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

Abstract

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.

Weighted Adaptive Opportunistic Scheduling Framework for Smartphone Sensor Data Collection in IoT

“…There are also some smartphone-based opportunistic data collection frameworks in the literature, specifically to facilitate IoT and smart city applications. Some researchers proposed an opportunistic mobile crowdsensing framework for collecting sensor data from pedestrian mobile devices in smart cities with the aim of minimizing the cost of sensing and reporting sensed data [20]. Other authors proposed a cost-efficient smart device application for opportunistic sensor data collection to monitor indoor air quality [21].…”

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

“…The organization of a MCS campaign requires to sustain costs to reward individuals for their involvement and to verify the accomplishment of the tasks. Consequently, it is crucial to investigate how to maximize the efficiency of a Data Collection Frameworks (DCF), which is defined in terms of the costs sustained by the organizer and the revenues [15]. The users sustain costs while contributing data too, i.e., the energy spent from the batteries for sensing and reporting data and, eventually, the data subscription plan if cellular connectivity is used for reporting.…”

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

“…The organization of a MCS campaign requires to sustain costs to reward individuals for their involvement and to verify the accomplishment of the tasks [10] . Consequently, it is crucial to investigate how to maximize the efficiency of a data collection framework (DCF), which is defined in terms of the costs sustained by the organizer and the revenues [11]. MCS follows a Sensing as a Service (S 2 aaS) business model, which makes data collected from sensors available to cloud users [12].…”