scholarly journals Erratum to: MRL-SCSO: Multi-agent Reinforcement Learning-Based Self-Configuration and Self-Optimization Protocol for Unattended Wireless Sensor Networks

2016 ◽  
Vol 96 (4) ◽  
pp. 5081-5081
Author(s):  
A. Pravin Renold ◽  
S. Chandrakala
Keyword(s):  
Wireless Sensor Networks ◽  
Reinforcement Learning ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Multi Agent ◽  
Unattended Wireless Sensor Networks

scholarly journals Multi-agent Reinforcement Learning for Autonomous Vehicles in Wireless Sensor Networks

2021 ◽  
Author(s):  
Haitham Afifi
Keyword(s):  
Wireless Sensor Networks ◽  
Reinforcement Learning ◽  
Sensor Networks ◽  
Real World ◽  
Autonomous Vehicles ◽  
Wireless Sensor ◽  
Acoustic Sensor Networks ◽  
Markov Decision ◽  
Multi Agent

<div>We develop a Deep Reinforcement Learning (DeepRL) based multi-agent algorithm to efficiently control</div><div>autonomous vehicles in the context of Wireless Sensor Networks (WSNs). In contrast to other applications, WSNs</div><div>have two metrics for performance evaluation. First, quality of information (QoI) which is used to measure the</div><div>quality of sensed data. Second, quality of service (QoS) which is used to measure the network’s performance. As</div><div>a use case, we consider wireless acoustic sensor networks; a group of speakers move inside a room and there</div><div>are microphones installed on vehicles for streaming the audio data. We formulate an appropriate Markov Decision</div><div>Process (MDP) and present, besides a centralized solution, a multi-agent Deep Q-learning solution to control the vehicles. We compare the proposed solutions to a naive heuristic and two different real-world implementations: microphones being hold or preinstalled. We show using simulations that the performance of autonomous vehicles in terms of QoI and QoS is better than the real-world implementation and the proposed heuristic. Additionally, we provide theoretical analysis of the performance with respect to WSNs dynamics, such as speed, rooms dimensions and speaker’s talking time.</div>

scholarly journals Multi-agent Reinforcement Learning for Autonomous Vehicles in Wireless Sensor Networks

2021 ◽  
Author(s):  
Haitham Afifi
Keyword(s):  
Wireless Sensor Networks ◽  
Reinforcement Learning ◽  
Sensor Networks ◽  
Real World ◽  
Autonomous Vehicles ◽  
Wireless Sensor ◽  
Acoustic Sensor Networks ◽  
Markov Decision ◽  
Multi Agent

<div>We develop a Deep Reinforcement Learning (DeepRL) based multi-agent algorithm to efficiently control</div><div>autonomous vehicles in the context of Wireless Sensor Networks (WSNs). In contrast to other applications, WSNs</div><div>have two metrics for performance evaluation. First, quality of information (QoI) which is used to measure the</div><div>quality of sensed data. Second, quality of service (QoS) which is used to measure the network’s performance. As</div><div>a use case, we consider wireless acoustic sensor networks; a group of speakers move inside a room and there</div><div>are microphones installed on vehicles for streaming the audio data. We formulate an appropriate Markov Decision</div><div>Process (MDP) and present, besides a centralized solution, a multi-agent Deep Q-learning solution to control the vehicles. We compare the proposed solutions to a naive heuristic and two different real-world implementations: microphones being hold or preinstalled. We show using simulations that the performance of autonomous vehicles in terms of QoI and QoS is better than the real-world implementation and the proposed heuristic. Additionally, we provide theoretical analysis of the performance with respect to WSNs dynamics, such as speed, rooms dimensions and speaker’s talking time.</div>

scholarly journals Multi-mobile agent itinerary planning algorithms for data gathering in wireless sensor networks: A review paper

2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668484 ◽  
Author(s):  
Huthiafa Q Qadori ◽  
Zuriati A Zulkarnain ◽  
Zurina Mohd Hanapi ◽  
Shamala Subramaniam
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Mobile Agent ◽  
Mobile Agents ◽  
Single Agent ◽  
Data Gathering ◽  
Optimal Number ◽  
Wireless Sensor ◽  
Itinerary Planning ◽  
Multi Agent

Recently, wireless sensor networks have employed the concept of mobile agent to reduce energy consumption and obtain effective data gathering. Typically, in data gathering based on mobile agent, it is an important and essential step to find out the optimal itinerary planning for the mobile agent. However, single-agent itinerary planning suffers from two primary disadvantages: task delay and large size of mobile agent as the scale of the network is expanded. Thus, using multi-agent itinerary planning overcomes the drawbacks of single-agent itinerary planning. Despite the advantages of multi-agent itinerary planning, finding the optimal number of distributed mobile agents, source nodes grouping, and optimal itinerary of each mobile agent for simultaneous data gathering are still regarded as critical issues in wireless sensor network. Therefore, in this article, the existing algorithms that have been identified in the literature to address the above issues are reviewed. The review shows that most of the algorithms used one parameter to find the optimal number of mobile agents in multi-agent itinerary planning without utilizing other parameters. More importantly, the review showed that theses algorithms did not take into account the security of the data gathered by the mobile agent. Accordingly, we indicated the limitations of each proposed algorithm and new directions are provided for future research.

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