COMPRESS NETS - Marie Skłodowska-Curie Individual Fellowship

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Compressed Sensing Techniques for Wireless Sensor Networks
H2020 Grant Agreement Number 793402

Research Topics

Wireless Sensor Networks

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The sensor observations may experience both spatial and temporal correlations, accounting for various typical environmental sensing applications in densely deployed wireless sensor networks. We want to understand the fundamental design principles and to mathematically characterize the optimal trade-offs between different performance metrics. This include deriving efficient data gathering protocols, distributed compression techniques as well as computationally efficient (vector) quantization schemes.

Age of Information – A New Metric for Information Freshness

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[video presentation]

The concept of information aging plays a central role in optimizing systems in which a process of interest is monitored via remote sensors that perform measurements (possibly at random times) and send status updates to a central node via wireless links. This is the case, e.g., in most wireless IoT systems. Status updates are transmitted as packets, containing information about one or more variables of interest, and the time of generation of the sample. Typically, it takes a random time until the packet is successfully delivered through the network. If the most recently received update carries the time stamp u(t), which specifies the time instant when the update was generated, then the age of information is defined as the random process Delta(t) = t-u(t). Hence, the age represents the time elapsed since the last received packet was generated and its sample path follows a sawtooth shape as illustrated in the figure above. The age increases linearly with time between two successive updates and, upon the reception of a new update, it is reset to the delay the packet experienced going through the transmission system. We want to understand and characterize mathematically the age of information performance for different status update transmission protocols, relevant for IoT applications.

Compressed Sensing

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Sparsity is an attribute present in a myriad of natural signals and systems, occurring either inherently or after a suitable projection. Such signals with lots of zeros possess minimal degrees of freedom and are thus attractive from an implementation perspective in wireless networks. While sparsity has appeared for decades in various mathematical fields, the emergence of compressed sensing in 2006 provided an efficient way to extract the information content of the input signal and condense it into a small amount of data, i.e., a mathematically elegant way to perform simultaneously signal acquisition and compression. This observation, which is the key idea behind compressed sensing, has profound implications on the fundamental structure of data acquisition systems. For more than 50 years, virtually all schemes followed the same basic structure: the input signal is first acquired at the desired resolution, and then compressed using (computationally intensive) signal processing techniques that remove the redundancy. Thus, a large amount of raw data is collected during the acquisition stage just to be thrown away at the compression stage. In sharp contrast, the novel CS approach is a computationally lighter method which collects only the minimum amount of data (i.e., already compressed) needed to reconstruct the input signal at the desired resolution.

Vector Quantized Compressed Sensing via Deep Neural Networks

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Sparse signals, encountered in many wireless and signal acquisition applications, can be acquired via compressed sensing (CS) to reduce computations and transmissions, crucial for resourcelimited devices such as wireless sensors. Since the information signals are often continuous-valued, digital communication of compressive measurements requires quantization. In such a quantized compressed sensing (QCS) context, we address remote acquisition of a sparse source through vector quantized noisy compressive measurements. We introduced a deep encoder-decoder architecture, consisting of an encoder deep neural network (DNN), a quantizer, and a decoder DNN, that realizes low-complexity vector quantization aiming at minimizing the mean-square error of the signal reconstruction for a given quantization rate.

Publications

Books & Monographs

[B1] M. Leinonen, M. Codreanu, and G. Giannakis, Compressed Sensing with Applications in Wireless Networks Foundations and Trends® in Signal Processing, Now Publishers, 2019.

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ISBN: 19328346
DOI: 10.1561/2000000107

Abstract:

Sparsity is an attribute present in a myriad of natural signals and systems, occurring either inherently or after a suitable projection. Such signals with lots of zeros possess minimal degrees of freedom and are thus attractive from an implementation perspective in wireless networks. While sparsity has appeared for decades in various mathematical fields, the emergence of compressed sensing (CS) – the joint sampling and compression paradigm – in 2006 gave rise to plethora of novel communication designs that can efficiently exploit sparsity. In this monograph, we review several CS frameworks where sparsity is exploited to improve the quality of signal reconstruction/detection while reducing the use of radio and energy resources by decreasing, e.g., the sampling rate, transmission rate, and number of computations. The first part focuses on several advanced CS signal reconstruction techniques along with wireless applications. The second part deals with efficient data gathering and lossy compression techniques in wireless sensor networks. Finally, the third part addresses CS-driven designs for spectrum sensing and multi-user detection for cognitive and wireless communications.

