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

Sensor networks are an indispensable part of the Internet of Things (IoT), where sensors perform data acquisition and information processing tasks to obtain the parameters of interest so that IoT-based monitoring, diagnosis and other systems respond quickly to the changing conditions, instantaneous faults, etc. Distributed estimation algorithms are usually employed to estimate the parameters of interest in these IoT-based applications. However, when sensor networks have highly correlated input signals and nonstationary behavior in which the parameters of interest are time-varying, conventional distributed estimation algorithms suffer from severely degraded learning performance due to the large eigenvalue spread in the covariance matrix of the input signals and the random perturbation of the parameters of interest. To address these problems, this paper proposes two diffusion Bayesian subband adaptive filter (DBSAF) algorithms from a Bayesian learning perspective. As the highly-correlated input signal is whitened in a multiband structure and an estimate of the uncertainty in the parameters of interest is obtained by performing Bayesian inference, the proposed DBSAF algorithms are able to achieve better learning performance in comparison with the competing diffusion algorithms. The transient and steady-state mean square error performance of the proposed DBSAF algorithms are analyzed, and are verified by numerical simulations. A lower bound on the time-varying step-size is derived to maintain the optimal steady-state performance in nonstationary scenarios. A new method for the estimation of the noise variance is also proposed. Numerical simulations demonstrate the excellent learning performance of the proposed algorithms in comparison with benchmark algorithms.

Diffusion Bayesian Subband Adaptive Filters for Distributed Estimation Over Sensor Networks

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

Li {\it et al}. introduced coded distributed computing (CDC) scheme to reduce the communication load in general distributed computing frameworks such as MapReduce. They also proposed cascaded CDC schemes, where each output function is computed multiple times. Such schemes achieved the fundamental trade-off between computation load and communication load on homogeneous computing networks, where all the nodes have the same storage, computing and communication capabilities. However, these schemes require exponentially large numbers of input files and output functions when the number of computing nodes gets large. In this paper, we give a construction of cascaded CDC schemes based on placement delivery arrays (PDAs), which was introduced to study coded caching schemes. Consequently, based on known results of PDAs, several infinite classes of cascaded CDC schemes could be obtained. We show that, in many cases, $\frac{L}{L_{Li}} \leq 2.1$, where $L$ and $L_{Li}$ are the communication loads of our new scheme and the scheme derived by Li {\it et al}. Most importantly, the number of output functions in all the new schemes are only a factor of the number of computing nodes, and the number of input files in our new schemes is much smaller than that of input files in CDC schemes derived by Li {\it et al}.

Cascaded Coded Distributed Computing Schemes Based on Placement Delivery Arrays

Censored regression distributed functional link adaptive filtering algorithm over nonlinear networks

In the Internet of Things (IoT), asynchronous networks with varying topology are quite common Meanwhile, Gaussian noise and impulsive noise widely exist in asynchronous networks Existing works on distributed estimation problems in networks primarily consider fixed topologies and Gaussian noise Thus, these algorithms are not suitable for distributed parameter estimation in asynchronous networks To overcome this issue, we propose a distributed diffusion kernel risk-sensitive loss (d-KRSL) algorithm, which can achieve a good performance in asynchronous networks with varying topology, and maintains the robustness to both Gaussian and impulsive noise The mean and mean square performances of the proposed algorithm are analyzed theoretically and verified by numerical simulation results

A Robust Diffusion Estimation Algorithm for Asynchronous Networks in IoT

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

We study the average Age of Information (AoI) and peak AoI (PAoI) of a dual-queue status update system that monitors a common stochastic process. Although the double queue parallel transmission is instrumental in reducing AoI, the out of order of data arrivals also imposes a significant challenge to the performance analysis. We consider two settings: the M-M system where the service time of two servers is exponentially distributed; the M-D system in which the service time of one server is exponentially distributed and that of the other is deterministic. For the two dual-queue systems, closed-form expressions of average AoI and PAoI are derived by resorting to the graphic method and state flow graph analysis method. Our analysis reveals that compared with the single-queue system with an exponentially distributed service time, the average PAoI and the average AoI of the M-M system can be reduced by 33.3% and 37.5%, respectively. For the M-D system, the reduction in average PAoI and the average AoI are 27.7% and 39.7%, respectively. Numerical results show that the two dual-queue systems also outperform the M/M/2 single queue dual-server system with optimized arrival rate in terms of average AoI and PAoI.

Analysis of Age of Information in Dual Updating Systems

High-fat diet (HFD) exposure has been proven to impair vagus nerve function. However, it is not yet known whether the HFD challenge impacts vagal efferent-based intestinal cholinergic anti-inflammation activity. This investigation aims to evaluate the effect of HFD on intestinal cholinergic anti-inflammatory activity in mice. Mice with or without intracerebroventricular treatment with an antibody against toll-like receptor 4 (TLR4) were fed with HFD or standard chow for 2 weeks. Vagus nerve-based anti-inflammatory activity was analyzed by heart rate variability. Acetylcholine (ACh) content, nicotinic acetylcholine receptor α7 subtype (α7nAChR), and pro-inflammatory cytokines were analyzed by biochemical kits or qRT-PCR. HFD feeding mice exhibit a significant increase in high frequency (HF) and a decrease in the ratio of low frequency/HF, which were accompanied by lower ACh levels and α7nAChR mRNA expression in the intestinal segments. However, anti-TLR4 antibody-treated HFD mice showed normal ACh levels and α7nAChR mRNA expression in the intestinal segments. Moreover, TNF-α production in small intestine was significantly reduced in HFD + antibody group compared with HFD + vehicle group. Collectively, our present results reveal that HFD challenge depresses intestinal cholinergic anti-inflammatory activity, which is mediated by hypothalamic inflammation. Impairment of intestinal cholinergic anti-inflammatory pathway is the cause of intestinal low-grade inflammation by HFD consumption. 

Limei Hu

Papers

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

A Robust Diffusion Estimation Algorithm for Asynchronous Networks in IoT

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

Analysis of Age of Information in Dual Updating Systems

Short-term exposure of HFD depresses intestinal cholinergic anti-inflammatory activity through hypothalamic inflammation in mice.