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IQABC-Based Hybrid deployment algorithm for Mobile Robotic Agents Providing Network Coverage

Tsinghua Science and Technology 2024年第2期29卷 589-604页

作者： Shuang Xu Xiaojie Liu Dengao Li Jumin Zhao College of Computer Science and Technology(College of Data Science) Taiyuan University of TechnologyTaiyuan 030024China Pengcheng Laboratory Shenzhen 518055China College of Information and Computer Taiyuan University of TechnologyTaiyuan 030024China

Working as aerial base stations,mobile robotic agents can be formed as a wireless robotic network to provide network services for on-ground mobile devices in a target ***,a challenging issue is how to deploy these mobile robotic agents to provide network services with good quality for more users,while considering the mobility of on-ground *** this paper,to solve this issue,we decouple the coverage problem into the vertical dimension and the horizontal dimension without any loss of optimization and introduce the network coverage model with maximum coverage ***,we propose a hybrid deployment algorithm based on the improved quick artificial bee *** algorithm is composed of a centralized deployment algorithm and a distributed *** proposed deployment algorithm deploy a given number of mobile robotic agents to provide network services for the on-ground devices that are independent and identically *** results have demonstrated that the proposed algorithm deploys agents appropriately to cover more ground area and provide better coverage uniformity.

关键词： wireless robotic networks network coverage deployment algorithm improved quick artificial bee colony

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Research on Service Function Chain deployment algorithm Based on Proximal Policy Optimization 22

Research on Service Function Chain Deployment Algorithm Base...

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Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering

作者： Peng Sun Hai Lin Huailong Yu You Zhong Xiaoping Wu Bing Sun Information and Communication Branch of State Grid Hunan Electric Power Company Limited China College of Electrical and Information Engineering Hunan University China State Grid Hunan Electric Power Company Limited China

ISBN: (纸本)9781450397148

In the future, business scenarios will become diversified, but it is difficult for the existing network architecture to provide strong support for them. Network function virtualization (NFV) technology decouples network functions from dedicated hardware devices and provides customized services for users in the form of service function chain (SFC). At present, the deployment of SFC has been proved to be a NP-hard problem. Most of the solutions are integer linear programming algorithms, but the process of such algorithms is complex. When the network topology scale becomes larger, the calculation process is very time-consuming, and the results sometimes fall into local optimal solutions, which makes it difficult to achieve the desired effect. In this case, reinforcement learning (RL) algorithms show great advantages, learning strategies through interaction with the environment to maximize rewards or achieve specific goals. Therefore, this paper proposes a SFC deployment algorithm based on proximal policy optimization (PPO) reinforcement learning, which aims at maximizing access rate and minimizing resource consumption. The simulation results show that the proposed algorithm has good convergence and stability, which is more conducive to the actual deployment of the SFC.

关键词： reinforcement learning (RL) Proximal policy optimization (PPO) Service function chain (SFC) deployment algorithm Network function virtualization (NFV)

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A Novel Heterogeneous Wireless Sensor Node deployment algorithm With Parameter-Free Configuration

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IEEE ACCESS 2018年 6卷 44951-44969页

作者： Kuawattanaphan, Rungrote Champrasert, Paskorn Aramkul, Somrawee Chiang Mai Univ Dept Comp Engn Chiang Mai 50200 Thailand Chiang Mai Univ Ctr Excellence Nat Disaster Management Chiang Mai 50200 Thailand Chiang Mai Rajabhat Univ Dept Comp Chiang Mai 50300 Thailand

The deployment of wireless sensor nodes affects the WSN performance, such as the sensing coverage, the connectivity between sensor nodes, and the network lifetime. The deployment is much more complicated when the sensor nodes are heterogeneous and there are some environmental obstacles in an area of interest. The deployment of heterogeneous sensor nodes in an area of interest with obstacles is a challenging problem and also proven to be an NP-complete problem. This paper proposes and evaluates a wireless sensor node deployment algorithm, called DeVForce-AP . The proposed algorithm combines the benefits of the two well-known deployment algorithms, Delaunay triangulation method (D) and extended virtual force algorithm (eVForce), with the adaptive parameter (AP) tuning mechanism. The DeVForce-AP attempts to improve the sensing coverage and network lifetime of the heterogeneous wireless sensor node. The practical deployment problems, such as the environmental obstacles, the network connectivity, and the sensing coverage area, have also been addressed. The DeVForce-AP consists of two steps. In the first step, the DeVForce-AP applies the Delaunay triangulation method to find and eliminate the largest coverage hole in the area of interest. In the second step, the DeVForce-AP applies eVForce method to avoid the existence of environmental obstacles and maintain the network connectivity. Moreover, the proposed DeVForce-AP automatically adjusts its parameters to the environmental scenario. Thus, there is no need to configure the parameters of the DeVForce-AP. The simulation results show that the proposed DeVForce-AP outperforms the random deployment, the traditional Delaunay triangulation deployment, and the traditional virtual force deployment methods.

