4,683 research outputs found
Deployment vs. data retrieval costs for caches in the plane
We consider the problem of finding the Pareto front of the expected deployment cost of wireless caches in the plane and the expected retrieval cost of a client requesting data from the caches. The data is allocated at the caches according to partitioning and coding strategies. We show that under coding, it is optimal to deploy many caches with low storage capacity. For partitioning, we derive a simple relation between the cost of the cache deployment and the cost of retrieving the data from the caches. Lastly, we show that coding results in a lower Pareto front than partitioning
Energy-efficient Internet of Things monitoring with low-capacity devices
The Internet of Things (IoT) allows users to gather data from the physical environment. While sensors in public spaces are already widely used, users are reluctant to deploy sensors for shared data at their homes. The deployment of IoT nodes at the users premises presents privacy issues regarding who can access to their data once it is sent to the Cloud which the users cannot control. In this paper we present an energy-efficient and low cost solution for environmental monitoring at the users home. Our system is built completely with open source components and is easy to reproduce. We leverage the infrastructure and trust of a community network to store and control the access to the monitored data. We tested our solution during several months on different low-capacity single board computers (SBC) and it showed to be stable. Our results suggest that this solution could become a permanently running service in SBCs at the users homes.Peer ReviewedPostprint (author's final draft
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
Estimating Fire Weather Indices via Semantic Reasoning over Wireless Sensor Network Data Streams
Wildfires are frequent, devastating events in Australia that regularly cause
significant loss of life and widespread property damage. Fire weather indices
are a widely-adopted method for measuring fire danger and they play a
significant role in issuing bushfire warnings and in anticipating demand for
bushfire management resources. Existing systems that calculate fire weather
indices are limited due to low spatial and temporal resolution. Localized
wireless sensor networks, on the other hand, gather continuous sensor data
measuring variables such as air temperature, relative humidity, rainfall and
wind speed at high resolutions. However, using wireless sensor networks to
estimate fire weather indices is a challenge due to data quality issues, lack
of standard data formats and lack of agreement on thresholds and methods for
calculating fire weather indices. Within the scope of this paper, we propose a
standardized approach to calculating Fire Weather Indices (a.k.a. fire danger
ratings) and overcome a number of the challenges by applying Semantic Web
Technologies to the processing of data streams from a wireless sensor network
deployed in the Springbrook region of South East Queensland. This paper
describes the underlying ontologies, the semantic reasoning and the Semantic
Fire Weather Index (SFWI) system that we have developed to enable domain
experts to specify and adapt rules for calculating Fire Weather Indices. We
also describe the Web-based mapping interface that we have developed, that
enables users to improve their understanding of how fire weather indices vary
over time within a particular region.Finally, we discuss our evaluation results
that indicate that the proposed system outperforms state-of-the-art techniques
in terms of accuracy, precision and query performance.Comment: 20pages, 12 figure
A File System Abstraction for Sense and Respond Systems
The heterogeneity and resource constraints of sense-and-respond systems pose
significant challenges to system and application development. In this paper, we
present a flexible, intuitive file system abstraction for organizing and
managing sense-and-respond systems based on the Plan 9 design principles. A key
feature of this abstraction is the ability to support multiple views of the
system via filesystem namespaces. Constructed logical views present an
application-specific representation of the network, thus enabling high-level
programming of the network. Concurrently, structural views of the network
enable resource-efficient planning and execution of tasks. We present and
motivate the design using several examples, outline research challenges and our
research plan to address them, and describe the current state of
implementation.Comment: 6 pages, 3 figures Workshop on End-to-End, Sense-and-Respond Systems,
Applications, and Services In conjunction with MobiSys '0
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