Sparse mobile crowdsensing (SMCS) achieves urban-scale environmental sensing by assigning tasks to workers in specific subareas and inferring global data from the collected information. However, the effectiveness of SMCS is often limited because many studies overlook workers’ mobility and data collection time during subarea selection, as well as the time constraints of the sensing cycle in task assignment. This may affect the task completion timeliness and data quality. To address these issues, we develop a subarea evaluation method based on deep reinforcement learning, considering both the temporal effectiveness of sensing tasks and the importance of subarea selection for data inference. Using the subarea evaluation values derived from this method, we establish an online urban sensing task assignment model which is subject to constraints of sensing cycle time and cost budget. This model aims to find the task assignment result that minimizes data inference error by maximizing the comprehensive utility value. Considering the characteristics of the task assignment model, we propose an evolutionary algorithm named OTA-EA, which is based on an improved genetic algorithm. Its enhanced evolutionary operators can avoid generating infeasible solutions while maintaining robust search and optimization performance. Lastly, we conduct experimental evaluations of these methods on the real-world datasets. The results demonstrate that our subarea evaluation method can significantly reduce the data inference error, and our evolutionary task assignment algorithm can achieve better task assignment results than the baseline algorithms.

In 3D intelligent transportation systems, effectively using multimodal data from cameras and LiDAR for object tracking is a key research area. Current methods struggle due to an overreliance on 3D LiDAR data that overlooks detailed 2D camera imagery and inefficient use of localization data, resulting in poor frame-by-frame object tracking. Therefore, we propose a Multimodal Localization Fusion Tracking (MLFT) method to overcome these problems in intelligent transportation systems. The proposed method enables precise localization of multimodal information inputs through a novel Multimodal Localization Distillation (MMLD) approach and achieves accurate frame-to-frame object tracking via an advanced Multimodal Knowledge Transform-Fusion Tracking (MKTF) technique. The proposed method is evaluated using three representative datasets: KITTI, NuScenes, and Waymo Open. The experimental results show that our proposed MLFT algorithm has strong performance, improving the success rate by about 3.0%, 4.1%, and 3.3% on the KITTI, NuScenes, and Waymo Open datasets, respectively, while improving the accuracy by about 3.2%, 3.1%, and 4.1%.

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 article 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 pheromone (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 ant colony optimization (ACO) algorithm. Moreover, paths are replanned 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.

The conventional equivalent-input-disturbance (EID) approach exhibits two estimation errors that degrade the disturbance-suppression performance. This article aims to suppress the effects of estimation errors and improve disturbance-suppression performance. Taking the errors to be an artificial disturbance, several other EID compensators are connected to suppress the disturbance. Simplifying the connected EID compensators yields the design of a parameter,$p$, in the conventional EID estimator. Based on such an idea, this article presents a parallel-EID (PEID) approach, which has many merits. The disturbance-suppression performance of this approach is better than that of previous related studies, while its configuration is simple and universal. The physical meaning of$p$is clear and explains the reason why the disturbance-suppression performance improves. Furthermore, this article designs a novel virtual filter for transforming the PEID-based control system into an equivalent configuration that is easy to derive stability conditions. Finally, the PEID approach is applied in controlling the speed of a permanent magnet synchronous motor. Additionally, comparisons between the conventional EID, improved EID, PEID, and modified EID approaches show the validity and superiority of the PEID approach.

An important issue for directional sensor network monitoring and detection is how to repair gaps in strong barrier and improve the barrier coverage. To address this problem, we study the optimization problem of heterogeneous barrier coverage with different sensing radii, sensing angles and rotation directions. We propose a rotation optimization based on GA, including distributed gap judgment algorithm, interval intersection judgment, a new chromosome encoding representation, and combining crossover operators. Its principle is to mend the barrier gaps by rotating the heterogeneous sensors, to achieve the optimization objectives of maximum coverage rate, minimum gap rate. The simulation results demonstrate its effectiveness and superiority.

Closed-barrier coverage is a new coverage problem in hybrid wireless sensor networks. An important issue in closed-barrier coverage is how to reduce the sensor usage and prolong the network lifetime when the coverage requirement is met. In this paper, a new closed-barrier covering optimization method (NCCOM) is proposed to solve this problem. Firstly, for the obstacle environment, we present a closed-barrier construction method which can actively avoid obstacles, and realize the closed-barrier coverage through mobile nodes. On this basis, we propose a node scheduling optimization method, by adjusting the coverage angle to reduce redundant nodes, to achieve coverage optimization. To achieve continuous monitoring, we employ a scheduling method based on multi-objective evolutionary algorithm, which schedules heterogeneous nodes to be on duty in turn, so as to prolong the network lifetime. Simulation results show that NCCOM has better performance than other approaches.

In wireless sensor networks (WSNs), the strong barrier coverage might become weak or even invalid due to node failure. Relocating the mobile sensor nodes to heal the barrier gap is an effective redeployment strategy. In order to decrease the moving energy consumption, we design a two-stage hybrid nodes redeployment (THNR) based on the virtual barrier curve concept to search the barrier gaps in directional WSNs and fully utilize the rotation ability of directional sensors to firstly minimize the barrier gaps based on distributed particle swarm optimization algorithm. Then, an efficient scheduling method is designed for mobile nodes to minimize mobile distance. Simulations are presented to prove that our method THNR has a good performance on healing barrier gaps.

An important issue in directional sensor networks is how to prolong the network lifetime when the coverage requirement is met in angle scenarios. To address this problem, this paper proposes a new angle coverage scheduling optimization method (NACSOM) for heterogeneous nodes. In this method, a coverage-enhancing algorithm of nodes with unadjustable coverage angle is first devised to improve the coverage ratio. For the uncovered angle, a hole-repairing algorithm of nodes with adjustable angle is proposed to repair the coverage hole and achieve the full-angle coverage. To achieve the constant scheduling, a sustainable multi-round angle coverage scheduling algorithm is developed to prolong the network lifetime when the coverage ratio is maximized. Simulation results demonstrate that our method has better performance in real situations.

Wireless sensor networks (WSNs) are usually deployed in the target area to carry out 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 article, 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 is 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.

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 article, 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.

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.

Coverage issue in directional sensor networks (DSNs) is different from traditional omni-directional wireless sensor networks (WSNs) due to the limited angle of view and adjustable working direction. This paper sets up a model of monitoring area and a model of sensing area according to the unique characteristics of directional sensor and then derives an optimization model for area coverage ratio maximization. A new method based on particle swarm optimization (PSO) is proposed to solve the model. The algorithm makes the directional sensor nodes adjust their sensing direction to get personal best and global best to optimize the coverage of DSNs. The simulation results under different circumstances show that the algorithm has good performance and high efficiency in real situations.

Demands for cloud video surveillance systems are growing rapidly. Addressing to the issue of low energy-efficiency in cloud video datacenters, a task scheduling method using a time-clustering-based genetic algorithm is proposed. Firstly, an off-line scheduling model with SLA (service level agreement) time constraint is proposed after the analysis of the constrain relationship between the SLA and surveillance tasks. Then, a time-clustering-based genetic algorithm (TCGA) is proposed to solve the model for an optimal energy-efficient solution. According to the solution, the service quality is guaranteed and the total operating time of virtual machines is minimized. Meanwhile, idle virtual machines are shut down to reduce energy consumption. Simulations of large scale tasks scheduling are conducted. Several comparison experiments verify that the proposed method can improve the resource utilization greatly and achieve energy saving extremely.