Quality Utilization Aware Based Data Gathering for Vehicular Communication Networks

Abstract

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.

Credibility on Crowdsensing Data Acquisition

“…They revealed that EM function enables the system to assign more accurate trust scores and subsequently aggregate more accurate data. Ren et al [139] presented a Quality Utilization (QU) metric that quantifies the ratio of quality of the collected sensing data to the cost of the system. Three data collection algorithms were proposed to maximize QU under different application requirements.…”

A Trust-Based Secure Routing Scheme Using the Traceback Approach for Energy-Harvesting Wireless Sensor Networks

“…However, the energy issue of sensor terminals poses significant challenges to the widespread use of IoT, in which the sensor devices generally have small volume and battery with limited capacity [ 10 , 11 , 12 , 13 , 14 , 15 ]. Therefore, the sustainable issue of IoT has attracted considerable attention from both academia and industry [ 16 , 17 , 18 , 19 ]. Wireless energy harvesting and transfer technology was recently proposed as an effective mean to address this issue.…”

Adaptive Aggregation Routing to Reduce Delay for Multi-Layer Wireless Sensor Networks

“…A wireless sensor network is a set of distributed hardware-constrained wireless devices that monitor a targeted area [ 6 , 7 , 8 , 9 , 10 , 11 ]. Wireless sensor networks are widely used in a variety of applications such as the environmental monitoring and urban health monitoring [ 12 , 13 , 14 ], healthcare [ 2 , 15 ], vehicular communication networks [ 16 ], cyber-physical cloud systems [ 17 ], multi-channel cognitive radio networks [ 18 , 19 ], crowdsourcing networks [ 20 , 21 , 22 ], and social networks [ 23 , 24 ]. Because of the growing demand for wireless sensor applications in various aspects, quality of service (QoS) is an important issue in wireless sensor applications [ 25 , 26 , 27 , 28 , 29 ].…”