Book Chapters

[BC1] Mohammad Moltafet, Markus Leinonen, and Marian Codreanu, Timely Status Updating via Packet Management in Multi-Source Systems, in Cambridge University Press Special Issue on Age of Information, in press, 2021.

Journal Papers

[J1] M. Leinonen, and M. Codreanu, “Low-complexity vector quantized compressed sensing via deep neural networks,” IEEE Open Journal of the Communications Society, vol. 1, pp. 1278–1294, Aug. 2020.

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Abstract:

Sparse signals, encountered in many wireless and signal acquisition applications, can be acquired via compressed sensing (CS) to reduce computations and transmissions, crucial for resourcelimited devices, e.g., wireless sensors. Since the information signals are often continuous-valued, digital communication of compressive measurements requires quantization. In such a quantized compressed sensing (QCS) context, we address remote acquisition of a sparse source through vector quantized noisy compressive measurements. We propose a deep encoder-decoder architecture, consisting of an encoder deep neural network (DNN), a quantizer, and a decoder DNN, that realizes low-complexity vector quantization aiming at minimizing the mean-square error of the signal reconstruction for a given quantization rate. We devise a supervised learning method using stochastic gradient descent and backpropagation to train the system blocks. Strategies to overcome the vanishing gradient problem are proposed. Simulation results show that the proposed non-iterative DNN-based QCS method achieves higher rate-distortion performance with lower algorithm complexity as compared to standard QCS methods, conducive to delay-sensitive applications with large-scale signals.

[J2] E. Belyaev, M. Codreanu, M. Juntti, and K. Egiazarian, “Compressive sensed video recovery via iterative thresholding with random transforms,” IET Image Processing, vol. 14, no. 6, pp. 1187–1199, May 2020.

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Abstract:

We consider the problem of compressive sensed video recovery via iterative thresholding algorithm. Traditionally, it is assumed that some fixed sparsifying transform is applied at each iteration of the algorithm. In order to improve the recovery performance, at each iteration the thresholding could be applied for different transforms in order to obtain several estimates for each pixel. Then the resulting pixel value is computed based on obtained estimates using simple averaging. However, calculation of the estimates leads to significant increase in reconstruction complexity. Therefore, we propose a heuristic approach, where at each iteration only one transform is randomly selected from some set of transforms. First, we present a simple example, when block-based 2-D discrete cosine transform is used as the sparsifying transform, and show that the random selection of the block size at each iteration significantly outperforms the case when fixed block size is used. Then, we show that similar improvement can be achieved, when a random shift of the blocks grid is introduced, or when random 2-D discrete wavelet transform is applied at each iteration. Second, building on these simple examples, we apply the proposed approach when video block-matching and 3D filtering (VBM3D) is used for the thresholding and show that the recovery performance could be improved as compared with the recovery based on VBM3D with fixed transform. Finally, extending the introduced approach, we propose to also randomly select a frame residual computation algorithm as well and show that it could provide additional gain as well.

[J3] M. Moltafet, M. Leinonen, and M. Codreanu, “Worst case age of information in wireless sensor networks: A multi-access channel,” IEEE Wireless Communications Letters, vol. 9, no. 3, pp. 321-325, Mar. 2020.

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Abstract:

Freshness of status update packets is essential for enabling a wide range of applications in wireless sensor networks (WSNs). Accordingly, we consider a WSN where sensors communicate status updates to a destination by contending for the channel access based on a carrier sense multiple access (CSMA) method. We analyze the worst case average age of information (AoI) and average peak AoI from the view of one sensor in a system where all the other sensors have a saturated queue. Numerical results illustrate the importance of optimizing the contention window size and the packet arrival rate to maximize the information freshness.

