uu.seUppsala University Publications
Change search
Link to record
Permanent link

Direct link
BETA
Alternative names
Publications (10 of 70) Show all publications
Özcelikkale, A., Koseoglu, M., Srivastava, M. & Ahlén, A. (2019). Deep reinforcement learning based energy beamforming for powering sensor networks. In: : . Paper presented at 29th IEEE International Workshop on Machine Learning for Signal Processing, October 13-16, Pittsburg, USA.
Open this publication in new window or tab >>Deep reinforcement learning based energy beamforming for powering sensor networks
2019 (English)Conference paper, Oral presentation with published abstract (Refereed)
National Category
Engineering and Technology
Research subject
Electrical Engineering with specialization in Signal Processing
Identifiers
urn:nbn:se:uu:diva-398752 (URN)
Conference
29th IEEE International Workshop on Machine Learning for Signal Processing, October 13-16, Pittsburg, USA
Available from: 2019-12-10 Created: 2019-12-10 Last updated: 2019-12-10
Voigt, T., Augustine, R., Asan, N. B., Perez, M. D., Ahlén, A., Teixeira, A., . . . Mani, M. (2019). LifeSec - Don’t Hack my Body. In: : . Paper presented at IEEE EuroS&P 2019 and CySeP'19.
Open this publication in new window or tab >>LifeSec - Don’t Hack my Body
Show others...
2019 (English)Conference paper, Poster (with or without abstract) (Refereed)
National Category
Computer Engineering Engineering and Technology
Identifiers
urn:nbn:se:uu:diva-397853 (URN)
Conference
IEEE EuroS&P 2019 and CySeP'19
Available from: 2019-12-03 Created: 2019-12-03 Last updated: 2020-01-08
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
Seifullaev, R., Knorn, S. & Ahlén, A. (2019). The effect of uniform quantization on parameter estimation of compound distributions. In: : . Paper presented at 2019 IEEE Conference on Decision and Control (CDC), Nice.
Open this publication in new window or tab >>The effect of uniform quantization on parameter estimation of compound distributions
2019 (English)Conference paper, Oral presentation with published abstract (Refereed)
National Category
Engineering and Technology
Research subject
Electrical Engineering with specialization in Signal Processing
Identifiers
urn:nbn:se:uu:diva-398748 (URN)
Conference
2019 IEEE Conference on Decision and Control (CDC), Nice
Available from: 2019-12-10 Created: 2019-12-10 Last updated: 2019-12-10
Seifullaev, R., Knorn, S. & Ahlén, A. (2019). The effect of uniform quantization on parameter estimation of compound distributions. IEEE Control Systems Letters (4), 1032-1037
Open this publication in new window or tab >>The effect of uniform quantization on parameter estimation of compound distributions
2019 (English)In: IEEE Control Systems Letters, no 4, p. 1032-1037Article in journal (Refereed) Published
National Category
Engineering and Technology
Research subject
Electrical Engineering with specialization in Signal Processing
Identifiers
urn:nbn:se:uu:diva-398811 (URN)10.1109/LCSYS.2019.2921239 (DOI)
Available from: 2019-12-11 Created: 2019-12-11 Last updated: 2019-12-11
Ahlén, A., Åkerberg, J., Eriksson, M., Isaksson, A. J., Iwaki, T., Johansson, K. H., . . . Sandberg, H. (2019). Towards Wireless Control in Industrial Process Automation: A Case Study at a Paper Mill. IEEE CONTROL SYSTEMS MAGAZINE, 39(5), 36-57
Open this publication in new window or tab >>Towards Wireless Control in Industrial Process Automation: A Case Study at a Paper Mill
Show others...
2019 (English)In: IEEE CONTROL SYSTEMS MAGAZINE, ISSN 1066-033X, Vol. 39, no 5, p. 36-57Article in journal (Refereed) Published
Abstract [en]

