Online Energy-Efficient Scheduling for Timely Information Downloads in Mobile Networks

Abstract

We consider a mobile network where a mobile device is running an application that requires timely information. The information at the device can be updated by downloading the latest information through neighboring access points. The freshness of the information at the device is characterized by the recently proposed age of information. However, minimizing the age of information by frequent downloading increases power consumption of the device. In this context, an energyefficient scheduling algorithm for timely information downloads is critical, especially for power-limited mobile devices. Moreover, unpredictable movement of the mobile device causes uncertainty of the channel dynamics, which is even non-stationary within a finite amount of time for running the application. Thus, in this paper we devise a randomized online scheduling algorithm for mobile devices, which can move arbitrarily and run the application for any amount of time. We show that the expected total cost incurred by the proposed algorithm, including an age cost and a downloading cost, is (asymptotically) at most e/(e − 1) ≈ 1.58 times the minimum total cost achieved by an optimal offline scheduling algorithm.Index Terms-Age of information, scheduling algorithms, competitive online algorithms.

Minimizing the Sum of Age of Information and Transmission Cost under Stochastic Arrival Model

“…In both the stochastic and the generate at will model, the actual cost of transmission can be significant, such as in an IoT setting, where devices are small and have limited energy [27]- [30]. The problem of minimizing the linear sum of sampling and transmission cost in a multiple-node system with generate at will model is analyzed in [27], subject to meeting average AoI constraints, and an upper bound on the objective function is derived.…”

“…A greedy algorithm is proposed that is shown to be 2-competitive. In [30], a node is considered that can download fresh updates (immediately) if a neighboring access-point (AP) is available, and decrease its own instantaneous AoI to 0. The goal is to minimize the linear sum of AoI and downloading cost.…”

“…The goal is to minimize the linear sum of AoI and downloading cost. When the time-slots in which a neigboring AP is available is arbitrary, [30] proposes a randomized online algorithm that is e/(e − 1)-competitive.…”

Delay-Aware Wireless Network Coding in Adversarial Traffic

“…That is because, if a packet arrives at queue Q 2 , the relay has to transmit a coded packet; thus, stop updating those variables for transmitting an uncoded packet. Following the line in [25,Theorem 5], the randomized online scheduling algorithm associated with the revised Alg. 4 can also achieve the same expected competitive ratio of e e−1 when C is large enough.…”