On the Age of Information in Status Update Systems With Packet Management

By Maice Costa, Marian Codreanu, Anthony Ephremides. IEEE Transactions on Information Theory, 2015

638 citations recorded by the source corpus.

We consider a communication system in which status updates arrive at a source node, and should be transmitted through a network to the intended destination node. The status updates are samples of a random process under observation, transmitted as packets, which also contain the time stamp to identify when the sample was generated. The age of the information available to the destination node is the time elapsed, since the last received update was generated. In this paper, we model the source-destination link using the queuing theory, and we assume that the time it takes to successfully transmit a packet to the destination is an exponentially distributed service time. We analyze the age of information in the case that the source node has the capability to manage the arriving samples, possibly discarding packets in order to avoid wasting network resources with the transmission of stale information. In addition to characterizing the average age, we propose a new metric, called peak age, which provides information about the maximum value of the age, achieved immediately before receiving an update.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Epidemiology

View Paper: https://www.semanticscholar.org/paper/893d05fe3677ad1e63c0aaa0e7bed83745664f22

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Citations in the corpus

Age of Information: A New Concept, Metric, and Tool (2018, methodology): “In [12] the performance of the three packet management schemes has been evaluated and compared with some of the models discussed previously.”
Optimal Status Update for Age of Information Minimization With an Energy Harvesting Source (2017, background): “A related metric, Peak Age of Information (PAoI), is introduced in [12] and [13], and has”
Age of Information: An Introduction and Survey (2020, methodology): “These strategies, identified as packet management [18], [19], include the M/M/1/1 queue that blocks and clears a new arrival while the server is busy, the M/M/1/2 queue that will queue one waiting packet but blocks an arrival when the waiting space is occupied, and the M/M/1/2∗ queue that will preem”
Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks (2018, methodology): “Evolution of hk when the Greedy policy is employed in a network with M = 5 clients, T = 2 slots per frame, error-free channels, pi = 1, ∀i, and h1 = [7 5 4 2 2]T .”
On the Role of Age of Information in the Internet of Things (2018, methodology): “Note that this is a generalization of the single source-destination pair model considered in most prior works on AoI in the literature [2], [3], [5], [7], [11].”
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications (2022, neutral): “applications in a broad range of system settings under a variety of queuing disciplines [286], [287], [288], [289], [290], [291], [292], [293], [294], [295], [296], it does not explicitly take into account the similarity or discrepancy between the status of information at the transmitter and the mon”
The Age of Information: Real-Time Status Updating by Multiple Sources (2016, methodology): “[14], [15] analyzed AoI and PAoI for M/M/1/1 and M/M/1/2 queues that discard arriving updates if the system is full and also for a third queue, dubbed M/M/1/2*, in which an arriving update would preempt a waiting update.”
UAV-Assisted Data Collection for Internet of Things: A Survey (2022, background): “The above parameters about queuing delay need to be modified according to the queuing models, such as first-come– first-service (FCFS), last-come–first-service (LCFS), prioritybased, and limited buffer strategies [41], [42].”