3,565 research outputs found
Context Aware Computing for The Internet of Things: A Survey
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
Exploiting affinity propagation for energy-efficient information discovery in sensor networks
Wireless sensor networks (WSN) are attractive for information gathering in large-scale data rich environments. Emerging WSN applications require dissemination of information to interested clients within the network requiring support for differing traffic patterns. Further, in-network query processing capabilities are required for autonomic information discovery. In this paper, we formulate the information discovery problem as a load-balancing problem, with the combined aim being to maximize network lifetime and minimize query processing delay. We propose novel methods for data dissemination, information discovery and data aggregation that are designed to provide significant QoS benefits. We make use of affinity propagation to group "similar" sensors and have developed efficient mechanisms that can resolve both ALL-type and ANY-type queries in-network with improved energy-efficiency and query resolution time. Simulation results prove the proposed method(s) of information discovery offer significant QoS benefits for ALL-type and ANY-type queries in comparison to previous approaches.<br /
Service-oriented middleware for wireless sensor networks
There is a wide range of applications for wireless sensor networks (WSNs) with different needs. The network infrastructure and data dissemination protocol change according to each specific application requirement. To achieve the best network performance, it is important to adapt the network operation to the application needs. We propose a middleware system for WSNs, which provides a layer between user applications and the network. Such middleware offers an automatic choice of the network configuration and data dissemination strategy
A Search Strategy of Level-Based Flooding for the Internet of Things
This paper deals with the query problem in the Internet of Things (IoT).
Flooding is an important query strategy. However, original flooding is prone to
cause heavy network loads. To address this problem, we propose a variant of
flooding, called Level-Based Flooding (LBF). With LBF, the whole network is
divided into several levels according to the distances (i.e., hops) between the
sensor nodes and the sink node. The sink node knows the level information of
each node. Query packets are broadcast in the network according to the levels
of nodes. Upon receiving a query packet, sensor nodes decide how to process it
according to the percentage of neighbors that have processed it. When the
target node receives the query packet, it sends its data back to the sink node
via random walk. We show by extensive simulations that the performance of LBF
in terms of cost and latency is much better than that of original flooding, and
LBF can be used in IoT of different scales
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