Age Of Information Performance Research Articles

Age-Oriented Access Control in GEO/LEO Heterogeneous Network for Marine IoRT: A Deep Reinforcement Learning Approach

With the growing interest in the smart ocean, the satellite-based marine Internet of Remote Things (IoRT) network has been regarded as a promising architecture for sensory data collection and transmission in infrastructure-limited offshore areas. In this article, we investigate the access control problem in the context of GEO/LEO heterogeneous IoRT networks, where multiple gateways are deployed to collect data generated by IoRT devices and then forward them to the terrestrial data center via satellite links. However, most existing access control strategies shed light on the traditional network performance (i.e., transmission delay and communication throughput) in single-layer satellite networks (i.e., low-Earth orbit (LEO) layer or geosynchronous orbit (GEO) layer), whereas the interplay between LEO and GEO layers and the freshness of information are rarely considered. To this end, we first formulate an age-oriented access control problem to minimize the long-term peak Age of Information (AoI) and transform it into a model-free Markov decision process (MDP). Then, a Deep-Double-Dueling- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -Learning (D3QN) policy is trained offline and can be deployed online to make decisions according to dynamic data arrivals and time-varying channels. Simulation results show that the proposed strategy significantly outperforms the state-of-the-art ones in terms of the long-term AoI performance. Furthermore, our strategy could make cooperative decisions for gateways and obtain a proper tradeoff between satellites on different layers.

Age Efficient Optimization in UAV-Aided VEC Network: A Game Theory Viewpoint

The timeless and efficient vehicle data transmission are the two common requirements for the Internet of Vehicles (IoV), especially the Unnamed Aircraft Vehicle (UAV)-aided Vehicular Edge Computing (VEC) network. Moreover, since the Age of Information (AoI) performance greatly influences these two indicators, data quality should be guaranteed in vehicle communication. However, few researchers pay attention to the AoI performance optimization issue regarding wireless resource constraint, transmission interference, and vehicle cooperation in recent years. To close this research gap, we propose an AoI-oriented channel access strategy in the UAV-aided VEC network from the game theory viewpoint. Firstly, the UAV-aided VEC network model and edge computing-based AoI expression are established and derived in the closed form, respectively. Subsequently, we transform the AoI minimization problem into an AoI-based channel access issue from the game theory viewpoint. Moreover, the stochastic learning-based algorithm is proposed to find the Nash Equilibrium (NE) solution of the formulated problem. Finally, simulation results evaluate the correctness and effectiveness of the proposed algorithms, where our scheme can achieve the better AoI value compared with baselines.

Age-Energy Tradeoff in Random-Access Poisson Networks

Energy efficiency and Age of Information (AoI) are two key performance metrics for many emerging battery-limited fresh-aware IoT networks, where random access protocols, such as Aloha, are usually adopted. Yet, in large-scale random access scenarios, the energy efficiency and AoI performance would degrade severely if the network is not configured properly. This paper aims to address this issue by studying the performance limit of energy efficiency under AoI constraint and how to achieve such limit in a slotted Aloha-based Poisson bipolar network. Specifically, we evaluate the energy efficiency via the lifetime throughput, which is defined as the number of update packets successfully decoded by the receiver during the lifetime of each transmitter. The explicit expression of the lifetime throughput is derived, based on which the maximum lifetime throughput with or without AoI constraint and the corresponding optimal channel access probability and packet arrival rate are characterized. The analysis reveals that both the maximum lifetime throughput and AoI-optimal lifetime throughput decline as the node density increases, while the gap between them would be non-negligible if the power ratio of the transmission state and the waiting state is large. Further with AoI constraint, it is shown that the AoI-constrained maximum lifetime throughput has to be sacrificed if the constraint is stringent. The effects of key system parameters on the optimal network configurations and age-energy tradeoff are also discussed, which provide important insights on practical battery-limited fresh-aware IoT network design.

