Middleware: Middleware Challenges and Approaches for Wireless Sensor Networks

Hadim, S.; Mohamed, Nader

doi:10.1109/mdso.2006.19

Cited by 370 publications

(184 citation statements)

References 27 publications

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“…Therefore, most wireless sensor network (WSN) kits have been designed either to demonstrate novel wireless sensor network architectures (e.g. [4,8]) or for industrial applications [2].…”

Section: Environmental Monitoringmentioning

confidence: 99%

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MobGeoSen: facilitating personal geosensor data collection and visualization using mobile phones

Kanjo

Benford

Paxton

et al. 2007

Pers Ubiquit Comput

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Mobile sensing and mapping applications are becoming more prevalent because sensing hardware is becoming more portable and more affordable. However, most of the hardware uses small numbers of fixed sensors that report and share multiple sets of environmental data which raises privacy concerns. Instead, these systems can be decentralized and managed by individuals in their public and private spaces. This paper describes a robust system called MobGeoSens which enables individuals to monitor their local environment (e.g. pollution and temperature) and their private spaces (e.g. activities and health) by using mobile phones in their day to day life.The MobGeoSen is a combination of software components that facilitates the phone's internal sensing devices (e.g. microphone and camera) and external wireless sensors (e.g. data loggers and GPS receivers) for data collection. It also adds a new dimension of spatial localization to the data collection process and provides the user with both textual and spatial cartographic displays. While collecting the data, individuals can interactively add annotations and photos which are automatically added and integrated in the visualization file/log. This makes it easy to visualize the data, photos and annotations on a spatial and temporal visualization tool. In addition, the paper will present ways in which mobile phones can be used as noise sensors using an on-device microphone. Finally, we present our experiences with school children using the above mentioned system to measure their exposure to environmental pollution.

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“…Therefore, most wireless sensor network (WSN) kits have been designed either to demonstrate novel wireless sensor network architectures (e.g. [4,8]) or for industrial applications [2].…”

Section: Environmental Monitoringmentioning

confidence: 99%

“…The system's development overlapped two separate research projects-Mobile phone as sensor and participate, 4 separate evaluation exercises were carried out, though both involved trials in an iterative manner. The iterative design process continoued across number of sessions all of which took place in the context of school classrooms.…”

Section: Pre Trial Testingmentioning

confidence: 99%

MobGeoSen: facilitating personal geosensor data collection and visualization using mobile phones

Kanjo

Benford

Paxton

et al. 2007

Pers Ubiquit Comput

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“…As argued in the introduction, the research community has created a rich suite of in-network technologies including specialized programming paradigms [12], [14], network support [19], [17], Operating Systems [9], [4], [11] and middleware [31], [32]. However, development support ends at the network gateway, leaving the WSN developer to implement ad-hoc solutions in terms of modeling and analyzing data gathered from the WSN.…”

Section: B Exploiting Cloud Resources In Wsn Environmentsmentioning

confidence: 99%

Extending sensor networks into the Cloud using Amazon Web Services

Lee

Murray

Hughes

et al. 2010

2010 IEEE International Conference on Networked Embedded Systems for Enterprise Applications

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Abstract-Sensor networks provide a method of collecting environmental data for use in a variety of distributed applications. However, to date, limited support has been provided for the development of integrated environmental monitoring and modeling applications. Specifically, environmental dynamism makes it difficult to provide computational resources that are sufficient to deal with changing environmental conditions. This paper argues that the Cloud Computing model is a good fit with the dynamic computational requirements of environmental monitoring and modeling. We demonstrate that Amazon EC2 can meet the dynamic computational needs of environmental applications. We also demonstrate that EC2 can be integrated with existing sensor network technologies to offer an end-to-end environmental monitoring and modeling solution.

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“…In system engineering, a great deal of investigation has been concentrated on the application programming interface for distributed computations, i.e., the middleware design (Hadim and Mohamed, 2006). It has been suggested by many important studies (Madden et al, 2005;Bonnet et al, 2001) that application-specific network design is necessary, in dealing with the requirements of application QoS, network scalability and node complexity constraints.…”

Section: Related Workmentioning

confidence: 99%

Cognitive networking of large scale wireless systems

Song

Hatzinakos²

2009

IJCNDS

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Abstract:We propose the concept of cognitive networking for large-scale wireless systems, which opportunistically utilises network resources including both spectrum bandwidth and radio availability. Both types of resources cannot be predetermined in large-scale wireless systems, due to various reasons such as interferences and dynamic traffic load. The proposed technology not only establishes dynamic wireless networks, but also provides for reliable network quality of services (QoS). The supporting network architecture, embedded wireless interconnect (EWI), is proposed to implement the cognitive networking concept and supply an effective application-programming interface for large-scale data management systems. Two example applications are presented, including wireless mesh networks for broadband wireless internet access and wireless sensor networks for target tracking. Major advantages of the technology are further discussed. We suggest that the performance of the proposed system would improve with larger network scale and the implementation complexity could be independent of the network scale.

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Middleware: Middleware Challenges and Approaches for Wireless Sensor Networks

Cited by 370 publications

References 27 publications

MobGeoSen: facilitating personal geosensor data collection and visualization using mobile phones

MobGeoSen: facilitating personal geosensor data collection and visualization using mobile phones

Extending sensor networks into the Cloud using Amazon Web Services

Cognitive networking of large scale wireless systems

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