[J4] M. Moltafet, M. Leinonen, and M. Codreanu, “On the age of information in a multi-source queuing model,” IEEE Transactions on Communications, vol. 68, no. 8, pp. 5003–5017, Aug. 2020.

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Abstract:

Freshness of status update packets is essential for enabling services where a destination needs the most recent measurements of various sensors. In this paper, we study the information freshness of single-server multi-source queueing models under a first-come first-served (FCFS) serving policy. In the considered model, each source independently generates status update packets according to a Poisson process. The information freshness of the status updates of each source is evaluated by the average age of information (AoI). We derive an exact expression for the average AoI for the case with exponentially distributed service time, i.e., for a multi-source M/M/1 queueing model. Moreover, we derive three approximate expressions for the average AoI for a multi-source M/G/1 queueing model having a general service time distribution. Simulation results are provided to validate the derived exact average AoI expression, to assess the tightness of the proposed approximations, and to demonstrate the AoI behavior for different system parameters.

[J5] M. Hatami, M. Leinonen, and M. Codreanu, “AoI minimization in status update control with energy harvesting sensors,” IEEE Transactions on Communications, submitted Sep. 2020.

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Abstract:

Information freshness is crucial for time-critical IoT applications, e.g., environment monitoring and control systems. We consider an IoT-based status update system with multiple users, multiple energy harvesting sensors, and a wireless edge node. The users are interested in time-sensitive information about physical quantities, each measured by a sensor. Users send requests to the edge node where a cache contains the most recently received measurements from each sensor. To serve a request, the edge node either commands the sensor to send a status update or retrieves the aged measurement from the cache. We aim at nding the best action of the edge node to minimize the age of information of the served measurements. We model this problem as a Markov decision process and develop reinforcement learning (RL) algorithms: a model-based value iteration method and a model-free Q-learning method. We also propose a Q-learning method for the realistic case where the edge node is informed about the sensors battery levels only via the status updates. Furthermore, properties of an optimal policy are analytically characterized. Simulation results show that an optimal policy is a threshold-based policy and that the proposed RL methods signicantly reduce the average cost as compared to several baseline methods.

[J6] M. Moltafet, M. Leinonen, and M. Codreanu, “Average AoI in multi-source systems with source-aware packet management,” IEEE Transactions on Communications, early access, 2021. (submitted, Mar. 2020, revised Sep. 2020)

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Abstract:

We study the information freshness under three different source aware packet management policies in a status update system consisting of two independent sources and one server. The packets of each source are generated according to the Poisson process and the packets are served according to an exponentially distributed service time. We derive the average age of information (AoI) of each source using the stochastic hybrid systems (SHS) technique for each packet management policy. In Policy 1, the queue can contain at most two waiting packets at the same time (in addition to the packet under service), one packet of source 1 and one packet of source 2. When the server is busy at an arrival of a packet, the possible packet of the same source waiting in the queue (hence, source-aware) is replaced by the arrived fresh packet. In Policy 2, the system (i.e., the waiting queue and the server) can contain at most two packets, one from each source. When the server is busy at an arrival of a packet, the possible packet of the same source in the system is replaced by the fresh packet. Policy 3 is similar to Policy 2 but it does not permit preemption in service, i.e., while a packet is under service all new arrivals from the same source are blocked and cleared. Numerical results are provided to assess the fairness between sources and the sum average AoI of the proposed policies.

[J7] M. Moltafet, M. Leinonen, M. Codreanu, and N. Pappas, “Dynamic radio resource allocation in wireless sensor networks for AoI-sensitive applications,” IEEE Transactions on Communications, submitted Oct. 2020.

