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Parallel and Distributed Computing in Wireless Sensor Networks

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 22264

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Special Issue Editors

Prof. Dr. Ling Wang

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Guest Editor

Department of Automation, Tsinghua University, Beijing, China
Interests: intelligent optimization theory; modeling, optimization and scheduling for complex industry process
Special Issues, Collections and Topics in MDPI journals

Dr. Chengyu Hu

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Guest Editor

School of Computer Science, China University of Geosciences, Wuhan, China
Interests: sensor placement; evolutionary computation; reinforcement learning

Prof. Dr. Yinan Guo

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Guest Editor

School of Information and Control Engineering, China University of Mining and Technology, Beijing, China
Interests: wireless sensor network; dynamic optimization; evolutionary optimization
Special Issues, Collections and Topics in MDPI journals

Dr. Feng Wang

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Guest Editor

School of Computer Science, Wuhan University, Wuhan, China
Interests: ad hoc network; intelligent optimization; machine learning

Special Issue Information

Dear Colleagues,

Wireless sensor networks (WSNs), empowered by wireless sensing and parallel-distributed computing technologies, represent a promising development trend. WSNs can be applied in a variety of domains, such as smart cities, smart homes, healthcare, smart traffic, smart buildings, smart industry, and smart agriculture.

WSN relies predominantly on the intelligent processing of the sensing data from various IoT devices, which are usually deployed in a geographically distributed manner. Besides the geographical distribution, the sensor data are also regarded as one of the main sources of big data characterized by their big volume and big velocity. To extract the big value, many computational resources are required in order to handle these big data analytic tasks. Cloud computing has shown great potential in this regard. However, a sole reliance on cloud computing may not be sufficient to satisfy the ever-growing resource requirements. To this end, more advanced distributed and parallel processing by exploring the widely available resources, such as edge computing, is needed. Recent development has recognized the potential of the intelligent edge computing paradigm as a supplement, or even alternative, to cloud computing. However, given the early stage of such development trends, there are still many problems to be addressed.

Therefore, in this Special Issue, we are motivated to collect articles which present state-of-the-art concepts and methodologies to address the challenges hampering distributed and parallel processing for WSN. We also welcome works on related technologies, such as 5G, 6G, blockchain, next-generation networking, and intelligent edge computing.

The aim of this Special Issue is to reflect the most recent developments in parallel, distributed, and intelligent computing in WSNs. The topics of interest include, but are not limited to:

Data-driven intelligent computing;
Distributed and cooperative methods for WSNs;
Intelligent optimization algorithms for WSNs;
Indoor/outdoor localization and tracking;
Localization and tracking using sensors;
Remote sensing;
Energy harvesting for autonomous sensors;
Intelligent optimization algorithms for sensor placement;
Optimal power allocation;
Evolutionary computation for wireless sensor networks;
Deep reinforcement learning for sensor deployment;
Preventing and detecting attacks in WSNs;
Scheduling of data traffic in WSNs;
Recluster and cluster of data in WSNs;
Scheduling the sleep and wake up in WSNs;
Aggregation of data in WSNs;
Efficient energy routing in WSNs;
Control of dynamic topology in WSNs.

Prof. Dr. Ling Wang
Dr. Chengyu Hu
Prof. Dr. Yinan Guo
Dr. Feng Wang
Guest Editors

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Keywords

wireless sensor networks
mobile sensors
parallel computing
distributed computing
intelligent computing
intelligent edge computing
cloud computing

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Published Papers (7 papers)

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Research

38 pages, 3083 KiB

Open AccessArticle

Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues

by Sedeng Danba, Jingjing Bao, Guorong Han, Siri Guleng and Celimuge Wu

Sensors 2022, 22(18), 6995; https://doi.org/10.3390/s22186995 - 15 Sep 2022

Cited by 8 | Viewed by 3391

Abstract

Internet of Vehicles (IoV) technology has been attracting great interest from both academia and industry due to its huge potential impact on improving driving experiences and enabling better transportation systems. While a large number of interesting IoV applications are expected, it is more challenging to design an efficient IoV system compared with conventional Internet of Things (IoT) applications due to the mobility of vehicles and complex road conditions. We discuss existing studies about enabling collaborative intelligence in IoV systems by focusing on collaborative communications, collaborative computing, and collaborative machine learning approaches. Based on comparison and discussion about the advantages and disadvantages of recent studies, we point out open research issues and future research directions. Full article

(This article belongs to the Special Issue Parallel and Distributed Computing in Wireless Sensor Networks)

