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A smart contract contains the \"terms\" of a blockchain transaction between a buyer and a seller as well as the capabilities to execute those terms. In order for smart contracts to include outside data from the world, such as stock market data, weather, sports data, etc..., the contract needs a third party service called an

Smart technologies have made our lives easier and allowed us to stay more connected...but is there more happening under the surface than we realize? With details on the ugly side of the technology we love, we were joined this morning by Dr. Future himself, Tom Keenan, Professor in the School of Architecture, Planning and Landscape at the  University of Calgary, author of the best selling book Technocreep and Fellow of the Canadian Global Affairs institute.

Smart home data is complicated. There are all kinds of devices, and they are in many different combinations, geographies, configurations, etc. This complicated data situation is further exacerbated during a pandemic when time series data seems to be filled with anomalies. Evan Welbourne joins us to discuss how Amazon is synthesizing this disparate data into functionality for the next generation of smart homes. He discusses the challenges of working with smart home technology, and he describes how they developed their latest feature called \"hunches.\"

Smart home data is complicated. There are all kinds of devices, and they are in many different combinations, geographies, configurations, etc. This complicated data situation is further exacerbated during a pandemic when time series data seems to be filled with anomalies. Evan Welbourne joins us to discuss how Amazon is synthesizing this disparate data into functionality for the next generation of smart homes. He discusses the challenges of working with smart home technology, and he describes how they developed their latest feature called \"hunches.\"

The rapid evolution of healthcare demands innovative solutions for managing the vast quantities of data generated daily. This paper examines the concept of Smart Health Records (SHRs) and their integration with Artificial Intelligence (AI) to improve healthcare delivery. SHRs represent an advanced iteration of electronic health records, equipped with AI-powered tools to ensure real-time data sharing, predictive analytics, and automated administrative processes. AI’s capabilities, including natural language processing, machine learning, and predictive modeling, streamline data management and enhance clinical decision-making. However, the transition to SHRs is not without challenges, such as data privacy, algorithm accuracy, and resistance to technological adoption. This paper discusses best practices for implementing AI-driven SHRs and examines future trends, emphasizing their potential to revolutionize healthcare by fostering patient-centered care, improving efficiency, and enabling personalized medicine. 

In this paper, we use data collected from over 2000 non-residential electric vehicle supply equipments (EVSEs) located in Northern California for the year of 2013 to estimate the potential benefits of smart electric vehicle (EV) charging. We develop a smart charging framework to identify the benefits of non-residential EV charging to the load aggregators and the distribution grid. Using this extensive dataset, we aim to improve upon past studies focusing on the benefits of smart EV charging by relaxing the assumptions made in these studies regarding: (i) driving patterns, driver behavior and driver types; (ii) the scalability of a limited number of simulated vehicles to represent different load aggregation points in the power system with different customer characteristics; and (iii) the charging profile of EVs. First, we study the benefits of EV aggregations behind-the-meter, where a time-of-use pricing schema is used to understand the benefits to the owner when EV aggregations shift load from high cost periods to lower cost periods. For the year of 2013, we show a reduction of up to 24.8% in the monthly bill is possible. Then, following a similar aggregation strategy, we show that EV aggregations decrease their contribution to the system peak load by approximately 40% when charging is controlled within arrival and departure times. Our results also show that it could be expected to shift approximately 0.25kWh (~2.8%) of energy per non-residential EV charging session from peak periods (12PM-6PM) to off-peak periods (after 6PM) in Northern California for the year of 2013.

The issue of road traffic congestion has become increasingly apparent in modern times. With the rise of urbanization, technological advancements, and an increase in the number of vehicles on the road, almost all major cities are experiencing poor traffic environments and low road efficiency. To address this problem, re-searchers have turned to diverse data resources and focused on predicting traffic flow, a crucial issue in intelligent transportation systems (ITS) that can help al-leviate congestion. By analyzing data from correlated roads and vehicles, such as speed, density, and flow rate, it is possible to anticipate traffic congestion and patterns. This paper presents an adaptive traffic system that utilizes supervised ma-chine learning and big data analytics to predict traffic flow. The system monitors and extracts relevant traffic flow data, analyzes and processes the data, and stores it to enhance the model's accuracy and effectiveness. A simulation was conducted by the authors to showcase the proposed solution. The outcomes of the study car-ry substantial implications for transportation systems, offering valuable insights for enhancing traffic flow management.

