A user-enabled testbed architecture with mobile crowdsensing support for smart, green buildings

Angelopoulos, Constantinos Marios; Evangelatos, Orestis; Nikoletseas, Sotiris; Raptis, Theofanis P.; Rolim, José D. P.; Veroutis, Konstantinos

doi:10.1109/icc.2015.7248383

Cited by 28 publications

(19 citation statements)

References 9 publications

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“…Based on available budget, Angelopoulos et al [67][68][69] recommend several strategies to reward the effort of data contributors in mobile crowdsensing, including proportional incentive policy, participation-aware incentive policy, behavioural-aware incentive policy, location-aware incentive policy, mobility-aware incentive policy, thrifty incentive policy and quality-aware incentive policy. Proportional incentive policy advocates that for each task segment, incentive proportionate to the expected utility and the current residual budget be allocated, from which the effort of participants who have contributed data to the task segment can be rewarded accordingly [69].…”

Section: Effortmentioning

confidence: 99%

“…Afterwards, the efforts of data contributors should be rewarded in a more conservative manner that is good enough to retain` existing participants, not necessarily attracting new ones [69]. Behavioural-aware incentive policy rewards participants based on historical records of their trustworthiness and commitment to the crowdsensing initiative [68]. Location-aware incentive policy selects participants by virtue of their locations or rewards participant's effort according to the cost associated with the given location where data is captured while mobility-aware incentive policy rewards participant's effort according to the frequency with which the user moves around the area of interest to capture data [68,70].…”

Section: Effortmentioning

confidence: 99%

“…Behavioural-aware incentive policy rewards participants based on historical records of their trustworthiness and commitment to the crowdsensing initiative [68]. Location-aware incentive policy selects participants by virtue of their locations or rewards participant's effort according to the cost associated with the given location where data is captured while mobility-aware incentive policy rewards participant's effort according to the frequency with which the user moves around the area of interest to capture data [68,70]. Thrifty incentive policy aims at rewarding participants in such a way that ensures available budget is used prudently and resourcefully.…”

Section: Effortmentioning

confidence: 99%

“…In this approach, different redeemable credit points are assigned to various locations and users motivated by these incentives can choose to make it to the locations of interest to capture data samples [93]. While the aforementioned solutions have made significant research contributions, further studies are required to address the problem of poor area coverage along various directions, such as the use of mobility profiles as one of the selection criteria when recruiting participants [65]; recruiting participants with high demographic diversity [66]; increasing the coverage area by understanding the mobility patterns of different groups [67]; involving participants with broad and diverse social interaction patterns [68]; and the use of density maps to estimate the number of participants in a given area [25]. It is also important to investigate the problem of poor area coverage from the perspective of unequal representation of various community stakeholders and interest groups in the participatory sensing process and how that influences the fairness and reliability of the crowdsensed data for urban decision making.…”

Section: Research Opportunities and Challengesmentioning

confidence: 99%

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Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework

Ogie

2016

Hum. Cent. Comput. Inf. Sci.

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Mobile crowdsensing is a burgeoning concept that allows smart cities to leverage the sensing power and ubiquitous nature of mobile devices in order to capture and map phenomena of common interest. At the core of any successful mobile crowdsensing application is active user participation, without which the system is of no value in sensing the phenomenon of interest. A major challenge militating against widespread use and adoption of mobile crowdsensing applications is the issue of how to identify the most appropriate incentive mechanism for adequately and efficiently motivating participants. This paper reviews literature on incentive mechanisms for mobile crowdsensing and proposes the concept of SPECTRUM as a guide for inferring the most appropriate type of incentive suited to any given crowdsensing task. Furthermore, the paper highlights research challenges and areas where additional studies related to the different factors outlined in the concept of SPECTRUM are needed to improve citizen participation in mobile crowdsensing. It is envisaged that the broad range of factors covered in SPECTRUM will enable smart cities to efficiently engage citizens in large-scale crowdsensing initiatives. More importantly, the paper is expected to trigger empirical investigations into how various factors as outlined in SPECTRUM can influence the type of incentive mechanism that is considered most appropriate for any given mobile crowdsensing initiative.

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Section: Effortmentioning

confidence: 99%

Section: Effortmentioning

confidence: 99%

Section: Effortmentioning

confidence: 99%

Section: Research Opportunities and Challengesmentioning

confidence: 99%

See 2 more Smart Citations

Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework

Ogie

2016

Hum. Cent. Comput. Inf. Sci.

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“…The following three scenarios showcase the capabilities and range of scenarios feasible by the architecture. Light control scenario for a building management system [6]. The end goals are to monitor the energy consumption, to automate the lighting and to save energy.…”

Section: Practical Applicationsmentioning

confidence: 99%

A Service Based Architecture for Multidisciplinary IoT Experiments with Crowdsourced Resources

Alexandrou

Angelopoulos

Evangelatos

et al. 2016

Ad-Hoc, Mobile, and Wireless Networks

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Abstract. Research on emerging networking paradigms, such as Mobile Crowdsensing Systems, requires new types of experiments to be conducted and an increasing spectrum of devices to be supported by experimenting facilities. In this work, we present a service based architecture for IoT testbeds which (a) exposes the operations of a testbed as services by following the Testbed as a Service (TBaaS) paradigm; (b) enables diverse facilities to be federated in a scalable and standardized way and (c) enables the seamless integration of crowdsourced resources (e.g. smartphones and wearables) and their abstraction as regular IoT resources. The architecture enables an experimenter to access a diverse set of resources and orchestrate experiments via a common interface by hiding the underlying heterogeneity and complexity. This way, the field of IoT experimentation with real resources is further promoted and broadened to also address researchers from other fields and disciplines.

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Frugal incentive mechanism in periodic mobile crowdsensing

Sun

Liu

2017

Int J Communication

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Nowadays, numerous incentive mechanisms of mobile crowdsensing have been designed to attract extensive user participation, but most of these mechanisms focus only on independent task scenarios, where the sensing tasks are independent of each other. On the contrary, we focus on a periodical task scenario, where each user participates in the same type of sensing tasks periodically. In this paper, we consider the long-term user participation incentive in a general periodical mobile crowdsensing system from a frugality payment perspective. We explore the issue under both semi-online (the intraperiod interactive process is synchronous while the interperiod interactive process is sequential and asynchronous during each period) and online user arrival models (the previous 2 interactive processes are sequential and asynchronous). In particular, we first propose a semi-online frugal incentive mechanism by introducing a Lyapunov method. Moreover, we also extend it to an online frugal incentive mechanism, which satisfies the long-term participation constraint and approximate optimality. Finally, extensive simulations show that our mechanisms satisfy the above theoretical properties. KEYWORDSfrugal payment, long-term incentive, periodical crowdsensing, submodular function Int J Commun Syst. 2018;31:e3493.wileyonlinelibrary.com/journal/dac

show abstract

A user-enabled testbed architecture with mobile crowdsensing support for smart, green buildings

Cited by 28 publications

References 9 publications

Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework

Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework

A Service Based Architecture for Multidisciplinary IoT Experiments with Crowdsourced Resources

Frugal incentive mechanism in periodic mobile crowdsensing

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