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.

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Context Aware Computing for The Internet of Things: A Survey

Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Meanwhile, with this revolutionary transformation, researchers, implementers, deployers, and users are faced with many challenges. IoT is a complicated, crowded, and complex field; there are various types of devices, protocols, communication channels, architectures, middleware, and more. Standardization efforts are plenty, and this chaos will continue for quite some time. What is clear, on the other hand, is that IoT deployments are increasing with accelerating speed, and this trend will not stop in the near future. As the field grows in numbers and heterogeneity, “intelligence” becomes a focal point in IoT. Since data now becomes “big data,” understanding, learning, and reasoning with big data is paramount for the future success of IoT. One of the major problems in the path to intelligent IoT is understanding “context,” or making sense of the environment, situation, or status using data from sensors, and then acting accordingly in autonomous ways. This is called “context-aware computing,” and it now requires both sensing and, increasingly, learning, as IoT systems get more data and better learning from this big data. In this survey, we review the field, first, from a historical perspective, covering ubiquitous and pervasive computing, ambient intelligence, and wireless sensor networks, and then, move to context-aware computing studies. Finally, we review learning and big data studies related to IoT. We also identify the open issues and provide an insight for future study areas for IoT researchers.

Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey

Research into ambient assisted living (AAL) strives to ease the daily lives of people with disabilities or chronic medical conditions. AAL systems typically consist of multitudes of sensors and embedded devices, generating large amounts of medical and ambient data. However, these biomedical sensors lack the processing power to perform key monitoring and data-aggregation tasks, necessitating data transmission and computation at central locations. The focus here is on the development of a scalable and context-aware framework and easing the flow between data collection and data processing. The resource-constrained nature of typical wearable body sensors is factored into our proposed model, with cloud computing features utilized to provide a real-time assisted-living service. With the myriad of distributed AAL systems at play, each with unique requirements and eccentricities, the challenge lies in the need to service these disparate systems with a middleware layer that is both coherent and flexible. There is significant complexity in the management of sensor data and the derivation of contextual information, as well as in the monitoring of user activities and in locating appropriate situational services. The proposed CoCaMAAL model seeks to address such issues and implement a service-oriented architecture (SOA) for unified context generation. This is done by efficiently aggregating raw sensor data and the timely selection of appropriate services using a context management system (CMS). With a unified model that includes patients, devices, and computational servers in a single virtual community, AAL services are enhanced. We have prototyped the proposed model and implemented some case studies to demonstrate its effectiveness.

CoCaMAAL: A cloud-oriented context-aware middleware in ambient assisted living

With the promising applications in e-Health and entertainment services, wireless body area network (WBAN) has attracted significant interest. One critical challenge for WBAN is to track and maintain the quality of service (QoS), e.g., delivery probability and latency, under the dynamic environment dictated by human mobility. Another important issue is to ensure the energy efficiency within such a resource-constrained network. In this paper, a new medium access control (MAC) protocol is proposed to tackle these two important challenges. We adopt a TDMA-based protocol and dynamically adjust the transmission order and transmission duration of the nodes based on channel status and application context of WBAN. The slot allocation is optimized by minimizing energy consumption of the nodes, subject to the delivery probability and throughput constraints. Moreover, we design a new synchronization scheme to reduce the synchronization overhead. Through developing an analytical model, we analyze how the protocol can adapt to different latency requirements in the healthcare monitoring service. Simulations results show that the proposed protocol outperforms CA-MAC and IEEE 802.15.6 MAC in terms of QoS and energy efficiency under extensive conditions. It also demonstrates more effective performance in highly heterogeneous WBAN.

Medium Access Control for Wireless Body Area Networks with QoS Provisioning and Energy Efficient Design

Wireless Body Area Network (WBAN) applications help reduce medical costs and improve people's quality of life by monitoring a user's biological signals via wearable and implantable wireless sensors. In order to satisfy burgeoning requirements, context-aware solutions are needed, thus allowing the system to adapt to changes in the user's mood, mental states, biological signals, and the environment. Thus, contextual information must be measured alongside biological signals in order to characterize and understand the current situation to adapt the system. With these issues in mind, this survey presents an overview of context-aware solutions at the Medium Access Control (MAC) and application layers. A clear distinction between medical and non-medical applications is made and some promising latest commercial WBAN products are highlighted. We show that, despite the importance of context-awareness in WBAN applications, there are limited solutions available, particularly at the MAC layer. The survey concludes with a discussion on open research challenges for future context-aware WBANs.

Context awareness in WBANs: a survey on medical and non-medical applications

Context awareness in Body Sensor Networks (BSNs) has the significance of associating physiological user activity and the environment to the sensed signals of the user. The context information derived from a BSN can be used in pervasive healthcare monitoring for relating importance to events and specifically for accurate episode detection. In this paper, we address the issue of context-aware sensing in BSNs, and survey different techniques for deducing context awareness.

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Barbara T. Korel

Papers

A Survey on Context-Aware Sensing for Body Sensor Networks