Active Status Update Packet Drop Control in an Energy Harvesting Node

Rafiee, Parisa; Özel, Ömur

doi:10.1109/spawc48557.2020.9154225

Cited by 16 publications

(7 citation statements)

References 21 publications

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“…Energy Harvesting Model: The energy supplier of the system harvests energy and stores it in a finite-capacity battery of E units of energy. Similarly to, e.g., [34], [35], we assume that the energy arrivals u(t) ∈ {0, 1} follows a Bernoulli process with parameter µ, i.e., Pr{u(t) = 1} = µ. Per occurrence of sampling and transmission, their cost will be deducted from the battery, thus, the evolution of the battery level at slot t, denoted by e(t) ∈ {0, .…”

Section: Sampling and Transmission Costsmentioning

confidence: 99%

Semantic-aware Sampling and Transmission in Real-time Tracking Systems: A POMDP Approach

Zakeri,

Moltafet,

Codreanu

2024

Preprint

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We address the problem of real-time remote tracking of a partially observable Markov source in an energy harvesting system with an unreliable communication channel. We consider both sampling and transmission costs. Different from most prior studies that assume the source is fully observable, the sampling cost renders the source partially observable. The goal is to jointly optimize sampling and transmission policies for two semantic-aware metrics: i) a general distortion measure and ii) the age of incorrect information (AoII). We formulate a stochastic control problem. To solve the problem for each metric, we cast a partially observable Markov decision process (POMDP), which is transformed into a belief MDP. Then, for both AoII under the perfect channel setup and distortion, we express the belief as a function of the age of information (AoI). This expression enables us to effectively truncate the corresponding belief space and formulate a finite-state MDP problem, which is solved using the relative value iteration algorithm. For the AoII metric in the general setup, a deep reinforcement learning policy is proposed to solve the belief MDP problem. Simulation results show the effectiveness of the derived policies and, in particular, reveal a non-monotonic switching-type structure of the real-time optimal policy with respect to AoI.

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Section: Sampling and Transmission Costsmentioning

confidence: 99%

Semantic-aware Sampling and Transmission in Real-time Tracking Systems: A POMDP Approach

Zakeri,

Moltafet,

Codreanu

2024

Preprint

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“…IPDs suffer frequent power failures due to a lack of available harvested energy. We do not explicitly consider energy as part of our system model, as in [9, Section V], [17], [20], rather assuming that the depleted energy will cause a number of random power failures. An amount of processing time L i,j clock ticks, where the i represents the overall cycle number and j the fail number within the cycle, will be wasted for each fail (since the updated system state including this processing has not been checkpointed from VM to NVM before failure).…”

Section: System Modelmentioning

confidence: 99%

Data Freshness in Mixed-Memory Intermittently-Powered Systems

Broadhead¹,

Pawełczak²

2021

Preprint

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Age of Information (AoI) is a key metric to understand data freshness in Internet of Things (IoT) devices. In this paper we analyse an intermittently-powered IoT sensorwith mixed-memory (volatile and non-volatile) architecturethat uses a Time-Dependent Checkpointing (TDC) scheme. We derive the average Peak Age of Information (PAoI) and average AoI of the system, and use these metrics to understand which device parameters most significantly influence performance. We go on to consider how the average PAoI of a mixed-memory system compares with entirely volatile or entirely non-volatile architecture, and also introduce an alternative TDC strategy to improve system resilience in unpredictable environmental conditions.

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“…At the same time, other sensors may be used for closed loop control, such as regulating temperature or humidity in a room, by activating HVAC systems. In this case, the delay between the ambient parameters changing and the sensor detecting their variation also depends on the effectiveness of the control systems adopted [10].…”

Section: Introductionmentioning

confidence: 99%

Status Update Scheduling in Remote Sensing Under Variable Activation Delay

Badia

Munari

2023

2023 International Balkan Conference on Communications and Networking (BalkanCom)

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Sensor data exchanges in IoT applications can experience a variable delay due to changes in the communication environment and sharing of processing capabilities. This variability can impact the performance and effectiveness of the systems being controlled, and is especially reflected on age of information (AoI), a performance metric that quantifies the freshness of updates in remote sensing. In this paper, we discuss the quantitative impact of a variable activation delay on AoI. We consider an offline scheduling over a finite horizon, and we show the main role of the first and second order moments of the activation delay. Our analysis gives a quantitative boundary on when such term can be neglected and also prompts possible further investigations that can be used to mitigate the increase of AoI and improve the overall performance.

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Active Status Update Packet Drop Control in an Energy Harvesting Node

Cited by 16 publications

References 21 publications

Semantic-aware Sampling and Transmission in Real-time Tracking Systems: A POMDP Approach

Semantic-aware Sampling and Transmission in Real-time Tracking Systems: A POMDP Approach

Data Freshness in Mixed-Memory Intermittently-Powered Systems

Status Update Scheduling in Remote Sensing Under Variable Activation Delay

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