The Internet of Things (IoT) has emerged as one of the key features of the next-generation wireless networks, where timely delivery of status update packets is essential for many real-time IoT applications. Age of Information (AoI) is a new metric to measure the freshness of update. The reduction of the violation probability that AoI of status updates exceeds a given age constraint is of great significance for guaranteeing the information freshness in IoT systems. By modeling the IoT networks as M/M/1 and M/D/1 queuing systems, this work focuses on characterizing the violation probability of peak AoI and AoI in IoT systems, where a sensor delivers updates to a monitor under M/M/1 and M/D/1 queues with first-come–first-served policy. From a time-domain perspective, we explore the correlation between interdeparture time and system time, by which the closed-form expressions of peak AoI distribution and the violation probability for any AoI constraint are derived. The obtained results induce accurate characterizations for probability distribution functions of peak AoI and AoI. Consequently, accurate characterizations of average AoI and the variance of AoI are obtained. Then, for peak AoI and AoI, the optimal generation rate of the status update that induces the minimal violation probability is also found. The numerical results show that the optimal update rate can significantly reduce the AoI violation probability for a wide range of AoI constraints. The theoretical findings and predictions are verified by numerical simulation results as well as provide guidance for the design of IoT networks.

Status Update in IoT Networks: Age-of-Information Violation Probability and Optimal Update Rate

This article studies the unmanned aerial vehicle (UAV)-assisted wireless powered network, where a UAV is dispatched to wirelessly charge multiple ground nodes (GNs) by using radio frequency (RF) energy transfer and then the GNs use their harvested energy to upload the sensed information to the UAV. At each moment, the UAV is scheduled to charge the GNs or only one GN is scheduled to upload its data. An optimization problem is formulated to minimize the average Age of Information (AoI) of the GNs by jointly optimizing the trajectory of the UAV and the scheduling of information transmission and energy harvesting of GNs. As the problem is a combinational optimization problem with a set of binary variables, it is difficult to be solved. Thus, it is modeled as a Markov problem with large state spaces and a deep <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> network (DQN)-based scheme is proposed to find its near-optimal solution on the basis of the deep reinforcement learning (DRL) framework. Two nets are structured with artificial neural network (ANN), where one is for evaluating the reward of the action performed in current state, and the other is for predicting realistic action. The corresponding state spaces, the efficient action spaces, and reward function are designed. Simulation results demonstrate the convergence of the proposed DQN scheme, which also show that the proposed DQN scheme gets much smaller average AoI than the three other known schemes. Moreover, by involving the energy punishment in the reward, the UAV may save its energy but yield higher AoI. Additionally, the effects of the packet size, the transmit power, and the distribution area of GNs on the GNs&#x2019; average AoI are also discussed, which are expected to provide some useful insights. 

Average AoI Minimization in UAV-Assisted Data Collection With RF Wireless Power Transfer: A Deep Reinforcement Learning Scheme

The age of information (AoI) has attracted increasing attentions as a new metric and tool to capture the timeliness of reception and freshness of data, which is different from the conventional metric such as outage, delay, and throughput While most existing works focused on AoI analysis and optimizing method, the information update system design by utilizing diversity to reduce AoI is not fully exploited so far Inspired by this fact, a novel information status update system is designed by jointing wireless energy harvesting diversity, status update transmitting diversity, and hybrid automatic repeat request (HARQ) Specially, the update system consists of two sensors that can be seen as an unified entity from the destination’s perspective, and one destination The two sensors, which have finite-size batteries and synchronously operate in half-duplex mode, harvest energy from a Bernoulli process source, and cooperatively deliver the status update to destination by sharing a common information source To exploit the information of the unsuccessfully decoding update packet, the HARQ and chase combining (HARQ-CC) are jointly used at destination so that the HARQ-CC receiving diversity is also exploited By using Bernoulli process energy arrival probability and sensor transmission probability, we first propose one novel sensor energy harvesting and update delivering model that can be represented by Markov Chain (MC) and develop one novel construction method of the transition matrix of sensor battery energy states Considering the developed energy state transmission matrix column stochastic, irreducible, and aperiodic, we obtain the stationary distribution of sensor battery energy states so that the steady transmitting power is derived Secondly, the average AoI of the dual-sensor status update system is derived in terms of a closed-form expression by accurately calculating the statistical descriptions of the maximum retransmission number (time) in HARQ-CC and the service time of one packet The analytical results are validated by using the comparison from simulations Moreover, compared with traditional single sensor system, the dual-sensor system not only harvests more energy, but also has smaller average AoI because of the joint exploitation of multi-diversity Besides, the impact of the system parameters on harvested energy and average AoI is provided, which provides insights in terms of the optimal design of Internet of things system

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Improvement on Age of Information for Information Update Systems With HARQ Chase Combining and Sensor Harvesting-Transmitting Diversities

Cooperative non-orthogonal multiple access (C-NOMA) has been gradually discussed in the ultra-reliable and low-latency communication (URLLC) scenarios. The recent research indicates that when optimizing blocklength, selection combining the re-transmission packet cannot sufficiently exploit the potential of C-NOMA. To tackle this problem, we propose a packet re-management framework to transmit different packets through direct and relay links, therefore, propose an energy-efficient transmission scheme. By investigating power consumption of unequal-blocklength non-orthogonal multiple access with appropriate successive interference cancelation order, we jointly optimize blocklength, power, and re-managed packet size to minimize the power consumption, under URLLC requirements and the maximum power constraint. Simulation results verify that the proposed scheme can reduce power consumption.