The issue of road traffic congestion has become increasingly apparent in modern times. With the rise of urbanization, technological advancements, and an increase in the number of vehicles on the road, almost all major cities are experiencing poor traffic environments and low road efficiency. To address this problem, re-searchers have turned to diverse data resources and focused on predicting traffic flow, a crucial issue in intelligent transportation systems (ITS) that can help al-leviate congestion. By analyzing data from correlated roads and vehicles, such as speed, density, and flow rate, it is possible to anticipate traffic congestion and patterns. This paper presents an adaptive traffic system that utilizes supervised ma-chine learning and big data analytics to predict traffic flow. The system monitors and extracts relevant traffic flow data, analyzes and processes the data, and stores it to enhance the model's accuracy and effectiveness. A simulation was conducted by the authors to showcase the proposed solution. The outcomes of the study car-ry substantial implications for transportation systems, offering valuable insights for enhancing traffic flow management.

The issue of road traffic congestion has become increasingly apparent in modern times. With the rise of urbanization, technological advancements, and an increase in the number of vehicles on the road, almost all major cities are experiencing poor traffic environments and low road efficiency. To address this problem, re-searchers have turned to diverse data resources and focused on predicting traffic flow, a crucial issue in intelligent transportation systems (ITS) that can help al-leviate congestion. By analyzing data from correlated roads and vehicles, such as speed, density, and flow rate, it is possible to anticipate traffic congestion and patterns. This paper presents an adaptive traffic system that utilizes supervised ma-chine learning and big data analytics to predict traffic flow. The system monitors and extracts relevant traffic flow data, analyzes and processes the data, and stores it to enhance the model's accuracy and effectiveness. A simulation was conducted by the authors to showcase the proposed solution. The outcomes of the study car-ry substantial implications for transportation systems, offering valuable insights for enhancing traffic flow management.

The widespread deployment of Advanced Metering Infrastructure has made granular data of residential electricity consumption available on a large scale. Smart meters enable a two way communication between residential customers and utilities. One field of research that relies on such granular consumption data is Residential Demand Response, where individual users are incentivized to temporarily reduce their consumption during periods of high marginal cost of electricity. To quantify the economic potential of Residential Demand Response, it is important to estimate the reductions during Demand Response hours, taking into account the heterogeneity of electricity users. In this paper, we incorporate latent variables representing behavioral archetypes of electricity users into the process of short term load forecasting with Machine Learning methods, thereby differentiating between varying levels of energy consumption. The latent variables are constructed by fitting Conditional Mixture Models of Linear Regressions and Hidden Markov Models on smart meter readings of a Residential Demand Response program in the western United States. We observe a notable increase in the accuracy of short term load forecasts compared to the case without latent variables. We then estimate the reductions during Demand Response events conditional on the latent variables, and discover a higher DR reduction among users with automated smart home devices compared to those without.

Petra Solar panels contain sensors that monitor the grid for potential problems, which could mean fewer blackouts.

In my connected home I've focused mostly on automation, but Stephen Sorkin, the chief strategy officer at Splunk has decided to go a much nerdier route. He focused on data, specifically gathering data from his circuit breaker, his connected weather station and his pool. He sends that data to Splunk and has used it to make some starting conclusions -- among them that his home generates about 200 MB of data a day. In this week's podcast Sorkin discusses how he is using data to make decisions about when to water his lawn and when to replace ol inefficient appliances. It's one thing to guess that a new washer or dryer will save you money, but Sorkin has the data to prove it. We discuss this and why users should be in control of their data. And Kevin Tofel was on vacation this week, but my colleague Kevin Fitchard joined us to talk about the Wink hub, the connected kitchen and new networks for the internet of things. Listen up. Host: Stacey Higginbotham Guests: Kevin Fitchard and Stephen Sorkin of Splunk A few thoughts on Wink. It has Lutron and a nice interface Stop trying to push the iOS v. Android worldview on the internet of things Does the internet of things need cellular networks? Why open data is not just useful, but essential Tips to monitor your electric meter and pool

