Update Packets Research Articles

Is Multichannel Access Useful for Timely Information Update?

This paper investigates information freshness of multichannel access in information update systems. Age of information (AoI) is a fundamentally important metric to characterize information freshness, defined as the time elapsed since the generation of the last successfully received update. When multiple devices share the same wireless channel to send updates to a common receiver, an interesting question is whether dividing the whole channel into several subchannels will lead to better AoI performance. Given the same frequency band, dividing it into different numbers of subchannels lead to different transmission times and packet error rates (PER) of short update packets, thus affecting information freshness. We focus on a multichannel access system where different devices take turns to transmit with a cyclic schedule repeated over time. We first derive the average AoI by estimating the PERs of short packets. Then we examine bounded AoI, for which the instantaneous AoI is required to be below a threshold a large percentage of the time. Simulation results indicate that multichannel access can provide low average AoI and uniform bounded AoI simultaneously across different received powers. Overall, our investigations provide insights into practical designs of multichannel access systems with AoI requirements.

Scaling Laws for Age of Information in Wireless Networks

We study age of information in a multiple source-multiple destination setting with a focus on its scaling in large wireless networks. There are n nodes uniformly and independently distributed on a fixed area that are randomly paired with each other to form n source-destination (S-D) pairs. Each source node wants to keep its destination node as up-to-date as possible. To accommodate successful communication between all n S-D pairs, we first propose a three-phase transmission scheme which utilizes local cooperation between the nodes along with what we call mega update packets to serve multiple S-D pairs at once. We show that under the proposed scheme average age of an S-D pair scales as O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/4</sup> logn) as the number of users, n, in the network grows. Next, we observe that communications that take place in Phases I and III of the proposed scheme are scaled-down versions of network-level communications. With this along with scale-invariance of the system, we introduce hierarchy to improve this scaling result and show that when hierarchical cooperation between users is utilized, an average age scaling of O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α(h)</sup> logn) per-user is achievable, where h denotes the number of hierarchy levels and α(h) = 1/3·2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</sup> +1. We note that α(h) tends to 0 as h increases, and asymptotically, the average age scaling of the proposed hierarchical scheme is O(logn). To the best of our knowledge, this is the best average age scaling result in a status update system with multiple S-D pairs.

Age-Delay Tradeoffs in Queueing Systems

We consider an m server system in which each server can service at most one update packet at a time. The system designer controls (1) scheduling - the order in which the packets get serviced, (2) routing - the server that an arriving update packet joins for service, and (3) the service time distribution with fixed service rate. Given a fixed update generation process, we prove a strong age-delay and age-delay variance tradeoff, wherein, as the average AoI approaches its minimum, the packet delay and its variance approach infinity. In order to prove this result, we consider two special cases of the m server system, namely, a single server system with last come first served with preemptive service and an infinite server system. In both these cases, we derive sufficient conditions to show that three heavy tailed service time distributions, namely Pareto, log-normal, and Weibull, asymptotically minimize the average AoI as their tail gets heavier, and establish the age-delay tradeoff results. We provide an intuitive explanation as to why such a seemingly counter intuitive age-delay tradeoff is natural, and that it should exist in many systems.

Throughput Maximization With an Average Age of Information Constraint in Fading Channels

In emerging fifth generation and beyond wireless communication systems, communication nodes are expected to support information flows that are freshness-sensitive , along with broadband traffic having high data rate requirements. Freshness-sensitive flows, where freshness is quantified by a metric called the age of information (AoI), are naturally assigned priority over resources. Motivated by this, we consider long-term average throughput maximization in a single user fading channel, subject to constraints on average AoI and power, and knowledge of channel state information at the transmitter (CSIT), which is the realization of channel power gains. We consider two scenarios: (i) when Perfect CSIT is available and (ii) when CSIT is not available. In both scenarios, the channel distribution information is available. We consider a generate-at-will model, in which update packets can be generated in any block of interest, at the transmitter. We propose simple age-independent stationary randomized policies (AI-SRP), which allocate powers at the transmitter based only on the channel state and/or distribution information, without any knowledge of the AoI. We show that the optimal long-term average throughputs achieved by the AI-SRPs are equal to at least half of the throughputs achieved by optimal policies, independent of all the parameters of the problem. Furthermore, we provide an expression that bounds the difference in throughputs achieved by the optimal policies and AI-SRPs. Finally, we provide extensive numerical results to illustrate the performance of AI-SRPs.

