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Juwaied, Abdulla, Lidia Jackowska-Strumillo, and Artur Sierszeń. "Enhancing Clustering Efficiency in Heterogeneous Wireless Sensor Network Protocols Using the K-Nearest Neighbours Algorithm." Sensors 25, no. 4 (2025): 1029. https://doi.org/10.3390/s25041029.
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Wireless Sensor Networks are formed by tiny, self-contained, battery-powered computers with radio links that can sense their surroundings for events of interest and store and process the sensed data. Sensor nodes wirelessly communicate with each other to relay information to a central base station. Energy consumption is the most critical parameter in Wireless Sensor Networks (WSNs). Network lifespan is directly influenced by the energy consumption of the sensor nodes. All sensors in the network send and receive data from the base station (BS) using different routing protocols and algorithms. These routing protocols use two main types of clustering: hierarchical clustering and flat clustering. Consequently, effective clustering within Wireless Sensor Network (WSN) protocols is essential for establishing secure connections among nodes, ensuring a stable network lifetime. This paper introduces a novel approach to improve energy efficiency, reduce the length of network connections, and increase network lifetime in heterogeneous Wireless Sensor Networks by employing the K-Nearest Neighbours (KNN) algorithm to optimise node selection and clustering mechanisms for four protocols: Low-Energy Adaptive Clustering Hierarchy (LEACH), Stable Election Protocol (SEP), Threshold-sensitive Energy Efficient sensor Network (TEEN), and Distributed Energy-efficient Clustering (DEC). Simulation results obtained using MATLAB (R2024b) demonstrate the efficacy of the proposed K-Nearest Neighbours algorithm, revealing that the modified protocols achieve shorter distances between cluster heads and nodes, reduced energy consumption, and improved network lifetime compared to the original protocols. The proposed KNN-based approach enhances the network’s operational efficiency and security, offering a robust solution for energy management in WSNs.
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Chen, Bowen. "Wireless Communication Chip Designs: analysis of the Wireless Integrated Network Sensors." Highlights in Science, Engineering and Technology 70 (November 15, 2023): 580–87. http://dx.doi.org/10.54097/hset.v70i.13989.
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With the development of wireless technology, wireless integrated network sensor is a new form of sensor network. It enables highly efficient data acquisition and transmission by connecting the sensor nodes wirelessly. The purpose of this study is to investigate the basic principles and techniques of wireless integrated network sensors, analyze their application fields, and conduct experimental studies to verify their performance. This study first introduces the basic principles of wireless integrated network sensors, including wireless communication, sensor nodes, and network topology. Then, related technologies, including energy management, routing protocols and network security, are studied to improve the performance and stability of wireless integrated network sensors. Wireless integrated network sensors have wide application prospects in environmental monitoring, intelligent transportation and agriculture. Meanwhile, the energy utilization efficiency and network stability of the sensor network can be improved by adopting the new energy management mechanism and routing protocol. This study reveals the potential and value in practical applications through the exploration and research of wireless integrated network sensors. In future studies, the energy management and routing mechanisms of sensor networks can be further optimized to improve their performance and reliability. In addition, more application scenarios suitable for wireless integrated network sensors can be explored to provide solutions for practical problems.
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HUANG, GUANGYAN, XIAOWEI LI, JING HE, and XIN LI. "DATA MINING VIA MINIMAL SPANNING TREE CLUSTERING FOR PROLONGING LIFETIME OF WIRELESS SENSOR NETWORKS." International Journal of Information Technology & Decision Making 06, no. 02 (2007): 235–51. http://dx.doi.org/10.1142/s0219622007002538.
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Clustering is applied in wireless sensor networks for increasing energy efficiency. Clustering methods in wireless sensor networks are different from those in traditional data mining systems. This paper proposes a novel clustering algorithm based on Minimal Spanning Tree (MST) and Maximum Energy resource on sensors named MSTME. Also, specified constrains of clustering in wireless sensor networks and several evaluation metrics are given. MSTME performs better than already known clustering methods of Low Energy Adaptive Clustering Hierarchy (LEACH) and Base Station Controlled Dynamic Clustering Protocol (BCDCP) in wireless sensor networks when they are evaluated by these evaluation metrics. Simulation results show MSTME increases energy efficiency and network lifetime compared with LEACH and BCDCP in two-hop and multi-hop networks, respectively.
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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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Idan, Zainab S., and Ahmed Al-Fatlawi. "Energy Efficient Clustering Using Improved Particle Swarm Optimization in Wireless Sensor Networks." BIO Web of Conferences 97 (2024): 00106. http://dx.doi.org/10.1051/bioconf/20249700106.
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The Wireless Sensor Network (WSN) includes many low-cost nodes that have the capacity to perceive, operate, and communicate wirelessly. WSN can spread the information to all around through a cooperative node approach. It also has many advantages in terms of both cost and cooperative intelligence. In a wireless sensor network, nodes have limited energy resources, so their life cycle is considered as one of the main concerns about wireless sensor networks. Energy efficiency grouping and routing are two well-known issues in optimization that have been widely studied in order to increase the lifetime of wireless sensor networks. In this paper, an improved particle swarm optimization (IPSO) clustering algorithm for energy efficiency network management is introduced in order to find a route for creating optimal clusters. To evaluate the efficiency of the proposed clustering algorithm, this algorithm is simulated and compared with the particle swarm optimization(PSO) algorithm based on parameters such as network energy, number of live nodes and network life.
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Chugh, Amit, and Supriya Panda. "Energy Efficient Techniques in Wireless Sensor Networks." Recent Patents on Engineering 13, no. 1 (2019): 13–19. http://dx.doi.org/10.2174/1872212112666180731114046.
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Background: Wireless Sensor Network (WSN) is a collection of tiny electromechanical nodes termed as sensors. Sensors are equipped with sensing unit, which is designed for application specific. When deployed either by planned or unplanned after deployment, sensor’s energy starts depleting due to various roles like sensing, communication and aggregation. Method: WSN is challenged with limited battery power. The aim is to enhance energy efficiency that leads to a prolonged lifetime of networks. Results: We have reviewed the patents related to energy efficiency in wireless sensor networks. This Paper presents the study of various energy efficient techniques, which can enhance the lifetime of sensor networks; it covers basics of WSN, their design, Classification, Communication in WSN and a survey of different techniques for effective utilization of sensor’s energy. Conclusion: Paper has emphasized on energy efficient clustering technique along with feature wise summary of existing clustering protocols.
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Voulkidis, Artemis C., Markos P. Anastasopoulos, and Panayotis G. Cottis. "Energy efficiency in wireless sensor networks." ACM Transactions on Sensor Networks 9, no. 4 (2013): 1–27. http://dx.doi.org/10.1145/2489253.2489260.
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Paliwal, Rakesh, and Irfan Khan. "Design and Analysis of Soft Computing Based Improved Routing Protocol in WSN for Energy Efficiency and Lifetime Enhancement." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 3 (2022): 12–24. http://dx.doi.org/10.17762/ijritcc.v10i3.5521.