The City of Los Angeles Smart City Showcase features the City’s use of technology to improve the quality of life of residents, streamline City operations, and foster digital inclusion. #SmartCity #DigitalInclusion #LAMayorsOffice Follow LA CityView35 on our socials: Subscribe ➔ youtube.com/LACityview35?sub_confirmation=1 Website ➔ LACityView.org Facebook ➔ facebook.com/LACityView35 Twitter ➔ twitter.com/LACityView35 About us: LA CityView35 is your local municipal government channel bringing you the latest information on everything that matters to you including city services, resources, politics, news, culture, entertainment, and more! LA CityView Media Group is responsible for video production, social media, and cable television programming on behalf of the City of Los Angeles.

This study investigates the optimization of durian farming practices in Eastern Thailand using data-driven clustering techniques. The research aims to identify distinct agricultural patterns and improve resource allocation in durian produc tion. K-means clustering is applied to durian production area and yield data from 2012 to 2023. Cluster quality is assessed using the Davies-Bouldin index (DBI), Dunn index, and Silhouette score. The methodology included comparing clustering results before and after log transformation of the data. Three main clusters are identified which are large-scale high-yield producers, small-scale lower-yield areas, and medium-scale producers with moderate yields. Notably, log transformation did not consistently improve clustering performance with original data often producing better-defined clusters. This finding highlights the importance of carefully considering data pre processing methods. Further more, the data-driven clustering offers valuable insights for precision agriculture by identifying regions with higher productivity allowing for targeted interven tions and better resource allocation. The results can guide farmers in optimizing durian cultivation strategies, potentially leading to increased yields and more sustainable farming practices in Eastern Thailand’s durian industry.

A document discusses the physical and engineering principles affecting the design of the Smart Data Node in the Sky (SDNITS) -- a proposed Earth-orbiting satellite for relaying scientific data from other Earth-orbiting satellites to one or more ground station(s). The document characterizes the problem of designing the telecommunication architecture of the SDNITS as consisting of two main parts: (1) finding the most advantageous orbit for the SDNITS to gather data from the scientific satellites and relay the data to the ground, taking account of such factors as visibility and range; and (2) choosing a telecommunication architecture appropriate for the intended relay function.

A convergence of technologies in data mining, machine learning, and a persuasive computer has led to an interest in the development of smart environment to help human with functions, such as monitoring and remote health interventions, activity recognition, energy saving. The need for technology development was confirmed again by the aging population and the importance of individual independent in their own homes. Pattern mining on sensor data from smart home is widely applied in research such as using data mining. In this paper, we proposed a periodic pattern mining in smart house data that is integrated between the FP-Growth PrefixSpan algorithm and a fuzzy approach, which is called as fuzzy-time interval periodic patterns mining. Our purpose is to obtain the periodic pattern of activity at various time intervals. The simulation results show that the resident activities can be recognized by analyzing the triggered sensor patterns, and the impacts of minimum support values to the number of fuzzy-time-interval periodic patterns generated. Moreover, fuzzy-time-interval periodic patterns that are generated encourages to find daily or anomalies resident’s habits.

The dominant inherent nature to TM in DoD testing is sampled time-history data from an ultimately analog world, (which) is not going to change drastically regardless of how data is transmitted to ground. A factor that could change that fact most is the degree to which answers instead of data are obtained on board the test vehicle (iNET Concept of Operations, v. 2007.1). SDS seeks to change this inherent nature of telemetry in DoD testing by: 1) Developing an on-board capability to monitor and analyze test data in order to reduce the amount of data sent to the ground, 2) Employing bandwidth efficient algorithms to reduce bandwidth requirements, and 3) Developing the capability to notify operators when data demonstrate abnormal behavior

The dominant inherent nature to TM in DoD testing is sampled time-history data from an ultimately analog world, (which) is not going to change drastically regardless of how data is transmitted to ground. A factor that could change that fact most is the degree to which answers instead of data are obtained on board the test vehicle (iNET Concept of Operations, v. 2007.1). SDS seeks to change this inherent nature of telemetry in DoD testing by: 1) Developing an on-board capability to monitor and analyze test data in order to reduce the amount of data sent to the ground, 2) Employing bandwidth efficient algorithms to reduce bandwidth requirements, and 3) Developing the capability to notify operators when data demonstrate abnormal behavior

Perma.cc archive of https://datasmart.ash.harvard.edu/news/article/how-can-data-and-analytics-be-used-to-enhance-city-operations-723 created on 2022-07-12 21:34:35.005108+00:00.