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Abstract:

We consider a system where multiple sensors communicate timely information about various random processes to a sink. The sensors share orthogonal sub-channels to transmit such information in the form of status update packets. A central controller can control the sampling actions of the sensors to trade-off between the transmit power consumption and information freshness which is quantified by the Age of Information (AoI). We jointly optimize the sampling action of each sensor, the transmit power allocation, and the sub-channel assignment to minimize the average total transmit power of all sensors subject to a maximum average AoI constraint for each sensor. To solve the problem, we develop a dynamic control algorithm using the Lyapunov drift-plus-penalty method and provide optimality analysis of the algorithm. According to the Lyapunov drift-plus-penalty method, to solve the main problem we need to solve an optimization problem in each time slot which is a mixed integer non-convex optimization problem. We propose a low-complexity sub-optimal solution for this per-slot optimization problem that provides near-optimal performance and we evaluate the computational complexity of the solution. Numerical results illustrate the performance of the proposed dynamic control algorithm and the performance of the sub-optimal solution for the per-slot optimization problems versus the different parameters of the system. The results show that the proposed dynamic control algorithm achieves more than 60 % saving in the average total transmit power compared to a baseline policy.

[J8] M. Moltafet, M. Leinonen, and M. Codreanu, “Moment generating function of the AoI in two-source system with packet management,” IEEE Wireless Communications Letters, early access, 2021. (submitted Sep. 2020)

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Abstract:

We consider a status update system consisting of two independent sources and one server in which packets of each source are generated according to the Poisson process and packets are served according to an exponentially distributed service time. We derive the moment generating function (MGF) of the age of information (AoI) for each source in the system by using the stochastic hybrid systems (SHS) under two existing source-aware packet management policies which we term self-preemptive and nonpreemptive policies. In the both policies, the system (i.e., the waiting queue and the server) can contain at most two packets, one packet of each source; when the server is busy and a new packet arrives, the possible packet of the same source in the waiting queue is replaced by the fresh packet. The main difference between the policies is that in the self-preemptive policy, the packet under service is replaced upon the arrival of a new packet from the same source, whereas in the non-preemptive policy, this new arriving packet is blocked and cleared. We use the derived MGF to find the first and second moments of the AoI and show the importance of higher moments.

Conference Papers

[C1] M. Leinonen, M. Codreanu, and M. Juntti, “Practical compression methods for quantized compressed sensing,” in Proc. IEEE INFOCOM, SMILING Workshop, Paris, France, Apr. 29 - May 2 2019.

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Abstract:

In order to save energy of low-power sensors in Internet of Things applications, minimizing the number of bits to compress and communicate real-valued sources with a predefined distortion becomes crucial. In such a lossy source coding context, we study rate-distortion (RD) performance of various single-sensor quantized compressed sensing (QCS) schemes for compressing sparse signals via quantized/encoded noisy linear measurements. The paper combines and refines the recent advances of QCS algorithm designs and theoretical analysis. In particular, several practical symbol-by-symbol quantizer based QCS methods of different complexities relying on 1) compress-and-estimate, 2) estimate-and-compress, and 3) support-estimation-and-compress strategies are proposed. Simulation results demonstrate the RD performances of different schemes and compare them to the information-theoretic limits.

[C2] M. Jahandideh, M. Moltafet, M. Codreanu, and M. Latva-aho, “Low complexity sparse channel estimation for wideband mmWave systems: Multi-stage approach,” in Proc. IEEE Wireless Commun. and Networking Conf., Marrakech, Morocco, Apr. 15-19 2019.

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Abstract:

We consider the problem of channel estimation in hybrid transceiver architectures operating in millimeter wave (mmWave) band. Due to the dynamic features of the environment and the sensitivity of mmWave bands to blockage and deafness, it is important to estimate mmWave channels with a low complexity and high performance algorithm. In this regard, we exploit the sparse structure of the frequency-selective mmWave channels and formulate the channel estimation problem as a sparse signal reconstruction in frequency domain. In order to solve the estimation problem, we propose a multi-stage based low complexity algorithm. Simulation results show that the proposed algorithm significantly reduces the computational complexity while preserving the quality of the estimation.

