What is the 'Internet of Things'?

CURATOR

A pinboard by

this curator

I am physicist who is interested in big data and new technologies

PINBOARD SUMMARY

Internet of Things describes the connection of computers embedded in common devices via the internet

It started with the computer. Now, appliances and machines that were once single function have additional uses that are well beyond their original intended purpose. From inside the home, to on the street, phones, lampposts, parking meters, etc are all acquiring new capabilities from having access to the internet, ie. Internet of Things (IoT).

Businesses and governments also adopting IoT to make their products and companies smarter. However, despite the appeal of lampposts that can sense when someone is walking below while simultaneously collecting air pollution data, it remains to be seen how current infrastructure will deal with internet traffic, how we can extract unbiased information from large data streams, and how the security of data large sets will be ensured.

Below is a collection of current research and news articles covering technical aspects of this topic including latest research in smart meters, PV modules, electricity load forecasting etc.

13 ITEMS PINNED

Google launches new IoT management system

Distributed energy source system

Abstract: An energy management system includes at least one distributed energy source for producing energy, the distributed energy source in electrical communication with at least one consumer device to be powered by electric power, and in electrical communication with an electric utility. The device also includes a CPU in communication with the at least one distributed energy source, the CPU operable to control the flow of energy produced by the at least one distributed energy source. The CPU manipulates the flow of energy to provide efficient usage of the energy based on information from the at least one consumer and the electric utility.

Pub.: 31 Jan '17, Pinned: 18 May '17

Method and apparatus for managing power of smart appliance

Abstract: A method and apparatus for managing power of a smart appliance is provided. The method includes acquiring, from the smart appliance, by an Energy Management System (EMS) for managing power of the smart appliance at home over a home network, terminal information including function information and power consumption information; monitoring power of the smart appliance and transmitting results of the results to an energy service provider; and controlling, upon receiving a power reduction command from the energy service provider, the power of the smart appliance based on the acquired terminal information.

Pub.: 07 Feb '17, Pinned: 18 May '17

Early-warning application for real-time detection of energy consumption anomalies in buildings

Abstract: Energy consumption data must be presented to office occupants to encourage them to save energy when in their office buildings. Therefore, this work develops an early warning application (EWA) that intelligently analyzes electricity consumption and provides a real-time visualization of anomalous consumption based on data from smart meters and sensors to various stakeholders. Although smart meters collect massive amounts of data from different sources, many systems cannot analyze and informatively present rapidly collected data. Accordingly, they do not motivate people to adopt energy-saving behaviors. The contribution of this study is to design an EWA architecture that visually presents real-time anomalous power consumption in an office space based on data that are obtained from various instruments (smart meters and sensors) to office occupants. The anomalous consumption of the EWA dashboard is designed to ensure that office occupants with limited technical skills understand the presented energy consumption data. The collected anomaly data provide post-occupancy information. Electricity consumption data from smart meters and sensors in a real office space are used to demonstrate the effectiveness of the proposed EWA. A building manager can use archived anomaly data to audit energy consumption, to produce an energy reduction policy, and to support a retrofitting strategy.

Pub.: 06 Feb '17, Pinned: 18 May '17

Smart sensor network for power quality monitoring in electrical installations

Abstract: Smart meters are one of the basic components of the future smart grid, they allow remotely monitoring each point in the grid in order to know in real-time the performance of the system and to detect potential failures. In this paper, a smart sensor network is introduced and the most important features are presented in three different scenarios: a residential home, an industrial installation, and a public building. The proposed system demonstrates its capabilities of in situ real-time processing and big-data off-line network processing. The suggested smart meter is based on field programmable gate array (FPGA) technology that allows a reconfigurable architecture, which lets the user to select the proper processing modules according to their application. The developed smart sensor network calculates standard figures such as effective values, power factor, and total harmonic distortion; in addition, it detects power quality disturbances such as dips, swells, or interruptions. Moreover, the smart sensor network can continuously detect events to identify certain kind of appliances or industrial equipment such as: fans, lighting, microwave ovens, refrigerators, among others; it is a powerful tool to analyze an entire building in a non-intrusive load monitoring approach.

