Minimizing Convergecast Time and Energy Consumption in Green Internet of Things

• Published in: IEEE transactions on emerging topics in computing Vol. 8; no. 3; pp. 797 - 813
• Main Authors: Li, Zhetao, Liu, Yuxin, Liu, Anfeng, Wang, Shiguo, Liu, Haolin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-07-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agglomeration; Algorithms; Clean energy; cluster radius; Clusters; Computer architecture; Computer simulation; convergecast time; Convergence; Data collection; Data management; Delay; Delays; Electricity consumption; Energy conservation; Energy consumption; Energy conversion efficiency; Green data gathering; Internet of Things; Network architecture; Optimization; Schedules; state transitions; Surveillance systems; Time division multiple access; Wireless networks; Wireless sensor networks
• ISSN: 2168-6750; 2168-6750
• DOI: 10.1109/TETC.2018.2844282

Real-time surveillance systems with green wireless sensor networks (WSNs) are vital for maintaining high energy efficiency in many situations. This paper considers a scenario utilizing green WSNs to monitor the situation of Internet of Things (IoT), which constitute one of the most crucial sources of electricity consumption in information and communications technologies (ICT). More specifically, we focus on optimizing the cluster structure to minimize the delay and energy consumption for aggregation convergecast in green WSNs. We first find the optimal value of the network cluster radius for minimizing the delay through theoretical analysis. We then propose a novel cluster network architecture in which clusters that are far from the sink are small, allowing inter-cluster data aggregation to be processed earlier, and clusters that are near the sink are relatively large to allow more time for intra-cluster data aggregation. Hence, the sensor nodes can be scheduled in consecutive time slots to reduce the number of state transitions, consequently achieving the goal of minimizing both delay and energy consumption. Simulation results indicate that the proposed Algorithm outperforms previously reported solutions in terms of both schedule length and lifetime, thereby demonstrating its effectiveness.