scholarly journals Reputation-Aware Recruitment and Credible Reporting for Platform Utility in Mobile Crowd Sensing with Smart Devices in IoT

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3305 ◽  
Author(s):  
Waqas Ahmad ◽  
Shengling Wang ◽  
Ata Ullah ◽  
Sheharyar ◽  
Muhammad Yasir Shabir
Keyword(s):  
Profit Maximization ◽  
Smart Devices ◽  
The Internet ◽  
Mobile Crowd Sensing ◽  
Quality Reporting ◽  
Crowd Sensing ◽  
False Reporting ◽  
The Impact ◽  
Mobile Crowd ◽  
Selection Of

The Internet of things (IoT) comprises a huge collection of electronic devices connected to the Internet to ensure the dependable exchange of sensing information. It involves mobile workers (MWs) who perform various activities to support enormous online services and applications. In mobile crowd sensing (MCS), a massive amount of sensing data is also generated by smart devices. Broadly, in the IoT, verifying the credibility and truthfulness of MWs’ sensing reports is needed for MWs to expect attractive rewards. MWs are recruited by paying monetary incentives that must be awarded according to the quality and quantity of the task. The main problem is that MWs may perform false reporting by sharing low-quality reported data to reduce the effort required. In the literature, false reporting is improved by hiring enough MWs for a task to evaluate the trustworthiness and acceptability of information by aggregating the submitted reports. However, it may not be possible due to budget constraints, or when malicious reporters are not identified and penalized properly. Recruitment is still not a refined process, which contributes to low sensing quality. This paper presents Reputation, Quality-aware Recruitment Platform (RQRP) to recruit MWs based on reputation for quality reporting with the intention of platform profit maximization in the IoT scenario. RQRP comprises two main phases: filtration in the selection of MWs and verifying the credibility of reported tasks. The former is focused on the selection of suitable MWs based on different criteria (e.g., reputation, bid, expected quality, and expected platform utility), while the latter is more concerned with the verification of sensing quality, evaluation of reputation score, and incentives. We developed a testbed to evaluate and analyze the datasets, and a simulation was performed for data collection scenario from smart sensing devices. Results proved the superiority of RQRP against its counterparts in terms of truthfulness, quality, and platform profit maximization. To the best of our knowledge, we are the first to study the impact of truthful reporting on platform utility.

scholarly journals Privacy protection in mobile crowd sensing: a survey

2019 ◽  
Vol 23 (1) ◽  
pp. 421-452 ◽  
Author(s):  
Yongfeng Wang ◽  
Zheng Yan ◽  
Wei Feng ◽  
Shushu Liu
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Large Scale ◽  
System Structure ◽  
Smart Devices ◽  
Future Research ◽  
Sensitive Information ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Mobile Crowd

AbstractThe unprecedented proliferation of mobile smart devices has propelled a promising computing paradigm, Mobile Crowd Sensing (MCS), where people share surrounding insight or personal data with others. As a fast, easy, and cost-effective way to address large-scale societal problems, MCS is widely applied into many fields, e.g., environment monitoring, map construction, public safety, etc. Despite the popularity, the risk of sensitive information disclosure in MCS poses a serious threat to the participants and limits its further development in privacy-sensitive fields. Thus, the research on privacy protection in MCS becomes important and urgent. This paper targets the privacy issues of MCS and conducts a comprehensive literature research on it by providing a thorough survey. We first introduce a typical system structure of MCS, summarize its characteristics, propose essential requirements on privacy on the basis of a threat model. Then, we survey existing solutions on privacy protection and evaluate their performances by employing the proposed requirements. In essence, we classify the privacy protection schemes into four categories with regard to identity privacy, data privacy, attribute privacy, and task privacy. Besides, we review the achievements on privacy-preserving incentives in MCS from four viewpoints of incentive measures: credit incentive, auction incentive, currency incentive, and reputation incentive. Finally, we point out some open issues and propose future research directions based on the findings from our survey.

