A Scalable and Energy-Efficient Context Monitoring Framework for Mobile Personal Sensor Networks

Kang, Seungwoo; Lee, Jinwon; Jang, Hyun‐Jun; Lee, Mi Kyung; Park, Souneil; Song, Junehwa

doi:10.1109/tmc.2009.154

Cited by 61 publications

(18 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several authors [36][37][38][39] in the past highlighted the importance of energy efficiency of context-aware mobile applications. In [40], authors have proposed an efficient context data processing pipeline where an early feature detection stage can eliminate the usage of expensive context recognition. Although such an approach can minimize the overall energy consumption cost of mobile devices, but it can incur high latency depending on the complexity of the context evaluation function.…”

Section: Related Workmentioning

confidence: 99%

Scarf

Shin

Ghosh

Mehrotra

et al. 2019

Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

View full text Add to dashboard Cite

This paper proposes a context data management framework for efficient context data collection and evaluation to support contextaware applications. The proposed framework named SCARF leverages two architectures: a server-based centralized architecture and an edge-based architecture to address the problem of scalability of context collection and evaluation. SCARF collects the context data efficiently from resource constrained mobile/edge and multiple insitu sensors (not resource constrained) deployed in smart environments. In particular, SCARF takes a set of contextual queries from applications and optimizes the rate at which the context data needs to be collected and/or transmitted from the sensor to the server. Furthermore, SCARF decides whether the code for evaluating context should be executed on the server or on the edge. Execution on the edge will result in reduced communication between the server and the edge but can result in low performance due to resource constraints of the edge. On the other hand, evaluation of context on the server, will require SCARF to transmit sensor data from the sensor to the server but can result in high performance due to high resource availability of the server. SCARF is an adaptive framework which generates an optimal context acquisition plan and a context evaluation plan such that the overall latency is minimized.

show abstract

Section: Related Workmentioning

confidence: 99%

Scarf

Shin

Ghosh

Mehrotra

et al. 2019

Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

View full text Add to dashboard Cite

show abstract

“…Seeking to minimize the energy consumption when detecting and processing the context, a new approach is proposed in [14] that modifies the general way of processing the context data. The general flow starts from the sensors, through the pre-processing feature extraction and context recognition, the context change being detected at the final step.…”

Section: Related Workmentioning

confidence: 99%

A Personalized Multi-Path and Multi-User Traffic Analysis and Visualization Tool

Babau¹,

Marcu²,

Tihu³

et al. 2017

Acta Cybern

View full text Add to dashboard Cite

Traffic optimization is a subject that has become vital for the world we live in. People these days need to get from a starting point to a destination point as fast and as safe as possible. Traffic congestion plays a key role in the frustration of people and it results in lost time, reduced productivity and wasted resources. In our study we seek to address these issues by proposing a real-time road traffic planning system based on mobile context and crowd sourcing efforts. The first step toward this goal is real-time traffic characterization using data collected from mobile sensors of drivers, pedestrians, cyclists, passengers, etc.. We started developing a data collection and analysis system composed of a mobile application in order to collect user context data and a Web application to view and analyze the data. This new system will eventually give the users an automatically optimized route to the destination and predict the users' traveling route based on live traffic conditions and historical data.

show abstract

“…Similarly, using frameworks decreases line-of-code [36], increases developer productivity [39,40], and reduces maintenance efforts [41]. In addition, frameworks can be designed specifically for mobile platforms, such as smartphones or PDAs [3,42]. Moreover, frameworks can be used to develop applications directly as well as to address the business activities in a family of related applications in a specific domain.…”

Section: Application Framework In Patient Monitoringmentioning

confidence: 99%

“…Using a patient profile hosted on the patient's mobile device can contribute significantly to the design of CMPMF. For example, it supports the privacy protection of the patient's contextual data [42]. Furthermore, it is adequate to avoid the continuous network communication costs required to transmit and receive data to and from the backend server [3,4,7].…”

Section: A) Wireless Body Sensors Context Sourcementioning

confidence: 99%

See 1 more Smart Citation

Context-Aware Mobile Patient Monitoring Frameworks: A Systematic Review and Research Agenda

Al-Bashayreh¹,

Hashim

Khorma³

2013

JSW

View full text Add to dashboard Cite

Designing Context-aware Mobile Patient Monitoring Frameworks (CMPMF) is an emerging study in the biomedical informatics domain. However, literature on this topic is fragmented: first, there are no categories in the literature to characterize these CMPMF; second, there are no success factors that must be satisfied to enhance the design of these CMPMF; third, there is a need for a research agenda that provides a foundation for further research and development to enhance the design of these CMPMF. To address this gap, this paper is a review of context-aware frameworks in general and a systematic review of 10 designed context-aware frameworks in the biomedical informatics domain. Consequently, in this paper, three results are provided. First, a number of categories to be used to gain an intensive and extensive understanding of the designed CMPMF are identified. Second, a set of success factors, called Factors of Successful Context-aware Application Frameworks (FSCAF) are identified, to be used to enhance the design of CMPMF. Third, a research agenda that shows lacks and gaps in the designed CMPMF and provides a foundation to help researchers to design enhanced CMPMF is presented. The results show that no study integrated all of the FSCAF to enhance the design of CMPMF. Therefore, there is a need for further research to enhance the design of the existing CMPMF.

show abstract

A Scalable and Energy-Efficient Context Monitoring Framework for Mobile Personal Sensor Networks

Cited by 61 publications

References 36 publications

Scarf

Scarf

A Personalized Multi-Path and Multi-User Traffic Analysis and Visualization Tool

Context-Aware Mobile Patient Monitoring Frameworks: A Systematic Review and Research Agenda

Contact Info

Product

Resources

About