ARES: Reliable and Sustainable Edge Provisioning for Wireless Sensor Networks

Wireless sensor networks have wide applications in monitoring applications. However, sensors' energy and processing power constraints, as well as the limited network bandwidth, constitute significant obstacles to near-real-time requirements of modern IoT applications. Offloading sensor data on an edge computing infrastructure instead of in-cloud or in-network processing is a promising solution to these issues. Nevertheless, due to geographical dispersion, ad-hoc deployment, and rudimentary support systems compared to cloud data centers, reliability is a critical issue. This forces edge service providers to deploy a huge amount of edge nodes over an urban area, with catastrophic effects on environmental sustainability. In this work, we propose ARES, a two-stage optimization algorithm for sustainable and reliable deployment of edge nodes in an urban area. Initially, ARES applies multi-objective optimization to identify a set of Pareto-optimal solutions for transmission time and energy; then it augments these candidates in the second stage to identify a solution that guarantees the desired level of reliability using a dynamic Bayesian network based reliability model. ARES is evaluated through simulations using data from the urban area of Vienna. Results demonstrate that it can achieve a better trade-off between transmission time, energy-efficiency, and reliability than the state-of-the-art solutions.

Automated summary

Wireless sensor networks are important for monitoring applications, but limitations in energy, processing power, and network bandwidth hinder real-time requirements in IoT applications. Deploying edge nodes in urban areas requires consideration of reliability and environmental sustainability. This paper proposes the ARES algorithm, which uses multi-objective optimization and a dynamic Bayesian network model to achieve sustainable and reliable edge node deployment in urban areas.