Context-Awareness for Mobile Sensing: A Survey and Future Directions

Yurur, Ozgur; Liu, Chi Harold; Sheng, Zhengguo; Leung, Victor C. M.; Moreno, Wilfrido; Leung, Kin K.

doi:10.1109/comst.2014.2381246

Cited by 170 publications

(57 citation statements)

References 153 publications

(137 reference statements)

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“…Another approach is to support access control in the application layer on a per-vendor basis. Relevant projects that estimate the network location of objects to perform context-aware services are reviewed in [98]. Current methods for location estimation are based on IP.…”

Section: Operational Requirementsmentioning

confidence: 99%

A Review on Internet of Things for Defense and Public Safety

Fraga‐Lamas

Fernández‐Caramés

Suárez-Albela

et al. 2016

Sensors

195

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The Internet of Things (IoT) is undeniably transforming the way that organizations communicate and organize everyday businesses and industrial procedures. Its adoption has proven well suited for sectors that manage a large number of assets and coordinate complex and distributed processes. This survey analyzes the great potential for applying IoT technologies (i.e., data-driven applications or embedded automation and intelligent adaptive systems) to revolutionize modern warfare and provide benefits similar to those in industry. It identifies scenarios where Defense and Public Safety (PS) could leverage better commercial IoT capabilities to deliver greater survivability to the warfighter or first responders, while reducing costs and increasing operation efficiency and effectiveness. This article reviews the main tactical requirements and the architecture, examining gaps and shortcomings in existing IoT systems across the military field and mission-critical scenarios. The review characterizes the open challenges for a broad deployment and presents a research roadmap for enabling an affordable IoT for defense and PS.

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Section: Operational Requirementsmentioning

confidence: 99%

A Review on Internet of Things for Defense and Public Safety

Fraga‐Lamas

Fernández‐Caramés

Suárez-Albela

et al. 2016

Sensors

195

View full text Add to dashboard Cite

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“…To achieve this objective, the data collector periodically broadcasts information about the most urgently needed samples, defined as the data collection utility. Then all participants can determine whether they can contribute the requested data taking into account their cost of sensing and reporting and on the basis of the environmental context [37], [38]. For example, capturing a picture while the smartphone is in a pocket does not bring any utility for the MCS system.…”

Section: Data Collection Frameworkmentioning

confidence: 99%

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

Capponi

Fiandrino

Kliazovich

et al. 2017

IEEE Trans. Sustain. Comput.

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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.

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“…Environmental context C s of sensor s defines the set of facts and circumstances happening around the mobile device, such as the location or the mobility pattern [16]. Having knowledge of the environmental context is essential to avoid performing sensing under unfavorable conditions that affect the overall energy budget without providing any benefit for the collector.…”

Section: Profiling the Environmental Contextmentioning

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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Context-Awareness for Mobile Sensing: A Survey and Future Directions

Cited by 170 publications

References 153 publications

A Review on Internet of Things for Defense and Public Safety

A Review on Internet of Things for Defense and Public Safety

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

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

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