The IoV 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 paper 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.

Using unmanned aerial vehicles (UAVs) to cover users in wireless networks has increased in recent years. Deploying UAVs in appropriate positions is important to cover users and nodes properly. In this article, we propose an efficient approach to determine the minimum number of required UAVs and their optimal positions. To this end, we use an iterative algorithm that updates the number of required UAVs at each iteration. To determine the optimal position for the UAVs, we present a mathematical model and solve it accurately after linearizing. One of the inputs of the mathematical model is a set of candidate points for UAV deployments in 2-D space. The mathematical model selects a set of points among candidate points and determines the altitude of each UAV. To provide a suitable set of candidate points, we also propose a candidate point selection method: the MergeCells method. The simulation results show that the proposed approach performs better than the 3-D P-median approach introduced in the literature. We also compare different candidate point selection approaches, and we show that the MergeCells method outperforms other methods in terms of the number of UAVs, user data rates, and simulation time.

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.

This paper proposes a new method for energy-efficient data gathering using multiple unmanned aerial vehicles (UAVs) in deadline-based wireless sensor networks (WSNs). This method collects WSN node data in minimum energy by providing the optimal position and trajectory of UAVs, the minimum travel time, and the minimum number of UAVs in a determined deadline. First, in order to minimize the energy consumption of WSN nodes and determine the positions of where to place UAVs for receiving network nodes data, this paper clusters the nodes in a distributed form and considers the centers of these clusters as a place to meet the UAVs. Then, beginning and ending virtual nodes are used for controlling the minimum number of UAVs. This paper attempts to complete the proposed solution and obtain the minimum travel time of UAV required for collecting data from the network. In order to find the optimal solution for this problem, a mixed integer linear programming mathematical model is presented, followed by normalizing and putting weights on each part of an objective function. Results obtained in the simulation step show that the presented model has an optimal performance in providing the position and optimal trajectory of UAVs, energy consumption, minimum travel time, and the number of UAVs used.