Energy-aware and quality-driven sensor management for green mobile crowd sensing

Marjanović, Martina; Skorin‐Kapov, Lea; Pripužić, Krešimir; Antonić, Aleksandar; Žarko, Ivana Podnar

doi:10.1016/j.jnca.2015.06.023

Cited by 62 publications

(33 citation statements)

References 35 publications

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“…On the contrary, it optimizes the QoISD decreasing the reward in the subarea where sensed data is more than demand. As shown in (21), represents that deviation rate between actual sensed data and demand in subarea and subperiod .…”

Section: Distributed Algorithm For Optimizing Qoisdmentioning

confidence: 99%

“…In such applications, due to the need to collect a wide range and large amount of data even in a long-term continuous period, the cost of traditional method deploying sensing devices or specialized employee to collect the data is so high limiting the development of applications [20,21]. Hence, a new method of data collection named crowd sensing (or participatory sensing) is adopted to collect data [21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

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A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks

Liu

et al. 2018

Wireless Communications and Mobile Computing

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To tackle the issue in deep crowd sensing, a Time and Location Correlation Incentive (TLCI) scheme is proposed for deep data gathering in crowdsourcing networks. In TLCI scheme, a metric named "Quality of Information Satisfaction Degree" (QoISD) is to quantify how much collected sensing data can satisfy the application's QoI requirements mainly in terms of data quantity and data coverage. Two incentive algorithms are proposed to satisfy QoISD with different view. The first algorithm is to ensure that the application gets the specified sensing data to maximize the QoISD. Thus, in the first incentive algorithm, the reward for data sensing is to maximize the QoISD. The second algorithm is to minimize the cost of the system while meeting the sensing data requirement and maximizing the QoISD. Thus, in the second incentive algorithm, the reward for data sensing is to maximize the QoISD per unit of reward. Finally, we compare our proposed scheme with existing schemes via extensive simulations. Extensive simulation results well justify the effectiveness of our scheme. The QoISD can be optimized by 81.92%, and the total cost can be reduced by 31.38%.

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Section: Distributed Algorithm For Optimizing Qoisdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks

Liu

et al. 2018

Wireless Communications and Mobile Computing

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“…Zheng et al [40] proposed reliable clustering anomaly detection in Smart Parking. SC R S C EE AV RB I Qiu et al [6] 2016 X Jiang et al [7] 2015 X X X Collotta and Pau [8] 2015 X X Santos et al [9] 2015 X Xue and Detera [10] 2015 X Mao et al [11] 2015 X Marjanovic et al [12] 2015 X Batzler et al [13] 2015 X…”

Section: Reliabilitymentioning

confidence: 99%

Internet of Things: A Review on Technologies, Architecture, Challenges, Applications, Future Trends

Kaur¹,

Kaur²

2017

IJCNIS

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Abstract-World Wide Web (1990"s) and Mobile Internet (the 2000"s) had consequential corroborated the way how people communicate. However, with evolution in technology, the cataclysm of Internet has stepped into a new phase-Internet of Things. Internet of Things, a prominent paradigm in the field of IT having a nominal intervention of humans allowing diverse things to communicate with each other, anticipate, sight, and perceive surroundings. IoT exploits RFID tags, NFC, sensors, smart bands, and wired or wireless communication technologies to build smart surroundings, smart Homes, quick-witted intelligence in medical care, ease of Transport, and more. This paper introduces IoT with emphasis on its driver technologies and system architecture. In addition to application layer protocols, we focus on identifying various issues and application areas of IoT as well as future research trends in the field of IoT. We have also highlighted how big data is associated with Internet of Things.

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“…In addition, the terms M2M communication (M2MC) and machine type communication (MTC) are used interchangeably as in [17][18][19][89][90][91][92][93]199,200]. Although IoT is a broader concept which evolves from M2M, this paper assumes that IoT and M2M are synonyms as in [94].…”

Section: Iot In 5g Service Classificationmentioning

confidence: 99%

IoT’s Tiny Steps towards 5G: Telco’s Perspective

Cero¹,

Husić

Baraković

2017

Symmetry

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Abstract:The numerous and diverse applications of the Internet of Things (IoT) have the potential to change all areas of daily life of individuals, businesses, and society as a whole. The vision of a pervasive IoT spans a wide range of application domains and addresses the enabling technologies needed to meet the performance requirements of various IoT applications. In order to accomplish this vision, this paper aims to provide an analysis of literature in order to propose a new classification of IoT applications, specify and prioritize performance requirements of such IoT application classes, and give an insight into state-of-the-art technologies used to meet these requirements, all from telco's perspective. A deep and comprehensive understanding of the scope and classification of IoT applications is an essential precondition for determining their performance requirements with the overall goal of defining the enabling technologies towards fifth generation (5G) networks, while avoiding over-specification and high costs. Given the fact that this paper presents an overview of current research for the given topic, it also targets the research community and other stakeholders interested in this contemporary and attractive field for the purpose of recognizing research gaps and recommending new research directions.

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Energy-aware and quality-driven sensor management for green mobile crowd sensing

Cited by 62 publications

References 35 publications

A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks

A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks

Internet of Things: A Review on Technologies, Architecture, Challenges, Applications, Future Trends

IoT’s Tiny Steps towards 5G: Telco’s Perspective

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