Efficient building management with IP-based wireless sensor network

Existing Building/Energy Management Systems (BMS/EMS) fail to convey holistic performance to the building manager. A 20% reduction in energy consumption can be achieved by efficiently operated buildings compared with current practice. However, in the majority of buildings, occupant comfort and energy consumption analysis is primarily restricted by available sensor and meter data. Installation of a continuous monitoring process can significantly improve the building systems’ performance. We present WSN-BMDS, an IP-based wireless sensor network building monitoring and diagnostic system. The main focus of WSN-BMDS is to obtain much higher degree of information about the building operation then current BMSs are able to provide. Our system integrates a heterogeneous set of wireless sensor nodes with IEEE 802.11 backbone routers and the Global Sensor Network (GSN) web server. Sensing data is stored in a database at the back office via UDP protocol and can be access over the Internet using GSN. Through this demonstration, we show that WSN-BMDS provides accurate measurements of air-temperature, air-humidity, light, and energy consumption for particular rooms in our target building. Our interactive graphical user interface provides a user-friendly environment showing live network topology, monitor network statistics, and run-time management actions on the network. We also demonstrate actuation by changing the artificial light level in one of the rooms.

Generic support for policy-based self-adaptive systems

This paper presents a policy definition language which forms part of a generic policy toolkit for autonomic computing systems in which the policies themselves can be modified dynamically and automatically. Targeted enhancements to the current state of practice include: policy self-adaptation where the policy itself is dynamically modified to match environmental conditions; improved support for non autonomics-expert developers; and facilitating easy deployment of adaptive policies into legacy code. The policy definition language permits powerful expression of self-managing behaviours and facilitates a diverse policy behaviour space. Features include support for multiple versions of a given policy type, multiple configuration templates, and meta policies to dynamically select between policy instances. An example deployment scenario illustrates advanced functionality in the context of a multi policy stock trading system which is sensitive to environmental volatility.

A prospective randomised trial of a "test and treat" policy versus endoscopy based management in young Helicobacter pylori positive patients with ulcer-like dyspepsia, referred to a hospital clinic

Management of dyspepsia remains a controversial area. Although the European Helicobacter pylori study group has advised empirical eradication therapy without oesophagogastroduodenoscopy (OGD) in young H pylori positive dyspeptic patients who do not exhibit alarm symptoms, this strategy has not been subjected to clinical trial.

Measurement-based Policy-driven QoS Management in Converged Next Generation Networks

Policy-based management is considered an effective approach to address the challenges of resource management in large complex networks. Within the IU-ATC QoS Frameworks project, a policy-based network management framework, CNQF (Converged Networks QoS Framework) is being developed aimed at providing context-aware, end-to-end QoS control and resource management in converged next generation networks. CNQF is designed to provide homogeneous, transparent QoS control over heterogeneous access technologies by means of distributed functional entities that co-ordinate the resources of the transport network through policy-driven decisions. In this paper, we present a measurement-based evaluation of policy-driven QoS management based on CNQF architecture, with real traffic flows on an experimental testbed. A Java based implementation of the CNQF Resource Management Subsystem is deployed on the testbed and results of the experiments validate the framework operation for policy-based QoS management of real traffic flows.

Requirements for building information modeling based lean production management systems for construction

Smooth flow of production in construction is hampered by disparity between individual trade teams' goals and the goals of stable production flow for the project as a whole. This is exacerbated by the difficulty of visualizing the flow of work in a construction project. While the addresses some of the issues in Building information modeling provides a powerful platform for visualizing work flow in control systems that also enable pull flow and deeper collaboration between teams on and off site. The requirements for implementation of a BIM-enabled pull flow construction management software system based on the Last Planner System™, called ‘KanBIM’, have been specified, and a set of functional mock-ups of the proposed system has been implemented and evaluated in a series of three focus group workshops. The requirements cover the areas of maintenance of work flow stability, enabling negotiation and commitment between teams, lean production planning with sophisticated pull flow control, and effective communication and visualization of flow. The evaluation results show that the system holds the potential to improve work flow and reduce waste by providing both process and product visualization at the work face.

Web-based occupant feedback for building energy management systems using fuzzy logic

This paper describes the development and validation of a novel web-based interface for the gathering of feedback from building occupants about their environmental discomfort including signs of Sick Building Syndrome (SBS). The gathering of such feedback may enable better targeting of environmental discomfort down to the individual as well as the early detection and subsequently resolution by building services of more complex issues such as SBS. The occupant's discomfort is interpreted and converted to air-conditioning system set points using Fuzzy Logic. Experimental results from a multi-zone air-conditioning test rig have been included in this paper.