A recurring problem faced when training neural networks is that there is typically not enough data to maximize the generalization capability of deep neural networks(DNN). There are many techniques to address this, including data augmentation, dropout, and transfer learning. In this paper, we introduce an additional method which we call Smart Augmentation and we show how to use it to increase the accuracy and reduce overfitting on a target network. Smart Augmentation works by creating a network that learns how to generate augmented data during the training process of a target network in a way that reduces that networks loss. This allows us to learn augmentations that minimize the error of that network. Smart Augmentation has shown the potential to increase accuracy by demonstrably significant measures on all datasets tested. In addition, it has shown potential to achieve similar or improved performance levels with significantly smaller network sizes in a number of tested cases.

The trend in smart city is partly due to advances in Internet of Things. In any smart city, a significant amount of data is generated at the edge of the network, such as traffic data collected by smart devices on vehicles in smart transportation, and educational multimedia materials captured by and disseminated using smart phones or tablets in smart education. Generally, data is transmitted to the cloud for further processing and storage, which requires significant network resources and has associated security and privacy risks. However, most of the data could be pre-processed to reduce the transmission cost of the network and protect the privacy of the user. For instance, duplicate data cloud be reduced and videos cloud be compressed. To achieve this, some computation is required to be performed at the edge of the network. One commonly seen application in smart city is smart grids, partly due to the rapid digitization of household appliances, service industries, manufacturing processes, etc. Smart meters and other intelligent devices been deployed in each household and business premise, detailed information about the usage of the electricity (and potentially other information) is periodically collected and transmitted to the cloud server. Cloud storage, Cryptography, Data security, Public audit, Secure deduplication, Bilinear pairing, Smart grid.

The trend in smart city is partly due to advances in Internet of Things. In any smart city, a significant amount of data is generated at the edge of the network, such as traffic data collected by smart devices on vehicles in smart transportation, and educational multimedia materials captured by and disseminated using smart phones or tablets in smart education. Generally, data is transmitted to the cloud for further processing and storage, which requires significant network resources and has associated security and privacy risks. However, most of the data could be pre-processed to reduce the transmission cost of the network and protect the privacy of the user. For instance, duplicate data cloud be reduced and videos cloud be compressed. To achieve this, some computation is required to be performed at the edge of the network. One commonly seen application in smart city is smart grids, partly due to the rapid digitization of household appliances, service industries, manufacturing processes, etc. Smart meters and other intelligent devices been deployed in each household and business premise, detailed information about the usage of the electricity (and potentially other information) is periodically collected and transmitted to the cloud server. Cloud storage, Cryptography, Data security, Public audit, Secure deduplication, Bilinear pairing, Smart grid.

With the emergence of IoT devices, data aggregation in the area of smart grids can be implemented based on IoT networks. However, the communication and computation resources of IoT devices are limited so it is not possible to apply conventional Internet protocols directly. On the other hand, gathering data of smart meters in the advanced metering infrastructure faces challenges such as privacy-preserving and heavy-loaded authentication and aggregation schemes. In this paper, we propose an improved lightweight, secure, and privacy-preserving scheme for aggregating data from smart meters in large-scale IoT-based smart grids. The proposed scheme adopts light-weight cryptography operations such as exclusive-OR, hash, and concatenation functions. In comparison with the schemes in the literature, the analysis and simulation results show that the proposed scheme satisfies the same security levels, while at the same time burdens lower computation and communication overheads. This observation makes the proposed scheme more suitable to be employed in large-scale and IoT-based smart grids for data aggregation.