Age-Critical and Secure Blockchain Sharding Scheme for Satellite-Based Internet of Things

It is witnessed that blockchain technology has been widely studied in Internet of Things (IoT) applications due to its decentralized tamper-resistance. Meanwhile, satellite-based IoT (S-IoT) becomes popular and has been regarded as a potential solution of the scalability due to its ubiquitous coverage inherited from satellites. Nevertheless, the large-scale blockchain network enabled S-IoT (BNS-IoT) would be limited by timely performing consensus. In this paper, we propose an age-critical blockchain sharding (ABS) scheme with the metric of information timeliness, i.e., age of information (AoI) to realize timely consensus in BNS-IoT. Specifically, we propose a forking-waiting-retransmission (FR) mechanism for the ABS scheme to deal with forking events, and realize a secure consensus. Then, we derive the closed-form expressions of average AoI (AAoI), throughput and security performance of the FR mechanism in ABS scheme, respectively, and compare with the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -block confirmation and select the longest-chain ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -LC) mechanism. Simulation results show that our ABS scheme can realize the linear expansion of throughput with the increasing number of shards, and our FR mechanism can greatly improve the security by sacrificing minor AAoI compared with the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -LC mechanism. Furthermore, our ABS scheme can outperform the conventional random sharding (RS) scheme in terms of AAoI and throughout.

Information Freshness and Energy Harvesting Tradeoff in Network-Coded Broadcasting

This letter investigates both information freshness and energy harvesting (EH) in downlink information update systems. Information freshness is characterized by age of information (AoI). We consider a scenario in which a large update message is packetized into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> small packets and encoded by random linear network coding (RLNC). A base station continuously broadcasts the RLNC-encoded packets to multiple users until all the users have received the message. When a user successfully receives the update message while at least one other user has not received it yet, the user harvests energy from the received signals. A key challenge is to decide the value of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> in the message packetization in order to achieve low AoI and high EH simultaneously. To this end, we conduct a theoretical analysis of the tradeoff between the AoI and EH performance under different values of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> . In particular, the packet error rates of short packets in the message packetization are estimated by the short packet theory, and the closed-form AoI and EH formulas are derived. Our numerical results reveal that there exists a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> (neither too small nor too large) that can achieve both low AoI and high EH.

Timely Device Status Updates in Industrial Wireless Monitoring Systems Under Resource Constraints

In Industrial Internet of Things (IIoT), it is essential to acquire timely device status information to ensure efficient operation. In this article, we consider a wireless monitoring system in IIoT and employ the concept of Age of Information (AoI) to characterize the timeliness of device status information in the system. Considering the impact of resource constraints on information acquisition, we apply a pull-based model to control the entire process of sampling, transmission, and processing associated with device status updates, which constitutes a system-wide AoI minimization problem. The formulated problem is a mixed-integer nonconvex problem, due to the temporal correlation of AoI and the intractability of the implicit AoI-associated objective function. We introduce the concept of average AoI earnings to equivalently substitute the optimization objective. The original problem in consecutive time slots is decomposed into the per-time slot average AoI earnings maximization problem to deal with the temporal correlation of AoI. Then, an online slot-by-slot optimization algorithm (SBSA) is proposed to control device status updates without long-term system state information. Simulation results show that SBSA can significantly improve the AoI performance of the system. However, the problem decomposition in SBSA inevitably brings approximation error. Hence, based on the actual transmission and processing in the system, we get the lower bound of the system total AoI by designing a multislot optimization algorithm (MSA) and analyze the approximate error caused by SBSA. Through simulation results, SBSA has a substantially lower computational complexity, while maintaining acceptable approximation error in comparison to MSA.

