of these networks are the sensor nodes, which, unfortunately, consume a substantial amount of energy when transmitting information directly to the base station (BS). To mitigate energy consumption associated with direct transmission, this paper proposes a two-phase approach utilizing hybrid clustering and routing algorithms. The proposed approach incorporates fuzzy and seagull techniques for clustering and adopts optimal CH (cluster head) selection, CBRP (Cluster-Based Routing Protocol), and AES (Advanced Encryption Standard) for secure routing. The system employs rule-based fuzzy logic to correlate input values in both clustering and routing algorithms. Decision-making is based on factors such as the residual energy of sensor nodes, distance from the BS, and the number of nodes within the communication range. Input variables' crisp values are transformed into diverse fuzzy values, and the fuzzy output values are converted back to crisp values using the centroid defuzzification method. Selection of cluster heads and routers is determined by the output values, with sensor nodes being allocated to respective cluster heads based on their load-handling capacity. The routing path is then generated considering the capacity of routers. Simulations are conducted to evaluate energy consumption, active sensor nodes per round, and the sustainability period of the network. This proposed hybrid clustering and routing system aim to enhance the overall efficiency of wireless sensor networks by optimizing energy consumption and ensuring secure data transmission. The optimization model identifies the most suitable nodes in the routing cycle, starting with chosen cluster heads. The overarching goal is to enhance network indicators, including network lifespan, power consumption per node, and packet delivery percentage. The proposed solution achieved a network lifetime of 100 hours and a data delivery rate of 98%. additionally, it consumed the least amount of energy, measuring at 95,000 joules.
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Wang, Xun, and Xiaohu You. "A Research Study on an Entropy-Weighted Multi-View Fusion Approach for Agricultural WSN Data Based on Fuzzy Clustering." Electronics 14, no. 12 (2025): 2424. https://doi.org/10.3390/electronics14122424.
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This study proposes an entropy-weighted multi-view collaborative fusion algorithm to address key challenges in agricultural Wireless Sensor Network (WSN) monitoring systems, including high redundancy in multi-modal data, low energy efficiency, and poor cross-parameter adaptability of traditional fusion methods. A fuzzy clustering framework based on principal property selection is established to enable dynamic compression of multi-source heterogeneous data at cluster head nodes. The algorithm innovatively distinguishes between principal and secondary properties based on their contributions to clustering. Clustering is performed using principal properties, allowing data from nodes with similar values to be fused into unified categories, thereby enhancing the reliability of environmental information. Experimental results show that, compared to existing agricultural WSN data fusion algorithms, the proposed method reduces fusion error by an average of 5.6%, lowers the computational complexity of the original multi-view algorithm, and is more suitable for small-sized, low-capacity sensor nodes. Moreover, it has better adaptability to multiple agricultural parameters, reduces network energy consumption, and improves computational accuracy.
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Al Dallal, Haroon Rashid Hammood. "Clustering protocols for energy efficiency analysis in WSNS and the IOT." Problems of Information Society 15, no. 1 (2024): 18–24. http://dx.doi.org/10.25045/jpis.v15.i1.03.
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Throughout the development of energy efficient routing protocol for wireless sensor network clustering technique has been widely adopted approach. The Selection of cluster heads is also very important for the energy efficiency of the network. In the past, researchers have proposed multiple routing protocols; however, problems are still alive and need to be resolved because of the diversity of WSN applications. This article presents an energy-efficient routing protocol using the advanced approaches of Artificial Intelligence, the most promising field of computer science currently providing the best solutions. The proposed model uses the Deep Q-network to select the cluster head. Moreover, collected data at the cluster head is generalized as low, moderate, and high values using the fuzzy logic technique. After that, the Predictive coding theory algorithm is used for the data compression, and the lossy compression technique is applied to the data. Its compressed form also gives complete information of the data in small size and is delivered to the base station. Again, the transmitted data is reconstructed into its actual format. In the end, to justify the performance of the newly designed routing protocol, simulations are performed using the Matlab tool, and its results are evaluated in quality of service matrices and compared with well-known routing protocols.
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Subramani, Neelakandan, Abbas Mardani, Prakash Mohan, Arunodaya Raj Mishra, and Ezhumalai P. "A fuzzy logic and DEEC protocol-based clustering routing method for wireless sensor networks." AIMS Mathematics 8, no. 4 (2023): 8310–31. http://dx.doi.org/10.3934/math.2023419.
