In target tracking wireless sensor network, choosing the proper working nodes can not only minimize the number of active nodes, but also satisfy the tracking reliability requirement. However, most existing works focus on selecting sensor nodes which are the nearest to the target for tracking missions and they did not consider the correlation of the location of the sensor nodes so that these approaches can not meet all the goals of the network. This work proposes an efficient and adaptive node selection approach for tracking a target in a distributed wireless sensor network. The proposed approach combines the distance-based node selection strategy and particle filter prediction considering the spatial correlation of the different sensing nodes. Moreover, a joint distance weighted measurement is proposed to estimate the information utility of sensing nodes. Experimental results show that EANS outperformed the state-of-the-art approaches by reducing the energy cost and computational complexity as well as guaranteeing the tracking accuracy.

Wireless Sensor Networks (WSNs) consist of a large amount of small, low-cost, and wirelessly connected sensor nodes deployed in an unattended natural environment. Since the sensor nodes are usually battery-powered and it is infeasible to replenish energy via replacing their battery after deployment, therefore optimization of energy consumption is essential in all aspects of WSN to prolong the network lifetime.

In WSNs, an important application of target tracking has received significant attention in recent years [

With these motivations, we propose an efficient and adaptive node selection (EANS) strategy to dynamically choose the best set of sensor nodes for target tracking in WSNs. EANS combines the distance-based node selection strategy and particle filter prediction. The major objective of EANS is to keep reliable object tracking with minimum energy consumption. More precisely, the main contributions of this paper include the following:

This paper proposed a novel spatial-correlated node selection strategy, called EANS, which selects the node with more residual energy and considers the spatial correlation of the sensors located at the different positions within the sensing range. Thus, EANS can balance energy consumption and guarantee the tracking reliability with the optimal set of sensor nodes and minimize working nodes so as to decrease the energy consumption significantly.

This paper proposed a joint distance weighted information utility measurement, in which the joint information utility can be presented as the overlap area of the sight lines of the possible sensors and the covariance-related ellipses. In this way, EANS evaluates the usefulness of a sensor node’s observation without the complex entropy calculation and the a posteriori distribution estimation. Therefore, EANS can reduce the computational complexity and save the computational cost.

EANS considers not only the virtual range between sensors and target, but also the parallel degree of sensor’s sight line to the target. In other words, it also considers the effect of angular diversity of sight lines so that the sensing range of the nodes in EANS is more reliable.

The rest of this paper is organized as follows: Section

Recently, the problem of selecting the best nodes for tracking a target in distributed WSNs has been attracting much research attention.

The simplest approach (such as [

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In this paper, we consider a static WSN which is composed of one sink and

The sink is fixed and has an infinite power supply. And it gathers the information sensed by sensor nodes.

The distribution of sensor nodes is mutually independent. Every node is homogeneous and energy constrained.

Each node knows its position by using GPS or any localization algorithm. Let

Sensor nodes have three states, that is, active, sleep, and idle state. They remain in the sleep state most of the time and switch to the active state at specified time slots scheduled by the sink. In the active time slots, sensor nodes receive the assignment messages from the sink and check if there are sensing or relaying tasks in the next time instant. If there are tasks, they will keep active; otherwise they will sleep in next sensing instant. Afterward, the sink node predicts the next position of target by received data using particle filter algorithm. Then, the sink chooses the best nodes for the next task according to the joint distance weighted information utility measurement.

The goal of our work is to select the best subset of nodes for the next tracking task. Moreover, we decide which subset of nodes is the best according to two factors: (1) a joint distance weighted information utility and (2) the successful detection probability and residual energy of the candidate nodes. Therefore, given any time

In target tracking research field, particle filter (PF) has become a very effective algorithm because of its potential of coping with difficult nonlinear or non-Gaussian problems. PF with parallel structure is based on Monte Carlo simulation and Bayesian sampling estimation theories [

As the sink node can obtain the collaborative sensing result of target positions, the PF algorithm is performed on the sink node to predict the target position in the next sensing instant. The schematic diagram of PF algorithm is shown in Figure

Schematic diagram of particle filter algorithm.

