852 research outputs found
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
When things matter: A survey on data-centric Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, but several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy and continuous. This paper reviews the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed
Extending Event Sequence Processing:New Models and Optimization Techniques
Many modern applications, including online financial feeds, tag-based mass transit systems and RFID-based supply chain management systems transmit real-time data streams. There is a need for event stream processing technology to analyze this vast amount of sequential data to enable online operational decision making. This dissertation focuses on innovating several techniques at the core of a scalable E-Analytic system to achieve efficient, scalable and robust methods for in-memory multi-dimensional nested pattern analysis over high-speed event streams. First, I address the problem of processing flat pattern queries on event streams with out-of-order data arrival. I design two alternate solutions: aggressive and conservative strategies respectively. The aggressive strategy produces maximal output under the optimistic assumption that out-of-order event arrival is rare. The conservative method works under the assumption that out-of-order data may be common, and thus produces output only when its correctness can be guaranteed. Second, I design the integration of CEP and OLAP techniques (ECube model) for efficient multi-dimensional event pattern analysis at different abstraction levels. Strategies of drill-down (refinement from abstract to specific patterns) and of roll-up (generalization from specific to abstract patterns) are developed for the efficient workload evaluation. I design a cost-driven adaptive optimizer called Chase that exploits reuse strategies for optimal E-Cube hierarchy execution. Then, I explore novel optimization techniques to support the high- performance processing of powerful nested CEP patterns. A CEP query language called NEEL, is designed to express nested CEP pattern queries composed of sequence, negation, AND and OR operators. To allow flexible execution ordering, I devise a normalization procedure that employs rewriting rules for flattening a nested complex event expression. To conserve CPU and memory consumption, I propose several strategies for efficient shared processing of groups of normalized NEEL subexpressions. Our comprehensive experimental studies, using both synthetic as well as real data streams demonstrate superiority of our proposed strategies over alternate methods in the literature in both effectiveness and efficiency
Capturing Data Uncertainty in High-Volume Stream Processing
We present the design and development of a data stream system that captures
data uncertainty from data collection to query processing to final result
generation. Our system focuses on data that is naturally modeled as continuous
random variables. For such data, our system employs an approach grounded in
probability and statistical theory to capture data uncertainty and integrates
this approach into high-volume stream processing. The first component of our
system captures uncertainty of raw data streams from sensing devices. Since
such raw streams can be highly noisy and may not carry sufficient information
for query processing, our system employs probabilistic models of the data
generation process and stream-speed inference to transform raw data into a
desired format with an uncertainty metric. The second component captures
uncertainty as data propagates through query operators. To efficiently quantify
result uncertainty of a query operator, we explore a variety of techniques
based on probability and statistical theory to compute the result distribution
at stream speed. We are currently working with a group of scientists to
evaluate our system using traces collected from the domains of (and eventually
in the real systems for) hazardous weather monitoring and object tracking and
monitoring.Comment: CIDR 200
Service Composition for IP Smart Object using Realtime Web Protocols: Concept and Research Challenges
The Internet of Things (IoT) refers to a world-wide network of interconnected physical things using standardized communication protocols. Recent development of Internet Protocol (IP) stacks for resource-constrained devices unveils a possibility for the future IoT based on the stable and scalable IP technology much like today's Internet of computers. One important question remains: how can data and events (denoted as services) introduced by a variety of IP networked things be exchanged and aggregated e ciently in various application domains. Because the true value of IoT lies in the interaction of several services from physical things, answers to this question are essential to support a rapid creation of new IoT smart and ubiquitous applications. The problem is known as service composition. This article explains the practicability of the future full-IP IoT with realtime Web protocols to formally state the problem of service composition for IP smart objects, provides literature review, and discusses its research challenges
A hierarchal framework for recognising activities of daily life
PhDIn today’s working world the elderly who are dependent can sometimes be
neglected by society. Statistically, after toddlers it is the elderly who are observed
to have higher accident rates while performing everyday activities. Alzheimer’s
disease is one of the major impairments that elderly people suffer from, and leads
to the elderly person not being able to live an independent life due to forgetfulness.
One way to support elderly people who aspire to live an independent life and
remain safe in their home is to find out what activities the elderly person is
carrying out at a given time and provide appropriate assistance or institute
safeguards.
The aim of this research is to create improved methods to identify tasks related to
activities of daily life and determine a person’s current intentions and so reason
about that person’s future intentions. A novel hierarchal framework has been
developed, which recognises sensor events and maps them to significant activities
and intentions. As privacy is becoming a growing concern, the monitoring of an
individual’s behaviour can be seen as intrusive. Hence, the monitoring is based
around using simple non intrusive sensors and tags on everyday objects that are
used to perform daily activities around the home. Specifically there is no use of
any cameras or visual surveillance equipment, though the techniques developed
are still relevant in such a situation.
Models for task recognition and plan recognition have been developed and tested
on scenarios where the plans can be interwoven. Potential targets are people in the
first stages of Alzheimer’s disease and in the structuring of the library of kernel
plan sequences, typical routines used to sustain meaningful activity have been
used. Evaluations have been carried out using volunteers conducting activities of
daily life in an experimental home environment. The results generated from the
sensors have been interpreted and analysis of developed algorithms has been
made. The outcomes and findings of these experiments demonstrate that the
developed hierarchal framework is capable of carrying activity recognition as well
as being able to carry out intention analysis, e.g. predicting what activity they are
most likely to carry out next
An Intelligent Complex Event Processing with D
Efficient matching of incoming mass events to persistent queries is fundamental to complex event processing systems. Event matching based on pattern rule is an important feature of complex event processing engine. However, the intrinsic uncertainty in pattern rules which are predecided by experts increases the difficulties of effective complex event processing. It inevitably involves various types of the intrinsic uncertainty, such as imprecision, fuzziness, and incompleteness, due to the inability of human beings subjective judgment. Nevertheless, D numbers is a new mathematic tool to model uncertainty, since it ignores the condition that elements on the frame must be mutually exclusive. To address the above issues, an intelligent complex event processing method with D numbers under fuzzy environment is proposed based on the Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS) method. The novel method can fully support decision making in complex event processing systems. Finally, a numerical example is provided to evaluate the efficiency of the proposed method
Emerging research directions in computer science : contributions from the young informatics faculty in Karlsruhe
In order to build better human-friendly human-computer interfaces,
such interfaces need to be enabled with capabilities to perceive
the user, his location, identity, activities and in particular his interaction
with others and the machine. Only with these perception capabilities
can smart systems ( for example human-friendly robots or smart environments) become posssible. In my research I\u27m thus focusing on the
development of novel techniques for the visual perception of humans and
their activities, in order to facilitate perceptive multimodal interfaces,
humanoid robots and smart environments. My work includes research
on person tracking, person identication, recognition of pointing gestures,
estimation of head orientation and focus of attention, as well as
audio-visual scene and activity analysis. Application areas are humanfriendly
humanoid robots, smart environments, content-based image and
video analysis, as well as safety- and security-related applications. This
article gives a brief overview of my ongoing research activities in these
areas
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