A Novel Prediction-Based Temporal Graph Routing Algorithm for Software-Defined Vehicular Networks

Zhao, Liang; Li, Zhuhui; Al‐Dubai, Ahmed; Min, Geyong; Li, Jiajia; Hawbani, Ammar; Zomaya, Albert Y.

doi:10.1109/tits.2021.3123276

Cited by 37 publications

(18 citation statements)

References 36 publications

(54 reference statements)

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“…where B represents the subchannel bandwidth. SI NR i,j represents the signal-to-interferenceplus-noise ratio (SINR) [29] between m i and s j , which is given by…”

Section: Data Transmission Modelmentioning

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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“…where B represents the subchannel bandwidth. SI NR i,j represents the signal-to-interferenceplus-noise ratio (SINR) [29] between m i and s j , which is given by…”

Section: Data Transmission Modelmentioning

confidence: 99%

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

et al. 2022

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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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“…ey consider both spatial dependency and temporal property and employ the residual neural network framework to dynamically aggregate them to predict the final traffic of crowds. ere are also some works that focus on vehicular network to prediction future location of vehicle [25][26][27].…”

Section: Related Workmentioning

confidence: 99%

A New Approach to Regional Traffic Estimation for Intelligent Transport Systems

Feng

Zhang

et al. 2022

Journal of Advanced Transportation

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With the great development of urban transportation systems, immediate urban traffic information has become an essential resource for the public. Traffic estimation is to predict current or future traffic situation (traffic speed and/or volume) in a road or a region of a city, and can benefit our daily life from many aspects, such as routing planning and traffic management. Existing works focus on estimating future traffic for individual road segments from a perspective of fine-grained level. This paper presents a new approach to estimating future traffic from a perspective of coarse-grained level, by which we estimate the traffic situation of a region, instead of an individual road segment. We propose a new concept about regional traffic named Ω-region, which aims to reflect the traffic situation of a region precisely. Two challenges in the regional traffic estimation problem are how to partition the road network into reasonable regions and how to estimate the regional traffic effectively. To address these challenges, first we define reasonable regions Ω-regions with traffic situations so that the all the road segment in the region has similar traffic. Then, we propose a three-phase partition method to divide the road network into Ω-regions based on historical trajectory data. Thirdly, we propose an effective linear-based model to estimate regional traffic. Experimental results on real-world dataset show that our proposed method achieves high performance.

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A Novel Prediction-Based Temporal Graph Routing Algorithm for Software-Defined Vehicular Networks

Cited by 37 publications

References 36 publications

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

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

Aggregated Boolean Query Processing for Document Retrieval in Edge Computing

A New Approach to Regional Traffic Estimation for Intelligent Transport Systems

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