1,203 research outputs found
Quantitative Verification: Formal Guarantees for Timeliness, Reliability and Performance
Computerised systems appear in almost all aspects of our daily lives, often in safety-critical scenarios such as embedded control systems in cars and aircraft
or medical devices such as pacemakers and sensors. We are thus increasingly reliant on these systems working correctly, despite often operating in unpredictable or unreliable environments. Designers of such devices need ways to guarantee that they will operate in a reliable and efficient manner.
Quantitative verification is a technique for analysing quantitative aspects of a system's design, such as timeliness, reliability or performance. It applies formal methods, based on a rigorous analysis of a mathematical model of the system, to automatically prove certain precisely specified properties, e.g. ``the airbag will always deploy within 20 milliseconds after a crash'' or ``the probability of both sensors failing simultaneously is less than 0.001''.
The ability to formally guarantee quantitative properties of this kind is beneficial across a wide range of application domains. For example, in safety-critical systems, it may be essential to establish credible bounds on the probability with which certain failures or combinations of failures can occur. In embedded control systems, it is often important to comply with strict constraints on timing or resources. More generally, being able to derive guarantees on precisely specified levels of performance or efficiency is a valuable tool in the design of, for example, wireless networking protocols, robotic systems or power management algorithms, to name but a few.
This report gives a short introduction to quantitative verification, focusing in particular on a widely used technique called model checking, and its generalisation to the analysis of quantitative aspects of a system such as timing, probabilistic behaviour or resource usage.
The intended audience is industrial designers and developers of systems such as those highlighted above who could benefit from the application of quantitative verification,but lack expertise in formal verification or modelling
Smart Wireless Sensor Networks
The recent development of communication and sensor technology results in the growth of a new attractive and challenging area - wireless sensor networks (WSNs). A wireless sensor network which consists of a large number of sensor nodes is deployed in environmental fields to serve various applications. Facilitated with the ability of wireless communication and intelligent computation, these nodes become smart sensors which do not only perceive ambient physical parameters but also be able to process information, cooperate with each other and self-organize into the network. These new features assist the sensor nodes as well as the network to operate more efficiently in terms of both data acquisition and energy consumption. Special purposes of the applications require design and operation of WSNs different from conventional networks such as the internet. The network design must take into account of the objectives of specific applications. The nature of deployed environment must be considered. The limited of sensor nodes� resources such as memory, computational ability, communication bandwidth and energy source are the challenges in network design. A smart wireless sensor network must be able to deal with these constraints as well as to guarantee the connectivity, coverage, reliability and security of network's operation for a maximized lifetime. This book discusses various aspects of designing such smart wireless sensor networks. Main topics includes: design methodologies, network protocols and algorithms, quality of service management, coverage optimization, time synchronization and security techniques for sensor networks
A brief network analysis of Artificial Intelligence publication
In this paper, we present an illustration to the history of Artificial
Intelligence(AI) with a statistical analysis of publish since 1940. We
collected and mined through the IEEE publish data base to analysis the
geological and chronological variance of the activeness of research in AI. The
connections between different institutes are showed. The result shows that the
leading community of AI research are mainly in the USA, China, the Europe and
Japan. The key institutes, authors and the research hotspots are revealed. It
is found that the research institutes in the fields like Data Mining, Computer
Vision, Pattern Recognition and some other fields of Machine Learning are quite
consistent, implying a strong interaction between the community of each field.
It is also showed that the research of Electronic Engineering and Industrial or
Commercial applications are very active in California. Japan is also publishing
a lot of papers in robotics. Due to the limitation of data source, the result
might be overly influenced by the number of published articles, which is to our
best improved by applying network keynode analysis on the research community
instead of merely count the number of publish.Comment: 18 pages, 7 figure
Driving Big Data – Integration and Synchronization of Data Sources for Artificial Intelligence Applications with the Example of Truck Driver Work Stress and Strain Analysis
This paper contributes to the issue of big data analysis and data quality with the specific field of time synchronization. As a highly relevant use case, big data analysis of work stress and strain factors for driving professions is outlined. Drivers experience work stress and strain due to trends like traffic congestion, time pressure or worsening work conditions. Although a large professional group with 2.5 million (US) and 3.5 million (EU) truck drivers, scientific analysis of work stress and strain factors is scarce. Driver shortage is growing into a large-scale economic and societal challenge, especially for small businesses. Empirical investigations require big data approaches with sources like physiological and truck, traffic, weather, planning or accident data. For such challenges, accurate data is required, especially regarding time synchronization. Awareness among researchers and practitioners is key and first solution approaches are provided, connecting to many further Machine Learning and big data applications
Time-Sensitive Networking for Industrial Automation: Challenges, Opportunities, and Directions
With the introduction of Cyber-Physical Systems (CPS) and Internet of Things
(IoT) into industrial applications, industrial automation is undergoing
tremendous change, especially with regard to improving efficiency and reducing
the cost of products. Industrial automation applications are often required to
transmit time- and safety-critical data to monitor and control industrial
processes, especially for critical control systems. There are a number of
solutions to meet these requirements (e.g., priority-based real-time schedules
and closed-loop feedback control systems). However, due to their different
processing capabilities (e.g., in the end devices and network switches),
different vendors may come out with distinct solutions, and this makes the
large-scale integration of devices from different vendors difficult or
impossible. IEEE 802.1 Time-Sensitive Networking (TSN) is a standardization
group formed to enhance and optimize the IEEE 802.1 network standards,
especially for Ethernet-based networks. These solutions can be evolved and
adapted into a cross-industry scenario, such as a large-scale distributed
industrial plant, which requires multiple industrial entities working
collaboratively. This paper provides a comprehensive review on the current
advances in TSN standards for industrial automation. We present the
state-of-the-art IEEE TSN standards and discuss the opportunities and
challenges when integrating each protocol into the industry domains. Finally,
we discuss some promising research about applying the TSN technology to
industrial automation applications
A Survey of Scheduling in Time-Sensitive Networking (TSN)
TSN is an enhancement of Ethernet which provides various mechanisms for
real-time communication. Time-triggered (TT) traffic represents periodic data
streams with strict real-time requirements. Amongst others, TSN supports
scheduled transmission of TT streams, i.e., the transmission of their packets
by edge nodes is coordinated in such a way that none or very little queuing
delay occurs in intermediate nodes. TSN supports multiple priority queues per
egress port. The TAS uses so-called gates to explicitly allow and block these
queues for transmission on a short periodic timescale. The TAS is utilized to
protect scheduled traffic from other traffic to minimize its queuing delay. In
this work, we consider scheduling in TSN which comprises the computation of
periodic transmission instants at edge nodes and the periodic opening and
closing of queue gates.
In this paper, we first give a brief overview of TSN features and standards.
We state the TSN scheduling problem and explain common extensions which also
include optimization problems. We review scheduling and optimization methods
that have been used in this context. Then, the contribution of currently
available research work is surveyed. We extract and compile optimization
objectives, solved problem instances, and evaluation results. Research domains
are identified, and specific contributions are analyzed. Finally, we discuss
potential research directions and open problems.Comment: 34 pages, 19 figures, 9 tables 110 reference
- …