A Distributed Energy-Balanced Topology Control Algorithm Based on a Noncooperative Game for Wireless Sensor Networks

Du, Yongwen; Gong, Junhui; Wang, Zhangmin; Xu, Ning

doi:10.3390/s18124454

Cited by 13 publications

(7 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Theorem 1 guarantees the existence of at least one NE solution. Moreover, this equilibrium that maximizes OPG is also the Pareto optimal solution [25,26]. Pareto optimality [27] refers to a combination of policies that maximizes the utility of all players involved, thereby constituting the global optimal solution to the problem.…”

Section: Definitionmentioning

confidence: 99%

Intelligent Frequency Decision Communication with Two-Agent Deep Reinforcement Learning

Liu,

Shi,

Wang

2023

Electronics

View full text Add to dashboard Cite

Traditional intelligent frequency-hopping anti-jamming technologies typically assume the presence of an ideal control channel. However, achieving this ideal condition in real-world confrontational environments, where the control channel can also be jammed, proves to be challenging. Regrettably, in the absence of a reliable control channel, the autonomous synchronization of frequency decisions becomes a formidable task, primarily due to the dynamic and heterogeneous nature of the transmitter and receiver’s spectral states. To address this issue, a novel communication framework for intelligent frequency decision is introduced, which operates without the need for negotiations. Furthermore, the frequency decision challenge between two communication terminals is formulated as a stochastic game, with each terminal’s utility designed to meet the requirements of a potential game. Subsequently, a two-agent deep reinforcement learning algorithm for best-response policy learning is devised to enable both terminals to achieve synchronization while avoiding jamming signals. Simulation results demonstrate that once the proposed algorithm converges, both communication terminals can effectively evade jamming signals. In comparison to existing similar algorithms, the throughput performance of this approach remains largely unaffected, with only a slightly extended convergence time. Notably, this performance is achieved without the need for negotiations, making the presented algorithm better suited for realistic scenarios.

show abstract

Section: Definitionmentioning

confidence: 99%

Intelligent Frequency Decision Communication with Two-Agent Deep Reinforcement Learning

Liu,

Shi,

Wang

2023

Electronics

View full text Add to dashboard Cite

show abstract

“…A potential game is formulated in [21] as a solution for the topology control optimisation problem in a WSN. Du et al [22] proposed a non‐cooperative potential game for topology control addressing the problem of power minimisation and energy balancing in a WSN. Another topology control game is proposed in [23] for rechargeable WSNs where sensor nodes have to make a compromise between energy exploitation and energy saving.…”

Section: Related Workmentioning

confidence: 99%

Game theoretical model for information transmission in structure‐free wireless sensor networks

Chaiel¹,

Abass

2020

IET Communications

View full text Add to dashboard Cite

“…Then, an improved local multi-objective power control algorithm was studied to obtain a network topology with less communication overhead. Du et al applied the Theil index to measure the energy imbalance of WSNs and constructed a network topology [9]. To maximize the SINR, the power control problem was modeled as a hybrid game [10].…”

Section: Introductionmentioning

confidence: 99%

Joint Power and Channel Optimization of Agricultural Wireless Sensor Networks Based on Hybrid Deep Reinforcement Learning

Han

Zhu

et al. 2021

Processes

View full text Add to dashboard Cite

The reduction of maintenance costs in agricultural wireless sensor networks (WSNs) requires reducing energy consumption. At the same time, care should be taken not to affect communication quality and network lifetime. This paper studies a joint optimization algorithm for transmitted power and channel allocation based on deep reinforcement learning. First, an optimization model to measure network reward was established under the constraint of the signal-to-interference plus-noise-ratio (SINR) threshold, which includes continuous power variables and discrete channel variables. Secondly, considering the dynamic changes of agricultural WSNs, the network control is described as a Markov decision process with continuous state and action space. A deep deterministic policy gradient (DDPG) reinforcement learning scheme suitable for mixed variables was designed. This method could obtain a control scheme that maximizes network reward by means of black-box optimization for continuous transmitted power and discrete channel allocation. Experimental results indicated that the studied algorithm has stable convergence. Compared with traditional protocols, it can better control the transmitted power and allocate channels. The joint power and channel optimization provides a reference solution for constructing an energy-balanced network.

show abstract

A Distributed Energy-Balanced Topology Control Algorithm Based on a Noncooperative Game for Wireless Sensor Networks

Cited by 13 publications

References 25 publications

Intelligent Frequency Decision Communication with Two-Agent Deep Reinforcement Learning

Intelligent Frequency Decision Communication with Two-Agent Deep Reinforcement Learning

Game theoretical model for information transmission in structure‐free wireless sensor networks

Joint Power and Channel Optimization of Agricultural Wireless Sensor Networks Based on Hybrid Deep Reinforcement Learning

Contact Info

Product

Resources

About