A genetic algorithm for energy efficient fog layer resource management in context-aware smart cities. (December 2020)

• Record Type: Journal Article
• Authors: Reddy, K. Hemant Kumar; Luhach, Ashish Kr.; Pradhan, Buddhadeb; Dash, Jatindra Kumar; Roy, Diptendu Sinha
• Is Part Of: Sustainable cities and society. Volume 63(2020)
• Publication Date: 2020-12
• Subjects: Internet of Things -- IoT applications -- Fog computing -- Cloud computing -- Context sharing -- Service delay -- Intelligent forecasting; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2020.102428
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: Smart cities' energy minimization at fog layer formulated as an optimization problem. Novel VM management approach proposed that uses GA for fog layer energy minimization. Further energy reduction attained by inducing fog nodes to sleep mode using RL. The proposed RL enacts an intelligent duty cycling by predicting sleep-wake cycles. Abstract: The development of novel Information and Communication Technology (ICT) based solutions becomes essential to meet the ever increasing rate of global urbanization in order to satiate the constraint in resources. The popular 'smart city paradigm is characterized by ubiquitous cyber provisions for the monitoring and control of such city's critical infrastructures, encompassing healthcare, environment, transportation and utilities among others. In order to manage the numerous services keeping their Quality of Service (QoS) demands upright, it is imperative to employ context aware computing as well as fog computing simultaneously. This paper investigates the feasibility of energy minimization at the fog layer through intelligent sleep and wake-up cycles of the fog nodes which are context-aware. It proposes a virtual machine management approach for effectively allocating service requests with a minimal number of active fog nodes using a genetic algorithm (GA); and thereafter, a reinforcement learning (RL) approach is incorporated to optimize the period of fog nodes' duty cycle. Simulations are carried out using MATLAB and the results demonstrate that the proposed scheme improves energy consumption of the fog layer by approximately 11–21% when compared to existing context sharing based algorithms.