scholarly journals Crowd-Based Cognitive Perception of the Physical World: Towards the Internet of Senses

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2437 ◽  
Author(s):  
Gianni Pasolini ◽  
Anna Guerra ◽  
Francesco Guidi ◽  
Nicolò Decarli ◽  
Davide Dardari
Keyword(s):  
Antenna Arrays ◽  
Physical World ◽  
Machine Learning Techniques ◽  
The Internet ◽  
User Cooperation ◽  
Learning Mechanisms ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Action Capabilities ◽  
Mobile Crowd

This paper introduces a possible architecture and discusses the research directions for the realization of the Cognitive Perceptual Internet (CPI), which is enabled by the convergence of wired and wireless communications, traditional sensor networks, mobile crowd-sensing, and machine learning techniques. The CPI concept stems from the fact that mobile devices, such as smartphones and wearables, are becoming an outstanding mean for zero-effort world-sensing and digitalization thanks to their pervasive diffusion and the increasing number of embedded sensors. Data collected by such devices provide unprecedented insights into the physical world that can be inferred through cognitive processes, thus originating a digital sixth sense. In this paper, we describe how the Internet can behave like a sensing brain, thus evolving into the Internet of Senses, with network-based cognitive perception and action capabilities built upon mobile crowd-sensing mechanisms. The new concept of hyper-map is envisioned as an efficient geo-referenced repository of knowledge about the physical world. Such knowledge is acquired and augmented through heterogeneous sensors, multi-user cooperation and distributed learning mechanisms. Furthermore, we indicate the possibility to accommodate proactive sensors, in addition to common reactive sensors such as cameras, antennas, thermometers and inertial measurement units, by exploiting massive antenna arrays at millimeter-waves to enhance mobile terminals perception capabilities as well as the range of new applications. Finally, we distillate some insights about the challenges arising in the realization of the CPI, corroborated by preliminary results, and we depict a futuristic scenario where the proposed Internet of Senses becomes true.

A mobile crowd sensing ecosystem enabled by CUPUS: Cloud-based publish/subscribe middleware for the Internet of Things

2016 ◽  
Vol 56 ◽  
pp. 607-622 ◽  
Author(s):  
Aleksandar Antonić ◽  
Martina Marjanović ◽  
Krešimir Pripužić ◽  
Ivana Podnar Žarko
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
The Internet Of Things ◽  
Mobile Crowd

scholarly journals Quality-Aware Incentive Mechanism for Mobile Crowd Sensing

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Ling-Yun Jiang ◽  
Fan He ◽  
Yu Wang ◽  
Li-Juan Sun ◽  
Hai-ping Huang
Keyword(s):  
Incentive Mechanism ◽  
Data Reliability ◽  
Mobile Crowd Sensing ◽  
Simulation Experiments ◽  
Universal System ◽  
Multiple User ◽  
Sensing Systems ◽  
Crowd Sensing ◽  
The Impact ◽  
Mobile Crowd

Mobile crowd sensing (MCS) is a novel sensing paradigm which can sense human-centered daily activities and the surrounding environment. The impact of mobility and selfishness of participants on the data reliability cannot be ignored in most mobile crowd sensing systems. To address this issue, we present a universal system model based on the reverse auction framework and formulate the problem as the Multiple Quality Multiple User Selection (MQMUS) problem. The quality-aware incentive mechanism (QAIM) is proposed to meet the quality requirement of data reliability. We demonstrate that the proposed incentive mechanism achieves the properties of computational efficiency, individual rationality, and truthfulness. And meanwhile, we evaluate the performance and validate the theoretical properties of our incentive mechanism through extensive simulation experiments.

scholarly journals Maximizing spatial–temporal coverage in mobile crowd-sensing based on public transports with predictable trajectory

