A Survey on Context-Aware Sensing for Body Sensor Networks

Korel, Barbara T.; Koo, Simon G. M.

doi:10.4236/wsn.2010.28069

Cited by 18 publications

(8 citation statements)

References 42 publications

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“…They are typically used to model complex relationships between inputs and outputs or to find patterns in data. Body sensor networks domain has employed this technique for pervasive healthcare monitoring in [154]. Support vector machines are widely used for pattern recognition in context-aware computing.…”

Section: Context Reasoning Decision Modelsmentioning

confidence: 99%

Context Aware Computing for The Internet of Things: A Survey

Perera

Zaslavsky

Christen

et al. 2014

IEEE Commun. Surv. Tutorials

2,281

1,354

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As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

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Section: Context Reasoning Decision Modelsmentioning

confidence: 99%

Context Aware Computing for The Internet of Things: A Survey

Perera

Zaslavsky

Christen

et al. 2014

IEEE Commun. Surv. Tutorials

2,281

1,354

View full text Add to dashboard Cite

show abstract

“…After collecting the data from all nodes, the controller processes and analyzes the received data, and can detect the change in the activity or the context by following one of the context recognition algorithms that are applied to the sensors data. Some of these algorithms can be found in [27]. Using such algorithms is independent from designing MAC protocols; thus, we will not go into details how those algorithms operate as this is beyond the scope of the study.…”

Section: Emergency Detectionmentioning

confidence: 99%

Reliability and Energy Efficiency Enhancement for Emergency-Aware Wireless Body Area Networks (WBANs)

Salayma

Al‐Dubai

Romdhani

et al. 2018

IEEE Trans. on Green Commun. Netw.

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Medium Access Control (MAC) protocols based on Time Division Multiple Access (TDMA) can improve the reliability and efficiency of WBAN. However, traditional static TDMA techniques adopted by IEEE 802.15.4 and IEEE 802.15.6 do not sufficiently consider the channel status or the buffer requirements of the nodes within heterogeneous contexts. Although there are some solutions that have been proposed to alleviate the effect of the deep fade in WBAN channel by adopting dynamic slot allocation, these solutions still suffer from some reliability and energy efficiency issues and they do not avoid channel deep fading. This paper presents two novel and generic TDMA based techniques to improve WBAN reliability and energy efficiency. Both techniques synchronize nodes adaptively whilst tackling their channel and buffer status in normal and emergency contexts. Extensive simulation experiments using various traffic rates and time slot lengths demonstrate that the proposed techniques improve the reliability and the energy efficiency compared to IEEE 802.15.4 and IEEE 802.15.6 in both situations, the normal and emergency contexts. This improvement has been achieved in terms of packet loss, up to 90% and energy consumption, up to 13%, confirming the significant enhancements made by the developed scheduling techniques.

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“…Context awareness may be defined as "associating physiological user activity and the environment to the sensed signals of the user" [4]. As such, a context-aware WBAN may adapt its processing strategy based on different contextual cues, such as location (e.g., via global positioning system (GPS)), social activity (e.g., face-to-face communication), a user's mental state (e.g., stress, fatigued), activity (e.g., walking), environment (e.g., noise, lighting), and interruptability (e.g., busy).…”

Section: Context Awareness In Wbans: a Survey On Medical And Non-medimentioning

confidence: 99%