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Mobile wireless sensor networks have been developed as a result of recent advancements in wireless technologies. Sensors in the network are low-cost and have a short battery life, in addition to their mobility. They are more applicable in terms of the essential properties of these networks. These networks have a variety of uses, including search and rescue operations, health and environmental monitoring, and intelligent traffic management systems, among others. According to the application requirements, mobile wireless sensor nodes are energy limited equipment, so energy conservation is one of the most significant considerations in the design of these networks. Aside from the issues posed by sensor node mobility, we should also consider routing and dynamic clustering. According to studies, cluster models with configurable parameters have a substantial impact on reducing energy usage and extending the network's lifetime. As a result, the primary goal of this study is to describe and select a smart method for clustering in mobile wireless sensor networks utilizing evolutionary algorithms in order to extend the network's lifetime and ensure packet delivery accuracy. For grouping sensor nodes in this work, the Genetic Algorithm is applied initially, followed by Bacterial Conjugation. The simulation's results show a significant increase in clustering speed acceleration. The speed of the nodes is taken into account in the suggested approach for calibrating mobile wireless sensor nodes.
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Hung, Li-Ling. "Charging Protocol for Partially Rechargeable Mobile Sensor Networks." Sensors 23, no. 7 (2023): 3438. http://dx.doi.org/10.3390/s23073438.
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Wireless sensor networks (WSNs) have wide applicability in services used in daily life. However, for such networks, limited energy is a critical issue. The efficiency of a deployed sensor network may be subject to energy supply. Wireless rechargeable sensor networks have recently been proposed and discussed. Most related studies have involved applying static rechargeable sensors to an entire rechargeable environment or having mobile chargers patrol the environment to charge sensors within it. For partially rechargeable environments, improving the recharge efficiency and extending the lifetime of WSNs are considerable challenges. Scientists have devoted attention to energy transmission technologies and mobile sensor network (MSN) applications. In this paper, we propose a flexible charging protocol in which energy can be transmitted from certain energy supply regions to other regions in an MSN. Mobile rechargeable sensors are deployed to monitor the environment. To share energy in a certain region, the sensors move to replenish their energy and transmit energy to sensors outside the energy supply region. The efficiency of the proposed protocol is also discussed in the context of various situations. The evaluation results suggest that the flexible protocol is more efficient than other charging protocols in several situations.
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M. Rajalakshmi. "Survey on Enhancing Energy Efficiency in Wireless Sensor Networks Based on Rapid Data Collection." Communications on Applied Nonlinear Analysis 31, no. 3s (2024): 74–81. http://dx.doi.org/10.52783/cana.v31.732.
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Wireless Sensor Networks (WSNs) are widely used in various applications such as environmental monitoring, healthcare, and business automation. These networks rely on the integration of sensors to collect data quickly. However, operating WSNs efficiently, especially in scenarios where quick and reliable data acquisition is crucial, faces significant challenges due to the limited power resources of sensor nodes. This survey paper provides a detailed review of current methodologies and techniques focused on improving energy efficiency in WSNs for fast data collection. It begins by discussing the basic concepts of wireless sensor networks, highlighting the important role sensors play in gathering accurate information. Afterwards, the paper provides a summary of the issues surrounding power usage, underscoring the importance of creative solutions to prolong the network's longevity. An extensive section of the study delves into various energy-efficient protocols and algorithms aimed at improving data collection in Wireless Sensor Networks (WSNs). The paper classifies these approaches according to their strategies for forming clusters, communication protocols, and optimization algorithms.
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Vijayan, Sneha, and Nagarajan Munusamy. "Deterministic Centroid Localization for Improving Energy Efficiency in Wireless Sensor Networks." Cybernetics and Information Technologies 22, no. 1 (2022): 24–39. http://dx.doi.org/10.2478/cait-2022-0002.
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Abstract Wireless sensor networks are an enthralling field of study with numerous applications. A Wireless Sensor Network (WSN) is used to monitor real-time scenarios such as weather, temperature, humidity, and military surveillance. A WSN is composed of several sensor nodes that are responsible for sensing, aggregating, and transmitting data in the system, in which it has been deployed. These sensors are powered by small batteries because they are small. Managing power consumption and extending network life is a common challenge in WSNs. Data transmission is a critical process in a WSN that consumes the majority of the network’s resources. Since the cluster heads in the network are in charge of data transmission, they require more energy. We need to know where these CHs are deployed in order to calculate how much energy they use. The deployment of a WSN can be either static or random. Although most researchers focus on random deployment, this paper applies the proposed Deterministic Centroid algorithm for static deployment. Based on the coverage of the deployment area, this algorithm places the sensors in a predetermined location. The simulation results show how this algorithm generates balanced clusters, improves coverage, and saves energy.
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Rao, K. Raghava, D. Sateesh Kumar, Mohiddin Shaw, and V. Sitamahalakshmi. "Energy Efficiency Analysis of LoRa and ZigBee Protocols in Wireless Sensor Networks." Revista Gestão Inovação e Tecnologias 11, no. 4 (2021): 2836–49. http://dx.doi.org/10.47059/revistageintec.v11i4.2322.
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Now a days IoT technologies are emerging technology with wide range of applications. Wireless sensor networks (WSNs) are plays vital role in IoT technologies. Construction of wireless sensor node with low-power radio link and high-speed processors is an interesting contribution for wireless sensor networks and IoT applications. Most of WSNs are furnished with battery source that has limited lifetime. The maximum operations of these networks require more power utility. Nevertheless, improving network efficiency and lifetime is a curtail issue in WSNs. Designing a low powered wireless sensor networks is a major challenges in recent years, it is essential to model its efficiency and power consumption for different applications. This paper describes power consumption model based on LoRa and Zigbee protocols, allows wireless sensor nodes to monitor and measure power consumption in a cyclic sleeping scenario. Experiential results reveals that the designed LoRa wireless sensor nodes have the potential for real-world IoT application with due consideration of communicating distance, data packets, transmitting speed, and consumes low power as compared with Zigbee sensor nodes. The measured sleep intervals achieved lower power consumption in LoRa as compared with Zigbee. The uniqueness of this research work lies in the review of wireless sensor node optimization and power consumption of these two wireless sensor networks for IoT applications.
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Wenxing, Liao, Wu Muqing, and Wu Yuewei. "Design of multi-energy-space-based energy-efficient algorithm in novel software-defined wireless sensor networks." International Journal of Distributed Sensor Networks 13, no. 7 (2017): 155014771771811. http://dx.doi.org/10.1177/1550147717718113.
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Energy efficiency has always been a hot issue in wireless sensor networks. A lot of energy-efficient algorithms have been proposed to reduce energy consumption in traditional wireless sensor networks. With the emergence of software-defined networking, researchers have demonstrated the feasibility of software-defined networking over traditional wireless sensor networks. Thus, energy-efficient algorithms in software-defined wireless sensor networks have been studied. In this article, we propose an energy-efficient algorithm based on multi-energy-space in software-defined wireless sensor networks. First, we propose a novel architecture of software-defined wireless sensor networks according to current research on software-defined wireless sensor networks. Then, we introduce the concept of multi-energy-space which is based on the residual energy. Based on the novel architecture of software-defined wireless sensor networks and the concept of multi-energy-space, we give a detailed introduction of the main idea of our multi-energy-space-based energy-efficient algorithm. Simulation results show that our proposed algorithm performs better in energy consumption balance and network lifetime extension compared with the typical energy-efficient algorithms in traditional wireless sensor networks.
