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The Age-of-Information (AoI) is a new performance metric recently proposed for measuring the freshness of information in information-update systems. In this work, we conduct a systematic and comparative study to investigate the impact of scheduling policies on the AoI performance in single-server queues and provide useful guidelines for the design of AoI-efficient scheduling policies. Specifically, we first perform extensive simulations to demonstrate that the update-size information can be leveraged for achieving a substantially improved AoI compared to non-size-based (or arrival-time-based) policies. Then, by utilizing both the update-size and arrival-time information, we propose three AoI-based policies. Observing improved AoI performance of policies that allow service preemption and that prioritize informative updates, we further propose preemptive, informative, AoI-based scheduling policies. Our simulation results show that such policies empirically achieve the best AoI performance among all the considered policies. Interestingly, we also prove sample-path equivalence between some size-based policies and AoI-based policies. This provides an intuitive explanation for why some size-based policies, such as Shortest-Remaining-Processing-Time (SRPT), achieve a very good AoI performance. 

We consider the problem of scheduling real-time traffic with hard deadlines in a wireless ad hoc network. In contrast to existing real-time scheduling policies that merely ensure a minimal timely throughput, our design goal is to provide guarantees on both the timely throughput and data freshness in terms of age-of-information (AoI), which is a newly proposed metric that captures the "age" of the most recently received information at the destination of a link. The main idea is to introduce the AoI as one of the driving factors in making scheduling decisions. We first prove that the proposed scheduling policy is feasibility-optimal, i.e., satisfying the per-traffic timely throughput requirement. Then, we derive an upper bound on a considered data freshness metric in terms of AoI, demonstrating that the network-wide data freshness is guaranteed and can be tuned under the proposed scheduling policy. Interestingly, we reveal that the improvement of network data freshness is at the cost of slowing down the convergence of the timely throughput. Extensive simulations are performed to validate our analytical results. Both analytical and simulation results confirm the capability of the proposed scheduling policy to improve the data freshness without sacrificing the feasibility optimality. 

With the rapid growth of Internet of Things (IoT) applications in recent years, there is a strong need for wireless uplink scheduling algorithms that determine when and which subset of a large number of users should transmit to the central controller. Different from the downlink case, the central controller in the uplink scenario typically has very limited information about the users. On the other hand, collecting all such information from a large number of users typically incurs a prohibitively high communication overhead. This motivates us to investigate the development of an efficient and low-overhead uplink scheduling algorithm that is suitable for large-scale IoT applications with limited amount of coordination from the central controller. Specifically, we first characterize a capacity outer bound subject to the sampling constraint where only a small subset of users are allowed to use control channels for system state reporting and wireless channel probing. Next, we relax the sampling constraint and propose a joint sampling and transmission algorithm, which utilizes full knowledge of channel state distributions and instantaneous queue lengths to achieve the capacity outer bound. The insights obtained from this capacity-achieving algorithm allow us to develop an efficient and low-overhead scheduling algorithm that can strictly satisfy the sampling constraint with asymptotically diminishing throughput loss. Moreover, the throughput performance of our proposed algorithm is independent of the number of users, a highly desirable property in large-scale IoT systems. Finally, we perform extensive simulations to validate our theoretical results. 

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.