Update Packets Research Articles

Low-power timely random access: Packet-based or connection-based?

This paper studies low-power random access protocols for timely status update systems with information freshness requirements, measured by age of information (AoI). A fundamental challenge for such networks is to schedule a large number of transmitters to access the wireless channel in a way that achieves a low network-wide AoI at low power consumption. Conventional packet-based random access protocols involve transmitters contending for the channel by sending their entire data packets. When packets are of long duration, the time and energy wasted due to packet collisions is considerable. In contrast, connection-based random access protocols establish connections with the receiver before transmitting data packets. From an information freshness perspective, there should be conditions that favor one approach over the other. Therefore, we conduct a comparative study of the average AoI of packet-based and connection-based random access protocols. Specifically, we consider frame slotted Aloha (FSA) as a representative of packet-based random access and design a request-then-access (RTA) protocol for connection-based random access. Our analyses indicate that the choice between packet-based and connection-based protocols depends mainly on the payload size of update packets and the transmit power budget. In particular, RTA significantly reduces AoI and saves power, especially when the payload size is large. Overall, our investigation offers insights into the practical design of random access protocols for low-power timely status update systems.

Age of correlated information-optimal dynamic policy scheduling for sustainable Green IoT devices: A multi-agent deep reinforcement learning approach

The rapid progress in communication technologies and the widespread adoption of connected devices have given rise to various information-centric Internet-of-Things (IoT) systems, typically necessitating timely updates of information. Green IoT (G-IoT) strives to enhance the environment by reducing the power consumption of billions of devices engaged in extensive data exchange, addressing the substantial energy demand in the process. The Age of Correlated Information (AoCI) measures the freshness of information shared among two or more devices that contribute to the same decision-making process. Optimizing AoCI using Deep Reinforcement Learning (DRL) in IoT reduces energy consumption, optimizes resource utilization, and promotes environmentally conscious communication, contributing to the development of a sustainable G-IoT system. This paper focuses on scheduling the transmission of status update packets among interconnected G-IoT devices to minimize the application-specific long-term average AoCI. The problem is modeled as an NP-hard episodic Markov Decision Process (MDP), highlighting its computational complexity. To handle correlations and the curse of dimensionality, a multi-agent deep reinforcement learning algorithm, specifically the Multi-Agent Deep Deterministic Policy Grading (MADDPG) algorithm, is developed. The training progress displays episode rewards, with an environment designed to penalize multiple agents transmitting simultaneously or none at all, promoting cooperative behavior and minimizing the average age of correlated information. We provide comprehensive simulation results, including reward convergence, the learning process of actors and critics, and the resulting average AoCI with the number of episodes, demonstrating the effectiveness of the MADDPG algorithm.

Age of information minimization in hybrid cognitive radio networks under a timely throughput constraint

Exchanging time-critical information is prevalent in various industrial applications where low latency and timely delivery are paramount. Through this work, we consider a cognitive radio network comprised of multiple secondary users with time-sensitive traffic, and they can access the licensed channel under the hybrid interweave/underlay scheme to enhance spectrum utilization. Traffic in the secondary system is divided into two distinct categories: deadline-constrained data and status updates. Quality of service of data with expiration time, such as multimedia streams, is assessed through the timely throughput metric. However, the age of information metric is used to characterize the freshness of the status update packets, which is vital in several emerging applications. Within an interference constraint imposed by the primary user, a dynamic scheduling policy is proposed to optimize the weighted sum of the average age of information of the status update users under a strict timely throughput requirement for each user with deadline-constrained traffic. We formulate the optimization problem as a constrained Markov decision process. Then, through the drift-plus-penalty method, the problem is reduced into a series of unconstrained Markov decision problems. Finally, each subproblem is tackled using the backward dynamic programming technique. Simulation results illustrate the effect of the main system parameters, such as the PU transmitted power and transmission rate level, on the performance of the secondary system. Moreover, the model feasibility regarding the fulfillment of the constraints against PU activity is experimentally investigated under the proposed hybrid mode and classical interweave mode. The performance of the proposed policy is compared to two other low-complexity scheduling schemes, which ensure the satisfaction of the constraints; results show the performance superiority of our proposed policy.