Scalable model-based configuration management of security services in complex enterprise networks

Security administrators face the challenge of designing, deploying and maintaining a variety of configuration files related to security systems, especially in large-scale networks. These files have heterogeneous syntaxes and follow differing semantic concepts. Nevertheless, they are interdependent due to security services having to cooperate and their configuration to be consistent with each other, so that global security policies are completely and correctly enforced. To tackle this problem, our approach supports a comfortable definition of an abstract high-level security policy and provides an automated derivation of the desired configuration files. It is an extension of policy-based management and policy hierarchies, combining model-based management (MBM) with system modularization. MBM employs an object-oriented model of the managed system to obtain the details needed for automated policy refinement. The modularization into abstract subsystems (ASs) segment the system-and the model-into units which more closely encapsulate related system components and provide focused abstract views. As a result, scalability is achieved and even comprehensive IT systems can be modelled in a unified manner. The associated tool MoBaSeC (Model-Based-Service-Configuration) supports interactive graphical modelling, automated model analysis and policy refinement with the derivation of configuration files. We describe the MBM and AS approaches, outline the tool functions and exemplify their applications and results obtained. Copyright (C) 2010 John Wiley & Sons, Ltd.

Competence Monitoring in Project Teams by using Web based portfolio management systems

This paper reports a learning experience related to the acquisition of project management competences. Students from three different universities and backgrounds, cooperate in a common project that drives the learning-teaching process. Previous related works on this initiative have already evaluated the goodness of this multidisciplinary, project-based learning approach in the context of a new educative paradigm. Yet the innovative experience has allowed the authors to define a rubric in order to measure specific competences in project management. The study shows the rubric’s main aspects as well as competence acquisition evaluation alternatives, based in the metrics defined. Key indicators and specific reports obtained from data base fields in the web tool will support this work. As a result, new competences can be assessed, such ones like teamwork, problem solving, communication and leadership. Final goal is to provide an overall competence map to the students at the same time they improve their skills.

Knowledge-based models for adaptive traffic management systems

This paper describes a general approach for real time traffic management support using knowledge based models. Recognizing that human intervention is usually required to apply the current automatic traffic control systems, it is argued that there is a need for an additional intelligent layer to help operators to understand traffic problems and to make the best choice of strategic control actions that modify the assumption framework of the existing systems.

A framework for policy based coordinated adaptation in mobile systems

Adaptation is an important requirement for mobile applications due to the varying levels of resource availability that characterizes mobile environments. However without proper control, multiple applications can each adapt independently in response to a range of different adaptive stimuli, causing conflicts or sub optimal performance. In this thesis we presented a framework, which enables multiple adaptation mechanisms to coexist on one platform. The key component of this framework was the 'Policy Server', which has all the system policies and governs the rules for adaptation. We also simulated our framework and subjected it to various adaptation scenarios to demonstrate the working of the system as a whole. With the help of the simulation it was shown that our framework enables seamless adaptation of multiple applications.

Model based integration of management systems (MSs) – case study

Abstract Purpose – The purpose of this paper is to present a case study regarding the deployment of a previously developed model for the integration of management systems (MSs). The case study is developed at a manufacturing site of an international enterprise. The implementation of this model in a real business environment is aimed at assessing its feasibility. Design/methodology/approach – The presented case study takes into account different management systems standards (MSSs) progressively implemented, along the years, independently. The implementation of the model was supported by the results obtained from an investigation performed according to a structured diagnosis that was conducted to collect information related to the organizational situation of the enterprise. Findings – The main findings are as follows: a robust integrated management system (IMS), objectively more lean, structured and manageable was found to be feasible; this study provided an holistic view of the enterprise’s global management; clarifications of job descriptions and boundaries of action and responsibilities were achieved; greater efficiency in the use of resources was attained; more coordinated management of the three pillars of sustainability – environmental, economic and social, as well as risks, providing confidence and added value to the company and interested parties was achieved. Originality/value – This case study is pioneering in Portugal in respect to the implementation, at the level of an industrial organization, of the model previously developed for the integration of individualized MSs. The case study provides new insights regarding the implementation of IMSs including the rationalization of several resources and elimination of several types of organizational waste leveraging gains of efficiency. Due to its intrinsic characteristics, the model is able to support, progressively, new or revised MSSs according to the principles of annex SL (normative) – proposals for MSSs – of the International Organization for Standardization and the International Electrotechnical Commission, that the industrial organization can adopt beyond the current ones.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.