CrowdSenSim: a Simulation Platform for Mobile Crowdsensing in Realistic Urban Environments

Fiandrino, Claudio; Capponi, Andrea; Cacciatore, Giuseppe; Kliazovich, Dzmitry; Sorger, Ulrich; Bouvry, Pascal; Kantarcı, Burak; Granelli, Fabrizio; Giordano, Stefano

doi:10.1109/access.2017.2671678

Cited by 97 publications

(47 citation statements)

References 24 publications

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“…III. PERFORMANCE EVALUATION We evaluate performance of the proposed framework with a custom crowdsensing simulator, CrowdSenSim [18]. We first present its detailed functionality, then the simulation scenario and the obtained results.…”

Section: Amount Of Reported Datamentioning

confidence: 99%

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

Capponi

Fiandrino

Kliazovich³

et al. 2017

2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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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.

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Section: Amount Of Reported Datamentioning

confidence: 99%

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

Capponi

Fiandrino

Kliazovich³

et al. 2017

2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

Self Cite

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“…Performance evaluation exploits CrowdSenSim [17]. This section first presents its detailed features, then the simulation scenario and the output results.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…We employ CrowdSenSim to perform extensive simulations for the analysis of the energy efficiency of the various DCFs [17]. The simulator models pedestrian mobility in realistic urban environments.…”

Section: Crowdsensimmentioning

confidence: 99%

“…Hence, simulations are an excellent alternative and viable solution. CrowdSenSim [17], the first simulator for MCS systems, was designed to fill this gap by providing the researchers a tool to perform large scale simulations over realistic urban environments. For example, its effectiveness has been demonstrated to evaluate performance of city-wide solutions for public street lighting [18].…”

Section: Introductionmentioning

confidence: 99%

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

Tomasoni

Capponi

Fiandrino

et al. 2018

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

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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.

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“…Bluetooth is currently one of the most promising available ad hoc wireless technologies [1][2][3][4], and the introduction of its advantageous features is considered to be one of the main enablers of the Internet of Things (IoT) [5][6][7][8][9][10]. Until now, Bluetooth Low Energy (BLE) technology [11] has been embedded in existing mobile and portable devices, offering the advantages of short-range radio, low power, and low cost wireless connectivity.…”

Section: Introductionmentioning

confidence: 99%

A Reconfigurable Mesh-Ring Topology for Bluetooth Sensor Networks

Wang¹,

Kao

2018

Energies

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Abstract:In this paper, a Reconfigurable Mesh-Ring (RMR) algorithm is proposed for Bluetooth sensor networks. The algorithm is designed in three stages to determine the optimal configuration of the mesh-ring network. Firstly, a designated root advertises and discovers its neighboring nodes. Secondly, a scatternet criterion is built to compute the minimum number of piconets and distributes the connection information for piconet and scatternet. Finally, a peak-search method is designed to determine the optimal mesh-ring configuration for various sizes of networks. To maximize the network capacity, the research problem is formulated by determining the best connectivity of available mesh links. During the formation and maintenance phases, three possible configurations (including piconet, scatternet, and hybrid) are examined to determine the optimal placement of mesh links. The peak-search method is a systematic approach, and is implemented by three functional blocks: the topology formation block generates the mesh-ring topology, the routing efficiency block computes the routing performance, and the optimum decision block introduces a decision-making criterion to determine the optimum number of mesh links. Simulation results demonstrate that the optimal mesh-ring configuration can be determined and that the scatternet case achieves better overall performance than the other two configurations. The RMR topology also outperforms the conventional ring-based and cluster-based mesh methods in terms of throughput performance for Bluetooth configurable networks.

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CrowdSenSim: a Simulation Platform for Mobile Crowdsensing in Realistic Urban Environments

Cited by 97 publications

References 24 publications

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

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

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

A Reconfigurable Mesh-Ring Topology for Bluetooth Sensor Networks

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