ARES: Reliable and Sustainable Edge Provisioning for Wireless Sensor Networks

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 (1) geographical dispersion, (2) ad-hoc deployment and (3) 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.
 EXISTING SYSTEM :
 ? In this paper, we report the deployment of a ZigBee-based WSN inside an existing building duct system utilized for intelligent waste collection in an industrial environment. ? WSNs operating in the 2.4 GHz ISM (industrial, scientific, and medical) band may also be coexisting with Bluetooth or Wi-Fi, causing further interference in the network. ? Utilizing existing automatic waste collection system (AWCS) ducts as a wireless duct area network (WDAN) combines the advantages of a safe and reliable communications channel with efficient and environmentally friendly garbage disposal. ? Integrating a WDAN into a building’s existing automatic waste collection processes can also increase the reliability of the company’s communication systems.
 DISADVANTAGE :
 ? The edge provisioning problem targeted in this work needs evaluation of the three objectives before ENs can be deployed and hence the actual values can be measured. ? The goal of this first phase is to find a set of non-dominated solutions for the provisioning problem that minimize both latency and energy consumption. ? Facility Location Problem (FLP) is a single-objective algorithm aiming at minimizing energy consumption without considering latency and reliability. ? FLP is implemented as an integer linear programming problem, following the description in, using the ECOS BB solver implemented using Python 3.5 CVXPY module.
 PROPOSED SYSTEM :
 • In contrast, the WDAN system proposed here does not incur the major expense involved in installing expensive coaxial communication cables and multiple wireless routers in a building. • Our proposed architecture for deploying WDAN inside existing ducts/hollow pipes in a factory or building environment. These waveguides can provide a safe communication channel for all types of wireless communications. • In the proposed architecture, sensor and/or actuator (SA) nodes are mounted inside a hollow pipe at some distance from each other. • Applying our proposed WDAN based HVAC system will provide more efficient control and thus achieve further energy savings.
 ADVANTAGE :
 ? We employ NSGA-II meta-heuristic, due to the better performance in comparison with other metaheuristics. ? We compare the performance of ARES to the baseline algorithms described in with respect to the energy efficiency, transmission time, and fault-tolerance. ? However, CI is comparatively very large, which indicates randomness in its energy efficiency performance. ? The reason is, JFP ignores the location of provisioned nodes, which also affects the energy consumption due to communication distance. This is clear in the per transmission results where it has the worst performance and again large CI.

We have more than 145000 Documents , PPT and Research Papers

Have a question ?

Mail us : info@nibode.com