Joint Offloading and Trajectory Optimization for Complex Status Updates in UAV-Assisted Internet of Things

Diao, Xianbang; Guan, Xinrong; Cai, Yueming

doi:10.1109/jiot.2022.3188608

Cited by 12 publications

(11 citation statements)

References 36 publications

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“…Sensors 2024, 24, x FOR PEER REVIEW 3 of 32 or path of the UAVs significantly influences this decision [16]. Figure 1 depicts an overall view of the trajectory-aware offloading decision process for UAV-aided MEC.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, ongoing studies have emphasized the importance of simultaneously optimizing offloading decisions and trajectories, highlighting the profound impact of trajectory-aware offloading decisions on the overall performance and efficiency of UAV-aided MEC systems. or path of the UAVs significantly influences this decision [16]. Figure 1 depicts an overall view of the trajectory-aware offloading decision process for UAV-aided MEC.…”

Section: Introductionmentioning

confidence: 99%

“…Both the IoT devices and UAVs need to take the decision of either computing a task locally or offloading it to the higher layer. This decision is crucial for enhancing the performance and efficiency of the network by maximizing device capabilities, balancing load, and reducing burdens [ 16 ]. Appropriate offloading decisions also aid in resource management and cost optimization in space–air–ground-integrated networks by matching resources to the task, reducing network traffic, and enabling real-time application by reducing latency [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, a recent trend has emerged in which researchers have realized the mutual reliance of these features and are investigating their joint optimization [ 20 ]. Offloading decisions are vital for optimizing the overall system performance in UAV-aided MEC, and the trajectory or path of the UAVs significantly influences this decision [ 16 ]. Figure 1 depicts an overall view of the trajectory-aware offloading decision process for UAV-aided MEC.…”

Section: Introductionmentioning

confidence: 99%

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Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey

Baidya,

Nabi,

Moh

2024

Sensors

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Recently, the integration of unmanned aerial vehicles (UAVs) with edge computing has emerged as a promising paradigm for providing computational support for Internet of Things (IoT) applications in remote, disaster-stricken, and maritime areas. In UAV-aided edge computing, the offloading decision plays a central role in optimizing the overall system performance. However, the trajectory directly affects the offloading decision. In general, IoT devices use ground offload computation-intensive tasks on UAV-aided edge servers. The UAVs plan their trajectories based on the task generation rate. Therefore, researchers are attempting to optimize the offloading decision along with the trajectory, and numerous studies are ongoing to determine the impact of the trajectory on offloading decisions. In this survey, we review existing trajectory-aware offloading decision techniques by focusing on design concepts, operational features, and outstanding characteristics. Moreover, they are compared in terms of design principles and operational characteristics. Open issues and research challenges are discussed, along with future directions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey

Baidya,

Nabi,

Moh

2024

Sensors

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“…In the work of Liu and Zheng, 23 the unmanned aerial vehicle (UAV) trajectory optimization was found in UAV‐enabled environmental monitoring systems. In the work of Diao et al, 24 the joint offloading and trajectory optimization was investigated in UAV‐assisted IoT system.…”

Section: Introductionmentioning

confidence: 99%

Age of information analysis for internet of things information status update system with cellular backhaul and dedicated energy harvesting beacons

Hao

Jia

2022

Int J Communication

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SummaryThe Internet of Things (IoT) that is usually deployed with the assistance of cellular backhaul and energy harvesting (EH) is characterized by the status update freshness. However, the traditional nature EH results in the loss of information freshness due to the randomness of nature energy process. Although using wireless energy transferring (WET) to charge sensors can overcome this problem, this method has low‐energy efficiency. With these considerations, this paper proposes a novel IoT system under cellular communication scenario by simultaneously exploiting the nature EH and WET. The proposed IoT system is consisted of a cellular backhaul subsystem, a wireless EH and transferring (WEHT) subsystem, and a wireless sensor status update subsystem. The WEHT subsystem employs the dedicated power beacons harvesting energy from natural energy source and transferring the harvested energy to sensor. For the proposed IoT system, this paper first investigates the WEHT subsystem and constructs the Markov Chain (MC) of discrete energy states as well as the MC state transition matrix. Second, the average AoI and peak AoI (PAoI) are formulated by separately considering cellular backhaul and EHTBs. The AoI (PAoI) comparison shows that the proposed EHTB‐based IoT system can outperform the conventional non‐EHTB one where the sensor directly harvests energy from natural source. At the same time, the numerical results exploit the impact of system parameters on AoI and PAoI, respectively. It is found that there exists a trade‐off between the nature energy arrival and EHTB energy transfer so that the average minimum AoI and PAoI are achieved.

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Fresh data collection for UAV-assisted IoTs based on proximity-remote region collaboration

Pan

2023

Ad Hoc Networks

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Joint Offloading and Trajectory Optimization for Complex Status Updates in UAV-Assisted Internet of Things

Cited by 12 publications

References 36 publications

Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey

Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey

Age of information analysis for internet of things information status update system with cellular backhaul and dedicated energy harvesting beacons

Fresh data collection for UAV-assisted IoTs based on proximity-remote region collaboration

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