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Ding, K., Ren, X., Quevedo, D., Dey, S. & Shi, L. (2019). DoS attacks on remote state estimation with asymmetric information. IEEE Transactions on Control of Network Systems, 6(2), 653-666
Open this publication in new window or tab >>DoS attacks on remote state estimation with asymmetric information
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2019 (English)In: IEEE Transactions on Control of Network Systems, ISSN 2325-5870, Vol. 6, no 2, p. 653-666Article in journal (Refereed) Published
Abstract [en]

Abstract: In this paper, we consider remote state estimation in an adversarial environment. A sensor forwards local state estimates to a remote estimator over a vulnerable network, which may be congested by an intelligent denial-of-service (DoS) attacker. It is assumed that the acknowledgement information from the remote estimator to the sensor is hidden from the attacker, which thus leads to asymmetric information between the sensor and attacker. Considering the infinite-time goals of the two agents and their asymmetric information structure, we model the conflicting nature between the sensor and the attacker by a stochastic Bayesian game. Solutions for this game under two different structures of public information history are investigated, that is, the open-loop structure (in which players cannot observe their opponents' play) and the closed-loop one (in which players can observe the play causally). For the open-loop history case, the original game problem is transformed into a static Bayesian game. We provide the unique mixed-strategy equilibrium explicitly for this game, and analyze the sensor's advantages brought by the extra information. When it comes to the closed-loo

Place, publisher, year, edition, pages
IEEE, 2019
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-365050 (URN)10.1109/TCNS.2018.2867157 (DOI)000469874200017 ()
Funder
Swedish Research Council, 2017-04053
Available from: 2018-11-08 Created: 2018-11-08 Last updated: 2019-06-26Bibliographically approved
Knorn, S., Dey, S., Ahlén, A. & Quevedo, D. E. (2019). Optimal Energy Allocation in Multisensor Estimation Over Wireless Channels Using Energy Harvesting and Sharing. IEEE Transactions on Automatic Control, 64(10), 4337-4344
Open this publication in new window or tab >>Optimal Energy Allocation in Multisensor Estimation Over Wireless Channels Using Energy Harvesting and Sharing
2019 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 64, no 10, p. 4337-4344Article in journal (Refereed) Published
Abstract [en]

We investigate the optimal power control for multisensor estimation of correlated random Gaussian sources. A group of wireless sensors obtains local measurements and transmits them to a remote fusion center (FC), which reconstructs the measurements using the minimum mean-square error estimator. All the sensors are equipped with an energy harvesting module and a transceiver unit for wireless, directed energy sharing between neighboring sensors. The sensor batteries are of finite storage capacity and prone to energy leakage. Our aim is to find optimal power control strategies, which determine the energies used to transmit data to the FC and shared between sensors, so as to minimize the long-term average distortion over an infinite horizon. We assume centralized causal information of the harvested energies and channel gains, which are generated by independent finite-state stationary Markov chains. The optimal power control policy is derived using a stochastic predictive control formulation. We also investigate the structure of the optimal solution, a Q-learning based sub-optimal power control scheme and two computationally simple and easy-to-implement heuristic policies. Extensive numerical simulations illustrate the performance of the considered policies.

Keywords
Energy harvesting, energy sharing, fading, multisensor estimation, networks, power control, Q-learning
National Category
Communication Systems
Identifiers
urn:nbn:se:uu:diva-396738 (URN)10.1109/TAC.2019.2896048 (DOI)000490772500037 ()
Funder
Swedish Research Council, 2017-04053Swedish Research Council, 2017-04186
Available from: 2019-11-26 Created: 2019-11-26 Last updated: 2019-11-26Bibliographically approved
Salimi, S., Dey, S. & Ahlén, A. (2019). Sequential Detection of Deception Attacks in Networked Control Systems with Watermarking. In: 2019 18th European Control Conference (ECC): . Paper presented at 18th European Control Conference (ECC), Naples, Italy, June 25-28, 2019 (pp. 883-890). IEEE
Open this publication in new window or tab >>Sequential Detection of Deception Attacks in Networked Control Systems with Watermarking
2019 (English)In: 2019 18th European Control Conference (ECC), IEEE, 2019, p. 883-890Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we investigate the role of a physical watermarking signal in quickest detection of a deception attack in a scalar linear control system where the sensor measurements can be replaced by an arbitrary stationary signal generated by an attacker. By adding a random watermarking signal to the control action, the controller designs a sequential test based on a Cumulative Sum (CUSUM) method that accumulates the log-likelihood ratio of the joint distribution of the residue and the watermarking signal (under attack) and the joint distribution of the innovations and the watermarking signal under no attack. As the average detection delay in such tests is asymptotically (as the false alarm rate goes to zero) upper bounded by a quantity inversely proportional to the Kullback-Leibler divergence(KLD) measure between the two joint distributions mentioned above, we analyze the effect of the watermarking signal variance on the above KLD. We also analyze the increase in the LQG control cost due to the watermarking signal, and show that there is a tradeoff between quick detection of attacks and the penalty in the control cost. It is shown that by considering a sequential detection test based on the joint distributions of residue/innovations and the watermarking signal, as opposed to the distributions of the residue/innovations only, we can achieve a higher KLD, thus resulting in a reduced average detection delay. We also present some new structural results involving the associated KLD and its behaviour with respect to the attacker's signal power and the watermarking signal power. These somewhat non-intuitive structural results can be used by either the attacker to choose their power to minimize the KLD, and/or by the system designer to choose its watermarking signal variance appropriately to increase the KLD. Numerical results are provided to support our claims.

