Sensor data scheduling over a packet-dropping network

Shi, Ling; Cheng, Peng; Chen, Jiming

doi:10.1109/wcica.2010.5554937

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Similar approaches can also be found in [5]. Shi et al [6] considered sensor data scheduling over packet-dropping networks. Due to its limited energy, a sensor has to decide whether to send its local estimate data to the remote estimator at low or high power level at each instance.…”

Section: Introductionmentioning

confidence: 82%

An improved hybrid sensor schedule for remote state estimation under limited communication resources

Johansson

Shi

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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Abstract-In this paper, we consider remote state estimation. A sensor locally processes its measurement data and sends its local estimate to a remote estimator for further processing. Due to the limited communication resources, the sensor can only communicate with the estimator for a pre-specified number within a given horizon. We propose a hybrid sensor data schedule which introduces an event-triggering mechanism on top of an optimal offline sensor schedule. This hybrid schedule, having a small implementation cost, leads to a smaller estimation error at the remote estimator when compared with the optimal offline sensor schedule.

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Section: Introductionmentioning

confidence: 82%

An improved hybrid sensor schedule for remote state estimation under limited communication resources

Johansson

Shi

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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“…In recent years, many researchers begin to study security issues in WSNs . For example, Shi et al study sensor data scheduling problem and construct a scheduling scheme to minimize the estimation error and satisfy the energy constraint. Li et al consider a scenario where the wireless communication channel between a sensor node and a remote estimator may be jammed by an external attacker.…”

Section: Introductionmentioning

confidence: 99%

False data injection attack on consensus‐based distributed estimation

Yang

et al. 2016

Intl J Robust & Nonlinear

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We consider security issues for consensus-based distributed estimation problem. In a sensor network, an attacker with limited power injects random false data into the communication links so as to degrade the network performance. First, an optimal estimator is designed by minimizing the mean-squared estimation error of each sensor under hostile attacks. Then, a sufficient condition is provided to guarantee the stability of the proposed estimator. Finally, a set of suboptimal attacking sequences is obtained for the attacker to maximize the network estimation error. Illustrative examples are provided to verify the effectiveness of the suboptimal attacking strategy.Kalman filter based on the Kalman filter updated step; Stankovi K c et al.[12] put a priori estimation in the Kalman filter predict step.In real applications, security issue has been a big obstacle to the applications of WSNs because of its vulnerabilities to various types of potential hostile attacks. In recent years, many researchers begin to study security issues in WSNs [13]. For example, Shi et al. [14] study sensor data scheduling problem and construct a scheduling scheme to minimize the estimation error and satisfy the energy constraint. Li et al. [15] consider a scenario where the wireless communication channel between a sensor node and a remote estimator may be jammed by an external attacker. Mo et al. [16] study the performance of control systems under a replay attack model.Currently, the existing works on the attacking strategy can be classified into two types: one is denial of service (DoS). For example, Zhang et al.[17] focus on the DoS attack and build an optimal attacking schedule to maximize the expected average estimation error of the target system. Ren et al. [18] develop an optimal DoS attack strategies over Bayesian quickest change detection to maximize the detection cost for the remote center. The other is false data injection attack. For example, Mo et al. [19] study the influence of false data injection attack on the state estimation of an objective system whose monitor failed to detect anomalies. Zou et al. [20] study the state estimation problem for complex networks subject to false data injection attack. Xie et al. [21] consider the false data injection attack in power market operations and study the economic impact of this attack. In addition, the security problem in multi-agent systems has also attracted increasing interests. In [22], a distributed adaptive controller was proposed for multi-agent systems with missing control input in some intervals and also delays in the transmission of control input to agents. The intervals could be provisional failures of communication links, packet loss or false data injection. To defend against two different typical attacks in multi-agent system, Feng et al.[23] studied a distributed secure consensus tracking problem with connected and disconnected directed switching topologies.Most of the aforementioned works are aiming primarily at the centralized state estimations. To the best of ...

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“…Savage and La Scala [5] provided the optimal schedule for a sensor measurement scheduling problem, which minimizes the terminal estimation error covariance under the constraint that only n < N measurements could be taken with a finite time horizon N . Shi et al [6] studied the state estimation problem in which the sensor can estimate the state locally and send it to a remote estimator via a packet-dropping channel, and gave an optimal periodic schedule. Mo et al [7] considered the problem to select a subset of sensors to communicate to a fusion center dynamically at each time step, with the purpose to minimize the asymptotic expected covariance matrix of the estimation error.…”

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confidence: 99%

Communication scheduling for decentralized state estimation

Yang

Shi

et al. 2012

2012 12th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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This paper considers decentralized state estimation subject to communication constraints. A group of agents measure the state of a process and obtain their state estimates by exchanging data with one another. Due to the communication constraint, only a few communication channels are available. The main objective of this paper is to allocate these channels among the agents so as to minimize their average estimation errors. Assuming the agents have the same sensing capability, we provide the optimal allocation strategy for both directed and un-directed communication channels.

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Sensor data scheduling over a packet-dropping network

Cited by 4 publications

References 15 publications

An improved hybrid sensor schedule for remote state estimation under limited communication resources

An improved hybrid sensor schedule for remote state estimation under limited communication resources

False data injection attack on consensus‐based distributed estimation

Communication scheduling for decentralized state estimation

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