Age of Information for Partial Earliest Relay Aided Short Packet Status Update With Energy Harvesting

This paper focuses on the timely delivery of status updates for a multi-relay assisted energy harvesting (EH) Internet of Things system with short packet communications (SPC), where an EH-powered source transmits status updates to a destination with the aid of multiple EH-powered relays. To improve the energy efficiency and information freshness, an earliest-ℓ partial relay selection scheme is proposed, in which the earliest ℓ relays that successfully receive the status update from the source will be selected to forward the status update to the destination, and the selection combining (SC) and maximal-ratio combining (MRC) schemes are used at the destination. Firstly, we investigate the update packet delivery schemes of source-relay (S-R) and relay-destination (R-D) links, and present the packet error probabilities under the SC and MRC schemes based on SPC theory. Secondly, after characterizing the full charge time durations of both the source and relay, we investigate the statistical description of the number of the packet transmission attempts from the source to relay as well as the one from the relay to destination by using order statistics. With the analysis of the evolution of the instantaneous age of information (AoI), the expression for average AoI is derived. Finally, we investigate the average AoI minimization, which is formulated as a coupled triplet problem of the packet lengths at the source and relays and the value of ℓ. To overcome the coupling, we present the convexity approximation of the average AoI as well as the concise proof. Then, an efficient gradient-based two-step optimal search algorithm is proposed to solve the optimization problem, which searches the local optimal packet lengths for S-R and R-D links by iteratively updating the value of ℓ until the global optimal objective parameters are found. Numerical analyses give the effect of the key parameters on the average AoI, and the comparison demonstrates that the proposed gradient-based method converges quickly than the conventional greedy search method.

Age of Information in UAV Aided Wireless Sensor Networks Relying on Blockchain

Nowadays, the freshness of status update data is critical for emergent tasks in wireless sensor networks (WSNs). This paper analyzes the peak age of information (PAoI) in the transaction-confirmation process of blockchain technology in the context of unmanned aerial vehicle (UAV) aided wireless sensor networks. Each access node transmits the latest packets to the associated access node, which processes the packets relying on blockchain technology. We investigate the transaction-confirmation process based on a finite-buffer batch-service <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\bf GI/M^{[b]}/1/N}$</tex-math></inline-formula> queueing system. Additionally, the embedded Markov chain techniques are utilized to obtain the state probabilities of the system. Moreover, we deduce the closed-form expressions of average PAoI, which regards as the worse case of the update packet's freshness. Numerical results verify that reducing the block size and the queue length leads to a lower PAoI. Besides, fixed interval network traffic and Markov property network traffic based situations display much better aging behavior than other situations.

Age of error information and throughput for truncated and layer‐coded HARQ‐based satellite‐IoT systems

SummaryThe work focuses on the simultaneous improvement on the throughput and information freshness of the promising satellite‐based Internet of Things (S‐IoT). To this end, we first propose a method based on the adaptive modulation coding (AMC) and truncated layer‐coded hybrid automatic repeat request (L‐HARQ) strategies by especially considering the difference in the channel state information (CSI) between the transmitter and receiver. On account of the large signal delay of the S‐IoT systems, this paper establishes the channel model based on the correlation of the signal‐to‐noise ratio (SNR) under the outdated channel and the ideal channel. Secondly, considering the fact that the improved backtrack decoding is used in the L‐HARQ, the classical definition of age of information (AoI) is insufficient to quantify the status update freshness, we propose the age of error information (AoEI), which is defined as the elapsed time since the generation of the lastly successfully backtrack‐decoded status update. Thirdly, we characterize the statistical descriptions of the maximum wait time of the successfully recovered status updates and the interdeparture time between the two consecutive status update packets that are lastly successfully received. Then, the closed‐form expression of average AoEI is derived under the assumption of independent and identically distributed fading channels. Finally, the Markov decision process (MDP) framework is used to optimize the packet parameters in each round of L‐HARQ process under the general fading scenario. Thereby, the optimized transmission scheme that minimizes AoI with throughput constraint is solved. The simulations investigate the effect of system parameters on the average AoEI.

