1,721,150 research outputs found
Intrinsic Price of Anarchy of Age of Information for Converging Sources with Individual Costs
No full textThis paper considers a sensor network for real-time monitoring, where multiple sensors can act as equivalent information sources for a common receiver. The sensing goal is to achieve minimal freshness of status updates at the receiver's end, which is captured through the metric known as age of information (AoI). A distributed uncoordinated management is applied to the sensors, so that they send their reports independently and according to a memoryless process. Thus, nodes share the common objective of minimizing AoI but at the same time they incur an individual transmission cost when sending their updates. The goal of the analysis is to evaluate the intrinsic price of anarchy of such a distributed management, even when the nodes are in the best possible conditions, i.e., the information is converging, free from errors and collisions, and fresher updates always pre-empt older ones
Partially Stateful Server Selection for Minimal Age of Information Scheduling Over a Finite Horizon
No full textWe consider a source reporting real-time information content over a finite horizon so as to obtain minimal age of information (AoI). We assume that the information content requires a computationally heavy handling, as typical of tasks involving AI-augmented interpretation. As such, scheduling an information update requires a processing can be either performed locally, or offloaded to a remote mobile edge computing (MEC) server shared by other sources. The former option is subject to a certain failure rate, whereas the latter is always successful, but taking a longer time subject to how many other similar sources use the remote server. Inspired by the literature for MEC offloading, we consider a partially stateful approach, where the scheduling decision is made according to the system state (comprising the current AoI, the number of updates available, and the current congestion at the remote server), whereas the server selection follows a randomized-alpha policy. Through a dynamic programming approach, we find that the optimal choice of the local processing rate, although dependent on the characteristics of the remote server, is relatively robust to its variations. Not only does this justify our approach, but it also highlight a practical low-complexity approach to draw meaningful considerations on server sharing in multi-source updating systems
Crowdsensing Strategies Inspired by Choir Management Analyzed via Game Theory
No full textIn this paper, we draw an analogy between crowd-sensing scenarios and the real life activity of singing in a choir. We identify some similarities, in particular for what concerns the role of the network coordinator and the choir director, as well as the common desirability of eliminating non-collaborative behavior (free-riding). Inspired by this comparison, we identify some strategies that the "director"can implement during "choir rehearsals"and we give a game theoretic analysis of their effectiveness. The general model is based on characterizing the willingness to undertake effort for the common task as a user's private type, which is compared to the contribution cost to decide whether to contribute or free-ride. Imposing some access penalty is known to reduce significantly the onset of free-riding, and we discuss possible ways to implement such a penalty, namely, we compare a probabilistic exclusion of free riders, as well as a multiplicative and an additive penalty to access, and we show the better effectiveness of the last strategy
Blockage-Peeking Game of Mobile Strategic Nodes in Millimeter Wave Communications
Full text linkGiven the importance of line-of-sight in mmWave communications, a strategic adversary can harm a transmission by obstructing the receiver, which in turn can react by trying to move around this hurdle. To expand on this point, we study one such scenario from the perspective of game theory, considering a mobile mmWave receiver and an adversary interacting strategically as players in a zero-sum game, where they want to maximize, or respectively minimize, the spectral efficiency of the communication. To do so, the adversary attempts at screening the receiver's line of sight as an obstacle, while the receiver can move around so as to avoid the blockage. We consider preset distances and the choices available to the players are to change their angular coordinates to go around each other. This is framed as a static game of complete information, for which we numerically find the Nash equilibrium in mixed strategies, drawing some interesting conclusions such as connecting it with the beamforming pattern of the transmitter
On the Anarchy of Multiple False Data Injectors for Age of Incorrect Information in Sensor Networks
No full textSensor networks, especially when deployed in a field with little supervision, are vulnerable to a broad range of attacks. In this paper, we study a scenario where multiple competitive adversaries inject false content in the sensed data with the intent of impairing network control. We use game theory to analyze the different behavior of adversaries acting independently or in a coordinated fashion. This analysis ultimately results in the evaluation of efficiency metrics for the utility of uncoordinated attackers, based on the Age of Incorrect Information (AoII), which is compared to the coordinated case. Our numerical results show that generally the lack of coordination is detrimental for the two attackers. With the exception of few edge cases, competition leads the attackers to be more concerned with prevailing over each other than actually compromising the system
Leveraging Random Access Techniques for Finite Horizon Uncoordinated Status Sensing
No full textThis study explores the minimization of age of information from multiple sources in a low-complexity scenario without centralized management. Multiple nodes transmit with-out coordination to a common sink with the objective of reporting status information about a monitored area (e.g., a forest, a greenhouse, or a field) for a finite horizon time window. We show how, although the problem is prone to high inefficiency in the solution, the exploitation of techniques inspired by random access at the multiplexing level of wireless networks, in particular introducing carrier sense over predefined operational points, enables considerable improvements. Further, we explore the sensitivity of these approaches to parameters such as the contention interval, the processing time, and the available offset over the schedule
Distributed and Timely Smart Microgrid Management Through Markov Games
No full textEnergy delivery within smart microgrids often requires prompt reaction to the system state. In the presence of multiple energy sources, inefficiency may arise due to their lack of coordination. In this paper, we frame the task of efficient energy management as a dynamic program, and we further expand it to the case of multiple agents. We combine this approach with game theory and we leverage the similarity between Markov games concerning information and energy exchanges in networks. This methodological motivation allows us to identify distributed control techniques for efficient energy delivery. Specifically, we highlight how a naive distributed and selfish control of individual nodes may be inefficient from a game theoretic perspective. Yet, a decentralized strategy that combines energy availability and global network cost as shared objectives can significantly improve the outcome, approaching the performance of a centralized resource allocation still in a distributed manner
Strategic Age of Information Under Different Correlation of Sources
No full textWe analyze a sensing system where multiple sources transmit status updates to a common receiver. We assume that the correlation of transmitted information allows updates from one source to enhance the information freshness of others. We study the objective of minimizing individual information staleness, quantified by the Age of Information (AoI), at the receiver's end. We evaluate both centralized and distributed optimization strategies. In the former case, we select the globally optimal transmission rates for each source to minimize the total average AoI of the system. For distributed optimization, sources are seen as players in a non-cooperative game of complete information, for which we compute the Nash equilibria. As an example, we consider a fixed correlation budget shared among two sources and evaluate the transmission rates depending on the specific level of correlation. Our results show that, under a centralized approach, it is convenient that only the source with more influential content transmits, while the other source reduces its data injection rate. In contrast, independent transmission in a distributed setup leads to greater congestion and higher average AoI. However, as correlation increases, the performance of the distributed system approaches that of the centralized model, indicating that decentralized management becomes effective in highly correlated scenarios
Massive Opportunistic Sensing with Limited Collaboration for Age of Information
No full textWe consider an Internet of thing scenario, where a set of sensors collect data and exchange them with a common receiver. We analyze their interaction, considering a shared goal to minimize Age of information at the receiver's side. We argue that a fully collaborative setup, albeit generally succeeding in this task at first, often leads to resource wastage in the long run. We try to achieve a similar level of cooperation through a purely opportunistic mechanism, in which nodes are driven by selfish objectives, but still aware of the ultimate goal of maximizing information freshness. We show how our proposed approach, allowing fewer nodes to participate in the task (up to one order of magnitude), results in a better resource management, still improving the long-term average age of information. At the same time, a target number of participating nodes can be set, e.g., to a given fraction of the network, by properly tuning the individual objectives and the communication costs
Status Update Scheduling in Remote Sensing under Variable Activation Delay
Full text linkSensor 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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