Exploiting Rush Hours for Energy-Efficient Contact Probing in Opportunistic Data Collection

Wu, Xiuchao; Brown, Kenneth N.; Sreenan, Cormac J.

doi:10.1109/icdcsw.2011.23

Cited by 13 publications

(14 citation statements)

References 17 publications

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“…More realistic mobility patterns are considered by Wu et al [21] that propose an approach to identify rush hours and to concentrate effort and energy on particular time frames, thus scheduling a higher energy duty cycle when there is more probability to interact with other elements. While properly exploiting the nature of some contacts, that in Smart Cities scenarios can appear in burst, the selection of the rush hours in this algorithm is achieved at deployment time by engineers and is not performed online, making the approach unsuitable for dynamic environments with changing rush hours, e.g., when alternating between weekdays and weekends.…”

Section: Related Workmentioning

confidence: 99%

CARD: Context-Aware Resource Discovery for mobile Internet of Things scenarios

Pozza

Nati

Georgoulas

et al. 2014

Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014

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The occurrence of short but recurrent opportunistic contacts between static infrastructure and mobile devices largely characterizes recent Internet of Things (IoT) applications for Smart Cities and Smart Buildings scenarios. In order to efficiently exploit such existing communication opportunities to access services, share and collect data, IoT applications cannot rely on standard discovery mechanisms that periodically probe the environment to discover resources. Discovery protocols resilient to different contact opportunities and able to optimize energy consumption when device contacts are not present are therefore required in order to avoid waste of energy especially in battery operated user devices. Additionally, such new discovery protocols need to optimize the time available for communication operations, while being able to adjust to application requirements, and balance energy consumption with respect to latency for contacts discovery. To this aim, we introduce CARD, a Context Aware Resource Discovery framework that leveraging QLearning techniques extends the functionalities of asynchronous neighbor discovery protocols, while being capable to reduce energy wastage and discovery latency. Simulation results show that CARD performs better than existing approaches and is resilient to a variety of real scenarios characterizing Smart Cities and Smart Building deployments.

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Section: Related Workmentioning

confidence: 99%

CARD: Context-Aware Resource Discovery for mobile Internet of Things scenarios

Pozza

Nati

Georgoulas

et al. 2014

Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014

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“…Hence, it should be better to let smart phones keep listening and let sensor nodes probe when they wake up according to their duty-cycle. This issue has been studied further in [20] [19].…”

Section: Contact Probingmentioning

confidence: 99%

A Shared Opportunistic Infrastructure for Long-Lived Wireless Sensor Networks

Sreenan

Brown

2012

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

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Abstract. In this paper, a Shared Opportunistic Infrastructure (SOI) is proposed to reduce total cost of ownership for long-lived wireless sensor networks through exploiting human mobility. More specifically, various sensor nodes are opportunistically connected with their corresponding servers through smart phones carried by people in their daily life. In this paper, we will introduce the motivations, present the architecture, discuss the feasibility, and identify several research opportunities of SOI.

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“…This work is supported in part by the Irish HEA PRTLI-IV NEMBES Grant and the CTVR Grant Preliminary results were presented in the 4th International Workshop on Sensor Networks (held with IEEE ICDCS 2011) [47] and the 1st Intl Workshop on Cyber-Physical Networking Systems (held with IEEE INFOCOM 2011) [48]. The authors thank the reviewers for their insightful comments.…”

Section: Acknowledgementsmentioning

confidence: 99%

Contact Probing Mechanisms for Opportunistic Sensor Data Collection

Brown

Sreenan

2015

The Computer Journal

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Type of publicationArticle (peer-reviewed) In many emerging wireless sensor network scenarios the use of a fixed infrastructure of base stations for data collection is either infeasible, or prohibitive in terms of deployment and maintenance costs. Instead, we consider the use of mobile devices (i.e., smartphones) carried by people in their daily life to collect data from sensor nodes opportunistically. As the movement of these mobile nodes is, by definition, not controlled for the purpose of data collection, synchronization through contact probing becomes a challenging task, particularly for sensor nodes, which need to be aggressively duty-cycled to conserve energy and achieve long lifetimes. This paper formulates this important problem, providing an analytical solution framework, and systematically investigating the effective use of contact probing for opportunistic data collection. We present two new solutions, Sensor Node-Initiated Probing (SNIP) and SNIP-Rush Hours (SNIP-RH), the latter taking advantage of the temporal locality of human mobility. These schemes are evaluated using numerical analysis and COOJA network simulations, and the results are validated on a small sensor testbed and with the real-world human mobility traces from Nokia MDC Dataset. Our experimental results quantify the relative performance of alternative solutions on sensor node energy consumption and the efficacy of contact probing for data collection, allowing us to offer insights on this important emerging problem.

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Exploiting Rush Hours for Energy-Efficient Contact Probing in Opportunistic Data Collection

Cited by 13 publications

References 17 publications

CARD: Context-Aware Resource Discovery for mobile Internet of Things scenarios

CARD: Context-Aware Resource Discovery for mobile Internet of Things scenarios

A Shared Opportunistic Infrastructure for Long-Lived Wireless Sensor Networks

Contact Probing Mechanisms for Opportunistic Sensor Data Collection

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