Relevant bibliographies by topics / Low energy adaptive clustering hierarchy algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Low energy adaptive clustering hierarchy algorithm'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Low energy adaptive clustering hierarchy algorithm"

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Sakharkar, Gourav. "Smart Grid Simulation with LEACH (Low-Energy Adaptive Clustering Hierarchy) Clustering." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 315–20. http://dx.doi.org/10.22214/ijraset.2023.56483.

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Power industry applications for smart grid are quite promising. In recent years, network life extension has attracted a lot of study attention. By examining the business needs of the Smart grid, this study enhances the wireless sensor network LEACH algorithm. To ensure the rationality of the cluster head selection, the remaining energy and node density are first taken into consideration. The cluster radius of various sizes is then constructed while clustering using a non-uniform clustering process. To achieve the goal of balancing the energy load, data is transmitted to the base station using
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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 tw
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Chen, Qi Gong, Yong Zhi Wang, Li Sheng Wei, and Wen Gen Gao. "Improvement of Clustering Algorithm for WSNs." Applied Mechanics and Materials 665 (October 2014): 745–50. http://dx.doi.org/10.4028/www.scientific.net/amm.665.745.

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Energy consumption is a hot issue in WSNs (Wireless Sensor Networks). In this paper, we present an improved clustering algorithm. By changing the order of traditional WSNs clustering algorithm, this algorithm uses k-means clustering firstly base on optimal number of cluster head is determined; Then selects cluster head by an improved LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm; Finally, Our experimental results demonstrate that this approach can reduces energy consumption and increases the lifetime of the WSNs.
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Kumar, Arun, Sumit Chakravarthy, Nishant Gaur, and Aziz Nanthaamornphong. "Ad hoc wireless network implementing BEE-LEACH." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (2024): 2945. http://dx.doi.org/10.11591/ijece.v14i3.pp2945-2954.

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Adaptations have been key to the development and advancement of the low energy adaptive clustering hierarchy (LEACH) protocol. Presented is an alteration to the traditional LEACH, low energy adaptive clustering hierarchy, algorithm. This algorithm focuses on the battery life optimization of sensors within a wireless sensor network (WSN). These sensors will be grouped into clusters with the aim of maximizing the battery life of the overall system by sorting each sensor by residual energy and assigning the highest residual energy the role of cluster head. The protocol will then assign sensors to
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Zhou, Xi Yi, Li Qiang Liu, and Ge Zhang. "An Improved Clustering Algorithm for Wireless Sensor Networks." Advanced Materials Research 926-930 (May 2014): 2654–57. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2654.

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An on-demand weighed clustering algorithm is proposed against the limitation of wireless sensor network nodes with limited energy. This algorithm obtains more local network information by means of information interaction between nodes, and by comprehensively considering such factors as the current energy value, degree and transmission power, etc. of nodes, it makes different clustering decisions as per different application backgrounds of networks. Simulation results show that this algorithm has a more rational clustering by reducing energy consumption and lengthening network lifetime, compare
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Sharmin, Sharmin, Ismail Ahmedy, and Rafidah Md Noor. "An Energy-Efficient Data Aggregation Clustering Algorithm for Wireless Sensor Networks Using Hybrid PSO." Energies 16, no. 5 (2023): 2487. http://dx.doi.org/10.3390/en16052487.

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Extending the lifetime of wireless sensor networks (WSNs) and minimizing energy costs are the two most significant concerns for data transmission. Sensor nodes are powered by their own battery capacity, allowing them to perform critical tasks and interact with other nodes. The quantity of electricity saved from each sensor together in a WSN has been strongly linked to the network’s longevity. Clustering conserves the most power in wireless transmission, but the absence of a mechanism for selecting the most suitable cluster head (CH) node increases the complexity of data collection and the powe
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Kumar, Arun, Sumit Chakravarthy, Nishant Gaur, and Aziz Nanthaamornphong. "Ad hoc wireless network implementing BEE-LEACH." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (2024): 2945–54. https://doi.org/10.11591/ijece.v14i3.pp2945-2954.

