Green Cloud and reduction of energy consumption

By using global application environments, cloud computing based data centers growing every day and this exponentially grows definitely effect on our environment. Researchers that have a commitment to their environment and others which was concerned about the electricity bills came up with a solution which called “Green Cloudâ€. Green cloud data centers based on how consume energy are known as high efficient data centers. In green cloud we try to reduce number of active devices and consume less electricity energy. In green data centers toke an advantage of VM and ability of copying, deleting and moving VMs over the data center and reduce energy consumption. This paper focused on which parts of data centers may change and how researchers found the suitable solution for each component of data centers. Also with all these problems why still the cloud data centers are the best technology for IT businesses

Evaluation of Automatic Vehicle Specific Identification (AVSI) in a traffic signal control system

Automatic Vehicle Specific Identification (AVSI) is a generic name for advanced vehicle detection systems. By automating the identification of vehicles by sensing the presence of vehicles with roadside detection sites or readers, AVSI is assumed to provide vehicle specific information in traffic signal control systems;In the application of AVSI to traffic signal control systems, as a vehicle passes a reader site, the reader records the arrival time and type of the detected vehicle. The reader would then send the information received to a local microprocessor-based traffic signal controller. The controller\u27s built-in signal control logic would then use the information to adjust traffic signal timing to reflect the present traffic stream\u27s characteristics;The purpose of this research is to evaluate the potential benefits of AVSI at an isolated intersection. The evaluation of the applicability of AVSI at an intersection is accomplished by using a new developed microscopic simulation model. This simulation model is coded in SIMAN simulation language. For the purpose of validating the simulation model, a delay study is conducted at an actual intersection. The validation of the model has established a level of confidence in the obtained simulation results;An important element of this simulation model is the development of a new Vehicle Specific Adaptive (VSA) traffic signal control strategy. VSA control strategy adjusts the signal timing based on AVSI traffic information, that is, it examines individual vehicle performance characteristics before extending a phase green time or implementing a new cycle split;Using the simulation model, the incorporated VSA control strategy is tested against a pretimed control system. The simulation results indicates that through the use of AVSI traffic information, the VSA control logic can improve intersection performance by reducing vehicles stopped delay at an intersection

BIM and Project Management in AEC Industry

Building Information Modeling has altered project managers’ role in architecture, Engineering and Construction (AEC) industry. In theory Building Information Modeling (BIM) has promised the project managers to enhance communication and collaboration aspect of managing their projects, thus increasing the efficiency and success rate of the AEC industry projects. However, in real world application, project managers had challenges and barriers with BIM implementation into their processes. This study intends to highlight the main characteristics of the BIM, its benefits, barriers and risk management capabilities to elaborate on why BIM adoption has been slower than anticipated. Based on quantitative data collection through surveys, this study then asserts BIM impact on production efficiency rate. Findings of this research paper show that project managers had been facing 2 main challenges when implementing BIM into their project management processes: 1. Lack of knowledge of the software 2. Lack of knowledge of project management role shifts that had to take place once BIM was implemented. The findings of the quantitative data analysis show that BIM increased the production efficiency rate where it was implemented. In conclusion, the research paper asserts on the main question identified in the problem statement on why project managers in AEC industry should implement BIM into their project management processes

Data Driven Approach To Characterize And Forecast The Impact Of Freeway Work Zones On Mobility Using Probe Vehicle Data

The presence of work zones on freeways causes traffic congestion and creates hazardous conditions for commuters and construction workers. Traffic congestion resulting from work zones causes negative impacts on traffic mobility (delay), the environment (vehicle emissions), and safety when stopped or slowed vehicles become vulnerable to rear-end collisions. Addressing these concerns, a data-driven approach was utilized to develop methodologies to measure, predict, and characterize the impact work zones have on Michigan interstates. This study used probe vehicle data, collected from GPS devices in vehicles, as the primary source for mobility data. This data was used to fulfill three objectives: develop a systematic approach to characterize work zone mobility, predict the impact of future work zones, and develop a business intelligence support system to plan future work zones. Using probe vehicle data, a performance measurement framework was developed to characterize the spatiotemporal impact of work zones using various data visualization techniques. This framework also included summary statistics of mobility performance for each individual work zone. The result was a Work Zone Mobility Audit (WZMA) template which summarizes metrics into a two-page summary which can be utilized for further monitoring and diagnostics of the mobility impact. A machine learning framework was developed to learn from historical projects and predict the spatiotemporal impact of future work zones on mobility. This approach utilized Random Forest, XGBoost, and Artificial Neural Network classification algorithms to determine the traffic speed range for highway segments while having freeway lane-closures. This framework used a distribution of speed for each freeway segment, as a substitute for hourly traffic volume, and were able to predict speed ranges for future scenarios with up to 85% accuracy. The ANN model reached up to 88% accuracy predicting queueing condition (speed less than 20 mph), which could be utilized to enhance queue warning systems and improve the overall safety and mobility. Mobility data for more than 1,700 historical work zone projects in state of Michigan were assessed to provide a comprehensive overview of the overall impact and significant factors affecting the mobility. A Business Intelligence (BI) approach was utilized to analyze these work zones and present actionable information which helps work zone mobility executives make informed decisions while planning their future work zones. The Pareto principle was also utilized to identify significant projects which accounted for a majority of the overall impact. Chi-square Automatic Interaction Detector, CHAID, algorithm was also applied to discover the relationship between variables affecting the mobility. This statistical method built several decision-trees which could be utilized to determine best, worst, and expected consequence of different work zone strategies

