The Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP) Method for Solving Wireless Sensor Networks Optimization Problems in Smart Grid Applications

Abstract

Prior studies in Wireless Sensor Network (WSN) optimization mostly concentrate on maximizing network coverage and minimizing network energy consumption. However, there are other factors that could affect the WSN Quality of Service (QoS). In this paper, four objective functions that affect WSN QoS, namely end-to-end delay, end-to-end latency, network throughput and energy efficiency are studied. Optimal value of packet payload size that is able to minimize the end-to-end delay and end-to-end latency, while also maximizing the network throughput and energy efficiency is sought. To do this, a smart grid application case study together with a WSN QoS model is used to find the optimal value of the packet payload size. Our proposed method, named Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP), along with other three state-of-the-art multi-objective optimization algorithms known as OMOPSO, NSGA-II and SPEA2, are utilized in this study. Different packet payload sizes are supplied to the algorithms and their optimal value is derived. From the experiments, the knee point and the intersection point of all the obtained Pareto fronts for all the algorithms show that the optimal packet payload size that manages the trade-offs between the four objective functions is equal to 45 bytes. The results also show that the performance of our proposed MOSFP method is highly competitive and found to have the best average value compared to the other three algorithms. Furthermore, the overall performance of MOSFP on four objective functions outperformed OMOPSO, NSGA-II and SPEA2 by 3%, 6% and 51%, respectively.

Solving Multi-Objective Fuzzy Optimization in Wireless Smart Sensor Networks under Uncertainty Using a Hybrid of IFR and SSO Algorithm

“…where the ith row represents the related triangular fuzzy number of the ith criterion, i.e., (2,3,7) is the triangular fuzzy number of the cost of sensor 1. Similarly, the fuzzy criteria matrices of sensors 2-6 can be displayed as:…”

“…Step F2. Calculate the weighted normalized triangular fuzzy number 3 ) and the aggregated triangular fuzzy number G i = (A i , B i , C i ) for alternative i, i = 1, 2, . .…”

“…Wireless (smart) sensor networks (WSN) consist of smart sensors deployed and operated in wireless sensor networks, and have rapidly become an important design widely applied in many modern applications, such as the Internet of Things (IoT) [1,2], smart grids [3], healthcare and medical systems [4], wind energy systems [5], industrial automation [6], the smart transportation industry [7,8], the semiconductor industry [9] and smart cities [10]. The signal, information or flow cannot be successfully transmitted through the nodes, which are the sensors in the WSN, if the operation of WSN system fails because nodes deplete their limited battery power.…”

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

“…They are used to evaluate design trade-offs, to assess control systems, and to find patterns in data. The solution to the optimization problem consists of a reduction of a complex problem into the more straightforward problems and then solving each straightforward problem, using its solution to solve the next problem [15]. Mathematical optimization is the selection of the best elements from some set of available alternatives.…”

Solving the Energy Efficient Coverage Problem in Wireless Sensor Networks: A Distributed Genetic Algorithm Approach with Hierarchical Fitness Evaluation

“…Wireless sensor networks (WSN) have become a hot research topic and have been widely used in numerous real-world applications, such as traffic monitoring [1], mobile computing [2], environmental observation [3], and many others [4][5][6]. In these sophisticated environments, in order to make full coverage and get more accurate results, many nodes should be randomly deployed in the area, causing a waste of resources.…”