Place, publisher, year, edition, pages
IEEE, 2019
National Category
Control Engineering Telecommunications
Identifiers
urn:nbn:se:uu:diva-396473 (URN)10.23919/ECC.2019.8796303 (DOI)000490488300142 ()978-3-907144-00-8 (ISBN)
Conference
18th European Control Conference (ECC), Naples, Italy, June 25-28, 2019
Available from: 2019-11-14 Created: 2019-11-14 Last updated: 2019-11-14Bibliographically approved
Biswas, S., Dey, S. & Shirazinia, A. (2019). Sum Throughput Maximization in a Cognitive Multiple Access Channel With Cooperative Spectrum Sensing and Energy Harvesting. IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING, 5(2), 382-399
Open this publication in new window or tab >>Sum Throughput Maximization in a Cognitive Multiple Access Channel With Cooperative Spectrum Sensing and Energy Harvesting
2019 (English)In: IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING, ISSN 2332-7731, Vol. 5, no 2, p. 382-399Article in journal (Refereed) Published
Abstract [en]

This paper focuses on the problem of sensing throughput optimization in a fading multiple access cognitive radio (CR) network, where the secondary user (SU) transmitters participate in cooperative spectrum sensing and are capable of harvesting energy and sharing energy with each other. We formulate the optimization problem as a maximization of the expected achievable sum-rate over a finite horizon, subject to an average interference constraint at the primary receiver, peak power constraints, and energy causality constraints at the SU transmitters. The optimization problem is a non-convex, mixed integer non-linear program (MINLP) involving the binary action to sense the spectrum or not, and the continuous variables, such as the transmission power, shared energy, and sensing time. The problem is analyzed under two different assumptions on the available information pattern: 1) non-causal channel state information (CSI), energy state information (ESI), and infinite battery capacity and 2) the more realistic scenario of the causal CSI/ESI and finite battery. In the non-casual case, this problem can be solved by an exhaustive search over the decision variable or an MINLP solver for smaller problem dimensions, and a novel heuristic policy for larger problems, combined with an iterative alternative optimization method for the continuous variables. The causal case with finite battery is optimally solved using a dynamic programming (DP) methodology, whereas a number of sub-optimal algorithms are proposed to reduce the computational complexity of DP. Extensive numerical simulations are carried out to illustrate the performance of the proposed algorithms. One of the main findings indicates that the energy sharing is more beneficial when there is a significant asymmetry between average harvested energy levels/channel gains of different SUs.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Keywords
Energy harvesting, cognitive radio, multiple access channel, spectrum sensing, fading channel
National Category
Communication Systems Telecommunications Signal Processing
Identifiers
urn:nbn:se:uu:diva-389809 (URN)10.1109/TCCN.2019.2908860 (DOI)000471115000016 ()
Funder
Swedish Research Council, 2017-04053
Available from: 2019-07-30 Created: 2019-07-30 Last updated: 2019-07-30Bibliographically approved
Leong, A. S., Quevedo, D., Dolz, D. & Dey, S. (2019). Transmission Scheduling for Remote State Estimation over Packet Dropping Links in the Presence of an Eavesdropper. IEEE Transactions on Automatic Control, 64(9), 3732-3739
Open this publication in new window or tab >>Transmission Scheduling for Remote State Estimation over Packet Dropping Links in the Presence of an Eavesdropper
2019 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 64, no 9, p. 3732-3739Article in journal (Refereed) Published
Abstract [en]