2018 ◽  
Vol 14 (8) ◽  
pp. 155014771879535 ◽  
Author(s):  
Chaowei Wang ◽  
Chensheng Li ◽  
Cai Qin ◽  
Weidong Wang ◽  
Xiuhua Li
Keyword(s):  
Optimization Problem ◽  
System Model ◽  
Trajectory Data ◽  
Mobile Crowd Sensing ◽  
Data Set ◽  
The Public ◽  
Crowd Sensing ◽  
Mobile Crowd ◽  
Temporal Coverage ◽  
Selection Of

Mobile crowd-sensing is a prospective paradigm especially for intelligent mobile terminals, which collects ubiquitous data efficiently in metropolis. The existing crowd-sensing schemes based on intelligent terminals mainly consider the current trajectory of the participants, and the quality highly depends on the spatial-temporal coverage which is easily weakened by the mobility of participants. Nowadays, public transports are widely used and affordable in many cities around the globe. Public transports embedded with substantial sensors act as participants in crowd-sensing, but different from the intelligent terminals, the trajectory of public transports is schedulable and predictable, which sheds an opportunity to achieve high-quality crowd-sensing. Therefore, based on the predictable trajectory of public transports, we design a novel system model and formulate the selection of public transports as an optimization problem to maximize the spatial–temporal coverage. After proving the public transport selection is non-deterministic polynomial-time hardness, an approximation algorithm is proposed and the coverage is close to 1. We evaluate the proposed algorithm with samples of real T-Drive trajectory data set. The results show that our algorithm achieves a near optimal coverage and outperforms existing algorithms.

scholarly journals Radio Environment Map Construction by Kriging Algorithm Based on Mobile Crowd Sensing

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Zhifeng Han ◽  
Jianxin Liao ◽  
Qi Qi ◽  
Haifeng Sun ◽  
Jingyu Wang
Keyword(s):  
Large Scale ◽  
Kriging Interpolation ◽  
Smart Devices ◽  
Prototype System ◽  
Interpolation Algorithm ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Environment Map ◽  
Mobile Crowd ◽  
Radio Environment Map

In the IoT era, 5G will enable various IoT services such as broadband access everywhere, high user and devices mobility, and connectivity of massive number of devices. Radio environment map (REM) can be applied to improve the utilization of radio resources for the access control of IoT devices by allocating them reasonable wireless spectrum resources. However, the primary problem of constructing REM is how to collect the large scale of data. Mobile crowd sensing (MCS), leveraging the smart devices carried by ordinary people to collect information, is an effective solution for collecting the radio environment information for building the REM. In this paper, we build a REM collecting prototype system based on MCS to collect the data required by the radio environment information. However, limited by the budget of the platform, it is hard to recruit enough participants to join the sensing task to collect the radio environment information. This will make the radio environment information of the sensing area incomplete, which cannot describe the radio information accuracy. Considering that the Kriging algorithm has been widely used in geostatistics principle for spatial interpolation for Kriging giving the best unbiased estimate with minimized variance, we utilize the Kriging interpolation algorithm to infer complete radio environment information from collected sample radio environment information data. The interpolation performance is analyzed based on the collected sample radio environment information data. We demonstrate experiments to analyze the Kriging interpolation algorithm interpolation results and error and compared them with the nearest neighbor (NN) and the inverse distance weighting (IDW) interpolation algorithms. Experiment results show that the Kriging algorithm can be applied to infer radio environment information data based on the collected sample data and the Kriging interpolation has the least interpolation error.

scholarly journals Intelligent Gaming for Mobile Crowd-Sensing Participants to Acquire Trustworthy Big Data in the Internet of Things

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 22209-22223 ◽  
Author(s):  
Maryam Pouryazdan ◽  
Claudio Fiandrino ◽  
Burak Kantarci ◽  
Tolga Soyata ◽  
Dzmitry Kliazovich ◽  
...  
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
The Internet ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
The Internet Of Things ◽  
Mobile Crowd
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