ROUTER:Fog Enabled Cloud based Intelligent Resource Management Approach for Smart Home IoT Devices

There is a growing requirement for Internet of Things (IoT) infrastructure to ensure low response time to provision latency-sensitive real-time applications such as health monitoring, disaster management, and smart homes. Fog computing offers a means to provide such requirements, via a virtualized intermediate layer to provide data, computation, storage, and networking services between Cloud datacenters and end users. A key element within such Fog computing environments is resource management. While there are existing resource manager in Fog computing, they only focus on a subset of parameters important to Fog resource management encompassing system response time, network bandwidth, energy consumption and latency. To date no existing Fog resource manager considers these parameters simultaneously for decision making, which in the context of smart homes will become increasingly key. In this paper, we propose a novel resource management technique (ROUTER) for fog-enabled Cloud computing environments, which leverages Particle Swarm Optimization to optimize simultaneously. The approach is validated within an IoT-based smart home automation scenario, and evaluated within iFogSim toolkit driven by empirical models within a small-scale smart home experiment. Results demonstrate our approach results a reduction of 12% network bandwidth, 10% response time, 14% latency and 12.35% in energy consumption

Cost-Effective Scheduling in Fog Computing: An Environment Based on Modified PROMETHEE Technique

With the rising use of Internet of Things (IoT)-enabled devices, there is a significant increase in the use of smart applications that provide their response in real time. This rising demand imposes many issues such as scheduling, cost, overloading of servers, etc. To overcome these, a cost-effective scheduling technique has been proposed for the allocation of smart applications. The aim of this paper is to provide better profit by the Fog environment and minimize the cost of smart applications from the user end. The proposed framework has been evaluated with the help of a test bed containing four analysis phases and is compared on the basis of five metrics- average allocation time, average profit by the Fog environment, average cost of smart applications, resource utilization and number of applications run within given latency. The proposed framework performs better under all the provided metrics.&nbsp