中国科学院自动化研究所机构知识库

Sensing network of the Internet of Things (IoT) has become the infrastructure to facilitate the near real-time monitoring of potential events, where the accuracy and energy-efficiency are essential factors to be considered when determining the boundary of continuous objects. This paper proposes an energy-efficient boundary detection mechanism in IoT sensing networks. Specifically, a sleeping mechanism is adapted to detect the relatively coarse boundary through applying the convex hull algorithm, where only the relay nodes are activated. Leveraging the analysis of the relation for corresponding boundary nodes, the boundary area around a boundary node is categorized as three types of sub-areas with the descending possibility of event occurrence, i.e., the most possible, possible, and impossible areas. An optimized greedy algorithm is adapted to selectively activate certain numbers of one-hop neighboring IoT nodes in respective sub-areas, to avoid the activation of all one-hop neighboring nodes in a flooding manner. Consequently, the boundary is refined and optimized according to sensory data of these activated IoT nodes. The experimental results demonstrate that this technique can achieve a better detection accuracy, while reducing energy consumption to a large extent, than the state of art's techniques.

Sensing network of the Internet of Things (IoT) has become the infrastructure to facilitate the near real-time monitoring of potential events, where the accuracy and energy-efficiency are essential factors to be considered when determining the boundary of continuous objects. This paper proposes an energy-efficient boundary detection mechanism in IoT sensing networks. Specifically, a sleeping mechanism is adapted to detect the relatively coarse boundary through applying the convex hull algorithm, where only the relay nodes are activated. Leveraging the analysis of the relation for corresponding boundary nodes, the boundary area around a boundary node is categorized as three types of sub-areas with the descending possibility of event occurrence, i.e., the most possible, possible, and impossible areas. An optimized greedy algorithm is adapted to selectively activate certain numbers of one-hop neighboring IoT nodes in respective sub-areas, to avoid the activation of all one-hop neighboring nodes in a flooding manner. Consequently, the boundary is refined and optimized according to sensory data of these activated IoT nodes. The experimental results demonstrate that this technique can achieve a better detection accuracy, while reducing energy consumption to a large extent, than the state of art's techniques.

Sensing network of the Internet of Things (IoT) has become the infrastructure to facilitate the near real-time monitoring of potential events, where the accuracy and energy-efficiency are essential factors to be considered when determining the boundary of continuous objects. This paper proposes an energy-efficient boundary detection mechanism in IoT sensing networks. Specifically, a sleeping mechanism is adapted to detect the relatively coarse boundary through applying the convex hull algorithm, where only the relay nodes are activated. Leveraging the analysis of the relation for corresponding boundary nodes, the boundary area around a boundary node is categorized as three types of sub-areas with the descending possibility of event occurrence, i.e., the most possible, possible, and impossible areas. An optimized greedy algorithm is adapted to selectively activate certain numbers of one-hop neighboring IoT nodes in respective sub-areas, to avoid the activation of all one-hop neighboring nodes in a flooding manner. Consequently, the boundary is refined and optimized according to sensory data of these activated IoT nodes. The experimental results demonstrate that this technique can achieve a better detection accuracy, while reducing energy consumption to a large extent, than the state of art's techniques.