Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications

Abstract

In unmanned aerial vehicle (UAV)-aided wireless sensor networks (UWSNs), a UAV is employed as a mobile sink to gather data from sensor nodes. Incorporating UAV helps prolong the network lifetime and avoid the energy-hole problem faced by sensor networks. In emergency applications, timely data collection from sensor nodes and transferal of the data to the base station (BS) is a prime requisite. The timely and safe path of UAV is one of the fundamental premises for effective UWSN operations. It is essential and challenging to identify a suitable path in an environment comprising various obstacles and to ensure that the path can efficiently reach the target point. This paper proposes a hybrid path planning (HPP) algorithm for efficient data collection by assuring the shortest collision-free path for UAV in emergency environments. In the proposed HPP scheme, the probabilistic roadmap (PRM) algorithm is used to design the shortest trajectory map and the optimized artificial bee colony (ABC) algorithm to improve different path constraints in a three-dimensional environment. Our simulation results show that the proposed HPP outperforms the PRM and conventional ABC schemes significantly in terms of flight time, energy consumption, convergence time, and flight path.

UAV Path Planning Based on Multi-Stage Constraint Optimization

“…Reference [14], the author proposed an algorithm combining simulated annealing and genetic algorithm(GA) to carry out path planning. In reference [15], the author proposes a hybrid path planning (HPP) algorithm to efficiently collect data by ensuring the shortest collision-free path for UAVs in emergency situations. The HPP algorithm first uses the probabilistic roadmap method to get the shortest collision-free route and then uses the improved artificial bee colony algorithm to dynamically optimize the collision constraints.…”

“…The update method of the relaxation constraint ε(T) is listed in Formula (15), r f T represents the ratio of feasible and infeasible solutions in the Tth generation, and α is to control the searching preference between the feasible and infeasible regions (α ∈ [0, 1]). Different from other relaxation constraint mechanism design, two control parameters τ and cp are introduced to control the speed of reducing the relaxation of constraints for different feasible solution ratios in Formula (15), which can help to find feasible solutions more quickly and efficiently. T c is the preset maximum relaxation generation, which is used to control the time when the relaxation constraint is reduced to zero.…”

Flight Planning Optimization of Multiple UAVs for Internet of Things

“…When it comes to communication infrastructure, UAVs can be useful to perform data collection on a network of IoT devices [7][8][9]. Data collection from static nodes is a typical application for IoT sensor networks.…”

“…For the drone to store the data collected from all visited clusters, the total amount of data collected by a specific UAV must be less than or equal to the storage available on that drone as formalized by restriction (7). The total energy consumption when visiting the points and hovering to perform the synchronization must be less than the autonomy in the specific vehicle (8). The e k ij is the energy spent by UAV to travel between points ij.…”

Multi-Agent System for Intelligent Urban Traffic Management Using Wireless Sensor Networks Data

“…In the context of smart cities, wireless sensor networks play an important role in developing smart applications [ 1 ], emergency applications [ 2 ], seismic monitoring buildings [ 3 ], air pollution monitoring [ 4 ], characterization of urban anomalous noise events [ 5 ], and many others.…”