[C3] M. Leinonen, M. Codreanu, and M. Juntti, “Signal reconstruction performance under quantized noisy compressed sensing,” in Proc. Data Compression Conference, Snowbird, UT, USA, Mar. 26-29 2019.

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Abstract:

We study rate-distortion (RD) performance of various single-sensor compressed sensing (CS) schemes for acquiring sparse signals via quantized/encoded noisy linear measurements, motivated by low-power sensor applications. For such a quantized CS (QCS) context, the paper combines and refines our recent advances in algorithm designs and theoretical analysis. Practical symbol-by-symbol quantizer based QCS methods of different compression strategies are proposed. The compression limit of QCS - the remote RDF - is assessed through an analytical lower bound and a numerical approximation method. Simulation results compare the RD performances of different schemes.

[C4] M. Moltafet, M. Leinonen, and M. Codreanu, “Closed-form expression for the average age of information in a multi-source M/G/1 queueing model,” in Proc. IEEE Inform. Theory Workshop, Visby, Gotland, Sweden, Aug.25-28 2019.

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Abstract:

In the context of the next generation wireless networks, freshness of status update packets is essential for enabling the services where a destination needs the most recent measurements of various sensors. In this paper, we study the information freshness of a multi-source MG1 first-come first-served (FCFS) queueing model, where each source independently generates status update packets according to a Poisson process. The information freshness of the status updates of each source is evaluated using the average age of information (AoI). To this end, we derive a closed-form expression for the average AoI of each source. As particular cases of our general expressions, we also derive closed-form expressions of the average AoI for both multi-source M/M/1 and single-source M/G/1 queueing models.

[C5] M. Moltafet, M. Leinonen, and M. Codreanu, “Worst case analysis of age of information in a shared-access channel,” in Proc Int. Symp. on Wireless Commun. Syst., Oulu, Finland, Aug.27-30 2019.

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Abstract:

Freshness of status update packets is essential for enabling a wide range of Internet of Things (IoT) applications. In this paper, we consider a status update system in which various sensors are assigned to transmit status update packets of a physical process to a desired destination. We consider that the sensors share a wireless channel and contend for the channel access based on a carrier sense multiple access (CSMA) method. We study freshness of the status update system at the destination using the age of information (AoI) metric. To this end, we analyze the worst case average AoI for each sensor in the CSMA-based system. Numerical results show that the AoI in the CSMA-based system may dramatically increase when the number of sensors increases. Moreover, we observe that the contention window size and the packet arrival rate must be optimized since they have a critical role in the performance of the system.

[C6] M. Hatami, M. Leinonen, and M. Codreanu, “Online caching policy with user preferences and time-dependent requests: A reinforcement learning approach,” in Proc. Annual Asilomar Conf. Signals, Syst., Comp., Pacific Grove, CA, Nov.3-6 2019.

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Abstract:

Content caching is a promising approach to reduce data traffic in the back-haul links. We consider a system where multiple users request items from a cache-enabled base station that is connected to a cloud. The users request items according to the user preferences in a time-dependent fashion, i.e., a user is likely to request the next chunk (item) of the file requested at a previous time slot. Whenever the requested item is not in the cache, the base station downloads it from the cloud and forwards it to the user. In the meanwhile, the base station decides whether to replace one item in the cache by the fetched item, or to discard it. We model the problem as a Markov decision process (MDP) and propose a novel state space that takes advantage of the dynamics of the users’ requests. We use reinforcement learning and propose a Q-learning algorithm to find an optimal cache replacement policy that maximizes the cache hit ratio without knowing the popularity profile distribution, probability distribution of items, and user preference model. Simulation results show that the proposed algorithm improves the cache hit ratio compared to other baseline policies.

[C7] M. Moltafet, M. Leinonen, M. Codreanu, and N. Pappas, “Power minimization in wireless sensor networks with constrained AoI using stochastic optimization,” in Proc. Annual Asilomar Conf. Signals, Syst., Comp., Pacific Grove, CA, Nov.3-6 2019.