Packet Re-Management-Based C-NOMA for URLLC: From the Perspective of Power Consumption

We consider a multi-source status update system, where each source generates status update packets according to a Poisson process which are then served according to a generally distributed service time. For this multi-source M/G/1/1 queueing model, we consider a self-preemptive packet management policy and derive the moment generating functions (MGFs) of the age of information (AoI) and peak AoI of each source. According to the policy, an arriving fresh packet preempts the possible packet of the same source in the system. Furthermore, we derive the MGFs of the AoI and peak AoI for the globally preemptive and non-preemptive policies, for which only the average AoI and peak AoI have been derived earlier. Finally, we use the MGFs to derive the average AoI and peak AoI in a two-source M/G/1/1 queueing model under each policy. Numerical results show the effect of the service time distribution parameters on the average AoI. The results also highlight the importance of higher moments of the AoI.

Moment Generating Function of Age of Information in Multisource M/G/1/1 Queueing Systems

The age of information (AoI) becomes a fashion and effective measurement for evaluating the timeliness and freshness of state updates in Internet of Things (IoT). The majority of existing works provide abundant insights for optimizing age through packet management. In this paper, the average AoI of a remote data transmission system in which the transmission process is modeled as a multiple stream M/M/1/1 non-preemptive queue process is considered. We first derive the exact theoretical expression of the average AoI of multiple stream M/M/1/1 non-preemptive queue and then extend this result to more general M/G/1/1 queues. Results suggest that the M/G/1/1 non-preemptive queue strategy can effectively improve the system performance. The comparison of preemption strategy and non-preemption strategy under different service processes shows that preemption of packets does not always lead to reduction of AoI, especially for the system with small coefficient of variance of service time. Moreover, it is found that given the same average service time, the M/G/1/1 queue with small coefficient of variance of service time performs better.

Age of Information In A Multiple Stream M/G/1/1 Non-preemptive Queue

Compared with orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA) has enormous advantages in enhancing fairness and spectral efficiency for downlink transmission. To further improve the performance of NOMA, cooperative NOMA (C-NOMA) has attracted attention while power allocation of C-NOMA is of great importance to ameliorate the system performance. To guarantee the requirements of ultra-reliable and low-latency communication (URLLC) nowadays, the code length is supposed to be short enough where finite blocklength (FBL) codes are adopted. This paper presents a two-user half-duplex downlink C-NOMA scheme with low latency constraint. To minimize the error probability, power allocation problems are formulated. For this purpose, the properties of power allocation to objective functions are investigated. First, the error probability is analysed in terms of signal-to-noise ratio. Afterwards, the convexity of the power allocation problems under certain conditions are shown and optimal algorithms that solve the problems are proposed. Numerical results demonstrate that the proposed C-NOMA with finite blocklength codes successfully achieve lower error probability with regard to the conventional NOMA.

Optimal Power Allocation of Cooperative NOMA with Finite Blocklength Codes

Internet of Things (IoT) has emerged as one of the key features of the next-generation wireless networks, where timely delivery of status update packets is essential for many real-time IoT applications. Age of Information (AoI) is a new metric to measure the freshness of update. Reduction of the violation probability that AoI of status updates exceeds a given age constraint is of great significance for guaranteeing the data freshness in IoT systems. This work focuses on characterizing the violation probability of AoI in IoT systems where a sensor delivers updates to a monitor under M/M/1 queue with first-come-first-served (FCFS) policy. By exploring the correlation between inter-departure time and system time, the closed-form expression of the violation probability for any AoI constraint is derived. The obtained result induces an accurate characterization of the probability distribution function of AoI. The optimal generation rate of the status update that induces the minimal violation probability is also found. Numerical results show that the optimal update rate can significantly reduce the AoI violation probability for a wide range of AoI constraints.

Optimal Status Update in IoT Systems: An Age of Information Violation Probability Perspective

Full-duplex (FD) is a promising technique to improve the spectrum efficiency in wireless communications. However, it is noteworthy that there is a game for both node performances in a two-way channel, since self-interference can not be eliminated completely in FD communications. In this paper, we investigate the node energy efficiency (EE) for FD two-way ultra-reliable short packet communications in an additive white Gaussian noise (AWGN) channel. We focus on two cases. One is that the two nodes are noncooperative, each node only intends to maximize its own EE at any moment. The other one is that two nodes are cooperative, i.e., either of them is willing to improve their optimal EE simultaneously. For the first case, we establish a Game in terms of node EE and design an iterative algorithm to search for a Nash Equilibrium. In the second case, we establish a convex optimization problem of node EE and find the optimal solution by a standard convex optimization algorithm. It is shown by numerical results that the iterative algorithm converges and each node can achieve a higher optimal EE with a lower transmit power if they cooperate with each other.

Min Wang

Papers

Status Update in IoT Networks: Age-of-Information Violation Probability and Optimal Update Rate

Age of Information In A Multiple Stream M/G/1/1 Non-preemptive Queue

Optimal Power Allocation of Cooperative NOMA with Finite Blocklength Codes

Optimal Status Update in IoT Systems: An Age of Information Violation Probability Perspective

On the Node Energy Efficiency of Full Duplex Two-way Ultra-Reliable Short Packet Communications