Energy Efficient Moving Target Tracking in Wireless Sensor Networks

Directional-Sensor Network Deployment Planning for Mobile-Target Search

In this paper, a novel time-phased directional-sensor network deployment strategy is presented for the mobile-target search problem, e.g., wilderness search and rescue (WiSAR). The proposed strategy uses probabilistic target-motion models combined with a variation of a standard direct search algorithm to plan the optimal locations of directional-sensors which maximize the likelihood of target detection. A linear sensing model is employed as a simplification for directional-sensor network deployment planning, while considering physical constraints, such as on-time sensor deliverability. Extensive statistical simulations validated our method. One such illustrative experiment is included herein to demonstrate the method’s operation. A comparative study was also carried out, whose summary is included in this paper, to highlight the tangible improvement of our approach versus three traditional deployment strategies: a uniform, a random, and a ring-of-fire type deployment, respectively.

Design of Shrewd Underwater Routing Synergy Using Porous Energy Shells

During the course of ubiquitous data monitoring in the underwater environment, achieving sustainable communication links among the sensor nodes with astute link quality seems an ordeal challenge. Energy utilization has a direct impact because all active devices are battery dependent and no charging or replacement actions can be made when cost- effective data packet delivery has been set as the benchmark. Hop link inspection and the selection of a Shrewd link through a resurrecting link factor have been nothing short of a bleak challenge, and only possible after meticulous research to develop a shrewd underwater routing synergy using extra porous energy shells (SURS-PES) which has never been conducted before. After broadcasting packets, the sensor node conducts a link inspection phase, thereby, if any link is found to be less than or equal to 50% shaky, the destination receiving node adds its residual energy status and returns it to the source node which adds some unusable energy porous shell to strengthen the link from 5% to a maximum of 90% and sends it only to the targeted node, therefore, an unaltered data packet delivery is anticipated. Performance evaluation was carried out using an NS2 simulator and the obtained results were compared with depth-based routing (DBR) and energy efficient DBR (EEDBR) to observe the outcomes with results that confirmed the previously mentioned direction for research in this area.

A Novel Loss Recovery and Tracking Scheme for Maneuvering Target in Hybrid WSNs

Tracking a mobile target, which aims to timely monitor the invasion of specific target, is one of the most prominent applications in wireless sensor networks (WSNs). Traditional tracking methods in WSNs only based on static sensor nodes (SNs) have several critical problems. For example, to void the loss of mobile target, many SNs must be active to track the target in all possible directions, resulting in excessive energy consumption. Additionally, when entering coverage holes in the monitoring area, the mobile target may be missing and then its state is unknown during this period. To tackle these problems, in this paper, a few mobile sensor nodes (MNs) are introduced to cooperate with SNs to form a hybrid WSN due to their stronger abilities and less constrained energy. Then, we propose a valid target tracking scheme for hybrid WSNs to dynamically schedule the MNs and SNs. Moreover, a novel loss recovery mechanism is proposed to find the lost target and recover the tracking with fewer SNs awakened. Furthermore, to improve the robustness and accuracy of the recovery mechanism, an adaptive unscented Kalman filter (AUKF) algorithm is raised to dynamically adjust the process noise covariance. Simulation results demonstrate that our tracking scheme for maneuvering target in hybrid WSNs can not only track the target effectively even if the target is lost but also maintain an excellent accuracy and robustness with fewer activated nodes.