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6829 results in Communications and signal processing

Page 112 of 342

  • 9 - Advanced Metering Infrastructure

  • from Part II - Cloud Computing Applications for Smart Grid
  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp 118-146

  • Summary

    Advanced metering infrastructure (AMI) is an important component of a smart grid that can help in fulfilling the objectives of the latter. It is a combination of smart meters and bi-directional communication networks. With the help of AMI, customers, service providers, and other entities exchange information among themselves in order to have a well-balanced smart grid environment. Consequently, with the wide deployment of smart meters supported by bi-directional communication networks, it is easier to monitor real-time energy supply–demand information, automatic billing, and many others. Moreover, it is one of the primary requirements for establishing a smart grid environment.

    Requirements

    Typically, an AMI system should meet the following requirements [1]:

  • • Data storage: Energy consumption data is required to be stored in the smart meter. Therefore, the AMI should help smart meters to store real-time and past energy consumption data. To fulfill this requirement, adequate memory should be installed in the smart meters. Moreover, it is also required to ensure that the stored data is accessible by only authenticated users.

  • • Communication: Different communication protocols are also required to communicate with different entities; examples include the IEEE 802.15.4, IEEE 802.11, and wide area network protocols. For example, home appliances may communicate with the home energy management unit (HEMU) using the IEEE 802.15.4 protocol. On the other hand, a HEMU may communicate with the local aggregator unit using the IEEE 802.11 protocol. Finally, the aggregator units may communicate with the service provider through the protocols used in wide area networks. Therefore, suitable communication protocols are required to be deployed, taking into consideration both their advantages and disadvantages.

  • • Management: Different management functions include remote management, billing management, and load control. All these tasks should be performed in an adaptive manner without the involvement of human beings. In case of remote management, fault in the smart meter must be rectified, and the corresponding action should be taken in order to avoid service disruption. In case of billing management, customers can be automatically billed according to their energy consumption. Finally, in case of load control, energy demand from some of the smart meters can be discarded to establish a balance between the energy supply and demand.

  • • User interface: It can be used as an alternative to users for accessing AMI information; the interface includes LCD displays, graphics constructions, keyboard, and a website with authorization facility.

  • 13 - Smart Buildings

  • from Part III - Smart Grid Data Management and Applications
  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp 207-224

  • Summary

    Buildings are one of the most prominent energy consumers in modern-day power grids. In particular, commercial buildings consume large amounts of energy for operating lighting systems, heating–ventilation and air-conditioning (HVAC), and IT equipment including servers. High energy consumption at the buildings increases energy consumption cost and environment pollution. To deal with these issues, buildings can be converted to ‘smart buildings’. Through the concept of smart buildings, electric loads can be controlled in an adaptive manner. Unused equipment can be switched off so that the total energy consumption in the building can be reduced. The appliances that are installed inside a building will generate huge data in order to have real-time information inside the building. To fulfill these objectives, different methodologies exist. In this chapter, we focus on commercial buildings as smart buildings for efficient energy management.

    Concept of Smart Building

    Most commercial buildings are equipped with HVAC systems, IT equipment, servers, and plug-in equipment. These equipments can be integrated with different sensing and actuation technologies, so that they can be controlled dynamically depending on their requirements. Different useful techniques that are deployed in different buildings to make them smart buildings, are discussed here.

  • • Sensing: It is the key technology required for setting up a smart building. Different sensors are installed in the building (inside and outside), so that different parameters (such as temperature, smoke, humidity, and motion) can be sensed in real-time. The sensed data are forwarded to the data center network for processing, computing, and decision making. For data collection and forwarding, wireless sensor network (WSN) is an emerging technology that can be used due to its unique features such as the capability of operating in low power. Typically, IEEE 802.15.4-based technologies (such as Zig-Bee and 6LowPAN) are used in WSN to forward real-time data. User context, environment, and energy consumption are three major factors to be monitored to establish a smart building. The sensors that are installed to monitor these components generate huge amount of data. This also requires suitable data management techniques for real-time energy management inside the building, as discussed in Chapter 11.

