Optimization Based Sink Deployment Technique in WSN to Improve Network Performance

Background & Objective: Currently, WSN (Wireless Sensor Networks) provides a variety of services in industrial and commercial applications. WSN consists of nodes that are used to sense the environments like humidity, temperature, pressure, sound, etc. As the use of WSN grows there are some issues like coverage, fault tolerance, a deployment problem, localization, Quality of Service, etc. which needs to be resolved. Sink deployment is a very important problem because it is not the only impact on performance, but also influence on deployment cost. In traditional WSN, a single sink is deployed in the network, which aggregates all the data. Due to this, the whole network is suffering from some serious issues like delay, congestion, network failure that reduces network performance. Methods: One solution is to deploy multiple sinks instead of a single sink. Deploying multiple sinks can improve network performance, but increases sink deployment cost. In this paper, an ISDOA (Improved Sink Deployment Optimization Algorithm) is proposed to find the optimum number of sinks and their optimum location in ROI. Simulation is carried out in Matlab simulator. The impact of sensors and sinks on various network performance parameters like throughput, network lifetime, packet delivery ratio, energy consumption and cost of the network is analyzed. Results & Conclusion: It is shown by simulation results that the number of sinks varies inversely with energy consumption of the nodes; and it is linearly proportional to the network lifetime, throughput and packet delivery ratio. Furthermore, results show that the proposed approach outperforms random deployment with 25% higher throughput, 30% better network lifetime, 15% lesser energy consumption and 21% optimized cost of the network, respectively.

Genetic algorithm for solving transportation problems on networks with one source and multiple sinks

In this paper we propose a genetic algorithm for solving the nonlinear transportation problem on a network with multiple sinks and concave piecewise cost functions. We prove that the complexity of one iteration of the algorithm is O(n2) and the algorithm converges to a local optimum solution. We show that the algorithm can be used to solve large-scale problems and present the implementation and several testing examples of the algorithm using Wolfram Language.

Deployment of Multiple Sink in the different Monitoring Area based on Optimization Technique

Wireless Sensor Networks are widely used in different applications like environmental monitoring, health monitoring, wildlife monitoring, etc. The monitored area may be of any shape such as circular, rectangular, and square. Finding an ideal node deployment technique in wireless sensor systems (WSNs) that would diminish cost, be powerful to node failure, shorten calculation, and communication overhead, and guarantee full coverage alongside network connectivity is a troublesome issue. Indeed, sensing coverage and system connectivity are two of the most basic issues in WSNs as they can straightforwardly affect the system lifetime and activity. In traditional WSNs, a single sink is deployed which results in more traffic load on that sink cause higher energy consumption. Thus, it is necessary to deploy multiple sinks. The efficient deployment of sensors and multiple sinks is a challenging task as the performance of the network is depending on it. In this paper, sensors and multiple sinks are deployed using SSDOA (Sensor Sink Deployment Optimization Algorithm) in a different monitoring area. Performance parameters like coverage, network lifetime, energy consumption are analyzed. Compared to existing methods our method performs better in any type of monitoring area. Reported numerical results show that our method outperforms PSO, GA and Random deployment in the square monitoring area with 9% better network lifetime, 4% full coverage and 7.3% lesser energy consumption respectively. Furthermore, our proposed approach also performs better in the circular and rectangular monitoring area.

Robust Evolutionary-Game-Based Routing for Wireless Multimedia Sensor Networks

Nowadays, wireless multimedia sensor networks (WMSNs) are used in various applications. An energy-efficient and robust routing protocol is essential for WMSNs because the quality of service is important for traffic-intensive multimedia data, such as images and videos. A WMSN with multiple sinks allows cluster heads (CHs) to deliver the collected data to the nearest sink, thereby mitigating the delivery overhead. In this study, we propose a novel evolutionary-game-based routing (EGR) protocol for WMSNs with multiple sinks, in which the evolutionary game theory is exploited for selecting CHs. In EGR, an algorithm to mitigate data redundancy, based on the overlapping field of views of the multimedia sensor nodes, is also presented. This algorithm decreases the number of redundant transmissions, thereby increasing energy efficiency and network performance. According to the performance evaluation results of this study, the proposed EGR significantly outperforms the state-of-art protocols in terms of energy efficiency, end-to-end delay, packet delivery ratio, cluster formation time, and network lifetime.