An effective data fusion-based routing algorithm with time synchronization support for vehicular wireless sensor networks

Tan, Chang; Ji, Sai; Gui, Ziyuan; Shen, Jian; Fu, Desheng; Wang, Jin

doi:10.1007/s11227-017-2145-0

Cited by 13 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To form the basis for any sensor integration, the sensor data must be time synchronized and the sensors' spatial relationship must be known. The time synchronization is typically an engineering problem, and a variety of solutions exists [50][51][52]. The spatial relationship between any two sensors, in particular, the rotation between two sensor frames, is of critical importance as it cannot be directly measured, compared to distances between sensors which can be easily surveyed.…”

Section: Motivationmentioning

confidence: 99%

High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation

İlçi

Toth

2020

Sensors

View full text Add to dashboard Cite

Recent developments in sensor technologies such as Global Navigation Satellite Systems (GNSS), Inertial Measurement Unit (IMU), Light Detection and Ranging (LiDAR), radar, and camera have led to emerging state-of-the-art autonomous systems, such as driverless vehicles or UAS (Unmanned Airborne Systems) swarms. These technologies necessitate the use of accurate object space information about the physical environment around the platform. This information can be generally provided by the suitable selection of the sensors, including sensor types and capabilities, the number of sensors, and their spatial arrangement. Since all these sensor technologies have different error sources and characteristics, rigorous sensor modeling is needed to eliminate/mitigate errors to obtain an accurate, reliable, and robust integrated solution. Mobile mapping systems are very similar to autonomous vehicles in terms of being able to reconstruct the environment around the platforms. However, they differ a lot in operations and objectives. Mobile mapping vehicles use professional grade sensors, such as geodetic grade GNSS, tactical grade IMU, mobile LiDAR, and metric cameras, and the solution is created in post-processing. In contrast, autonomous vehicles use simple/inexpensive sensors, require real-time operations, and are primarily interested in identifying and tracking moving objects. In this study, the main objective was to assess the performance potential of autonomous vehicle sensor systems to obtain high-definition maps based on only using Velodyne sensor data for creating accurate point clouds. In other words, no other sensor data were considered in this investigation. The results have confirmed that cm-level accuracy can be achieved.

show abstract

Section: Motivationmentioning

confidence: 99%

High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation

İlçi

Toth

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Figure 1 shows the VSN architecture with vehicles (Active Sensor) and Road Side Sensor (RSS) (Passive Sensor) nodes [28]. However, most of the research studies concentrated only on the data gathering and fusion based routing in VSN [29], [30]. Yet, there is in need of clustering and collision avoidance since vehicles having high mobility behavior thus leads to frequent communication losses and collisions due to frequent data transmissions.…”

Section: A Research Purposementioning

confidence: 99%

NSSC: Novel Segment Based Safety Message Broadcasting in Cluster-Based Vehicular Sensor Network

Alkhalifa

Almogren

2020

IEEE Access

View full text Add to dashboard Cite

Extensive research attention has been devoted to the Vehicular Sensor Network (VSN) owing to its great potential in environment monitoring. Still, it is difficult to diminish the broadcast storm and data collisions in vehicular senor environment. Due to improper broadcasting of safety message and transmission of packet at same time from multiple vehicles leads to collision. Our key intention is to overwhelm these shortcomings in VSN. Hence, we propose Novel Segment based Safety message broadcasting in Cluster (NSSC) based VSN. Our NSSC is mainly concentrated in three successive processes that are Cluster Formation, Collision Avoidance and Safety Message Broadcasting. Cluster formation is performed originally to sustain stable vehicular environment. Here, Variant based Clustering (VbC) Scheme is proposed to elect Cluster Head (CH) and to form clusters. CH is selected using Chaotic Crow Search (CCS) algorithm. In accord to mitigate the data collision during transmission between CH and Cluster Member, we propose Adaptive Carrier Sense Multiple Access/Collision Avoidance (Ada-CSMA/CA) protocol. Safety message broadcasting adopts Segment based Forwarder Selection (SFS) scheme which selects optimal forwarder using Fuzzy-Vikor method. In this, optimal forwarder is selected concerning to broadcast safety message which reduces the broadcast storm. In regard to validate the proposed NSSC, we have conducted simulations on Omnet++ and SUMO simulator based Veins framework. The acquired results are auspicious in terms of succeeding metrics reachability, average number of collision, duplicate data packets, latency, packet delivery ration and throughput.

show abstract

“…The results has shown the optimal number of result with in the suitable computational complexity. Tan et al [53] proposed the clustering algorithm TSDEGA for the structural health monitoring. The primary objective of the novel method is based on the increasing the network lifetime by using the battery energy as a metric.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Moth Flame Clustering Algorithm for Internet of Vehicle (MFCA-IoV)

et al. 2019

View full text Add to dashboard Cite

A network of wirelessly connected vehicles by using any mean of connectivity is termed as the Internet of Vehicle (IoV). Managing this type of network is a challenging task. Clustering is a technique to efficiently manage resources in this type of network. In a cluster, all inter/intra cluster communication is managed by a cluster head (CH). Load on each CH, the lifetime of the cluster and the total number of clusters in a network are some parameters to measure the efficiency of the network. In this paper, a novel technique based on moth flame clustering algorithm for IoV (MFCA-IoV) is proposed. Moth flame optimizer is a nature-inspired algorithm. MFCA-IoV generates optimized clusters for robust transmission and is evaluated experimentally with renowned techniques. These techniques are Grey-Wolf-optimizationbased method used for the clustering called as GWOCNETs, multi-objective particle-swarm-optimization (MOPSO), clustering algorithm based on Ant colony optimization for vehicular ad-hoc networks termed as CACONET and comprehensive learning particle-swarm-optimization (CLPSO). To assess the comparative efficiency of these algorithms, numerous experiments are performed. The parameters like network gridsize, number of nodes, speed, direction, and transmission-range of the nodes are considered for optimized clustering. The results indicate, MFCA-IoV is showing 73% nodes, which are not selected as a cluster head while existing techniques are providing 57%, 50%, 51%, and 58% for GWOCNETs, CLPSO, MOPSO, and CACONET, respectively. Hence, lesser the nodes are selected as CH, the more optimal result will be considered. INDEX TERMS Internet of Vehicle (IoV), vehicular ad-hoc networks (VANETs), intelligent transportation system (ITS), Ant-colony-optimization (ACO), particle swarm optimization (PSO), MFO; clustering, meta-heuristic algorithms, population-based algorithm.

show abstract

An effective data fusion-based routing algorithm with time synchronization support for vehicular wireless sensor networks

Cited by 13 publications

References 20 publications

High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation

High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation

NSSC: Novel Segment Based Safety Message Broadcasting in Cluster-Based Vehicular Sensor Network

Moth Flame Clustering Algorithm for Internet of Vehicle (MFCA-IoV)

Contact Info

Product

Resources

About