Object detection and tracking is one of the key applications of wireless sensor networks (WSNs). The key issues associated with this application include network lifetime, object detection and localization accuracy. To ensure the high quality of the service, there should be a trade-off between energy efficiency and detection accuracy, which is challenging in a resource-constrained WSN. Most researchers have enhanced the application lifetime while achieving target detection accuracy at the cost of high node density. They neither considered the system cost nor the object localization accuracy. Some researchers focused on object detection accuracy while achieving energy efficiency by limiting the detection to a predefined target trajectory. In particular, some researchers only focused on node clustering and node scheduling for energy efficiency. In this study, we proposed a mobile object detection and tracking framework named the Energy Efficient Object Detection and Tracking Framework (EEODTF) for heterogeneous WSNs, which minimizes energy consumption during tracking while not affecting the object detection and localization accuracy. It focuses on achieving energy efficiency via node optimization, mobile node trajectory optimization, node clustering, data reporting optimization and detection optimization. We compared the performance of the EEODTF with the Energy Efficient Tracking and Localization of Object (EETLO) model and the Particle-Swarm-Optimization-based Energy Efficient Target Tracking Model (PSOEETTM). It was found that the EEODTF is more energy efficient than the EETLO and PSOEETTM models.

Node localization is the process of determining the location of sensor nodes in the area of operation. To determine the location of the moving object in a heterogeneous wireless sensor network accurately, it is important to know the location of sensor nodes that sense the presence of the object in their vicinity area. Sensor nodes equipped with GPS facility can know their exact location with reference to some point in space. But this makes the network system expensive. So, alternate method of determination of exact location of the nodes are always inevitable. But unfortunately, no such error free method is proposed till date. Hence, it is an open research problem for the researchers. In this paper, we propose an intelligent algorithm based on swarm intelligence for node localization problem. The proposed algorithm is a hybrid swarm intelligence algorithm in which the location estimation error resulted in DV-Hop algorithm is corrected using hybrid Particle Swarm Optimization (PSO)-Grey Wolf Optimization (GWO) algorithm with Poor-for-Change strategy. The performance of the algorithm is evaluated and compared with existing DV-Hop using PSO and GWO based node localization algorithm. It is found that the object localization error is less in case of proposed model in comparison to the above said models.

No abstract