This paper studies remote state estimation in the presence of an eavesdropper. A sensor transmits local state estimates over a packet dropping link to a remote estimator, while an eavesdropper can successfully overhear each sensor transmission with a certain probability. The objective is to determine when the sensor should transmit, in order to minimize the estimation error covariance at the remote estimator, while trying to keep the eavesdropper error covariance above a certain level. This is done by solving an optimization problem that minimizes a linear combination of the expected estimation error covariance and the negative of the expected eavesdropper error covariance. Structural results on the optimal transmission policy are derived, and shown to exhibit thresholding behaviour in the estimation error covariances. In the infinite horizon situation, it is shown that with unstable systems one can keep the expected estimation error covariance bounded while the expected eavesdropper error covariance becomes unbounded. An alternative measure of security, constraining the amount of information revealed to the eavesdropper, is also considered, and similar structural results on the optimal transmission policy are derived. In the infinite horizon situation with unstable systems, it is now shown that for any transmission policy which keeps the expected estimation error covariance bounded, the expected amount of information revealed to the eavesdropper is always lower bounded away from zero. An extension of our results to the transmission of measurements is also presented.

National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-365051 (URN)10.1109/TAC.2018.2883246 (DOI)000484210500015 ()
Available from: 2018-11-08 Created: 2018-11-08 Last updated: 2019-10-17Bibliographically approved
Biasson, A., Dey, S. & Zorzi, M. (2018). A decentralized optimization framework for energy harvesting devices. IEEE Transactions on Mobile Computing, 17(11), 2483-2496
Open this publication in new window or tab >>A decentralized optimization framework for energy harvesting devices
2018 (English)In: IEEE Transactions on Mobile Computing, ISSN 1536-1233, E-ISSN 1558-0660, Vol. 17, no 11, p. 2483-2496Article in journal (Refereed) Published
Abstract [en]

Designing decentralized policies for wireless communication networks is a crucial problem, which has only been partially solved in the literature so far. In this paper, we propose a Decentralized Markov Decision Process (Dec-MDP) framework to analyze a wireless sensor network with multiple users which access a common wireless channel. We consider devices with energy harvesting capabilities, that aim at balancing the energy arrivals with the data departures and with the probability of colliding with other nodes. Over time, an access point triggers a SYNC slot, wherein it recomputes the optimal transmission parameters of the whole network, and distributes this information. Every node receives its own policy, which specifies how it should access the channel in the future, and, thereafter, proceeds in a fully decentralized fashion, with no interactions with other entities in the network. We propose a multi-layer Markov model, where an external MDP manages the jumps between SYNC slots, and an internal Dec-MDP computes the optimal policy in the short term. We numerically show that, because of the harvesting, stationary policies are suboptimal in energy harvesting scenarios, and the optimal trade-off lies between an orthogonal and a random access system.

National Category
Telecommunications
Identifiers
urn:nbn:se:uu:diva-365046 (URN)10.1109/TMC.2018.2810269 (DOI)000446655000002 ()
Available from: 2018-11-08 Created: 2018-11-08 Last updated: 2018-11-27Bibliographically approved
Ren, X., Wu, J., Dey, S. & Shi, L. (2018). Attack allocation on remote state estimation in multi-systems: Structural results and asymptotic solution. Automatica, 87, 187-194
Open this publication in new window or tab >>Attack allocation on remote state estimation in multi-systems: Structural results and asymptotic solution
2018 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 87, p. 187-194Article in journal (Refereed) Published
Abstract [en]

Abstract

This paper considers optimal attack attention allocation on remote state estimation in multi-systems. Suppose there are M" role="presentation">

independent systems, each of which has a remote sensor monitoring the system and sending its local estimates to a fusion center over a packet-dropping channel. An attacker may generate noises to exacerbate the communication channels between sensors and the fusion center. Due to capacity limitation, at each time the attacker can exacerbate at most N" role="presentation"> of the M" role="presentation"> channels. The goal of the attacker side is to seek an optimal policy maximizing the estimation error at the fusion center. The problem is formulated as a Markov decision process (MDP) problem, and the existence of an optimal deterministic and stationary policy is proved. We further show that the optimal policy has a threshold structure, by which the computational complexity is reduced significantly. Based on the threshold structure, a myopic policy is proposed for homogeneous models and its optimality is established. To overcome the curse of dimensionality of MDP algorithms for general heterogeneous models, we further provide an asymptotically (as M" role="presentation"> and N" role="presentation"> go to infinity) optimal solution, which is easy to compute and implement. Numerical examples are given to illustrate the main results.

