Efficient Capacity Constrained Assignment for Dynamic Network Coverage

With the fast development of the 5G wireless communications, the Internet of Things (IoT) becomes a hot research topic. Unmanned aerial vehicles (UAVs), due to the high mobility and low labor cost, have a big potential to be applied in the future IoT communication networks, e.g., data collection in remote areas. In this paper, we take a UAV as a monitor and an IoT device as an agent, and study how to utilize UAVs to establish network coverage and enhance the overall performance. Given ground agents and aerial monitors, each agent needs to be supervised by one monitor that can at most take charge of certain amount, while such assignment should guarantee the required service quality. This is much different from the conventional assumption that each monitor owns exactly a fixed number of agents without considering sensing quality under limited transmit power. Suppose that a monitor supervises an agent with a cost as the negative value of transmission rate in Rician fading, we then maximize the sum of transmission induced by every monitor-agent connection constrained with workload capacity for each monitor. To achieve the above goals, we first present a fast algorithm to report the least-cost assignment plan. Then, we seek for the minimum number of monitors to maintain the required service quality. Last, we discuss the assignment problem in the scenario of dynamic agents and dynamic monitors. We also give a set of strategies on how to initialize assignment, optimize monitor locations and manage power consumption. Extensive experimental results on both simulated datasets and real-life traffic data demonstrate our effectiveness and high performance.