Age of Information With Joint Packet Coding in Industrial IoT

Chan, Tse-Tin; Pan, Haoyuan; Liang, Jiaxin

doi:10.1109/lwc.2021.3105304

Cited by 21 publications

(5 citation statements)

References 19 publications

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“…Currently, AoI has gained popularity as a metric to assess data timeliness in IoT networks [10][11][12][13][14][15]. It underscores the "freshness" of data within target nodes.…”

Section: Introductionmentioning

confidence: 99%

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Deep Reinforcement Learning-Driven UAV Data Collection Path Planning: A Study on Minimizing AoI

Huang,

Li,

Song

et al. 2024

Electronics

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As a highly efficient and flexible data collection device, Unmanned Aerial Vehicles (UAVs) have gained widespread application because of the continuous proliferation of Internet of Things (IoT). Addressing the high demands for timeliness in practical communication scenarios, this paper investigates multi-UAV collaborative path planning, focusing on the minimization of weighted average Age of Information (AoI) for IoT devices. To address this challenge, the multi-agent twin delayed deep deterministic policy gradient with dual experience pools and particle swarm optimization (DP-MATD3) algorithm is presented. The objective is to train multiple UAVs to autonomously search for optimal paths, minimizing the AoI. Firstly, considering the relatively slow learning speed and susceptibility to local minima of neural network algorithms, an improved particle swarm optimization (PSO) algorithm is utilized for parameter optimization of the multi-agent twin delayed deep deterministic policy gradient (MATD3) neural network. Secondly, with the introduction of the dual experience pools mechanism, the efficiency of network training is significantly improved. Experimental results show DP-MATD3 outperforms MATD3 in average weighted AoI. The weighted average AoI is reduced by 33.3% and 27.5% for UAV flight speeds of v = 5 m/s and v = 10 m/s, respectively.

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“…Currently, AoI has gained popularity as a metric to assess data timeliness in IoT networks [10][11][12][13][14][15]. It underscores the "freshness" of data within target nodes.…”

Section: Introductionmentioning

confidence: 99%

“…It underscores the "freshness" of data within target nodes. In reference [10], a balance between high reliability and information freshness was achieved through finetuning jointly encoded packets. Simultaneous transmission over multiple sub-channels effectively reduced AoI.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Driven UAV Data Collection Path Planning: A Study on Minimizing AoI

Huang,

Li,

Song

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Recently, information timeliness has become a novel and very critical metric for the newly emerged status update applications, including hazard monitoring, autonomous driving, and AR services, because outdated information may result in serious impacts, such as production accidents and economic loss or even casualties. In order to enhance the information timeliness for IIoT, age of information (AoI), which is defined as the time interval from the time a data packet is generated to the current time, has been presented and widely studied in various systems network scenarios [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

AoI-Minimal Power Adjustment in RF-EH-Powered Industrial IoT Networks: A Soft Actor-Critic-Based Method

Ge,

Xiong,

Wang

et al. 2024

IEEE Trans. on Mobile Comput.

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This paper investigates the radio-frequency-energy-harvesting-powered (RF-EH-powered) wireless Industrial Internet of Things (IIoT) networks, where multiple sensor nodes (SNs) are first powered by a wireless power station (WPS), and then collect status updates from the industrial environment and finally transmit the collected data to the monitor with their harvested energy. To enhance the timeliness of data, age of information (AoI) is used as a metric to optimize the system. Particularly, an expected sum AoI (ESA) minimization problem is formulated by optimizing the power adjustment policy for the SNs under multiple practical constraints, including the EH, the minimal signal-to-noise-plus-interference ratio (SINR) and the battery capacity constraints. To solve the non-convex problem with no explicit AoI expression, we transform it into a Markov decision problem (MDP) with continuous state space and action space. Then, inspired by the Soft Actor-Critic (SAC) framework in deep reinforcement learning, a SAC-based age-aware power adjustment (SAPA) method is proposed by modeling the power adjustment as a stochastic strategy. Furthermore, to reduce the communication overhead of SAPA, a multi-agent version of SAPA, i.e., MSAPA, is proposed, with which each SN is able to adjust its transmit power based on its local observations. The communication overhead of SAPA and MSAPA is also analyzed theoretically. Simulation results show that the proposed SAPA and MSAPA converge well with different numbers of SNs. It is also shown that the ESA achieved by the proposed SAPA and MSAPA is lower than that achieved by the baseline methods.

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“…For example, multiple relayaided sensors can communicate with the relay using time division multiple access (TDMA), frequency division multiple access (FDMA), etc. The relay then forwards the aggregate packets to the receiver [5].…”

Section: Introductionmentioning

confidence: 99%

Enhancing information freshness for coordinated direct and relay transmission

Chan

Pan

2023

J. Commun. Netw.

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This paper investigates the information freshness of relay-aided status updating in coordinated direct and relay transmission (CDRT) for the first time. Information freshness is measured by age of information (AoI), defined as the time elapsed since the generation of the last successfully received update packet. In CDRT, one sensor communicates directly with the receiver while another sensor requires the assistance of a decodeand-forward relay. Conventional CDRT is achieved via time division multiple access (TDMA) or non-orthogonal multiple access (NOMA). Interestingly, our experiments on software-defined radios (SDR) indicate that in error-prone wireless networks, TDMA-CDRT and NOMA-CDRT are not superior to each other in terms of peak AoI in all signal-to-noise ratio (SNR) regimes. Specifically, the direct sensor prefers NOMA-CDRT while the relay-aided sensor prefers TDMA-CDRT, leading to a dilemma in optimizing the peak AoI of the system. To this end, we put forth a hybrid CDRT scheme that combines TDMA and NOMA. The SDR experimental results show that the hybrid CDRT scheme not only balances the peak AoI of the direct and relay-aided sensors, but also achieves better information freshness for the whole CDRT system.Index Terms-Age of information (AoI), coordinated direct and relay transmission (CDRT), Internet of things (IoT), nonorthogonal multiple access (NOMA).

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Age of Information With Joint Packet Coding in Industrial IoT

Cited by 21 publications

References 19 publications

Deep Reinforcement Learning-Driven UAV Data Collection Path Planning: A Study on Minimizing AoI

Deep Reinforcement Learning-Driven UAV Data Collection Path Planning: A Study on Minimizing AoI

AoI-Minimal Power Adjustment in RF-EH-Powered Industrial IoT Networks: A Soft Actor-Critic-Based Method

Enhancing information freshness for coordinated direct and relay transmission

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