关键词： deployment algorithm wireless sensor network heterogeneous Delaunay triangulation virtual forces

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MobiBar: An autonomous deployment algorithm for barrier coverage with mobile sensors

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AD HOC NETWORKS 2017年 54卷 111-129页

作者： Silvestri, Simone Goss, Ken Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA

Critical homeland security applications, such as international border surveillance and zone monitoring in case of biological attacks, require the timely creation of a barrier of sensors along the border to be monitored. Mobile Wireless Sensor Networks have the potential to meet the desired coverage requirements, by exploiting the device coordination and self-deployment capabilities. However the design of effective and efficient algorithms is challenging. In this paper we propose MOBIBAR, an autonomous deployment algorithm for k-barrier coverage with mobile sensors. MOBIBAR coordinates sensor movements in order to construct k distinct complete barriers and to ensure the desired level of redundancy. We formally prove that MosIBAR terminates in a finite time and that the final deployment provides the maximum level of barrier coverage with the available sensors. Furthermore, we show that MOBIBAR is able to self-reconfigure and self-heal the network to deal with dynamic coverage requirements and sudden sensor failures. We study the performance of MOBIBAR by means of simulations. Results show that it achieves performance close to centralized solutions and it outperforms recent distributed approaches with respect to several performance metrics. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Mobile wireless sensor networks Barrier coverage deployment algorithm Self-* properties

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An Improved Force-Based deployment algorithm for Wireless Sensor Network 17

An Improved Force-Based Deployment Algorithm for Wireless Se...

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IEEE 17th International Conference on Communication Technology (ICCT)

作者： Zhang, Chun Gong, Lejun Nanjing Univ Posts & Telecommun Sch Comp Sci & Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781509039449

In this paper, an improved force-based deployment algorithm for wireless sensor network is proposed. In order to maximize the network coverage while enhancing the efficiency of the algorithm implementation, the resultant of force exerted on sensors is set based on the ratio of the transmission range and sensing range. To decrease the waste overlap, the distance threshold is set, so the oscillating movement and the total moving distance of sensors decreases. Theoretical deduction proves that the network maintains connectivity when the network is connected initially. Simulation results clearly indicate the benefits of this algorithm in terms of network coverage.

关键词： deployment algorithm wireless sensor network network coverage connectivity

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An Improved Force-Based deployment algorithm for Wireless Sensor Network

An Improved Force-Based Deployment Algorithm for Wireless Se...

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2017 17th IEEE International Conference on Communication Technology (ICCT 2017)

作者： Chun Zhang Lejun Gong School of Computer Science and Technology Nanjing University of Posts and Telecommunications

关键词： deployment algorithm wireless sensor network network coverage connectivity

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A deployment algorithm FOR MOBILE WIRELESS SENSOR NETWORKS BASED ON THE ELECTROSTATIC FIELD THEORY

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INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS 2015年第1期8卷 516-537页

作者： Xu, Jiang Qian, Huanyan Ying, Wenhao Zhang, Jun Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210091 Peoples R China Changshu Inst Sci & Technol Sch Comp Sci & Engn Changshu 215500 Peoples R China Shenyang Artillery Acad Dept Coastal Def Artillery Shenyang 110161 Peoples R China

This paper proposes a deployment algorithm based on the electrostatic field theory for mobile wireless sensor networks. The nodes and obstacles in the deployment area are taken as the charged particles;and the particles will move due to the Coulomb's force from other particles or obstacles. Finally, the nodes automatically spread to the whole area by the resultant action and complete the deployment. Four metrics, including coverage, uniformity, deployment time and average displacement distance, are used to evaluate the performance of the algorithm. The simulation results show that the proposed algorithm can give full play to its self-adaptive advantages and achieve the desired deployment effect;it is a kind of deployment algorithm with self-adaptive characteristics.