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Talib, Mohammed Saad. "Minimizing the Energy Consumption in Wireless Sensor Networks." JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences 26, no. 1 (2017): 17–28. http://dx.doi.org/10.29196/jub.v26i1.349.
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Energy in Wireless Sensor networks (WSNs) represents an essential factor in designing, controlling and operating the sensor networks. Minimizing the consumed energy in WSNs application is a crucial issue for the network effectiveness and efficiency in terms of lifetime, cost and operation. Number of algorithms and protocols were proposed and implemented to decrease the energy consumption. Principally, WSNs operate with battery-powered sensors. Since Sensor's batteries have not been easily recharge. Therefore, prediction of the WSN represents a significant concern. Basically, the network failure occurs due to the inefficient sensor's energy. MAC protocols in WSNs achieved low duty-cycle by employing periodic sleep and wakeup. Predictive Wakeup MAC (PW-MAC) protocol was made use of the asynchronous duty cycling. It reduces the consumption of the node energy by allowing the senders to predict the receiver′s wakeup time. The WSN must be applied in an efficient manner to utilize the sensor nodes and their energy to ensure effective network throughput. To ensure energy efficiency the sensors' duty cycles must be adjusted appropriately to meet the network traffic demands. The energy consumed in each node due to its switching between the active and idle states was also estimated. The sensors are assumed to be randomly deployed. This paper aims to improve the randomly deployed network lifetime by scheduling the effects of transmission, reception and sleep states on the energy consumption of the sensor nodes. Results for these states with much performance metrics were also studied and discussed.
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Liu, Yong, Baohua Liang, and Jiabao Jiang. "Information Processing and Data Management Technology in Wireless Sensor Networks." International Journal of Online Engineering (iJOE) 14, no. 09 (2018): 66. http://dx.doi.org/10.3991/ijoe.v14i09.8270.
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<p>The wireless sensor network is essentially a data-centric network that processes the continuous stream of data, which is collected by different sensors. Therefore, the existing data management technologies regard the wireless sensor network, which is named WSN as a distributed database, and it is composed of continuous data streams from the physical world. Wireless sensor networks are emerging next-generation sensor networks, but their transmission of information is highly dependent. The wireless sensor network processes the continuous stream of data collected by the sensor. Based on the features of wireless sensor networks, this paper presents a topology-dependent model of cluster evolution with fault tolerance. Through the limited data management, resources have reasonably configured, while also saving energy. The model is based on the energy-aware routing protocol in its network layer protocols. The key point is the energy routing principle. According to its own local view, the cluster head node builds the inter-cluster topology to achieve fault-tolerant and energy-saving goals. Simulation results show that the model has good fault tolerance and energy efficiency.</p>
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Varghese, Liji. "Energy Efficient Sensory Data Collection in Wireless Sensor Networks." International Journal of Science and Research (IJSR) 10, no. 6 (2021): 914–19. https://doi.org/10.21275/sr21609151823.
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Saravana G, Jegadeeswari J S, NaveenKumar V, and PushpaValli M. "Energy Efficiency in Wireless Sensor Networks using Advanced LEACH Protocol." international journal of engineering technology and management sciences 6, no. 6 (2022): 74–83. http://dx.doi.org/10.46647/ijetms.2022.v06i06.011.
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The wireless sensor network is the best research subject matter with huge applications in various domains. By and large, a wireless sensor network involves hundreds to thousands of sensor nodes, which transmit and communicate with each other by the utilization of radio transmissions or signals. A portion of the difficulties that exist in the sketch of wireless sensor networks are confined computation power, storage capacity, battery, and transmission transfer speed or bandwidth. To determine these issues, clustering and routing algorithm have been introduced. Clustering and routing processes are viewed as an optimization issue in wireless sensor networks which can be settled by the utilization of swarm intelligence-based approaches. This article presents an original multitude of insight-based grouping and multihop routing protocol for wireless sensor report giving a new swarm optimization technique applied for picking the cluster heads and organizing the cluster capably. Then, at that point, the grey wolf optimization algorithm-based routing process takes place to choose the ideal ways in the network. The introduced better particle swarm optimization-grey wolf optimization approach consolidates the advantages of both the clustering and routing processes which prompts the greatest energy efficiency and network lifetime. The proposed model is reproduced under a broad arrangement of experiments, and the outcomes are approved under a few measures. The acquired trial result exhibited the predominant qualities of the improved particle swarm optimization–grey wolf optimization method under all the test cases. It enhances LEACH protocols in terms of energy efficiency, network Lifetime, and throughput. Maintaining the Energy Efficiency of a wireless sensor network has been a great concern nowadays. The main aim is to overcome the drawback of Improvement of the energy efficiency of a wireless sensor network. It may be improved with the performance of an optimization algorithm using swarm Intelligence. The novelty of the project is Network lifetime and Energy Consumption. The future of this project is to share the complete information which is present in the cluster head without any interruption
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Zong Chen, Dr Joy Iong, and Lu-Tsou Yeh. "Data Forwarding in Wireless Body Area Networks." June 2020 2, no. 2 (2020): 80–87. http://dx.doi.org/10.36548/jei.2020.2.002.
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One of the most crucial application of Wireless Body Area Networks in healthcare applications is the process of monitoring human bodies and gather physiological data. Network performance degradation in the form of energy efficiency and latency are caused because of energy depletions which arises due to limited energy resource availability. The heterogeneity of body sensors will lead to variation in the rate of energy consumption. Based on this, a novel Data Forwarding Strategy is presented in this research work to enhance collaborative WBAN operations, improve network lifetime and restrict energy consumption of the sensors. In this paper, we have contributed towards reducing the size of data to be transmitted by compressed sensing and selection of relay sensor based on sampling frequency, energy levels and sensor importance. Using the proposed methodology, it is possible to improve both reliability and energy-efficiency of WBAN data transmission. moreover, it is also possible to adapt to the changing WBAN topologies when the proposed methodology is used, balancing energy efficiency and consumption.
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Gu, Yi, Qishi Wu, and Nageswara S. V. Rao. "Optimizing Cluster Heads for Energy Efficiency in Large-Scale Heterogeneous Wireless Sensor Networks." International Journal of Distributed Sensor Networks 6, no. 1 (2010): 961591. http://dx.doi.org/10.1155/2010/961591.
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Many complex sensor network applications require deploying a large number of inexpensive and small sensors in a vast geographical region to achieve quality through quantity. Hierarchical clustering is generally considered as an efficient and scalable way to facilitate the management and operation of such large-scale networks and minimize the total energy consumption for prolonged lifetime. Judicious selection of cluster heads for data integration and communication is critical to the success of applications based on hierarchical sensor networks organized as layered clusters. We investigate the problem of selecting sensor nodes in a predeployed sensor network to be the cluster heads to minimize the total energy needed for data gathering. We rigorously derive an analytical formula to optimize the number of cluster heads in sensor networks under uniform node distribution, and propose a Distance-based Crowdedness Clustering algorithm to determine the cluster heads in sensor networks under general node distribution. The results from an extensive set of experiments on a large number of simulated sensor networks illustrate the performance superiority of the proposed solution over the clustering schemes based on k-means algorithm.
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K. Kavitha, R. Rajalingam,. "Energy-Recognition Clustering Technique Based on Reinforcement Learning In WSN." Journal of Electrical Systems 20, no. 2 (2024): 2493–502. http://dx.doi.org/10.52783/jes.2022.
