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Clustering in Wireless Sensor Networks
Published in Ankur Dumka, Sandip K. Chaurasiya, Arindam Biswas, Hardwari Lal Mandoria, A Complete Guide to Wireless Sensor Networks, 2019
Ankur Dumka, Sandip K. Chaurasiya, Arindam Biswas, Hardwari Lal Mandoria
Low Energy Adaptive Clustering Hierarchy (LEACH): Low energy adaptive clustering hierarchy (LEACH) is the first scheme which explores the advantages of clustering at its most, i.e., LEACH is the first clustering-based routing scheme for wireless sensor networks devised by Heinzelman et al. (2000). The key features of the scheme are: Localized coordination and control for cluster setup and operationRandomized rotation of the cluster heads and the corresponding clustersLocal compression to reduce global communication (Heinzelman et al., 2000)
Adaptive Routing in Wireless Sensor Networks
Published in Mohamed Ibnkahla, Adaptation and Cross Layer Design in Wireless Networks, 2018
Hong Luo, Guohua Zhang, Yonghe Liu, Sajal K. Das
Low-energy adaptive clustering hierarchy (LEACH) [6] is a cluster-based protocol that minimizes energy dissipation in sensor networks. The purpose of LEACH is to randomly select sensor nodes as cluster heads so that high energy dissipation in communicating with the base station is spread to all sensor nodes in the network. The operation of LEACH is separated into two phases, the setup phase and the steady phase. The duration of the steady phase is longer than the duration of the setup phase in order to minimize the overhead. During the setup phase, a sensor node chooses a random number between 0 and 1. If this random number is less than the threshold T(n), the sensor node is a cluster head. Here, T(n) is calculated according to () T(n)={P1−P[rmod(1/P)]if n∈G,0otherwise
Internet of Things for Structural Health Monitoring
Published in Jayantha Ananda Epaarachchi, Gayan Chanaka Kahandawa, Structural Health Monitoring Technologies and Next-Generation Smart Composite Structures, 2016
Aravinda S. Rao, Jayavardhana Gubbi, Tuan Ngo, Priyan Mendis, Marimuthu Palaniswami
The proposed method performs better than TPSN and LTS, both in terms of synchronization error and energy efficiency. It is evident from the results that LTS relaxes accuracy constraints while reducing accuracy, confining to authors claim. It is important to note that energy can be saved at the cost of sacrificing synchronization error in all the three methods. Low-energy adaptive clustering hierarchy (LEACH) is a clustering-based energy distribution protocol that distributes the load of local cluster head for communication between cluster head and the base station. It uses randomized rotation of cluster head to balance the local cluster head energy [22]. Numerous LEACH-based clustering algorithms are also implemented [1,14,21,30,32,51,56,60]. The combination of LEACH (for cluster-energy distribution) and VCTS (for time synchronization between nodes in a given cluster) can assist to design better energy-efficient time synchronization approaches as shown in Figure 4.11.
Optimal Relay Selection Strategy for Energy-Efficient Cooperative Multi-Hop Image Transmission in Wireless Multimedia Sensor Network
Published in Cybernetics and Systems, 2022
Praveen Kumar Devulapalli, Sushanth Babu Maganti, Satish Kumar Gae, Sumalatha Rachapogula
The transmission of multimedia data in WSN is a tough problem that has a considerable influence on its widespread adoption. In the literature, a collaborative multipath routing system for picture transmission in WMSN is presented (Xu et al. 2012). The bandwidth-power aware cooperative multipath routing problem is addressed using a polynomial heuristic method in this technique (BP-CMPR). This strategy may efficiently reduce energy use by utilizing multi-node collaboration and resource allocation. To save energy, clustering routing strategies have been proposed in the literature (Lee and Teng 2017; El Alami and Najid 2019). The low energy adaptive clustering hierarchy (LEACH) approach is developed to evenly distribute the energy load throughout the network’s sensors. This technique for dynamic networks uses localized coordination to provide scalability and durability. To limit the amount of data that must be transferred to the base station, it adds data fusion into the routing protocol. Based on simulation results it is observed that the LEACH technique distributes the energy dissipation uniformly among the sensors.
Optimization of energy consumption in wireless sensor networks using density-based clustering algorithm
Published in International Journal of Computers and Applications, 2021
Hasan Jafari, Mousa Nazari, Shahaboddin Shamshirband
Heinzelman et al. [9] introduced the LEACH, Low-Energy Adaptive Clustering Hierarchy, protocol algo-rithm and the first and most well-known clustering based protocol in wireless sensor networks. In this method, clusters are formed in a distributed manner. The most important goal of the LEACH is having local clusters to reduce energy consumption due to data transfer to a remote base station. This algorithm selects a number of sensor nodes randomly as cluster-heads and organizes local nodes as local clusters. The nodes join the respective cluster-head based on their distance. Intra-communications have overhead for nodes because normal nodes transfer their data to the cluster heads. Cluster-head nodes have higher energy consumption, compared to normal nodes. Thus their constant selection leads to early energy dissipation and their early death [9].
MH-CACA: multi-objective harmony search-based coverage aware clustering algorithm in WSNs
Published in Enterprise Information Systems, 2020
Heinzelman et al. (2000) have developed Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol. This technique is based on a random selection of CHs among sensors which in fact suffer from the limitation to select the low-energy sensor as CH during communication. As a result, with slight variation in operating condition, a network can die quickly even though its load balancing is ideal. To overcome this problem, authors have developed several heuristics and approximation approaches as discussed in (Bari, Jaekel, and Bandyopadhyay 2008) and greedy approach for load-balanced clustering is appeared in (Low et al. 2008).