SPIDER: A Social Computing Inspired Predictive Routing Scheme for Softwarized Vehicular Networks

Zhao, Liang; Zheng, Tong; Lin, Mingwei; Hawbani, Ammar; Shang, Jiaxing; Fan, Cheng

doi:10.1109/tits.2021.3122438

Cited by 40 publications

(19 citation statements)

References 38 publications

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“…First, the queueing model can be applied to analyze the performances of the VEC system [39]. Second, the Software Defined Networking (SDN) technology can be applied to the VEC system [2,5]. Third, the safety issue of the VEC systems needs to be further explored [40].…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, due to the developments of the communication protocols [1] as well as the advancements in the Internet of Things (IoT) and intelligent transportation system (ITS) [2], varieties of applications such as autonomous driving and image-aided navigation are emerging [3]. These applications covering the aspects of driving safety and information entertainment demand a lot of computation resources and have strict requirements for processing time.…”

Section: Introductionmentioning

confidence: 99%

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Computational Resources Allocation and Vehicular Application Offloading in VEC Networks

Gu¹,

Yang²,

et al. 2022

Electronics

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With the advances in wireless communications and the Internet of Things (IoT), various vehicular applications such as image-aided navigation and autonomous driving are emerging. These vehicular applications require a significant number of computation resources and a lower processing delay. However, these resource-limited and power-constrained vehicles may not meet the requirements of processing these vehicular applications. By offloading these vehicular applications to the edge cloud, vehicular edge computing (VEC) is deemed a novel paradigm for improving vehicular performance. However, how to optimize the allocation of computation resources of both vehicles and VEC servers to reduce the energy and delay is a challenging issue when deploying the VEC systems. In this article, we try to address this issue and propose a vehicular application offloading and computational resources allocation strategy. We formulate an optimization problem and present an efficient offloading scheme for vehicular applications. Extensive simulation results are offered to analyze the performances of the proposed scheme. In comparison with the benchmark schemes, the proposed scheme can outperform them in terms of computation cost.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computational Resources Allocation and Vehicular Application Offloading in VEC Networks

Gu¹,

Yang²,

et al. 2022

Electronics

View full text Add to dashboard Cite

show abstract

“…To solve P1, it is feasible to traverse all possible results due to the high computational complexity [32,33]. Therefore, we convert P1 into a many-to-one two-sided matching problem between devices and gateways.…”

Section: Problem Transformationmentioning

confidence: 99%

Delay and Energy-Efficiency-Balanced Task Offloading for Electric Internet of Things

et al. 2022

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With the development of the smart grid, massive electric Internet of Things (EIoT) devices are deployed to collect data and offload them to edge servers for processing. However, the task of offloading optimization still faces several challenges, such as the differentiated quality of service (QoS) requirements, decision coupling among multiple devices, and the impact of electromagnetic interference. In this paper, a low-complexity delay and energy-efficiency-balanced task offloading algorithm based on many-to-one two-sided matching is proposed for an EIoT. The proposed algorithm shows its novelty in the dynamic tradeoff between energy efficiency and delay as well as in low-complexity and stable task offloading. Specifically, we firstly formulate the minimization problem of weighted difference between delay and energy efficiency to realize the joint optimization of differentiated QoS requirements. Then, the joint optimization problem is transformed into a many-to-one two-sided matching problem. Through continuous iteration, a stable matching between devices and servers is established to cope with decision coupling among multiple devices. Finally, the effectiveness of the proposed algorithm is validated through simulations. Compared with two advanced algorithms, the weighted difference between the energy efficiency and delay of the proposed algorithm is increased by 29.01% and 45.65% when the number of devices is 120, and is increased by 11.57% and 22.25% when the number of gateways is 16.

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“…Query processing as an important ingredient of the IoT applications in edge computing has been studied recently, where the applications are processed in a decentralized fashion as much as possible [17][18][19][20]. A typical work is presented in [18], which processes the multiattribute aggregation query in a decentralized fashion by constructing the energy-aware IR-tree in single-edge networks.…”

Section: Decentralized Query Processing In Edge Computingmentioning

confidence: 99%

Aggregated Boolean Query Processing for Document Retrieval in Edge Computing

et al. 2022

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Search engines use significant hardware and energy resources to process billions of user queries per day, where Boolean query processing for document retrieval is an essential ingredient. Considering the huge number of users and large scale of the network, traditional query processing mechanisms may not be applicable since they mostly depend on a centralized retrieval method. To remedy this issue, this paper proposes a processing technique for aggregated Boolean queries in the context of edge computing, where each sub-region of the network corresponds to an edge network regulated by an edge server, and the Boolean queries are evaluated in a distributed fashion on the edge servers. This decentralized query processing technique has demonstrated its efficiency and applicability for the document retrieval problem. Experimental results on two real-world datasets show that this technique achieves high query performance and outperforms the traditional centralized methods by 2–3 times.

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SPIDER: A Social Computing Inspired Predictive Routing Scheme for Softwarized Vehicular Networks

Cited by 40 publications

References 38 publications

Computational Resources Allocation and Vehicular Application Offloading in VEC Networks

Computational Resources Allocation and Vehicular Application Offloading in VEC Networks

Delay and Energy-Efficiency-Balanced Task Offloading for Electric Internet of Things

Aggregated Boolean Query Processing for Document Retrieval in Edge Computing

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