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<abstract> <p>Power-efficient wireless sensor network routing techniques (WSN). Clustering is used to extend WSNs' lifetimes. One node act as the cluster head (CH) to represent the others in communications. The member nodes are less important than the cluster hub (CH) in the clustering procedure. Fuzzy techniques based on clustering theory may provide evenly distributed loads. In this study, we provide a fuzzy-logic-based solution that factors in distance to base station (BS), number of nodes, remaining energy, compactness, distance to communicate within a cluster, number of CH, and remaining energy. Fuzzy clustering has a preliminary and final step. First, we select CH based on distance to the base station (BS), remaining node vigor, and node compactness. In the second phase, clusters are created by combining nodes that aren't already in a CH, using density, outstanding vigor, and detachment as limitations. The proposed solution increases load balancing and node longevity. This work provides a unique hybrid routing technique for forming clusters and managing data transfer to the base station. Simulation findings confirm the protocol's functionality and competence. Reduced energy use keeps network sensor nodes online longer. The framework outperforms Stable Election Protocol (SEP), hybrid energy-efficient distributed clustering (HEED), and Low Energy Adaptive Clustering Hierarchy (LEACH). Using the nodes' energy levels to create a grid pattern for the clusters gave four clusters. In addition, the proposed method has a 4347%, 41.46%, 39.26%, 37.57% and 35.67% reduction in average energy consumption when compared with the conventional algorithms. The proposed technologies could increase the network's lifetime, stability interval, packet transfer rate (throughput), and average energy. The suggested protocol is at least 50% better in every statistic that was looked at, such as network lifetime, stability interval, packet transmission rate (throughput), and average energy use.</p> </abstract>
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Varun, Rajesh Kumar, Rakesh C. Gangwar, Omprakash Kaiwartya, and Geetika Aggarwal. "Energy-Efficient Routing Using Fuzzy Neural Network in Wireless Sensor Networks." Wireless Communications and Mobile Computing 2021 (August 3, 2021): 1–13. http://dx.doi.org/10.1155/2021/5113591.
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In wireless sensor networks, energy is a precious resource that should be utilized wisely to improve its life. Uneven distribution of load over sensor devices is also the reason for the depletion of energy that can cause interruptions in network operations as well. For the next generation’s ubiquitous sensor networks, a single artificial intelligence methodology is not able to resolve the issue of energy and load. Therefore, this paper proposes an energy-efficient routing using a fuzzy neural network (ERFN) to minimize the energy consumption while fairly equalizing energy consumption among sensors thus as to prolong the lifetime of the WSN. The algorithm utilizes fuzzy logic and neural network concepts for the intelligent selection of cluster head (CH) that will precisely consume equal energy of the sensors. In this work, fuzzy rules, sets, and membership functions are developed to make decisions regarding next-hop selection based on the total residual energy, link quality, and forward progress towards the sink. The developed algorithm ERFN proofs its efficiency as compared to the state-of-the-art algorithms concerning the number of alive nodes, percentage of dead nodes, average energy decay, and standard deviation of residual energy.
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Rama, P., C. Pretty Diana Cyril, and W. Gracy Theresa. "ENERGY EFFECTIVE HETEROGENEOUS GROUPING IDEAL TRANSMISSION SYSTEM WITH FUZZY IDENTIFICATION IN UWSNTION IN UWSN." ASEAN Engineering Journal 14, no. 2 (2024): 135–45. http://dx.doi.org/10.11113/aej.v14.20850.
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Network connectivity is the fundamental issue for ensuring the longevity of the networks in underground wireless sensor networks. Other important factors are consider in the WSN is minimum path count, energy usage, and packet delivery ratio. There are numerous approaches that might be used to extend the life of UWSN, however sustaining the energy level of network in underground locations in soil is still a challenge. It is projected that the Energy Effective Heterogeneous Grouping Ideal Transmission System with Fuzzy Identification (EEHG-ITSFI) technique will significantly lower the amount of energy used to transport data from node to bottom station and will increase the network of subterranean devices' useful life. Using EEHC-OTSFI, which randomly chooses a cluster head from the collection of clusters, the sensors are grouped into clusters. A fuzzy identification technique is used to focus this connectivity, which is seen as a QoS indication. The network's packet delivery is sped up and delayed less thanks to fuzzy identification technology. The ability to combine different variables into a single indicator, which demonstrates creative presentation for the growth of routing performance in WSNs, is the key benefit.The suggested methodology maximises lifetime in heterogeneous Underground Wireless Sensor Networks (UWSN) while lowering energy consumption by 25–30%, average hop count by 38–42%, and packet latency by up to 40–44%.