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Adaptations have been key to the development and advancement of the low energy adaptive clustering hierarchy (LEACH) protocol. Presented is an alteration to the traditional LEACH, low energy adaptive clustering hierarchy, algorithm. This algorithm focuses on the battery life optimization of sensors within a wireless sensor network (WSN). These sensors will be grouped into clusters with the aim of maximizing the battery life of the overall system by sorting each sensor by residual energy and assigning the highest residual energy the role of cluster head. The p
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Jemal, Adem Fanos, Redwan Hassen Hussen, Do-Yun Kim, Zhetao Li, Tingrui Pei, and Young-June Choi. "Energy-efficient selection of cluster headers in wireless sensor networks." International Journal of Distributed Sensor Networks 14, no. 3 (2018): 155014771876464. http://dx.doi.org/10.1177/1550147718764642.

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Clustering is vital for lengthening the lives of resource-constrained wireless sensor nodes. In this work, we propose a cluster-based energy-efficient router placement scheme for wireless sensor networks, where the K-means algorithm is used to select the initial cluster headers and then a cluster header with sufficient battery energy is selected within each cluster. The performance of the proposed scheme was evaluated in terms of the energy consumption, end-to-end delay, and packet loss. Our simulation results using the OPNET simulator revealed that the energy consumption of our proposed schem
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Fachrizal, Ferry, Muhammad Zarlis, Poltak Sihombing, and Suherman Suherman. "Optimization of the LEACH algorithm in the selection of cluster heads based on residual energy in wireless sensor networks." Eastern-European Journal of Enterprise Technologies 1, no. 9 (127) (2024): 14–21. http://dx.doi.org/10.15587/1729-4061.2024.298268.

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This research has a research object, namely the optimization of the LEACH (Low-Energy Adaptive Clustering Hierarchy) algorithm in the context of wireless sensor networks. The problem in this research is the imbalance in energy consumption across clusters, which has an impact on battery life and affects network performance. Other problems include selecting a cluster head that is not focused so that it is difficult to balance network performance as well as computational limitations that require optimization. The results obtained from this research are in the form of optimizing the leaching algor
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Saleh, Mohammed Mehdi, Ruslan Saad Abdulrahman, and Aymen Jaber Salman. "Energy‑harvesting and energy aware routing algorithm for heterogeneous energy WSNs." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 2 (2021): 910. http://dx.doi.org/10.11591/ijeecs.v24.i2.pp910-920.

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Wireless sensor networks are regarded as the most essential components of contemporary technologies since they are in charge of sensing and monitoring processes, which are the primary functions of these technologies. Because these nodes rely on an unchangeable battery and are randomly deployed in the environment, node energy management is the most essential issue to consider when designing algorithms to enhance the network's life. Clustering is a wireless sensor network (WSN) routing technique that has been implemented in order to extend network lifetime. Also, it is trendy to increase the ene
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Dissertations / Theses on the topic "Low energy adaptive clustering hierarchy algorithm"

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Pawa, Taran Deep Singh. "Analysis of Low Energy Adaptive Clustering Hierarchy (LEACH) protocol." Thesis, 2011. http://ethesis.nitrkl.ac.in/2493/1/reportfinal.pdf.

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Sensor network consists of tiny sensors and actuators with general purpose computing elements to cooperatively monitor physical or environmental conditions, such as temperature, pressure, etc. Wireless Sensor Networks are uniquely characterized by properties like limited power they can harvest or store, dynamic network topology, large scale of deployment. Sensor networks have a huge application in fields which includes habitat monitoring, object tracking, fire detection, land slide detection and traffic monitoring. Based on the network topology, routing protocols in sensor networks can be clas
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Chavali, Sai Prakash. "Design of Modified LEACH(Low Energy Adaptive Clustering hierarchy) Protocol for Wireless Sensor Networks." Thesis, 2018. http://ethesis.nitrkl.ac.in/9629/1/2018_MT_216CS1145_CSPrakash_Design.pdf.

