Parallel Algorithm on GPU for Wireless Sensor Data Acquisition Using a Team of Unmanned Aerial Vehicles

Abstract

This paper proposes a framework for the wireless sensor data acquisition using a team of Unmanned Aerial Vehicles (UAVs). Scattered over a terrain, the sensors detect information about their surroundings and can transmit this information wirelessly over a short range. With no access to a terrestrial or satellite communication network to relay the information to, UAVs are used to visit the sensors and collect the data. The proposed framework uses an iterative k‑means algorithm to group the sensors into clusters and to identify Download Points (DPs) where the UAVs hover to download the data. A Single-Source–Shortest-Path algorithm (SSSP) is used to compute optimal paths between every pair of DPs with a constraint to reduce the number of turns. A genetic algorithm supplemented with a 2-opt local search heuristic is used to solve the multi-travelling salesperson problem and to find optimized tours for each UAVs. Finally, a collision avoidance strategy is implemented to guarantee collision-free trajectories. Concerned with the overall runtime of the framework, the SSSP algorithm is implemented in parallel on a graphics processing unit. The proposed framework is tested in simulation using three UAVs and realistic 3D maps with up to 100 sensors and runs in just 20.7 s, a 33.3× speed-up compared to a sequential execution on CPU. The results show that the proposed method is efficient at calculating optimized trajectories for the UAVs for data acquisition from wireless sensors. The results also show the significant advantage of the parallel implementation on GPU.

Parallel Cooperative Coevolutionary Grey Wolf Optimizer for Path Planning Problem of Unmanned Aerial Vehicles

“…In such a model of m ∈  slaves, the simultaneous evaluation of m individuals is possible, which leads to a significant reduction in the total evaluation time of the population. The parallel software implementation will be more meaningful in large-scale optimization problems [33][34][35][36][37][38][39][40].…”

“…Over the past decades, there has been a growing interest in the parallelization of metaheuristics algorithms [33][34][35][36][37][38][39][40]. Such advanced mechanisms for computation accelerating and enhancement greatly contribute to the success of metaheuristics for solving hard and large-scale optimization problems.…”

“…In study [35], a parallel GA algorithm on GPU is investigated and compared to a sequential execution on CPU for wireless sensor data acquisition using a team of unmanned aerial vehicles. In study [36], an island model-based parallel GA is proposed and implemented on a GPU for solving the unequal area facility layout problem.…”

Combining Parallel Computing and Biased Randomization for Solving the Team Orienteering Problem in Real-Time

“…The parallel algorithms were utilized by a number of researchers in solving routing problems. For example, Roberge and Tarbouchi [24] developed a framework to download data from sensors by unmanned aerial vehicles. In their research, they used a genetic algorithm as the single source algorithm to optimize the routes of the vehicles.…”

Power Allocation and Energy Cooperation for UAV-Enabled MmWave Networks: A Multi-Agent Deep Reinforcement Learning Approach

“…In Ref. [ 4 ], a complete framework for the data acquisition from wireless sensor nodes using a swarm of UAVs is introduced. It covers all the steps from the sensor clustering to the collision-avoidance strategy.…”