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


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.


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

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

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

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