A clustering method for locating services based on fog computing for the internet of things

Fog computing enables cloud and edge resource integration. It provides intelligent, decentralized processing of the amount of data generated by the Internet of Things (IoT) sensors for seamless integration of physical and cyber environments. This can create many benefits for society. The IoT framework uses wireless nodes to collect data and monitor the environment. Therefore, the limited power of batteries and the network lifetime are two essential problems in an IoT-based environment. Reducing energy consumption and fast transmission of collected data for reducing the response time can be improved the performance of IoT. Sending data packets by the appropriate header nodes can reduce the communication latency to the Fog. So, in the first step of the proposed method, the header nodes are selected based on many factors including the criteria of residual energy, speed of nodes, and number of neighbors in the Fog-IoT method. In the second phase, we implement a self-adapting algorithm using a control loop to solve the service location problem in the Fog-IoT approach. By considering the feature of self-adapting computing which refers to the self-management features of the distributed computing resources, the proposed method provides an optimal and centralized framework and methodology for solving the problem of deploying the Fog service based on self-adapting computing. Therefore, the real-time application requested and tasks allocation can be dynamically accepted in the shortest possible time in the Fog and the result returns to the IoT-based wireless devices. The simulation of the proposed method under the several performance metrics including the packet delivery ratio, throughput, and network lifetime has been compared with the previous works. The results show that the proposed Fog-IoT method improves all metrics in various scenarios.