Age of Information-Based Scheduling for Wireless D2D Systems With a Deep Learning Approach

Device-to-device (D2D) links scheduling for avoiding excessive interference is critical to the success of wireless D2D communications. Most of the traditional scheduling schemes only consider the maximum throughput or fairness of the system and do not consider the freshness of information. In this paper, we propose a novel D2D links scheduling scheme to optimize an age of information (AoI) and throughput jointly scheduling problem when D2D links transmit packets under the last-come-first-serve policy with packet-replacement (LCFS-PR). It is motivated by the fact that the maximum throughput scheduling may reduce the activation probability of links with poor channel conditions, which results in terrible AoI performance. Specifically, We derive the expression of the overall average AoI and throughput of the network under the spatio-temporal interfering queue dynamics with the mean-field assumption. Moreover, a neural network structure is proposed to learn the mapping from the geographic location to the optimal scheduling parameters under a stationary randomized policy, where the scheduling decision can be made without estimating the channel state information(CSI) after the neural network is well-trained. To overcome the problem that implicit loss functions cannot be back-propagated, we derive a numerical solution of the gradient. Finally, numerical results reveal that the performance of the deep learning approach is close to that of a local optimal algorithm which has a higher computational complexity. The trade-off curve of AoI and throughput is also obtained, where the AoI tends to infinity when throughput is maximized.

Real-Time Status Updates in Wireless HARQ With Imperfect Feedback Channel

We study the impact of the erroneous wireless control feedback channel on the Age of Information (AoI) performance. We consider a point-to-point communication setup employing packet combining strategies to transmit status update packets over an erroneous wireless data channel. The sender receives the positive acknowledgment (ACK) or negative acknowledgment (NACK) of packet reception over an error-prone wireless feedback channel. To mitigate the impact of the imperfect feedback channel on the system performance, we adopt an asymmetric signal detection model to control the detection accuracy of ACK and NACK signals. We then compute the explicit expressions for the average AoIs under preemptive and non-preemptive service management policies. We show the optimum parameter design for the control channel model in order to minimize the average AoI. The numerical results validate the analysis and provide detailed perspectives on the optimal signal detection setup minimizing the average AoI, and the possible trade-off between AoI and resource utilization. Generally, the analysis for a preemption setting illustrates that a better protection for the NACK messages compared to the ACK messages can preserve the minimum AoI performance. Especially, under a high noisy feedback channel setup, we show that the viable solution minimizing the average AoI is a blind transmission mechanism at the cost of increasing unnecessary utilization of the channel resources. Moreover, the analysis for a non-preemptive policy reveals the dependence of the optimal feedback signal detection design on the status packet generation rate at the sensor. Such a dependency makes the feedback signal detection approach to provide a more reliable ACK detection compared to NACK messages under the condition of more frequent packet arrival, whereas the opposite holds under the condition of less frequent packet arrival.

Optimizing Age of Information in Random-Access Poisson Networks

Timeliness is an emerging requirement for many Internet of Things (IoT) applications. In IoT networks with a large number of nodes, severe interference may incur that leads to Age-of-Information (AoI) degradation. It is, therefore, important to study how to optimize the AoI performance. This article focuses on the AoI minimization in random-access Poisson networks. By considering the spatiotemporal interactions amongst the transmitters, an expression of the peak AoI is derived, based on which the optimal peak AoI and the corresponding optimal packet arrival rate and channel access probability are further characterized. The analysis shows that when the channel access probability (resp., the packet arrival rate) is given, the optimal packet arrival rate (resp., the optimal channel access probability) is equal to one when nodes are sparsely deployed, and decreases as the node deployment density increases. With a joint tuning of these two system parameters, the optimal channel access probability always equals one. Moreover, with the sole tuning of the channel access probability, the optimal peak AoI is improved with a smaller packet arrival rate only when the node deployment density is high. In contrast, a higher channel access probability always improves peak AoI performance when the packet arrival rate is solely tuned. The analysis in this article sheds important light on freshness-aware design for large-scale networks.

