Modeling Value of Information in remote sensing from correlated sources

Zancanaro, Alberto; Cisotto, Giulia; Badia, Leonardo

doi:10.1016/j.comcom.2023.03.008

Cited by 8 publications

(6 citation statements)

References 35 publications

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“…The sensed information may be correlated at different locations. We aim to take advantage of this correlation to decrease the number of useless transmissions but keep the average AoI as low as possible [ 15 , 16 , 27 ]. Particularly, in each time slot, we consider either of the following two possibilities: a sensor, e.g., sensor 1, senses a new sample of information and transmits the fresh sample to the central server, and this event is assumed to happen with a probability equal to p .…”

Section: Scenario and Methodologymentioning

confidence: 99%

“…Particularly, at each time slot, the probability that a sensor transmits is p . We investigate the behavior of the system in this setting using a Markov chain to model the average AoI of a sensor with a variable number of neighbors N [ 16 ], especially in case of poorly or strongly correlated information coming from different locations, i.e., sensors. The states of the Markov chain are used to model the AoI of a sensor and the transition represents its increase or decrease.…”

Section: Multiple Accessmentioning

confidence: 99%

“…From the point of view of the AoI an update due to an anomaly or a routine update have the same importance, which is undesirable whenever the scenario is supposed to provide some application in a smart living context. Correlation among multiple neighboring sources [ 13 , 14 , 15 , 16 ] is another important factor to consider when taking AoI into account since an update could also deliver extra information related to other data sources. This occurs especially in the case of uncoordinated sensors monitoring the same process (or correlated metrics of the same process) or in the simplest case of redundancy.…”

Section: Introductionmentioning

confidence: 99%

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Tackling Age of Information in Access Policies for Sensing Ecosystems

Zancanaro

Cisotto

Badia

2023

Sensors

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Recent technological advancements such as the Internet of Things (IoT) and machine learning (ML) can lead to a massive data generation in smart environments, where multiple sensors can be used to monitor a large number of processes through a wireless sensor network (WSN). This poses new challenges for the extraction and interpretation of meaningful data. In this spirit, age of information (AoI) represents an important metric to quantify the freshness of the data monitored to check for anomalies and operate adaptive control. However, AoI typically assumes a binary representation of the information, which is actually multi-structured. Thus, deep semantic aspects may be lost. In addition, the ambient correlation of multiple sensors may not be taken into account and exploited. To analyze these issues, we study how correlation affects AoI for multiple sensors under two scenarios of (i) concurrent and (ii) time-division multiple access. We show that correlation among sensors improves AoI if concurrent transmissions are allowed, whereas the benefits are much more limited in a time-division scenario. Furthermore, we discuss how ML can be applied to extract relevant information from data and show how it can further optimize the transmission policy with savings of resources. Specifically, we demonstrate, through simulations, that ML techniques can be used to reduce the number of transmissions and that classification errors have no influence on the AoI of the system.

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Section: Scenario and Methodologymentioning

confidence: 99%

Section: Multiple Accessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tackling Age of Information in Access Policies for Sensing Ecosystems

Zancanaro

Cisotto

Badia

2023

Sensors

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“…In general, the constraint on the accuracy is due to a communication bottleneck, which occurs due to limited bandwidth and energy, such that sensors need to reduce their transmissions as much as possible [23]. The selection of the subset of sensors that will transmit has been studied in this context [24], often considering the correlation between neighboring sensors' measurements [25]. 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].…”

Section: Related Workmentioning

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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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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“…The metric is particularly relevant for applications involving sporadic sensing of the environment, real-time decision-making processes and machine to machine (M2M) communication, to quantify how timely are the system reactions to variations in the system conditions. Relevant examples cover a wide array of applications of next generation communication scenarios, including smart Industry [4], eHealth [5], vehicular networks [6], and more applications of the Internet of Things (IoT) [7].…”

Section: Introductionmentioning

confidence: 99%

Impact of Transmission Delays Over Age of Information Under Finite Horizon Scheduling

Zancanaro,

Cisotto,

Munari

et al. 2023

2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

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Sensor communications in real-time systems may be required to report status updates to minimize the so-called age of information metric, which quantifies the freshness of exchanged data. This situation can be heavily impacted by the delays in data transmission, as some updates may reach the destination when their information content is already stale. In this paper, we consider the problem of scheduling sensing updates over a finite time horizon, and discuss the impact of their transmission delay on the resulting data freshness. We tackle the adjustments required for an offline schedule aimed at minimizing the average age of information which, as long as they do not cause the updates to go off the boundaries of the finite horizon, are only dependent on the average transmission delay. We derive a closed form expression for the average age of information, also verified through simulations, and the resulting performance is evaluated under different system conditions. This can be used to further explore the task of delivering timely system updates under general scenarios, e.g., when the statistics of the delay is not known a priori, or under other non idealities.

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Modeling Value of Information in remote sensing from correlated sources

Cited by 8 publications

References 35 publications

Tackling Age of Information in Access Policies for Sensing Ecosystems

Tackling Age of Information in Access Policies for Sensing Ecosystems

Goal-Oriented Scheduling in Sensor Networks With Application Timing Awareness

Impact of Transmission Delays Over Age of Information Under Finite Horizon Scheduling

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