Data Collection Maximization in IoT-Sensor Networks Via an Energy-Constrained UAV

Abstract : In this paper, we study sensing data collection of IoT devices in a sparse IoT-sensor network, using an energy-constrained Unmanned Aerial Vehicle (UAV), where the sensory data is stored in IoT devices while the IoT devices may or may not be within the transmission range of each other. We formulate two novel data collection problems to fully or partially collect data stored from IoT devices using the UAV, by finding a closed tour for the UAV such that the accumulative volume of data collected within the tour is maximized, subject to the energy capacity on the UAV. To this end, we first propose a novel data collection framework that enables the UAV to collect sensory data from multiple IoT devices simultaneously if the IoT devices are within the coverage range of the UAV. We then formulate two data collection maximization problems to deal with full or partial data collection from sensors and show that both problems are NP-hard. We instead devise approximation and heuristic algorithms for them. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrated that the proposed algorithms are promising.

 EXISTING SYSTEM :

 ? To the best of our knowledge, existing research attempts have not addressed UAV technology's importance to green IoT applications to improve smart cities' quality of life. ? In, the authors introduced the drones' coexistence techniques and distributed IoT devices on the ground-based stations in Machine to Machine (M2M) communication networks. ? Flexible deployment has been found an appropriate technology for air pollution monitoring. ? Authors in studied the existing UAV techniques for monitoring the application environment. Heuristic and PSO techniques monitor the particular area and focus on the most polluted zones.

 DISADVANTAGE :

 ? We will focus on developing efficient approximation and heuristic algorithms for the data collection optimization problems. ? We formulate two data collection maximization problems, and develop efficient approximation and heuristic algorithms for the problems that strive for a fine tradeoff between the energy usages of the UAV on hovering and traveling. ? We study the data collection maximization problem, by deploying an energy-constrained UAV. ? They assumed that the hovering time of the UAV at each hovering location is identical, for which they proposed an approximation algorithm for the coverage quality maximization problem. ? This paper aims to address this issue and to provide efficient approximation and heuristic algorithms for the problem.

 PROPOSED SYSTEM :

 • The proposed techniques and strategies could reduce energy consumption, extend flying time, and reduce data collection latency. • Both techniques were shown to contribute to saving time and energy consumption of sensor nodes. More so, in, the authors proposed a model of one IoT device's efficient energy. • This survey presents an overview of the techniques and strategies proposed recently to achieve green IoT using UAVs infrastructure for a reliable and sustainable smart world. • This paper presents a comprehensive survey on efficient and intelligent UAV techniques, which have been proposed in the literature in recent years for greening IoT. • The devices are proposed to be equipped with sensors and communication add-ons.

 ADVANTAGE :

 ? We evaluate the performance of the proposed algorithms for the full (or partial) data collection maximization problems through experimental simulations. ? We also investigate the impact of important parameters on the algorithm performance. ? The use of mobile charging vehicles or mobile data collection vehicles on the ground for sensor charging and sensory data collection has been widely studied in the past. ? To evaluate the performance of the proposed algorithms for the data collection maximization problems, we here introduce a heuristic benchmark which proceeds iteratively. ? We first investigate the performance of different algorithms for the data collection maximization problem without hovering coverage overlapping.