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In Wireless Sensor Networks,the network lifespan is affected by the energy utilization of each node, more than the utilization of energy rises more than the lifespan of the network falls, so to prolong the network lifetime needs a protocol which minimizes the power utilization of sending or receiption of data by the sensor nodes. Recently much study is carried out to prolong the network lifetime. To handle with this, the hierarchical protocols developed to minimize the traffic of network toward the sink and hence increase the lifespan of the network. Out of these, clustering algorithms have i
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Huang, Bo. "Multi-level low energy adaptive clustering hierarchy (ML-LEACH) : a routing solution for periodic data sensing in large-area wireless sensor network." Thesis, 2007. http://hdl.handle.net/10125/20566.

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Book chapters on the topic "Low energy adaptive clustering hierarchy algorithm"

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Jadhav, Savita, Ishwar Panpaliya, and Sangeeta Jadhav. "Comparative Study of Low-Energy Adaptive Clustering Hierarchy Protocols." In Futuristic Communication and Network Technologies. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4625-6_33.

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Pundir, Sumit, Mohammad Wazid, Ayan Bakshi, and Devesh Pratap Singh. "Optimized Low-Energy Adaptive Clustering Hierarchy in Wireless Sensor Network." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4851-2_4.

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Yadav, Amita, and Suresh Kumar. "Analysis and Simulation of Low-Energy Adaptive Clustering Hierarchy Protocol." In Advances in Intelligent Systems and Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6005-2_38.

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Thakkar, Ankit, and Ketan Kotecha. "Alive Nodes Based Improved Low Energy Adaptive Clustering Hierarchy for Wireless Sensor Network." In Advanced Computing, Networking and Informatics- Volume 2. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07350-7_6.

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Chidambaram, S., Pretty Diana Cyril C., and S. Geetha. "Evolutionary Algorithm-Based Solutions for Energy Efficiency in Wireless Sensor Networks." In Advances in Computer and Electrical Engineering. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7600-3.ch008.

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In recent years, technological developments in the fields of industrial control, healthcare, environmental monitoring, and wireless sensor networks (WSN) have opened new opportunities. Numerous sectors employ wireless sensor networks, which offer a number of advantages including low cost, easy installation, reliable transmission, and superior anti-interference capability. Energy is the primary barrier reducing the existence of sensor networks, and wireless network nodes may be complicated or unfeasible to recharge or restore their power supply. The network nodes in charge of processing data and transferring information are constrained by the battery. Increasing the network's total lifespan is therefore essential to network optimization. Two important factors that are relevant in any application are the network's lifetime and the energy consumption for routing. In this chapter, the functionalities of genetic algorithms and low energy adaptive clustering hierarchy algorithm are discussed. This study provides a comparison of various energy optimization algorithms in terms of supporting mobility, power management, and multi-path routing.
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Mukherjee, Proshikshya. "LEACH-VD." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9004-0.ch005.

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Wireless sensor networks act as an important role in the wireless communication area because of its properties, its intelligence, cheaper costs, and its smaller size. Multiple nodes are required for coperative communication, the low energy adaptive clustering hierarchy and LEACH-Vector Quantization are used for cluster and active cluster headformation. Further, Dijkstra Algorithm is used to find the shortest path between the active CHs and high-energy utilization, respectively. The main issue of inter-cluster communication is carried out in earlier work using LEACH and LEACH-V protocols. The chapter illustrates the LEACH-Vector Quantization Dijkstra protocol for shortest path active CH communication in a cooperative communication network. In the application point of view, LEACH-VD performs the lowest energy path. LEACH-V provides the intra-cluster communication between the cluster head, and using Dijkstra Algorithm, the minimum distance is calculated connecting the active cluster heads, which creates the shortest path results using an energy-efficient technique.
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Haque, Enamul, and Norihiko Yoshida. "Clustering in Wireless Sensor Networks." In Internet and Distributed Computing Advancements. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0161-1.ch008.

