Optimizing Information Freshness in a Multiple Access Channel With Heterogeneous Devices

Chen, Zheng; Παππάς, Νικόλαος; Björnson, Emil; Larsson, Erik G.

doi:10.1109/ojcoms.2021.3062678

Cited by 41 publications

(29 citation statements)

References 40 publications

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“…The work [16] investigated AoI-optimal scheduling in a cognitive radio EH system. In [17], the authors studied age-optimal scheduling under stability constraints in a multiple access channel with two heterogeneous nodes (including an EH node) transmitting to a common destination. In [18], the sensor monitors a stochastic process and tracks its evolution and thereby, a modified definition of AoI is proposed to account for the discrepancy in the remote destination.…”

Section: B Related Workmentioning

confidence: 99%

“…where Jπ R,µ is the average number of command actions under policy π R,µ , which is calculated using (17). From (17) and the fact that (P3) is decoupled into K per-sensor problems (P4), Jπ R,µ is calculated as Jπ R,µ = 1 K K k=1 Jπ R,µ,k , where Jπ R,µ,k denotes the per-sensor time average number of command actions under the per-sensor policy π R,µ,k , which is defined as…”

Section: A Cmdp Formulationmentioning

confidence: 99%

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On-Demand AoI Minimization in Resource-Constrained Cache-Enabled IoT Networks with Energy Harvesting Sensors

Hatami¹,

Leinonen²,

Chen³

et al. 2022

Preprint

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We consider a resource-constrained IoT network, where multiple users make on-demand requests to a cache-enabled edge node to send status updates about various random processes, each monitored by an energy harvesting sensor. The edge node serves users' requests by deciding whether to command the corresponding sensor to send a fresh status update or retrieve the most recently received measurement from the cache. Our objective is to find the best actions of the edge node to minimize the average age of information (AoI) of the received measurements upon request, i.e., average on-demand AoI, subject to per-slot transmission and energy constraints. First, we derive a Markov decision process model and propose an iterative algorithm that obtains an optimal policy. Then, we develop an asymptotically optimal low-complexity algorithm -termed relax-then-truncate -and prove that it is optimal as the number of sensors goes to infinity. Simulation results illustrate that the proposed relax-then-truncate approach significantly reduces the average on-demand AoI compared to a request-aware greedy (myopic) policy and also depict that it performs close to the optimal solution even for moderate numbers of sensors. Index TermsAge of information (AoI), energy harvesting (EH), constrained Markov decision process (CMDP).

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Section: B Related Workmentioning

confidence: 99%

Section: A Cmdp Formulationmentioning

confidence: 99%

On-Demand AoI Minimization in Resource-Constrained Cache-Enabled IoT Networks with Energy Harvesting Sensors

Hatami¹,

Leinonen²,

Chen³

et al. 2022

Preprint

Self Cite

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“…It is assumed that the jammer does not have a constant source of power supply, but it has energy harvesting ability. The energy arrival process at the jammer is modeled by a Bernoulli process where the rate of energy arrival is assumed to be δ [32]- [36]. These chunks are stored in a battery (an energy buffer).…”

Section: A Network Layer Modelmentioning

confidence: 99%

Performance Analysis of a MIMO System with Bursty Traffic in the presence of Energy Harvesting Jammer

Allipuram¹,

Mohapatra²,

Παππάς³

et al. 2021

Preprint

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This paper explores the role of multiple antennas in mitigating jamming attacks for the Rayleigh fading environment with exogenous random traffic arrival. The jammer is assumed to have energy harvesting ability where energy arrives according to Bernoulli process. The outage probabilities are derived with different assumptions on the number of antennas at the transmitter and receiver. The outage probability for the Alamouti space-time code is also derived. The work characterizes the average service rate for different antenna configurations taking into account of random arrival of data and energy at the transmitter and jammer, respectively. In many practical applications, latency and timely updates are of importance, thus, delay and Average Age of Information (AAoI) are the meaningful metrics to be considered. The work characterizes these metrics under jamming attack. The impact of finite and infinite energy battery size at the jammer on various performance metrics is also explored. Two optimization problems are considered to explore the interplay between AAoI and delay under jamming attack. Furthermore, our results show that Alamouti code can significantly improve the performance of the system even under jamming attack, with less power budget. The paper also demonstrates how the developed results can be useful for multiuser scenarios.

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“…For the second moment, the computations lead to a more involved expression, shown at the top of the next page in (18). Finally, using ( 16), (17), and ( 18), together with ( 7) and ( 8), one can substitute in (10) and evaluate the long-term average AoI achieved with a γ-threshold policy.…”

Section: Lemmamentioning

confidence: 99%

“…with E ∆ (j) given by ( 16) after replacing λ d with λ d,j , and the first and second moments of w max{e (κ) , d (κ) } given by ( 17) and (18), respectively, after replacing λ d with λ d,κ .…”

Section: The Multiple Sources Casementioning

confidence: 99%

Timely Updating with Intermittent Energy and Data for Multiple Sources over Erasure Channels

Daniel¹,

Arafa²

2021

Preprint

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A status updating system is considered in which multiple data sources generate packets to be delivered to a destination through a shared energy harvesting sensor. Only one source's data, when available, can be transmitted by the sensor at a time, subject to energy availability. Transmissions are prune to erasures, and each successful transmission constitutes a status update for its corresponding source at the destination. The goal is to schedule source transmissions such that the collective longterm average age-of-information (AoI) is minimized. AoI is defined as the time elapsed since the latest successfully-received data has been generated at its source. To solve this problem, the case with a single source is first considered, with a focus on threshold waiting policies, in which the sensor attempts transmission only if the time until both energy and data are available grows above a certain threshold. The distribution of the AoI is fully characterized under such a policy. This is then used to analyze the performance of the multiple sources case under maximum-age-first scheduling, in which the sensor's resources are dedicated to the source with the maximum AoI at any given time. The achievable collective longterm average AoI is derived in closed-form. Multiple numerical evaluations are demonstrated to show how the optimal threshold value behaves as a function of the system parameters, and showcase the benefits of a threshold-based waiting policy with intermittent energy and data arrivals.

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Optimizing Information Freshness in a Multiple Access Channel With Heterogeneous Devices

Cited by 41 publications

References 40 publications

On-Demand AoI Minimization in Resource-Constrained Cache-Enabled IoT Networks with Energy Harvesting Sensors

On-Demand AoI Minimization in Resource-Constrained Cache-Enabled IoT Networks with Energy Harvesting Sensors

Performance Analysis of a MIMO System with Bursty Traffic in the presence of Energy Harvesting Jammer

Timely Updating with Intermittent Energy and Data for Multiple Sources over Erasure Channels

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