Optimal Status Update for Age of Information Minimization With an Energy Harvesting Source

Abstract

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. We assume energy arrives randomly at the sensor according to aPoisson process, and each status update consumes one unit of energy. 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, i.e., the status update epochs form a renewal process, and the length of each renewal interval depends on the first energy arrival over that interval only. We then focus on a renewal interval, and prove that if the AoI in the system is below a threshold when the first energy arrives, the sensor should store the energy and hold status update until the AoI reaches the threshold; otherwise, it updates the status immediately. We analytically characterize the long-term average AoI under such a threshold-based policy, and explicitly identify the optimal threshold. Simulation results corroborate the theoretical bounds.

Optimizing Information Freshness in Wireless Networks: A Stochastic Geometry Approach

“…Moreover, the queueing stability shall be guaranteed in the average sense, as shown in (10), according to Loynes' theorem [39]. However, according to (5), we find the optimization problem (8) is hardly solvable because µ Φ 0 does not even possess an analytical expression. For that reason, we leverage the dominant system [40], in which every transmitter keeps sending out packets in each time slot (if one transmitter has an empty buffer at any given time slot, it sends out a dummy packet), for an approximation.…”

“…We next deal with the distribution of the conditional transmission success probability, µ Φ 0 , as defined in (4) 5 . It is worthwhile to mention that although we leverage the dominant system in Section 3 to devise the scheduling policy, the analysis presented in this section is conducted upon the original system which does not assume full buffer at the transmitters.…”

Context-Aware Information Lapse for Timely Status Updates in Remote Control Systems

“…Letting π = (π 1 , π 2 , · · · , π N ) be a stationary scheme of program (24), with which the i-th terminal is scheduled with probability π i at each time slot. Since scheme (28) minimizes the right-hand side of Eq.…”

How Often Should I Access My Online Social Networks?

“…Since social networks are currently one of the largest sources of traffic, recommendations on the rate at which users should access their pages can have cascading effects on the network as a whole. The age of information (AoI) has gained considerable visibility in the scientific community, and we envision that the contributions of this work are relevant in this new domain [2], [4], [7], [9], [23], [25], [38], [39].…”