Tensor Decomposition Based Electrical Data Recovery

An energy-efficient data gathering protocol called E2DGP that takes advantage of spatial and temporal correlation of sampling data for WSNs is proposed in this paper. E2DGP includes a clustering method of balancing energy consumption, a data prediction transmission strategy and an energy-aware multihop routing algorithm. In clustering process phase, the initial probability of node for cluster head election is derived from mathematical relation between applicationpsilas seamless coverage fraction and numbers of required cluster heads. In data aggregation phase, the spatial correlation of data within a cluster is utilized by cluster head to aggregate sampling data. According to temporal correlation of sampling data, cluster heads send data to sink node using prediction transmission strategy while satisfying the transmission precision in the data transmission phase, and the lifetime of network is greatly prolonged by this strategy. In order to mitigate the hot spot problem among cluster heads, a greedy geographic and energy-aware multihop routing algorithm is presented for inter-cluster communication. Simulation results show that E2DGP outperforms EECS and LEACH in terms of network lifetime by balancing energy consumption and decrease of transmission while meeting desired application-specific requirements.

Most studies in wind integration ignore either the temporal or spatial correlation of the power data, which decreases the accuracy of the results. The objective of this paper is to model and verify the temporal correlation of wind power data using Copula and nonparametric kernel density estimation (KDE). A model for reconstructing wind power time series is proposed based on Copula theory. This model preserves the time correlation characteristics of the original wind power sequence as much as possible, which facilitates the subsequent research on the uncertainty of evolution over time. Then, based on the nonparametric KDE and maximum likelihood estimation algorithm, by selecting appropriate correlation coefficients, a time correlation model of Gaussian Copula is established and applied to the actual dataset of a wind farm in GEFCOM2014. Finally, the boxwhisker plot and the autocorrelation function are used to verify the uncertainty characteristics of the reconstructed sequence, and the accuracy of the fit is further proved by the root mean square error in both short-term and long-term case studies. From the comparison with original time series data, as well as the numerical results, the effectiveness of the proposed method is illustrated.

Certificateless privacy-preserving multidimensional data aggregation scheme with fault-tolerance for fog-based smart grids

In the quest of increasing production capacity to meet future demands E&P companies have embarked in the journey of re-developing their matured assets. This is enabled using several field development programmes that include in-fill drilling, horizontal drilling, underbalanced drilling and drilling unconventional reservoirs. These programmes require high quality offset well data along with real time data for better planning and execution to successfully manage drilling risk, reduce non-productive time and cost to achieve the field development objective. Historically a huge volume of operational data has been acquired from the large number of wells drilled. Data include well trajectories, casing points, casing analysis, drilling risks, drilling events, BHA performance, operational window, cost, time etc. Maintaining the historical offset drilling data systematically in a well organized manner is vital to unlock its business value and enable better drilling data analysis along with real time data. Technology aided programs such as collaborative well planning, geo steering, real time operations center, require offset well data integrated into real time data for integrated and optimized workflows during planning and execution. This includes building better engineering models and earth models. This paper discuss present data challenges and the approach towards building an Drilling Integrated Workflow Environment (DIWE) comprising of high quality drilling knowledge base, real time data enabled by data standards and the framework to support next generation well planning and drilling workflows. DIWE enables improved planning and execution by supporting integrated workflows that aim to mitigate drilling hazards, improve operational awareness, optimize well design, cost effective construction, and better well placement moving towards achieving drilling excellence

To achieve low‐carbon sustainable energy development, new technologies such as Internet of Energy (IoE), intelligent systems and Internet of Things (IoT) as well as distributed energy generations via smart grids (SG) are gaining attention. The interoperability between intelligent energy systems, realised through the web, enables automatic consumption optimisation and increases network efficiency and intelligent management. IoE is an intriguing topic in close connection with the IoT, communication systems, SG and electrical mobility that contributes to energy efficiency to achieve zero‐carbon technologies and green environments. Furthermore, nowadays, the widespread growth and utilisation of processors for mining digital currency in homes and small warehouses are some other factors to be considered in terms of electric energy consumption and greenhouse gas emission. However, research on the use of the Internet for evaluating the misallocation of energy and the effect it can have on CO 2 emissions is often neglected. In this study, the authors present a detailed overview regarding the evolution of SG in conjunction with the employment of IoE systems as well as the essential components of IoE for decarbonisation. Also, mathematical models with simulation are provided to evaluate the role of IoE in reducing CO 2 emission.

