New Game-Theoretic Approach to Decentralized Path Selection and Sleep Scheduling for Mobile Edge Computing

Wu, Baosheng; Zeng, Jie; Shao, Shihai; Ni, Wei; Tang, Youxi

doi:10.1109/twc.2022.3146514

Cited by 9 publications

(5 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Even though each AGV may achieve optimal coordination on its own, CS strategies must ensure that the entire system is optimally coordinated to handle global tasks in logistics transportation efficiently. This can be achieved by ensuring that the NE has global convergence, which will result in the optimal coordination of the system and the most efficient operating conditions for the entire logistics transportation system, as described in [18].…”

Section: The Description and Results Of Ocs In Logistics Warehousingmentioning

confidence: 99%

“…As an alternative, game theory optimizes the action of each AGV from the perspective of group performance to achieve the Nash equilibrium (NE) of group OCS, thereby improving group performance [17,18]. In particular, differential games can quantify the dynamic interaction and confrontation process between multi-AGV, which are often used to solve coordination control problems [19], such as pursuit-evasion (PE) [20] and formation control problems [21].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal coordination in logistics warehousing with sensing and communication limits: A distributed differential game approach

Xue,

Zhan,

Chen

et al. 2023

Asian Journal of Control

View full text Add to dashboard Cite

Optimal coordination is essential for multi‐automated guided vehicle (AGV) systems, particularly in logistic transportation cases, where the system task completion time needs to be minimized, with the guarantee of safe operation. This is because an optimal coordination strategy (OCS), if achieved, can significantly improve the transportation system's efficiency. In this paper, to deal with the dynamic interaction process among AGVs, and sensing and communication range limits, we formulate the optimal coordination problem into a distributed differential game (DDG) framework, where individual AGVs only use information communicated from nearby AGVs to design their optimal operation trajectories. This helps to significantly reduce the computational and communication requirements for the multi‐AGV logistic transportation systems. Targeting operation safety and working efficiency requirements, we incorporate collision avoidance and trajectory optimization objectives into the proposed framework. It is shown that local OCS, obtained by solving the DDG problem for each AGV, will converge to the global Nash equilibrium, which represents the most efficient operating condition for the entire logistic transportation system. Finally, the efficacy of the proposed method is demonstrated, based on simulations and experiments, benchmarked with existing logistic warehousing planning and differential game methods. Compared with conventional methods, the proposed framework successfully helps reduce the task completion time by up to 16%.

show abstract

Section: The Description and Results Of Ocs In Logistics Warehousingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal coordination in logistics warehousing with sensing and communication limits: A distributed differential game approach

Xue,

Zhan,

Chen

et al. 2023

Asian Journal of Control

View full text Add to dashboard Cite

show abstract

“…In the past, the de nition of the overload function included the term "ascending function." Work in [17,18,19] developed and published a model for cascading failure in scale-free wireless sensor networks (WSNs) [20]. They investigated how the sensor nodes were related to the stress that was eventually put on the largest portion of the interconnected system as a consequence of a series of failures.…”

Section: Literature Reviewmentioning

confidence: 99%

LCSMIBR: Design of a Low complexity Sleep-scheduling Model with Iterative Bioinspired learning for improved Routing efficiency under heterogeneous network condition

Kuthe,

Khandelwal

2024

Preprint

View full text Add to dashboard Cite

One of the best methods for increasing energy efficiency in large-scale wireless networks is sleep scheduling. The scalability of existing sleep scheduling algorithms is limited in real-time applications due to their higher complexity or lack of coordination. Furthermore, these models' routing performance needs to be adjusted for various use scenarios. This essay suggests designing a low complexity sleep-scheduling model with iterative bioinspired learning for better routing efficiency in the context of diverse networks in order to integrate these qualities. At first, the suggested model gathers progressively larger data sets on node location, energy usage, fault frequency, and additional quality of service measures. These parameter sets are used to train a set of scalar nodes (nodes with low efficiency levels), that decide initial sleep scheduling cycles for high-efficiency multimedia nodes. The sleep schedules are co-ordinated by multimedia nodes during their inter-node communications, which assists the scalar nodes to iteratively improve sleep-and-wake cycles. These cycles are decided by a Bacterial Foraging Optimization (BFO) process, which assists in improving energy efficiency without compromising on QoS performance levels. The BFO process is cascaded with a fault-tolerant Grey Wolf Particle Swarm Optimizer (GWPSO), that evaluates high-efficiency routing paths that can provide low delay, low energy consumption, with lower probability of faults. Due to these operations, the proposed model is able to reduce communication delay by 4.9%, and energy needed for communication by 8.5%, while improving throughput by 3.4%, and consistency of routing-performance by 1.9% when compared with existing routing techniques. This performance was observed to be consistent even under node-level faults for heterogenous network scenarios.

show abstract

“…Many works consider either a single edge computing node [1][2][3] or a set of independent nearby edge computing nodes [4][5][6][7][8][9][10] which might also collaborate with a remote cloud (a cloud-edge) [11][12][13][14][15][16][17]. Only few studies, however, consider an edge computing environment that includes network connections among the edge nodes in a local area due to the complexity of the problem [18,19].…”

Section: Related Workmentioning

confidence: 99%

“…Yan et al [1] propose a Stackelberg game of service caching and task offloading. Another well applied game is the potential game [7,9,19]. In all these cases network planning is not studied and the impact of network latency and topology is not considered.…”

Section: Related Workmentioning

confidence: 99%

Game Theoretic Resource Planning and Request Scheduling in Mobile Edge Computing Networks

Xiang¹,

Elias

Martignon

et al. 2023

2023 IFIP Networking Conference (IFIP Networking)

View full text Add to dashboard Cite

Mobile Edge Computing (MEC) networks offer an increasing computing power through the collaboration among MEC nodes. This opens a large computing market and brings challenges for efficient resource management. In this paper, we study the joint optimization problem of planning cost-efficient edge networks, allocating link and computation resources, as well as scheduling and routing user requests in edge computing networks with arbitrary topologies and multiple ingress nodes. We formulate this problem as a Stackelberg game where the network operator, as the leader, aims at maximizing its profit, and the edge nodes, as the followers, minimize their users' costs and latency. Then, we prove the existence of the generalized Nash equilibrium for the follower subgame, and the Stackelberg equilibrium for the leader-follower game. We further propose a distributed best-response algorithm for the follower game and an alternating leader-follower optimization algorithm for the full game to compute the equilibrium and prove its convergence. A centralized optimization incorporating both profit and network latency targets is formulated and solved, which serves as benchmark for the game solution. Extensive numerical results demonstrate the effectiveness of the proposed game, achieving near-optimal planning and scheduling solutions in a very short time even for large-scale edge networks.

show abstract

New Game-Theoretic Approach to Decentralized Path Selection and Sleep Scheduling for Mobile Edge Computing

Cited by 9 publications

References 49 publications

Optimal coordination in logistics warehousing with sensing and communication limits: A distributed differential game approach

Optimal coordination in logistics warehousing with sensing and communication limits: A distributed differential game approach

LCSMIBR: Design of a Low complexity Sleep-scheduling Model with Iterative Bioinspired learning for improved Routing efficiency under heterogeneous network condition

Game Theoretic Resource Planning and Request Scheduling in Mobile Edge Computing Networks

Contact Info

Product

Resources

About