418 research outputs found

    Next Generation M2M Cellular Networks: Challenges and Practical Considerations

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    In this article, we present the major challenges of future machine-to-machine (M2M) cellular networks such as spectrum scarcity problem, support for low-power, low-cost, and numerous number of devices. As being an integral part of the future Internet-of-Things (IoT), the true vision of M2M communications cannot be reached with conventional solutions that are typically cost inefficient. Cognitive radio concept has emerged to significantly tackle the spectrum under-utilization or scarcity problem. Heterogeneous network model is another alternative to relax the number of covered users. To this extent, we present a complete fundamental understanding and engineering knowledge of cognitive radios, heterogeneous network model, and power and cost challenges in the context of future M2M cellular networks

    LiSA: A Lightweight and Secure Authentication Mechanism for Smart Metering Infrastructure

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    Smart metering infrastructure (SMI) is the core component of the smart grid (SG) which enables two-way communication between consumers and utility companies to control, monitor, and manage the energy consumption data. Despite their salient features, SMIs equipped with information and communication technology are associated with new threats due to their dependency on public communication networks. Therefore, the security of SMI communications raises the need for robust authentication and key agreement primitives that can satisfy the security requirements of the SG. Thus, in order to realize the aforementioned issues, this paper introduces a lightweight and secure authentication protocol, "LiSA", primarily to secure SMIs in SG setups. The protocol employs Elliptic Curve Cryptography at its core to provide various security features such as mutual authentication, anonymity, replay protection, session key security, and resistance against various attacks. Precisely, LiSA exploits the hardness of the Elliptic Curve Qu Vanstone (EVQV) certificate mechanism along with Elliptic Curve Diffie Hellman Problem (ECDHP) and Elliptic Curve Discrete Logarithm Problem (ECDLP). Additionally, LiSA is designed to provide the highest level of security relative to the existing schemes with least computational and communicational overheads. For instance, LiSA incurred barely 11.826 ms and 0.992 ms for executing different passes across the smart meter and the service providers. Further, it required a total of 544 bits for message transmission during each session.Comment: To appear in IEEE Globecom 201

    Non-intrusive Zigbee power meter for load monitoring in smart buildings

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    Energy efficiency in smart buildings requires distributed sensing infrastructure to monitor the power consumption of appliances, machines and lighting sources. The analysis of current and voltage waveforms is fundamental for gathering diagnostic information about the power quality and for reducing power wastage. Moreover, it enables Non-intrusive Load Monitoring (NILM), which is the process of disaggregating a household's total electricity consumption into its contributing appliances, by analysing the voltage and current changes. In this paper, an innovative full Energy-neutral (i.e. battery free) and Non-intrusive Wireless Energy Meter (NIWEM) is presented to measure current, voltage and power factor. As key features, the NIWEM is completely non-invasive and it can self-sustain its operations by harvesting energy from the monitored load. It also features a standard (Zigbee) wireless interface for communication with the smart-building system. Experimental results have confirmed that complete energy sustainability can be achieved also with very low-power loads

    Predicción de perfiles de consumo eléctrico usando polinomios potenciales de grado uno y redes neuronales artificiales en la infraestructura de medición inteligente

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    Este trabajo analiza métodos y algoritmos de predicción del comportamiento de consumo eléctrico basados en redes neuronales, usando datos obtenidos de la infraestructura de medición avanzada (AMI) de una institución educativa. También, se ha realizado un contraste entre el uso de redes neuronales convencionales (ANN), redes neuronales basadas en wavelets (WNN) y los polinomios potenciales de grado uno (P1P). Se analiza la correlación de cada método de predicción, así como el comportamiento del error cuadrático medio (MSE) para finalmente establecer si existe un desbalance en el coste computacional a través del análisis de Big-O y el tiempo de ejecución. Los resultados cuantitativos del error MSE están por debajo del 0,05% para predicciones con ANN y usan un alto costo computacional. Para P1P se presentan errores alrededor del 1,2% mostrando como método de predicción de bajo consumo computacional pero aplicable de forma principal para un análisis a corto plazo. Este trabajo se da en respuesta a la necesidad de establecer una plataforma que permita aprovechar la estructura de medición inteligente, a través de la predicción de perfil de consumo eléctrico con el objetivo de elaborar una planificación de mantenimiento y gestión de la demanda eléctrica para reducir costos de operación desde el consumidor final hasta el gestor de la distribución de energía eléctrica. Para el análisis de las proyecciones sobre el perfil de carga eléctrica se consideran las características estadísticas del consumo para seleccionar los algoritmos de predicción según la cantidad de días a proyectar, usando los datos de cualquiera de los medidores inteligentes, que pueden ser monitoreados en una red eléctrica orientada a las Smart Grids

