Energy-Efficient Boundary Detection for Continuous Objects in Collaborative Edge Networks

Along with a large and increasing number of resource-constrained and battery-powered smart things being deployed in networks, an energy-efficient mechanism for continuous object boundary detection is fundamental for prolonging the network lifetime. Most of the existing studies focus on how to improve the boundary accuracy of continuous objects while ignoring the energy consumption of smart things in edge networks, which may lead to some smart things being in an unusable state. To address this issue, we propose an
${E}$
nergy-efficient
${C}$
ontinuous object boundary
${D}$
etection
${M}$
echanism, namely
ECDM
. This mechanism consists of two stages: (i) edge-collaboration monitoring mechanism, where only sensory data relevant to toxic gas are transmitted among multi-edge networks, thus minimizing the number of exchanged sensory data involved in the detection, and reducing the transmission energy consumption of smart things; (ii) gas diffusion model is used to predict the diffusion of toxic gases, such that suspicious edge networks can be located quickly and efficiently, further reducing the energy consumption of smart things transmitting anomalous sensory data. Experimental results show that the energy consumption of
ECDM
is reduced by 34.5% compared to the latest boundary detection method that introduces a gas diffusion model while maintaining detection accuracy.