Long-term Average Age Of Information Research Articles

AoI-Bounded Scheduling for Industrial Wireless Sensor Networks

Age of information (AoI) is an emerging network metric that measures information freshness from an application layer perspective. It can evaluate the timeliness of information in industrial wireless sensor networks (IWSNs). Previous research has primarily focused on minimizing the long-term average AoI of the entire system. However, in practical industrial applications, optimizing the average AoI does not guarantee that the peak AoI of each data packet is within a bounded interval. If the AoI of certain packets exceeds the predetermined threshold, it can have a significant impact on the stability of the industrial control system. Therefore, this paper studies the scheduling problem subject to a hard AoI performance requirement in IWSNs. First, we propose a low-complexity AoI-bounded scheduling algorithm for IWSNs that guarantees that the AoI of each packet is within a bounded interval. Then, we analyze the schedulability conditions of the algorithm and propose a method to decrease the peak AoI of nodes with higher AoI requirements. Finally, we present a numerical example that illustrates the proposed algorithm step by step. The results demonstrate the effectiveness of our algorithm, which can guarantee bounded AoI intervals (BAIs) for all nodes.

Importance-Aware Optimization for Age of Information in Multi-Cluster IoT Systems With Random Delays

Age of information (AoI) is a newly developed real-time metric that is frequently employed to assess the timeliness of received data. In this letter, we consider a multi-cluster Internet of Things system with random delays and develop scheduling policy to minimize the ratio of long-term average AoI to the importance of received data. Due to the strong coupling between inter-cluster channel allocation and intra-cluster link selection, traditional methods may be difficult to implement due to excessive action space. To overcome this issue, we develop a virtual queue-based sub-policy to make link selection decision, and then utilize deep reinforcement learning to design a master policy for channel allocation. The scheduling policy is obtained by embedding the sub-policy into the master policy for training. Simulation results show up to 55.7% performance improvement compared to the existing algorithms.

AoI Minimization for WSN Data Collection With Periodic Updating Scheme

In this paper, we consider the design of a wireless sensor network (WSN) that aims at monitoring the environment and collecting data periodically. In view of the limited energy and computational capability of the sensor nodes, a mobile edge computing (MEC) server is deployed in the WSN as a data processing unit. The goal of the design is to maintain the freshness of the data, which is characterized by the criterion of the age of information (AoI). Therefore, we analyze the long-term average AoI of the considered network. Then, the energy and time constraints for the WSN are modeled with consideration of transmission and computation. Next, a non-convex average AoI minimization problem is formulated subject to the energy and time constraints by jointly optimizing the sampling rate, computing scheduling, and transmit power. To tackle the challenging problem, the geometric programming and successive convex approximation (SCA) technique are applied to develop an algorithm with convergence guarantee. Moreover, to exhibit the benefits of the MEC server, a joint design is investigated for the WSN without the MEC server. Finally, the numerical results demonstrate the efficiency of our proposed SCA-based algorithm and show the impact of the sampling rate on the AoI performance.

Scheduling Schemes for Age Optimization in IoT Systems With Limited Retransmission Times

Age of Information (AoI) is a recently introduced metric to capture data freshness. In this article, we consider a multiuser single-destination Internet of Things (IoT) system with periodic state updating, and investigate the scheduling methods of minimizing the long-term average AoI with limited retransmission times. In view of the retransmission mode, the AoI optimization problems for retransmission with and without feedback are analyzed. For the retransmission without feedback, we formulate the expected decision loss (EDL) function of the AoI optimization problem with the finite retransmission times and propose a loss-greedy policy by minimizing the EDL at each step. For the retransmission with feedback, a potential optimal solution is to construct the AoI optimization problem as an infinite-horizon Markov decision process (MDP) and solve the corresponding Bellman optimal equations. However, this optimal solution is prone to suffer from the curse of dimensionality and is hard to implement. To address this issue, we decouple the infinite-horizon MDP into the finite-state MDP in each single frame, and then propose a low-complexity slot-based max-weight (SBMW) policy to minimize the long-term average AoI. Numerical results show that, compared with an ALOHA-like baseline policy, the proposed Loss-Greedy policy can achieve up to 48% reduction of the average AoI for the retransmission without feedback, while the SBMW policy can reduce the average AoI by 57% in the retransmission with feedback. The average performance gain of the proposed policies over the state-of-the-art policies is at least 10%.

Average Age of Information in Wireless Powered Relay Aided Communication Network

In order to satisfy timeliness requirements arising from environmental sensing applications, Age of Information (AoI) was proposed to characterize the freshness of the received updates. In this article, we consider a wireless powered relay aided communication network (WPRCN), in which a relay wirelessly powered by a hybrid-access point (H-AP) receives environmental monitoring information update from a sensor and forwards it to the H-AP. The long-term average AoI is studied since the decision correctness depends on the update timely uploaded by the relay. The wireless powered relay adopts either a decode-and-forward (DF) or an amplify-and-forward (AF) protocols, respectively, subject to the energy causality. We also consider the decoding cost of DF protocol owing to the relay’s limited energy storage. Since the expression of the average AoI is nonelementary, we propose a Taylor approximation-based algorithm to obtain its integral. We optimize the transmit power/the equivalent average power consumption of the relay for the sake of minimizing the average AoI in the whole WPRCN with different forwarding protocols. Our simulation results demonstrate the accuracy of the theoretical analysis, while the average AoI of the WPRCN is optimized by our power allocation scheme at the relay. The proposed algorithm is verified to achieve a great approximation effect.