  • • Actuation: After sensing the users’ contexts, environment, and energy consumption, actuation is another important task to be employed. HVACs should be controlled in an efficient manner considering the sensed information received from individual sensors.

  • 5 - Demand Response

  • from Part II - Cloud Computing Applications for Smart Grid
  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp 61-80

  • Summary

    Typically, in traditional power delivery systems, energy is supplied to the customers using non-renewable energy sources. Therefore, the energy generators are based on fossil fuels. Consequently, carbon emission in the environment is very high, and there is potentially a great impact on the environment. In contrast, in the smart grid system, energy is supplied to the end-users using renewable and non-renewable energy sources. Additionally, the implementation of distributed energy sources plays an important role for real-time energy management in the smart grid. Consequently, in the presence of distributed energy sources at the customers’ end, electricity consumers can schedule their shiftable appliances to the off-peak hours to relieve the extra load on the grid during on-peak hours. This mechanism is widely known as demand response (DR). The objective of any demand response mechanism is to reduce energy consumption cost to the customers and reduce imbalance between the energy supply and demand, while modifying the energy consumption pattern according to real-time price signals.

    Fundamentals of Demand Response and Challenges

    Different technologies are introduced in a smart grid system in order to establish the demand response mechanism – demand scheduling, online energy monitoring, and deployment of distributed energy sources. As mentioned earlier, customers can schedule their shiftable appliances to off-peak hours so as to minimize their energy consumption cost. Additionally, the real-time energy demand to the grid can also be relieved during peak hours. In the traditional power delivery system, such strategies cannot be deployed by the service providers in order to schedule the customers’ energy demand. Therefore, we have on-peak and off-peak hours in which energy demand is more and less, respectively, than the energy supply to the grid. On the other hand, using the demand response mechanism, the energy demand from the customers can be modified according to the energy supply to the grid in order to minimize the imbalance between the energy supply and the demand to the grid. However, there are several challenges that should be considered in order to install demand response mechanisms in real-life environments.

  • 1. Peak load reduction: In the smart grid, there are three different time-slots depending on the energy load on the grid – on-peak, mid-peak, and off-peak. During on-peak hours, energy demand from the customers is high. On the other hand, during mid-peak and off-peak hours, demands from the customers are moderate and low, respectively.

  • 10 - Cloud-Based Security and Privacy

  • from Part II - Cloud Computing Applications for Smart Grid
  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp 147-166

  • Summary

    Smart grid is essentially a cyber-physical system (CPS) integrated with electric distribution supported with bi-directional communication networks. To support the communication and computing requirements, third party service providers are also expected to participate. Consequently, the third party service providers also access customers’ information that is sent through smart meters and backbone networks. As a result, the smart grid system should have appropriate security and privacy policies to secure the smart meters data and other components from unauthorized access. It is a challenging issue to secure the smart grid from cyber attacks in the presence of online connectivity of all components with the Internet [1]. Among different threats that are present in the smart grid in terms of security, energy theft is one of the most important issues to consider. The intruder can access the control point of the smart meters, and thereby, modify real-time information to change energy consumption information reported to utility providers. Therefore, adequate security policies are needed to deal with such issues. In this chapter, we will discuss general security requirements in a communication network. Then specific security issues present in the smart grid and their remedies are discussed.

    Security in Data Communication

    In data communication, at least two parties (the sender and the receiver) are involved in message exchange. The sender sends a message for an intended receiver. On receiving the message, the receiver takes adequate actions or decisions. In such a scenario, if the sender sends the message directly to the receiver, it may happen that the message is modified or accessed by an attacker. However, the receiver assumes that the received message is the original one sent by the sender. Additionally, it may also happen that an attacker uses the sender's identity to send malicious data to the receiver. Finally, after sending a message, the sender should not be able to repudiate the sent message. Therefore, it is required to implement security mechanisms to prevent such incidents. For example, in banking transactions, appropriate security mechanisms should be in place so that no one other than the actual user can withdraw money. In this chapter, we focus on the cryptography-based security aspects in data communication instead of focusing on other aspects such as intrusion detection and denial-of-service (DoS) attacks. The basic terms used in security are as follows.

