A large body of applications that involve monitoring, decision making, and forecasting require timely status updates for their efficient operation. Age of Information (AoI) is a newly proposed metric that effectively captures this requirement. Recent research on the subject has derived AoI optimal policies for the generation of status updates and AoI optimal packet queueing disciplines. Unlike previous research we focus on low-end devices that typically support monitoring applications in the context of the Internet of Things. We acknowledge that these devices host a diverse set of applications some of which are AoI sensitive while others are not. Furthermore, due to their limited computational resources they typically utilize a simple First-In First-Out (FIFO) queueing discipline. We consider the problem of optimally controlling the status update generation process for a system with a source-destination pair that communicates via a wireless link, whereby the source node is comprised of a FIFO queue and two applications, one that is AoI sensitive and one that is not. We formulate this problem as a dynamic programming problem and utilize the framework of Markov Decision Processes to derive optimal policies for the generation of status update packets. Due to the lack of comparable methods in the literature, we compare the derived optimal policies against baseline policies, such as the zero-wait policy, and investigate the performance of all policies for a variety of network configurations. Results indicate that existing status update policies fail to capture the trade-off between frequent generation of status updates and queueing delay and thus perform poorly.
Next we present works that focus on scheduling. The work in @cite_2 considers a wireless broadcast network with a base station sending time-sensitive information to a number of nodes. A discrete-time decision problem is formulated to find a scheduling policy that minimizes the expected weighted sum of AoI for all nodes in the network. The authors in @cite_31 consider a stream of status updates where each update is either of high priority or an ordinary one. Then, a transmission policy that treats updates depending on their priority is considered. The arrival processes of the two kinds of updates are modeled as independent Poisson processes while the service times are modeled as two exponentials. The work in @cite_17 considers a problem of sampling a Wiener process, the samples are forwarded to a remote estimator via a channel that consists of a queue with random delay. The estimator reconstructs a real-time estimate of the signal. The optimal sampling strategy that minimizes the mean square estimation error subject to a sampling frequency constraint is studied.
@cite_31 @cite_17 @cite_2
2783520079 2582952306 2588468001
Consider a stream of status updates generated by a source, where each update is of one of two types: priority or ordinary; these updates are to be transmitted through a network to a monitor. We analyze a transmission policy that treats updates depending on their content: ordinary updates are served in a first- come first-served fashion, whereas the priority updates receive preferential treatment. An arriving priority update discards and replaces any currently-in-service priority update, and preempts (with eventual resume) any ordinary update. We model the arrival processes of the two kinds of updates as independent Poisson processes and the service times as two (possibly different rate) exponentials. We find the arrival and service rates under which the system is stable and give closed-form expressions for average peak age and a lower bound on the average age of the ordinary stream. We give numerical results on the average age of both streams and observe the effect of each stream on the age of the other. In this paper, we consider a problem of sampling a Wiener process, with samples forwarded to a remote estimator via a channel with queueing and random delay. The estimator reconstructs a real-time estimate of the signal from causally received samples. Motivated by recent research on Age-of-Information, we study the optimal sampling strategy for minimizing the mean square estimation error subject to a sampling-rate constraint. We prove that the optimal sampling strategy is a threshold policy, and find the optimal threshold. This threshold is determined by the sampling-rate constraint and how much the Wiener process varies during the channel delay. An unexpected consequence is that even in the absence of the sampling-rate constraint, the optimal strategy is not zero-wait sampling in which a new sample is taken once the previous sample is delivered; rather, there is a non-zero waiting time before taking the next sample. Further, if the sampling times are independent of the observed Wiener process, the optimal sampling problem reduces to an age-of-information optimization problem that has been recently solved. Our comparisons show that the estimation error of the optimal sampling policy is much smaller than those of age-optimal sampling, zero-wait sampling, and classic uniform sampling. We consider a wireless broadcast network with a base station sending time-sensitive information to a number of clients. The Age of Information (AoI), namely the amount of time that elapsed since the most recently delivered packet was generated, captures the freshness of the information. We formulate a discrete-time decision problem to find a scheduling policy that minimizes the expected weighted sum AoI of the clients in the network. To the best of our knowledge, this is the first work to provide a scheduling policy that optimizes AoI in a wireless network with unreliable channels. The results are twofold: first, we show that a Greedy Policy, which transmits the packet with highest current age, is optimal for the case of symmetric networks. Then, for the general network case, we establish that the problem is indexable and obtain the Whittle Index in closed-form. Numerical results are presented to demonstrate the performance of the policies.