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Yang, Shun, Xian’ai Long, Hao Peng, and Haibo Gao. "Optimization of Heterogeneous Clustering Routing Protocol for Internet of Things in Wireless Sensor Networks." Journal of Sensors 2022 (January 19, 2022): 1–9. http://dx.doi.org/10.1155/2022/4327414.
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Wireless sensor network technology is widely used in various modern scenarios, and various industries have higher and higher requirements for the performance indicators of wireless sensor networks. A reasonable and effective layout of wireless sensor networks is conducive to the monitoring of environmental quality, various transactions, and status and transmits a large number of sensing data to the data aggregation center for processing and analysis. However, the operation and development of traditional wireless sensor networks are extremely dependent on the energy supply of the network. When the corresponding supply energy is limited, the operation life of the corresponding wireless sensor network will be greatly reduced. Based on the above situation, this paper proposes a nonuniform clustering routing protocol optimization algorithm from the energy loss of cluster head and clustering form algorithm in wireless sensor networks. At the level of cluster head calculation in wireless sensor networks, firstly, based on the adaptive estimation clustering algorithm, the core density is used as the estimation element to calculate the cluster head radius of wireless sensor networks. At the same time, this paper creatively proposes a fuzzy logic algorithm to further solve the uncertainty of cluster head selection, integrate the residual energy of cluster head nodes, and finally complete the reasonable distribution of cluster heads and realize the balance of node energy consumption. In order to further reduce the algorithm overhead of transmission between cluster heads and realize energy optimization, an intercluster routing optimization algorithm based on the ant colony algorithm is proposed. The pheromone is updated and disturbed by introducing chaotic mapping to ensure the optimal solution of the algorithm, and the optimal path is selected from the perspective of energy dispersion coefficient and distance coefficient, so as to optimize the energy consumption between cluster heads. The experimental results show that compared with the traditional algorithm, the proposed nonuniform clustering routing protocol optimization algorithm prolongs the corresponding life cycle by 75% and reduces the total network energy consumption by about 20%. Therefore, the algorithm achieves the purpose of optimizing network energy consumption and prolonging network life to a certain extent and has certain practical value.
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Jubair, Ahmed Mahdi, Rosilah Hassan, Azana Hafizah Mohd Aman, et al. "Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols." Applied Sciences 11, no. 23 (2021): 11448. http://dx.doi.org/10.3390/app112311448.
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Recently, Wireless Sensor Network (WSN) technology has emerged extensively. This began with the deployment of small-scale WSNs and progressed to that of larger-scale and Internet of Things-based WSNs, focusing more on energy conservation. Network clustering is one of the ways to improve the energy efficiency of WSNs. Network clustering is a process of partitioning nodes into several clusters before selecting some nodes, which are called the Cluster Heads (CHs). The role of the regular nodes in a clustered WSN is to sense the environment and transmit the sensed data to the selected head node; this CH gathers the data for onward forwarding to the Base Station. Advantages of clustering nodes in WSNs include high callability, reduced routing delay, and increased energy efficiency. This article presents a state-of-the-art review of the available optimization techniques, beginning with the fundamentals of clustering and followed by clustering process optimization, to classifying the existing clustering protocols in WSNs. The current clustering approaches are categorized into meta-heuristic, fuzzy logic, and hybrid based on the network organization and adopted clustering management techniques. To determine clustering protocols’ competency, we compared the features and parameters of the clustering and examined the objectives, benefits, and key features of various clustering optimization methods.
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Vishnupriya, Gurunathan, Sakthivel Anusha, and Datchanamoorthy Kavitha. "Fuzzy based clustering and improved ant colony optimization for collecting data via mobile sink in wireless sensor networks." Fuzzy based clustering and improved ant colony optimization for collecting data via mobile sink in wireless sensor networks 32, no. 1 (2023): 276–83. https://doi.org/10.11591/ijeecs.v32.i1.pp276-283.