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WSN (Wireless Sensor Network) technology has recently gained a lot of attention. This began with the deployment of small WSNs and moved to the deployment of big and IoT WSNs, all with an emphasis on energy conservation. Wireless sensor networks can benefit from network clustering to increase their energy efficiency. The practise of dividing nodes into clusters before picking multiple cluster heads is known as network clustering (CHs). Clustering in wireless sensor networks is known to save energy and extend the network's lifetime (WSNs). Energy efficiency is a hot topic in existing wireless sensor networks, although it's not generally discussed. In this research, we offer a reinforcement learning (RL) based energy-aware clustering approach, whereby peripheral cluster nodes monitor environmental factors like energy use and choose an optimal cluster leader (CH). Connect the CH (BS) to the base station. In the simulation (PDR), performance factors such as network lifetime, energy tax, network stability period, and packet delivery rate are all taken into account. The simulation results show that the proposed QL-ReLeC performs around 11% better than the reference protocol in terms of PDR and 11% better in terms of energy tax.
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Kusuma, S. M., K. N. Veena, B. P. Vijaya Kumar, and B. V. Varun. "Performance Modeling of Energy Efficiency for Sensors Deployment in Embedded Wireless Sensor Networks." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 4515–24. http://dx.doi.org/10.1166/jctn.2020.9107.
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Present trend of Internet of Things (IoT) and sensors deployment increased in every sectors enormously from last one decade. But the deployment challenges of sensors and their networks with respect to their contextual dynamics and system performance is not much investigated. Hence there is a need to investigate the deployment challenges of sensors supporting the computing system that exactly imitates the phenomenon by understanding the context and other influencing parameters, i.e., to sense the environmental parameter values accurately and precisely from the respective embedded sensor system. In this paper, a methodology is proposed to analyze the performance of embedded Wireless Sensor Networks (eWSNs) with respect to energy efficiency based on sensors deployment. The method involves in clustering the sensor nodes based on distance from the phenomenon and its physical location. Sensors and sensor network lifetime energy consumption for data acquisition is analyzed using Markovian model. Simulation platform for random deployment of sensor nodes along with Self Organizing map neural network for clustering with various cases of sensors deployment, network dynamics and environment are studied to understand the performance of the embedded WSN system for energy efficiency.
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Jobanputra, Paresh Ashokkumar, and Arun Jhapate Prof. "A Review of Sensor Node in Wireless Sensor Networks." International Journal of Trend in Scientific Research and Development 3, no. 3 (2019): 124–27. https://doi.org/10.31142/ijtsrd23620.
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Wireless Sensor Networks WSNs are collection of tiny sensor nodes capable of sensing, processing and broadcasting data correlated to some occurrence in the network area. The sensor nodes have severe limitation, such as bandwidth, short communication range, limited CPU processing facility, memory and energy. Enhancing the lifetime of wireless sensors network and efficient utilizations of bandwidth are essential for the proliferation of wireless sensor network in different applications. We provide an in depth study of applying wireless sensor networks WSNs to real world habitat monitoring. A set of system design requirements were developed that cover the hardware design of the nodes, the sensor network software, protective enclosures, and system architecture to meet the requirements of biologists. Although researchers anticipate some challenges arising in real world deployments of WSNs, many problems can only be discovered through experience. We present a set of experiences from a four month long deployment on a remote island. We analyze the environmental and node health data to evaluate system performance. The close integration of WSNs with their environment provides environmental data at densities previously impossible. We show that the sensor data is also useful for predicting system operation and network failures. Based on over one million data readings, we analyze the node and network design and develop network reliability profiles and failure models. Jobanputra Paresh Ashokkumar | Prof. Arun Jhapate "A Review of Sensor Node in Wireless Sensor Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23620.pdf
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Onkar, Singh. "Energy Efficient Routing Protocol for Wireless Sensor Networks: A Review." International Journal of Advances In Scientific Research and Engineering (IJASRE) 3, no. 4 (2017): 95–100. https://doi.org/10.5281/zenodo.583818.
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A wireless sensor network is a collection of sensor nodes arranged into a Prespecified/random way in the concerned Geographical region. Nowadays, wireless sensor networks (WSNs) are dramatically becoming more popular and widely being used in variety of application like battlefield, medicals and several other areas because they are cheap in cost and have the ability to sense data irrespective of environment conditions. Routing in WSNs consumes the most of sensors nodes energy if we are able to make an energy conserving routing protocol then we will be able to conserve the considerable amount of energy which will enhance the Network lifetime.
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Kannan, K. Nattar, and B. Paramasivan. "Enhancing Energy Efficiency in Wireless Sensor Networks Using Optimal Gradient Routing Protocol." International Journal of Computer and Communication Engineering 3, no. 6 (2014): 408–12. http://dx.doi.org/10.7763/ijcce.2014.v3.359.
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Park, Sea Young, Dai Yeol Yun, TaeHyeon Kim, Jong-Yong Lee, and Daesung Lee. "An Energy Efficient Enhanced Dual-Fuzzy Logic Routing Protocol for Monitoring Activities of the Elderly Using Body Sensor Networks." Electronics 9, no. 5 (2020): 723. http://dx.doi.org/10.3390/electronics9050723.
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Wireless body area networks (WBANs) are an important application in wireless sensor networks (WSNs). Specifically, in healthcare monitoring systems, it is important to screen the patient’s biometric signals. For example, the elderlies’ vital signs, such as ECG (Electrocardiogram), blood pressure, heart rate, and blood glucose, can be used as measures of their well-being and are all critically important for remote elderly care in tracking their physical and cognitive capabilities. Therefore, WBANs require higher energy efficiency and data transmission. This paper proposes a cluster-based routing protocol which is suitable for WBANs while analyzing energy efficiency issue in data transmission. Considering the importance of sensor nodes in a specific environment for improving the network’s lifetime, the protocol based on the LEACH (low energy adaptive clustering hierarchy) algorithm is proposed. Due to its avoidance of long-distance transmission, the clustering technique is an efficient algorithm for prolonging the lifetimes of sensor networks. Therefore, this paper suggests an enhanced LEACH-dual fuzzy logic (ELEACH-DFL) protocol based-on clustering for CH (cluster head) selection and cluster configuration in wireless sensor networks. The simulation and analysis results address that the enhanced algorithm reduces the energy consumption effectively and extends the lifespan of the entire network. For wired sensors, attaching sensors to the user may cause problems and inconvenience of mobility. This leads to the use of wireless sensors to proceed with body sensors, which should consider the problem of battery efficiency, which concerns the configuration of wireless sensors. The LEACH protocol is energy efficient until the first node dead is generated. However, there is a sharp drop in energy efficiency after that. The ELEACH-DFL protocol has the advantage of maintaining energy efficiency even after the first node dead is generated, with the utmost consideration being given to stability in consideration of cluster selection and cluster head selection. In a field of 50 × 50, the FND efficiency improvement rate of ELEACH-DFL versus LEACH protocol is approximately 32%. In addition, in a field of 50 × 150, the FND efficiency improvement rate of ELEACH-DFL versus LEACH protocol is approximately 159%.
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Sendra, Sandra, Jaime Lloret, Raquel Lacuesta, and Jose Miguel Jimenez. "Energy Efficiency in Cooperative Wireless Sensor Networks." Mobile Networks and Applications 24, no. 2 (2016): 678–87. http://dx.doi.org/10.1007/s11036-016-0788-3.