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Abstract:

In this paper, we consider a system where multiple low-power sensors communicate timely information about a random process to a sink. The sensors share orthogonal subchannels to transmit such information in the form of status update packets. Freshness of the sensors’ information at the sink is characterized by the Age of Information (AoI), and the sensors can control the sampling policy by deciding whether to take a sample or not. We formulate an optimization problem to minimize the time average total transmit power of sensors by jointly optimizing the sampling action of each sensor, the transmit power allocation, and the subchannel assignment under the constraints on the maximum time average AoI and maximum power of each sensor. To solve the optimization problem, we use the Lyapunov drift-plus-penalty method. Numerical results show the performance of the proposed algorithm versus the different parameters of the system.

[C8] M. Leinonen and M. Codreanu, “Quantized compressed sensing via deep neural networks,” in Proc. 6G Wireless Summit, Levi, Finland, Mar.17-20 2020.

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Abstract:

Compressed sensing (CS) is an efficient technique to acquire sparse signals in many wireless applications to, e.g., reduce the amount of data and save low-power sensors’ batteries. This paper addresses efficient acquisition of sparse sources through quantized noisy compressive measurements where the encoder and decoder are realized by deep neural networks (DNNs). We devise a DNN based quantized compressed sensing (QCS) method aiming at minimizing the mean-square error of the signal reconstruction. Once trained offline, the proposed method enjoys extremely fast and low complexity decoding in the online communication phase. Simulation results demonstrate the superior rate-distortion performance of the proposed method compared to a polynomial-complexity QCS reconstruction scheme.

[C9] M. Moltafet, M. Leinonen, and M. Codreanu, “An approximate expression for the average AoI in a multi-source M/G/1 queueing model,” in Proc. 6G Wireless Summit, Levi, Finland, Mar.17-20 2020.

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Abstract:

Freshness of status update packets is essential for wide range of real-time Internet of things applications. In this paper, we study the information freshness of a single-server multi-source queueing model under a first-come first-served (FCFS) serving policy. In the considered model, each source independently generates status update packets according to a Poisson process. The information freshness of the status updates of each source is evaluated by the average age of information (AoI). We derive an approximate expression for the average AoI for a multi-source MG1 queueing model having a general service time distribution. Simulation results are provided to validate and assess the tightness of the proposed approximate expression for the average AoI in the M/G/1 queueing model where the service time follows a gamma distribution.

[C10] M. Moltafet, M. Leinonen, and M. Codreanu, “Average age of information for a multi-source M/M/1 queueing model with packet management and self-preemption in service,” in Proc. Int. Symp. on Modelling and Opt. in Mobile, Ad-hoc and Wireless Networks, Volos, Greece, Jun.15-19 2020.

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Abstract:

We consider an M/M/1 status update system consisting of two independent sources and one server. We derive the average age of information (AoI) of each source using the stochastic hybrid systems (SHS) technique under the following packet management with self-preemptive serving policy. The system can contain at most two packets with different source indexes at the same time, i.e., one packet under service and one packet in the queue. When the system is empty, any arriving packet immediately enters the server. When the server is busy at an arrival of a packet, the possible packet of the same source in the system (either waiting in the queue or being served) is replaced by the fresh packet. Numerical results illustrate the effectiveness of the proposed packet management with self-preemptive serving policy compared to several baseline policies.

[C11] M. Fountoulakis, N. Pappas, M. Codreanu, and A. Ephremides, “Optimal sampling cost in wireless networks with age of information constraints,” in Proc. IEEE INFOCOM, Age of Information Workshop, Toronto, Canada, Jul.6-9 2020.