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17 pages, 3814 KiB

Open AccessArticle

A Dynamic Task Scheduling Method for Multiple UAVs Based on Contract Net Protocol

by Zhenshi Zhang, Huan Liu and Guohua Wu

Sensors 2022, 22(12), 4486; https://doi.org/10.3390/s22124486 - 14 Jun 2022

Cited by 9 | Viewed by 2240

Abstract

Unmanned aerial vehicles are becoming promising platforms for disaster relief, such as providing emergency communication services in wireless sensor networks, delivering some living supplies, and mapping for disaster recovery. Dynamic task scheduling plays a very critical role in coping with emergent tasks. To solve the multi-UAV dynamic task scheduling, this paper constructs a multi-constraint mathematical model for multi-UAV dynamic task scheduling, involving task demands and platform capabilities. Three objectives are considered, which are to maximize the total profit of scheduled tasks, to minimize the time consumption, and to balance the number of scheduled tasks for multiple UAVs. The multi-objective problem is converted into single-objective optimization via the weighted sum method. Then, a novel dynamic task scheduling method based on a hybrid contract net protocol is proposed, including a buy-sell contract, swap contract, and replacement contract. Finally, extensive simulations are conducted under three scenarios with emergency tasks, pop-up obstacles, and platform failure to verify the superiority of the proposed method. Full article

(This article belongs to the Special Issue Parallel and Distributed Computing in Wireless Sensor Networks)

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17 pages, 1142 KiB

Open AccessArticle

Domain Knowledge-Based Evolutionary Reinforcement Learning for Sensor Placement

by Mingxuan Song, Chengyu Hu, Wenyin Gong and Xuesong Yan

Sensors 2022, 22(10), 3799; https://doi.org/10.3390/s22103799 - 17 May 2022

Cited by 1 | Viewed by 2084

Abstract

Reducing pollutant detection time based on a reasonable sensor combination is desirable. Clean drinking water is essential to life. However, the water supply network (WSN) is a vulnerable target for accidental or intentional contamination due to its extensive geographic coverage, multiple points of access, backflow, infrastructure aging, and designed sabotage. Contaminants entering WSN are one of the most dangerous events that may cause sickness or even death among people. Using sensors to monitor the water quality in real time is one of the most effective ways to minimize negative consequences on public health. However, it is a challenge to deploy a limited number of sensors in a large-scale WSN. In this study, the sensor placement problem (SPP) is modeled as a sequential decision optimization problem, then an evolutionary reinforcement learning (ERL) algorithm based on domain knowledge is proposed to solve SPP. Extensive experiments have been conducted and the results show that our proposed algorithm outperforms meta-heuristic algorithms and deep reinforcement learning (DRL). Full article

(This article belongs to the Special Issue Parallel and Distributed Computing in Wireless Sensor Networks)

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25 pages, 1058 KiB

Open AccessArticle

Cooperatively Routing a Truck and Multiple Drones for Target Surveillance

by Shuangxi Tian, Xupeng Wen, Bin Wei and Guohua Wu

Sensors 2022, 22(8), 2909; https://doi.org/10.3390/s22082909 - 10 Apr 2022

Cited by 5 | Viewed by 2757

Abstract

With the development of drone technology, drones have been deployed in civilian and military fields for target surveillance. As the endurance of drones is limited, large-scale target surveillance missions encounter some challenges. Based on this motivation, we proposed a new target surveillance mode via the cooperation of a truck and multiple drones, which enlarges the range of surveillance. This new mode aims to rationally plan the routes of trucks and drones and minimize the total cost. In this mode, the truck, which carries multiple drones, departs from its base, launches small drones along the way, surveils multiple targets, recycles all drones and returns to the base. When a drone is launched from the truck, it surveils multiple targets and flies back to the truck for recycling, and the energy consumption model of the drone is taken into account. To assist the new problem-solving, we developed a new heuristic method, namely, adaptive simulated annealing with large-scale neighborhoods, to optimize truck and drone routes, where a scoring strategy is designed to dynamically adjust the selection weight of destroy operators and repair operators. Additionally, extensive experiments are conducted on several synthetic cases and one real case. The experimental results show that the proposed algorithm can effectively solve the large-scale target surveillance problem. Furthermore, the proposed cooperation of truck and drone mode brings new ideas and solutions to targets surveillance problems. Full article

(This article belongs to the Special Issue Parallel and Distributed Computing in Wireless Sensor Networks)

