Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search

Abstract

Underwater wireless sensor networks (UWSNs) can provide a promising solution to underwater target tracking. Due to the limited computation and bandwidth resources, only a small part of nodes are selected to track the target at each interval. How to improve tracking accuracy with a small number of nodes is a key problem. In recent years, a node depth adjustment system has been developed and applied to issues of network deployment and routing protocol. As far as we know, all existing tracking schemes keep underwater nodes static or moving with water flow, and node depth adjustment has not been utilized for underwater target tracking yet. This paper studies node depth adjustment method for target tracking in UWSNs. Firstly, since a Fisher Information Matrix (FIM) can quantify the estimation accuracy, its relation to node depth is derived as a metric. Secondly, we formulate the node depth adjustment as an optimization problem to determine moving depth of activated node, under the constraint of moving range, the value of FIM is used as objective function, which is aimed to be minimized over moving distance of nodes. Thirdly, to efficiently solve the optimization problem, an improved Harmony Search (HS) algorithm is proposed, in which the generating probability is modified to improve searching speed and accuracy. Finally, simulation results are presented to verify performance of our scheme.

Large-Scale Object Monitoring in Internet-of-Things: Energy-Efficient Perspectives

“…Remote monitoring of events and phenomena is one of the most important applications of IoT [4,5,[12][13][14]. Events or phenomena for sensing are generated by objects in target fields, and data evaluating events or phenomena are reported to a sink for recognizing what object exists and tracking how it moves.…”

“…Studies on energy efficiency typically focus on reducing communication costs since the energy consumption for transmitting and receiving data packets via wireless media is the major factor of battery power consumption of a sensor node [3]. The technological evolution model of IMO monitoring shows key features of all the proposed works in [4,5,[12][13][14][15].…”

Sensor-Networked Underwater Target Tracking Based on Grubbs Criterion and Improved Particle Filter Algorithm

“…Assuming that there is only one maneuvering target moving within the underwater three-dimensional space [15], the dynamic system can be represented by the state space model, and we adopt the uniform velocity turning model [16], [17]. The target moving state of the tracking system is given by formula (1).…”

“…According to the definition of the conditional entropy, the conditional entropy between the measured value of the j-th sensor node and the target state at time k is calculated as formula (15).…”