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Abstract:

We consider the problem of minimizing the time average cost of sampling and transmitting status updates by users over a wireless channel subject to average Age of Information constraints (AoI). Errors in the transmission may occur and the scheduling algorithm has to decide if the users sample a new packet or attempt for retransmission of the packet sampled previously. The cost consists of both sampling and transmission costs. The sampling of a new packet after a failure imposes an additional cost in the system. We formulate a stochastic optimization problem with time average cost in the objective under time average AoI constraints. To solve this problem, we apply tools from Lyapunov optimization theory and develop a dynamic algorithm that takes decisions in a slot-by-slot basis. The algorithm decides if a user: a) samples a new packet, b) transmits the old one, c) remains silent. We provide optimality guarantees of the algorithm and study its performance in terms of time average cost and AoI through simulation results.

[C12] M. Moltafet, M. Leinonen, and M. Codreanu, “Average age of information in a multisource M/M/1 queueing model with LCFS prioritized packet management,” in Proc. IEEE INFOCOM, Age of Information Workshop, Toronto, Canada, Jul.6-9 2020.

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Abstract:

In this paper, we consider an M/M/1 status update system consisting of two independent sources, one server, and one sink. We consider the following last-come first-served (LCFS) prioritized packet management policy. When the system is empty, any arriving packet immediately enters the server; when the server is busy, a packet of a source waiting in the queue is replaced if a new packet of the same source arrives and the fresh packet goes at the head of the queue. We derive the average age of information (AoI) of the considered M/M/1 queueing model by using the stochastic hybrid systems (SHS) technique. Numerical results illustrate the effectiveness of the proposed packet management policy compared to several baseline policies.

[C13] M. Moltafet, M. Leinonen, and M. Codreanu, “Average age of information for a multi-source MM1 queueing model with packet management,” in Proc. IEEE Int. Symp. Inform. Theory, Los Angeles, California, USA, Jun.21-26 2020.

[download preprint]    [download slides]

Abstract:

We consider a status update system consisting of two independent sources, one server, and one sink. The packets of different sources are generated according to the Poisson process and the packets are served according to an exponentially distributed service time. We consider the following packet management policy. When the system is empty, any arriving packet immediately enters the server; when the server is busy, a packet of a source waiting in the queue is replaced if a new packet of the same source arrives. We derive the average age of information (AoI) of the considered MM1 queueing model by using the stochastic hybrid systems (SHS) technique. Numerical results are provided to show the effectiveness of the proposed policy.

[C14] M. Moltafet, M. Leinonen, and M. Codreanu, "An exact expression for the average AoI in a multi-source M/M/1 queueing model,’’ in Proc. IEEE Int. Symp. Pers., Indoor, Mobile Radio Commun., London, UK, Aug. 31–Sep. 3 2020.

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Abstract:

Information freshness is crucial in a wide range of wireless applications where a destination needs the most recent measurements of a remotely observed random process. In this paper, we study the information freshness of a single-server multi-source MM1 queueing model under a first-come first-served (FCFS) serving policy. The information freshness of the status updates of each source is evaluated by the average age of information (AoI). We derive an exact expression for the average AoI for the multi-source MM1 queueing model. Simulation results are provided to validate the derived exact expression for the average AoI.

[C15] M. Hatami, M. Jahandideh, M. Leinonen, and M. Codreanu, “Age-aware status update control for energy harvesting IoT sensors via reinforcement learning,” in Proc. IEEE Int. Symp. Pers., Indoor, Mobile Radio Commun., London, UK, Aug. 31–Sep. 3 2020.

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Abstract:

We consider an IoT sensing network with multiple users, multiple energy harvesting sensors, and a wireless edge node acting as a gateway between the users and sensors. The users request for updates about the value of physical processes, each of which is measured by one sensor. The edge node has a cache storage that stores the most recently received measurements from each sensor. Upon receiving a request, the edge node can either command the corresponding sensor to send a status update, or use the data in the cache. We aim to find the best action of the edge node to minimize the average long-term cost which trade-offs between the age of information and energy consumption. We propose a practical reinforcement learning approach that finds an optimal policy without knowing the exact battery levels of the sensors. Simulation results show that the proposed method significantly reduces the average cost compared to several baseline methods.

Meetings, Seminars, Research Visits, Workshops, and Conferences

  1. January 15, 2019: kick-off meeting at the Linköping University.

  2. February 8, 2019: research visit at the University of Oulu (Finland) to discuss potential research directions of common interest.

