A greedy-proof incentive-compatible mechanism for group recruitment in mobile crowd sensing

Suliman, Ahmed El‐Raie Emam; Otrok, Hadi; Mizouni, Rabeb; Singh, Shakti; Ouali, Anis

doi:10.1016/j.future.2019.07.060

Cited by 21 publications

(7 citation statements)

References 20 publications

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“…(4) Efficiency and energy consumption: Efficiency and energy consumption not only mean that the server needs an efficient algorithm to process the incentive procedure, but also that the participant hopes that the incentive procedure can lower the resource usage on the sensing device end [84]. The resource consumption of the sensing device is an important factor that prevents participants from participating in a sensing campaign [85].…”

Section: User Incentivesmentioning

confidence: 99%

Federated Learning in Smart City Sensing: Challenges and Opportunities

Ji-chu

Kantarcı

Oktuğ

et al. 2020

Sensors

213

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Smart Cities sensing is an emerging paradigm to facilitate the transition into smart city services. The advent of the Internet of Things (IoT) and the widespread use of mobile devices with computing and sensing capabilities has motivated applications that require data acquisition at a societal scale. These valuable data can be leveraged to train advanced Artificial Intelligence (AI) models that serve various smart services that benefit society in all aspects. Despite their effectiveness, legacy data acquisition models backed with centralized Machine Learning models entail security and privacy concerns, and lead to less participation in large-scale sensing and data provision for smart city services. To overcome these challenges, Federated Learning is a novel concept that can serve as a solution to the privacy and security issues encountered within the process of data collection. This survey article presents an overview of smart city sensing and its current challenges followed by the potential of Federated Learning in addressing those challenges. A comprehensive discussion of the state-of-the-art methods for Federated Learning is provided along with an in-depth discussion on the applicability of Federated Learning in smart city sensing; clear insights on open issues, challenges, and opportunities in this field are provided as guidance for the researchers studying this subject matter.

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Section: User Incentivesmentioning

confidence: 99%

Federated Learning in Smart City Sensing: Challenges and Opportunities

Ji-chu

Kantarcı

Oktuğ

et al. 2020

Sensors

213

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“…Suliman et al proposed an incentive-compatible mechanism for group recruitment [50]. They considered the greediness of participants of in-group recruitment, and the proposed mechanism can increase the quality of the collected information by selecting participants who are expected to give high-quality data at a low cost.…”

Section: Incentive Mechanism and Trustworthiness For Mobile Crowdsensingmentioning

confidence: 99%

Differentially Private Mobile Crowd Sensing Considering Sensing Errors

Sei

Ohsuga

2020

Sensors

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An increasingly popular class of software known as participatory sensing, or mobile crowdsensing, is a means of collecting people’s surrounding information via mobile sensing devices. To avoid potential undesired side effects of this data analysis method, such as privacy violations, considerable research has been conducted over the last decade to develop participatory sensing that looks to preserve privacy while analyzing participants’ surrounding information. To protect privacy, each participant perturbs the sensed data in his or her device, then the perturbed data is reported to the data collector. The data collector estimates the true data distribution from the reported data. As long as the data contains no sensing errors, current methods can accurately evaluate the data distribution. However, there has so far been little analysis of data that contains sensing errors. A more precise analysis that maintains privacy levels can only be achieved when a variety of sensing errors are considered.

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“…From Fig. 2, MCS architecture contains the four major components: sensors, mobile devices, communication infrastructure, and processing infrastructure [26]. These components can further be grouped into two that is the mobile data collection components, and the web-based data server [27].…”

Section: Architecture Of Mobile Crow-dsensing Systemmentioning

confidence: 99%

Simulation of Trust-Based Mechanism for Enhancing User Confidence in Mobile Crowdsensing Systems

et al. 2020

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With the rapid development of mobile technology and subsequent mass adoption of mobile devices, mobile crowdsensing (MCS) has gained a lot of research attention. In MCS systems, trust is a key focus in the overall improvement in the participant uptake of the sensing tasks. The trust-based scheme of MCS is studied to predict the damage level, the scores of quality-of-service (QoS), and the levels of qualityof-data (QoD) of MCS systems. Users can participate in MCS sensing based on trustworthy indicators that are related to user experience and system reputation, as well as the knowledge obtained about the MCS systems. This paper illustrates the establishment of user confidence during recruitment in MCS as it is very critical for the success of MCS systems and proposes a simulation trust-based mechanism (SiTBaM) approach. The level of MCS security is enhanced to protect the privacy of participants, so that participants can be assured that the MCS system they are working with during sensing moment is trustworthy. The application of SiTBaM in MCS is verified to yield better results as the simulations show that it offers higher QoS levels, QoD scores, as well as low damage levels in the presence of any task or many malicious users. These results were validated through comparisons with other schemes. INDEX TERMS Mobile crowdsensing, quality-of-data, quality-of-service, security, trust-based scheme.

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A greedy-proof incentive-compatible mechanism for group recruitment in mobile crowd sensing

Cited by 21 publications

References 20 publications

Federated Learning in Smart City Sensing: Challenges and Opportunities

Federated Learning in Smart City Sensing: Challenges and Opportunities

Differentially Private Mobile Crowd Sensing Considering Sensing Errors

Simulation of Trust-Based Mechanism for Enhancing User Confidence in Mobile Crowdsensing Systems

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