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Energy efficient routing of data from sensor to base station (BS) is attained utilizing the clustering of sensor nodes, thus minimizing the number of hops and circling the task of the cluster head (CH) sporadically. In addition, the clusters near to the BS take a substantial load over multi-hop communication. The hot spot problem affects wireless sensor networks (WSNs) with BS nodes, which is caused by sensor nodes close to the BS allowing for increased traffic load. So, the entire network lifespan is minimized owing to the element some nodes drain their energy resources much faster equated to the break. To solve these issues fuzzy based clustering and form the optimal route (FCOR) by mobile sink (MS) approach for efficient data collection is proposed. The fuzzy logic method is used for elected the CH by node remaining energy, node connectivity and node distance parameters. Discovering an optimal movable trajectory for the MS is serious so as to attain energy efficiency. Improved ant colony optimization (IACO) method is a better solution to discovering an optimal traversal route. Simulation results proves that FCOR increases the energy efficiency, throughput and minimized the network delay in the WSN.
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Enaam, Abd Al-Hussain, and Abdulrazzaq Al-Suhail Ghaida. "EEIT2-F: energy-efficient aware IT2-fuzzy based clustering protocol in wireless sensor networks." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (2022): 2672–80. https://doi.org/10.11591/ijece.v12i3.pp2672-2680.
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Improving the network lifetime is still a vital challenge because most wireless sensor networks (WSNs) run in an unreached environment and offer almost impossible human access and tracking. Clustering is one of the most effective methods for ensuring that the relevant device process takes place to improve network scalability, decrease energy consumption and maintain an extended network lifetime. Many researches have been developed on the numerous effective clustering algorithms to address this problem. Such algorithms almost dominate on the cluster head (CH) selection and cluster formation; using the intelligent type1 fuzzy-logic (T1-FL) scheme. In this paper, we suggest an interval type2 FL (IT2-FL) methodology that assumes uncertain levels of a decision to be more efficient than the T1-FL model. It is the so-called energy-efficient interval type2 fuzzy (EEIT2-F) low energy adaptive clustering hierarchical (LEACH) protocol. The IT2-FL system depends on three inputs of the residual energy of each node, the node distance from the base station (sink node), and the centrality of each node. Accordingly, the simulation results show that the suggested clustering protocol outperforms the other existing proposals in terms of energy consumption and network lifetime.
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Gurunathan, Vishnupriya, Anusha Sakthivel, and Kavitha Datchanamoorthy. "Fuzzy based clustering and improved ant colony optimization for collecting data via mobile sink in wireless sensor networks." Indonesian Journal of Electrical Engineering and Computer Science 32, no. 1 (2023): 276. http://dx.doi.org/10.11591/ijeecs.v32.i1.pp276-283.
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<span>Energy efficient routing of data from sensor to base station (BS) is attained utilizing the clustering of sensor nodes, thus minimizing the number of hops and circling the task of the cluster head (CH) sporadically. In addition, the clusters near to the BS take a substantial load over multi-hop communication. The hot spot problem affects wireless sensor networks (WSNs) with BS nodes, which is caused by sensor nodes close to the BS allowing for increased traffic load. So, the entire network lifespan is minimized owing to the element some nodes drain their energy resources much faster equated to the break. To solve these issues fuzzy based clustering and form the optimal route (FCOR) by mobile sink (MS) approach for efficient data collection is proposed. The fuzzy logic method is used for elected the CH by node remaining energy, node connectivity and node distance parameters. Discovering an optimal movable trajectory for the MS is serious so as to attain energy efficiency. Improved ant colony optimization (IACO) method is a better solution to discovering an optimal traversal route. Simulation results proves that FCOR increases the energy efficiency, throughput and minimized the network delay in the WSN.</span>
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Hussain, Abid, Saima Munawar, and Nasir Naveed. "Near-optimal energy-aware approach through INSTANT-OFF and NEVER-OFF clustering by fuzzy logic for wireless sensor networks." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 83–98. http://dx.doi.org/10.3233/jifs-200382.