  • 3 - Introduction to Big Data Analytics

  • from Part I - Introduction
  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp 38-48

  • Summary

    Big data is conceptualized as a large data set with high complexity that cannot be managed with the traditional data processing schemes in an adequate manner. Big data technology is described as a holistic information management of new types of data alongside traditional data [1]. For example, traditionally, the backbone network carries different homogeneous information generated by end-users. In addition to this, due to the deployment of more number of sensors, actuators, and other different data sources, the network needs to carry heterogeneous information. It is also expected that the same network channel would be used to carry this heterogeneous information. The challenges in big data analytics include analyzing, capturing, processing, searching, storing, and visualizing the large data set. The term ‘big data’ refers to predictive analysis, i.e., predicting the future condition based on the results obtained from processing existing data. Thus, we can predict the system status in advance to take adequate measures to deal with situations in real-time.

    According to the existing literature, there is no hard and fast definition of big data. As mentioned earlier, big data is a conceptualized term that is used to represent a large data set, which is complex and heterogeneous in nature. Moreover, it is hard to process such large data set using the traditional applications. Due to the growing need to digitize data, it is required to store and process large amounts of data generated from heterogeneous devices. To get an idea about the amount of data generated over several past years, Figure 3.1 presents certain statistics regarding growth of information storage capacity with digitization of data (adopted from [2]). As can be seen in Figure 3.1, there is a large amount of data being stored in digital storage devices. However, we need efficient processing applications to process such large amount of data before storing it for future use. Different attributes of big data are discussed in the subsequent sections.

    Attributes of Big Data

    As the name suggests, one of the main attributes of ‘big data’ is volume, i.e., the size of the data. However, it is not only about the volume, but also other factors such as velocity and variety of the data being analyzed. Figure 3.2 presents the attributes diagrammatically. Let us discuss the attributes in detail.

  • 11 - Smart Meter Data Management

  • from Part III - Smart Grid Data Management and Applications
  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp 169-188

  • Summary

    It is evident that smart meters will play a key role in managing real-time energy supply–demand and billing customers, as discussed in Chapter 9. Typically, smart meters are expected to be deployed at the distribution side to monitor real-time power usage by the customers. The smart meters’ information (such as energy demand) is sent to the service providers to take adequate decisions (such as real-time price of energy and switching on more generators). Consequently, it is required to have adequate mechanisms in place to store, manage, and process the smart meter data. In this chapter, we will learn about different methodologies, which are useful to manage smart meter data and take appropriate decisions in order to establish an improved smart grid environment.

    Smart Metering Architecture

    Figure 11.1 presents a schematic view of the smart metering architecture, while focusing on different layers in smart meter information collection. Smart meters are deployed at the customers’ end to monitor in-house or in-building energy consumption. The data from the smart meters are reported to the utility provider both in real-time and on a day-ahead basis. The information sent by the smart meters are aggregated by multiple aggregators deployed on the distribution side. The collected information is aggregated depending on the content of the information. Further, the aggregated information is stored in the local servers, which are maintained by specific service providers. For simplicity, we can assume that such local servers are maintained by the owners of the micro-grids, in order to have energy consumption information within that micro-grid area. Finally, the information collected from all local servers are stored at the central data center. The data center helps all authenticated users to have real-time energy supply–demand information of all generators and consumers present in the smart grid architecture.

    Challenges and Opportunities

    In this section, we will discuss some of the challenges and opportunities present in managing smart meter data in the smart grid.

    Requirement of scalable computing facility

    In the smart grid, there are at least three different energy periods – on-peak, off-peak, and mid-peak, as discussed in Chapter 5. Therefore, the number of customers in different time periods will vary depending on their energy requirements. Consequently, the number of smart meters reporting the real-time energy consumption information will also vary.

