scholarly journals Quality Utilization Aware Based Data Gathering for Vehicular Communication Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-25 ◽  
Author(s):  
Yingying Ren ◽  
Anfeng Liu ◽  
Ming Zhao ◽  
Changqin Huang ◽  
Tian Wang
Keyword(s):  
Data Collection ◽  
Communication Networks ◽  
Data Gathering ◽  
Quality Data ◽  
Vehicular Communication ◽  
Sensing Data ◽  
Fine Grained ◽  
Vehicular Communication Networks ◽  
The Cost

The vehicular communication networks, which can employ mobile, intelligent sensing devices with participatory sensing to gather data, could be an efficient and economical way to build various applications based on big data. However, high quality data gathering for vehicular communication networks which is urgently needed faces a lot of challenges. So, in this paper, a fine-grained data collection framework is proposed to cope with these new challenges. Different from classical data gathering which concentrates on how to collect enough data to satisfy the requirements of applications, a Quality Utilization Aware Data Gathering (QUADG) scheme is proposed for vehicular communication networks to collect the most appropriate data and to best satisfy the multidimensional requirements (mainly including data gathering quantity, quality, and cost) of application. In QUADG scheme, the data sensing is fine-grained in which the data gathering time and data gathering area are divided into very fine granularity. A metric named “Quality Utilization” (QU) is to quantify the ratio of quality of the collected sensing data to the cost of the system. Three data collection algorithms are proposed. The first algorithm is to ensure that the application which has obtained the specified quantity of sensing data can minimize the cost and maximize data quality by maximizing QU. The second algorithm is to ensure that the application which has obtained two requests of application (the quantity and quality of data collection, or the quantity and cost of data collection) could maximize the QU. The third algorithm is to ensure that the application which aims to satisfy the requirements of quantity, quality, and cost of collected data simultaneously could maximize the QU. Finally, we compare our proposed scheme with the existing schemes via extensive simulations which well justify the effectiveness of our scheme.

scholarly journals A vehicle to vehicle relay-based task offloading scheme in Vehicular Communication Networks

2021 ◽  
Vol 7 ◽  
pp. e486
Author(s):  
Salman Raza ◽  
Muhammad Ayzed Mirza ◽  
Shahbaz Ahmad ◽  
Muhammad Asif ◽  
Muhammad Babar Rasheed ◽  
...  
Keyword(s):  
Communication Networks ◽  
Edge Computing ◽  
Vehicular Communication ◽  
Network Resources ◽  
Vehicle To Vehicle ◽  
Communication Modes ◽  
Heavy Burden ◽  
Vehicular Communication Networks ◽  
The Cost ◽  
Task Offloading

Vehicular edge computing (VEC) is a potential field that distributes computational tasks between VEC servers and local vehicular terminals, hence improve vehicular services. At present, vehicles’ intelligence and capabilities are rapidly improving, which will likely support many new and exciting applications. The network resources are well-utilized by exploiting neighboring vehicles’ available resources while mitigating the VEC server’s heavy burden. However, due to the vehicles’ mobility, network topology, and the available computing resources change rapidly, which are difficult to predict. To tackle this problem, we investigate the task offloading schemes by utilizing vehicle to vehicle and vehicle to infrastructure communication modes and exploiting the vehicle’s under-utilized computation and communication resources, and taking the cost and time consumption into account. We present a promising relay task-offloading scheme in vehicular edge computing (RVEC). According to this scheme, the tasks are offloaded in a vehicle to vehicle relay for computation while being transmitted to VEC servers. Numerical results illustrate that the RVEC scheme substantially enhances the network’s overall offloading cost.

scholarly journals A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Fulong Ma ◽  
Xiao Liu ◽  
Anfeng Liu ◽  
Ming Zhao ◽  
Changqin Huang ◽  
...  
Keyword(s):  
Data Gathering ◽  
Incentive Scheme ◽  
Quality Of Information ◽  
Total Cost ◽  
Sensing Data ◽  
Crowd Sensing ◽  
Data Coverage ◽  
Simulation Results ◽  
The Cost

To tackle the issue in deep crowd sensing, a Time and Location Correlation Incentive (TLCI) scheme is proposed for deep data gathering in crowdsourcing networks. In TLCI scheme, a metric named “Quality of Information Satisfaction Degree” (QoISD) is to quantify how much collected sensing data can satisfy the application’s QoI requirements mainly in terms of data quantity and data coverage. Two incentive algorithms are proposed to satisfy QoISD with different view. The first algorithm is to ensure that the application gets the specified sensing data to maximize the QoISD. Thus, in the first incentive algorithm, the reward for data sensing is to maximize the QoISD. The second algorithm is to minimize the cost of the system while meeting the sensing data requirement and maximizing the QoISD. Thus, in the second incentive algorithm, the reward for data sensing is to maximize the QoISD per unit of reward. Finally, we compare our proposed scheme with existing schemes via extensive simulations. Extensive simulation results well justify the effectiveness of our scheme. The QoISD can be optimized by 81.92%, and the total cost can be reduced by 31.38%.

scholarly journals Improving Specimen Labelling and Data Collection in Bio-science Research using Mobile and Web Applications

2020 ◽  
Vol 10 (1) ◽  
pp. 1-16
Author(s):  
Isaac Nyabisa Oteyo ◽  
Mary Esther Muyoka Toili
Keyword(s):  
Data Collection ◽  
Web Applications ◽  
Data Entry ◽  
Science Research ◽  
Quality Data ◽  
Quality Of Data ◽  
Data Entry Form ◽  
Research Activities ◽  
Daunting Task

AbstractResearchers in bio-sciences are increasingly harnessing technology to improve processes that were traditionally pegged on pen-and-paper and highly manual. The pen-and-paper approach is used mainly to record and capture data from experiment sites. This method is typically slow and prone to errors. Also, bio-science research activities are often undertaken in remote and distributed locations. Timeliness and quality of data collected are essential. The manual method is slow to collect quality data and relay it in a timely manner. Capturing data manually and relaying it in real time is a daunting task. The data collected has to be associated to respective specimens (objects or plants). In this paper, we seek to improve specimen labelling and data collection guided by the following questions; (1) How can data collection in bio-science research be improved? (2) How can specimen labelling be improved in bio-science research activities? We present WebLog, an application that we prototyped to aid researchers generate specimen labels and collect data from experiment sites. We use the application to convert the object (specimen) identifiers into quick response (QR) codes and use them to label the specimens. Once a specimen label is successfully scanned, the application automatically invokes the data entry form. The collected data is immediately sent to the server in electronic form for analysis.

scholarly journals Securing Vehicle Communication Systems by the KLJN Key Exchange Protocol

2014 ◽  
Vol 13 (03) ◽  
pp. 1450020 ◽  
Author(s):  
Y. Saez ◽  
X. Cao ◽  
L. B. Kish ◽  
G. Pesti
Keyword(s):  
Communication Networks ◽  
Communication Systems ◽  
Security Requirements ◽  
Communication Security ◽  
Vehicular Communication ◽  
Johnson Noise ◽  
Key Exchange Protocol ◽  
Communication Architecture ◽  
Vehicle Communication ◽  
Vehicular Communication Networks

We review the security requirements for vehicular communication networks and provide a critical assessment of some typical communication security solutions. We also propose a novel unconditionally secure vehicular communication architecture that utilizes the Kirchhoff-law–Johnson-noise (KLJN) key distribution scheme.

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