Timely Distributed Computation With Stragglers

We consider a status update system in which the update packets need to be processed to extract the embedded useful information. The source node sends the acquired information to a computation unit (CU) which consists of a master node and n worker nodes. The master node distributes the received computation task to the worker nodes. Upon computation, the master node aggregates the results and sends them back to the source node to keep it updated. We investigate the age performance of uncoded and coded (repetition coded, MDS coded, and multi-message MDS (MM-MDS) coded) schemes in the presence of stragglers under i.i.d. exponential transmission delays and i.i.d shifted exponential computation times. We show that asymptotically MM-MDS coded scheme outperforms the other schemes. Furthermore, we characterize the optimal codes such that the average age is minimized.

On the Age of Information in Multi-Source Queueing Models

Freshness of status update packets is essential for enabling services where a destination needs the most recent measurements of various sensors. In this paper, we study the information freshness of single-server multi-source queueing models under a first-come first-served (FCFS) serving policy. In the considered model, each source independently generates status update packets according to a Poisson process. The information freshness of the status updates of each source is evaluated by the average age of information (AoI). We derive an exact expression for the average AoI for the case with exponentially distributed service time, i.e., for a multi-source M/M/1 queueing model. Moreover, we derive three approximate expressions for the average AoI for a multi-source M/G/1 queueing model having a general service time distribution. Simulation results are provided to validate the derived exact average AoI expression, to assess the tightness of the proposed approximations, and to demonstrate the AoI behavior for different system parameters.

A Reinforcement Learning Framework for Optimizing Age of Information in RF-Powered Communication Systems

In this paper, we study a real-time monitoring system in which multiple source nodes are responsible for sending update packets to a common destination node in order to maintain the freshness of information at the destination. Since it may not always be feasible to replace or recharge batteries in all source nodes, we consider that the nodes are powered through wireless energy transfer (WET) by the destination. For this system setup, we investigate the optimal online sampling policy (referred to as the age-optimal policy ) that jointly optimizes WET and scheduling of update packet transmissions with the objective of minimizing the long-term average weighted sum of Age of Information (AoI) values for different physical processes (observed by the source nodes) at the destination node, referred to as the sum-AoI . To solve this optimization problem, we first model this setup as an average cost Markov decision process (MDP) with finite state and action spaces. Due to the extreme curse of dimensionality in the state space of the formulated MDP, classical reinforcement learning algorithms are no longer applicable to our problem even for reasonable-scale settings. Motivated by this, we propose a deep reinforcement learning (DRL) algorithm that can learn the age-optimal policy in a computationally-efficient manner. We further characterize the structural properties of the age-optimal policy analytically, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. We extend our analysis to characterize the structural properties of the policy that maximizes average throughput for our system setup, referred to as the throughput-optimal policy . Afterwards, we analytically demonstrate that the structures of the age-optimal and throughput-optimal policies are different. We also numerically demonstrate these structures as well as the impact of system design parameters on the optimal achievable average weighted sum-AoI.

Design and Evaluation of Flooding-Based Location Service in Vehicular Ad Hoc Networks.

