The BikeNet mobile sensing system for cyclist experience mapping

Eisenman, Shane B.; Miluzzo, Emiliano; Lane, Nicholas D.; Peterson, Ronald; Ahn, G-S.; Campbell, Andrew T.

doi:10.1145/1322263.1322273

Cited by 334 publications

(251 citation statements)

References 18 publications

(13 reference statements)

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“…The development of IoT technologies can support a variety of applications, including the intelligent power grid [1][2][3][4], intelligent transportation [5,6], intelligent medicine and healthcare [7][8][9][10], intelligent art [11,12], intelligent logistics [13,14], intelligent environmental monitoring [15,16], smart life [17][18][19][20], etc. In a smart home context, numerous smart objects need to use wireless communications due to the requirements of supporting mobile applications and maintaining neat living space.…”

Section: Open Accessmentioning

confidence: 99%

Design and Implementation of an IoT Access Point for Smart Home

et al. 2015

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Network communication and micro-electro-mechanical embedded technologies have attracted much attention in recent years. Through these technologies, the capabilities of sensing, identification, and communication can be embedded in various smart devices. These smart devices can automatically connect to the Internet and form an intelligent network called Internet of Things (IoT). However, these devices are embedded with different wireless communication interfaces such as Wi-Fi and ZigBee. This paper presents the design and implementation of an IoT access point that supports functionalities of coordination of various wireless transmission protocols. Based on the existing Wi-Fi access point, we have embedded a ZigBee module and implemented ZigBee and UPnP protocols into the designed IoT access point, which supports ZigBee communication capabilities over the Internet.

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Section: Open Accessmentioning

confidence: 99%

Design and Implementation of an IoT Access Point for Smart Home

et al. 2015

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“…Such applications feature continuous sharing of data that can be utilized to create a number of collaborative scenarios (e.g., BikeNet [15]). A central component to realize such scenarios is the availability of some high-level communication structure, such as QRTs.…”

Section: Background and Related Workmentioning

confidence: 99%

Intelligent search in social communities of smartphone users

Konstantinidis

Zeinalipour-Yazti

Andreou

et al. 2012

Distrib Parallel Databases

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Abstract. Social communities of smartphone users have recently gained significant interest due to their wide social penetration. The applications in this domain, however, currently rely on centralized or cloud-like architectures for data sharing and searching tasks, introducing both data-disclosure and performance concerns. In this paper, we present a distributed search architecture for intelligent search of objects in a mobile social community. Our framework, coined SmartOpt, is founded on an in-situ data storage model, where captured objects remain local on smartphones and searches then take place over an intelligent multi-objective lookup structure we compute dynamically. Our MO-QRT structure optimizes several conflicting objectives, using a multi-objective evolutionary algorithm that calculates a diverse set of high quality non-dominated solutions in a single run. Then a decision-making subsystem is utilized to tune the retrieval preferences of the query user. We assess our ideas both using trace-driven experiments with mobility and social patterns derived by Microsoft's GeoLife project, DBLP and Pics 'n' Trails but also using our real Android SmartP2P 3 system deployed over our SmartLab 4 testbed of 40+ smartphones. Our study reveals that SmartOpt yields high query recall rates of 95%, with one order of magnitude less time and two orders of magnitude less energy than its competitors.

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“…Some systems (e.g., BikeNet [7], PEIR [17], CarTel [11,8]) rely on a centralized approach, where mobile nodes propagate sensing data to a central node responsible for processing information. Our system is also based on a centralized approach, but unlike previous works focus on the scalability of data processing in the servers.…”

Section: Related Workmentioning

confidence: 99%

Scalable Data Processing for Community Sensing Applications

Ferreira¹,

Duarte²,

Preguiça³

et al. 2012

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Participatory Sensing is a new computing paradigm that aims to turn personal mobile devices into advanced mobile sensing networks. For popular applications, we can expect a huge number of users to both contribute with sensor data and request information from the system. In such scenario, scalability of data processing becomes a major issue. In this paper, we present a system for supporting participatory sensing applications that leverages cluster or cloud infrastructures to provide a scalable data processing infrastructure. We propose and evaluate three strategies for data processing in this architecture.

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The BikeNet mobile sensing system for cyclist experience mapping

Cited by 334 publications

References 18 publications

Design and Implementation of an IoT Access Point for Smart Home

Design and Implementation of an IoT Access Point for Smart Home

Intelligent search in social communities of smartphone users

Scalable Data Processing for Community Sensing Applications

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