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Zhang, Meiyan, and Wenyu Cai. "Data Collecting and Energy Charging Oriented Mobile Path Design for Rechargeable Wireless Sensor Networks." Journal of Sensors 2022 (April 8, 2022): 1–14. http://dx.doi.org/10.1155/2022/5004507.
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Energy efficiency is one of the most important concerns in wireless sensor networks (WSNs). As far as we know, almost all energy efficiency researches of WSNs focus on energy conservation in some respects such as wireless data transmission and minimal data collection. Recently, wireless energy transfer has been a promising technology to prolong the lifetime of microsensor nodes, and so the traditional WSNs can be extended to rechargeable WSNs. Rechargeable WSNs is a new type of wireless sensor networks, where each sensor node can replenish energy through wireless charging. For rechargeable WSNs, it is powered by reusable energy or harvested energy, so the energy efficiency problem can be completely solved. Furthermore, mobile data collection has been well recognized to have significant advantages over sensory data collection manner using static sinks. In this paper, by employing one or multiple recharging sinks to replenish energy for sensor nodes and collect sensory data concurrently, we propose a novel wireless charging and mobile data collecting method based on self-organizing map (SOM) unsupervised learning for rechargeable WSNs. In other words, the sink mobility and energy replenishment are jointly considered in this paper. Finally, we evaluate the performance of the proposed algorithms through software simulation. Extensive results verify that the performance of the proposed algorithm can reduce the travel cost of mobile sink and improve the residual energy level for sensor nodes. As a results, it is very promising in the field of data acquisition in wireless sensor networks.
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Xiao, Wen Hong, and Xiang Dong Cai. "A Novel Wireless Sensor Network Model Based on Complex Network Theory." Advanced Materials Research 546-547 (July 2012): 1276–82. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.1276.
Full textAbstract:
The key issue of wireless sensor networks is to balance the energy costs of the entire network, to enhance the robustness of the entire sensor network. Sensor networks as a special kind of complex network, in particular, environmental constraints, and more from the traditional complex networks, such as Internet networks, ecological networks, social networks, is to introduce a way of wireless sensor networks way of complex networks theory and analytical method, the key lies in, which is a successful model of complex network theory and analysis methods, more suitable for the application of wireless sensor networks, in order to achieve certain characteristics of some wireless sensor networks to optimize the network. Considering multi-hop transmission of sensor network, this paper has proposed a maximum restriction on the communication radius of each sensor node; in order to improve the efficiency of energy consumption and maintain the sparsely of the entire network, this paper has also added a minimum restriction on the communication radius of each sensor node to the improved model; to balance the energy consumption of the entire network, The simulation results show that proposed improvements to the entire network more robust to random failure and energy costs are more balanced and reasonable. This is more applicable to wireless sensor networks.
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Manhas, Prabal, Amrit Raj Shankar, and Girjanand Tiwary. "Enhancing the Wireless Sensor Network Efficiency using Coverage and Energy Utilization Technique." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 3979–82. http://dx.doi.org/10.22214/ijraset.2023.51181.
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Abstract: Wireless sensor networks have witnessed a major growth in several domains such as the industrial sector and research. Sensor network is one of the major part of automation application. Data from a small area to a large area can be collected efficiently using these sensor networks. But these sensors also have some major drawbacks such as the coverage issues, battery life, so in this research paper we have described about some major challenges and the algorithms using which we can ultimately enhance the coverage and life span of battery
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Bendjima, Mostefa, and Mohammed Feham. "Intelligent Communication in Wireless Sensor Networks." Future Internet 10, no. 9 (2018): 91. http://dx.doi.org/10.3390/fi10090091.
Full textAbstract:
Wireless sensor networks (WSN) are designed to collect information by means of a large number of energy-limited battery sensor nodes. Therefore, it is important to minimize the energy consumed by each sensor, in order to extend the network life. The goal of this work is to design an intelligent WSN that collects as much information as possible to process it intelligently. To achieve this goal, an agent is sent to each sensor in order to process the information and to cooperate with neighboring sensors while mobile agents (MA) can be used to reduce information shared between source nodes (SN) and send them to the base station (Sink). This work proposes to use communication architecture for wireless sensor networks based on the multi-agent system (MAS) to ensure optimal information collection. The collaboration of these agents generates a simple message that summarizes the important information in order to transmit it by a mobile agent. To reduce the size of the MA, the sensors of the network have been grouped into sectors. For each MA, we have established an optimal itinerary, consuming a minimum amount of energy with data aggregation efficiency in a minimum time. Successive simulations in large-scale wireless sensor networks through the SINALGO (published under a BSD license) simulator show the performance of the proposed method, in terms of energy consumption and package delivery rate.
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Liang, Xiurong, and You Qian. "Energy Balance Routing Protocol for Wireless Sensor Networks Based on Fuzzy Control Strategy." Wireless Communications and Mobile Computing 2022 (May 27, 2022): 1–12. http://dx.doi.org/10.1155/2022/4597992.
Full textAbstract:
The existing routing protocols for wireless sensor networks were not reasonable in design, which limited their application. Most of the existing studies did not take into account the energy consumption of the network and the balanced use of the energy of sensor nodes, which led to the unsatisfactory application effect of wireless sensor networks in some fields. Therefore, from the perspective of energy balance in wireless sensor networks, this paper proposed a construction method of an energy balance routing protocol in wireless sensor networks based on a fuzzy control strategy. Firstly, based on the analysis of the basic composition of wireless sensor networks and the structure of sensor nodes, this paper expounded the basic process of wireless data transmission and summarized the classification and characteristics of routing protocols in wireless sensor networks from different angles. Secondly, according to the node data transmission characteristics of wireless sensor networks, the energy balance use model of sensor nodes was established, and the design method of the energy balance routing protocol based on fuzzy control strategy was proposed, and the data transmission link was optimized. Finally, through experimental comparative analysis, the results showed that the energy balanced routing protocol proposed in this paper can effectively realize the energy balanced use of the network data transmission process. Compared with other common routing protocols, the wireless sensor network routing protocol proposed in this paper can not only improve the data transmission efficiency and reduce the data redundancy but also save energy consumption and prolong the network running time. The design method of routing protocol proposed in this paper will be conducive to the optimization and application of routing protocol in wireless sensor networks and provide a theoretical basis for the related research of wireless sensor networks.
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Onyango, Catherine, Kibet Lang’at, and Dominic Konditi. "A Fuzzy Logic Based Cluster Head Election Technique for Energy Consumption Reduction in Wireless Sensor Networks." International Journal of Electrical and Electronics Research 11, no. 4 (2023): 1136–46. http://dx.doi.org/10.37391/ijeer-110434.
Full textAbstract:
Wireless sensor networks deploy sensor nodes to different areas for data collection. The small size of these sensor nodes allows limited energy storage capacity, and most applications of the networks do not support recharging the batteries once their energy is depleted. Research on energy efficiency in wireless sensor networks is thus an active area that seeks to minimize energy consumption so that the sensor nodes can live longer. Clustering, one of the energy consumption optimization techniques, is employed in this research. It splits the network into smaller groups for data collection and forwards the data to the base station via appointed cluster heads. A fuzzy-based cluster head election strategy is proposed here to improve energy efficiency in wireless sensor networks. The input parameters of the fuzzy inference system are chosen as the residual energy, the node centrality, and the mobility factor. The system generates an output of the chance of a node being selected as a cluster head based on the combination of the values of the given inputs. The simulation results show that the proposed model reduces the network’s overall energy consumption and extends the sensor nodes’ lifetime.