With the emergence of IoT devices, data aggregation in the area of smart grids can be implemented based on IoT networks. However, the communication and computation resources of IoT devices are limited so it is not possible to apply conventional Internet protocols directly. On the other hand, gathering data of smart meters in the advanced metering infrastructure faces challenges such as privacy-preserving and heavy-loaded authentication and aggregation schemes. In this paper, we propose an improved lightweight, secure, and privacy-preserving scheme for aggregating data from smart meters in large-scale IoT-based smart grids. The proposed scheme adopts light-weight cryptography operations such as exclusive-OR, hash, and concatenation functions. In comparison with the schemes in the literature, the analysis and simulation results show that the proposed scheme satisfies the same security levels, while at the same time burdens lower computation and communication overheads. This observation makes the proposed scheme more suitable to be employed in large-scale and IoT-based smart grids for data aggregation.

Perma.cc archive of https://www.wateronline.com/doc/smart-water-meter-considered-evidence-murder-case-0001 created on 2022-08-05 17:15:58.639129+00:00.

Research on Smart Sports Training Platform Based on Data Post-Processing Algorithm of Gasp Frequency Signal Acquisition Instrument

Research on Smart Sports Training Platform Based on Data Post-Processing Algorithm of Gasp Frequency Signal Acquisition Instrument

Research on Smart Sports Training Platform Based on Data Post-Processing Algorithm of Gasp Frequency Signal Acquisition Instrument

Learn how to transfer contacts, messages, pictures and all your data from your old phone to your new Samsung device in a few simple steps with Samsung Smart Switch. 1. Backup your data on iCloud 2. Open Samsung Smart Switch application on your new phone 3. Tap 'Receive Data' 4. Tap 'Galaxy/Android' 5. Select the data you want to transfer 6. Tap 'Transfer' Learn more about Samsung Smart Switch and different ways to transfer data from Android to Android phone at https://bit.ly/Smart_switch For more tips, tricks and support go to: https://bit.ly/Samsung_Fold3_Tips #SamsungSmartSwitch #SamsungUK Source: https://www.youtube.com/watch?v=YTUz8ZmI8vs Uploader: Samsung UK

https://media.ccc.de/v/Camp2019-10355-privacy_leaks_in_smart_devices_extracting_data_from_used_smart_home_devices Remember the good old fun sport, where people bought random hard drives from ebay and did forensics on them? Did you know you can do the same thing with used IoT devices too? Most end-users have no idea what kind of information their devices are storing and how to securely clean their devices (if that even is possible). Lets explore together what the risks are and how we can extract that data. Many IoT devices collect a lot of data and log files. Of course, most of this data is sent to the Cloud. However, often this data is also stored locally on the device and never deleted in the lifetime of those devices, not even on a factory reset (in contrast to Smart Phones nowadays). This might surprise many people, and especially end users might not be aware of that. Due to the design of IoT devices, there is usually no real way, like for notebooks or PCs, for end users to clean the devices before they sell them on eBay or discard them. The devices may hold sensitive information like Wi-Fi credentials, nearby access points, cloud communication log files, maps, or audio samples. In this talk I will show some examples of interesting IoT devices from various vendors and how to extract the corresponding information. We will use software methods (rooting) and hardware methods (flash dumping). Using this information, I will show how I am able to find the original owner of the device. Also I discuss various challenges and tricks of the methods, and how to prevent this kind of data leakage for yourself. Dennis Giese https://fahrplan.events.ccc.de/camp/2019/Fahrplan/events/10355.html Source: https://www.youtube.com/watch?v=sUGKhcbUzfg Uploader: media.ccc.de

Multiple industries have been revolutionized by the incorporation of data science advancements into intelligent environment technologies, specifically in the context of smart grids. Smart grids offer a dynamic and efficient framework for the management and optimization of electricity generation, distribution, and consumption, thanks to developments in big data analytics. This review delves into the integration of Smart Grid applications and Big Data analytics by reviewing 25 papers screened with PRISMA standard. The paper matter encompasses critical domains including adaptive energy management, canonical correlation analysis, and novel methodologies including blockchain and machine learning. The paper emphasizes contributions to energy efficiency, security, and sustainability by means of a rigorous methodology.