In the past two decades, interest in demand response (DR) schemes has grown exponentially. The need for DR has been driven by sustainability (environmental and socioeconomic) and cost-efficiency. The main premise of DR is to influence the timing and magnitude of consumption to match energy supply by sharing the benefits with consumers, ultimately aiming to optimize generation cost. As such, the first and primary enabler to DR was the establishment of contemporary electricity markets. Increased proliferation of Distributed Energy Resources (DER) and microgeneration further motivated the participation of consumers as active players in the market, popularizing DR and the wider category of Demand-Side Management (DSM) programs. Smart Grids (SG) have been an enabler to modern DR schemes, with smart metering data providing input to the underlying optimization and forecasting tools. The more recent emergence of the Internet of Energy (IoE), seen as the evolution of SG, is driven by increased Internet of Things (IoT)-enabling and high penetration of scalable and distributed energy resources. In this IoE paradigm being a fully decentralized network of energy prosumers, DR will continue to be a vital aspect of the grid in future Transactive Energy (TE) schemes, aiming for a more user-centered, energy-efficient, cost-saving, energy management approach. This paper investigates original motives and identifies the first mentions of DR in the legislative and scientific literature. Afterwards, the evolution of DR is tracked over the past four decades, attempting to study the co-influence of legislation and research by performing a thorough statistical analysis of research trends on the IEEE Xplore digital library. Finally, conclusions are made as to the current state of DR and future prospects of DR are discussed.

Most of the existing packet scheduling mechanisms of the wireless sensor network use First Come First Served (FCFS) non pre emptive priority and pre emptive priority scheduling algorithms. The above algorithms have high processing overhead and also long end-to-end data transmission delay. In FCFS concept the data packet which is entering the node first will go out first from the node, and the packet which will enter last will leave at last. But in FCFS scheduling of real time data packets coming to the node have to wait for a long time period.In non pre emptive priority scheduling algorithm there is starvation of real time data packets because once the processor enters the running state, it will not allow remove until it is completed, so there is starvation of real time data packets. In pre emptive scheduling, starvation of non real time data packets, due to continuous arrival of real time data. Therefore the data packets are to be schedule in multilevel queue. But the multilevel queue scheduling scheme is not suitable for dynamic inputs, and hence the scheme is designed for dynamically change in the inputs. The Dynamic Multilevel Priority (DMP) packet scheduling is the scheme for dynamically changes in the inputs. In this scheme each node except the last level of the virtual hierarchy in the zone based topology of wireless sensor network has three levels of priority queues. Real time data packets are placed into highest priority queue and can preempt the data packets in the other queues. Non real time data packets are placed into other two queues based on threshold of their estimated processing time. The leaf node have two queues, one for real time data packet and another for non real time data packet since they do not receive data from other nodes and thus reduces end to end delay. This scheme reduces the average waiting time and end-to -end delay of data packets.

Interoperability among ICT systems of the power grid is crucial for achieving stable and reliable power supply. Interutility real time data exchange has become critical to the operation of interconnected systems. Current Energy Management System (EMS)/Supervisory Control and Data Acquisition (SCADA) provides an ICCP connection for exchanging the real time data within control centers or across control centers. The proposed Self Healing ICCP system uses multi agents for logical port management. It can also collocate the existing ICCP systems which don't have self healing with complete ICCP stack based on IEC 60870 part 6. It reduces the real time data losses between the control centers and which leads to better interoperability. The multi agents are used for diagnosis, analysis and configuration of logical port management.

