scholarly journals Energy Optimized Routing Protocol Based on Binary Particle Swarm Optimization Successive Interference Cancellation for WSN

2020 ◽  
Vol 9 (4) ◽  
pp. 2020-2026
Keyword(s):  
Particle Swarm Optimization ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Interference Cancellation ◽  
Shortest Paths ◽  
Particle Swarm ◽  
Sensor Nodes ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Limited Energy

A large variety of Wireless Sensor Network (WSN) applications encourage researchers to develop and improve protocols and algorithms for rising challenges. During data transmission, energy consumption is main issue because sensor nodes have limited energy capacity. In fact, WSN needs load balancing algorithms that keep use of the limited energy source to route the collected data to the receiving node. While considering adhoc network in sensitive areas, one amongst the necessary aspects to consider is energy as a result of while sending information all communicating nodes exhausts its battery life. For mobile nodes in adhoc situation one and only one source of energy is battery. As compared to single path multipath routing helps to find best path that needs less energy and enhances the network life. In this paper an energy efficient routing protocol is proposed. In routing algorithm, route that have shortest path among multipaths chosen by particle swarm optimization algorithm. Among these shortest paths, that path is chosen that need minimum route selection parameter. The proposed algorithm is based on selection of energy efficient paths from multiple shortest SIC path from source to destination. For selection Binary Particle Swarm Optimization (BPSO) algorithm is applied. The Binary Particle Swarm Optimization (BPSO) algorithm selects energy efficient route among different shortest paths. The k-shortest route is selected on the basis of bandwidth and minimum SIC. The performance of the proposed algorithm is compared with multipath Ad hoc On-Demand Distance Vector (AODV) routing protocol and concluded that BPSO optimized energy efficient path is more efficient with respect to remaining energy of the network. The result is analyzed with variable number of packets send.

scholarly journals Energy Efficient Particle Swarm Optimization Based Multipath Routing in WSN

2018 ◽  
Vol 4 (10) ◽  
pp. 5
Author(s):  
Roshni Jha ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Particle Swarm Optimization ◽  
Shortest Path ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Energy Requirement ◽  
Shortest Paths ◽  
Multipath Routing ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Shortest Path Routing

Wireless Networks includes a larger advantage in today’s communication application like environmental, traffic, military, and health observation. To realize these applications it's necessary to possess a reliable routing protocol. discusses about the working of proposed energy efficient bandwidth aware shortest path routing protocol for multipath routing in wireless sensor network. The proposed algorithm is based for choosing energy efficient shortest path. In routing algorithm, route that have shortest path among multipaths selected by particle swarm optimization algorithm. Among these shortest paths, that path is selected which require minimum route selection parameter. The proposed algorithm uses distance as well as energy of nodes as a parameter to find optimum paths using particle swarm optimization. Among these selected paths, only one optimum path is selected which reduces the energy requirement of the network. According to this work there would be improvement in other parameters also such as end to end delay as well as throughput.

scholarly journals Deployment of Wireless Sensor Networks for Oilfield Monitoring by Multiobjective Discrete Binary Particle Swarm Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Zhen-Lun Yang ◽  
Angus Wu ◽  
Hua-Qing Min
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Real Time ◽  
Monitoring System ◽  
Particle Swarm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization

The deployment problem of wireless sensor networks for real time oilfield monitoring is studied. As a characteristic of oilfield monitoring system, all sensor nodes have to be installed on designated spots. For the energy efficiency, some relay nodes and sink nodes are deployed as a delivery subsystem. The major concern of the construction of the monitoring system is the optimum placement of data delivery subsystem to ensure the full connectivity of the sensor nodes while keeping the construction cost as low as possible, with least construction and maintenance complexity. Due to the complicated landform of oilfields, in general, it is rather difficult to satisfy these requirements simultaneously. The deployment problem is formulated as a constrained multiobjective optimization problem and solved through a novel scheme based on multiobjective discrete binary particle swarm optimization to produce optimal solutions from the minimum financial cost to the minimum complexity of construction and maintenance. Simulation results validated that comparing to the three existing state-of-the-art algorithms, that is, NSGA-II, JGGA, and SPEA2, the proposed scheme is superior in locating the Pareto-optimal front and maintaining the diversity of the solutions, thus providing superior candidate solutions for the design of real time monitoring systems in oilfields.

Using Multi-Objective Particle Swarm Optimization for Energy-Efficient Clustering in Wireless Sensor Networks

2012 ◽  
pp. 291-304
Author(s):  
Hamid Ali ◽  
Waseem Shahzad ◽  
Farrukh Aslam Khan
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Efficient ◽  
Efficient Solution ◽  
Particle Swarm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Battery Power ◽  
Energy Efficient Clustering

In this chapter, the authors propose a multi-objective solution to the problem by using multi-objective particle swarm optimization (MOPSO) algorithm to optimize the number of clusters in a sensor network in order to provide an energy-efficient solution. The proposed algorithm considers the ideal degree of nodes and battery power consumption of the sensor nodes. The main advantage of the proposed method is that it provides a set of solutions at a time. The results of the proposed approach were compared with two other well-known clustering techniques: WCA and CLPSO-based clustering. Extensive simulations were performed to show that the proposed approach is an effective approach for clustering in WSN environments and performs better than the other two approaches.

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