Efficient traffic management poses a significant challenge in smart cities, requiring the integration of diverse approaches. This paper presents an artificial intelligence framework that integrates internet of things (IoT) road traffic data to optimize traffic flow in smart city environments. Real-time traffic data is collected using IoT edge sensors, processed using machine learning (support vector machines, logistic regression, k-nearest neighbors) and deep learning long short-term memory (LTSM) algorithms, and utilized to develop accurate short-term and long-term traffic forecasting models. The proposed framework showcases superior performance compared to existing approaches, making it a widely applicable solution for smart city traffic management. By leveraging IoT road traffic data and artificial intelligence (AI) techniques, real-time monitoring, proactive decision-making, and dynamic traffic control can be achieved, leading to optimized traffic flow, reduced congestion, and enhanced urban mobility. This research provides valuable insights into the potential of IoT and AI technologies in addressing urban traffic challenges and lays the foundation for intelligent transportation systems in smart city environments. 

FabLabs are makerspaces, where you can transform your sketch into a real product with easy-to-use machines such as 3D printers, laser cutters, or desktop CNC mills. However, FabLabs, like most production environments, have a problem: the digital manufacturing machines provide only a one-way-interaction: human to computer to machine – from bits to atoms. A 3D printer, for example, produces objects unaware of its environment. But, what if we could change the machine so it could react to outside influences, such as other machines, people, or environmental conditions? This is the starting premise for the project in which we implemented a vast number of sensors at FabLab Graz and carried out Big Data Analyses. The research project is structured along the Big Data approach under supervision of Prof. Viktor Mayer-Schönberger from Oxford University, and is divided into three main project phases: First, integration of sensors in the laser cutter, 3D printer, CNC mill and also the environment of FabLab Graz. Second, the collection, analysis and processing of the collected data. In the third phase we focus on the derivation of use cases and product improvements based on data correlation. The aim of the project is to establish a smart FabLab that provides insights into operational enhancements and new product concepts to optimize machine interaction. These product concepts and the basic set up of the sensor system are open source and can be implemented easily at other FabLabs throughout the worldwide community. The paper describes the first and second phase of this ongoing research project in detail and gives first insights in phase three. Data, however, is unstoppable and while it is being gathered new correlations or enhancements can be discovered even in the course of writing this sentence.  

To extract value from evergrowing volumes of data, coming from a number of different sources, and to drive decision making, organizations frequently resort to the composition of data processing workflows, since they are expressive, flexible, and scalable. The typical workflow model enforces strict temporal synchronization across processing steps without accounting the actual effect of intermediate computations on the final workflow output. However, this is not the most desirable behavior in a multitude of scenarios. We identify a class of applications for continuous data processing where workflow output changes slowly and without great significance in a short-to-medium time window, thus wasting compute resources and energy with current approaches. To overcome such inefficiency, we introduce a novel workflow model, for continuous and data-intensive processing, capable of relaxing triggering semantics according to the impact input data is assessed to have on changing the workflow output. To assess this impact, learn the correlation between input and output variation, and guarantee correctness within a given tolerated error constant, we rely on Machine Learning. The functionality of this workflow model is implemented in SmartFlux, a middleware framework which can be effortlessly integrated with existing workflow managers. Experimental results indicate we are able to save a significant amount of resources while not deviating the workflow output beyond a small error constant with high confidence level.

To extract value from evergrowing volumes of data, coming from a number of different sources, and to drive decision making, organizations frequently resort to the composition of data processing workflows, since they are expressive, flexible, and scalable. The typical workflow model enforces strict temporal synchronization across processing steps without accounting the actual effect of intermediate computations on the final workflow output. However, this is not the most desirable behavior in a multitude of scenarios. We identify a class of applications for continuous data processing where workflow output changes slowly and without great significance in a short-to-medium time window, thus wasting compute resources and energy with current approaches. To overcome such inefficiency, we introduce a novel workflow model, for continuous and data-intensive processing, capable of relaxing triggering semantics according to the impact input data is assessed to have on changing the workflow output. To assess this impact, learn the correlation between input and output variation, and guarantee correctness within a given tolerated error constant, we rely on Machine Learning. The functionality of this workflow model is implemented in SmartFlux, a middleware framework which can be effortlessly integrated with existing workflow managers. Experimental results indicate we are able to save a significant amount of resources while not deviating the workflow output beyond a small error constant with high confidence level.