关键词： Mobile Wireless Sensor Networks Electrostatic field deployment algorithm

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Adaptive scheduling framework of streaming applications based on performance-to-cost ratio in heterogeneous cloud environment

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JOURNAL OF SUPERCOMPUTING 2025年第5期81卷 1-36页

作者： Li, Hongjian Tan, Gangfan Wang, Chenzi Gao, Yuan Zhou, Tao Duan, Xiaolin Chongqing Univ Posts & Telecommun Dept Comp Sci & Technol Chongqing 400065 Peoples R China China Telecom Corp Ltd Chongqing Branch Chongqing Peoples R China

With the rapidly growing demand for real-time processing of large amounts of stream data, the global deployment of cloud infrastructure for stream processing has brought tremendous economic benefits to cloud service providers, yet large-scale stream processing continues to increase resource cost consumption. In stream processing, the trade-off between performance and cost for workflows scheduling at lower resource cost remains to be explored. In this paper, a deployment method of streaming applications is proposed to maximize the performance-to-cost ratio in Storm, and a communication traffic optimization method for inter-node communication based on graph partitioning is proposed to reduce resource cost. First, we propose a cost-efficient model based on performance-to-cost ratio and a Storm-based performance-to-cost ratio maximization deployment algorithm PC-Storm. The PC-Storm first detects and collects task node information in the heterogeneous cluster. Then, it calculates the performance-to-cost ratio of the nodes and builds a performance-to-cost ratio table. Finally, the Storm task threads are assigned to nodes with the high performance-to-cost ratio. In addition, a maxSubtopology algorithm is also proposed to optimize the communication traffic between Storm nodes based on graph partitioning. The maxSubtopology algorithm assigns the tasks with heavy communication traffic to the same node under the premise of satisfying the performance constraint. Compared with the existing algorithms, the PC-Storm improves the resource performance-to-cost ratio by 40.92%, and the maxSubtopology algorithm reduces the communication cost by 42.8%. The proposed method can maximize performance-to-cost ratio and reduce the communication cost effectively for deployment of streaming applications.

关键词： Storm deployment algorithm Performance-to-cost ratio Graph partitioning Real-time processing Streaming application

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Area Surveillance With Low Detection Probability Using UAV Swarms

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第2期73卷 1736-1752页

作者： Fan, Jianrui Lei, Lei Cai, Shengsuo Shen, Gaoqing Cao, Pan Zhang, Lijuan Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 211106 Peoples R China

Unmanned aerial vehicles (UAVs) deployed as a swarm can offer a flexible and cost-efficient solution for surveillance missions in large-scale adversarial environments. UAV swarms possess superior real-time surveillance capabilities in detecting time-sensitive targets with speed and security, surpassing alternative techniques such as satellites and ground-based sensors. However, the area surveillance problem in adversarial scenarios using UAV swarms necessitates careful consideration of factors such as swarm positioning, flight attitudes, antenna directionality, the threat posed by adversary detection systems (ADSs), and swarm network topology. To address this challenge, we propose the Integrated Low Detection Probability (ILDP) deployment method for UAV swarms, which incorporates the Danger Avoidance Distributed Motion control algorithm (DADM) for swarm collaboration and the Low Detection Probability Topology Control algorithm (LDPTC) for swarm networking. The DADM algorithm facilitates swarm cooperation in achieving area coverage and evading ADSs by leveraging neighboring, environmental, and threat information in adversarial scenarios. Furthermore, the LDPTC algorithm establishes a topology optimization model that comprehensively considers swarm distribution and the impact of directional antenna sidelobes to reduce transmitting energy leakage on the ground. Our strategy significantly decreases the detection probability of UAV swarms by ADSs, ensuring the operational effectiveness of UAV swarms in dynamic adversarial scenarios. Extensive simulations validate the superiority of our proposed ILDP method, demonstrating considerably lower detection probabilities compared to other approaches in static and dynamic adversary environments across various swarm scales. Moreover, our method excels in real-time surveillance capabilities with lower computational complexity, freeing up computing resources for UAVs to fulfill additional tasks.

关键词： UAV swarm low detection probability area coverage directional antenna deployment algorithm

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Quantum connectivity optimization algorithms for entanglement source deployment in a quantum multi-hop network

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Frontiers of physics 2018年第2期13卷 1-12页

作者： Zhen-Zhen Zou Xu-Tao Yu Zai-Chen Zhang State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China National Mobile Communications Research Lab Southeast University Nanjing 210096 China

At first, the entanglement source deployment problem is studied in a quantum multi-hop network, which has a significant influence on quantum connectivity. Two optimization algorithms are introduced with limited entanglement sources in this paper. A deployment algorithm based on node position （DNP） improves connectivity by guaranteeing that all overlapping areas of the distribution ranges of the entanglement sources contain nodes. In addition, a deployment algorithm based on an improved genetic algorithm （DIGA） is implemented by dividing the region into grids. From the simulation results, DNP and DIGA improve quantum connectivity by 213.73% and 248.83% compared to random deployment, respectively, and the latter performs better in terms of connectivity. However, DNP is more flexible and adaptive to change, as it stops running when all nodes are covered.

关键词： entanglement source deployment quantum connectivity deployment algorithm

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