Age of Information in Physical-Layer Network Coding Enabled Two-Way Relay Networks

This paper investigates information freshness of two-way relay networks (TWRNs) operated with physical-layer network coding (PNC). Information freshness is quantified by age of information (AoI), defined as the time elapsed since the generation time of the latest received information update. PNC reduces communication latency of TWRNs by turning superimposed electromagnetic waves into network-coded messages so that end users can send update packets to each other more frequently via the relay. While sending update packets more frequently has the potential to reduce AoI, how to handle packet corruption has not been investigated. Specifically, if an old packet is corrupted in any hop of a TWRN, one needs to decide whether to drop or to retransmit the old packet. Therefore, we study the average AoI with and without automatic repeat request (ARQ) in PNC-enabled TWRNs. Interestingly, our analysis shows that neither the non-ARQ scheme nor the pure ARQ scheme achieves a good average AoI. Hence, we put forth an uplink-lost-then-drop (ULTD) protocol that combines packet drop and ARQ. Experiments on software-defined radios indicate that ULTD significantly outperforms non-ARQ and pure ARQ schemes in terms of average AoI, especially when the two end users have imbalanced channel conditions. We believe the insight of ULTD on TWRNs generally applies to other two-hop networks.

Optimizing Age of Information in Wireless Uplink Networks With Partial Observations

This paper considers a wireless uplink network consisting of multiple end devices and an access point (AP). Each device monitors a physical process with randomly generated status updates and sends these update packets to the AP in the uplink. The AP aims to schedule the transmissions of these devices to optimize the network-wide information freshness, quantified by the age of information (AoI) metric. Due to the stochastic arrival of the status updates at end devices, the AP only has partial observations of system times of the latest status update packets at end devices when making scheduling decisions. Such a decision-making problem can be naturally formulated as a partially observable Markov decision process (POMDP). We reformulate the POMDP into an equivalent belief Markov decision process (belief-MDP), by defining fully observable belief states of the POMDP as the states of the belief-MDP. The belief-MDP in its original form is difficult to solve as the dimension of its states can go to infinity and its belief space is uncountable. Fortunately, by carefully leveraging the properties of the status update arrival processes (i.e., Bernoulli processes), we manage to simplify the belief-MDP substantially, where every feasible state is characterized by a two-dimensional vector. Based on the simplified belief-MDP, we devise a low-complexity scheduling policy, termed Partially Observing Max-Weight (POMW) policy, for the formulated AoI-oriented scheduling problem. We derive upper bounds for the time-average AoI performance of the proposed POMW policy. We analyze the performance guarantee for the POMW policy by comparing its performance with a universal lower bound available in the literature. Numerical results validate our analyses and demonstrate that the performance gap between the POMW policy and its fully observable counterpart is proportional to the inverse of the lowest arrival rate of all end devices.

G-SC-IRSA: Graph-Based Spatially Coupled IRSA for Age-Critical Grant-Free Massive Access

In this paper, we focus on a grant-free massive access setup and analyze its age of information (AoI), where a large number of user equipments (UEs) are randomly activated and attempt transmitting status update packets to a base station (BS) over a common shared channel. To support this age-critical grant-free massive access, we propose a graph-based spatially coupled irregular repetition slotted ALOHA (G-SC-IRSA) random access protocol, which utilizes the pseudo-random access pattern (PRAP), coupled frames, and sliding window decoder (SWD) to improve the packet loss rate (PLR) and AoI performance. Specifically, we derive the approximate expressions to the normalized average AoI (AAoI) as a function of the PRAP and system load. Then, we establish the problem of minimizing the AAoI under the G-SC-IRSA protocol. Furthermore, we utilize the density evolution (DE) with a bipartite graph to evaluate the system load threshold of G-SC-IRSA in asymptotic regime, and achieve an optimal degree distribution via the differential evolution algorithm, and finally obtain the optimal PRAP with PEG algorithm. Simulation results validate the accuracy of our theoretical derivations, and show the G-SC-IRSA can achieve the minimum AAoI with the optimal PRAP, and outperforms the existing benchmark schemes in terms of PLR and AAoI.