  3. March 26 – 29, 2019: project results ([C3]) presented at the Data Compression Conference (DCC 2019), Snowbird, UT, USA.

  4. April 15-19, 2019: project results ([C2]) presented at the IEEE Wireless Communications and Networking Conference (WCNC 2019), Marrakech, Morocco.

  5. April 1–4, 2019: organized a Compressed Sensing Special Session and presented project results at the Nordic Workshop on System and Network Optimization for Wireless (SNOW 2019), Ruka, Finland.

  6. April 29 – May 2, 2019: project results ([C1]) presented at the IEEE INFOCOM, SMILING Workshop, Paris, France.

  7. May 24, 2019: seminar at the University of Oulu (Finland) to discuss research results and future directions with the collaborators from the University of Oulu.

  8. June 25, 2019: project meeting at the Linköping University for intermediate evaluation of the results.

  9. August 25 – 28, 2019: project results ([C4]) presented at the IEEE Information Theory Workshop (ITW 2019), Visby, Gotland, Sweden.

  10. August 27 – 30, 2019: project results ([C5]) presented at the International Symposium on Wireless Communication Systems (ISWCS 2019), Oulu, Finland.

  11. August 30, 2019: seminar at the University of Oulu (Finland) to discuss research results and future directions with the collaborators from the University of Oulu.

  12. September 24, 2019: visit at Ericsson Site Linköping to discuss possible collaboration and transfer of results to local industry.

  13. September 30 – October 13: hosted Mohammad Hatami (doctoral student), research visitor from University of Oulu (Finland) involved in collaborative research related to the COMPRESS NETS.

  14. October 7 – 11: hosted Dr. Markus Leinonen, research visitor from University of Oulu (Finland) involved in collaborative research related to the COMPRESS NETS.

  15. October 7 – 20: hosted Mohammad Moltafet (doctoral student), research visitor from University of Oulu (Finland) involved in collaborative research related to the COMPRESS NETS.

  16. October 10, 2019: seminar at the Linköping University with guests (M. Hatami, M. Moltrafet and Dr. M. Leinonen) from University of Oulu (Finland).

  17. October 15 – 16, 2019: project results presented at the ELLIIT Annual Workshop, Blekinge Institute of Technology, Karlskrona, Sweden.

  18. November 3 – 6, 2019: project results ([C6], [C7]) presented at the Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, California, USA.

  19. January 9, 2019: project meeting at the Linköping University for intermediate evaluation at the mid-term.

  20. March 17 – 20, 2020: project results ([C8], [C9]) presented at the 6G Wireless Summit, Levi, Finland (virtual conference).

  21. March 30 – April 2, 2020: organized the Nordic Workshop on Systems and Network Optimization for Wireless communication (SNOW 2020); workshop program (cancelled due to COVID-19 outbreak)

  22. May 15, 2020: (online) meeting with Combitech and Nokia to discuss possible collaboration and transfer of results to local industry.

  23. May 22, 2020: online seminar with guests (M. Hatami, M. Moltrafet and Dr. M. Leinonen) from University of Oulu (Finland).

  24. June 15 – 19, 2020: project results ([C10]) presented at the International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt 2020), Volos, Greece (virtual conference).

  25. June 21 – 26, 2020: project results ([C13]) presented at the IEEE International Symposium on Information Theory (ISIT 2020), Los Angeles, California, USA (virtual conference).

  26. July 6 – 9, 2020: project results ([C11], [C12]) presented at the IEEE INFOCOM, Age of Information Workshop, Toronto, Canada (virtual conference).

  27. August 31 – September 3, 2020: project results ([C14], [C15]) presented at the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2020), London, UK (virtual conference).

  28. December 10, 2020: online meeting for final project evaluation.

  29. December 18, 2020: online seminar with guests (M. Hatami, M. Moltrafet and Dr. M. Leinonen) from University of Oulu (Finland).