A hybrid localization model using node segmentation and improved particle swarm optimization with obstacle-awareness for wireless sensor networks

Abstract

Other than energy consumption, precision is of the utmost importance in node localization. Various wireless-sensor-network applications require the accurate information of sensor nodes’ locations. For instance, an enemy intrusion detection system (e.g., geo-fencing) needs accurate sensor nodes’ locations to detect where intruding enemies are located. As practical examples, forest fire, landslide, and water quality monitoring systems require the early identification of root causes’ exact locations before they can widely spread. In general, range-based localization techniques often yield higher accuracies because the localization estimation can be directly derived from the distance between hops and can leverage received signal strength indicator (RSSI) values but require model approximation of various hops and distances as in range-free localization techniques. However, the important factor that affects the accuracies is sensor node positioning, especially when sensor nodes (SNs) are spread across areas filled with obstructions causing less localization accuracy. Due to the diffraction caused by obstructions, the approximate distances between pairs of anchor nodes and unknown nodes using RSSI can differ substantially from the actual values. This research, therefore, aims to improve sensor node localization in situations where SNs are in areas with obstructions. We propose a novel technique, node segmentation with improved particle swarm optimization (NS-IPSO) that divides SNs into segments to improve the accuracy of the estimated distances between pairs of anchor nodes and unknown nodes. First, we determine candidate sensor nodes that could potentially be used to segment anchor nodes in the area. Such sensor nodes (STs) are those on the shortest paths between anchor nodes that appear more often than the average appearances of all sensor nodes. Then, segment nodes (SMs, sensor nodes for segmenting the anchor nodes) are selected from all the other STs based on certain specified conditions. To further improve the localization precision, we enhance the fitness function for each anchor node by taking into account the number of hops between each anchor node and unknown nodes. Furthermore, we enhance particle swarm optimization (PSO) by considering only particles that do not change positions to possibly reduce the chance of the local optimal trap. In this research, we test our proposed scheme's performance considering three forms of sensor node positioning: C-shape, H-shape, and S-shape. The simulation results show that the proposed scheme achieves higher accuracy in comparison with the recent state-of-the-art methods, i.e., hybrid discrete PSO (HDPSO), Hybrid PSO, approximate distances node localization (ADNL), the weight-search localization algorithm (WSLA), and min-max PSO techniques, particularly the situation where sensor nodes are in areas with obstacles.