Wireless sensors and networks are used only occasionally in current control loops in the process industry. With rapid developments in embedded and highperformance computing, wireless communication, and cloud technology, drastic changes in the architecture and operation of industrial automation systems seem more likely than ever. These changes are driven by ever-growing demands on production quality and flexibility. However, as discussed in "Summary," there are several research obstacles to overcome. The radio communication environment in the process industry is often troublesome, as the environment is frequently cluttered with large metal objects, moving machines and vehicles, and processes emitting radio disturbances [1], [2]. The successful deployment of a wireless control system in such an environment requires careful design of communication links and network protocols as well as robust and reconfigurable control algorithms.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019
Keywords
Wireless communication, Process control, Wireless sensor networks, Automation, Control systems, Job shop scheduling, Industries
National Category
Robotics
Research subject
Electrical Engineering with specialization in Signal Processing
Identifiers
urn:nbn:se:uu:diva-396957 (URN)10.1109/MCS.2019.2925226 (DOI)000492188200001 ()
Funder
Vinnova
Available from: 2019-11-13 Created: 2019-11-13 Last updated: 2019-12-16Bibliographically approved
Voigt, T., Augustine, R., Asan, N. B., Perez, M. D., Ahlén, A., Teixeira, A., . . . Mani, M. (2019). Tumor Sensing Privacy in In-Body Networks. In: : . Paper presented at EEE EuroS&P 2019 and CySeP'19.
Open this publication in new window or tab >>Tumor Sensing Privacy in In-Body Networks
Show others...
2019 (English)Conference paper, Poster (with or without abstract) (Refereed)
National Category
Computer Engineering Engineering and Technology
Identifiers
urn:nbn:se:uu:diva-398222 (URN)
Conference
EEE EuroS&P 2019 and CySeP'19
Available from: 2019-12-03 Created: 2019-12-03 Last updated: 2020-01-08
Olofsson, T. & Ahlén, A. (2018). Computing probability density functions of compound distributions: A comparative investigation. In: : . Paper presented at IEEE International COnference on Communications, ICC, 20-24 May, Kansas City, USA.
Open this publication in new window or tab >>Computing probability density functions of compound distributions: A comparative investigation
2018 (English)Conference paper, Published paper (Refereed)
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-363239 (URN)
Conference
IEEE International COnference on Communications, ICC, 20-24 May, Kansas City, USA
Available from: 2018-10-15 Created: 2018-10-15 Last updated: 2019-01-03Bibliographically approved
Biswas, S., Knorn, S., Dey, S. & Ahlén, A. (2018). Quantized non-Bayesian quickest change detection with energy harvesting. In: : . Paper presented at IEEE Global Communications Conference (GLOBECOM), 9-13 December 2018, Abu Dhabi, United Arab Emirates. IEEE
Open this publication in new window or tab >>Quantized non-Bayesian quickest change detection with energy harvesting
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper focuses on the analysis of an optimal sensing and quantization strategy in a multi-sensor network where each individual sensor sends its quantized log-likelihood information to the fusion center (FC) for non-Bayesian quickest change detection. It is assumed that the sensors are equipped with a battery/energy storage device of finite capacity, capable of harvesting energy from the environment. The FC is assumed to have access to either non-causal or causal channel state information (CSI) and energy state information (ESI) from all the sensors while performing the quickest change detection. The primary observations are assumed to be generated from a sequence of random variables whose probability distribution function changes at an unknown time point. The objective of the detection problem is to minimize the average detection delay of the change point with respect to a lower bound on the rate of false alarm. In this framework, the optimal sensing decision and number of quantization bits for information transmission can be determined with the constraint of limited available energy due to finite battery capacity. This optimization is formulated as a stochastic control problem and is solved using dynamic programming algorithms for both non-causal and causal CSI and ESI scenario. A set of non-linear equations is also derived to determine the optimal quantization thresholds for the sensor log-likelihood ratios, by maximizing an appropriate Kullback-Leibler (KL) divergence measure between the distributions before and after the change. A uniform threshold quantization strategy is also proposed as a simple sub-optimal policy. The simulation results indicate that the optimal quantization is preferable when the number of quantization bits is low as its performance is significantly better compared to its uniform counterpart in terms of average detection delay. For the case of a large number of quantization bits, the performance benefits of using the optimal quantization as compared to its uniform counterpart diminish, as expected.

Place, publisher, year, edition, pages
IEEE, 2018
Series
IEEE Global Communications Conference, E-ISSN 2576-6813
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-363299 (URN)10.1109/GLOCOM.2018.8647715 (DOI)000465774303093 ()978-1-5386-4727-1 (ISBN)
Conference
IEEE Global Communications Conference (GLOBECOM), 9-13 December 2018, Abu Dhabi, United Arab Emirates
Available from: 2018-10-16 Created: 2018-10-16 Last updated: 2019-06-24Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-9066-5468

Search in DiVA

Show all publications