Abstract
Large-scale wireless sensor network (LSWSN) is composed of a huge number of sensor nodes that are distributed in some region of interest (ROI), to sense and measure the environmental conditions like pressure, temperature, pollution levels, humidity, wind, and so on. The objective is to collect data for real-time monitoring so that appropriate actions can be taken promptly. One of the sensor nodes used in an LSWSN is called the sink node, which is responsible for processing and analyzing the collected information. It works as a station between the network sensor nodes and the administrator. Also, it is responsible for controlling the whole network. Determining the sink node location in an LSWSN is a challenging task, as it is crucial to the network lifetime, for keeping the network activity to the most possible extent. In this paper, the Harris' hawks optimization (HHO) algorithm is employed to solve this problem and subsequently the Prim's shortest path algorithm is used to reconstruct the network by making minimum transmission paths from the sink node to the rest of the sensor nodes. The performance of HHO is compared with other well-known algorithms such as particle swarm optimization (PSO), flower pollination algorithm (FPA), grey wolf optimizer (GWO), sine cosine algorithm (SCA), multi-verse optimizer (MVO), and whale optimization algorithm (WOA). The simulation results of different network sizes, with single and multiple sink nodes, show the superiority of the employed approach in terms of energy consumption and localization error, and ultimately prolonging the lifetime of the network in an efficacious way.
Highlights
Along with the remarkable developments in wireless sensor networks (WSNs), large-scale wireless sensor networks (LSWSNs) have appeared, which are used in our daily lives for monitoring, tracking, sensing, measuring, and collecting real-time data in various settings [1], [2], such as smart buildings, health care monitoring, industrial monitoring, and other surveillance systems
Banka and Jana in [17] have employed particle swarm optimization (PSO) to best place sink node in WSNs, and the results indicate the superiority of their approach compared to the exhaustive grid search algorithm
The sink node localization problem is solved by PSO, flower pollination algorithm (FPA), grey wolf optimizer (GWO), sine cosine algorithm (SCA), multi-verse optimizer (MVO), and whale optimization algorithm (WOA) approaches with the transmission paths built by the greedy algorithm for the comparison purpose
Summary
Along with the remarkable developments in wireless sensor networks (WSNs), large-scale wireless sensor networks (LSWSNs) have appeared, which are used in our daily lives for monitoring, tracking, sensing, measuring, and collecting real-time data in various settings [1], [2], such as smart buildings, health care monitoring, industrial monitoring, and other surveillance systems. Similar to other hard optimization problems posed by LSWSNs deployment, locating the sink node in LSWSNs is a challenging task, as determining the best location of the sink node means reducing the number of message hops from a sensor node to its sink [3] This subsequently lowers sensors’ energy consumption ratios, because the process of sending and receiving data from a sensor to another consumes energy. The major problem with the topology construction algorithms remains that there is no generally an agreed mechanism for choosing the optimal location of the sink node [10]. Proposing a new fitness function for determining the optimal position of the sink node in LSWSNs. The remainder of this paper is organized as follows: in Section II, a review of previous studies related to optimization algorithms employed on WSNs is presented.
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