scholarly journals Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems

Automatica ◽  
2020 ◽  
Vol 113 ◽  
pp. 108759 ◽  
Author(s):  
Alex S. Leong ◽  
Arunselvan Ramaswamy ◽  
Daniel E. Quevedo ◽  
Holger Karl ◽  
Ling Shi
Keyword(s):  
Reinforcement Learning ◽  
Cyber Physical Systems ◽  
Wireless Sensor ◽  
Sensor Scheduling ◽  
Physical Systems

scholarly journals Enabling Cyber Physical Systems with Wireless Sensor Networking Technologies, Multiagent System Paradigm, and Natural Ecosystems

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Nafaâ Jabeur ◽  
Nabil Sahli ◽  
Sherali Zeadally
Keyword(s):  
Cyber Physical Systems ◽  
Wireless Sensor ◽  
Natural Ecosystems ◽  
Distributed Sensors ◽  
Physical Systems ◽  
Structure Topology ◽  
Spatially Distributed ◽  
Engineered Systems ◽  
Networking Technologies ◽  
Living Organisms

Wireless sensor networks (WSNs) are key components in the emergent cyber physical systems (CPSs). They may include hundreds of spatially distributed sensors which interact to solve complex tasks going beyond their individual capabilities. Due to the limited capabilities of sensors, sensor actions cannot meet CPS requirements while controlling and coordinating the operations of physical and engineered systems. To overcome these constraints, we explore the ecosystem metaphor for WSNs with the aim of taking advantage of the efficient adaptation behavior and communication mechanisms of living organisms. By mapping these organisms onto sensors and ecosystems onto WSNs, we highlight shortcomings that prevent WSNs from delivering the capabilities of ecosystems at several levels, including structure, topology, goals, communications, and functions. We then propose an agent-based architecture that migrates complex processing tasks outside the physical sensor network while incorporating missing characteristics of autonomy, intelligence, and context awareness to the WSN. Unlike existing works, we use software agents to map WSNs to natural ecosystems and enhance WSN capabilities to take advantage of bioinspired algorithms. We extend our architecture and propose a new intelligent CPS framework where several control levels are embedded in the physical system, thereby allowing agents to support WSNs technologies in enabling CPSs.

scholarly journals A Critical Analysis of Mobility Management Related Issues of Wireless Sensor Networks in Cyber Physical Systems

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 16363-16376 ◽  
Author(s):  
Jalal Al-Muhtadi ◽  
Ma Qiang ◽  
Khan Zeb ◽  
Junaid Chaudhry ◽  
Kashif Saleem ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Mobility Management ◽  
Critical Analysis ◽  
Cyber Physical Systems ◽  
Wireless Sensor ◽  
Physical Systems

scholarly journals Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems Using Multi-objective Reinforcement Learning

2021 ◽  
Vol 5 (4) ◽  
pp. 1-24
Author(s):  
Jianguo Chen ◽  
Kenli Li ◽  
Keqin Li ◽  
Philip S. Yu ◽  
Zeng Zeng
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Balance ◽  
Supply And Demand ◽  
Pareto Frontier ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Optimal Dispatch ◽  
Multi Objective ◽  
Dispatching Algorithm ◽  
Multiple Dispatch

As a new generation of Public Bicycle-sharing Systems (PBS), the Dockless PBS (DL-PBS) is an important application of cyber-physical systems and intelligent transportation. How to use artificial intelligence to provide efficient bicycle dispatching solutions based on dynamic bicycle rental demand is an essential issue for DL-PBS. In this article, we propose MORL-BD, a dynamic bicycle dispatching algorithm based on multi-objective reinforcement learning to provide the optimal bicycle dispatching solution for DL-PBS. We model the DL-PBS system from the perspective of cyber-physical systems and use deep learning to predict the layout of bicycle parking spots and the dynamic demand of bicycle dispatching. We define the multi-route bicycle dispatching problem as a multi-objective optimization problem by considering the optimization objectives of dispatching costs, dispatch truck's initial load, workload balance among the trucks, and the dynamic balance of bicycle supply and demand. On this basis, the collaborative multi-route bicycle dispatching problem among multiple dispatch trucks is modeled as a multi-agent and multi-objective reinforcement learning model. All dispatch paths between parking spots are defined as state spaces, and the reciprocal of dispatching costs is defined as a reward. Each dispatch truck is equipped with an agent to learn the optimal dispatch path in the dynamic DL-PBS network. We create an elite list to store the Pareto optimal solutions of bicycle dispatch paths found in each action, and finally get the Pareto frontier. Experimental results on the actual DL-PBS show that compared with existing methods, MORL-BD can find a higher quality Pareto frontier with less execution time.

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