To extract value from evergrowing volumes of data, coming from a number of different sources, and to drive decision making, organizations frequently resort to the composition of data processing workflows, since they are expressive, flexible, and scalable. The typical workflow model enforces strict temporal synchronization across processing steps without accounting the actual effect of intermediate computations on the final workflow output. However, this is not the most desirable behavior in a multitude of scenarios. We identify a class of applications for continuous data processing where workflow output changes slowly and without great significance in a short-to-medium time window, thus wasting compute resources and energy with current approaches. To overcome such inefficiency, we introduce a novel workflow model, for continuous and data-intensive processing, capable of relaxing triggering semantics according to the impact input data is assessed to have on changing the workflow output. To assess this impact, learn the correlation between input and output variation, and guarantee correctness within a given tolerated error constant, we rely on Machine Learning. The functionality of this workflow model is implemented in SmartFlux, a middleware framework which can be effortlessly integrated with existing workflow managers. Experimental results indicate we are able to save a significant amount of resources while not deviating the workflow output beyond a small error constant with high confidence level.

We propose an integrated architecture for smart grids, that supports data aggregation and access control. Data can be aggregated by home area network, building area network and neighboring area network in such a way that the privacy of customers is protected. We use homomorphic encryption technique to achieve this. The consumer data that is collected is sent to the substations where it is monitored by remote terminal units (RTU). The proposed access control mechanism gives selective access to consumer data stored in data repositories and used by different smart grid users. Users can be maintenance units, utility centers, pricing estimator units or analyzing and prediction groups. We solve this problem of access control using cryptographic technique of attribute-based encryption. RTUs and users have attributes and cryptographic keys distributed by several key distribution centers (KDC). RTUs send data encrypted under a set of attributes. Users can decrypt information provided they have valid attributes. The access control scheme is distributed in nature and does not rely on a single KDC to distribute keys. Bobba \emph{et al.} \cite{BKAA09} proposed an access control scheme, which relies on a centralized KDC and is thus prone to single-point failure. The other requirement is that the KDC has to be online, during data transfer which is not required in our scheme. Our access control scheme is collusion resistant, meaning that users cannot collude and gain access to data, when they are not authorized to access. We theoretically analyze our schemes and show that the computation overheads are low enough to be carried out in smart grids. To the best of our knowledge, ours is the first work on smart grids, which integrates these two important security components (privacy preserving data aggregation and access control) and presents an overall security architecture in smart grids.

As the quantity of content required by AAA games continues to grow, new approaches to structuring this content must be considered in order to quickly build your game without compromising on quality due to memory and performance restraints. This presentation will discuss the technologies developed by Frostbite to support rapid content creation using modular sound design coupled with data inheritance, allowing for a succinct separation between the modeled behavior and the rendered result. This technique was used extensively to deliver the latest games built on Frostbite, including Battlefield 4, Need for Speed Rivals, and most recently, Dragon Age Inquisition. Use-cases from these games will be discussed to provide concrete examples of this approach.

Architecture

Data Crawler Data Collection and QoS Optimization Path of Smart Course Online + Offline Evaluation Platform

අද අපි ගොඩක් වාහන  ගැන,  ගන්න හොද smart home device ගැන, ලංකාවේ rap හා සින්දු ගැන. Free Whatsapp backup , DJI Mavic 2 Pro Drone ගැන, Plane පොලිසියෙන් අල්ලයි. 

This book sets out to address some of the issues that a smart city needs to overcome to make use of both the data currently available to them and how this can be enhanced by using emerging technology enabling a citizen to share their personal data, adding value. It provides answers for those within a smart city, advising their mayors or leaders on introducing new technology. We will cover the topic so as to enable many different public officials to be able to understand the situation from their own perspective, be they lawyers, financial people, service providers, those looking at governance structures, policy makers, etc. We are contributing to the new model for the European Data Economy. Case studies of existing best practice in the use of data are augmented with examples of embracing a citizen’s personal data in the mix, to enable better services to develop and potential new revenue streams to occur. This will enable new business models and investment opportunities to emerge. We will address the topic of how to put a value on data and will conclude by looking at what new technologies will be emerging in the coming years, to help cities with carbon-neutral targets to have more chance of succeeding.

This item represents a case in PACER, the U.S. Government's website for federal case data. If you wish to see the entire case, please consult PACER directly.