Pub.: 20 Feb '17, Pinned: 18 May '17

Privacy-preserving protocols for secure and reliable data aggregation in IoT-enabled Smart Metering systems

Abstract: As the Internet of Things (IoT) gets more pervasive, its areas of usage expands. Smart Metering systems is such an IoT-enabled technology that enables convenient and high frequency data collection compared to existing metering systems. However, such a frequent data collection puts the consumers’ privacy in risk as it helps expose the consumers’ daily habits. Secure in-network data aggregation can be used to both preserve consumers’ privacy and reduce the packet traffic due to high frequency metering data. The privacy can be provided by performing the aggregation on concealed metering data. Fully homomorphic encryption (FHE) and secure multiparty computation (secure MPC) are the systems that enable performing multiple operations on concealed data. However, both FHE and secure MPC systems have some overhead in terms of data size or message complexity. The overhead is compounded in the IoT-enabled networks such as Smart Grid (SG) Advanced Metering Infrastructure (AMI). In this paper, we propose new protocols to adapt FHE and secure MPC to be deployed in SG AMI networks that are formed using wireless mesh networks. The proposed protocols conceal the smart meters’ (SMs) reading data by encrypting it (FHE) or computing its shares on a randomly generated polynomial (secure MPC). The encrypted data/computed shares are aggregated at some certain aggregator SM(s) up to the gateway of the network in a hierarchical manner without revealing the readings’ actual value. To assess their performance, we conducted extensive experiments using the ns-3 network simulator. The simulation results indicate that the secure MPC-based protocol can be a viable privacy-preserving data aggregation mechanism since it not only reduces the overhead with respect to FHE but also almost matches the performance of the Paillier cryptosystem when it is used within a proper sized AMI network.

Pub.: 24 Apr '17, Pinned: 18 May '17

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Abstract: Traditional power grids are being transformed into Smart Grids (SGs) to solve the problems of uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide the first comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems.

Pub.: 28 Apr '17, Pinned: 18 May '17

Safety and Security for Intelligent Infrastructure

Abstract: Increasingly, smart computing devices, with powerful sensors and internet connectivity, are being embedded into all new forms of infrastructure, from hospitals to roads to factories. These devices are part of the Internet of Things (IoT) and the economic value of their widespread deployment is estimated to be trillions of dollars, with billions of devices deployed. Consider the example of "smart meters" for electricity utilities. Because of clear economic benefits, including a reduction in the cost of reading meters, more precise information about outages and diagnostics, and increased benefits from predicting and balancing electric loads, such meters are already being rolled out across North America. With residential solar collection, smart meters allow individuals to sell power back to the grid providing economic incentives for conservation. Similarly, smart water meters allow water conservation in a drought. Such infrastructure upgrades are infrequent (with smart meters expected to be in service for 20-30 years) but the benefits from the upgrade justify the significant cost. A long-term benefit of such upgrades is that unforeseen savings might be realized in the future when new analytic techniques are applied to the data that is collected. The same benefits accrue to any infrastructure that embeds increased sensing and actuation capabilities via IoT devices, including roads and traffic control, energy and water management in buildings, and public health monitoring.

Pub.: 04 May '17, Pinned: 18 May '17

An intelligent reconfiguration technique for enhancing the power from photovoltaic module in partially shaded environments

Abstract: Partial shading in PV panels occur due to passing of clouds, shadows of tree, building and poles which in turn greatly reduces the power output. In the proposed work, the solar PV modules are rearranged using a novel reconfiguration topology resulting in the effective distribution of the shadowing effect over the entire PV array without modifying the electrical connections. This proposed reconfiguration topology is so versatile that it can be extended for any number of PV modules. The system performances are investigated for different shadowing pattern and the results obtained using the intelligent reconfiguring technique yields improved performance compared with Su Do Ku configuration.

