Scheduling of Sensor Transmissions Based on Value of Information for Summary Statistics

Chiariotti, Federico; Kalør, Anders E.; Holm, Josefine; Soret, Beatriz; Popovski, Petar

doi:10.1109/lnet.2022.3172591

Cited by 6 publications

(11 citation statements)

References 17 publications

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“…Other scenarios in which VoI is used are data muling applications [26], in which drones, robots, or underwater vehicles need to physically move close to sensors to collect the information [27], and sensor placement problems, in which the issue is not to schedule transmissions, but rather to design the network to maximize accuracy and minimize cost [28]. Our own previous work [8] extends the definition of VoI from the MSE of the state to arbitrary functions, presenting a one-step optimal scheduling procedure. Another interesting development involves the modeling of the state of each sensor as a Markov chain, posing the polling problem as a POMDP [29] to identify sensors reporting abnormal values with the minimum energy expenditure [30].…”

Section: Related Workmentioning

confidence: 99%

“…In the system model in the figure, a Mobile Edge Computing (MEC)-enabled base station polls a set of sensors, which respond with their latest measurements. The edge node uses the data to estimate the overall state of the process measured by the sensors, and receives queries from client applications, which might be different functions of the state, e.g., the highest value among all sensors, or the number of sensors measuring values in a certain range [8]. In this work, we will consider that the edge node has enough computational power to run the estimation in real-time, but the interplay between the complexity of the scenario and the allocation of computing resources is an interesting problem that could complement our analysis [9].…”

Section: Introductionmentioning

confidence: 99%

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Goal-Oriented Scheduling in Sensor Networks With Application Timing Awareness

Holm

Chiariotti

Kalør

et al. 2023

IEEE Trans. Commun.

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Taking inspiration from linguistics, the communications theoretical community has recently shown a significant recent interest in pragmatic, or goal-oriented, communication. In this paper, we tackle the problem of pragmatic communication with multiple clients with different, and potentially conflicting, objectives. We capture the goal-oriented aspect through the metric of Value of Information (VoI), which considers the estimation of the remote process as well as the timing constraints. However, the most common definition of VoI is simply the Mean Square Error (MSE) of the whole system state, regardless of the relevance for a specific client. Our work aims to overcome this limitation by including different summary statistics, i.e., value functions of the state, for separate clients, and a diversified query process on the client side, expressed through the fact that different applications may request different functions of the process state at different times. A query-aware Deep Reinforcement Learning (DRL) solution based on statically defined VoI can outperform naive approaches by 15-20%.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Goal-Oriented Scheduling in Sensor Networks With Application Timing Awareness

Holm

Chiariotti

Kalør

et al. 2023

IEEE Trans. Commun.

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“…The importance of timely information in communication system has been studied extensively in the literature on the Age of Information (AoI) metric [7], [8], which measures the time elapsed since the generation of the last measurement received by the sink node. The AoI of pull-based transmission strategies has previously been studied in [2], and the related Value of Information (VoI) metric was analyzed in [9]. The use of wake-up radio to collect data has been studied, amongst others, in the context of Unmanned Aerial Vehicles (UAVs) [10], and to obtain the top-k values in a WSN [11].…”

Section: Introductionmentioning

confidence: 99%

Query Timing Analysis for Content-Based Wake-Up Realizing Informative IoT Data Collection

Shiraishi¹,

Kalør²,

Chiariotti³

et al. 2023

IEEE Wireless Commun. Lett.

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Information freshness and high energy-efficiency are key requirements for sensor nodes serving Industrial Internet of Things (IIoT) applications, where a sink node must collect informative data before a deadline to control an external element. Pull-based communication is an interesting approach for optimizing information freshness and saving wasteful energy. To this end, we apply Content-based Wake-up (CoWu), in which the sink can activate a subset of nodes observing informative data at the time that wake-up signal is received. In this case, the timing of the wake-up signal plays an important role: early transmission leads to high reliability in data collection, but the received data may become obsolete by the deadline, while later transmission ensures a higher timeliness of the sensed data, but some nodes might not manage to communicate their data before the deadline. This letter investigates the timing for data collection using CoWu and characterizes the gain of CoWu. The obtained numerical results show that CoWu improves accuracy, while reducing energy consumption by about 75% with respect to round-robin scheduling.

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“…The problem of scheduling IoT sensors based on Value of Information (VoI) under given underlying communication constraints has been considered in [7], [8], with the aim to minimize the MSE of the state estimate with imprecise measurements. The authors in [9] proposed a way to schedule sensing agents based on VoI to maximize the accuracy of various summary statistics of the state, which has potential applications in industrial automation or safety. The aforementioned works and related literature thereof [7]- [9], however, do not consider the incurred communication costs or evaluate the importance of each feature within the state space of PA.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in [9] proposed a way to schedule sensing agents based on VoI to maximize the accuracy of various summary statistics of the state, which has potential applications in industrial automation or safety. The aforementioned works and related literature thereof [7]- [9], however, do not consider the incurred communication costs or evaluate the importance of each feature within the state space of PA. Furthermore, measurement errors of sensing agents have not been taken into account, which is critical.…”

Section: Introductionmentioning

confidence: 99%

Scheduling Policy for Value-of-Information (VoI) in Trajectory Estimation for Digital Twins

Bui¹,

Pandey²,

Chiariotti³

et al. 2023

Preprint

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This paper presents an approach to schedule observations from different sensors in an environment to ensure their timely delivery and build a digital twin (DT) model of the system dynamics. At the cloud platform, DT models estimate and predict the system's state, then compute the optimal scheduling policy and resource allocation strategy to be executed in the physical world. However, given limited network resources, partial state vector information, and measurement errors at the distributed sensing agents, the acquisition of data (i.e., observations) for efficient state estimation of system dynamics is a non-trivial problem. We propose a Value of Information (VoI)-based algorithm that provides a polynomial-time solution for selecting the most informative subset of sensing agents to improve confidence in the state estimation of DT models. Numerical results confirm that the proposed method outperforms other benchmarks, reducing the communication overhead by half while maintaining the required estimation accuracy.

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Scheduling of Sensor Transmissions Based on Value of Information for Summary Statistics

Cited by 6 publications

References 17 publications

Goal-Oriented Scheduling in Sensor Networks With Application Timing Awareness

Goal-Oriented Scheduling in Sensor Networks With Application Timing Awareness

Query Timing Analysis for Content-Based Wake-Up Realizing Informative IoT Data Collection

Scheduling Policy for Value-of-Information (VoI) in Trajectory Estimation for Digital Twins

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