Cooperative Data Collection With Multiple UAVs for Information Freshness in the Internet of Things

Maintaining the freshness of information in the Internet of Things (IoT) is a critical yet challenging problem. In this paper, we study cooperative data collection using multiple Unmanned Aerial Vehicles (UAVs) with the objective of minimizing the total average Age of Information (AoI). We consider various constraints of the UAVs, including kinematic, energy, trajectory, and collision avoidance, in order to optimize the data collection process. Specifically, each UAV, which has limited on-board energy, takes off from its initial location and flies over sensor nodes to collect update packets in cooperation with the other UAVs. The UAVs must land at their final destinations with non-negative residual energy after the specified time duration to ensure they have enough energy to complete their missions. It is crucial to design the trajectories of the UAVs and the transmission scheduling of the sensor nodes to enhance information freshness. We model the multi-UAV data collection problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP), as each UAV is unaware of the dynamics of the environment and can only observe a part of the sensors. To address the challenges of this problem, we propose a multi-agent Deep Reinforcement Learning (DRL)-based algorithm with centralized learning and decentralized execution. In addition to the reward shaping, we use action masks to filter out invalid actions and ensure that the constraints are met. Simulation results demonstrate that the proposed algorithms can significantly reduce the total average AoI compared to the baseline algorithms, and the use of the action mask method can improve the convergence speed of the proposed algorithm.

Age and Energy Analysis for $L$th Best Relay Enabled Cooperative Status Update Systems With Short Packet Communications

In the Internet of Things monitoring systems, the fresh information delivery and efficient energy usage are two significant design concerns. However, they generally cannot be simultaneously achieved. In this paper, we investigate the fundamental tradeoff between the age of information (AoI) and energy consumption (EC) for a generalized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell$</tex-math></inline-formula> th best relay selection policy in two-hop cooperative status update systems with automatic repeat request (ARQ), where the source delivers the sensed status update to the destination through multiple decode-and-forward relays. Among these relays, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell$</tex-math></inline-formula> th best relay (i.e., the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell$</tex-math></inline-formula> -th earliest one that correctly decodes the update packet) will be selected as a forwarder to relay the data to the sink. When a decoding failure occurs, we can either retransmit the old packet to save sensing EC or sense and transmit a new one to reduce AoI. Accordingly, the classical ARQ and truncated ARQ protocols are employed in the second hop. By considering short packet-based transmissions, the average AoI and average EC expressions are derived, and the weighted combination of them is minimized to attain the tradeoff. Numerical results demonstrate that an optimal packet size that can trade off age and energy always exists. Moreover, the best relay does not always minimize the weighted combination, and the truncated ARQ is superior to the classical one. As a promising candidate, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell$</tex-math></inline-formula> th best relay selection can be implemented when either the system energy resource is limited or the best relay is unavailable.

Is proportional fair scheduling suitable for age-sensitive traffic?

Proportional Fair (PF) scheduling with successful deployments in various cellular wireless networks and wireless LANs, aims at maximizing the sum of the logarithms of user throughputs. PF scheduling is known to strike an appropriate balance between fairness and throughput, for conventional data traffic. On the other hand, there has recently been a surge of interest in status update networks carrying age-sensitive traffic for which information freshness is crucial and therefore network performance metrics driven by Age of Information (AoI) are instrumental, as opposed to conventional performance metrics such as delay, loss, or throughput, used for conventional data traffic. This paper studies the scheduling problem for the downlink of a cellular wireless network with a transmitter sending age-sensitive status update packets from multiple information sources to users with the goal of keeping the information as fresh as possible for the users. For this purpose, under the generate-at-will scenario, an age-agnostic model-free scheduler is proposed with the goal of minimizing the weighted sum peak AoI of the network, which is the performance metric used in this paper for quantifying information freshness. With numerical examples, the proposed scheduler is compared and contrasted with weighted PF scheduling in terms of implementation and performance, in both non-opportunistic and opportunistic scenarios.

