Clustering Objectives in Wireless Sensor Networks: A Survey and Research Direction Analysis

Strategies based on various aspects of clustering in wireless sensor networks using classical, optimization and machine learning techniques: Review, taxonomy, research findings, challenges and future directions

The most famous wireless sensor networks is one of the cheapest and rapidly evolving networks in modern communication. It can be used to sense various substantial and environmental specifications by providing cost-effective sensor devices. The development of these sensor networks is exploited to provide an energy-efficient weighted clustering method to increase the lifespan of the network. We propose a novel energy-efficient method, which utilizes the brainstorm algorithm in order to adopt the ideal cluster head (CH) to reduce energy draining. Furthermore, the effectiveness of the BrainStorm Optimization (BSO) algorithm is enhanced with the incorporation of the modified teacher–learner optimized (MTLBO) algorithm with it. The modified BSO–MTLBO algorithm can be used to attain an improved throughput, network lifetime, and to reduce the energy consumption by nodes and CH, death of sensor nodes, routing overhead. The performance of our proposed work is analyzed with other existing approaches and inferred that our approach performs better than all the other approaches.

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Energy-Efficient Cluster-Based Routing Protocol for WSN Based on Hybrid BSO–TLBO Optimization Model

Vehicle ad-hoc networks (VANETs) are a subclass of mobile ad hoc networks (MANETs). The VANETs communication framework is used to provide communication between moving vehicles in highway and urban road scenarios. Dynamic properties of VANETs, such as high dynamic topology, frequent route failure, high mobility of nodes, and bandwidth constraints, reduce the efficiency of routing. The long length route between source and destination affects the efficiency of the protocol in the form of high overhead, frequent disconnections, high packet loss rate, low packet delivery rate, and low throughput. In this paper, we propose an Enhanced Hybrid Ant Colony Optimization Routing Protocol (EHACORP) to improve the efficiency of the routing process using the shortest path. The shortest path in the proposed protocol has low communication costs and the least number of hops between source and destination vehicles. The EHACORP has two phases. In phase 1, the EHACORP relies on a distance calculation method to compute the distance between vehicles. In phase 2, the source-based ant colony optimization is used to guide the ants to build a shorter path with the least number of hops to transmit data. The shortest path improves the efficiency of protocol in all aspects. The simulation results show that the EHACORP has improved packet delivery rate, throughput, end-to-end delay, routing overhead, and packet loss rate compared to Fuzzy based ant colony optimization (F-ANT), Ad hoc on-demand distance vector (AODV), ant colony optimization routing algorithm (ARA), and AntNet routing protocols.

An enhanced hybrid ant colony optimization routing protocol for vehicular ad-hoc networks

Wireless sensor networks (WSNs) plays a significant role in the field of surveillance, monitoring the real time applications. Regardless its strong ability to handle such tasks, it is difficult to maintain a trade-off between the conflicting goals of network lifetime, transmission delay, high coverage and packet loss. Various solutions have been proposed by the researchers to address these issues comprising the solution in real-time network scenarios. This paper delivers a brief analysis of the solutions addressing recent research problems in WSN comprising conflicting goals, i.e. multi-objective optimization (MOO) technique. Firstly, an illustration of key optimization objective in WSNs is given which constitutes existing issues such as power control, rate control ant routing. Then, an elaboration of various objective functions used in MOO with its merits and demerits is also provided. Later, existing approaches for improving optimizing metric, applications performance of existing approaches and proposed architecture have been discussed.

Multi-Objective Optimization in WSN: Opportunities and Challenges

Wireless Sensor Networks (WSNs) consist of a large number of spatially distributed sensor nodes connected through the wireless medium to monitor and record the physical information from the environment. The nodes of WSN are battery powered, so after a certain period it loose entire energy. This energy constraint affects the lifetime of the network. The objective of this study is to minimize the overall energy consumption and to maximize the network lifetime. At present, clustering and routing algorithms are widely used in WSNs to enhance the network lifetime. In this study, the Butterfly Optimization Algorithm (BOA) is employed to choose an optimal cluster head from a group of nodes. The cluster head selection is optimized by the residual energy of the nodes, distance to the neighbors, distance to the base station, node degree and node centrality. The route between the cluster head and the base station is identified by using Ant Colony Optimization (ACO), it selects the optimal route based on the distance, residual energy and node degree. The performance measures of this proposed methodology are analyzed in terms of alive nodes, dead nodes, energy consumption and data packets received by the BS. The outputs of the proposed methodology are compared with traditional approaches LEACH, DEEC and compared with some existing methods FUCHAR, CRHS, BERA, CPSO, ALOC and FLION. For example, the alive nodes of the proposed methodology are 200 at 1500 iterations which is higher compared to the CRHS and BERA methods.

Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization

WSN is a network technology which is currently in use in many existing and latest applications. Among the major problems in Wireless Sensor Networks, Energy Conservation is of utmost importance since batteries used in the sensor devices are not replaceable. There are number of cluster based routing protocols such as LEACH, TEEN, SEP proposed which handle these issues. This paper introduces an extension to TEEN protocol, a reactive homogeneous protocol. Modified TEEN (MTEEN) handles hotspot problem and the problem of load balancing in existing WSN routing algorithms. On the basis of our simulation carried on MATLAB, it is found that the proposed algorithm outperforms LEACH and TEEN in network lifetime.

Modified TEEN for Handling Inconsistent Cluster Size Problem in WSN

Wireless sensor network (WSN) are used to sense, process, and send important information relating to surrounding environment. In these network, one of the important issue is timely deliver...

Prachi Maheshwari

Papers

Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization

Modified TEEN for Handling Inconsistent Cluster Size Problem in WSN

Game theoretic application for energy efficient mobility handling in wireless sensor network