The steps of PF are outlined as follows.

Thus, the state of target position is updated as

Dynamically choosing the best set of sensor nodes for tracking task can reduce the energy consumption of the network and improve tracking accuracy. As Figure

Node selection in target tracking WSNs.

In the entropy-based method, the entropy is used as information utility measure. The information utility of node

According to Section

Illustration of information utility measure in weighted distance method.

Assuming the measurement error

Unfortunately, because the locations of sensing nodes can affect each other on the estimation of target’s state and position, just considering one sensor node each time is not enough when node selecting algorithm is implemented. As shown in Figure

Effect of node’s location on information utility measure.

The equation of the uncertainty ellipse is known as

The line crossed node

Thus, the distance from node

Calculating the degree of the angle

Calculating the distance from node

When the sink obtains the estimated position of the target

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To evaluate the performance of the proposed approach of EANS, we simulated a network with 100 sensors nodes randomly laid out in a 100 m × 100 m area. We also compare our simulation results with the closest node selection (in which the nodes closest to the target are selected to track), the weighted distance node selection method (in which a node is selected once time according to its own information utility) and entropy-based method in terms of Mean square errors, execution time and energy cost.

The sink is fixed and located at (100, 150). The sensing range of each sensor node is

For a moving target, we assume a target in the sensing area moves randomly with maximum acceleration

Target trajectory in the sensing area.

Mean square positioning errors in different methods.

Figure

In order to investigate the computational complexities of these approaches in the simulations, the experiments were carried out keeping the other parameters fixed and progressively increasing the number of active candidate nodes to be selected for tracking in the sensing area. Specifically, the candidate nodes increase from 1 to 10. Figure

Comparison of execution time versus the number of active sensors in the sensing area.

Moreover, nowadays, sensor nodes are available with a longer or variable sensing range. If sensing ranges of sensors are changed, the number of sensor nodes within the sensing range is also changed. Table

Comparison of different approaches of average execution time.

Terms | Sensing ranges | |||
---|---|---|---|---|

Sensing range of sensors | ||||

10 m | 20 m | 40 m | 80 m | |

Average number of nodes within sensing range | 4 | 9 | 28 | 76 |

Average execution time of node selection (s) | ||||

Weighted distance | 0.535 | 1.168 | 3.116 | 6.389 |

Closest | 0.115 | 0.492 | 0.834 | 2.529 |

Entropy-based | 0.763 | 2.218 | 6.273 | 13.517 |

EANS | 0.563 | 1.272 | 3.656 | 7.395 |

Figure

Mean square positioning errors versus the number of selected nodes.

Finally, Table

Comparison of different approaches for tracking.

Approaches | Terms | |
---|---|---|

Average energy cost of the system (J) | Fail tracking percentage (%) | |

Weighted distance | 0.226 | 0.73 |

Closest | 0.251 | 1.26 |

Entropy-based | 0.243 | 0.63 |

EANS | 0.213 | 0.66 |

This paper proposed an efficient and adaptive node selection (EANS) approach for target tracking WSNs. EANS combines the distance-based node selection strategy and particle filter which is implemented at the sink to predict target states. The proposed spatial-correlated node selection algorithm uses a joint distance weighted measurement to estimate the information utility of sensing nodes. EANS has better performance than the other approaches by considering the spatial correlations of the sensing nodes in the process of node selections. The simulation results proved that EANS outperformed the state-of-the-art approaches by reducing the energy cost and computational complexity as well as guaranteeing the tracking accuracy.

The authors declare that there is no conflict of interests regarding the publication of this paper.

This work was supported by the National Natural Science Foundation of China under Grant 61301094, the Aerospace Innovation Foundation of Shandong province under Grant 2014JJ009, and the Fundamental Research Funds for the Central Universities under Grant 3102015ZY040.