Scheduling 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 a constraint on the average number of transmissions at the source node. After each transmission, the source receives an instantaneous ACK NACK feedback, and decides on the next update without prior knowledge on the success of future transmissions. First, the optimal scheduling policy is studied under different feedback mechanisms when the channel statistics are known; in particular, the standard automatic repeat request (ARQ) and hybrid ARQ (HARQ) protocols are considered. Then, for an unknown environment, an average-cost reinforcement learning (RL) algorithm is proposed that learns the system parameters and the transmission policy in real time. The effectiveness of the proposed methods are verified through numerical simulations.
  Age of information is a newly proposed metric that captures delay from an application layer perspective. The age measures the amount of time that elapsed from the moment the mostly recently received update was generated until the present time. In this paper, we study an age minimization problem over a wireless broadcast network with many users, where only one user can be served at a time. We formulate a Markov decision process (MDP) to find dynamic transmission scheduling schemes, with the purpose of minimizing the long-run average age. While showing that an optimal scheduling algorithm for the MDP is a simple stationary switch-type, we propose a sequence of finite-state approximations for our infinite-state MDP and prove its convergence. We then propose both optimal off-line and online scheduling algorithms for the finite-approximate MDPs, depending on knowledge of time-varying arrivals. We consider managing the freshness of status updates sent from a source (such as a sensor) to a monitoring node. The time-varying availability of energy at the sender limits the rate of update packet transmissions. At any time, the age of information is defined as the amount of time since the most recent update was successfully received. An offline solution that minimizes not only the time average age, but also the peak age for an arbitrary energy replenishment profile is derived. The related decision problem under stochastic energy arrivals at the sender is studied through a discrete time dynamic programming formulation, and the structure of the optimal policy that minimizes the expected age is shown. It is found that tracking the expected value of the current age (which is a linear operation), together with the knowledge of the current energy level at the sender side is sufficient for generating an optimal threshold policy. An effective online heuristic, Balance Updating (BU), that achieves performance close to an omniscient (offline) policy is proposed. Simulations of the policies indicate that they can significantly improve the age over greedy approaches. An extension of the formulation to stochastically formed updates is considered. Emerging applications rely on wireless broadcast to disseminate time-critical information. For example, vehicular networks may exchange vehicle position and velocity information to enable safety applications. The number of nodes in one-hop communication range in such networks can be very large, leading to congestion and undesirable levels of packet collisions. Earlier work has examined such broadcasting protocols primarily from a MAC perspective and focused on selective aspects such as packet error rate. In this work, we propose a more comprehensive metric, the average system information age, which captures the requirement of such applications to maintain current state information from all other nearby nodes. We show that information age is minimized at an optimal operating point that lies between the extremes of maximum throughput and minimum delay. Further, while age can be minimized by saturating the MAC and setting the CW size to its throughput-optimal value, the same cannot be achieved without changes in existing hardware. Also, via simulations we show that simple contention window size adaptations like increasing or decreasing the window size are unsuitable for reducing age. This motivates our design of an application-layer broadcast rate adaptation algorithm. It uses local decisions at nodes in the network to adapt their messaging rate to keep the system age to a minimum. Our simulations and experiments with 300 ORBIT nodes show that the algorithm effectively adapts the messaging rates and minimizes the system age. 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. Increasingly ubiquitous communication networks and connectivity via portable devices have engendered a host of applications in which sources, for example people and environmental sensors, send updates of their status to interested recipients. These applications desire status updates at the recipients to be as timely as possible; however, this is typically constrained by limited network resources. In this paper, we employ a time-average age metric for the performance evaluation of status update systems. We derive general methods for calculating the age metric that can be applied to a broad class of service systems. We apply these methods to queue-theoretic system abstractions consisting of a source, a service facility and monitors, with the model of the service facility (physical constraints) a given. The queue discipline of first-come-first-served (FCFS) is explored. We show the existence of an optimal rate at which a source must generate its information to keep its status as timely as possible at all its monitors. This rate differs from those that maximize utilization (throughput) or minimize status packet delivery delay. While our abstractions are simpler than their real-world counterparts, the insights obtained, we believe, are a useful starting point in understanding and designing systems that support real time status updates.