Pub.: 12 May '16, Pinned: 18 May '17

Efficient resource management system based on 4Vs of big data streams

Abstract: Big data streams are generated continuously at unprecedented speed by thousands of data sources. The analysis of such streams need cloud resources. Due to growth of big data over cloud, allocating appropriate cloud resources has emerged as a major research problem. The current methodologies allocate cloud resources based upon data characteristics. But due to random nature of data generation, the characteristics of data in big data streams are unknown. This poses difficulty in selecting and allocating appropriate resources to big data stream. Solving this problem, an efficient resource management system is proposed in this paper. The proposed system initially estimates the data characteristics of big data stream in terms of volume, velocity, variety and variability. The estimated values are expressed in terms of a vector called Characteristics of Data (CoD). On the other hand, clusters of cloud resources are created dynamically with the help of Self-Organizing Maps (SOM). SOM uses CoD to create and allocate cluster to big data stream. Moreover, the topological ordering of clusters formed by SOM is used to reduce waiting time. The proposed system is tested experimentally. The experimental results show that the proposed system not only efficiently predicts data characteristics but also effectively enhanced the performance of cloud resources.

Pub.: 07 Apr '17, Pinned: 18 May '17

Sensing as a Service and Big Data

Abstract: Internet of Things (IoT) will comprise billions of devices that can sense, communicate, compute and potentially actuate. Data streams coming from these devices will challenge the traditional approaches to data management and contribute to the emerging paradigm of big data. This paper discusses emerging Internet of Things (IoT) architecture, large scale sensor network applications, federating sensor networks, sensor data and related context capturing techniques, challenges in cloud-based management, storing, archiving and processing of sensor data.

Pub.: 01 Jan '13, Pinned: 18 May '17

Dynamic resource allocation for big data streams based on data characteristics (5Vs)

Abstract: Various Internet-based applications such as social media, business transactions, mobile applications, cyber-physical systems, and Internet of Things have led to the generation of big data streams in every field. The growing need to extract knowledge from big data streams has pioneered the challenge of selecting appropriate cloud resources. The current techniques allocate resources based on data characteristics. But because of the stochastic nature of data generation, the characteristics of data in big data streams are unknown. This poses difficulty in selecting and allocating appropriate resources to big data stream. Working towards this direction, this paper proposes a system that predicts the data characteristics in terms of volume, velocity, variety, variability, and veracity. The predicted values are expressed in a quadruple called Characteristics of Big data (CoBa). Thereafter, the proposed system uses self-organizing maps to dynamically create clusters of cloud resources. One of these clusters is allocated to the big data stream based on its CoBa. The proposed system is dynamic in the sense that it changes the cloud cluster allocated to big data stream if its CoBa changes. Experimental results show that the proposed system has a performance edge over other streaming data processing tools such as Storm, Flume, and S4.

Pub.: 12 May '17, Pinned: 18 May '17

Intra-Minute Cloud Passing Forecasting Based on a Low Cost IoT Sensor-A Solution for Smoothing the Output Power of PV Power Plants.

Abstract: Clouds moving at a high speed in front of the Sun can cause step changes in the output power of photovoltaic (PV) power plants, which can lead to voltage fluctuations and stability problems in the connected electricity networks. These effects can be reduced effectively by proper short-term cloud passing forecasting and suitable PV power plant output power control. This paper proposes a low-cost Internet of Things (IoT)-based solution for intra-minute cloud passing forecasting. The hardware consists of a Raspberry PI Model B 3 with a WiFi connection and an OmniVision OV5647 sensor with a mounted wide-angle lens, a circular polarizing (CPL) filter and a natural density (ND) filter. The completely new algorithm for cloud passing forecasting uses the green and blue colors in the photo to determine the position of the Sun, to recognize the clouds, and to predict their movement. The image processing is performed in several stages, considering selectively only a small part of the photo relevant to the movement of the clouds in the vicinity of the Sun in the next minute. The proposed algorithm is compact, fast and suitable for implementation on low cost processors with low computation power. The speed of the cloud parts closest to the Sun is used to predict when the clouds will cover the Sun. WiFi communication is used to transmit this data to the PV power plant control system in order to decrease the output power slowly and smoothly.

Pub.: 16 May '17, Pinned: 18 May '17