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Al Kabi, Amin. "Enhancement of Energy Harvesting Efficiency in Mobile Wireless Sensor Networks." WSEAS TRANSACTIONS ON COMMUNICATIONS 21 (May 19, 2022): 129–34. http://dx.doi.org/10.37394/23204.2022.21.18.
Full textAbstract:
Mobile wireless sensor networks suffer from the restricted availability of energy supplies. In this research work, a proposed method for extending the lifetime of energy-constrained mobile wireless sensor networks (MWSNs) is presented. This method is based on the fact that RF signal carries both information and energy at the same time. Hence, by increasing the efficiency of energy harvesting from radio frequency (RF) signals, the lifetime of the wireless network can be significantly extended. The Simultaneous Wireless Information and Power Transfer (SWIPT) technique enables harvesting of energy by relay nodes which in turn can be used for wireless data transmission. To enhance the lifetime of the mobile wireless network, the transmitted RF energy can be recycled at the receiver side. On the other hand, a balance between energy harvesting and wireless data transmission is required in to maximize the overall efficiency of the system. Particle Swarm Optimization (PSO) is employed to obtain the optimum resource allocation policy which maximizes the system energy efficiency. A cost function is framed for this purpose and PSO attains the maximum energy efficiency by improving the solution of the cost function at each iteration with respect to given constraints.
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Shao, Lingfeng, Junjie Yang, and Jicheng Fang. "A Distributed Optimization Algorithm for Energy of Wireless Sensor Networks Based on Potential Game." International Journal of Photoenergy 2020 (January 21, 2020): 1–11. http://dx.doi.org/10.1155/2020/4745678.
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Recently, we have witnessed the rapid development of techniques on upgrading energy efficiency for wireless sensor networks (WSN). With the improvement of the detection range and the detection intensity, the lifetime of wireless sensor networks (WSN) is still limited by sensor node batteries (BA). Due to the need for wireless sensor network energy optimization, the power supply side has been putting forward higher requirements, and the traditional wireless sensor network with energy supplement has difficulty in meeting this development trend. The game and potential game concepts were introduced to take economics into account. Taking the wireless sensor network (WSN) with photovoltaic (PV) array charging and mobile-charging car (MCC) as an example, a running optimization model based on potential game is proposed, and the existence of Nash equilibrium has been proven. The iterative solution is completed by communication between the players, and the energy utilization rate is effectively improved. This paper verifies that potential game theory can be used to improve the feasibility and efficiency of wireless sensor network energy optimization.
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Lokesh, Deepika, and N. V. Uma Reddy. "Energy Efficient Low Latency Routing Design for Target Tracking Applications of Wireless Sensor Network." International Journal of Circuits, Systems and Signal Processing 16 (June 1, 2022): 1018–26. http://dx.doi.org/10.46300/9106.2022.16.124.
Full textAbstract:
Target tracking is the greatest important applications in Wireless Sensor Networks (WSNs). The wireless sensor network applications have been increasing since the IoT has been established. Most of the applications have various kind of sensors to transmit the information from one source to another. The basic operation of a wireless sensor network is to sense the data, collect the data and transmit the data from time to time whenever the base station requires the data for evaluation. Improving the reliability, performance for the collection of the data is the main role of the wireless sensor device. Moreover, the objective of the wireless sensor network device is to minimize the latency and improve the energy efficiency in order to provide more reliability is a major performance metric for provisioning WSNs. In this paper, we have presented an Energy Efficient Low Latency Routing (EELLR) design for Target Tracking (TT) Applications of Wireless Sensor Network. This model provides reliability and has a better performance in terms of communication overhead, energy efficiency and packet processing latency reduction when compared with the existing routing-based models.
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Ordouei, Mohammad, Ali Broumandnia, Touraj Banirostam, and Alireza Gilani. "Providing A Novel Distributed Method For Energy Management In Wireless Sensor Networks Based On The Node Importance Criteria." Journal of Namibian Studies: History Politics Culture 34 (November 20, 2023): 5252–65. https://doi.org/10.5281/zenodo.10161751.
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Energy management in wireless sensor networks has great importance. In these networks, sensor nodes typically operate on batteries and due to limited energy resources, energy management is crucial for optimal performance and battery lifetime of these nodes. The use of suitable energy management algorithms in wireless sensor networks can help improve network efficiency and increase the battery life of sensor nodes. In this paper, a new method is proposed for optimal energy consumption management in wireless sensor networks. In the proposed method, each node selects a cluster for itself in a distributed manner or chooses itself as the cluster head. This method is designed based on the importance of nodes. The proposed method was implemented in different scenarios for wireless sensor networks such as sparse and dense wireless sensor networks. In all simulations, the proposed method demonstrated good capabilities in optimal energy consumption management.
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Zang, Yue, Yuyang Peng, Sangdon Park, Han Hai, Fawaz AL-Hazemi, and Mohammad Meraj Mirza. "A Novel Cooperative Transmission Scheme in UAV-Assisted Wireless Sensor Networks." Electronics 11, no. 4 (2022): 600. http://dx.doi.org/10.3390/electronics11040600.
Full textAbstract:
In wireless sensor networks (WSNs), the efficiency of data transmission within a limited time is critical, especially for sensors designed with small batteries. In this paper, we design a cooperative transmission scheme with an energy-charging function in a WSN where an unmanned aerial vehicle (UAV) is considered for sensory data collection and energy charging. Specially, the sensor nodes are powered by the UAV for their data transmission. In the first phase, the UAV transmits the energy signal to the sensor nodes distributed on the ground. All the energy received by the sensor nodes is used to collect and transmit the sensory data to the UAV. In the second phase, local data transmissions are conducted among the collaborating sensor nodes in one cluster. In the third phase, the cooperative nodes send the collected sensory data to the UAV in the form of cooperative transmission. In the proposed scheme, we discovered that the size of the modulation constellation and the assigned time ratio of each phase were the key factors affecting the data transmission efficiency. In order to achieve the maximum data transmission, the optimal modulation constellation size and the optimal time ratio of each phase were found using the Lagrange multiplier method. Numerical results show that the proposed scheme with the optimal constellation size and the optimal time ratio can outperform the existing scheme in terms of the data transmission efficiency.
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Pranjal, Singh. "Increasing energy efficiency in wireless sensor networks using cognitive radios." Journal of Information Sciences and Computing Technologies 1, no. 1 (2015): 55–58. https://doi.org/10.5281/zenodo.4014586.
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Energy saving has been one of the major design criteria for wireless sensor networks. Cognitive radio is an enabling technology for dynamic spectrum access in opportunistic manner. In the present work, we assume that wireless sensor network consists of smart radios embedded with cognitive radio capability. These smart radio nodes can sense white space available and may decide the use of channels with the best propagation characteristics. In this paper, we investigate the use of cognitive radio in wireless sensor networks which can use the frequency from white space for their operation in opportunistic manner. Cognitive radio enabled sensor nodes can adapt their system parameters according to the propagation environment. Through numerical results, we have reported that communication range available to the cognitive radio enabled sensor nodes is significantly large as compared to that available to the conventional sensor nodes. This arises due to better radio propagation characteristics. Enhanced communication range can be traded off for energy saving for the given network coverage or communication range, which ultimately leads to higher energy efficiency. Numerical results show relative energy saving on the order of 0.0055 for given system parameters.