    Design and analysis of dynamic compressive sensing in distribution grids

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    Doctor of PhilosophyDepartment of Electrical and Computer EngineeringBalasubramaniam NatarajanThe transition to a smart distribution grid is powered by enhanced sensing and advanced metering infrastructure that can provide situational awareness. However, aggregating data from spatially dispersed sensors/smart meters can present a significant challenge. Additionally, the lack of reliability in communication network used for aggregating this data, prevents its use for real time operations such as state estimation and control. With these challenges associated with measurement availability and accessibility, current distribution systems are typically unobservable. To cope with the unobservability issue, compressive sensing (CS) theory allows us to recover system state information from a small number of measurements provided the states of the distribution system exhibit sparsity. The spatio-temporal correlation of loads and/or rooftop photovoltaic (PV) generation results in sparsity of distribution system states. In this dissertation, we first validate this system sparsity property and exploit it to develop two (direct/indirect) voltage state estimation strategies for a three-phase unbalanced distribution network. Secondly, we focus on addressing the challenge of sparse signal recovery from limited measurements while incorporating their temporal dependence. Specifically, we implement two recursive dynamic CS approaches namely, streaming modified weighted-L1 CS and Kalman filtered CS that reconstruct a sparse signal using the current underdetermined measurements and the prior information about the sparse signal and its support set. Using practical distribution system power measurements as a case study, we quantify, for the first time, the performance improvement achievable with such recursive techniques relative to batch algorithms. CS based signal recovery efforts typically assume that a limited number of measurements are available. However, in practice, due to communication network impairments, there is no guarantee that even this limited set of information might be available at the time of processing at the fusion/control center. Therefore, for the first time, we investigate the impact of intermittent measurement availability and random delays on recursive dynamic CS. Specifically, we quantify the error dynamics in both sparse signal estimation and support set estimation for a modified Kalman filter-CS based strategy in the presence of measurement losses. Using input-to-state stability analysis, we provide an upper bound for the expected covariance of the estimation error for a given rate of information loss. Next, we develop a modified CS algorithm that leverages apriori knowledge of signal correlation to project delayed measurements to the current signal recovery instant. We derive a new result quantifying the impact of errors in the apriori correlation model on signal recovery error. Lastly, we study the robustness of CS based state estimation to uncertainty in distribution network topology knowledge. Topology identification is a challenging problem in distribution systems in general and especially, when there are limited number of available measurements. We tackle this problem by jointly estimating the states and network topology via an integrated mixed integer nonlinear program formulation. By developing convex relaxations of the original formulation as well Markovian models for dynamic topology transitions, we illustrate the superior performance achieved in both state estimation and in topology identification. In summary, this dissertation offers the first comprehensive treatment of dynamic CS in smart distribution grids and can serve as the foundation of numerous follow-on efforts related to networked state estimation and control

    IoT & environmental analytical chemistry: Towards a profitable symbiosis

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    [EN] In a constantly evolving world, where the rising population and increased social awareness have led to a higher concern for the environment, research in this field (most notably in Environmental Analytical Chemistry) should take advantage of the great opportunities offered by new technologies such as Internet of Things (IoT) and Cloud-based services. Both of them are especially suitable when chemical sensors and related devices are used in the continuous in-line monitoring of environmental parameters. In this sense, it is very important to obtain spatially distributed information of these parameters as well as their temporal evolution. In this work, a friendly approach to IoT world for environmental applications is carried out. To get a global vision of these concepts, the starting point is their historical evolution. New trends are also identified along with associated challenges and potential threats. Furthermore, not only there will be (in the near future) a need to rely on distributed analytical sensors but also on even more complex, lab-based techniques that are connected to the IoT through appropriate mechanisms. A revision of the recent literature relating IoT with environmental issues has also been performed, the most relevant contributions being discussed. Finally, the need of a mutual cooperation between IoT and Environmental Analytical Chemistry is outlined and commented in detail. Ignoring the new capabilities offered by Cloud computing and IoT environments is no further an option. In this sense, the main contribution of this paper consists of highlighting the fact that the wiser course is to embrace these opportunities consciously for mutual profit.Capella Hernández, JV.; Bonastre Pina, AM.; Campelo Rivadulla, JC.; Ors Carot, R.; Peris Tortajada, M. (2020). IoT & environmental analytical chemistry: Towards a profitable symbiosis. Trends in Environmental Analytical Chemistry. 27:1-8. https://doi.org/10.1016/j.teac.2020.e00095S182
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