scholarly journals A hybrid Meta-heuristic solution for Energy-Efficient routing in Software-Defined Wireless Sensor Network 

2021 ◽  
Author(s):  
POOJA MISHRA ◽  
NEETESH KUMAR ◽  
W WILFRED GODFREY
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Fitness Function ◽  
Life Time ◽  
Residual Energy ◽  
Wireless Sensor ◽  
Energy Efficient Routing ◽  
Nature Inspired Algorithm ◽  
Network Life Time

Abstract Software-Defined Networking (SDN) has been adopted as an emerging networking paradigm within Wireless Sensor Networks (WSNs). SDN enables WSNs with self-configuration and programmable control to dynamically and efficiently manage the network functionalities. Generally, in WSN, smart sensing devices suffer from the low battery issue and they may be deployed in such environments where frequent recharge is not possible after the deployment. Therefore, this work focuses on energy-efficient routing problem considering Software-Defined Wireless Sensor Networks (SD-WSN) architecture. In SD-WSN, Control Server (CS) assigns the tasks to selected Control Nodes (CNs) dynamically. Thus, the CNs' selection process is developed as one optimization (NP-Hard) problem to make the network functional. To solve this problem effectively, a nature-inspired algorithm i.e., Grey Wolf Optimization (GWO) is hybridized with Particle Swarm Optimization (PSO) in order to improve its convergence and overall performance. This hybrid variant of GWO is dedicated to offering a Balanced clustering (BC) based routing protocol, this variant is referred to as HGWO-BC. Further, to solve the problem effectively, a fitness function is designed that considers several parameters e.g., intracluster distance, CS to CNs distance, nodes' residual energy, and cluster size. Thus, the proposed approach performs balanced, energy-efficient, and scalable clustering and prolongs the network life-time. To verify its effectiveness, an exhaustive simulation study is done. Comparative results show that the HGWO-BC approach outperforms other state-of-the-art approaches concerning network life-time, residual energy, network throughput, and convergence rate.

scholarly journals Energy-efficient genetic algorithm variants of PEGASIS for 3D Wireless Sensor Networks

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Aaqil Somauroo ◽  
Vandana Bassoo
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Residual Energy ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Efficient Routing ◽  
Delay Performance

Due to its boundless potential applications, Wireless Sensor Networks have been subject to much research in the last two decades. WSNs are often deployed in remote environments making replacement of batteries not feasible. Low energy consumption being of prime requisite led to the development of energy-efficient routing protocols. The proposed routing algorithms seek to prolong the lifetime of sensor nodes in the relatively unexplored area of 3D WSNs. The schemes use chain-based routing technique PEGASIS as basis and employ genetic algorithm to build the chain instead of the greedy algorithm. Proposed schemes will incorporate an energy and distance aware CH selection technique to improve load balancing. Clustering of the network is also implemented to reduce number of nodes in a chain and hence reduce delay. Simulation of our proposed protocols is carried out for homogeneous networks considering separately cases for a static base-station inside and outside the network. Results indicate considerable improvement in lifetime over PEGASIS of 817% and 420% for base station inside and outside the network respectively. Residual energy and delay performance are also considered.

scholarly journals Energy-Efficient Routing Using Fuzzy Neural Network in Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Rajesh Kumar Varun ◽  
Rakesh C. Gangwar ◽  
Omprakash Kaiwartya ◽  
Geetika Aggarwal
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Fuzzy Neural Network ◽  
Residual Energy ◽  
Wireless Sensor ◽  
Energy Efficient Routing ◽  
Fuzzy Neural

In wireless sensor networks, energy is a precious resource that should be utilized wisely to improve its life. Uneven distribution of load over sensor devices is also the reason for the depletion of energy that can cause interruptions in network operations as well. For the next generation’s ubiquitous sensor networks, a single artificial intelligence methodology is not able to resolve the issue of energy and load. Therefore, this paper proposes an energy-efficient routing using a fuzzy neural network (ERFN) to minimize the energy consumption while fairly equalizing energy consumption among sensors thus as to prolong the lifetime of the WSN. The algorithm utilizes fuzzy logic and neural network concepts for the intelligent selection of cluster head (CH) that will precisely consume equal energy of the sensors. In this work, fuzzy rules, sets, and membership functions are developed to make decisions regarding next-hop selection based on the total residual energy, link quality, and forward progress towards the sink. The developed algorithm ERFN proofs its efficiency as compared to the state-of-the-art algorithms concerning the number of alive nodes, percentage of dead nodes, average energy decay, and standard deviation of residual energy.

scholarly journals Energy Efficient Rule based intelligent routing using Fitness Functions in Wireless Sensor Networks

2019 ◽  
Vol 8 (12) ◽  
pp. 5414-5420
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Efficient ◽  
Fitness Function ◽  
Life Time ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Packet Delivery ◽  
Fitness Functions ◽  
End To End Delay ◽  
Network Life Time