On the Prediction Policy for Timely Status Updates in Space-Air-Ground Integrated Transportation Systems

In this paper, we investigate the timeliness of the vehicular status updates in space-air-ground integrated networks (SAGIN) for intelligent transportation systems (ITS). The Age of Information (AoI) is introduced to capture the timeliness of the vehicular status updates. To overcome the inherent end-to-end latency taken by the long-distance communications in SAGIN for ITS, prediction has attracted extensive attention in the existing literature and shown its superiority. Nevertheless, it is not clear whether prediction is beneficial to the AoI. Inspired by the motivation, we first formulate a model of a real-time vehicular communication link with prediction, where the generated update can be predicted and transmitted to the receiver in advance. Then, we derive the explicit expression of the average age and show that the prediction is not always beneficial to the AoI. Instead, prediction is more applicable for the short-distance communications than long-distance communications. Further, to improve the AoI performance, a MDP framework is presented to obtain a switching structure of the optimal prediction policy. The results show the advantage of the optimal prediction policy over the policy of predicting all the time or with no predicting.

Throughput and Age of Information in a Cellular-Based IoT Network

This paper studies the interplay between device-to-device (D2D) communications and real-time monitoring systems in a cellular-based Internet of Things (IoT) network. In particular, besides the possibility that the IoT devices communicate directly with each other in a D2D fashion, we consider that they frequently send time-sensitive information/status updates (about some underlying physical processes observed by them) to their nearest cellular base stations (BSs). Specifically, we model the locations of the IoT devices as a bipolar Poisson Point Process (PPP) and that of the BSs as another independent PPP. For this setup, we characterize the performance of D2D communications using the average network throughput metric whereas the performance of the real-time applications is quantified by the Age of Information (AoI) metric. The IoT devices are considered to employ a distance-proportional fractional power control scheme while sending status updates to their serving BSs. Hence, depending upon the maximum transmission power available, the IoT devices located within a certain distance from the BSs can only send status updates. This association strategy, in turn, forms the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Johnson-Mehl (JM)</i> tessellation, such that the IoT devices located in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JM cells</i> are allowed to send status updates. The average network throughput is obtained by deriving the mean success probability for the D2D links. On the other hand, the temporal mean AoI of a given status update link can be treated as a random variable over space since its success delivery rate is a function of the interference field seen from its receiver. Thus, in order to capture the spatial disparity in the AoI performance, we characterize the spatial moments of the temporal mean AoI. In particular, we obtain these spatial moments by deriving the moments of both the conditional success probability and the conditional scheduling probability for status update links. Our results provide useful design guidelines on the efficient deployment of future massive IoT networks that will jointly support D2D communications and several cellular network-enabled real-time applications.

Age of Information With Joint Packet Coding in Industrial IoT

This letter studies the information freshness in an industrial Internet of Things (IIoT) network, measured by Age of Information (AoI). We consider a scenario where a sink node collects information update packets from different sensors and then uploads the collected packets to an edge server. This scenario has two main requirements: high information freshness (low AoI) and high reliability (low packet error rate, PER). Since update packets are usually short in practice, previous works usually packed and encoded multiple short packets from different sensors into a long packet to improve PER performances. However, while such a joint coding approach improves reliability, it generally leads to longer delay and hence possibly higher AoI. This letter investigates the AoI performance tradeoff by examining the number of packets to be jointly encoded. We consider two AoI metrics, average AoI and bounded AoI. In particular, bounded AoI is the threshold below which the instantaneous AoI falls for a given percentage of the time. Our theoretical analysis and numerical results show that there exist optimal numbers of jointly coded packets that minimize the average AoI and the bounded AoI. Specifically, a smaller number of packets is usually sufficient to achieve both high information freshness and high reliability.

