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In the Internet of Things networks deploying fixed infrastructure is not always the best and most economical solution. Advances in efficiency and durability of Unmanned Aerial Vehicles (UAV) made flying small cell base stations (BS) a promising approach by providing coverage and capacity in environments where using fixed infrastructure is not economically justified. A key challenge in covering an area with UAV-based small cell BSs is optimal positioning the UAVs to maximize the coverage and minimize the number of required UAVs. In this paper, we propose an optimization problem that helps to determine the number and position of the UAVs. Moreover, to have efficient results in a reasonable time, we propose complementary heuristic methods that effectively reduce the search space. The simulation results show that our proposed method performs better than genetic algorithms. [ABSTRACT FROM AUTHOR]

The Internet of Vehicle sensors generate huge data that are processed by cloud servers, but this leads to high latency and bandwidth occupies. This was the main motivation behind fog computing. Software-defined network (SDN) was developed to simplify the traditional networks’ architecture. Sometimes, when a fog server fails to execute some tasks, it sends them to cloud servers while other fog servers are idle. This leads to inefficient utilization of fog computing resources. This letter aims to optimize the resource utilization of fog computing with meeting the deadlines of tasks via assigning them to an optimal fog server using SDN. Moreover, local and global load balance techniques are proposed. The simulation results revealed that our proposed system decreases the transference of tasks to the cloud and as a result decreases the latency and bandwidth consumption. [ABSTRACT FROM AUTHOR]