Cuttings transportation in coiled tubing drilling for mineral exploration

Cuttings transportation in micro-borehole annulus in coiled tubing drilling for mineral exploration was studied. The effect of cuttings size and mud properties as well as hole inclination was simulated physically using a flow loop. Computational fluid dynamics was applied to simulate lab experiments and do sensitivity analysis of various parameters. The results show significant differences in cuttings transport response in mineral exploration comparing to the oil and gas drilling applications

Energy efficiency in wireless sensor networks

University of Technology Sydney. Faculty of Engineering and Information Technology.Wireless sensor networks (WSNs), as distributed networks of sensors with the ability to sense, process and communicate, have been increasingly used in various fields including engineering, health and environment, to intelligently monitor remote locations at low cost. Sensors (a.k.a. nodes) in such networks are responsible for four major tasks: data aggregation, sending and receiving data, and in-network data processing. This implies that they must effectively utilise their resources, including memory usage, CPU power and, more importantly, energy, to increase their lifetime and productivity. Besides harvesting energy, increasing the lifetime of sensors in the network by decreasing their energy consumption has become one of the main challenges of using WSNs in practical applications. In response to this challenge, over the last few years there have been increasing efforts to minimise energy consumption via new algorithms and techniques in different layers of the WSN, including the hardware layer (i.e., sensing, processing, transmission), network layer (i.e., protocols, routing) and application layer; most of these efforts have focused on specific and separate components of energy dissipation in WSNs. Due to the high integration of these components within a WSN, and therefore their interplay, each component cannot be treated independently without regard for other components; in another words, optimising the energy consumption of one component, e.g. MAC protocols, may increase the energy requirements of other components, such as routing. Therefore, minimising energy in one component may not guarantee optimisation of the overall energy usage of the network. Unlike most of the current research that focuses on a single aspect of WSNs, we present an Energy Driven Architecture (EDA) as a new architecture for minimising the total energy consumption of WSNs. The architecture identifies generic and essential energy-consuming constituents of the network. EDA as a constituent-based architecture is used to deploy WSNs according to energy dissipation through their constituents. This view of overall energy consumption in WSNs can be applied to optimising and balancing energy consumption and increasing the network lifetime. Based on the proposed architecture, we introduce a single overall model and propose a feasible formulation to express the overall energy consumption of a generic wireless sensor network application in terms of its energy constituents. The formulation offers a concrete expression for evaluating the performance of a wireless sensor network application, optimising its constituent’s operations, and designing more energy-efficient applications. The ultimate aim is to produce an energy map architecture of a generic WSN application that comprises essential and definable energy constituents and the relationships between these constituents so that one can explore strategies for minimising the overall energy consumption of the application. Our architecture focuses on energy constituents rather than network layers or physical components. Importantly, it allows the identification and mapping of energy-consuming entities in a WSN application to energy constituents of the architecture. Furthermore, we perform a comprehensive study of all possible tasks of a sensor in its embedded network and propose an energy management model. We categorise these tasks into five energy consuming constituents. The sensor's energy consumption (EC) is modelled based on its energy consuming constituents and their input parameters and tasks. The sensor's EC can thus be reduced by managing and executing efficiently the tasks of its constituents. The proposed approach can be effective for power management, and it also can be used to guide the design of energy efficient wireless sensor networks through network parameterisation and optimisation. Later, parameters affecting energy consumption in WSNs are extracted. The dependency between these parameters and the average energy consumption of a specific application is then investigated. A few statistical tools are applied for parameter reduction, then random forest regression is employed to model energy consumption per delivered packet with and without parameter reduction to determine the reduction in accuracy due to reduction. Finally, an energy-efficient dynamic topology management algorithm is proposed based on the EDA model and the prevalent parameters. The performance of the new topology management algorithm, which employs Dijkstra to find energy-efficient lowest cost paths among nodes, is compared to similar topology management algorithms. Extensive simulation tests on randomly simulated WSNs show the potential of the proposed topology management algorithm for identifying the lowest cost paths. The challenges of future research are revealed and their importance is explained