Average Peak Age-of-Information Minimization in UAV-Assisted IoT Networks

Abd-Elmagid, Mohamed A.; Dhillon, Harpreet S.

doi:10.1109/tvt.2018.2885871

Cited by 245 publications

(136 citation statements)

References 19 publications

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“…IoT devices can collect the real-time status information of the associated underlying physical processes and update the status information packets to the common destination. We assume that the time needed for generating the status packets is negligible for each IoT device 2 , as done in [15]- [17], [25]. This is relevant to several practical IoT applications such as environmental monitoring or surveillance with smart camera systems [26], [27], in which, the devices can instantaneously capture an image or a short video.…”

Section: Thementioning

confidence: 99%

Minimum Age of Information in the Internet of Things With Non-Uniform Status Packet Sizes

Zhou

Saad

2020

IEEE Trans. Wireless Commun.

100

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In this paper, a real-time Internet of Things (IoT) monitoring system is considered in which the IoT devices are scheduled to sample associated underlying physical processes and send the status updates to a common destination. In a real-world IoT, due to the possibly different dynamics of each physical process, the sizes of the status updates for different devices are often different and each status update typically requires multiple transmission slots. By taking into account such multi-time slot transmissions with nonuniform sizes of the status updates under noisy channels, the problem of joint device scheduling and status sampling is studied in order to minimize the average age of information (AoI) at the destination.This stochastic problem is formulated as an infinite horizon average cost Markov decision process (MDP). The monotonicity of the value function of the MDP is characterized and then used to show that the optimal scheduling and sampling policy is threshold-based with respect to the AoI at each device.To overcome the curse of dimensionality, a low-complexity suboptimal policy is proposed through a semi-randomized base policy and linear approximated value functions. The proposed suboptimal policy is shown to exhibit a similar structure to the optimal policy, which provides a structural base for its effective performance. A structure-aware algorithm is then developed to obtain the suboptimal policy.The analytical results are further extended to the IoT monitoring system with random status update arrivals, for which, the optimal scheduling and sampling policy is also shown to be threshold-based with the AoI at each device. Simulation results illustrate the structures of the optimal policy and show a near-optimal AoI performance resulting from the proposed suboptimal solution approach. Index TermsInternet of things, status update, age of information, optimization, scheduling.

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Section: Thementioning

confidence: 99%

Minimum Age of Information in the Internet of Things With Non-Uniform Status Packet Sizes

Zhou

Saad

2020

IEEE Trans. Wireless Commun.

100

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“…Note that the destination node was commonly assumed to be a static node in [12]- [23]. More recently, in [22] and [23], the authors considered the optimization of AoI in UAV-assisted wireless networks. However, the analyses in these works were limited to scenarios where UAVs acted as relay nodes and are hence not broadly applicable.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Minimizing Age-of-Information in UAV-Assisted Networks

Abd-Elmagid

Ferdowsi

Dhillon

et al. 2019

2019 IEEE Global Communications Conference (GLOBECOM)

Self Cite

122

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Unmanned aerial vehicles (UAVs) are expected to be a key component of the next-generation wireless systems. Due to their deployment flexibility, UAVs are being considered as an efficient solution for collecting information data from ground nodes and transmitting it wirelessly to the network. In this paper, a UAV-assisted wireless network is studied, in which energy-constrained ground nodes are deployed to observe different physical processes. In this network, a UAV that has a time constraint for its operation due to its limited battery, moves towards the ground nodes to receive status update packets about their observed processes. The flight trajectory of the UAV and scheduling of status update packets are jointly optimized with the objective of achieving the minimum weighted sum for the age-of-information (AoI) values of different processes at the UAV, referred to as weighted sum-AoI. The problem is modeled as a finite-horizon Markov decision process (MDP) with finite state and action spaces. Since the state space is extremely large, a deep reinforcement learning (RL) algorithm is proposed to obtain the optimal policy that minimizes the weighted sum-AoI, referred to as the age-optimal policy. Several simulation scenarios are considered to showcase the convergence of the proposed deep RL algorithm. Moreover, the results also demonstrate that the proposed deep RL approach can significantly improve the achievable sum-AoI per process compared to the baseline policies, such as the distance-based and random walk policies. The impact of various system design parameters on the optimal achievable sum-AoI per process is also shown through extensive simulations.

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“…The authors in [4] propose an age-optimal trajectory planning for UAVs to collect data from ground sensor nodes. In [5], a framework is developed to exploit UAVs, serving as relay nodes, to minimize the peak ageof-information for a pair of nodes in an Internet-of-Things (IoT) network. The authors in [6] find an energy-efficient path planning scheme for a fixed-wing UAV that communicates with a ground user.…”

Section: Introductionmentioning

confidence: 99%

“…Although interesting, the body of work in [4], [5], [7]- [9] mainly focuses on communications aspects of UAVs, without considering specific constraints that affect inspection operations, such as the impact of UAV's weight, energy requirements for hovering, and flight conditions. Moreover, some of the existing works consider path planning for specific scenarios, such as the works in [6] and [7], that solely focus on circular trajectory for the UAVs.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, [10] does not consider communications constraints such as minimum required data rate. Meanwhile, several works in the literature, such as [4], [5], [7] and [8], primarily focus on minimizing the time that a wireless user is visited by the UAV swarm. While this metric is important in sensor networks and IoT, for inspection operations, energy-efficient path planning is more critical.…”

Section: Introductionmentioning

confidence: 99%

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Optimized Path Planning for Inspection by Unmanned Aerial Vehicles Swarm with Energy Constraints

Monwar

Semiari

Saad

2018

2018 IEEE Global Communications Conference (GLOBECOM)

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Autonomous inspection of large geographical areas is a central requirement for efficient hazard detection and disaster management in future cyber-physical systems such as smart cities. In this regard, exploiting unmanned aerial vehicle (UAV) swarms is a promising solution to inspect vast areas efficiently and with low cost. In fact, UAVs can easily fly and reach inspection points, record surveillance data, and send this information to a wireless base station (BS). Nonetheless, in many cases, such as operations at remote areas, the UAVs cannot be guided directly by the BS in real-time to find their path. Moreover, another key challenge of inspection by UAVs is the limited battery capacity. Thus, realizing the vision of autonomous inspection via UAVs requires energy-efficient path planning that takes into account the energy constraint of each individual UAV. In this paper, a novel path planning algorithm is proposed for performing energy-efficient inspection, under stringent energy availability constraints for each UAV. The developed framework takes into account all aspects of energy consumption for a UAV swarm during the inspection operations, including energy required for flying, hovering, and data transmission. It is shown that the proposed algorithm can address the path planning problem efficiently in polynomial time. Simulation results show that the proposed algorithm can yield substantial performance gains in terms of minimizing the overall inspection time and energy. Moreover, the results provide guidelines to determine parameters such as the number of required UAVs and amount of energy, while designing an autonomous inspection system.

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Average Peak Age-of-Information Minimization in UAV-Assisted IoT Networks

Cited by 245 publications

References 19 publications

Minimum Age of Information in the Internet of Things With Non-Uniform Status Packet Sizes

Minimum Age of Information in the Internet of Things With Non-Uniform Status Packet Sizes

Deep Reinforcement Learning for Minimizing Age-of-Information in UAV-Assisted Networks

Optimized Path Planning for Inspection by Unmanned Aerial Vehicles Swarm with Energy Constraints

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