Assessing Performance of Internet of Things-Based Mobile Crowdsensing Systems for Sensing as a Service Applications in Smart Cities

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 Resultsmentioning

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

“…Garbagewatch [12] relies on citizen participation to improve the collection and recycling process, while the National Environment Agency of Singapore employs on a daily basis HazeWatch 2 to perform air pollution monitoring, an application previously developed at The University of New South Wales [13]. In all such applications, the collected data is sent though cellular (e.g., 4G/LTE) or WiFi data connection to a central collector, which is usually located in the cloud [14]. The organizer is usually responsible for user recruitment and task assignment prior to the start of the sensing campaign and for data analysis and processing during the sensing campaign.…”

Section: Introductionmentioning

confidence: 99%

Why energy matters? Profiling energy consumption of mobile crowdsensing data collection frameworks

Tomasoni

Pervasive and Mobile Computing

et al. 2018

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Mobile Crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. The citizens actively participate in the sensing process by contributing data with their mobile devices. To produce data, citizens sustain costs, i.e., the energy consumed for sensing and reporting operations. Hence, devising energy efficient data collection frameworks (DCF) is essential to foster participation. In this work, we investigate from an energy-perspective the performance of different DCFs. Our methodology is as follows: (i) we developed an Android application that implements the DCFs, (ii) we profiled the energy and network performance with a power monitor and Wireshark, (iii) we included the obtained traces into CrowdSenSim simulator for large-scale evaluations in city-wide scenarios such as Luxembourg, Turin and Washington DC. The amount of collected data, energy consumption and fairness are the performance indexes evaluated. The results unveil that DCFs with continuous data reporting are more energy-efficient and fair than DCFs with probabilistic reporting. The latter exhibit high variability of energy consumption, i.e., to produce the same amount of data, the associated energy cost of different users can vary significantly.

“…MCS follows a Sensing as a Service (S 2 aaS) business model, which makes data collected from sensors available to cloud users [8]. Consequently, companies and organizations have no longer the need to acquire an infrastructure to perform a sensing campaign, but they can exploit existing ones in a pay-as-you-go basis.…”

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.