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.

为了满足日益增长的全球城市化速度，以缓解资源限制，基于信息和通信技术（ICT）的新型解决方案的开发变得至关重要。流行的“智能城市范例”的特征是无处不在的网络条款，用于监视和控制该城市的关键基础设施，包括医疗保健，环境，交通和公用事业等。为了管理众多服务，使其服务质量（QoS）要求保持直立，必须同时采用上下文感知计算和雾计算。本文研究了通过情境感知的雾节点的智能睡眠和唤醒周期，将雾层能量最小化的可行性。它提出了一种虚拟机管理方法，可使用遗传算法（GA）有效地分配具有最少数量的活动雾节点的服务请求。然后，采用强化学习（RL）方法来优化雾节点的占空比周期。使用MATLAB进行仿真，结果表明，与现有的基于上下文共享的算法相比，该方案可将雾层的能耗降低约11–21％。