Continuous Objects Detection Based on Optimized Greedy Algorithm in IoT Sensing Networks

Sensing network of the Internet of Things (IoT) has become the infrastructure for facilitating the monitoring of potential events, where the accuracy and energy-efficiency are essential factors to be considered when determining the boundary of continuous objects. This article proposes an energy-efficient boundary detection mechanism in IoT sensing network. Specifically, a sleeping mechanism is adopted to detect the relatively coarse boundary through applying the convex hull algorithm. Leveraging the analysis of the relation for corresponding boundary nodes, the area around a boundary node is categorized as three types of sub-areas with descending possibility of event occurrence. An optimized greedy algorithm is adopted to selectively activate certain numbers of 1-hop neighboring IoT nodes in respective sub-areas, to avoid the activation of all 1-hop neighboring nodes in a flooding manner. Consequently, the boundary is refined and optimized according to sensory data of these activated IoT nodes. Experimental results demonstrate that our method can achieve better detection accuracy, while reducing energy consumption to a large extent, compared to the state of arts.