Electric vehicles (EVs) are viewed as the future vehicles since they are very efficient, environmentally friendly (no pollution and less noisy) and helpful for power regulation in grid through various charging topologies. In order to analyze the performance of EV, proper hardware testing model is required and hence in this work basic EV testing model is considered for the real time data analysis. Proposed EV testing model incorporates trending IOT technology to Cloud based real time data acquisition, provides the live interaction with electric vehicle. This enables user to study the performance parameters in fixed time interval with varied drive cycles. This paper presents the details of Basic EV module built using Arduino controller to acquire and process real time data for Battery Electric Vehicles (BEVs) through IoT in which all acquired performance parameters are stored in things speak server and analyzed using MATLAB

The streaming of real-time audio/video data is very challenging because of the time-varying and unreliable wireless channels, video content characteristics, limited bandwidth, dynamic topology, heterogeneous and distributed environment and high packet loss rate because of wireless interference and channel fading. Because of such open issues present in wireless networks, it is very difficult to satisfy the requirements of audio/video streaming applications such as low delay, low packet loss, jitter control etc. In such scenarios of opportunistic networks where real time data is being streamed from server node to client node, Network Coding and its variants can be used by such nodes to meet different requirements. Network coding changes the role of such multimedia nodes from store and forward to encode data packets. Encoding process includes mathematical operations on data packets. This survey paper has considered number of speculative and practical approaches and cases where network coding or its variant applied either fully or partially on multimedia traffic with the aim to improve performance and to provide protection against packet losses. This review has mainly focused on the opportunity of performance enhancement of MPEG-4 traffic over Wireless Network using Random Linear Network Coding (RLNC) with Multi Generation Mixing (MGM). Using Multi Generation Mixing, packets of greater importance has got more protection, less loss, more reconstruction and recovery of real time data.

Electrical power data of machining has become more and more vital due to the increasing concerns on the energy efficiency. As a kind of easily available real-time signal, electrical power data in machining involves diverse characteristics related to technical specification and operational production of machine tools and workpieces. To efficiently exploit the various characteristics involved in electrical power data, this paper proposes a framework to analyze characteristics of electrical power data in machining. Firstly, the sample experiment for turning the round stock on a lathe is carried out to investigate the characteristics implied in the real-time electrical power data. Based on it, a framework is derived to analyze characteristics from three aspects, energy-specific characteristic of machining, state-specific characteristic of machine tools and operation-specific characteristic of workpieces. The case with three scenarios is presented to demonstrate the potential applications of the framework, which is effectively utilized in analyzing energy consumption of machining, monitoring states of machine tools and automatically identifying the types of machining workpieces.

A new numerical procedure for the study of finite temperature quantum dynamics is developed. The method is based on the observation that the real and imaginary time dynamical data contain complementary types of information. Maximum entropy methods, based on a combination of real and imaginary time input data, are used to calculate the spectral densities associated with real time correlation functions. Model studies demonstrate that the inclusion of even modest amounts of short-time real time data significantly improves the quality of the resulting spectral densities over that achievable using either real time data or imaginary time data separately.

Internet of Energy is considered as a promising approach to solve the problems of energy crisis and carbon emission. It needs to collect user’s real-time data for optimizing the energy utilization. However, such data may disclose user’s privacy information. Previous works usually adopt specific obfuscation value to mask user’s data and counteract the deviation through data aggregation; these works can preserve the data privacy effectively, but most of them consider less about the data-utility (precision). In this paper, we propose a utility-privacy tradeoff scheme based on random data obfuscation in Internet of Energy. In the proposed scheme, we adopt random data-obfuscation to mask the real-time data and realize the fault-tolerance during data aggregation, and the random obfuscation value obeys the Laplace distribution. We use the signal-to-noise ratio to quantify the level of utility; we measure the level of privacy through information entropy. Based on these two Indicators, we balance the utility-privacy tradeoff by calculating the optimal parameters of the Laplace distribution. The analysis shows that our scheme can meet the security requirement, and it also has better performance than that of other popular methods.

