Age of Information From Two Strategic Sources Analyzed via Game Theory

Analysis of Age of Information Under SR ARQ

“…For all the curves, the trend is linearly increasing; this is interesting because it shows that SR ARQ error control is effective in keeping the AoI from exploding when the erasure probability ε increases. For stand-alone data content (without sequential decoding and SR ARQ) [30], increasing the error rate ε causes the average AoI to follow (1 − ε) −1 , whose Taylor series for ε < 1 is 1 + ε + ε 2 + o(ε 2 ), i.e., it causes a superlinear increase. It seems that the interdependencies among multiple packets, while overall increasing the average AoI, also regularize its growth as a function of the erasure probability.…”

Modeling Value of Information in Remote Sensing from Correlated Sources

“…To this end, it is worth mentioning that we specifically focus on discrete time MC models, so our investigation can be seen as an extension of [17]. The use of such models allows for matrix-geometric approaches and is convenient in many scenarios, where a discrete time axis can be considered [23]. Also, this would make it immediate to merge these investigations with the special cases where the source or the channel follow an embedded MC [24]- [26].…”

Correlation of Multiple Strategic Sources Decreases Their Age of Information Anarchy

“…However, no previous game theoretic study focuses on queueing, and the interaction takes place among disconnected information sources, creating a mors tua vita mea situation [14], where updates sent by an agent are to detriment of the others, as they congest the queue at the receiver. Tripathi & Modiano [6] Abd-Elmagid & Dhillon [7] Yates & Kaul [8] Zhang et al [10] Badia & Munari [11] Saurav & Vaze [12] Miguélez et al [13] Buratto et al [15] Kalør & Popovski [16] Crosara et al [17] Badia [19] our contribution Yet, many sensing scenarios involve multiple sources from the same deployment, and whose data are correlated with one another [15]. This can be the case of cyberphysical systems used in smart agriculture, eHealth, or intelligent transportations: Multiple sensors can be placed at different locations to measure the same quantity, or physically located in the same spot but reporting on different, yet related, quantities (e.g., temperature and humidity, or multiple vital parameters) [16], [17].…”

“…This correlation can, and should, be taken into account, without any data exchange between the sensors, but relying on the presence of other sensors, as well as the statistical correlation in the information content [7]. The individual minimization of AoI calls for a game theoretic analysis, to assess whether the anarchical behavior of a distributed system can be efficient in terms of AoI [18], [19].…”