Location-based routing protocols for vehicular ad hoc networks (VANETs) use location information to determine routing decisions. This information is provided by a location service that is queried by nodes in order to properly forward packets to communication partners. This paper presents the semiflooding location service, a proactive flooding-based location service that drastically reduces the number of update packets sent over the network compared to traditional flooding-based location services. This goal is achieved by each node partially forwarding location information. We present both deterministic and probabilistic approaches for this algorithm, which remains very simple. A mathematical model is proposed to show the effectiveness of this solution. The cases of homogeneous 1D, 2D, and 3D networks were studied for both deterministic and probabilistic forwarding decisions. We compare our algorithm with simple flooding and with the multipoint-relay (MPR) flooding of the optimized-link-state-routing (OLSR) protocol, and we show that our algorithm, despite being very simple, has excellent scalability properties. The mean number of generated messages ranges with the mean number of the neighbors of one random network node.

Information Update: TDMA or FDMA?

This letter studies information freshness in information update systems operated with TDMA and FDMA. Information freshness is characterized by a recently introduced metric, age of information (AoI), defined as the time elapsed since the generation of the last successfully received update. In an update system with multiple users sharing the same wireless channel to send updates to a common receiver, how to divide the channel among users affects information freshness. We investigate the AoI performances of two basic multiple access schemes, TDMA and FDMA. We first derive the time-averaged AoI by estimating the packet error rate of short update packets based on Gallager’s random coding bound. For time-critical systems, we further define a new AoI metric, termed bounded AoI, which corresponds to an AoI threshold for the instantaneous AoI. Specifically, the instantaneous AoI is below the bounded AoI a large percentage of the time. We give a theoretical upper bound for bounded AoI. Our simulation results are consistent with our theoretical analysis. Although TDMA outperforms FDMA in terms of average AoI, FDMA is more robust against varying channel conditions since it gives a more stable bounded AoI across different received powers. Overall, our findings give insight to the design of practical multiple access systems with AoI requirements.

On the Age of Information in Wireless Networks Using Rateless Codes

The information freshness, quantified by the emerging idea of Age of Information (AoI), has recently received widespread attentions from both academia and industry. However, most prior works on AoI considered a single source-destination pair and provided the performance analysis merely through queueing-theoretic models, which do not lend themselves to the analysis of large-scale wireless networks. This paper focuses on the AoI analysis in a large-scale setting using random spatial models in stochastic geometry. Specifically, we first adopt rateless coding for packet delivery to improve the transmission reliability while reducing the transmission delay. Then, to quantify the enhancement of the information freshness by rateless coding, we derive the average peak age of information (PAoI) in Poisson bipolar and cellular networks, which characterizes the maximum value of AoI immediately before an update packet is received accounting for the spatial variation of network nodes and temporal dynamics of service packets. With these results, we further give the conditions that make the average PAoI to be finite in each type of the network. Numerical results show the significant benefits of rateless codes relative to the commonly used fixed-rate codes in terms of the PAoI and the feasible region of system parameters such as the traffic arrival rate, transmit probability, etc., in Poisson bipolar and cellular networks.

Optimal Policies for Status Update Generation in an IoT Device With Heterogeneous Traffic

A large body of applications that involve monitoring, decision making, and forecasting require timely status updates for their efficient operation. Age of Information (AoI) is a newly proposed metric that effectively captures this requirement. Recent research on the subject has derived AoI optimal policies for the generation of status updates and AoI optimal packet queueing disciplines. Unlike previous research, we focus on low-end devices that typically support monitoring applications in the context of the Internet of Things. We acknowledge that these devices host a diverse set of applications some of which are AoI sensitive while others are not. Furthermore, due to their limited computational resources, they typically utilize a simple first-in–first-out (FIFO) queueing discipline. We consider the problem of optimally controlling the status update generation process for a system with a source–destination pair that communicates via a wireless link, whereby the source node is composed of a FIFO queue and serves two applications, one that is AoI sensitive and one that is not. We formulate this problem as a dynamic programming problem and utilize the framework of Markov decision processes to derive the optimal policy for the generation of status update packets. Due to the lack of comparable methods in the literature, we compare the derived optimal policies against baseline policies such as the zero-wait policy. Results indicate that the baseline policy fails to capture the complex system dynamics that determine the relationship between the frequency of status update generation and the resulting queueing delay and thus perform poorly. To the best of our knowledge, the derived optimal policy does not exhibit a simple structure; thus, we utilized the baseline policies, whose operation is intuitive, to gain insight into the inner workings of the optimal policy.