Real time solutions: an innovative approach to making business-time critical driven decisions. It is designed to monitor, support, and optimize operations in real time and remotely to mitigate cost and risk at work sites. The workflows allowed real time monitoring and intervention of the drilling operating parameters from the office which resulted in greater operating efficiency and thus greater financial saving. This technology was instrumental to the Angsi development team's success in drilling Malaysia's longest development well to-date, Angsi-A028ST1 at a total depth of 6,339 meters.Real time well construction workflows begin when real time data on the rig site are gathered and transmitted to PETRONAS Carigali Sdn.Bhd, PCSB office via VSAT link. Data can be accessed via a web-based interface for 24 hours coverage anytime anywhere. The real time data are integrated into a geological and geophysical project database and fed into a 3D visualization tool which amalgamates the planned and current well trajectories within the geological earth model incorporating neighbouring wells with operations data. The simultaneous display of "knowledge-management-cubes" from daily drilling operational reporting software enables the team to review contingency plans and be pre-emptive by the captured lesson learnt.This technology creates multidisciplinary collaborations through the ether space, reducing communication gaps and greatly improving the decision making process. Examples: reducing the number of check trips, shortening the planned TD by comparing the real time data against correlated model, and helping in handling operational problems. The team has greatly benefited from RTO - technologies, people and processes - with a total cost saving of more than USD 1 million.This paper presents the real time system setup, architecture and scope of work in enabling 2D and 3D well visualization and integration. The value-added and the relative cost savings, observations and feedback are also presented

The Bulgarian National Operational Marine Observing System (NOMOS) is a system of systems designed to allow the real-time assessment of weather and marine conditions in the coastal, shelf and open sea areas of the western part of Black Sea. NOMOS consists of several subsystems: POMOS, Galata, Shkorpilovtci, BulSeaL and BulARGO. POMOS is an observing system located in the ports (14 measuring sites, 45 instruments, 14 weather and seastate parameters), designed to provide weather and sea state operational information for the areas of major Bulgarian ports (Varna, Burgas and Balchik), intended to secure safety shipping in ports, canals and bays. Galata is a platform which provides open sea Real-time meteorological and oceanographic information from 24 sensors, measuring 31 major oceanographic and meteorological parameters. Shkorpilovtci is a coastal oceanographic station compiling: weather station, sea level gauge, UV radiation sensor and Recording Doppler Current Profiler with wave sensor. BulSeaL is a network of sea level stations including four historical sites, measuring sea level from more than 80 years. BulARGO is a set of Argo floats deployed in the frame if the project supported by Bulgarian National Science Fund, Ministry of education, youth and science. The purpose of the BulARGO project is to develop a new national marine research infrastructure for in situ observation in Black Sea based on autonomous profiling floats as a Bulgarian component of the Euro Argo network. These subsystems collect real time data using various instruments and sensors placed at different strategic locations. All instruments are connected to communication system via intranet which provides direct access to the sensors. The measured data are transmitted to the central collecting system, where the information is processed and stored in database. Access to database is through internet/intranet with the help of browsers. Actual data is controlled by Data Management System an can be displayed on the computer screens using report server supporting thereby needs of different group of users. Next future development will be to install new system which includes five open sea moorings equipped with meteorological and oceanographic sensors as well as with seismometers as a main component of a marine hazards early warning system. The main goal of NOMOS is to support sustainable development of the Bulgarian Black Sea coast and EEZ, and the main task is to provide operational information for the needs of: national security, civil protection, Search and Rescue, government and local authorities, port authorities, shipping, marine industry, fishery and aquaculture, tourist industry, environmental protection, coast protection, oil spills combat and other interested. NOMOS real time data is one of the most important sources of multi-parameter operational information in the Black Sea which will serve for monitoring, verification and improvement of model results and forecasts as well as for collecting long time series of data needed for climatic research, marine physics, chemistry and biology. Free real time data access and developed user friendly WEB and WAP interfaces made the system useful not only for science, marine industry and governmental agencies but also for general public, especially for yachtsmen and windsurfers.