Timely Status Update in Internet of Things Monitoring Systems: An Age-Energy Tradeoff

Gu, Y. T.; Chen, He; Zhou, Yong; Li, Yonghui; Vucetic, Branka

doi:10.1109/jiot.2019.2900528

Cited by 177 publications

(83 citation statements)

References 34 publications

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“…These devices can range from a sensor that detects movement to report intruders in a company's facilities to a household appliance with integrated circuits and connections that can be configured or programmed remotely. Smart cities [2], transportation [3], healthcare [4], [5], public services [6], and monitoring [7] are some of the areas in which the use of IoT is essential for the solution of different problems. These solutions are possible because the IoT paradigm application allows the collection, composition, and analysis of critical data generated in these existing scenarios.…”

Section: Introductionmentioning

confidence: 99%

WRF-RPL: Weighted Random Forward RPL for High Traffic and Energy Demanding Scenarios

et al. 2021

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The wireless sensor networks (WSN) are immersed in multiple areas of knowledge and are the alternative that allows analyzing, solving, and preventing problems under different facets. The RPL protocol (IPv6 Routing Protocol for Low power a Lossy Networks) arises for the management of this type of network restricted by their physical capabilities through different rules and operations that simplify the communications and constructions of the WSN. Although RPL is an efficient and standardized protocol, it does not consider high traffic handling and presents severe problems regarding load balancing, which leads to service disruption. In this article, WRF-RPL is proposed as a protocol for considering load balancing over RPL to distribute communications and messages into a network topology to avoid one preferred parent's congestions. The proposed protocol aims to improve the network's lifetime and the packet delivery through source nodes to the sink in an energy-efficient manner. Different simulated scenarios were conducted over the Cooja simulator. The results show that WRF-RPL protocols outperform the standard RPL protocol over Network Lifetime, PDR, control message overhead, and energy consumption compared with other existing protocols.

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Section: Introductionmentioning

confidence: 99%

WRF-RPL: Weighted Random Forward RPL for High Traffic and Energy Demanding Scenarios

et al. 2021

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“…Afterwards, a series of works [5]- [12] aimed at characterizing the average AoI and its variations (e.g., Peak Age-of-Information (PAoI) [8]- [10] and Value of Information of Update (VoIU) [11]) for adaptations of the queueing model studied in [4]. Another direction of research [13]- [33] focused on employing AoI as a performance metric for different communication systems that deal with time critical information while having limited resources, e.g., multi-server information-update systems [14], broadcast networks [15]- [17], multi-hop networks [18], cognitive networks [19], unmanned aerial vehicle (UAV)assisted communication systems [20]- [22], IoT networks [2], [23], [24], ultra-reliable low-latency vehicular networks [25], multicast networks [26], decentralized random access schemes [32], and multi-state time-varying networks [33]. Particularly, the objective of this research direction was to characterize optimal policies that minimize average AoI, referred to as ageoptimal polices, by applying different tools from optimization theory.…”

Section: A Related Workmentioning

confidence: 99%

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

Abd-Elmagid

Dhillon

Παππάς

2020

IEEE Trans. Commun.

148

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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 throughputoptimal 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.

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“…To provide accurate and effective services, an IoT monitoring network has to maintain the data freshness of status updates for the monitored physical parameters [5], [6]. The age of information (AoI) was proposed in the seminal works [7] and [8] as a new metric to measure the data freshness in a network.…”

Section: Introductionmentioning

confidence: 99%

Decode-and-Forward Short-Packet Relaying in the Internet of Things: Timely Status Updates

Zheng

Yang

Wei

et al. 2021

IEEE Trans. Wireless Commun.

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In this paper, a decode-and-forward (DF) shortpacket relaying model is developed to achieve timely status updates for intelligent monitoring within the Internet of Things (IoT), where the status updates generated at an IoT device are delivered to a remote server with the aid of a relay in both halfduplex (HD) and full-duplex (FD) modes. To characterise the data freshness of status updates, we exploit the age of information (AoI) as a metric, which is defined as the time elapsed since the generation of the latest successfully decoded status update. The average AoI is formulated and minimised for both HD-DF and FD-DF relaying IoT networks in finite blocklength regime. For the HD-DF relaying, we introduce a perfect approximation of the average AoI to solve the problem of average AoI minimisation with the optimal blocklengths in two phases. For the FD-DF relaying, we propose an iterative algorithm to solve the problem of average AoI minimisation by optimising the relay's transmit power and the blocklength. Illustrative numerical results not only substantiate the validity of our proposed algorithms, but also provide useful references for the IoT monitoring network design, specifically for the transmit power thresholds at the IoT device and the relay.Index Terms-Age of information (AoI), decode-and-forward (DF), finite blocklength regime, full duplex (FD), half duplex (HD), short-packet relaying, status updates.

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Timely Status Update in Internet of Things Monitoring Systems: An Age-Energy Tradeoff

Cited by 177 publications

References 34 publications

WRF-RPL: Weighted Random Forward RPL for High Traffic and Energy Demanding Scenarios

WRF-RPL: Weighted Random Forward RPL for High Traffic and Energy Demanding Scenarios

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

Decode-and-Forward Short-Packet Relaying in the Internet of Things: Timely Status Updates

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