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20 pages, 14311 KiB

Open AccessArticle

An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship

by Jiawei Chen, Qizhang Luo and Guohua Wu

Sensors 2022, 22(5), 2050; https://doi.org/10.3390/s22052050 - 6 Mar 2022

Cited by 1 | Viewed by 2372

Abstract

Airship-based Earth observation is of great significance in many fields such as disaster rescue and environment monitoring. To facilitate efficient observation of high-altitude airships (HAA), a high-quality observation scheduling approach is crucial. This paper considers the scheduling of the imaging sensor and proposes a hierarchical observation scheduling approach based on task clustering (SA-TC). The original observation scheduling problem of HAA is transformed into three sub-problems (i.e., task clustering, sensor scheduling, and cruise path planning) and these sub-problems are respectively solved by three stages of the proposed SA-TC. Specifically, a novel heuristic algorithm integrating an improved ant colony optimization and the backtracking strategy is proposed to address the task clustering problem. The 2-opt local search is embedded into a heuristic algorithm to solve the sensor scheduling problem and the improved ant colony optimization is also implemented to solve the cruise path planning problem. Finally, extensive simulation experiments are conducted to verify the superiority of the proposed approach. Besides, the performance of the three algorithms for solving the three sub-problems are further analyzed on instances with different scales. Full article

(This article belongs to the Special Issue Parallel and Distributed Computing in Wireless Sensor Networks)

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15 pages, 6184 KiB

Open AccessArticle

LeGO-LOAM-SC: An Improved Simultaneous Localization and Mapping Method Fusing LeGO-LOAM and Scan Context for Underground Coalmine

by Guanghui Xue, Jinbo Wei, Ruixue Li and Jian Cheng

Sensors 2022, 22(2), 520; https://doi.org/10.3390/s22020520 - 11 Jan 2022

Cited by 39 | Viewed by 6263

Abstract

Simultaneous localization and mapping (SLAM) is one of the key technologies for coal mine underground operation vehicles to build complex environment maps and positioning and to realize unmanned and autonomous operation. Many domestic and foreign scholars have studied many SLAM algorithms, but the mapping accuracy and real-time performance still need to be further improved. This paper presents a SLAM algorithm integrating scan context and Light weight and Ground-Optimized LiDAR Odometry and Mapping (LeGO-LOAM), LeGO-LOAM-SC. The algorithm uses the global descriptor extracted by scan context for loop detection, adds pose constraints to Georgia Tech Smoothing and Mapping (GTSAM) by Iterative Closest Points (ICP) for graph optimization, and constructs point cloud map and an output estimated pose of the mobile vehicle. The test with KITTI dataset 00 sequence data and the actual test in 2-storey underground parking lots are carried out. The results show that the proposed improved algorithm makes up for the drift of the point cloud map, has a higher mapping accuracy, a better real-time performance, a lower resource occupancy, a higher coincidence between trajectory estimation and real trajectory, smoother loop, and 6% reduction in CPU occupancy, the mean square errors of absolute trajectory error (ATE) and relative pose error (RPE) are reduced by 55.7% and 50.3% respectively; the translation and rotation accuracy are improved by about 5%, and the time consumption is reduced by 2~4%. Accurate map construction and low drift pose estimation can be performed. Full article

(This article belongs to the Special Issue Parallel and Distributed Computing in Wireless Sensor Networks)

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17 pages, 411 KiB

Open AccessArticle

An Improved Multioperator-Based Constrained Differential Evolution for Optimal Power Allocation in WSNs

by Wei Li and Wenyin Gong

Sensors 2021, 21(18), 6271; https://doi.org/10.3390/s21186271 - 18 Sep 2021

Cited by 4 | Viewed by 1564

Abstract

Optimal power allocation (OPA), which can be transformed into an optimization problem with constraints, plays a key role in wireless sensor networks (WSNs). In this paper, inspired by ant colony optimization, an improved multioperator-based constrained adaptive differential evolution (namely, IMO-CADE) is proposed for the OPA. The proposed IMO-CADE can be featured as follows: (i) to adaptively select the proper operator among different operators, the feedback of operators and the status of individuals are considered simultaneously to assign the selection probability; (ii) the constrained reward assignment is used to measure the feedback of operators; (iii) the parameter adaptation is used for the parameters of differential evolution. To extensively evaluate the performance of IMO-CADE, it is used to solve the OPA for both the independent and correlated observations with different numbers of sensor nodes. Compared with other advanced methods, simulation results clearly indicate that IMO-CADE yields the best performance on the whole. Therefore, IMO-CADE can be an efficient alternative for the OPA of WSNs, especially for WSNs with a large number of sensor nodes. Full article

(This article belongs to the Special Issue Parallel and Distributed Computing in Wireless Sensor Networks)

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