Age-Optimal Power Control for Status Update Systems With Packet-Based Transmissions

This letter investigates the average age of information (AoI) minimization in status update systems in which the update packets are transmitted with fixed rate. It is assumed that the source avoids queue-induced delay by generating and sending a new status update packet in each time slot. Assuming that the transmit power can be adapted based on the number of successive negative acknowledgments (NACKs) received, a closed-form expression for the average AoI is derived considering transmissions over block fading channel models. An optimization problem for minimizing the average AoI under average power constraints is formulated and a stochastic optimization algorithm for obtaining a feasible solution is proposed. Numerical results on the proposed power control policy show that more power is preferred to be allocated to packets after a certain number of failure packets in the low-power regime and the proposed power control reduces the average AoI compared to the constant power policy. An alternative on–off power control policy is also proposed and shown to achieve satisfactory performance.

Performance Analysis of Delay-Constrained Traffic in a Cognitive Radio Network With RF Energy Harvesting

We consider a cognitive radio network where a secondary user (SU) tries to opportunistically access the channel time unused by the primary users (PUs) to transmit real-time status update packets with delivery deadline. For the radio frequency (RF)- powered SU, the channel occupancy times of the PUs are opportunities to harvest energy, whereas the idle channel times are opportunities to transmit data. Thus, there is a balance characterizing the SU performance in terms of channel occupancy probability of the PUs. We investigate the timely-delivery probability of data packets for the RF-powered SU. Specifically, we reveal the impact of the PU's channel occupancy probability on the timely-delivery probability and packet sojourn time.

On the Role of Age of Information in the Internet of Things

In this article, we provide an accessible introduction to the emerging idea of Age of Information (AoI) that quantifies freshness of information and explore its possible role in the efficient design of freshness-aware Internet of Things (IoT). We start by summarizing the concept of AoI and its variants with emphasis on the differences between AoI and other well-known performance metrics in the literature, such as throughput and delay. Building on this, we explore freshness-aware IoT design for a network in which IoT devices sense potentially different physical processes and are supposed to frequently update the status of these processes at a destination node (e.g., a cellular base station). Inspired by recent interest, we also assume that these IoT devices are powered by wireless energy transfer by the destination node. For this setting, we investigate the optimal sampling policy that jointly optimizes wireless energy transfer and scheduling of update packet transmissions from IoT devices with the goal of minimizing long-term weighted sum-AoI. Using this, we characterize the achievable AoI region. We also compare this AoI-optimal policy with the one that maximizes average throughput (throughput-optimal policy), and demonstrate the impact of system state on their structures. Several promising directions for future research are also presented.

Worst Case Age of Information in Wireless Sensor Networks: A Multi-Access Channel

Freshness of status update packets is essential for enabling a wide range of applications in wireless sensor networks (WSNs). Accordingly, we consider a WSN where sensors communicate status updates to a destination by contending for the channel access based on a carrier sense multiple access (CSMA) method. We analyze the worst case average age of information (AoI) and average peak AoI from the view of one sensor in a system where all the other sensors have a saturated queue. Numerical results illustrate the importance of optimizing the contention window size and the packet arrival rate to maximize the information freshness.

TOPVISOR: Two‐level controller‐based approach for service advertisement and discovery in vehicular cloud network

SummaryVehicular cloud is a kind of mobile cloud in which vehicles share their resources and provide services for each other. The first step in establishing a vehicular cloud network is the service advertisement and discovery. Due to the dynamic nature of vehicular cloud networks, services are not continuous and the service location may vary at any time depending on the vehicle location. Therefore, higher network traffic is generated to access the consistent and up‐to‐date information. In this paper, a two‐level hierarchical approach is proposed for service advertisement and discovery in vehicular cloud networks. To register the services' specification in this approach, the distributed directories in RSUs and central controllers are used. Moreover, a method is used to avoid extra update packets by localizing the updates. The simulation results show improvement in the packet delivery rate as well as a reduction in transmission bandwidth preventing the network congestion.

