یک چارچوب یکپارچه برای پوشش KK و جمع آوری داده ها در شبکه های حسگر بی سیم ناهمگن
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|67523||2016||13 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Parallel and Distributed Computing, Volume 89, March 2016, Pages 37–49
One of the fundamental tasks in the development of wireless sensor networks is coverage, which measures the network effectiveness and accuracy in event detection. While most existing studies on coverage focus on homogeneous and static wireless sensor networks, where the sensors have the same features, such as sensing, communication, and initial energy reserve, this paper considers heterogeneous sensors and sink mobility, which provide a more realistic and accurate view of the network design for a variety of sensing applications. In this paper, we exploit Helly’s Theorem to address the joint problem of kk-coverage and data collection in heterogeneous wireless sensor networks, where each point in a field of interest is simultaneously covered by at least kk active heterogeneous sensors. More precisely, we introduce a global framework that jointly considers kk-coverage and data collection. Precisely, we propose a multi-tier (or hierarchical) architecture of heterogeneous sensors along with two data collection protocols. While the first protocol is based on an adaptive hybrid forwarding scheme, the second one uses a mobile sink to collect the sensed data from all the sensors in the network. To this end, we investigate the optimal mobility strategy of the sink in order to minimize the average total energy consumption due to both of data communication and sink mobility in a circular sensor field. We divide the field into concentric circular bands with the same width, and derive a closed-form solution for the optimal sink mobility. We corroborate our analysis with simulation results to assess our proposed framework. We find that our sink mobility-based data collection protocol outperforms our hybrid geographic forwarding-based data collection protocol.