Wireless Sensor Networks (WSNs) is a distributed collection of tiny wireless nodes which forms an ad hoc network dynamically to sense the natural phenomenon and sent it to the control station. Due to the resource constrained nature of WSN, maximizing the nodes life time is main and challenging issue. In this paper, Fitness Function based Routing Protocol (FFBRP) is proposed which provides the optimal routing to increase the life time of nodes in a network. The proposed protocol selects the fitness functions based on the important routing parameters like nodes energy consumption, nodes life time, packet Delivery ratio of nodes, distance between nodes, end to end delay of nodes and routing overhead of nodes. Based on the combination of selected fitness function parameters, the intelligent rules are generated and the optimal routes are discovered to perform energy efficient effective routing in WSN. By doing so, the proposed protocol provides better performance in terms of network life time and has better Quality of Service (QoS) than other existing techniques. The implementation of the proposed scheme is carried out using Network Simulator (NS2) with mannasim framework. Simulation results justifies that, proposed protocol outperforms the existing techniques and has better Packet Delivery Ratio, throughput , network life time, energy consumption, end to end delay and routing overhead .

scholarly journals A Research on Energy Efficient Cluster Routing for the Wireless Sensor Networks

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1344-1347
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Search Algorithm ◽  
Routing Algorithm ◽  
Life Time ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Efficient Routing ◽  
Small Clusters

Clustering with energy efficient routing is the most important technique for the wireless sensor networks. Cluster converts group of sensor nodes into small clusters and electing the cluster heads with energy efficient cluster routing for all the clusters in the Wireless sensor networks. By selecting the proper energy efficient cluster routing algorithm we can increase the life time of the wireless sensor networks. Lot of techniques are used for energy efficient cluster routing for Wireless sensor networks like Particle Swarm Optimization, Artificial Bees Colony Optimization, Crow Search Algorithm, Energy-efficient Intracluster Routing (EIR) algorithm and Dolphin Echolocation Algorithm (DEA). In this paper we have given the comparative analysis report of energy efficient cluster routing algorithms for the wireless sensor networks in terms of energy efficiency and sensor node lifetime of the networks.

scholarly journals QOS OPTIMIZATION OF ENERGY EFFICIENT ROUTING IN IOT WIRELESS SENSOR NETWORKS

2019 ◽  
Vol 01 (01) ◽  
pp. 12-23 ◽  
Author(s):  
Jennifer S. Raj ◽  
Abul Basar
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Power Consumption ◽  
Energy Efficient ◽  
Life Time ◽  
Packet Delivery Ratio ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Packet Delivery ◽  
Network Life Time

The internet of things is a group of connected computing, digital and mechanical machines with the capability of being identified by other devices that are internet enabled. The wireless sensor networks is a gathering of sovereign sensing elements in combination with actuating, computing, communicating and energy storing devices to keep track of the continuous physical world changes. These clique of independent sensors that commune wirelessly incurring advantages such as low cost, limited power consumption, high scalability with adaptableness to hostile and harsh environments afford them to be connected with IOT to become a part of it, to trace the physical changes encountered in the things that are internet enabled. The conventional methods for connection establishment between WSNs with IOT are more energy consuming and prone to failures in terms of network life time, packet delivery ratio and delay. So the proposed methodology that uses the concatenation of clustering with neural and simple fuzzy rule based system supported by the shortest route determination to provide with an energy efficient and enhanced routing capabilities for IOT with WSNs ensures to have a route entrenchment with reduced power consumption and improvised QOS metrics. The performance analysis is done with regard to the packet delivery ratio, energy consumption, sensor network life time and delay to evidence it perfect functioning.

scholarly journals An Energy Efficient Approach to Extend Network Life Time of Wireless Sensor Networks

2016 ◽  
Vol 92 ◽  
pp. 425-430 ◽  
Author(s):  
Veena Anand ◽  
Deepika Agrawal ◽  
Preety Tirkey ◽  
Sudhakar Pandey
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Life Time ◽  
Wireless Sensor ◽  
Efficient Approach ◽  
Network Life Time

An Energy Efficient Routing Approach to Enhance Coverage for Application Specific Wireless Sensor Networks using Genetic Algorithm

2019 ◽  
Vol 13 ◽  
Author(s):  
Amandeep Kaur Sohal ◽  
Ajay Kumar Sharma ◽  
Neetu Sood
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Transmission ◽  
Energy Efficient ◽  
Search Algorithm ◽  
Cluster Formation ◽  
Green Computing ◽  
Wireless Sensor ◽  
Energy Efficient Routing

Background: An information gathering is a typical and important task in agriculture monitoring and military surveillance. In these applications, minimization of energy consumption and maximization of network lifetime have prime importance for green computing. As wireless sensor networks comprise of a large number of sensors with limited battery power and deployed at remote geographical locations for monitoring physical events, therefore it is imperative to have minimum consumption of energy during network coverage. The WSNs help in accurate monitoring of remote environment by collecting data intelligently from the individual sensors. Objective: The paper is motivated from green computing aspect of wireless sensor network and an Energy-efficient Weight-based Coverage Enhancing protocol using Genetic Algorithm (WCEGA) is presented. The WCEGA is designed to achieve continuously monitoring of remote areas for a longer time with least power consumption. Method: The cluster-based algorithm consists two phases: cluster formation and data transmission. In cluster formation, selection of cluster heads and cluster members areas based on energy and coverage efficient parameters. The governing parameters are residual energy, overlapping degree, node density and neighbor’s degree. The data transmission between CHs and sink is based on well-known evolution search algorithm i.e. Genetic Algorithm. Conclusion: The results of WCEGA are compared with other established protocols and shows significant improvement of full coverage and lifetime approximately 40% and 45% respectively.

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