Invulnerability Analysis of Wireless Sensor Networks based on Cellular Automata

Fu, Xiuwen; He, Xiaolin; Yang, Yongsheng

doi:10.1109/ichms49158.2020.9209541

Cited by 4 publications

(6 citation statements)

References 16 publications

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“…In order to better understand the research progress of distributed filtering in the field of LSR, Table 1 compares the existing methods according to six aspects. [27][28][29] As shown in Table 1, the existing methods focus on estimation consensus. However, the optimal (estimation accuracy) is ignored.…”

Section: Related Workmentioning

confidence: 99%

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Distributed filtering in sensor networks based on linear minimum mean square error criterion with limited sensing range

Shao

2022

International Journal of Distributed Sensor Networks

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One of the fundamental problems in sensor networks is to estimate and track the target states of interest that evolve in the sensing field. Distributed filtering is an effective tool to deal with state estimation in which each sensor only communicates information with its neighbors in sensor networks without the requirement of a fusion center. However, in the majority of the existing distributed filters, it is assumed that typically all sensors possess unlimited field of view to observe the target states. This is quite restrictive since practical sensors have limited sensing range. In this article, we consider distributed filtering based on linear minimum mean square error criterion in sensor networks with limited sensing range. To achieve the optimal filter and consensus, two types of strategies based on linear minimum mean square error criterion are proposed, that is, linear minimum mean square error filter based on measurement and linear minimum mean square error filter based on estimate, according to the difference of the neighbor sensor information received by the sensor. In linear minimum mean square error filter based on measurement, the sensor node collects measurement from its neighbors, whereas in linear minimum mean square error filter based on estimate, the sensor node collects estimate from its neighbors. The stability and computational complexity of linear minimum mean square error filter are analyzed. Numerical experimental results further verify the effectiveness of the proposed methods.

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Section: Related Workmentioning

confidence: 99%

“…In order to better understand the research progress of distributed filtering in the field of LSR, Table 1 compares the existing methods according to six aspects. 27–29…”

Section: Introductionmentioning

confidence: 99%

Distributed filtering in sensor networks based on linear minimum mean square error criterion with limited sensing range

Shao

2022

International Journal of Distributed Sensor Networks

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“…For the reasons listed above, it is considered that emulation workbenches are a good compromise between simulation and testbed, since they reproduce some characteristics intrinsic to hardware problems but guarantee greater repeatability for the production of statistical data. Note that a wireless network workbench may be a suitable option to obtain probabilistic data for NCS design, since the employment of closed mathematical models [ 3 , 4 ] or those based on algorithms [ 2 , 5 ] requires more theoretical knowledge. On the other hand, open simulators such as ns-3 [ 33 ] or proprietary simulators such as Scalable-Networks accessed on 11 September 2021 (specialized solutions) also require prior knowledge.…”

Section: Related Workmentioning

confidence: 99%

“…The most recent research in the WSN literature is concerned with improving the robustness of the network by using, for instance, topology optimization models to help WSN resist cascading failures [ 2 , 3 ], information fusion processes to lower the negative impact of the environment on message routing [ 4 ], and more realistic failure models to analyse the network invulnerability [ 5 ]; etc. However, if the network is used as a communication channel for NCS projects, there are some alternative techniques that guarantee the robustness of the closed-loop system even under, for instance, denial of service in cyber-physical attacks [ 6 , 7 ], network-induced delays [ 8 ], and packet loss, among several other issues [ 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Robust Static Output-Feedback Control for MJLS with Non-Homogeneous Markov Chains: A Comparative Study Considering a Wireless Sensor Network with Time-Varying PER

Serafini

Delforno

Palma

et al. 2021

Sensors

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This paper deals with the problem of control through a semi-reliable communication channel, such as wireless sensor networks (WSN). Particularly, the case investigated is the one where the packet loss rate of the network is time-varying due to, for instance, variation in the distance between the nodes. Considering this practical motivation, the control system is modeled using a formulation based on discrete-time Markov jump linear systems (MJLS) with non-homogeneous Markov chains (time-varying transition probabilities). New control design conditions based on parameter-dependent linear matrix inequalities are proposed in order to solve this problem. The purpose is to demonstrate that this strategy is suitable to handle the networked control problem by comparing the temporal behavior of the closed-loop system with the Markovian controller and a standard proportional-integral-derivative (PID) controller. The case study presented in the paper considers the problem of the remote control of a Vertical Take-Off and Landing (VTOL) vehicle through a wireless communication channel. The network packet loss model employed in the case study is based on data collected on a wireless network workbench, which was previously developed and validated by the authors.

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“…For example, in wireless sensor networks, the limited energy of sensor nodes is an important problem that puzzles its practical application. The energy consumption of sensor networks is mainly concentrated in the mutual communication between nodes, and its communication energy consumption is 2-4 times of the calculated energy consumption [3]. If each node can process the collected information and then spread it, the whole sensor network will have a longer network lifetime.…”

Section: Introductionmentioning

confidence: 99%

Analysis of wireless English multimedia communication based on spatial state model equation

Long¹,

Li²,

Jin

2022

Applied Mathematics and Nonlinear Sciences

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In order to improve the quality of wireless English multimedia communication, according to the characteristics of space multimedia communication environment and communication equipment, this paper proposes a wireless English synchronous QoS control algorithm with low complexity and easy implementation. Using RTCPSRRTP timestamp and NTP absolute timestamp, the wireless English RTP timestamp is mapped to the same absolute reference clock (sender system time) respectively, and the wireless English synchronization detection decision rule is established according to the synchronization QoS performance requirements between wireless English media. Wireless English synchronization control system model at the receiver. At the receiving end, the wireless English stream is the main media stream and the video stream is the slave media stream. The synchronization detection and judgment is implemented in real time before the video frame is played. The synchronization between audio and video media is realized by corresponding synchronization control of wireless English playback. State space model reflects all the information of system dynamic behavior and is a complete description of system behavior. The state space model has a wide range of applications. It is not only suitable for SISO linear constant systems, but also suitable for nonlinear systems, time-varying systems, MIMO systems and stochastic systems. The more outstanding advantage is that the state space model adopts local calculation and uses the state variables inside the system to describe its dynamic characteristics. This property of local calculation not only greatly simplifies the mathematical expression of multi-dimensional system, but also facilitates the analysis and research of the system. The air link rate of the wireless transceiver is set to 512Kbps. The video is encoded based on H.264 and the audio is encoded based on G.729 Speech coding standard. Compared with the wireless English media synchronization control algorithm based on RTP / RTCP, the results show that the RMSE value after adopting the synchronization algorithm in this paper is reduced by 60.09% and 77.69% at most; Compared with the wireless English media synchronization control algorithm based on RTP / RTCP, the synchronization performance of this algorithm is better than the wireless English media synchronization control algorithm. The RMSE value is reduced by 2% on average and 5.2% at most. In addition, the synchronization control algorithm between wireless English media performs synchronization control by losing frames or pausing decoding before video decoding at the receiver, which affects the quality of wireless English video decoding and reconstruction.

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Invulnerability Analysis of Wireless Sensor Networks based on Cellular Automata

Cited by 4 publications

References 16 publications

Distributed filtering in sensor networks based on linear minimum mean square error criterion with limited sensing range

Distributed filtering in sensor networks based on linear minimum mean square error criterion with limited sensing range

Robust Static Output-Feedback Control for MJLS with Non-Homogeneous Markov Chains: A Comparative Study Considering a Wireless Sensor Network with Time-Varying PER

Analysis of wireless English multimedia communication based on spatial state model equation

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