Sociability-Driven User Recruitment in Mobile Crowdsensing Internet of Things Platforms

IEEE Trans. Sustain. Comput.

Kliazovich

et al. 2017

Self Cite

Abstract-Mobile crowd sensing received significant attention in the recent years and has become a popular paradigm for sensing. It operates relying on the rich set of built-in sensors equipped in mobile devices, such as smartphones, tablets and wearable devices. To be effective, mobile crowd sensing systems require a large number of users to contribute data. While several studies focus on developing efficient incentive mechanisms to foster user participation, data collection policies still require investigation. In this paper, we propose a novel distributed and sustainable framework for gathering information in cloud-based mobile crowd sensing systems with opportunistic reporting. The proposed framework minimizes cost of both sensing and reporting, while maximizing the utility of data collection and, as a result, the quality of contributed information. Analytical and simulation results provide performance evaluation for the proposed framework by providing a fine-grained analysis of the energy consumed. The simulations, performed in a real urban environment and with a large number of participants, aim at verifying the performance and scalability of the proposed approach on a large scale under different user arrival patterns.

Section: Simulation Set-upmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Cost-Effective Distributed Framework for Data Collection in Cloud-Based Mobile Crowd Sensing Architectures

IEEE Trans. Sustain. Comput.

Kliazovich

et al. 2017

Self Cite

“…On the contrary, opportunistic sensing systems minimize user involvement and sensing decisions are device-driven (e.g., recording ambient light). In both types of frameworks, proper recruitment of the participants is essential [5]. Hassani et al [6] proposed Context-Aware Task Allocation (CATA), which allocates tasks after having recruited users in opportunistic MCS systems.…”

Section: Introductionmentioning

confidence: 99%

Energy efficient data collection in opportunistic mobile crowdsensing architectures for smart cities

2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

Kliazovich³

et al. 2017

Self Cite

Abstract-Smart cities employ latest information and communication technologies to enhance services for citizens. Sensing is essential to monitor current status of infrastructures and the environment. In Mobile Crowdsensing (MCS), citizens participate in the sensing process contributing data with their mobile devices such as smartphones, tablets and wearables. To be effective, MCS systems require a large number of users to contribute data. While several studies focus on developing efficient incentive mechanisms to foster user participation, data collection policies still require investigation. In this paper, we propose a novel distributed and energy-efficient framework for data collection in opportunistic MCS architectures. Opportunistic sensing systems require minimal intervention from the user side as sensing decisions are application-or device-driven. The proposed framework minimizes the cost of both sensing and reporting, while maximizing the utility of data collection and, as a result, the quality of contributed information. We evaluate performance of the framework with simulations, performed in a real urban environment and with a large number of participants. The simulation results verify cost-effectiveness of the framework and assess efficiency of the data generation process.

“…Developing efficient DCFs is crucial to regulate the degree of user involvement to prevent excessive battery drain from the mobile devices. This is a fundamental limiting factor to foster user participation and contribution [14]. At the same time, the DCFs have to gather a sufficient amount of data to ensure quality of sensed information [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Profiling Energy Efficiency of Mobile Crowdsensing Data Collection Frameworks for Smart City Applications

Tomasoni

2018 6th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud)

et al. 2018

Self Cite

Abstract-Mobile crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. In MCS, citizens actively participate in the sensing process by contributing data with their smartphones, tablets, wearables and other mobile devices to a collector. As citizens sustain costs while contributing data, i.e., the energy spent from the batteries for sensing and reporting, devising energy efficient data collection frameworks (DCFs) is essential. In this work, we compare the energy efficiency of several DCFs through CrowdSenSim, which allows to perform largescale simulation experiments in realistic urban environments. Specifically, the DCFs under analysis differ one with each other by the data reporting mechanism implemented and the signaling between users and the collector needed for sensing and reporting decisions. Results reveal that the key criterion differentiating DCFs' energy consumption is the data reporting mechanism. In principle, continuous reporting to the collector should be more energy consuming than probabilistic reporting. However, DCFs with continuous reporting that implement mechanisms to block sensing and data delivery after a certain amount of contribution are more effective in harvesting data from the crowd.