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.
The work in @cite_7 considers a sequential estimation and sensor scheduling problem in the presence of multiple communication channels. In @cite_8 , scheduling the transmission of status updates over an error-prone communication channel is studied in order to minimize the average AoI at the destination under a constraint on the average number of transmissions at the source node. The work in @cite_32 , introduced a deep reinforcement learning-based approach that can learn to minimize the AoI with no prior assumptions about network topology.
@cite_32 @cite_7 @cite_8
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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. In this paper, we consider a scenario where an energy harvesting sensor continuously monitors a system and sends time-stamped status updates to a destination. The destination keeps track of the system status through the received updates. We use the metric Age of Information (AoI), the time that has elapsed since the last received update was generated, to measure the “freshness” of the status information available at the destination. Our objective is to design optimal online status update policies to minimize the long-term average AoI, subject to the energy causality constraint at the sensor. We consider three scenarios, i.e., the battery size is infinite, finite, and one unit only, respectively. For the infinite battery scenario, we adopt a best-effort uniform status update policy and show that it minimizes the long-term average AoI. For the finite battery scenario, we adopt an energy-aware adaptive status update policy, and prove that it is asymptotically optimal when the battery size goes to infinity. For the last scenario where the battery size is one, we first show that within a broadly defined class of online policies, the optimal policy should have a renewal structure. We then focus on a renewal interval, and prove that the optimal policy should have a threshold structure, i.e., if the AoI in the system is below a threshold when an energy arrival enters an empty battery, the sensor should store the energy first and then update when the AoI reaches the threshold; otherwise, it updates the status immediately. Simulation results corroborate the theoretical bounds. We consider a wireless broadcast network with a base station sending time-sensitive information to a number of clients through unreliable channels. 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 transmission scheduling policy that minimizes the expected weighted sum AoI of the clients in the network. We first show that in symmetric networks a Greedy policy, which transmits the packet with highest current age, is optimal. For general networks, we develop three low-complexity scheduling policies: a randomized policy, a Max-Weight policy and a Whittle's Index policy, and derive performance guarantees as a function of the network configuration. To the best of our knowledge, this is the first work to derive performance guarantees for scheduling policies that attempt to minimize AoI in wireless networks with unreliable channels. Numerical results show that both Max-Weight and Whittle's Index policies outperform the other scheduling policies in every configuration simulated, and achieve near optimal performance.