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.
In @cite_27 , the authors introduce the metrics of Cost of Update Delay (CoUD) and Value of Information of Update (VoIU) in order to characterize the cost of having stale information at a remote destination and to capture the reduction of CoUD upon reception of an update respectively. The work in @cite_12 studied the optimal control of status updates from a source to a remote monitor. The authors showed that in some cases, the optimal policy is to wait for a certain time before submitting a new update. In @cite_1 the authors study the average AoI of a primary node and the throughput of multiple secondary nodes in a shared access network with priorities. In @cite_16 , the average AoI for an @math queueing system with packet deadlines is studied.
@cite_27 @cite_16 @cite_1 @cite_12
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We consider a real-time status update system consisting of a source-destination network. A stochastic process is observed at the source, and samples, so called status updates, are extracted at random time instances, and delivered to the destination. In this paper, we expand the concept of information ageing by introducing the Cost of Update Delay (CoUD) metric to characterize the cost of having stale information at the destination. We introduce the Value of Information of Update (VoIU) metric that captures the reduction of CoUD upon reception of an update. The importance of the VoIU metric lies on its tractability which enables an alternative performance criterion in status update systems. Scheduling of the transmission of status updates over an error-prone communication channel is studied in order to minimize the long-term average age of information (AoI) at the destination, under an average resource constraint at the source node, which limits the average number of transmissions. After each transmission, the source receives an instantaneous ACK NACK feedback, and decides on the next update, without a priori knowledge on the success of the future transmissions. The optimal scheduling policy is studied under different feedback mechanisms; in particular, standard automatic repeat request (ARQ) and hybrid ARQ (HARQ) protocols are considered. Average-cost reinforcement learning algorithms are proposed when the error probabilities for the HARQ system are unknown. In this paper, we consider a status monitoring system where an energy harvesting sensor continuously sends time-stamped status updates to a destination. With a non-zero probability, each update will be corrupted by noise and result in an updating failure. The destination keeps track of the system status through the successfully delivered updates. We assume that there is a perfect feedback channel between the destination and the source, so that the source is aware of the updating failures once they occur. With the feedback information, our objective is to design the optimal online status updating policy to minimize the expected long-term average Age of Information (AoI) at the destination, subject to the energy causality constraint at the sensor. We propose a status updating policy called Best-effort Uniform updating with Retransmission (BUR), under which the source tries to equalize the delay between two successful updates as much as possible, and retransmits an update immediately if the previous transmission fails. We show that the BUR policy achieves the minimum expected long-term average AoI among a broad class of online policies. The Age-of-Information (AoI) has recently been proposed as an important metric for investigating the timeliness performance in information-update systems. Prior studies on AoI optimization often consider a Push model, which is concerned about when and how to "push" (i.e., generate and transmit) the updated information to the user. In stark contrast, in this paper we introduce a new Pull model, which is more relevant for certain applications (such as the real-time stock quotes service), where a user sends requests to the servers to proactively "pull" the information of interest. Moreover, we propose to employ request replication to reduce the AoI. Interestingly, we find that under this new Pull model, replication schemes capture a novel tradeoff between different levels of information freshness and different response times across the servers, which can be exploited to minimize the expected AoI at the user's side. Specifically, assuming Poisson updating process at the servers and exponentially distributed response time, we derive a closed-form formula for computing the expected AoI and obtain the optimal number of responses to wait for to minimize the expected AoI. Finally, we conduct numerical simulations to elucidate our theoretical results. Our findings show that waiting for more than one response can significantly reduce the AoI in most scenarios.