  • 2 - Introduction to Cloud Computing

  • from Part I - Introduction
  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp 18-37

  • Summary

    The term ‘cloud computing’ was initially coined to refer to on-demand computing services that were offered by commercial service providers such as Amazon, Google, and Microsoft. Cloud computing is an emerging computation model that shares resources over the Internet and provides on-demand facilities to its customers. In other words, it can be seen as a computation platform combined with shared computer processing resources over the Internet. Computing resources can be shared from anywhere using Internet connectivity. Thus, cloud computing enables ubiquitous and on-demand access to a shared pool of resources to meet users’ requirements [1]. Figure 2.1 depicts an overview of the cloud computing technology.

    As depicted in Figure 2.1, cloud computing consists of several shared computer resources, such as computing devices, software, and applications, which are accessed in an on-demand basis. Additionally, different consumers, who access the cloud platform through mobile phones, laptops, desktops, and other peripheral devices, are also present in the cloud computing technological framework. All the resources and consumers are linked through Internet connectivity. The entire framework is part of a cloud platform. Therefore, a cloud platform helps its users to store and process their data in data centers. The important feature of cloud computing is that a central management entity controls all operations associated with different users situated in different geographical regions. Thus, multiple agents can participate in a single platform to share/procure resources in an

    on-demand basis. Figure 2.2 shows an abstracted view of the cloud computing technology. The cloud manager maps the incoming requests with the available resources, which are shared with the cloud manager. So, it is evident that the cloud manager only manages the available resources and incoming requests in an optimum manner, so that the users of the cloud-services can get seamless and ubiquitous services. However, the cloud-service subscribers do not know from which hardware/software resources, the requested services are provided. Similarly, the owners of the shared resources also do not know to whom their resources are mapped. Therefore, although a common platform is used for multiple parties, an abstraction level is always used for security and privacy concerns. As evident from Figure 2.2, there are multiple parties that participate in the resource sharing/procuring/managing process – publisher, subscriber, and manager. The publishers publish their shared resources to the cloud-service providers.

Stochastic Geometry Analysis of Cellular Networks
  • Bartłomiej Błaszczyszyn, Martin Haenggi, Paul Keeler, Sayandev Mukherjee
  • Published online:
    26 March 2018
    Print publication:
    19 April 2018
  • Achieve faster and more efficient network design and optimization with this comprehensive guide. Some of the most prominent researchers in the field explain the very latest analytic techniques and results from stochastic geometry for modelling the signal-to-interference-plus-noise ratio (SINR) distribution in heterogeneous cellular networks. This book will help readers to understand the effects of combining different system deployment parameters on key performance indicators such as coverage and capacity, enabling the efficient allocation of simulation resources. In addition to covering results for network models based on the Poisson point process, this book presents recent results for when non-Poisson base station configurations appear Poisson, due to random propagation effects such as fading and shadowing, as well as non-Poisson models for base station configurations, with a focus on determinantal point processes and tractable approximation methods. Theoretical results are illustrated with practical Long-Term Evolution (LTE) applications and compared with real-world deployment results.

Media and Radio Signal Processing for Mobile Communications
  • Kyunghun Jung, Russell M. Mersereau
  • Published online:
    23 March 2018
    Print publication:
    19 April 2018
  • Get to grips with the principles and practice of signal processing used in mobile communications systems. Focusing particularly on speech, video, and modem signal processing, pioneering experts employ a detailed, top-down analytical approach to outline the network architectures and protocol structures of multiple generations of mobile communications systems, identify the logical ranges where media and radio signal processing occur, and analyze the procedures for capturing, compressing, transmitting, and presenting media. Chapters are uniquely structured to show the evolution of network architectures and technical elements between generations up to and including 5G, with an emphasis on maximizing service quality and network capacity through re-using existing infrastructure and technologies. Implementation examples and data taken from commercial networks provide an in-depth insight into the operation of real mobile communications systems, including GSM, cdma2000, W-CDMA, LTE, and LTE-A, making this a practical, hands-on guide for both practicing engineers and graduate students in wireless communications.