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Wireless Sensor Networks (WSNs) consist of various low-cost devices with limited battery power for surveillance of certain vicinity. The main concern was to prolong the network lifetime to save energy. The heterogeneous nodes are deployed in the given setting divided into two INSTANT-OFF and NEVER-OFF states. Then each one is further subdivided by a Fuzzy Inference System (FIS). The INSTANT-OFF (Good, Better, and Best) has three states active, idle, sleep, and always worked as Cluster Members (CMs) to sense the physical environment. The NEVER-OFF (Good, Better, and Best) has active and idle states. The first two most optimum NEVER-OFF selected as Cluster Head (CH) and Data Collector (DC), and the remaining belonged to CMs. The cluster boundary was defined by parameter Distance from Base Station (DisBS) to meet the unequal clustering approach. The energy consumes during sensing, processing, and transmission phases by its appropriate nodes. The CMs worked reactively and saved energy by idle and sleep states, while the CH and DC worked in a proactive mode and saved energy in an idle state. The sensing job was done by CMs that consumed a minor amount of energy and transmitted packets of 200 bits length to DC. The DC received packets of 200 bits length from CMs and aggregated them into 6400 bits length packets, then delivered them to CH. The reactive and proactive mechanisms saved the energy as 85.1033% in 2000 rounds; increased lifetime up to 774 rounds, re-clustering setup took place after 1912 rounds, and enhanced the throughput as 100% and latency time 0.001123 by experiment evaluation. The result shows that most energy consumption job were communicated with BS performed by CH hop by hop through other CH. The unequal clustering approach maintained the consumption of energy levels throughout WSNs processing.
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Clavijo-López, Rosa, Jesús Merino Velásquez, Wayky Alfredo Luy Navarrete, et al. "Energy-aware and Context-aware Fault Detection Framework for Wireless Sensor Networks." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 14, no. 3 (2023): 01–13. http://dx.doi.org/10.58346/jowua.2023.i3.001.
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Wireless sensor networks (WSNs) consist of many sensor nodes that are densely deployed throughout a randomized geographical area to monitor, detect, and analyze various physical phenomena. The primary obstacle encountered in WSNs pertains to the significant reliance of sensor nodes on finite battery power for wireless data transfer. Sensors as a crucial element inside Cyber-Physical Systems (CPS) renders them vulnerable to failures arising from intricate surroundings, substandard manufacturing, and the passage of time. Various anomalies can appear within WSNs, mostly attributed to defects such as hardware and software malfunctions and anomalies and assaults initiated by unauthorized individuals. These anomalies significantly impact the overall integrity and completeness of the data gathered by the networks. Therefore, it is imperative to provide a critical mechanism for the early detection of faults, even in the presence of constraints imposed by the sensor nodes. Machine Learning (ML) techniques encompass a range of approaches that may be employed to identify and diagnose sensor node faults inside a network. This paper presents a novel Energy-aware and Context-aware fault detection framework (ECFDF) that utilizes the Extra-Trees algorithm (ETA) for fault detection in WSNs. To assess the effectiveness of the suggested methodology for identifying context-aware faults (CAF), a simulated WSN scenario is created. This scenario consists of data from humidity and temperature sensors and is designed to emulate severe low-intensity problems. This study examines six often-seen categories of sensor fault, including drift, hard-over/bias, spike, erratic/precision, stuck, and data loss. The ECFDF approach utilizes an Energy-Efficient Fuzzy Logic Adaptive Clustering Hierarchy (EE-FLACH) algorithm to select a Super Cluster Head (SCH) within WSNs. The SCH is responsible for achieving optimal energy consumption within the network, and this selection process facilitates the early detection of faults. The results of the simulation indicate that the ECFDF technique has superior performance in terms of Fault Detection Accuracy (FDA), False-Positive Rate (FPR), and Mean Residual Energy (MRE) when compared to other detection and classification methods.
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Sharma, Shweta, Amandeep Kaur, Deepali Gupta, Sapna Juneja, and Mukesh Kumar. "Dragon fly algorithm based approach for escalating the security among the nodes in wireless sensor network based system." SN Applied Sciences 5, no. 12 (2023). http://dx.doi.org/10.1007/s42452-023-05614-2.