Age of Information Analysis With Preemptive Packet Management

The timely delivery of status update packets is essential for many real-time Internet of Things (IoT) applications. For monitoring and control, a key performance measure of interest is the Age of Information (AoI). We study the AoI for a status update system with two independent sources and a single server, where a status update packet of a source renders earlier status update packets irrelevant. As new updates might arrive during the service of a status update packet, we study various preemptive update mechanisms. In preemptive scenarios, the packet being served is updated, which affects the remaining service time. Using an effective service time approach, we find explicit expressions for the Laplace-Stieltjes transform of the time average age of information, and of the peak age of information.

On the Age of Information and Energy Efficiency in Cellular IoT Networks With Data Compression

The Age of Information (AoI), which evaluates the information freshness, and the energy efficiency (EE) play key roles in cellular IoT networks. This is due to that outdated data can hardly provide any useful information for delay-sensitive applications and the IoT devices usually have limited battery life. In particular, the AoI can be significantly affected by the transmission latency, which becomes the bottleneck for the AoI performance in ultradensely deployed cellular IoT networks. Moreover, it is desirable for cellular IoT networks to achieve low AoI with high EE. The data compression (DC) can decrease the AoI and improve the EE by reducing the transmission latency. Therefore, in this work, the AoI and EE performance in a large-scale densely deployed uplink cellular IoT network are jointly analyzed with the DC technology. Specifically, the closed-form results of AoI are derived and validated by Monte Carlo simulations. Based on these results, the AoI–EE ratio is defined to evaluate the tradeoff between the AoI and the EE. Equipped with these results, the effects of compression ratio (CR) and status update packet generation rate (SUPGR) on both the AoI and the AoI–EE ratio are analyzed numerically. The results show that by jointly optimizing the CR and SUPGR, the AoI can be decreased by up to 82% and the AoI–EE ratio can be reduced by up to 83% as compared with the case that only adjusts the SUPGR without the DC. It indicates that the DC should be widely adopted in IoT devices, which can improve the information freshness with low-energy consumption, especially in an ultradensely deployed scenario.

Learning-Based Data Gathering for Information Freshness in UAV-Assisted IoT Networks

Unmanned aerial vehicle (UAV) has been widely deployed in efficient data collection for Internet of Things (IoT) networks. Information freshness in data collection can be characterized by the Age of Information (AoI). It is highly challenging to schedule multiple energy-constrained UAVs to improve information freshness especially when the generation instants of sensing samples are unpredictable. To deal with this issue, we leverage state-of-art reinforcement learning (RL) methods to design flight trajectories of UAVs without knowing the sampling mode each sensor node (SN) adopts. Each SN can sample the environment at periodical or random intervals. Multiple energy-constrained UAVs are dispatched to collect update packets from the SNs when flying over them. The UAV trajectory planning problem for AoI minimization is formulated as a Markov decision process (MDP). The objective is to minimize the average AoI of the SNs under the constraints of energy capacity and collision avoidance for the UAVs. Then, we propose two learning algorithms based on the Sarsa and value-decomposition network (VDN), respectively, which allow the UAVs to fulfill data collection tasks requested by the SNs. By learning directly from the environment, the Sarsa-based algorithm can approach the optimal policy asymptotically when certain conditions are satisfied. As one of the most popular multiagent deep RL methods, the VDN-based algorithm enables each UAV to make its own decision independently on its flight and data collection based on the partially observed network information. Simulation results validate the effectiveness of the proposed two learning-based algorithms compared with baseline policies.