In order to improve the effect of smart home control and management, a new smart home control and management method based on big data analysis is designed. The basic hardware of smart home control and management is designed, including smoke sensor hardware, temperature and humidity sensor hardware, and infrared sensor hardware, so as to collect smart home data and realize data visualization and buzzer alarm. The collected data are transmitted through the indoor wireless network of smart home gateway equipment, and the data distributed cache architecture based on big data analysis is used to store smart home data. Based on the relevant data, the hybrid particle swarm optimization algorithm is used to schedule the control and management tasks of smart home to complete the control and management of smart home. Dr. Rajendra Kumar Bharti "Framework to Manage Big Data in Smart Home Services" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-6 , October 2022, URL: https://www.ijtsrd.com/papers/ijtsrd52102.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/52102/framework-to-manage-big-data-in-smart-home-services/dr-rajendra-kumar-bharti

In order to improve the effect of smart home control and management, a new smart home control and management method based on big data analysis is designed. The basic hardware of smart home control and management is designed, including smoke sensor hardware, temperature and humidity sensor hardware, and infrared sensor hardware, so as to collect smart home data and realize data visualization and buzzer alarm. The collected data are transmitted through the indoor wireless network of smart home gateway equipment, and the data distributed cache architecture based on big data analysis is used to store smart home data. Based on the relevant data, the hybrid particle swarm optimization algorithm is used to schedule the control and management tasks of smart home to complete the control and management of smart home. Dr. Rajendra Kumar Bharti "Framework to Manage Big Data in Smart Home Services" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-6 , October 2022, URL: https://www.ijtsrd.com/papers/ijtsrd52102.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/52102/framework-to-manage-big-data-in-smart-home-services/dr-rajendra-kumar-bharti

In order to improve the effect of smart home control and management, a new smart home control and management method based on big data analysis is designed. The basic hardware of smart home control and management is designed, including smoke sensor hardware, temperature and humidity sensor hardware, and infrared sensor hardware, so as to collect smart home data and realize data visualization and buzzer alarm. The collected data are transmitted through the indoor wireless network of smart home gateway equipment, and the data distributed cache architecture based on big data analysis is used to store smart home data. Based on the relevant data, the hybrid particle swarm optimization algorithm is used to schedule the control and management tasks of smart home to complete the control and management of smart home. Dr. Rajendra Kumar Bharti "Framework to Manage Big Data in Smart Home Services" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-6 , October 2022, URL: https://www.ijtsrd.com/papers/ijtsrd52102.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/52102/framework-to-manage-big-data-in-smart-home-services/dr-rajendra-kumar-bharti

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New research out of Cardiff University in Wales. looks at Parkinson's disease, and how data from smart watches could potentially help diagnose symptoms in early stages. Our house doctor Peter Lin tells us about it.

This letter presents an experimental study and a novel modelling approach of the wireless channel of smart utility meters placed in basements or sculleries. The experimental data consist of signal strength measurements of consumption report packets. Since such packets are only registered if they can be decoded by the receiver, the part of the signal strength distribution that falls below the receiver sensitivity threshold is not observable. We combine a Rician fading model with a bias function that captures the cut-off in the observed signal strength measurements. Two sets of experimental data are analysed. It is shown that the proposed method offers an approximation of the distribution of the signal strength measurements that is better than a na\"ive Rician fitting.

Due to limited metering infrastructure, distribution grids are currently challenged by observability issues. On the other hand, smart meter data, including local voltage magnitudes and power injections, are communicated to the utility operator from grid buses with renewable generation and demand-response programs. This work employs grid data from metered buses towards inferring the underlying grid state. To this end, a coupled formulation of the power flow problem (CPF) is put forth. Exploiting the high variability of injections at metered buses, the controllability of solar inverters, and the relative time-invariance of conventional loads, the idea is to solve the non-linear power flow equations jointly over consecutive time instants. An intuitive and easily verifiable rule pertaining to the locations of metered and non-metered buses on the physical grid is shown to be a necessary and sufficient criterion for local observability in radial networks. To account for noisy smart meter readings, a coupled power system state estimation (CPSSE) problem is further developed. Both CPF and CPSSE tasks are tackled via augmented semi-definite program relaxations. The observability criterion along with the CPF and CPSSE solvers are numerically corroborated using synthetic and actual solar generation and load data on the IEEE 34-bus benchmark feeder.