Optimizing Information Freshness in Computing-Enabled IoT Networks

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. To provide users with context-aware services and lighten the transmission burden, the raw data usually needs to be preprocessed before being transmitted to the destination. However, the effect of computing on the overall information freshness is not well understood. In this work, we first develop an analytical framework to investigate the information freshness, in terms of peak age of information (PAoI), of a computing enabled IoT system with multiple sensors. Specifically, we model the procedure of computing and transmission as a tandem queue, and derive the analytical expressions of the average PAoI for different sensors. Based on the theoretical results, we formulate a min-max optimization problem to minimize the maximum average PAoI of different sensors. We further design a derivative-free algorithm to find the optimal updating frequency, with which the complexity for checking the convexity of the formulated problem or obtaining the derivatives of the object function can be largely reduced. The accuracy of our analysis and effectiveness of the proposed algorithm are verified with extensive simulation results.

Virtually Zero Delay Interaction Between Online Game Players Using Kalman Filter-Based Dead Reckoning with Density and Distance Gain Control Adaptation

Online 3D games require fast and efficient user interaction support over the network environments, and the networking support is usually implemented by the use of a network game engine. The network game engine should minimize the network delay and mitigate the network traffic congestion. To minimize the network traffic between game users, a client-based prediction (dead reckoning (DR) algorithm) is used. Each game entity uses the algorithm to estimate its own movement as well as the others’. In case the estimation error exceeds the threshold, the entity sends an UPDATE packet which includes velocity, position and the like to other entities. As the estimation accuracy is increased, each entity can minimize the transmission of the UPDATE packet. In this paper, a Kalman filter-based approach is proposed in order to improve the prediction accuracy and an adaptive Kalman gain control in order to minimize the number of UPDATE packets to distant devices. The BZFlag game was used in the experiment in order to verify the proposed approach and the results have shown that it is possible to increase prediction accuracy and reduce the network traffic by 12%.

Age of information in multihop multicast networks

We consider the age of information in a multihop multicast network where there is a single source node sending time-sensitive updates to nL end nodes, and L denotes the number of hops. In the first hop, the source node sends updates to n first-hop receiver nodes, and in the second hop each first-hop receiver node relays the update packets that it has received to n further users that are connected to it. This network architecture continues in further hops such that each receiver node in hop I is connected to n further receiver nodes in hop l +1. We study the age of information experienced by the end nodes, and in particular, its scaling as a function of n. We show that, using an earliest k transmission scheme in each hop, the age of information at the end nodes can be made a constant independent of n. In particular, the source node transmits each update packet to the earliest k1 of the n first-hop nodes, and each first-hop node that receives the update relays it to the earliest k2 out of n second-hop nodes that are connected to it and so on. We determine the optimum kl stopping value for each hop l for arbitrary shifted exponential link delays.

CURE: Consistent Update With Redundancy Reduction in SDN

In this paper, we address the issue of rule duplication during network updates in software-defined networking (SDN). In SDN, network update involves the controller in sending update packets to desired set of switches, where the update rules are installed. To ensure update consistency, old flow rules are stored until the total update procedure is complete. Higher consumption of ternary content addressable memories (TCAMs) during update increases the cost of network update and decreases the scalability of SDN. In this paper, we propose an approach for consistent update with redundancy reduction, named CURE, which reduces the TCAM usage during update. CURE prioritizes switches according to their usage pattern and schedules updates based on priority zones. The proposed approach guarantees that highly loaded switches are updated first. CURE also maintains packet-level consistency by implementing a multilevel queuing approach. In this framework, each switch in the current update region stores the incoming packets in individual device queues until the switch completes update. Therefore, after the initiation of an update, packets are processed according to new rules only. The results of performance evaluation depict that the average rule space utilization during update using CURE is 29.954% less than using the two-phase update proposed in the existing literature.