Cooperative Lossy Communications in Unmanned Aerial Vehicle Networks: Age-of-Information With Outage Probability

This article analyzes the robustness and timeliness for cooperative lossy communications in unmanned aerial vehicle (UAV) networks. The analytical framework consists of two steps: 1) calculating the outage probability, and 2) characterizing the Age-of-Information (AoI) for a given outage probability. Initially, we determine the outage probability based on the Shannon's lossy source-channel separation theorem. Numerical results indicate that joint decoding reduces the outage probability, and the system can achieve higher diversity order for less stringent distortion requirement. Then, we derive a closed-form expression of the lower bound on the average AoI for the communication system where outage events are constrained to an acceptable level. Moreover, we conduct a series of simulations for verifying the lower bound on the average AoI and evaluating the impact of UAV locations on the average AoI. It is demonstrated that the lower bound is tight when the server utilization ratio is either relatively busy or idle. For the case with a relatively large outage probability, we propose an intermittent transmission scheme to refine the AoI performance without extra energy consumption. The effectiveness of the intermittent transmission scheme for reducing the average AoI is verified by both the simulations and the lower bound analysis.

Age-Aware Utility Maximization in Relay-Assisted Wireless Powered Communication Networks.

This article investigates a relay-assisted wireless powered communication network (WPCN), where the access point (AP) inspires the auxiliary nodes to participate together in charging the sensor, and then the sensor uses its harvested energy to send status update packets to the AP. An incentive mechanism is designed to overcome the selfishness of the auxiliary node. In order to further improve the system performance, we establish a Stackelberg game to model the efficient cooperation between the AP–sensor pair and auxiliary node. Specifically, we formulate two utility functions for the AP–sensor pair and the auxiliary node, and then formulate two maximization problems respectively. As the former problem is non-convex, we transform it into a convex problem by introducing an extra slack variable, and then by using the Lagrangian method, we obtain the optimal solution with closed-form expressions. Numerical experiments show that the larger the transmit power of the AP, the smaller the age of information (AoI) of the AP–sensor pair and the less the influence of the location of the auxiliary node on AoI. In addition, when the distance between the AP and the sensor node exceeds a certain threshold, employing the relay can achieve better AoI performance than non-relaying systems.

Anti-aging scheduling in single-server queues: A systematic and comparative study

The age of information (AoI) is a new performance metric recently proposed for measuring the freshness of information in information-update systems. In this work, we conduct a systematic and comparative study to investigate the impact of scheduling policies on the AoI performance in single-server queues and provide useful guidelines for the design of AoI-efficient scheduling policies. Specifically, we first perform extensive simulations to demonstrate that the update-size information can be leveraged for achieving a substantially improved AoI compared to non-size-based (orarrival-time-based) policies. Then, by utilizing both the update-size and arrival-time information, we propose three AoI-based policies. Observing improved AoI performance of policies that allow service preemption and that prioritize informative updates, we further propose preemptive, informative, AoI-based scheduling policies. Our simulation results show that such policies empirically achieve the best AoI performance among all the considered policies. However, compared to the best delay-efficient policies (such as shortest remaining processing time (SRPT)), the AoI improvement is rather marginal in the settings with exogenous arrivals. Interestingly, we also prove sample-path equivalence between some size-based policies and AoI-based policies. This provides an intuitive explanation for why some size-based policies (such as SRPT) achieve a very good AoI performance.