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Applications of Wireless Sensor Networks (WSN) have been expanded from industrial operation to daily common use. With the pace of development, a good number of state-of-the-art routing protocols have been proposed for WSN. Among many of these protocols, hierarchical or cluster-based protocol technique is adopted from the wired network because of its scalability, better manageability, and implicit energy efficiency. In this chapter, the authors have surveyed Low Energy Adaptive Clustering Hierarchy, Power-Efficient Gathering in Sensor Information Systems, Adaptive Periodic Threshold-Sensitive Energy Efficient Sensor Network, and Hybrid Energy-Efficient Distributed Routing Protocols. These protocols exhibit notable characteristics and advantages compared to their contemporaries. Again, context aware computing and applications have been greatly emphasized in recent articles by renowned technologists. This approach is considered as a momentous technology that will change the way of interaction with information devices. Accordingly, context aware clustering technique carries a great deal of importance among WSN routing protocols. Therefore, the authors have investigated noteworthy context aware routing protocols such as: Context Adaptive Clustering, Data-Aware Clustering Hierarchy, Context-Aware Clustering Hierarchy, and Context-Aware Multilayer Hierarchical Protocol. Their investigation and analysis of these protocols has been included in this chapter with useful remarks. Context awareness is considered an integral part of Body Sensor Networks (BSN), which is one kind of WSN. Thus, the authors have also discussed issues related to context aware techniques used in BSN.
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Sarella, Venkata Ramana, Deshai Nakka, Sekhar B. V. D. S., Krishna Rao Sala, and Sameer Chakravarthy V. V. S. S. "An Experimental Analysis of Modified EEECARP." In Advances in Library and Information Science. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1786-4.ch012.

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Designing various energy-saving routing protocols for real-time internet of things (IoT) applications in modern secure wireless sensor networks (MS-WSN) is a tough task. Many hierarchical protocols for WSNs were not well scalable to large-scale IoT applications. Low energy adaptive two-level-CH clustering hierarchy (LEATCH) is an optimized technique reduces the energy-utilization of few cluster heads, but the LEATCH is not suitable for scalable and dynamic routing. For dynamic routing in MS-WSN, energy efficiency and event clustering adaptive routing protocol (EEECARP) with event-based dynamic clustering and relay communication by selecting intermediates nodes as relay-nodes is necessary. However, EEECARP cannot consider the hop-count, different magnitude ecological conditions, and energy wastage in cluster formation while collisions occur. So, the authors propose the modified EEECARP to address these issues for better dynamic event clustering adaptive routing to improve the lifetime of MS-WSNs. The experimental outcomes show that proposed protocol achieves better results than EEECARP and LEATCH.
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El-Basioni, Basma M. Mohammad, Sherine M. Abd El-Kader, Hussein S. Eissa, and Mohammed M. Zahra. "Clustering in Wireless Sensor Network." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5170-8.ch013.

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The purpose of this chapter is the study of the clustering process in Wireless Sensor Networks (WSN), starting with clarifying why there are different clustering protocols for WSN by stating and briefly describing some of the variate features in their design; these features can represent questions the clustering protocol designer asks before the design, and their brief description can be considered probabilities for these questions’ answers to represent design options for the designer. The designer can choose the best answer to each design question or, in better words, the best design options that will make its protocol different from the others and make the resultant clustered network satisfies some requirements for improving the overall performance of the network. The chapter also mentions some of these requirements. The chapter then gives illustrative examples for these design variations and requirements by studying them on three well-known clustering protocols: Low-Energy Adaptive Clustering Hierarchy (LEACH), Energy-Efficient Clustering Scheme (EECS), and Hybrid, Energy-Efficient, Distributed clustering approach for ad-hoc sensor networks (HEED).
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Fatima, Bouakkaz, Ali Wided, Guemmadi Sabrina, and Derdour Makhlouf. "K-Means Efficient Energy Routing Protocol for Maximizing Vitality of WSNs." In Computational Optimization Techniques and Applications. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.96567.

