Abstract
In recent years, Wireless Sensor Networks (WSNs) have benefitted from their integration with Internet of Things (IoT) applications. WSN usage for monitoring and tracing applications shows massive acceleration, whether indoors or outdoors. WSN is constructed from interconnected sensors, limited resource (battery), which requires considerable importance on deployment and routing strategies, to improve the performance of Quality of Service (QoS) in WSNs. Many of the existing strategies are based on metaheuristics algorithms such as Genetic Algorithms to resolve the problem. This research proposes a new algorithm, Enhanced Non-Dominated Sorting Genetic Routing Algorithm (ENSGRA), to improve the QoS in WSNs. The proposed algorithm relies on Non-Dominated Sorting Genetic Algorithm 3 (NSGA-III), but adjusts reference points through the use of a dynamic weighted clustered scheduled vector to obtain new solutions. Moreover, ENSGRA can be used to find an integration between two parents crossover with multi-parent crossover (MPX), to produce multiple children and improve new offspring to obtain the optimal Pareto Fronts (PF). This algorithm excels when compared with the lagged multi-objective jumping particle swarm optimization, Non-dominated Sorting Genetic Algorithm–II and NSGA-III in terms of the QoS model (31% optimization percentage). Results show that the proposed ENSGRA is superior over other algorithms in evaluation measures for multi-objective algorithms.
Highlights
The importance of Wireless Sensor Networks (WSNs) come from using them in different applications, including monitoring various kinds of conditions such as temperature, humidity, pressure, vehicular movements and soil makeup
The properties of this dataset are represented as sensors and sink nodes that are placed on the same 2D-surface of size Dx x Dy
Sensors provided by the dataset and sink node coordinates are provided by the optimisation algorithm
Summary
The importance of Wireless Sensor Networks (WSNs) come from using them in different applications, including monitoring various kinds of conditions such as temperature, humidity, pressure, vehicular movements and soil makeup. A WSN consists of a large number of low power wireless sensor nodes, which have limited transmission range and cannot directly send data to sink nodes that need multihop communication. WSNs applications can be classified into two types; first for monitoring by analyzing or supervising a real-time system and second for tracking event change on a person or animal. A new important example of applications, known as IoT application based on WSNs, is a method used to extract big data from things, mining the data to extract necessary information [2]. Integrating WSNs with the Internet of Things (IoT) is considered an important and essential issue in the future
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