Energy-Efficient Message Bundling with Delay and Synchronization Constraints in Wireless Sensor Networks

• Published in: Sensors (Basel, Switzerland) Switzerland), 2022-07, Vol.22 (14), p.5276
• Main Authors: Li, Sihao, Kim, Kyeong Soo, Zhang, Linlin, Huan, Xintao, Smith, Jeremy
• Format: Article
• Language: English
• Subjects: Accuracy; Bundling; Emulators; end-to-end delay; Energy consumption; Energy efficiency; Integer programming; Linear programming; message bundling; Network topologies; Nodes; Sensors; Time synchronization; time synchronization accuracy; Wireless networks; Wireless sensor networks; wireless sensor networks (WSNs)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22145276

In a wireless sensor network (WSN), reducing the energy consumption of battery-powered sensor nodes is key to extending their operating duration before battery replacement is required. Message bundling can save on the energy consumption of sensor nodes by reducing the number of message transmissions. However, bundling a large number of messages could increase not only the end-to-end delays and message transmission intervals, but also the packet error rate (PER). End-to-end delays are critical in delay-sensitive applications, such as factory monitoring and disaster prevention. Message transmission intervals affect time synchronization accuracy when bundling includes synchronization messages, while an increased PER results in more message retransmissions and, thereby, consumes more energy. To address these issues, this paper proposes an optimal message bundling scheme based on an objective function for the total energy consumption of a WSN, which also takes into account the effects of packet retransmissions and, thereby, strikes the optimal balance between the number of bundled messages and the number of retransmissions given a link quality. The proposed optimal bundling is formulated as an integer nonlinear programming problem and solved using a self-adaptive global-best harmony search (SGHS) algorithm. The experimental results, based on the Cooja emulator of Contiki-NG, demonstrate that the proposed optimal bundling scheme saves up to 51.8% and 8.8% of the total energy consumption with respect to the baseline of no bundling and the state-of-the-art integer linear programming model, respectively.