Abstract
An important criterion of wireless sensor network is the energy efficiency inspecified applications. In this wireless multimedia sensor network, the observations arederived from acoustic sensors. Focused on the energy problem of target tracking, this paperproposes a robust forecasting method to enhance the energy efficiency of wirelessmultimedia sensor networks. Target motion information is acquired by acoustic sensornodes while a distributed network with honeycomb configuration is constructed. Thereby,target localization is performed by multiple sensor nodes collaboratively through acousticsignal processing. A novel method, combining autoregressive moving average (ARMA)model and radial basis function networks (RBFNs), is exploited to perform robust targetposition forecasting during target tracking. Then sensor nodes around the target areawakened according to the forecasted target position. With committee decision of sensornodes, target localization is performed in a distributed manner and the uncertainty ofdetection is reduced. Moreover, a sensor-to-observer routing approach of the honeycombmesh network is investigated to solve the data reporting considering the residual energy ofsensor nodes. Target localization and forecasting are implemented in experiments.Meanwhile, sensor node awakening and dynamic routing are evaluated. Experimentalresults verify that energy efficiency of wireless multimedia sensor network is enhanced bythe proposed target tracking method.
Highlights
Wireless sensor networks (WSNs) consist of a large number of intelligent sensor nodes integrated into the environment, accomplishing complicated tasks, such as target tracking and environment surveillance
The difference of our work from aforementioned approaches mainly includes the following three aspects: (1) the wireless multimedia sensor networks (WMSNs) is organized in a distributed manner, where the honeycomb configuration is discussed; (2) the energy efficiency of WMSN benefits from the prior information of target motion, which is extracted by autoregressive moving average (ARMA)-Radial basis functions (RBF) algorithm; (3) target localization with committee decision based on the acoustic energy attenuation model
The efficiency of the proposed energy-efficient target tracking method will be evaluated by acoustic signal processing and energy consumption analysis in WMSN
Summary
Wireless sensor networks (WSNs) consist of a large number of intelligent sensor nodes integrated into the environment, accomplishing complicated tasks, such as target tracking and environment surveillance. To enhance the energy efficiency and the detection accuracy of WMSN, the sleep coordination and collaborative localization of sensor nodes can be performed with the prior target motion information derived from target tracking procedure. An algorithm with low complexity is desirable for target tracking in WMSN, especially for the distributed computation on general sensor nodes. A novel algorithm is proposed for target position forecasting, which is so-called ARMA-RBF It is a combination of ARMA model and RBFN. Experiments analysis is presented to justify the efficiency of the proposed target tracking method while the localization accuracy improvement and energy saving of WMSN are illustrated. The future target position forecasted by ARMA-RBF is adopted in the sleep mode scheduling and committee decision. The experiments results are presented, where the energy-efficient target tracking method with robust target forecasting is applied in WMSN.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call