Wireless sensor networks (WSNs) are usually deployed in the target area to carry out the detecting or monitoring tasks. Sweep coverage is an important concern of WSNs that more targets in the area can be monitored with fewer mobile sensor nodes. With the rapid development of unmanned aerial vehicle (UAV) technology, UAV has been increasingly used in military and civil fields. UAVs can be regarded as sensor nodes to perform specific tasks. Due to the limited battery lifetime of UAV in flight mission, it is difficult to achieve full coverage of all targets in a large-scale monitoring scenario. In this paper, we study how to plan the paths of multiple UAVs for sweep coverage. Based on the background of forest fire early warning and monitoring, we consider a Min-Time Max-Coverage (MTMC) issue in sweep coverage where a set of UAVs are dispatched to efficiently patrol the targets in the given area to achieve maximum coverage in minimum time. We establish a mathematical model considering the different coverage quality requirements of the targets in the given area. Then, we analyze the characteristics of the model and propose a heuristic algorithm Weighted Targets Sweep Coverage (WTSC) to find the optimal path, which considers the weights of the targets and the performance constraints of UAVs. Finally, we provide various numerical experiments and comparisons with several previous works to validate the superiority of the proposed algorithm.

Exploiting the possibility of an unmanned aerial vehicle (UAV) as a powerful tool for the Internet of Things applications, such as intelligent agricultural monitoring, intelligent transportation monitoring, etc., has gradually become a hot research topic at home and abroad. While some optimization algorithms have been devised to plan the flight route of UAVs, there are still some problems with the feasibility and effectiveness of these algorithms. This paper presents a solution to the UAV path planning problem for target coverage task in a dynamic environment. The methodology applies a greedy allocation strategy for task assignment and an improved ant colony optimization algorithm based on variable pheromones (ACO-VP) for path planning. First, we specify the optimal number of UAVs for the task and allocate target points to each UAV, through the greedy allocation strategy. Then, to improve the efficiency of path planning, we adjust the pheromone update rule by introducing a variable pheromone enhancement factor and a variable pheromone evaporation coefficient into the ACO algorithm. Moreover, paths are re-planned when the coverage task changes due to the increase of new target points. This method is verified through simulations and compared with other algorithms. The results show that the ACO-VP algorithm is more efficient and effective for UAV path planning than others.

One of the keys to making cloud data-centers (CDCs) proliferate impressively is the implementation of efficient task scheduling. Since all the resources of CDCs, even including operating systems (OSes) and application programs, can be stored and managed on remote data-centers, this study first analyzed the task scheduling problem for CDCs and established a mathematical model of the scheduling of two-stage tasks. The Johnson's rule was combined with the genetic algorithm to create a Johnson's-rule-based genetic algorithm (JRGA), which takes into account the characteristics of multiprocessor scheduling in CDCs. New crossover and mutation operations were devised to make the algorithm converge more quickly. In the decoding process, the Johnson's rule is used to optimize the makespan for each machine. Simulations were used to compare the performance of the JRGA with that of the list scheduling algorithm and an improved list scheduling algorithm. The results demonstrate the validity of the JRGA.

In wireless sensor networks (WSNs), the barrier coverage’s sensing belt can be open or closed. A closed belt is more difficult to construct than an open belt, as it has higher requirements, especially for the directional sensor networks (DSNs) with rotatable sensors. A target-barrier is a closed annular belt around a target with anti-intrusion and surveillance protection functions for both the inside and outside of the belt. Thus, a target-barrier is well suited to many applications including ecological reserve monitoring and contamination monitoring. In this paper, we employ the Virtual Target-barrier Construction (VTBC) method to facilitate its construction. First, we define and calculate a virtual barrier curve in order to convert the complicated target-barrier construction problem in a rotatable DSN into a much simpler optimization problem. Then, a distributed particle swarm optimization (D-PSO) algorithm is designed to achieve a near optimal coverage of the closed virtual barrier curve. Finally, simulations are presented to prove that VTBC has a better performance than other approaches.

One of the keys for the success of sweep coverage is to organize the participants to patrol effectively in large-scale target areas in order to satisfy the quality requirements of the sweep tasks. In this paper, we first analyze the possibility of applying crowdsensing technology for sweep coverage and propose a framework to solve the problem of arranging participants to sweep large-scale target areas when the quality requirements change dynamically over time. First, this problem is formulated as a task assignment problem with the goal of maximizing social welfare. Then, we establish a sweep coverage quality model for the area, which is a different approach from conventional methods that focus on the point of interest (PoI), and we propose a participant incentive model that considers the sustainability of the crowdsensing platform. Since determining the optimal assignment solution is an NP-hard problem, we design two approximate algorithms to arrange participants with the goal of maximizing social welfare; these are the participant-task oriented search (PTOS) algorithm based on a two-stage greed search and the bipartite graph-based participant search (BGPS) algorithm. We evaluate our methods using a population density map dataset from real-world cities as the large-scale target area and create dynamic task requirements for the sweep coverage. The experimental results show that the performance of our two algorithms is significantly better than the performance of the baseline algorithm.