Target Detection In Sensor Networks Research Articles

Decision fusion using fuzzy threshold scheme for target detection in sensor networks

Spectrum sensing is a fundamental surveillance task and is used to detect target signal. Energy detection is a popular spectrum sensing technique. But detection performance of energy detector deteriorates in low signal-to-noise ratio (SNR) conditions and under noise uncertainty. In this paper, we proposed an energy detector with fuzzy threshold scheme for spectrum sensing, in which each sensor node sends local decision to the fusion center depending on the region in which the observed energy lies. Fusion center then makes a final global decision by combining local decisions. Analysis and simulations show that the proposed fuzzy threshold scheme could improve the detect probability effectively under ‘OR’,‘AND’ and ‘K-out-of-N’ fusion rules, and overcome the confused region problem. Monte Carlo Simulation results also show that proposed scheme achieves better detection performance and outperforms both conventional energy detector of both single and double threshold, respectively.

Bayesian Data Fusion for Distributed Target Detection in Sensor Networks

In this correspondence, we study different approaches for Bayesian data fusion for distributed target detection in sensor networks. Due to communication and bandwidth constraints, we assume that each sensor can only transmit a local decision to the fusion center (FC), which is in charge to take the final decision about the presence of a target. The optimal Bayesian test statistic at the FC is derived in the case where both the number and locations of the sensors are known. On the other hand, if both the number and the locations of the sensors are unknown, the optimal Bayesian test statistic is computed based on the same observations that the Scan Statistic test utilizes. The performances of the different approaches are compared through simulation.

Distributed Target Detection in Sensor Networks Using Scan Statistics

We introduce a sequential procedure to detect a target with distributed sensors in a two dimensional region. The detection is carried out in a mobile fusion center which successively counts the number of binary decisions reported by local sensors lying inside its moving field of view. This is a two-dimensional scan statistic-an emerging tool from the statistics field that has been applied to a variety of anomaly detection problems such as of epidemics or computer intrusion, but that seems to be unfamiliar to the signal processing community. We show that an optimal size of the field of view exists. We compare the sequential two-dimensional scan statistic test and two other tests. Results for system level detection are presented.

Random and Periodic Sleep Schedules for Target Detection in Sensor Networks

We study random and periodic sleep schedules from the point of view of delay in detecting the target. We consider sleep schedules in which a sensor in “inactive” mode wakes up either randomly or periodically to detect presence of the target within its vicinity resulting into two sleep schedules: (a) random wake-up schedule, and (b) periodic wake-up schedule respectively. Specifically, we analyse and obtain for the random wake-up schedule the expected delay in detection, and the delay, such that with probability P, the delay is less than the computed value. For the periodic wake-up schedule we show that there exists an upper bound on the delay. Further we compute the average value of delay. We have shown that the theoretically computed averages and the upper bounds on the delay match with the simulation results for the random wake-up and periodic wake-up schedules.

Energy‐efficient target detection in sensor networks using line proxies

AbstractOne of the fundamental and important operations in sensor networks is sink–source matching, i.e. target detection. Target detection is about how a sink finds the location of source nodes observing the event of interest (i.e. target activity). This operation is very important in many sensor network applications such as military battlefield and environment habitats. The mobility of both targets and sinks brings significant challenge to target detection in sensor networks. Most existing approaches are either energy inefficient or lack of fault tolerance in the environment of mobile targets and mobile sinks. Motivated by these, we propose an energy‐efficient line proxy target detection (LPTD) approach in this paper. The basic idea of LPTD is to use designated line proxies as rendezvous points (or agents) to coordinate mobile sinks and mobile targets. Instead of having rendezvous nodes for each target type as used by most existing approaches, we adopt the temporal‐based hash function to determine the line in the given time. Then the lines are alternated over time in the entire sensor network. This simple temporal‐based line rotation idea allows all sensor nodes in the network to serve as rendezvous points and achieves overall load balancing. Furthermore, instead of network‐wide flooding, interests from sinks will be flooded only to designated line proxies within limited area. The interest flooding can further decrease if the interest has geographical constraints. We have conducted extensive analysis and simulations to evaluate the performance of our proposed approach. Our results show that the proposed approach can significantly reduce overall energy consumption and target detection delay. Copyright © 2007 John Wiley & Sons, Ltd.

Optimized placement of nodes for target detection in sensor networks

In order to improve the precision of the target detection in wireless sensor networks, a new approach based on genetic algorithm (GA) was proposed to optimize the placement of the sensor. The target location problem was transformed into locating a target at a grid point through modeling the sensor field as a grid of points. Moreover, the sensor placement problem was formulated as a combinatorial optimization problem, which is aimed at minimizing the maximum discrimination error under the restraints of limited cost and complete coverage. The GA approach uses binary coding to represent the location, and both single parent cross-over operator and single parent mutation operator are used to improve its speed and efficiency. Experimental results have shown that a global optimal solution can be quickly obtained using the proposed method and the precision requirement for target location is satisfied.