Energy-efficient adaptive sensor scheduling for target tracking in wireless sensor networks

Sensor scheduling is essential to collaborative target tracking in wireless sensor networks （WSNs）. In the existing works for target tracking in WSNs, such as the information-driven sensor query （IDSQ）, the tasking sensors are scheduled to maximize the information gain while minimizing the resource cost based on the uniform sampling intervals, ignoring the changing of the target dynamics and the specific desirable tracking goals. This paper proposes a novel energy-efficient adaptive sensor scheduling approach that jointly selects tasking sensors and determines their associated sampling intervals according to the predicted tracking accuracy and tracking energy cost. At each time step, the sensors are scheduled in alternative tracking mode, namely, the fast tracking mode with smallest sampling interval or the tracking maintenance mode with larger sampling interval, according to a specified tracking error threshold. The approach employs an extended Kalman filter （EKF）-based estimation technique to predict the tracking accuracy and adopts an energy consumption model to predict the energy cost. Simulation results demonstrate that, compared to a non-adaptive sensor scheduling approach, the proposed approach can save energy cost significantly without degrading the tracking accuracy.

Sensor Scheduling for Target Tracking in Networks of Active Sensors

Wireless sensor network (WSN) of active sensors suffers from serious inter-sensor interference (ISI) and imposes new design and implementation challenges. In this paper, based on the ultrasonic sensor network, two time-division based distributed sensor scheduling schemes are proposed to deal with ISI by scheduling sensors periodically and adaptively respectively. Extended Kalman filter (EKF) is used as the tracking algorithm in distributed manner. Simulation results show that the adaptive sensor scheduling scheme can achieve superior tracking accuracy with faster tracking convergence speed.

Sensor scheduling for ground maneuvering target tracking in presence of detection blind zone

Continuous and stable tracking of the ground maneuvering target is a challenging problem due to the complex terrain and high clutter. A collaborative tracking method of the multisensor network is presented for the ground maneuvering target in the presence of the detection blind zone(DBZ). First, the sensor scheduling process is modeled within the partially observable Markov decision process(POMDP) framework. To evaluate the target tracking accuracy of the sensor, the Fisher information is applied to constructing the reward function. The key of the proposed scheduling method is forecasting and early decisionmaking. Thus, an approximate method based on unscented sampling is presented to estimate the target state and the multi-step scheduling reward over the prediction time horizon. Moreover, the problem is converted into a nonlinear optimization problem, and a fast search algorithm is given to solve the sensor scheduling scheme quickly. Simulation results demonstrate the proposed nonmyopic scheduling method(Non-MSM) has a better target tracking accuracy compared with traditional methods.

Optimal sensor scheduling for hybrid estimation

A sensor scheduling problem was considered for a class of hybrid systems named as the stochastic linear hybrid system （SLHS）. An algorithm was proposed to select one （or a group of） sensor at each time from a set of sensors. Then, a hybrid estimation algorithm was designed to compute the estimates of the continuous and discrete states of the SLHS based on the observations from the selected sensors. As the sensor scheduling algorithm is designed such that the Bayesian decision risk is minimized, the true discrete state can be better identified. Moreover, the continuous state estimation performance of the proposed algorithm is better than that of hybrid estimation algorithms using only predetermined sensors. Finallyo the algorithms are validated through an illustrative target tracking example.

Multi-objective optimization sensor node scheduling for target tracking in wireless sensor network

Target tracking in wireless sensor network usually schedules a subset of sensor nodes to constitute a tasking cluster to collaboratively track a target.For the goals of saving energy consumption,prolonging network lifetime and improving tracking accuracy,sensor node scheduling for target tracking is indeed a multi-objective optimization problem.In this paper,a multi-objective optimization sensor node scheduling algorithm is proposed.It employs the unscented Kalman filtering algorithm for target state estimation and establishes tracking accuracy index,predicts the energy consumption of candidate sensor nodes,analyzes the relationship between network lifetime and remaining energy balance so as to construct energy efficiency index.Simulation results show that,compared with the existing sensor node scheduling,our proposed algorithm can achieve superior tracking accuracy and energy efficiency.

Using approximate dynamic programming for multi-ESM scheduling to track ground moving targets

This paper researches the adaptive scheduling problem of multiple electronic support measures(multi-ESM) in a ground moving radar targets tracking application. It is a sequential decision-making problem in uncertain environment. For adaptive selection of appropriate ESMs, we generalize an approximate dynamic programming(ADP) framework to the dynamic case. We define the environment model and agent model, respectively. To handle the partially observable challenge, we apply the unsented Kalman filter(UKF) algorithm for belief state estimation. To reduce the computational burden, a simulation-based approach rollout with a redesigned base policy is proposed to approximate the long-term cumulative reward. Meanwhile, Monte Carlo sampling is combined into the rollout to estimate the expectation of the rewards. The experiments indicate that our method outperforms other strategies due to its better performance in larger-scale problems.

A multi-sensor-system cooperative scheduling method for ground area detection and target tracking

A multi-sensor-system cooperative scheduling method for multi-task collaboration is proposed in this paper.We studied the method for application in ground area detection and target tracking.The aim of sensor scheduling is to select the optimal sensors to complete the assigned combat tasks and obtain the best combat benefits.First,an area detection model was built,and the method of calculating the detection risk was proposed to quantify the detection benefits in scheduling.Then,combining the information on road constraints and the Doppler blind zone,a ground target tracking model was established,in which the posterior Carmér-Rao lower bound was applied to evaluate future tracking accuracy.Finally,an objective function was developed which considers the requirements of detection,tracking,and energy consumption control.By solving the objective function,the optimal sensor-scheduling scheme can be obtained.Simulation results showed that the proposed sensor-scheduling method can select suitable sensors to complete the required combat tasks,and provide good performance in terms of area detection,target tracking,and energy consumption control.

Reliable and Energy Efficient Target Coverage for Wireless Sensor Networks

A critical aspect of applications with Wireless Sensor Networks （WSNs） is network lifetime. Power-constrained WSNs are usable as long as they can communicate sense data to a processing node. Poor communication links and hazardous environments make the WSNs unreliable. Existing schemes assume that the state of a sensor covering targets is binary： success （covers the targets） or failure （cannot cover the targets）. However, in real WSNs, a sensor covers targets with a certain probability. To improve WSNs＇ reliability, we should consider that a sensor covers targets with users＇ satisfied probability. To solve this problem, this paper first introduces a failure probability into the target coverage problem to improve and control the system reliability. Furthermore, we model the solution as the a-Reliable Maximum Sensor Covers （a-RMSC） problem and design a heuristic greedy algorithm that efficiently computes the maximal number of a-Reliable sensor covers. To efficiently extend the WSNs lifetime with users＇ pre-defined failure probability requirements, only the sensors from the current active sensor cover are responsible for monitoring all targets, while all other sensors are in a low-energy sleep mode. Simulation results validate the performance of this algorithm, in which users can precisely control the system reliability without sacrificing much energy consumption.