791 research outputs found
A Stochastic Geometry-based Demand Response Management Framework for Cellular Networks Powered by Smart Grid
In this paper, the production decisions across multiple energy suppliers in
smart grid, powering cellular networks are investigated. The suppliers are
characterized by different offered prices and pollutant emissions levels. The
challenge is to decide the amount of energy provided by each supplier to each
of the operators such that their profitability is maximized while respecting
the maximum tolerated level of CO2 emissions. The cellular operators are
characterized by their offered quality of service (QoS) to the subscribers and
the number of users that determines their energy requirements. Stochastic
geometry is used to determine the average power needed to achieve the target
probability of coverage for each operator. The total average power requirements
of all networks are fed to an optimization framework to find the optimal amount
of energy to be provided from each supplier to the operators. The generalized
-fair utility function is used to avoid production bias among the
suppliers based on profitability of generation. Results illustrate the
production behavior of the energy suppliers versus QoS level, cost of energy,
capacity of generation, and level of fairness.Comment: 6 pages, 4 figure
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Software-Defined Infrastructure for IoT-based Energy Systems
Internet of Things (IoT) devices are becoming an essential part of our everyday lives. These physical devices are connected to the internet and can measure or control the environment around us. Further, IoT devices are increasingly being used to monitor buildings, farms, health, and transportation. As these connected devices become more pervasive, these devices will generate vast amounts of data that can be used to gain insights and build intelligence into the system. At the same time, large-scale deployment of these devices will raise new challenges in efficiently managing and controlling them.
In this thesis, I argue that the IoT devices need programmability and need to provide software controls in order to manage them efficiently. Further, it will need data-driven modeling techniques to process and analyze a vast amount of data from heterogeneous devices to derive actionable insights. My thesis explores the problems posed by software-defined IoT energy infrastructure. I present four techniques that use systems and machine learning principles to design, analyze and deploy the next generation of smart IoT energy systems.
First, I discuss how current state-of-the-art LIDAR-based approaches in identifying ideal locations on rooftops for deploying energy systems such as solar do not scale to many regions of the world. To address the challenges, I propose DeepRoof, a data-driven approach that uses deep learning to estimate the solar potential of roofs using satellite imagery and identify ideal locations for installation. We evaluate our approach on different types of roof and show that our technique is comparable to LIDAR-based methods.
Second, I study how excessive solar can cause problems in the grid and examine how programmatic control of the solar output can prevent congestion in the electric grid. Further, I present a decentralized approach that can control the solar arrays in a grid-friendly manner. Also, my approach provides flexible control of solar output, and I show that such mechanisms allow for higher solar penetration in the grid.
Third, I discuss the challenges in community-owned (and shared) distributed energy resources that do not provide independent control to users. To do so, I propose vSolar, an approach to virtualize the solar arrays and energy storage that allows independent control. Further, I show how using vSolar users can exercise independent control, implement their custom energy sharing policies, and reduce energy costs through energy trading.
Finally, I present the challenges, and the high throughput needs to enable a peer-to-peer energy trading platform using permissioned blockchains. I propose FabricPlus, an enhanced Hyperledger Fabric blockchain, that contains a series of optimizations to enable high throughput transactions. FabricPlus increases the transaction throughput many folds, without requiring any changes to its external interfaces. I also show considerable performance improvement over the baseline Fabric
Energy sustainable paradigms and methods for future mobile networks: A survey
In this survey, we discuss the role of energy in the design of future mobile
networks and, in particular, we advocate and elaborate on the use of energy
harvesting (EH) hardware as a means to decrease the environmental footprint of
5G technology. To take full advantage of the harvested (renewable) energy,
while still meeting the quality of service required by dense 5G deployments,
suitable management techniques are here reviewed, highlighting the open issues
that are still to be solved to provide eco-friendly and cost-effective mobile
architectures. Several solutions have recently been proposed to tackle
capacity, coverage and efficiency problems, including: C-RAN, Software Defined
Networking (SDN) and fog computing, among others. However, these are not
explicitly tailored to increase the energy efficiency of networks featuring
renewable energy sources, and have the following limitations: (i) their energy
savings are in many cases still insufficient and (ii) they do not consider
network elements possessing energy harvesting capabilities. In this paper, we
systematically review existing energy sustainable paradigms and methods to
address points (i) and (ii), discussing how these can be exploited to obtain
highly efficient, energy self-sufficient and high capacity networks. Several
open issues have emerged from our review, ranging from the need for accurate
energy, transmission and consumption models, to the lack of accurate data
traffic profiles, to the use of power transfer, energy cooperation and energy
trading techniques. These challenges are here discussed along with some
research directions to follow for achieving sustainable 5G systems.Comment: Accepted by Elsevier Computer Communications, 21 pages, 9 figure
Internet of Vehicles and Real-Time Optimization Algorithms: Concepts for Vehicle Networking in Smart Cities
Achieving sustainable freight transport and citizens’ mobility operations in modern cities are becoming critical issues for many governments. By analyzing big data streams generated through IoT devices, city planners now have the possibility to optimize traffic and mobility patterns. IoT combined with innovative transport concepts as well as emerging mobility modes (e.g., ridesharing and carsharing) constitute a new paradigm in sustainable and optimized traffic operations in smart cities. Still, these are highly dynamic scenarios, which are also subject to a high uncertainty degree. Hence, factors such as real-time optimization and re-optimization of routes, stochastic travel times, and evolving customers’ requirements and traffic status also have to be considered. This paper discusses the main challenges associated with Internet of Vehicles (IoV) and vehicle networking scenarios, identifies the underlying optimization problems that need to be solved in real time, and proposes an approach to combine the use of IoV with parallelization approaches. To this aim, agile optimization and distributed machine learning are envisaged as the best candidate algorithms to develop efficient transport and mobility systems
A Survey on the Security and the Evolution of Osmotic and Catalytic Computing for 5G Networks
The 5G networks have the capability to provide high compatibility for the new
applications, industries, and business models. These networks can tremendously
improve the quality of life by enabling various use cases that require high
data-rate, low latency, and continuous connectivity for applications pertaining
to eHealth, automatic vehicles, smart cities, smart grid, and the Internet of
Things (IoT). However, these applications need secure servicing as well as
resource policing for effective network formations. There have been a lot of
studies, which emphasized the security aspects of 5G networks while focusing
only on the adaptability features of these networks. However, there is a gap in
the literature which particularly needs to follow recent computing paradigms as
alternative mechanisms for the enhancement of security. To cover this, a
detailed description of the security for the 5G networks is presented in this
article along with the discussions on the evolution of osmotic and catalytic
computing-based security modules. The taxonomy on the basis of security
requirements is presented, which also includes the comparison of the existing
state-of-the-art solutions. This article also provides a security model,
"CATMOSIS", which idealizes the incorporation of security features on the basis
of catalytic and osmotic computing in the 5G networks. Finally, various
security challenges and open issues are discussed to emphasize the works to
follow in this direction of research.Comment: 34 pages, 7 tables, 7 figures, Published In 5G Enabled Secure
Wireless Networks, pp. 69-102. Springer, Cham, 201
Optimization and Communication in UAV Networks
UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects
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