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The progress of wireless communication and microelectronics create wireless sensor network, which is a very important field of research, The utilization of Wireless Sensor Network is growing and have a diversity applications like Military applications, Agriculture, Health care, Medical monitoring. The main issue of WSN is energy consumption, where prolonged network lifetime, is important necessity. From the solution proposed the Clustering with k-means is a successful technique for achieving these goals. This work is adaptation of one of the most famous protocol in WSN witch is Low Energy Adaptive Clustering Hierarchy (LEACH) in the clustering phase where the choice of number of clusters and their CHs.sing the k-means method and the distance between nodes and residual energy. Clustering k-means given a best partition with cluster separation. This chapter regulated as below, in section two we discussed related work used k-means to improved vitality of WSN. In the next section, we introduce the proposed adaptation protocol. The simulation resultsusing MATLAB have shown that the proposed protocol outperforms LEACH protocol and optimizes the nodes energy and thenetwork lifetime.
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Conference papers on the topic "Low energy adaptive clustering hierarchy algorithm"

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Lagraini, Hajar, Mostafa Chhiba, Abdelmoumen Tabyaoui, and Ahmed Mouhsen. "New Approach for Cluster-head Selection Based on Low Energy Adaptive Clustering Hierarchy algorithm." In the 2nd International Conference. ACM Press, 2017. http://dx.doi.org/10.1145/3167486.3167536.

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Abdulsalam, Hanady M., and Layla K. Kamel. "W-LEACH: Weighted Low Energy Adaptive Clustering Hierarchy Aggregation Algorithm for Data Streams in Wireless Sensor Networks." In 2010 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2010. http://dx.doi.org/10.1109/icdmw.2010.28.

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Chen, Young-Long, Fu-Kai Cheung, and Yung-Chi Chang. "A Low-Energy Adaptive Clustering Hierarchy Architecture with an Intersection-Based Coverage Algorithm in Wireless Sensor Networks." In 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS). IEEE, 2013. http://dx.doi.org/10.1109/imis.2013.110.

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Alvarado, Gino, Carlos Bosquez, Fernando Palacios, and Luis Cordoba. "Low-energy Adaptive Clustering Hierarchy protocol and optimal number of cluster head algorithm in a randomized wireless sensor network deployment." In 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT). IEEE, 2017. http://dx.doi.org/10.1109/iceeccot.2017.8284632.

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Rafiq, Asma, Ehsan Ullah Munir, M. Mustafa Rafique, and Samee U. Khan. "A consistent approach towards clustering in low energy adaptive clustering hierarchy protocol." In 2015 12th International Conference on High-capacity Optical Networks and Enabling/Emerging Technologies (HONET). IEEE, 2015. http://dx.doi.org/10.1109/honet.2015.7395429.

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Bendjeddou, Amira, Houria Laoufi, and Saadi Boudjit. "LEACH-S: Low Energy Adaptive Clustering Hierarchy for Sensor Network." In 2018 International Symposium on Networks, Computers and Communications (ISNCC). IEEE, 2018. http://dx.doi.org/10.1109/isncc.2018.8531049.

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Palan, N. G., B. V. Barbadekar, and Suahs Patil. "Low energy adaptive clustering hierarchy (LEACH) protocol: A retrospective analysis." In 2017 International Conference on Inventive Systems and Control (ICISC). IEEE, 2017. http://dx.doi.org/10.1109/icisc.2017.8068715.

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Kishore, K. R., and N. V. S. N. Sarma. "AES based secure low energy adaptive clustering hierarchy for WSNs." In International Conference on Communication and Electronics System Design, edited by Vijay Janyani, M. Salim, and K. K. Sharma. SPIE, 2013. http://dx.doi.org/10.1117/12.2012337.

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Saha, Anik Kumar, Sharif Uddin Khan, and Mezanur Rahman. "Region based low energy adaptive clustering hierarchy (R-LEACH) protocol." In the 6th International Conference. ACM Press, 2019. http://dx.doi.org/10.1145/3362966.3362978.

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Farooq, Muhammad Omer, Abdul Basit Dogar, and Ghalib Asadullah Shah. "MR-LEACH: Multi-hop Routing with Low Energy Adaptive Clustering Hierarchy." In 2010 Fourth International Conference on Sensor Technologies and Applications (SENSORCOMM). IEEE, 2010. http://dx.doi.org/10.1109/sensorcomm.2010.48.

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Reports on the topic "Low energy adaptive clustering hierarchy algorithm"

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Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, 2022. http://dx.doi.org/10.4271/2022-01-0616.

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As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of ext
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