National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-365044 (URN)10.1016/j.automatica.2017.09.021 (DOI)
Available from: 2018-11-08 Created: 2018-11-08 Last updated: 2019-01-23Bibliographically approved
Leong, A., Quevedo, D. E. & Dey, S. (2018). Optimal control of energy resources for state estimation over wireless channels. In: Başar, Tamer; Krstic, Miroslav (Ed.), SpringerBriefs in Control, Automation and Robotics: . Springer Publishing Company
Open this publication in new window or tab >>Optimal control of energy resources for state estimation over wireless channels
2018 (English)In: SpringerBriefs in Control, Automation and Robotics / [ed] Başar, Tamer; Krstic, Miroslav, Springer Publishing Company, 2018Chapter in book (Refereed)
Place, publisher, year, edition, pages
Springer Publishing Company, 2018
Series
SpringerBriefs in Control, Automation and Robotics, ISSN 2192-6786
National Category
Signal Processing Robotics
Identifiers
urn:nbn:se:uu:diva-371939 (URN)
Available from: 2019-01-03 Created: 2019-01-03 Last updated: 2019-09-11Bibliographically approved
Wu, S., Ren, X., Dey, S. & Shi, L. (2018). Optimal scheduling of multiple sensors over shared channels with packet transmission constraint. Automatica, 96, 22-31
Open this publication in new window or tab >>Optimal scheduling of multiple sensors over shared channels with packet transmission constraint
2018 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 96, p. 22-31Article in journal (Refereed) Published
Abstract [en]

In this work, we consider the optimal sensory data scheduling of multiple process. A remote estimator is deployed to monitor S" role="presentation"> independent linear time-invariant processes. Each process is measured by a sensor, which is capable of computing a local estimate and sending its local state estimate wrapped up in packets to the remote estimator. The lengths of the packets are different due to different dynamics of each process. Consequently, it takes different time durations for the sensors to send the local estimates. In addition, only a portion of all the sensors are allowed to transmit at each time due to bandwidth limitation. We are interested in minimizing the sum of the average estimation error covariance of each process at the remote estimator under such packet transmission and bandwidth constraints. We formulate the problem as an average cost Markov decision process (MDP) over an infinite horizon. We first study the special case when S=1" role="presentation"> and find that the optimal scheduling policy always aims to complete transmitting the current estimate. We also derive a sufficient condition for boundedness of the average remote estimation error. We then study the case for general S" role="presentation">. We establish the existence of a deterministic and stationary policy for the optimal scheduling problem. We find that the optimal policy has a consistent property among the sensors and a switching type structure. A stochastic algorithm is designed to utilize the structure of the policy to reduce computation complexity. Numerical examples are provided to illustrate the theoretical results.

National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-365049 (URN)10.1016/j.automatica.2018.06.019 (DOI)
Available from: 2018-11-08 Created: 2018-11-08 Last updated: 2019-02-07Bibliographically approved
Guo, X., He, Y., Atapattu, S., Dey, S. & Evans, J. (2018). Power allocation for distributed detection systems in wireless sensor networks with limited fusion centre feedback. IEEE Transactions on Communications, 66(10), 4753-4766
Open this publication in new window or tab >>Power allocation for distributed detection systems in wireless sensor networks with limited fusion centre feedback
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2018 (English)In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 66, no 10, p. 4753-4766Article in journal (Refereed) Published
Abstract [en]

We consider a distributed detection system for a wireless sensor network over slow-fading channels. Each sensor only has knowledge of quantized channel state information (CSI) which is received from the fusion center via a limited feedback channel. We then consider transmit power allocation at each sensor in order to maximize a J-divergence based detection metric subject to a total and individual transmit power constraints. Our aim is to jointly design the quantization regions of all sensors CSI and the corresponding power allocations. A locally optimum solution is obtained by applying the generalized Lloyd algorithm (GLA). To overcome the high computational complexity of the GLA, we then propose a low-complexity near-optimal scheme which performs very close to its GLA based counterpart. This enables us to explicitly formulate the problem and to find the unique solution despite the non-convexity of the optimization problem. An asymptotic analysis is also provided when the number of feedback bits becomes large. Numerical results illustrate that only a small amount of feedback is needed to achieve a detection performance close to the full CSI case.

National Category
Signal Processing Communication Systems Telecommunications
Identifiers
urn:nbn:se:uu:diva-365047 (URN)10.1109/TCOMM.2018.2837101 (DOI)
Available from: 2018-11-08 Created: 2018-11-08 Last updated: 2019-02-13Bibliographically approved
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