4,530 research outputs found
Sensor Search Techniques for Sensing as a Service Architecture for The Internet of Things
The Internet of Things (IoT) is part of the Internet of the future and will
comprise billions of intelligent communicating "things" or Internet Connected
Objects (ICO) which will have sensing, actuating, and data processing
capabilities. Each ICO will have one or more embedded sensors that will capture
potentially enormous amounts of data. The sensors and related data streams can
be clustered physically or virtually, which raises the challenge of searching
and selecting the right sensors for a query in an efficient and effective way.
This paper proposes a context-aware sensor search, selection and ranking model,
called CASSARAM, to address the challenge of efficiently selecting a subset of
relevant sensors out of a large set of sensors with similar functionality and
capabilities. CASSARAM takes into account user preferences and considers a
broad range of sensor characteristics, such as reliability, accuracy, location,
battery life, and many more. The paper highlights the importance of sensor
search, selection and ranking for the IoT, identifies important characteristics
of both sensors and data capture processes, and discusses how semantic and
quantitative reasoning can be combined together. This work also addresses
challenges such as efficient distributed sensor search and
relational-expression based filtering. CASSARAM testing and performance
evaluation results are presented and discussed.Comment: IEEE sensors Journal, 2013. arXiv admin note: text overlap with
arXiv:1303.244
A Low-Overhead Script Language for Tiny Networked Embedded Systems
With sensor networks starting to get mainstream acceptance, programmability is of increasing importance.
Customers and field engineers will need to reprogram existing deployments and software developers
will need to test and debug software in network testbeds. Script languages, which are a popular
mechanism for reprogramming in general-purpose computing, have not been considered for wireless sensor
networks because of the perceived overhead of interpreting a script language on tiny sensor nodes.
In this paper we show that a structured script language is both feasible and efficient for programming
tiny sensor nodes. We present a structured script language, SCript, and develop an interpreter for the
language. To reduce program distribution energy the SCript interpreter stores a tokenized representation
of the scripts which is distributed through the wireless network. The ROM and RAM footprint of the
interpreter is similar to that of existing virtual machines for sensor networks. We show that the interpretation
overhead of our language is on par with that of existing virtual machines. Thus script languages,
previously considered as too expensive for tiny sensor nodes, are a viable alternative to virtual machines
Gravitational Clustering: A Simple, Robust and Adaptive Approach for Distributed Networks
Distributed signal processing for wireless sensor networks enables that
different devices cooperate to solve different signal processing tasks. A
crucial first step is to answer the question: who observes what? Recently,
several distributed algorithms have been proposed, which frame the
signal/object labelling problem in terms of cluster analysis after extracting
source-specific features, however, the number of clusters is assumed to be
known. We propose a new method called Gravitational Clustering (GC) to
adaptively estimate the time-varying number of clusters based on a set of
feature vectors. The key idea is to exploit the physical principle of
gravitational force between mass units: streaming-in feature vectors are
considered as mass units of fixed position in the feature space, around which
mobile mass units are injected at each time instant. The cluster enumeration
exploits the fact that the highest attraction on the mobile mass units is
exerted by regions with a high density of feature vectors, i.e., gravitational
clusters. By sharing estimates among neighboring nodes via a
diffusion-adaptation scheme, cooperative and distributed cluster enumeration is
achieved. Numerical experiments concerning robustness against outliers,
convergence and computational complexity are conducted. The application in a
distributed cooperative multi-view camera network illustrates the applicability
to real-world problems.Comment: 12 pages, 9 figure
Δομή λεξικού για ασύρματα δίκτυα
Η κατανάλωση ενέργειας και η αποδοτηκότητα πρόσβασης είναι δύο βασικοί
στόχοι και ανταγωνιστικοί στην ασύρματη εκπομπή δεδομένων. Για να
αντιμετωπιστεί το ενεργειακό πρόβλημα στην ακολουθιακή αναζήτηση μαζί
με τα δεδομένα έχουν προστεθεί δείκτες ( index ). Στην εργασία αυτή
προτείνουμε ένα παραμετροποιήσημο αλγόριθμο εκπομπής δεδομένων τον
Interpolation Index. Ο αλγόριθμος έχει δυνατότητα να βελτιστοποιήσει το
χρόνο αναζήτησης κρατώντας σταθερό τον χρόνο συντονισμού (tuning time )
και αντίστροφα
Exploratory study to explore the role of ICT in the process of knowledge management in an Indian business environment
In the 21st century and the emergence of a digital economy, knowledge and the knowledge base economy are rapidly growing. To effectively be able to understand the processes involved in the creating, managing and sharing of knowledge management in the business environment is critical to the success of an organization. This study builds on the previous research of the authors on the enablers of knowledge management by identifying the relationship between the enablers of knowledge management and the role played by information communication technologies (ICT) and ICT infrastructure in a business setting. This paper provides the findings of a survey collected from the four major Indian cities (Chennai, Coimbatore, Madurai and Villupuram) regarding their views and opinions about the enablers of knowledge management in business setting. A total of 80 organizations participated in the study with 100 participants in each city. The results show that ICT and ICT infrastructure can play a critical role in the creating, managing and sharing of knowledge in an Indian business environment
Rate-distortion Balanced Data Compression for Wireless Sensor Networks
This paper presents a data compression algorithm with error bound guarantee
for wireless sensor networks (WSNs) using compressing neural networks. The
proposed algorithm minimizes data congestion and reduces energy consumption by
exploring spatio-temporal correlations among data samples. The adaptive
rate-distortion feature balances the compressed data size (data rate) with the
required error bound guarantee (distortion level). This compression relieves
the strain on energy and bandwidth resources while collecting WSN data within
tolerable error margins, thereby increasing the scale of WSNs. The algorithm is
evaluated using real-world datasets and compared with conventional methods for
temporal and spatial data compression. The experimental validation reveals that
the proposed algorithm outperforms several existing WSN data compression
methods in terms of compression efficiency and signal reconstruction. Moreover,
an energy analysis shows that compressing the data can reduce the energy
expenditure, and hence expand the service lifespan by several folds.Comment: arXiv admin note: text overlap with arXiv:1408.294
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