Abstract
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.
Highlights
Advancements in microelectromechanical technology have enabled mass production of various inexpensive sensors, enhanced with limited data processing and transmission capabilities
Distance corresponds to the variable d in Algorithm 3. 90% service level agreement (SLA) can always be achieved without augmenting the solutions from stage1 (d = 0)
We propose ARES, a two-stage optimization method for offline provisioning of edge nodes (ENs)
Summary
Advancements in microelectromechanical technology have enabled mass production of various inexpensive sensors, enhanced with limited data processing and transmission capabilities. The Smart City Wien initiative by the city of Vienna has announced that all Viennese traffic signal systems (Figure 1) are being equipped with a total of approximately 10,000 weather and environmental sensors2 Such complex systems face the following challenges: (1) coverage of a geographically wide area; (2) continuous generation of a large amount of sensor data; (3) near-real-time processing of streaming data. These challenges cannot be solved by typical cloudbased WSN architectures, due to the high latency required to transfer data to cloud data centers for aggregation and processing. Among various prospective deployment strategies for edge computing, we consider the devices at the extreme edge of the network infrastructure to achieve the lowest latency
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