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Wireless sensor networks are generally used to assist in collecting and transmitting data where humans cannot directly explore. But in a scenario with complex terrestrial environment, the ground communication links between sensors become so weak to provide reliable and high-speed services. Unmanned aerial vehicles (UAVs) can be used as flying relays to enhance connective reliability of terrestrial wireless sensor networks. However, in a UAV-assisted wireless senor network, if the UAV shares the same spectrum with sensors, the interference degrades the quality of communication links when sensors exist in pairs under co-channel conditions. Motivated thereby, we manage the interference by optimizing the transmit power of all communication nodes and planning the trajectory of UAV to achieve the goal of maximizing the sum throughput of the target sensor. Due to the nonconvexity of the optimization problems, we utilize difference of two convex functions (D.C.) programming and successive convex approximation to obtain the suboptimal solutions. Simulation results prove that the minimum signal-to-interference-plus-noise ratio (SINR) required by sensor pairs, flight altitude and maximum transmit power of the UAV can be carefully selected to maximize the sum throughput of target sensor, when the UAV's trajectory is pre-planned. The successive trajectory planning algorithm is also employed to significantly improve the sum throughput.INDEX TERMS Wireless sensor network, UAV communication, throughput maximization, power control, trajectory planning.The associate editor coordinating the review of this manuscript and approving it for publication was Yonghui Li. not be able to communicate without manual intervention. Considering the complexity of the ground conditions, the deployment of unmanned aerial vehicle (UAV) is an efficient solution to enhance the network capability in emergency sensor communications.Unprecedented advances in aeronautics and electronic technologies have led to a widespread deployment of UAVs, such as drones, aircrafts, balloons, and airships, etc., [2]-[4]. In particular, if properly planned and deployed, UAVs can provide reliable and cost-effective wireless communications solutions for a variety of scenarios. Therefore, attentions of UAVs from academia and industry are increasingly paid due to their high mobility, wide coverage and low cost. UAVs can be used as aerial base stations (BS) to provide reliable

The results of the current study demonstrate that the inclusion of factors associated with autonomic activities in the BP estimation model would improve the BP estimation accuracy, especially for hypertensive subjects.

Ensuring real-time reporting of fresh information and maintaining the sustainability of power supply is of great importance in time-critical green Internet of Things (IoT). In this paper, we investigate the mobile element scheduling problem in a network with multiple independent and rechargeable sensors, in which mobile elements are dispatched to collect data packets from the sensor nodes and to recharge them. The age of information (AoI) is used to measure the time elapsed of the most recently delivered packet since the generation of the packet. We propose an age-optimal mobile elements scheduling (AMES), which decides the trajectories of mobile elements based on a cooperative enforcement game and completes the time-slot allocation in each meeting point, to minimize the average AoI and maximize the energy efficiency. The cooperative enforcement game enables the mobile elements to make optimal visiting decisions and avoid the visiting conflicts, and the outcome of the game is pareto-optimal. Compared to the existing approaches, i.e., greedy algorithm (GA), greedy-neighborhood algorithm (GA-neighborhood), simulation results demonstrate that AMES can achieve a lower average AoI and a higher energy efficiency with a higher successful visiting ratio of the sensor node. INDEX TERMS Mobile element scheduling, data collection, age of information, energy efficiency, green IoT.

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