Understanding Age of Information in Large-Scale Wireless Networks

The notion of age-of-information (AoI) is investigated in the context of large-scale wireless networks, in which transmitters need to send a sequence of information packets, which are generated as independent Bernoulli processes, to their intended receivers over a shared spectrum. Due to interference, the rate of packet depletion at any given node is entangled with both the spatial configurations, which determine the path loss, and temporal dynamics, which influence the active states, of the other transmitters, resulting in the queues to interact with each other in both space and time over the entire network. To that end, variants in the packet update frequency affect not just the inter-arrival time but also the departure process, and the impact of such phenomena on the AoI is not well understood. In this paper, we establish a theoretical framework to characterize the AoI performance in the aforementioned setting. Particularly, tractable expressions are derived for both the peak and average AoI under two different transmission protocols, namely the first-come-first-serve (FCFS) and the last-come-first-serve with preemption (LCFS-PR). Additionally, our analysis also accounts for the effects of channel access controls such as ALOHA on the AoI. The accuracy of the analysis is verified via simulations, and based on the theoretical outcomes, we find that: i) networks operating under LCFS-PR are able to attain smaller values of peak and average AoI than that under FCFS, whereas the gain is more pronounced when the infrastructure is densely deployed, ii) in sparsely deployed networks, ALOHA with a universally designed channel access probability is not instrumental in reducing the AoI, thus calling for more advanced channel access approaches, and iii) when the infrastructure is densely rolled out, there exists a non-trivial ALOHA channel access probability that minimizes the peak and average AoI under both FCFS and LCFS-PR.

Discrete-Time Queueing Model of Age of Information With Multiple Information Sources

Information freshness in IoT-based status update systems has recently been studied through the Age of Information (AoI) and Peak AoI (PAoI) performance metrics. In this article, we study a discrete-time server arising in multisource IoT systems, which accepts incoming information packets from multiple information sources so as to be forwarded to a remote monitor for status update purposes. Under the assumption of Bernoulli information packet arrivals and a common general discrete phase-type service time distribution across all the sources, we numerically obtain the exact per-source distributions of AoI and PAoI in matrix-geometric form for three different queueing disciplines: 1) nonpreemptive bufferless; 2) preemptive bufferless; and 3) nonpreemptive single buffer with replacement. The proposed numerical algorithm employs the theory of discrete-time Markov chains of quasi-birth-death type and is matrix analytical. Numerical examples are provided to validate the accuracy and effectiveness of the proposed queueing model. We also present a numerical example on the optimum choice of the Bernoulli parameters in a practical IoT system with two sources with diverse AoI requirements.

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.

Age of Information Optimized MAC in V2X Sidelink via Piggyback-Based Collaboration

Real-time status update in future vehicular networks is vital to enable control-level cooperative autonomous driving. Cellular Vehicle-to-Everything (C-V2X), as one of the most promising vehicular wireless technologies, adopts a Semi-Persistent Scheduling (SPS) based Medium-Access-Control (MAC) layer protocol for its sidelink communications. Despite the recent and ongoing efforts to optimize SPS, very few work has considered the status update performance of SPS. In this paper, Age of Information (AoI) is first leveraged to evaluate the MAC layer performance of C-V2X sidelink. Critical issues of SPS, i.e., persistent packet collisions and Half-Duplex (HD) effects, are identified to hinder its AoI performance. Therefore, a piggyback-based collaboration method is proposed accordingly, whereby vehicles collaborate to inform each other of potential collisions and collectively afford HD errors, while entailing only a small signaling overhead. Closed-form AoI performance is derived for the proposed scheme, optimal configurations for key parameters are hence calculated, and the convergence property is proved for decentralized implementation. Simulation results show that compared with the standardized SPS and its state-of-the-art enhancement schemes, the proposed scheme shows significantly better performance, not only in terms of AoI, but also of conventional metrics such as transmission reliability.

Age of Information in SWIPT-Enabled Wireless Communication System for 5GB

With the advancements in real-time applications, age of information (AoI) emerges to describe the freshness of data. Here, we investigate AoI performance metric in simultaneous wireless information and power transfer (SWIPT) enabled cooperative wireless communication system. We use two SWIPT protocols: time-switching (TS) and power- splitting (PS) at the relay node and compute the AoI performance. SWIPT has a great potential to energize energy constrained communication nodes in wireless sensor networks (WSNs) and Internet of Things (IoT) applications while maintaining a high degree of Quality-of-Service (QoS). Finally, we discuss the possible future directions in terms of 5G and beyond (5GB) to realize the integration of SWIPT with the enhancement of industrial IoT together with ultra-reliable low latency communication (URLLC) for future 3GPP releases.