  • 1 - Introduction to Interference Management

  • Venugopal V. Veeravalli, University of Illinois, Urbana-Champaign, Aly El Gamal, Purdue University, Indiana
  • Book:
    Interference Management in Wireless Networks
    Published online:
    09 February 2018
    Print publication:
    22 February 2018, pp 1-5

  • Summary

    In this chapter, we provide a high-level introduction to interference management in wireless networks, including a historical perspective on wireless cellular networks, and an overview of the remaining chapters in the book. We also summarize the notation used in the book.

    Interference Management in Cellular Networks: A Historical Perspective

    Managing interference from other users sharing the same frequency bands has been the key driver for mobile wireless communications. The first wireless phone systems served as extensions to the wired public switched telephone network [1]. These systems were “single cell” systems in the sense that mobile terminals could be connected to only one basestation during a call, with the call being lost when out of range of the basestation, akin to losing an FM radio signal while driving out of range of the station. Interference in these networks could be managed by simply orthogonalizing the users in the time–frequency plane, i.e., through the use of time-division multiple-access (TDMA) or frequency-division multiple-access (FDMA), or some combination of the two. Interference between basestations operating in the same frequency band was managed by ensuring that they are geographically far apart, again akin to the way in which radio stations operating in the same frequency band are placed.

    Cellular Concept

    A major breakthrough toward improving both the capacity and the mobility in wireless phone systems came with the introduction of the cellular concept [2]. In the cellular system design, a given geographical region is split into contiguous regions called “cells,” without any gaps in coverage. The system is designed so that cells that use the same frequency band are far enough from each other to cause little interference to each other. The number of different frequency bands is called the reuse factor of the system. The reuse factor is a measure of spectral efficiency in the system, with a larger reuse factor corresponding to a smaller efficiency. A key innovation in the cellular concept is the introduction of handoff between neighboring cells operating in different frequency bands, which allows a mobile user to maintain a continuous connection while moving through the geographical region. Interference management within each of the cells is achieved by orthogonalizing the users in the time–frequency plane.

  • 8 - Cellular Uplink

  • Venugopal V. Veeravalli, University of Illinois, Urbana-Champaign, Aly El Gamal, Purdue University, Indiana
  • Book:
    Interference Management in Wireless Networks
    Published online:
    09 February 2018
    Print publication:
    22 February 2018, pp 148-164

  • Summary

    Our treatment of the CoMP concept of communication has been restricted so far to transmission schemes and their applications in the cellular downlink. In this chapter, we investigate whether the ideas studied for enabling rate gains and minimizing delay requirements through CoMP can be applied to reception schemes and their applications in the cellular uplink. Throughout our discussion of CoMP transmission, we assumed that messages are distributed through a backhaul network to transmitters. More specifically, we assumed that sharing of information between transmitters occurs through sharing of digital messages, instead of assuming that quantized analog signals are being shared. This is a natural assumption when considering the cellular downlink for the following reasons. First, sharing of analog signals is prone to quantization errors, which complicate the interference management problem, and make it harder to obtain clear insights through information-theoretic analyses. Second, since our purpose is to model fairly general scenarios for cellular networks, and in particular, those anticipated in next generation wireless networks, it is natural to assume that a message is being delivered from a central controller (or basestation controller) to each basestation transmitter. This is the case even if we are not considering cooperative transmission, and hence it is also natural to extend this assumption with a more powerful backhaul to allow for delivering the digital message to more than one basestation transmitter. For the considered setting of cellular uplink, the first reason mentioned above will still hold. However, the second aspect is different. Here, we can think of two different ways for cooperative reception. The first is when sharing of analog received signals is permitted; we call such schemes CoMP reception schemes and we discuss information-theoretic results for this case in Section 8.1. The second is when only sharing of decoded messages is permitted; we call this the message passing model and we discuss information-theoretic results for this case in Section 8.2.

    There are noticeable similarities between the message passing model and the CoMP transmission schemes that are based on zero-forcing. By passing one message from one basestation to another through the backhaul, the interference caused by this message at exactly one receiver could be eliminated. However, unlike the downlink case, when the interference caused by a message is canceled at one receiver through cooperation, it does not propagate to other receivers.

Page 112 of 342