Modelling and Analysis of the Epidemic Model under Pulse Charging in Wireless Rechargeable Sensor Networks

Liu, Guiyun; Huang, Ziyi; Wu, Xilai; Liang, Zhongwei; Hong, Fenghuo; Su, Xiaokai

doi:10.3390/e23080927

Cited by 6 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(4) If the residual energy of MC does not satisfy the charging demand of the next selected sensor, but it is enough to return to the depot, the MC is allowed to return to the depot to charge itself, and the charging time of the MC is ignored. (5) The charging decision of two adjacent time steps cannot be the same sensor or depot. (6) If the residual energy of the MC does not meet the charging demand for the next sensor, is not enough to return to the depot, or the preset network lifetime is reached, the charging plan will be ended no matter whether the sensors are still alive or not.…”

Section: States Of the Environmentmentioning

confidence: 99%

See 1 more Smart Citation

Attention-Shared Multi-Agent Actor–Critic-Based Deep Reinforcement Learning Approach for Mobile Charging Dynamic Scheduling in Wireless Rechargeable Sensor Networks

Jiang

Wang

Chen

et al. 2022

Entropy

View full text Add to dashboard Cite

The breakthrough of wireless energy transmission (WET) technology has greatly promoted the wireless rechargeable sensor networks (WRSNs). A promising method to overcome the energy constraint problem in WRSNs is mobile charging by employing a mobile charger to charge sensors via WET. Recently, more and more studies have been conducted for mobile charging scheduling under dynamic charging environments, ignoring the consideration of the joint charging sequence scheduling and charging ratio control (JSSRC) optimal design. This paper will propose a novel attention-shared multi-agent actor–critic-based deep reinforcement learning approach for JSSRC (AMADRL-JSSRC). In AMADRL-JSSRC, we employ two heterogeneous agents named charging sequence scheduler and charging ratio controller with an independent actor network and critic network. Meanwhile, we design the reward function for them, respectively, by considering the tour length and the number of dead sensors. The AMADRL-JSSRC trains decentralized policies in multi-agent environments, using a centralized computing critic network to share an attention mechanism, and it selects relevant policy information for each agent at every charging decision. Simulation results demonstrate that the proposed AMADRL-JSSRC can efficiently prolong the lifetime of the network and reduce the number of death sensors compared with the baseline algorithms.

show abstract

Section: States Of the Environmentmentioning

confidence: 99%

“…The status information of sensors is highly controllable and predictable. Theoretically, WRSNs could work indefinitely under a well-designed charging scheme [ 5 ]. Therefore, the design of the charging scheme in WRSN is critical, and it has drawn extensive attention from the research community.…”

Section: Introductionmentioning

confidence: 99%

Attention-Shared Multi-Agent Actor–Critic-Based Deep Reinforcement Learning Approach for Mobile Charging Dynamic Scheduling in Wireless Rechargeable Sensor Networks

Jiang

Wang

Chen

et al. 2022

Entropy

View full text Add to dashboard Cite

show abstract

“…Furthermore, the SEIR (susceptible-exposed-infected-recovered) model is adapted to study the spread of malware [22], while the SEIRS-V (susceptible-exposed-infected-recoveredsusceptible and vaccinated) model explores the application of Hopf bifurcation during the malware spreading [23,24]. With the development, the research on WRSNs was pushed forward recently [25][26][27][28][29][30][31], considering the new concept of L (low-energy status) nodes. It is worth noting that the transition from the low-energy status nodes to the normal status nodes is called charging.…”

Section: Introductionmentioning

confidence: 99%

Dynamical Behavior Analysis of a Time-Delay SIRS-L Model in Rechargeable Wireless Sensor Networks

Liu

Liang

et al. 2021

Mathematics

Self Cite

View full text Add to dashboard Cite

The traditional SIRS virus propagation model is used to analyze the malware propagation behavior of wireless rechargeable sensor networks (WRSNs) by adding a new concept: the low-energy status nodes. The SIRS-L model has been developed in this article. Furthermore, the influence of time delay during the charging behavior of the low-energy status nodes needs to be considered. Hopf bifurcation is studied by discussing the time delay that is chosen as the bifurcation parameter. Finally, the properties of the Hopf bifurcation are explored by applying the normal form theory and the center manifold theorem.

show abstract

“…Li et al [26] proposed an energyefficient patching strategy to solve the problems of virus attacks and energy constraints. Liu et al [27,28] considered the impact of pulse charging and charging delay on the system. Keshri et al [29] proposed an SEIR model with two time delays (including the period exposed and the period of temporary immunity).…”

mentioning

confidence: 99%

Analysis and Control of Malware Mutation Model in Wireless Rechargeable Sensor Network with Charging Delay

et al. 2022

Self Cite

View full text Add to dashboard Cite

In wireless rechargeable sensors (WRSNs), the system is vulnerable to be attacked by malware. Because of the distributed network structure of WRSNs, the malware attack has great influence on the security system of WRSNs. With the variability in malware, the problem of decryption and coding errors will lead to the malware mutating. In this paper, there are two problems to be solved, including the malware mutation and the charging delay in WRSNs. The malware mutation state and the low-energy state are introduced. Firstly, three different equilibrium solutions of the mutation model are given. Then, the local stability is proven by the characteristic equation, and the system will be stabilized at different equilibrium solutions when the base reproductive number is different. With the condition of charging delay, the bifurcation phenomenon is investigated by using the Hopf bifurcation theory. Furthermore, to improve the security of WRSNs and decrease the control cost, the Pontryagin’s Maximum principle is applied to obtain an optimal control scheme under mutation and charging delay. Finally, the numerical simulation is applied by Matlab to confirm this model. The simulation results show that the mutation malware can be controlled when the delay is less than the maximum threshold.

show abstract

Modelling and Analysis of the Epidemic Model under Pulse Charging in Wireless Rechargeable Sensor Networks

Cited by 6 publications

References 25 publications

Attention-Shared Multi-Agent Actor–Critic-Based Deep Reinforcement Learning Approach for Mobile Charging Dynamic Scheduling in Wireless Rechargeable Sensor Networks

Attention-Shared Multi-Agent Actor–Critic-Based Deep Reinforcement Learning Approach for Mobile Charging Dynamic Scheduling in Wireless Rechargeable Sensor Networks

Dynamical Behavior Analysis of a Time-Delay SIRS-L Model in Rechargeable Wireless Sensor Networks

Analysis and Control of Malware Mutation Model in Wireless Rechargeable Sensor Network with Charging Delay

Contact Info

Product

Resources

About