An Edge Federated MARL Approach for Timeliness Maintenance in MEC Collaboration

Zhu, Zheqi; Wan, Shuo; Fan, Pingyi; Letaief, Khaled B.

doi:10.1109/iccworkshops50388.2021.9473729

Cited by 11 publications

(12 citation statements)

References 16 publications

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“…The flight power in the semicircle section is defined as p cir and it can be obtained by constraint (19).…”

Section: ) Age Of Information Constraintsmentioning

confidence: 99%

“…In our algorithm, we choose the speed that minimizes the flight power to reduce the energy consumption. Based on constraint (19), the speed that minimizes flight power is obtained as:…”

Section: An Efficient Algorithmmentioning

confidence: 99%

“…The trajectory of UAV is supposed to contain the sequence of the non‐repeating nodes and the trajectory is optimized to minimize the age of collected information. In reference [19], Zhu et al. investigated on the timeliness maintenance of the MEC system and formulated the average AoI minimization problem.…”

Section: Introductionmentioning

confidence: 99%

“…The trajectory of UAV is supposed to contain the sequence of the non-repeating nodes and the trajectory is optimized to minimize the age of collected information. In reference [19], Zhu et al investigated on the timeliness maintenance of the MEC system and formulated the average AoI minimization problem. The UAV collects data, executes locally and offloads the tasks to cloud center and the trajectory of UAV is characterized by the position in each time slot.…”

Section: Introductionmentioning

confidence: 99%

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Age–energy‐aware trajectory planning for UAV‐assisted data collection in Internet of Things

Chen

Jia

et al. 2023

IET Communications

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Unmanned aerial vehicles (UAVs) are employed as mobile relay nodes to enable timely remote monitoring by collecting information from monitoring devices and transferring the collected information to base station. The freshness of delivered information is critical to system performance, which can be quantified by the concept of age of information (AoI). Nevertheless, the fresher information comes at the cost of higher energy consumption at UAVs. Considering the limited onboard energy, it is essential to strike a balance between the age of delivered information and the required energy budget. here, both straight trajectory and circular trajectory of UAV are considered, and study a problem with the goal of supporting timely data collection while minimizing the energy consumption at UAV. The problem is formulated as a multi‐criteria optimization problem by jointly considering the aging of collected information, the trajectory planning of UAV and energy consumption at UAV. To solve the formulated problem, a solution procedure to find a sequence of Pareto‐optimal points is proposed. Simulation results demonstrate the Pareto‐optimal curve, which yields the energy‐efficient UAV trajectory for timely data collection.

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“…The flight power in the semicircle section is defined as p cir and it can be obtained by constraint (19).…”

Section: ) Age Of Information Constraintsmentioning

confidence: 99%

“…In our algorithm, we choose the speed that minimizes the flight power to reduce the energy consumption. Based on constraint (19), the speed that minimizes flight power is obtained as:…”

Section: An Efficient Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Age–energy‐aware trajectory planning for UAV‐assisted data collection in Internet of Things

Chen

Jia

et al. 2023

IET Communications

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“…. ., N do (16) Sample (s t , a t , r t , s t+1 , a t+1 ) from D; (17) Calculate q t (a t , s t ; w t ) using the critic network; (18) Predict a t+1 using the target actor network; (19) Calculate q t+1 (a t+1 , s t+1 ; w t ) using the target critic network; (20) Update the actor network according to equation ( 20); (21) Update the critic network according to equation ( 21); (22) end for (23) if t mod Tu � � 1 then (24) Update the target actor network and the target critic network with following method; (25) θ t+1 � ξθ t+1 + (1 − ξ)θ t (26) w t+1 � ξw t+1 + (1 − ξ)w t (27) end if (28) if t mod Ef � � 1 then (29) Run edge-federated updating according to equations ( 23) and ( 24…”

Section: Experiments Settingsmentioning

confidence: 99%

“Fed-DRL”: A Timeliness Optimization Method for Dynamic Data Acquisition System Based on Mobile Edge Computing

Zheng

Qiu

et al. 2022

Mathematical Problems in Engineering

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Age of Information (AoI) is a metric to describe the timeliness of a system proposed in recent years. It measures the freshness of the latest received data from the perspective of the target node in the system. This work studies a kind of dynamic data acquisition system for urban security that can update and control the situation of urban environmental security by collecting environmental data. The collected data packets need to be uploaded to the cloud center in time for data update, which has high requirements on the timeliness of the system and freshness of data. However, due to the limited computing capacity of mobile terminals and the pressure of bandwidth for data transmission, problems such as high data execution delay and transmission interruption are caused. Emerging mobile edge computing (MEC), a new model of computing that extends cloud computing capabilities to the edge network, promises to solve these problems. This work focuses on the timeliness of the system, as measured by the average AoI across all mobile terminals. First, a timeliness optimization model is defined, and a multi-agent deep reinforcement learning (DRL) algorithm combined with an attention mechanism is proposed to carry out computing offloading and resource allocation through the continuous interaction between agent and environment; then, in order to improve algorithm performance and data security, the federated learning mode is proposed to train agents; finally, the proposed algorithm is compared with other main baseline algorithms based on deep reinforcement learning. The simulation results show that the proposed algorithm not only outperforms other algorithms in optimizing system timeliness, but also improves the stability of training.

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