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Gaudence, Tesha, and Eliphas Frank Tongora. "ENERGY HARVESTING AND RECHARGING FOR WIRELESS SENSOR NETWORKS." POLISH JOURNAL OF SCIENCE, no. 55 (October 19, 2022): 29–33. https://doi.org/10.5281/zenodo.7223717.
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Minimizing energy consumption for wireless sensor networks lifetime is one of the main problems in wireless sensor networks. The problem is long term autonomous operation of wireless sensor networks. This implies harvesting and recharging the sensor nodes during it is operation by employing the energy harvesting method using the sunlight available. The optimized solar energy harvesting and recharging wireless sensor networks operate for an infinite wireless sensor networks lifetime. The simulation results indicate the highest stability for energy harvesting and recharging the wireless sensor networks that indicates good throughput.
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Yang, Chun Xi, Chao Sun, Sha Fan, and Ning Wu. "Cluster-Gossip Based Distributed Kalman Consensus Filter Algorithm with Energy Efficiency." Applied Mechanics and Materials 667 (October 2014): 291–99. http://dx.doi.org/10.4028/www.scientific.net/amm.667.291.
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According to these constrains that wireless sensor networks are composed of many wireless nodes with limited power, a new energy efficient cluster-based distributed consensus kalman filtering algorithm is proposed in this paper. In this algorithm, each cluster contains a cluster-head and some member nodes where the cluster-head is used to fuse data which come from member nodes and consensus process between neighbor cluster-head. This clustering method divide wireless sensor networks into two classes of networks: cluster units network and cluster-heads network. In this way, numbers of information transmission among nodes are reduced efficiently and communication distances among nodes are also shortened. As a result, node’s energy in wireless sensor network can be saved greatly. Moreover, Gossip algorithm is introduced to deal with the consensus problem between cluster-heads for improving power consumption and the convergence analysis for the algorithm which is given by applying to graph theory and matrix theory. Finally, a simulation example is given to show the effectively of our method.
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Habibi, Payman, Goran Hassanifard, Abdulbaghi Ghaderzadeh, and Arez Nosratpour. "Offering a Demand-Based Charging Method Using the GBO Algorithm and Fuzzy Logic in the WRSN for Wireless Power Transfer by UAV." Journal of Sensors 2023 (May 2, 2023): 1–19. http://dx.doi.org/10.1155/2023/6326423.
Full textAbstract:
An extremely high number of geographically dispersed, energy-limited sensor nodes make up wireless sensor networks. One of the critical difficulties with these networks is their network lifetime. Wirelessly charging the sensors continuously is one technique to lengthen the network’s lifespan. In order to compensate for the sensor nodes’ energy through a wireless medium, a mobile charger (MC) is employed in wireless sensor networks (WRSN). Designing a charging scheme that best extends the network’s lifetime in such a situation is difficult. In this paper, a demand-based charging method using unmanned aerial vehicles (UAVs) is provided for wireless rechargeable sensor networks. In this regard, first, sensors are grouped according to their geographic position using the K-means clustering technique. Then, with the aid of a fuzzy logic system, these clusters are ranked in order of priority based on the parameters of the average percentage of battery life left in the sensor nodes’ batteries, the number of sensors, and critical sensors that must be charged, and the distance between each cluster’s center and the MC charging station. It then displays the positions of the UAV to choose the crucial sensor nodes using a routing algorithm based on the shortest and most vital path in each cluster. Notably, the gradient-based optimization (GBO) algorithm has been applied in this work for intracluster routing. A case study for a wireless rechargeable sensor network has been carried out in MATLAB to assess the performance of the suggested design. The outcomes of the simulation show that the suggested technique was successful in extending the network’s lifetime. Based on the simulation results, compared to the genetic algorithm, the proposed algorithm has been able to reduce total energy consumption, total distance during the tour, and total travel delay by 26%, 17.2%, and 25.4%, respectively.
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Kaur, Chamanpreet, and Vikramjit Singh. "A REVIEW ON DATA COLLECTION USING MOBILE NODES IN WSN." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 16, no. 5 (2017): 6926–32. http://dx.doi.org/10.24297/ijct.v16i5.6263.
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Wireless sensor networks have become increasingly popular due to their wide range of application. Clustering sensor nodes organizing them hierarchically have proven to be an effective method to provide better data aggregation and scalability for the sensor network while conserving limited energy. Minimizing the energy consumption of a wireless sensor network application is crucial for effective realization of the intended application in terms of cost, lifetime, and functionality. However, the minimizing task is hardly possible as no overall energy cost function is available for optimization. The need for energy-efficient infrastructures for sensor networks is becoming increasingly important. Wireless sensor networks are networks consisting of many sensor nodes that communicate over a wireless media. A sensor node is equipped with a sensor module, a processor, a radio module and a battery. Since the battery limits the lifetime of the sensor nodes it also limits the lifetime of the sensor network, thus energy efficiency is a major issue for sensor networks. An important goal in many sensor networks is to monitor an area as long time as possible. Hence, it is important to distribute energy consumption evenly across the network. When the energy consumption is evenly distributed, the major part of the sensor nodes will stay alive approximately equally long time.
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ChinnaDurai, M., S. MohanKumar, and S. Sharmila. "Underwater Wireless Sensor Networks." COMPUSOFT: An International Journal of Advanced Computer Technology 04, no. 07 (2015): 1899–902. https://doi.org/10.5281/zenodo.14786460.
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The applications of underwater sensor network are oceanographic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation and tactical surveillance applications. The routing protocol designed for specific roles, leads to issues in the network. The major issues for development of routing protocol for underwater sensor network are harsh deployment environment, low bandwidth, high propagation delay, requires high battery energy, Temporary losses, Fouling and corrosion and High bit error rates. In this project the certain issues to be rectified are low bandwidth, energy efficiency and data delivery.
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C. Ramesh Kumar, C. Ramesh Kumar, T. Ganesh Kumar C. Ramesh Kumar, A. Hemlathadhevi T. Ganesh Kumar, and D. R. Thirupurasundari A. Hemlathadhevi. "An Energy Efficiency Based Secure Data Transmission in WBSN Using Novel Id-Based Group Signature Model and SECC Technique." 網際網路技術學刊 24, no. 3 (2023): 683–96. http://dx.doi.org/10.53106/160792642023052403014.
