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 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

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.

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.

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