Journal Article10.1109/TVT.2017.2714704
AVE: Autonomous Vehicular Edge Computing Framework with ACO-Based Scheduling
344
TL;DR: Efficient job caching is proposed to better schedule jobs based on the information collected on neighboring vehicles, including GPS information, and a scheduling algorithm based on ant colony optimization is designed to solve this job assignment problem.
read more
Abstract: With the emergence of in-vehicle applications, providing the required computational capabilities is becoming a crucial problem. This paper proposes a framework named autonomous vehicular edge (AVE) for edge computing on the road, with the aim of increasing the computational capabilities of vehicles in a decentralized manner. By managing the idle computational resources on vehicles and using them efficiently, the proposed AVE framework can provide computation services in dynamic vehicular environments without requiring particular infrastructures to be deployed. Specifically, this paper introduces a workflow to support the autonomous organization of vehicular edges. Efficient job caching is proposed to better schedule jobs based on the information collected on neighboring vehicles, including GPS information. A scheduling algorithm based on ant colony optimization is designed to solve this job assignment problem. Extensive simulations are conducted, and the simulation results demonstrate the superiority of this approach over competing schemes in typical urban and highway scenarios.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Computation Offloading and Resource Allocation For Cloud Assisted Mobile Edge Computing in Vehicular Networks
TL;DR: The simulation results show that the proposed algorithm can effectively improve the system utility and computation time, especially for the scenario where the MEC servers fail to meet demands due to insufficient computation resources.
835
Learning-Based Computation Offloading for IoT Devices With Energy Harvesting
TL;DR: A reinforcement learning (RL) based offloading scheme for an IoT device with EH to select the edge device and the offloading rate according to the current battery level, the previous radio transmission rate to each edge device, and the predicted amount of the harvested energy.
Edge of Things: The Big Picture on the Integration of Edge, IoT and the Cloud in a Distributed Computing Environment
Hesham El-Sayed,Sharmi Sankar,Mukesh Prasad,Deepak Puthal,Akshansh Gupta,Manoranjan Mohanty,Chin-Teng Lin +6 more
TL;DR: After analyzing the different network properties in the system, the results show that EC systems perform better than cloud computing systems, and this paper aims to validate the efficiency and resourcefulness of EC.
Computation Offloading Toward Edge Computing
Li Lin,Xiaofei Liao,Hai Jin,Peng Li +3 more
- 09 Jul 2019
TL;DR: This paper reviews the state-of-the-art research on computation offloading in terms of application partitioning, task allocation, resource management, and distributed execution, with highlighting features for edge computing.
409
Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues
TL;DR: The significance and technical challenges of applying FL in vehicular IoT, and future research directions are discussed, and a brief survey of existing studies on FL and its use in wireless IoT is conducted.
References
Ad hoc On-Demand Distance Vector (AODV) Routing
Charles E. Perkins,Elizabeth M. Belding-Royer,Samir R. Das +2 more
- 01 Jul 2003
TL;DR: A logging instrument contains a pulsed neutron source and a pair of radiation detectors spaced along the length of the instrument to provide an indication of formation porosity which is substantially independent of the formation salinity.
Ad-hoc on-demand distance vector routing
C.E. Perkins,E.M. Royer +1 more
- 25 Feb 1999
TL;DR: An ad-hoc network is the cooperative engagement of a collection of mobile nodes without the required intervention of any centralized access point or existing infrastructure and the proposed routing algorithm is quite suitable for a dynamic self starting network, as required by users wishing to utilize ad- hoc networks.
A view of cloud computing
Michael Armbrust,Armando Fox,Rean Griffith,Anthony D. Joseph,Randy H. Katz,Andy Konwinski,Gunho Lee,David A. Patterson,Ariel Rabkin,Ion Stoica,Matei Zaharia +10 more
TL;DR: The clouds are clearing the clouds away from the true potential and obstacles posed by this computing capability.
10.4K
Ant colony system: a cooperative learning approach to the traveling salesman problem
TL;DR: The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and it is concluded comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.
8.3K
Edge Computing: Vision and Challenges
TL;DR: The definition of edge computing is introduced, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge Computing.
7.1K