Learning to Transmit Fresh Information in Energy Harvesting Networks

We study age of information (AoI) minimization in an ad hoc network consisting of energy harvesting transmitters that are scheduled to send status updates to their intended receivers. The transmission scheduling with power allocation problem over a communication session is first studied assuming apriori knowledge of channel state information, harvested energy, and update packet arrivals, i.e., the offline setting. The global optimal scheduling policy in this case is the solution of a mixed integer linear program which is known to be computationally hard. We propose a supervised-learning-based algorithm to mitigate the high computational complexity. A bidirectional recurrent neural network that interprets user scheduling as a time-series classification problem is trained and tested to achieve near-optimal AoI. Next, we consider online scheduling and power allocation with causal knowledge of the system state, which is an infinite-state Markov decision problem. In this case, the related reinforcement learning problem is solved by a model-free on-policy deep reinforcement learning, where the actor-critic algorithm with deep neural network function approximation is implemented. Comparable AoI to the optimal is demonstrated and faster runtime of learning solvers is observed, verifying the efficacy of learning in terms of both optimality and computational energy efficiency for AoI-focused scheduling and resource allocation problems in wireless networks.

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 of Information With Collision-Resolution Random Access

This paper studies the collision-resolution random access (CRRA) protocol for information update systems with age of information (AoI) requirements. AoI measures the information freshness, defined as the time elapsed since the generation of the last successfully received update. When a large number of users contend and send update packets to a common access point (AP), conventional random access protocols such as slotted Aloha (SA) simply ignore the collisions and inform the users to contend for the wireless channel again, thus leading to high average AoI due to the high collision probability. This paper argues that resolving collisions can improve the information freshness. When a collision occurs in CRRA, only the collided users enter a collision resolution procedure (CRP) to contend and access the channel until all of their update packets are received successfully. In particular, the time to receive a user’s update packet in the CRP is random, which complicates the AoI analysis. This paper theoretically analyze the average AoI of CRRA. Simulations show that CRRA significantly reduces the average AoI of SA, especially when update packets have a large payload. Furthermore, CRRA is more robust against the estimation error of the number of random-access users, and it is thus a promising solution to networks with time-varying traffic.

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

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.

Age of information and energy efficiency of amplify‐and‐forward relay‐assisted Internet of Things with nonlinear energy harvesting and imperfect channel state information over Nakagami‐ fading channels

AbstractThe emergence of the fifth generation of communication networks (5G) has paved the way for the fast development of Internet of Things (IoT) as well as many real‐time and mission‐critical applications. Because the status update required to be as timely as possible, a new metric termed as age of information (AoI) was born with the ability of capturing the timeliness. However, in practical implementation the sensor is energy constrained and transmits very low‐power signals, which yield high AoI so that the received status update can be stale or even outdated. To overcome this problem, this work proposes an IoT system model by integrating energy harvesting (EH), amplify‐and‐forward (AF) relay cooperation, and short packet communication (SPC) techniques. In the proposed scheme, the relay that is connected to fixed power grid, transfers energy radio frequency signals to charge the sensor and assists forwarding status update by using time switching scheme. The employment of SPC overcomes the freshness loss caused by relay cooperation and EH. Then, this work investigates the AoI and energy efficiency of the proposed scheme under finite block length communications over Nakagami‐ fading channels by specially considering channel state information (CSI) and nonlinear EH model. Under these practical constraints, the tractable AF relay transmission model and the end‐to‐end approximated signal‐to‐noise ratio are found. The end‐to‐end block error probability of finite block length transmission is derived, too. Third, under the simultaneous consideration of nonlinear EH and outdated CSI, the statistical descriptions of the time duration for the sensor fully charging its battery are derived as well as the ones of the time intervals of update packet delivery. As a result, the average AoI is achieved with closed‐form expression and the tradeoff model of AoI to energy efficiency is established by simultaneously exploiting outdated CSIs and nonlinearity of EH circuit. The presented numerical analysis exploits the impact of the outdated CSIs and nonlinear EH model and gives the insights of such model.