Fairness for Freshness: Optimal Age of Information Based OFDMA Scheduling With Minimal Knowledge

It is becoming increasingly clear that an important task for wireless networks is to minimize the age of information (AoI), i.e., the timeliness of information delivery. While mainstream approaches generally rely on the real-time observation of user AoI and channel state, there has been little attention to solve the problem in a complete (or partial) absence of such knowledge. In this article, we present a novel study to address the optimal blind radio resource scheduling problem in orthogonal frequency division multiplexing access (OFDMA) systems towards minimizing long-term average AoI, which is proven to be the composition of time-domain-fair clustered round-robin and frequency-domain-fair intra-cluster sub-carrier assignment. Heuristic solutions that are near-optimal as shown by simulation results are also proposed to effectively improve the performance upon presence of various degrees of extra knowledge, e.g., channel state and AoI.

A Reinforcement Learning Approach to Age of Information in Multi-User Networks With HARQ

Scheduling the transmission of time-sensitive information from a source node to multiple users over error-prone communication channels is studied with the goal of minimizing the long-term average age of information (AoI) at the users. A long-term average resource constraint is imposed on the source, which limits the average number of transmissions. The source can transmit only to a single user at each time slot, and after each transmission, it receives an instantaneous ACK/NACK feedback from the intended receiver, and decides when and to which user to transmit the next update. Assuming the channel statistics are known, the optimal scheduling policy is studied for both the standard automatic repeat request (ARQ) and hybrid ARQ (HARQ) protocols. Then, a reinforcement learning (RL) approach is introduced to find a near-optimal policy, which does not assume any a priori information on the random processes governing the channel states. Different RL methods including average-cost SARSA with linear function approximation (LFA), upper confidence reinforcement learning (UCRL2), and deep Q-network (DQN) are applied and compared through numerical simulations.

Age of Information Driven Cache Content Update Scheduling for Dynamic Contents in Heterogeneous Networks

The recent development in mobile edge computing necessitates caching of dynamic contents, where new versions of contents become available around-the-clock, thus timely update is required to ensure their relevance. The age of information (AoI) is a performance metric that evaluates the freshness of contents. Existing works on AoI-optimization of cache content update algorithms focus on minimizing the long-term average AoI of all cached contents. Sometimes, user requests that need to be served in the future are known in advance and can be stored in user request queues. In this paper, we propose dynamic cache content update scheduling algorithms that exploit the user request queues. We consider a use case, where the trained neural networks (NNs) from deep learning models are being cached in a heterogeneous network (HetNet), as a motivating example. A queue-aware cache content update scheduling algorithm based on constrained Markov decision process (CMDP) is developed to minimize the average AoI of the dynamic contents delivered to the users. By using enforced decomposition technique and deep reinforcement learning, we propose two low-complexity suboptimal scheduling algorithms. Simulation results show that our proposed algorithms outperform the periodic cache content update scheme and reduce the average AoI by up to 30%.

Minimizing the Age of Information of Cognitive Radio-Based IoT Systems Under a Collision Constraint

This article considers a cognitive radio-based IoT monitoring system, consisting of an IoT device that aims to update its measurement to a destination using cognitive radio technique. Specifically, the IoT device as a secondary user (SIoT), seeks and exploits the spectrum opportunities of the licensed band vacated by its primary user (PU) to deliver status updates without causing visible effects to the licensed operation. In this context, the SIoT should carefully make use of the licensed band and schedule when to transmit to maintain the timeliness of the status update. The timeliness of the status update characterizes how the destination knows the latest information of the SIoT. We adopt a recent metric, Age of Information (AoI), to characterize the timeliness of the status update of the SIoT. We aim to minimize the long-term average AoI of the SIoT while satisfying the collision constraint imposed by the PU by formulating a constrained Markov decision process (CMDP) problem. We first prove the existence of optimal stationary policy of the CMDP problem. The optimal stationary policy (termed age-optimal policy) is shown to be a randomized simple policy that randomizes between two deterministic policies with a fixed probability. We prove that the two deterministic policies have a threshold structure and further derive the closed-form expression of average AoI and collision probability for the deterministic threshold-structured policy by conducting Markov Chain analysis. The analytical expression offers an efficient way to calculate the threshold and randomization probability to form the age-optimal policy. For comparison, we also consider the throughput maximization policy (termed throughput-optimal policy) and analyze the average AoI performance under the throughput-optimal policy in the considered system. Numerical simulations show the superiority of the derived age-optimal policy over the throughput-optimal policy. We also unveil the impacts of various system parameters on the corresponding optimal policy and the resultant average AoI.

AoI-Optimal Joint Sampling and Updating for Wireless Powered Communication Systems

This paper characterizes the structure of the Age of Information (AoI)-optimal policy in wireless powered communication systems while accounting for the time and energy costs of generating status updates at the source nodes. In particular, for a single source-destination pair in which a radio frequency (RF)-powered source sends status updates about some physical process to a destination node, we minimize the long-term average AoI at the destination node. The problem is modeled as an average cost Markov Decision Process (MDP) in which, the generation times of status updates at the source, the transmissions of status updates from the source to the destination, and the wireless energy transfer (WET) are jointly optimized. After proving the monotonicity property of the value function associated with the MDP, we analytically demonstrate that the AoI-optimal policy has a threshold-based structure w.r.t. the state variables. Our numerical results verify the analytical findings and reveal the impact of state variables on the structure of the AoI-optimal policy. Our results also demonstrate the impact of system design parameters on the optimal achievable average AoI as well as the superiority of our proposed joint sampling and updating policy w.r.t. the generate-at-will policy.

Average Age of Information With Hybrid ARQ Under a Resource Constraint

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 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. The optimal scheduling policy is first 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. The structural results are derived for the optimal policy under HARQ, while the optimal policy is determined analytically for ARQ. For the case of unknown environments, an average-cost reinforcement learning algorithm is proposed that learns the system parameters and the transmission policy in real time. The effectiveness of the proposed methods is verified through the numerical results.

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

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.