A Power Allocation-Based Overlapping Transmission Scheme in Internet of Vehicles

Zhang, Dalong; Zheng, Liming; Chen, Qixiao; Wei, Baodian; Ma, Xiao

doi:10.1109/jiot.2018.2873480

Cited by 11 publications

(3 citation statements)

References 23 publications

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“…In addition, an efficient mathematical model was not used to find the positional information of the nodes. An overlapping transmission system for IoVs was introduced in [16] to provide high reliability and less latency communication. But optimal path estimation was not performed.…”

Section: Introductionmentioning

confidence: 99%

Trilateral Location based Maximum Weighted Directive Spanning Tree for Optimal Routing in IoV

Devi¹,

Bhuvaneswari²

2021

IJC

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Internet of Vehicles (IoV) is one of the developing models in the Vehicular adhoc networks (VANETs) with the vast improvement of communication technologies. In order to improve data transmission among the multiple communities without link breakage, a novel Trilateral Location Identified Maximum Weighted Directive Spanning Tree (TLIMWDST) technique is introduced. The proposed TLIMWDST technique consists of two major phases, namely location identification and optimal path identification to improve the reliability of data transmission from source vehicle to destination vehicle. In the first phase, the location of the neighboring vehicles is identified by applying a trilateration technique. After the location identification, an optimal route path between the source and destination is identified using Maximum Weighted Directive Spanning Tree (MWDST) through the intermediate nodes. The performance of the TLIMWDST technique is assessed through simulation as compared to the previous path selection techniques in terms of different routing metrics such as packet delivery ratio, packet loss rate, end-to-end delay and throughput with respect to the number of data packets.

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Section: Introductionmentioning

confidence: 99%

Trilateral Location based Maximum Weighted Directive Spanning Tree for Optimal Routing in IoV

Devi¹,

Bhuvaneswari²

2021

IJC

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“…Smart devices such as smartphones and tablets with network access have experienced swift growth in number and variety, among which, smart vehicles are considered as the next frontier in the automotive revolution, in which the number of interconnected vehicles is predicted to reach 250 million by 2020 [1]. As a result, the Internet of Vehicles (IoV) [2] is experiencing the significant transition of merging with wireless networks to create a powerful Intelligent Transportation System (ITS), where autonomous and semi-autonomous driving represents a future trend [3], [4] . This technology promises to substantially improve driving safety through intelligent operations such as obstacle detection, collision avoidance, and experience of onboard entertainment including online games and augmented reality (AR) [5].…”

Section: Introductionmentioning

confidence: 99%

Allocation of Computation-Intensive Graph Jobs Over Vehicular Clouds in IoV

Liwang

Hosseinalipour

Gao

et al. 2020

IEEE Internet Things J.

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“…Internet-of-things (IoT) has recently emerged as a promising enabling technique for a wide class of applications [1]- [7]. For IoT-aided sensing networks, the sink node may not be interested in the original messages.…”

Section: Introductionmentioning

confidence: 99%

Computing Algorithms for LDPC Coded Internet-of-Things

Zhao

2020

IEEE Access

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Low-density parity-check (LDPC) codes are widely employed in communication systems. We focus on the computing of messages at the sink node of internet-of-things (IoT). As opposed to decoding all the messages, we consider the case that the sink node is interested in computing a linear transformation of the messages. We assume that all the IoT devices are identical. We first present three representations of the considered systems, based on which three multistage computing algorithms are proposed, which are decoding-computing (DC) algorithm, computing-decoding (CD) algorithm, and computing-decodingcomputing (CDC) algorithm. Secondly, we show that the considered system admits a compact normal graph representation, based on which a joint computing algorithm is proposed. Thirdly, we present numerical results to show the advantages of the proposed algorithms. Numerical results show that the optimality of the proposed algorithms depends on the channel conditions and the computing functions. Numerical results also show that the joint computing algorithm has the best performances for a variety of scenarios. Finally, we present a simulation-based optimization procedure to design finite-length LDPC codes for the joint computing algorithm. INDEX TERMS LDPC codes, linear superposition, joint computing algorithm, internet-of-things.

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A Power Allocation-Based Overlapping Transmission Scheme in Internet of Vehicles

Cited by 11 publications

References 23 publications

Trilateral Location based Maximum Weighted Directive Spanning Tree for Optimal Routing in IoV

Trilateral Location based Maximum Weighted Directive Spanning Tree for Optimal Routing in IoV

Allocation of Computation-Intensive Graph Jobs Over Vehicular Clouds in IoV

Computing Algorithms for LDPC Coded Internet-of-Things

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