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<p>A wireless network composed of wearable sensing along with computing systems connected via a wireless communication channel is termed Wireless Body Sensor Network (WBSN). It enables continuous monitoring through sensors for medical and nonmedical applications. WBSN faces several security problems such as loss of information, access control, and authentication. As WBSN collects vital information and operates in an unfriendly environment, severe security mechanisms are needed in order to prevent the network from anonymous interactions. The different security threats are evaluated with the support of the data transmitted via the sensor networks amongst smart wearable devices. The whole network lifetime together with the Data Transmission (DT) quality is mitigated whilst performing DT utilizing sensor networks, which consume more energy. Hence, in this paper, an energy-efficient secure data transmission mechanism is proposed in WBSN using a novel authentication id-based group signature model and SECC technique. At first, the Group Manager (GM) is selected from the sensors in the remote body sensor system using Normalized Opposition Based Learning BAT Optimization Algorithm (NOBL-BOA). Afterward, clustering with Information Entropy induced K-Means Algorithm (IEKMA) takes place to improve energy efficiency. Next, to provide security to the WBSN, message authentication is carried out based on novel authentication ID-based group signature protocol. Finally, Secret key induced Elliptic Curve Cryptography (SECC) is used to encrypt the message for secure transmission. The simulation results reveal that in comparison with existing works, the proposed work achieves improved security and energy efficiency.</p> <p>&nbsp;</p>
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Dora, Sidhartha Sankar, and Prasanta Kumar Swain. "Feature Selection and Energy Management in Wireless Sensor Networks using Deep Learning." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9s (2023): 628–33. http://dx.doi.org/10.17762/ijritcc.v11i9s.7476.
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In wireless sensor networks, when the available energy sources and battery capacity are extremely constrained, energy efficiency is a major issue to be adressed. One of the main goals in the design of wireless sensor networks (WSNs) is to maximize longevity of battery life. Designers can benefit from the use of intelligent power utilization models to accomplish this goal. These models seek to decrease the number of chosen sensors used to record environmental measures in order to minimize power utilization while retaining the acceptable level of measurement accuracy. In order to simulate wireless sensor networks, we looked at real world datasets. Our simulation findings demonstrate that the suggested strategy can be used to accomplish significant goals by using the right number of sensors using deep learning, extend the lifespan of the wireless sensor networks.
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Benhadji, Mohammed, Mohammed Kaddi, and Mohammed Omari. "Atomic Energy Optimization for Wireless Sensor Network Clustering (AEOWSNC) Protocol for Energy-Efficient Wireless Sensor Networks." Engineering, Technology & Applied Science Research 15, no. 3 (2025): 22802–10. https://doi.org/10.48084/etasr.10631.
Full textAbstract:
This paper presents AEOWSNC (Atomic Energy Optimization for Wireless Sensor Network Clustering), a novel clustering protocol for Wireless Sensor Networks (WSNs), designed to optimize energy efficiency and extend network lifetime. Inspired by Atomic Energy Optimization (AEO), the algorithm aims to address key challenges in WSNs, such as efficient energy usage, live node maintenance, and ensuring high throughput to the Base Station (BS). AEOWSNC is evaluate through a series of experiments and its performance is compared with the ones of eight well-established meta-heuristic protocols, namely LEACH, LEACH-PWO, GWOC, CGC, LEACH-SAGA, PSO-ECHs, SA-LEACH, and PSCH-CH. The results demonstrate that AEOWSNC outperforms the other protocols in terms of network lifetime, residual energy, live nodes, and throughput at the BS. The protocol achieves superior energy management, prolonging the network's operational life while maintaining a high data transmission rate.
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Tang, Churan, and Linghua Zhang. "An Improved Flooding Routing Protocol for Wireless Sensor Networks Based on Network-Coding." ITM Web of Conferences 17 (2018): 02001. http://dx.doi.org/10.1051/itmconf/20181702001.
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A central question in wireless sensor network research is how to reduce the consumption of the energy of the sensor nodes. Theoretically, the network coding technology proposed by Ahlswede et al (2000) can improve the network reliability and network throughput, increase the robustness and save energy. Based on the classic flooding routing protocol, the present study proposes a new flooding control protocol, i.e. NC-Flooding for wireless sensor networks. NC-Flooding protocol introduces five mechanisms to enhance the efficiency of wireless sensor networks. As shown by MATLAB simulation results, NC-Flooding protocol reduces the number of broadcasts of wireless sensor networks, increases the throughput of the network and increases the bandwidth utilization. We conclude that NC-Flooding protocol reduces data forwarding cost and node energy consumption and extends nodes’ life cycle, thus increasing network utilization.
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Hamed Mahmood Ahmed and Dr Alireza Meshkin. "Anew Clustering Method to lmprove the Lifetime of Wireless Sensor Network Using Meta- Heuristic Methods." Journal of the College of Basic Education 30, no. 125 (2024): 35–52. http://dx.doi.org/10.35950/cbej.v30i125.12099.
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For greater performance, wireless sensor networks must have a longer lifespan. The lifespan of a wireless sensor network is based on how much energy each node consumes. Node energy consumption decreases as network lifespan grows. Because it requires less energy for information to go between nodes, proper clustering and the optimum selection of cluster heads are crucial to extending the network's lifespan. In this study, the K-means cluster method and the bat optimization algorithm were used to identify the optimum cluster head for the aggregation of a wireless sensor network. Based on the results of the simulated work, the K-means method and the bat algorithm together boost the efficiency of the wireless sensor system.
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Buzura, Sorin, Bogdan Iancu, Vasile Dadarlat, Adrian Peculea, and Emil Cebuc. "Optimizations for Energy Efficiency in Software-Defined Wireless Sensor Networks." Sensors 20, no. 17 (2020): 4779. http://dx.doi.org/10.3390/s20174779.
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Software-defined wireless sensor networking (SDWSN) is an emerging networking architecture which is envisioned to become the main enabler for the internet of things (IoT). In this architecture, the sensors plane is managed by a control plane. With this separation, the network management is facilitated, and performance is improved in dynamic environments. One of the main issues a sensor environment is facing is the limited lifetime of network devices influenced by high levels of energy consumption. The current work proposes a system design which aims to improve the energy efficiency in an SDWSN by combining the concepts of content awareness and adaptive data broadcast. The purpose is to increase the sensors’ lifespan by reducing the number of generated data packets in the resource-constrained sensors plane of the network. The system has a distributed management approach, with content awareness being implemented at the individual programmable sensor level and the adaptive data broadcast being performed in the control plane. Several simulations were run on historical weather and the results show a significant decrease in network traffic. Compared to similar work in this area which focuses on improving energy efficiency with complex algorithms for routing, clustering, or caching, the current proposal employs simple computing procedures on each network device with a high impact on the overall network performance.
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Prabhu, Boselin, and Bala Kumar. "HIGHLY DISTRIBUTED AND ENERGY EFFICIENT CLUSTERING ALGORITHM FOR WIRELESS SENSOR NETWORKS." International Journal of Research -GRANTHAALAYAH 4, no. 9 (2016): 30–38. http://dx.doi.org/10.29121/granthaalayah.v4.i9.2016.2531.
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Wireless sensor network (WSN) is a low-powered prestigious network fashioned by sensor nodes that treasures application in civilian, military, visual sense models and many others. Reduced energy utilization is an exigent task for these sensor networks. By the data aggregation procedure, needless communication between sensor nodes, cluster head and the base station is eluded. An evaluation of energy efficient optical low energy adaptive clustering hierarchy has been performed and the enactments have been compared with the prevailing low energy adaptive clustering hierarchy algorithm, between two detached wireless sensor network fields. The proposed clustering procedure has been primarily implemented to join two distinct wireless sensor fields. An optical fiber is used to join two reserved wireless sensor fields. This distributed clustering methodology chiefly targets in exploiting the parameters like network lifetime, throughput and energy efficiency of the whole wireless sensor system.