SONATA: Social Network Assisted Trustworthiness Assurance in Smart City Crowdsensing

SONATA: Social Network Assisted Trustworthiness Assurance in Smart City Crowdsensing

Burak Kantarci (Department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY, USA), Kevin G. Carr (Department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY, USA) and Connor D. Pearsall (Department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY, USA)
Copyright: © 2016 |Pages: 20
DOI: 10.4018/IJDST.2016010104
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

With the advent of mobile cloud computing paradigm, mobile social networks (MSNs) have become attractive tools to share, publish and analyze data regarding everyday behavior of mobile users. Besides revealing information about social interactions between individuals, MSNs can assist smart city applications through crowdsensing services. In presence of malicious users who aim at misinformation through manipulation of their sensing data, trustworthiness arises as a crucial issue for the users who receive service from smart city applications. In this paper, the authors propose a new crowdsensing framework, namely Social Network Assisted Trustworthiness Assurance (SONATA) which aims at maximizing crowdsensing platform utility and minimizing the manipulation probability through vote-based trustworthiness analysis in dynamic social network architecture. SONATA adopts existing Sybil detection techniques to identify malicious users who aim at misinformation/disinformation at the crowdsensing platform. The authors present performance evaluation of SONATA under various crowdsensing scenarios in a smart city setting. Performance results show that SONATA improves crowdsensing utility under light and moderate arrival rates of sensing task requests when less than 7% of the users are malicious whereas crowdsensing utility is significantly improved under all task arrival rates if the ratio of malicious users to the entire population is at least 7%. Furthermore, under each scenario, manipulation ratio is close to zero under SONATA while trustworthiness unaware recruitment of social network users leads to a manipulation probability of 2.5% which cannot be tolerated in critical smart city applications such as disaster management or public safety.
Article Preview

Introduction

The smart city concept denotes an intelligent platform consisting of interconnected sensors, embedded devices, decision making systems that process real time data (Chourabi, et al., 2012) (Meier & Lee, 2011) (Batty, 2014). Innovations in smart city design and applications can be accelerated by the integration of cloud computing into several smart city services such as transport, education, energy and water monitoring, healthcare, public safety and other ICT-based applications (Clohessy, Acton, & Morgan, 2014).A promising solution that would improve the benefits of smart city applications can be the integration of built-in sensors in mobile devices and providing the sensing resources as a service to particular smart city applications when needed (Kantarci & Mouftah, Trustworthy Sensing for Public Safety in Cloud-Centric Internet of Things, 2014). This concept is defined as Sensing as a Service (S2aaS) in the literature (Sheng, Xiao, Tang, & Xue, 2013). In S2aaS, distributed smart mobile devices participate in crowdsensing tasks that are requested by end users that are using either fixed computers or smart mobile devices.A comprehensive survey of S2aaS in the context of cloud-centric Internet of Things has been presented in (Kantarci & Mouftah, Sensing as a Service in Cloud-Centric Internet of Things Architecture, 2015).

Trustworthiness of crowdsensed data is a crucial challenge in S2aaS applications (French, Bessis, Maple, & Asimakopoulou, 2012). Kantarci and Mouftah have raised the trustworthiness issue in the recruitment process trough user centric incentives (Kantarci & Mouftah, Trustworthy Sensing for Public Safety in Cloud-Centric Internet of Things, 2014). Trustworthiness of crowdsensed data has been improved by applying a user reputation system that uses statistical accuracy of the crowdsensed data per mobile device. This approach has later been improved by incorporating mobility-awareness (Kantarci & Mouftah, Mobility-aware trustworthy crowdsourcing in cloud-centric Internet of Things, 2014) and social interaction-awareness (Kantarci & Mouftah, Trustworthy crowdsourcing via mobile social networks, 2014). In order to address trustworthiness issue, there have been studies which take social ties into consideration, and some mobile devices are defined as social sensors denoting the trusted crowdsensing nodes (Hao, Mingjie, Geyong, & Yang, 2014). Furthermore, social trust and reciprocity have been combined to maximize the crowdsensing utility by modeling the problem as maximizing utility of a circulation flow (Gong, Chen, Zhang, & Poor, 2014).

Complete Article List

Search this Journal:
Reset
Open Access Articles
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing