Task unit bid- spatial coverage and post input density (TUBSC_PID) based crowd sourcing network
- PDF / 685,289 Bytes
- 14 Pages / 439.37 x 666.142 pts Page_size
- 2 Downloads / 131 Views
Task unit bid- spatial coverage and post input density (TUBSC_PID) based crowd sourcing network G Rajathilagam 1 & K. Kavitha 1 Received: 24 May 2020 / Revised: 26 August 2020 / Accepted: 16 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
A huge number of items are associated with the Internet of Things (IoT) which is fixed with software, electronics and sensors. It has a wide variety of applications, namely smart homes, smart grids and smart cities. The sensor devices combine with Internet of Things (IoT) operates as robot system to execute data collection task. The IoT control objects, sense devices and gathers data. In crowd sourcing network there are two main issues, namely to guarantee the Quality of Service (QoS) of tasks and to reduce the data collection cost. There is also some problems arise between the task circulator and the data reporter in terms of profit. Since, IoT sensing devices have increased a lot, the relationship for finishing the task is very much important. In this paper, a novel framework called Task Unit Bit-based Spatial Coverage and Post Input density (TUBSC_PID) has been proposed. The input density is applied to estimate the contribution of a single data collector to a particular sensing task. A Task Unit Bid-based task selection strategy is proposed to choose the task which provides more contribution density and higher profit to the system. A novel spatial coverage technique is also applied to cover all the information obtained from the data collector. The present and post input density is applied to estimate the contribution of a single data collector to a particular sensing task as well as future sensing tasks. This method reduces the cost of data selection and maximizes the system profit. Experimental results predict that compared to the traditional techniques, namely Random Task selection with Input Density Reporter selection (RTCDR) and Collaborative MultiTasks Data Collection Scheme (CMDCS), the profit of the system is improved by 96.1%. Keywords Task selection . Spatial . Temporal . Profit . Offer . Input density
* G Rajathilagam [email protected] K. Kavitha [email protected]
1
Department of Computer Science, Mother Teresa Women’s University, Kodaikanal, Tamilnadu 624101, India
Multimedia Tools and Applications
1 Introduction Current development in computer expertises has led to the execution, the conceptualization and the growth of the cloud computing methods [13]. Cloud computing provides smart computational and storage space resolutions to deal with bandwidth, energy consumption, privacy and latency [1]. In IoT, through the sensing, the objects control can be abstracted as a task, in which a lot of sensing devices are applied to sense and to gather the data [15].IoT and cloud computing has a balance relationship [21]. IoT generates large amounts of data through sensing. While cloud providers allow data transfer using the internet, which means assisting a way to navigate the data. Using Cloud platform, IoT de
Data Loading...
