Bio-inspired Group Modeling and Analysis for Intruder Detection in Mobile Sensor/Robotic Networks

Fu, Bo; Xiao, Yang; Liang, Xiannuan; Chen, C. L. Philip

doi:10.1109/tcyb.2014.2320717

Cited by 23 publications

(7 citation statements)

References 23 publications

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“…Experimental environment is 50 m Â 50 m. N sta static nodes uniformly distribute in the environment for 1-coverage and the minimum N sta is computed based on equation (3). Mobile robots and events randomly deploy in the environment.…”

Section: Simulations and Discussionmentioning

confidence: 99%

Dynamic k-coverage planning for multiple events with mobile robots

Deng

2017

International Journal of Advanced Robotic Systems

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Dynamic k-coverage planning for multiple events with mobile robots is proposed in the article. In mobile sensor networks, movement with the minimum energy for multiple events detection is a challenge which is discussed in the article. The problem of multiple events coverage is divided into two subproblems, namely mobile robots' uniform deployment and nodes' selection. Assuming that sparse mobile robots randomly deploy in the environment, mobile robots need to uniformly deploy firstly in order to effectively communicate with static nodes and extremely cover the entire region. A weighted-sub-Voronoi-halfgravity method and a weighted-sub-Voronoi-half-incenter method are presented for mobile robots' uniform deployment. Two algorithms guarantee mobile robots are deploying with a higher coverage ratio. Meanwhile, analog game theoretic algorithm is proposed for nodes' selection (static node's selection and mobile robots' selection). Only one static node is selected to detect an event and notifies candidate mobile robots which can communicate with the selected one of the event's occurrence. Moreover, k mobile robots are selected for event coverage. The proposed algorithm achieves k-coverage of each event with less energy consumption. Performance analysis and simulations show that the proposed algorithm achieves very good results.

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Section: Simulations and Discussionmentioning

confidence: 99%

Dynamic k-coverage planning for multiple events with mobile robots

Deng

2017

International Journal of Advanced Robotic Systems

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“…Swarms can be useful because they can deliver performance that is better than the sum of the parts. These algorithms have been applied to applications of multi-agent exploration and path formation [ 39 ], energy optimization in sensor networks [ 40 ], multi-site deployment [ 41 ], parallel computing optimization [ 42 ], cooperative transport and vehicle routing [ 43 , 44 ], feature selection [ 45 ], intruder detection [ 46 ], resource allocation [ 47 ], multi-robot task allocation and tracking applications [ 48 , 49 ], and so forth. Disadvantages of swarm intelligence may include needless activities of the agents, conflicts, and slow global response to a change in the environment [ 38 , 50 ].…”

Section: Related Workmentioning

confidence: 99%

A Modified Distributed Bees Algorithm for Multi-Sensor Task Allocation

Tkach

Jevtić

Nof

et al. 2018

Sensors

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Multi-sensor systems can play an important role in monitoring tasks and detecting targets. However, real-time allocation of heterogeneous sensors to dynamic targets/tasks that are unknown a priori in their locations and priorities is a challenge. This paper presents a Modified Distributed Bees Algorithm (MDBA) that is developed to allocate stationary heterogeneous sensors to upcoming unknown tasks using a decentralized, swarm intelligence approach to minimize the task detection times. Sensors are allocated to tasks based on sensors’ performance, tasks’ priorities, and the distances of the sensors from the locations where the tasks are being executed. The algorithm was compared to a Distributed Bees Algorithm (DBA), a Bees System, and two common multi-sensor algorithms, market-based and greedy-based algorithms, which were fitted for the specific task. Simulation analyses revealed that MDBA achieved statistically significant improved performance by 7% with respect to DBA as the second-best algorithm, and by 19% with respect to Greedy algorithm, which was the worst, thus indicating its fitness to provide solutions for heterogeneous multi-sensor systems.

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“…A huge quantity of such sensors arranged in a network form for many applications that require inaccessible activity, thus creating a wireless sensor network (WSN). In present technological world WSNs have different usages, including objective detecting and monitoring (Zhu et al, 2013), social security health care monitoring (Acampora et al, 2013), classification of data (Yao et al, 2015), distributing computing (Li et al, 2013;Zhao et al, 2016) and security observations (Fu et al, 2014;Semnani and Basir, 2014).…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning based Node Sleep or Wake-up Time Scheduling Algorithm for Wireless Sensor Network

Verma¹,

Dumka²,

Vyas³

et al. 2020

Int J Math, Eng, Manag Sci

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A wireless sensor network is a collection of small sensor nodes that have limited energy and are usually not rechargeable. Because of this, the lifetime of wireless sensor networks has always been a challenging area. One of the basic problems of the network has been the ability of the nodes to effectively schedule the sleep and wake-up time to overcome this problem. The motivation behind node sleep or wake-up time scheduling is to take care of nodes in sleep mode for as long as possible (without losing data packet transfer efficiency) and thus extend their useful life. This research going to propose scheduling of nodes sleeps and wake-up time through reinforcement learning. This research is not based on the nodes' duty cycle strategy (which creates a compromise between data packet delivery and nodes energy saving delay) like other existing researches. It is based on the research of reinforcement learning which gives independence to each node to choose its own activity from the transmission of packets, tuning or sleep node in each time band which works in a decentralized way. The simulation results show the qualified performance of the proposed algorithm under different conditions.

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Bio-inspired Group Modeling and Analysis for Intruder Detection in Mobile Sensor/Robotic Networks

Cited by 23 publications

References 23 publications

Dynamic k-coverage planning for multiple events with mobile robots

Dynamic k-coverage planning for multiple events with mobile robots

A Modified Distributed Bees Algorithm for Multi-Sensor Task Allocation

Reinforcement Learning based Node Sleep or Wake-up Time Scheduling Algorithm for Wireless Sensor Network

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