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AbstractA new technology that is gaining popularity today is the Wireless Sensor Network. Smart sensors are being used in a variety of wireless network applications, including intruder detection, transportation, the Internet of Things, smart cities, the military, industrial, agricultural, and health monitoring, as a result of their rapid expansion. Sensor network technologies improve social advancement and life quality while having little to no negative impact on the environment or natural resources of the planet are examined in sensor networks for sustainable development. Real-world applications face challenges ensuring Quality of Service (QoS) due to dynamic network topology changes, resource constraints, and heterogeneous traffic flow. By enhancing its properties, such as maintainability, packet error ratio, reliability, scalability, availability, latency, jitter, throughput, priority, periodicity, deadline, security, and packet loss ratio, the optimized QoS may be attained. Real-world high performance is difficult to attain since sensors are spread out in a hostile environment. The performance parameters are divided into four categories: network-specific, deployment phase, layered WSN architecture, and measurability. Integrity, secrecy, safety, and security are among the privacy and security levels. This article leads emphasis on the trustworthiness of the routes as well as the nodes involved in those routes from where the data has to pass from source to destination. First of all, the nodes are deployed and cluster head selection is done by considering the total number of nodes and the distance from the base station. The proposed work uses AODV architecture for computing QoS parameters that are throughput, PDR and delay. K-means clustering algorithm is used to divide the aggregated data into three possible segments viz. good, moderate and bad as this process does not involve the labelling of aggregated data due to its supervised behavior. The proposed trust model works in two phases. In first phase, data is divided into 3 segments and labelling is done. In second phase, uses generated class objects are to be applied viz. the route records to publicize the rank of the routes followed by the rank of nodes. The proposed technique employed the statistical machine learning and swarm intelligence strategy with dragon fly algorithm in order to address the issues related effective rank generation of nodes and improving the network lifetime. Deep learning concepts can be combined with fuzzy logics approach for resolving issues like secure data transmission, trustworthiness of ranking nodes and efficient route discovery.
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"Routing Algorithm using Fuzzy Logic Based Clustering with Mobile Sink for Wireless Sensor Network." International Journal of Recent Technology and Engineering 8, no. 4 (2019): 4000–4005. http://dx.doi.org/10.35940/ijrte.d8629.118419.
Full textAbstract:
Minimization of the energy consumption in Wireless Sensor Network (WSN) is one of the most important area which has been explored by researchers through different methods. The use of non-stationary mobile sink has undoubtedly decreased the energy consumption within the sensor nodes and hence the life time of the system. Applying the Fuzzy Logic could effectively optimize the selection of Cluster Head. In this paper, Fuzzy Logic has been implemented for Cluster Head selection along with a mobile sink. The energy remaining in the sensor node, distance between the sink and the node, and the node degree are considered as the fuzzy inference variables. The life time of the node has been compared with the LEACH and Fuzzy logic based Clustering Combined with Mobile Sink (FCCMS) with mobile sink.
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"DHCFL: Dual Head Clustering Based on Fuzzy Logic for Wireless Sensor Networks." International Journal of Recent Technology and Engineering 8, no. 5 (2020): 1049–54. http://dx.doi.org/10.35940/ijrte.e6279.018520.
Full textAbstract:
Wireless Sensor Networks (WSN) are constructed by interconnecting miniature sensor nodes for monitoring the environment uninterrupted. These miniature nodes are having the sensing, processing and communication capability in a smaller scale powered by a battery unit. Proper energy conservation is required for the entire system. Clustering mechanism in WSN advances the lifetime and stability in the network. It achieves data aggregation and reduces the number of data transmission to the Base station (BS). But the Cluster Head (CH) nodes are affected by rapid energy depletion problem due to overload. A CH node spends its energy for receiving data from its member nodes, aggregation and transmission to the BS. In CH election, multiple overlapping factors makes it difficult and inefficient which costs the lifetime of the network. In recent years, Fuzzy Logic is widely used for CH election mechanism for WSN. But the underlying problem of the CHs node continues. In this research work, a new clustering algorithm DHCFL is proposed which elects two CHs for a cluster which shares the load of a conventional CH node. Data reception and aggregation will be done by CH aggregator (CH-A) node and data transmission to the BS will be carried over by CH relay (CH-R) node. Both CH-A and CH-R nodes are elected through fuzzy logic which addresses the uncertainty in the network too. The proposed algorithm DHCFL is compared and tested in different network scenarios with existing clustering algorithms and it is observed that DHCFL outperforms other algorithms in all the network scenarios.