An Efficent Coverage and Maximization of Network Lifetime in WSN Through Metaheuristics

<span>In wireless sensor networks (WSNs), energy, connectivity, and coverage are the three most important constraints for guaranteed data forwarding from every sensor node to the base station. Due to continuous sensing and transmission tasks, the sensor nodes deplete more quickly and hence they seek the help of data forwarding nodes, called relay nodes. However, for a given set of sensor nodes, finding optimal locations to place relay nodes is a very challenging problem. Moreover, from the earlier studies, the relay node placement is defined as a non-deterministic polynomial tree hard (NP-Hard) problem. To solve this problem, we propose a multi-objective firefly algorithm-based relay node placement (MOFF-RNP) to deploy an optimal number of relay nodes while considering connectivity, coverage, and energy constraints. To achieve network lifetime, this work adopted energy harvesting capabilities to the sensor nodes and backup relay strategy such that every sensor node is always connected to at least one relay to forward the data. The optimal relay placement is formulated as an objective function and MOFF is applied to achieve a better solution. Extensive Simulations are carried out over the proposed model to validate the performance and the obtained results are compared with state-of-art methods)</span>

Clustered Relay Node Placement for WSN through Metaheuristics Algorithm

In the field of Wireless Sensor Networks (WSNs), after the deployment of sensor nodes, transmission of data with an effective energy is becoming a critical issue due to the limited battery of sensor nodes. To solve this issue, most of the past developed approaches focused on the implementation of clustering in which the sensor nodes forward their data through Cluster Heads (CHs) to Base Station (BS). However, the main issue is to select an optimal CH or CHs such that every sensor node is ensured with at least one path to base station, directly, through CHs or through relay nodes. Towards this objective, we have proposed a new method called Multi-Objective Assisted Clustered Relay Node Placement (MOCRNP) to achieve improved energy efficiency along with an enhanced network lifespan. Firstly, to select the CH, the Multi-Objective Genetic Algorithm is introduced based on energy, distance and node density. Secondly, in the process of rely node placement, Multi-objective firefly algorithm is introduced and for this purpose, two constraints such as Coverage and Connectivity are considered. We have conducted several experimentations using several setups of the WSN and the performance is evaluated in terms of Network lifetime, average energy consumption, average residual energy, and throughput. On an average, the proposed approach has gained an improvement of 150 sec network lifetime, 104 Kbps Throughput when compared with state-of-art methods.

Relay Node Placement Techniques in Wireless Sensor Networks

<div>Relay node placement is one of the critical need of wireless sensor networks when connectivity, lifetime, fault tolerance like factors are desired in the network. In this study, we review the present relay node placement techniques and provide an overall view of this study by summarizing previous achievements. We categorize the placement strategies into four broad categories, namely, approximation, algorithms, heuristics, meta-heuristics based techniques. A taxonomy is presented to enlist the present techniques. The paper also emphasizes on the research challenges and gives an idea of potential future scope in this research domain. </div>

Relay Node Placement Techniques in Wireless Sensor Networks

<div>Relay node placement is one of the critical need of wireless sensor networks when connectivity, lifetime, fault tolerance like factors are desired in the network. In this study, we review the present relay node placement techniques and provide an overall view of this study by summarizing previous achievements. We categorize the placement strategies into four broad categories, namely, approximation, algorithms, heuristics, meta-heuristics based techniques. A taxonomy is presented to enlist the present techniques. The paper also emphasizes on the research challenges and gives an idea of potential future scope in this research domain. </div>