A smart resource management mechanism with trust access control for cloud computing environment

The core of the computer business now offers subscription-based on-demand services with the help of cloud computing. We may now share resources among multiple users by using virtualization, which creates a virtual instance of a computer system running in an abstracted hardware layer. It provides infinite computing capabilities through its massive cloud datacenters, in contrast to early distributed computing models, and has been incredibly popular in recent years because to its continually growing infrastructure, user base, and hosted data volume. This article suggests a conceptual framework for a workload management paradigm in cloud settings that is both safe and performance-efficient. A resource management unit is used in this paradigm for energy and performing virtual machine allocation with efficiency, assuring the safe execution of users' applications, and protecting against data breaches brought on by unauthorised virtual machine access real-time. A secure virtual machine management unit controls the resource management unit and is created to produce data on unlawful access or intercommunication. Additionally, a workload analyzer unit works simultaneously to estimate resource consumption data to help the resource management unit be more effective during virtual machine allocation. The suggested model functions differently to effectively serve the same objective, including data encryption and decryption prior to transfer, usage of trust access mechanism to prevent unauthorised access to virtual machines, which creates extra computational cost overhead

Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems

Smart buildings are increasingly using Internet of Things (IoT)-based wireless sensing systems to reduce their energy consumption and environmental impact. As a result of their compact size and ability to sense, measure, and compute all electrical properties, Internet of Things devices have become increasingly important in our society. A major contribution of this study is the development of a comprehensive IoT-based framework for smart city energy management, incorporating multiple components of IoT architecture and framework. An IoT framework for intelligent energy management applications that employ intelligent analysis is an essential system component that collects and stores information. Additionally, it serves as a platform for the development of applications by other companies. Furthermore, we have studied intelligent energy management solutions based on intelligent mechanisms. The depletion of energy resources and the increase in energy demand have led to an increase in energy consumption and building maintenance. The data collected is used to monitor, control, and enhance the efficiency of the system

Drone Base Station Trajectory Management for Optimal Scheduling in LTE-Based Sparse Delay-Sensitive M2M Networks

Providing connectivity in areas out of reach of the cellular infrastructure is a very active area of research. This connectivity is particularly needed in case of the deployment of machine type communication devices (MTCDs) for critical purposes such as homeland security. In such applications, MTCDs are deployed in areas that are hard to reach using regular communications infrastructure while the collected data is timely critical. Drone-supported communications constitute a new trend in complementing the reach of the terrestrial communication infrastructure. In this study, drones are used as base stations to provide real-time communication services to gather critical data out of a group of MTCDs that are sparsely deployed in a marine environment. Studying different communication technologies as LTE, WiFi, LPWAN and Free-Space Optical communication (FSOC) incorporated with the drone communications was important in the first phase of this research to identify the best candidate for addressing this need. We have determined the cellular technology, and particularly LTE, to be the most suitable candidate to support such applications. In this case, an LTE base station would be mounted on the drone which will help communicate with the different MTCDs to transmit their data to the network backhaul. We then formulate the problem model mathematically and devise the trajectory planning and scheduling algorithm that decides the drone path and the resulting scheduling. Based on this formulation, we decided to compare between an Ant Colony Optimization (ACO) based technique that optimizes the drone movement among the sparsely-deployed MTCDs and a Genetic Algorithm (GA) based solution that achieves the same purpose. This optimization is based on minimizing the energy cost of the drone movement while ensuring the data transmission deadline missing is minimized. We present the results of several simulation experiments that validate the different performance aspects of the technique

Advanced Communication and Control Methods for Future Smartgrids

Proliferation of distributed generation and the increased ability to monitor different parts of the electrical grid offer unprecedented opportunities for consumers and grid operators. Energy can be generated near the consumption points, which decreases transmission burdens and novel control schemes can be utilized to operate the grid closer to its limits. In other words, the same infrastructure can be used at higher capacities thanks to increased efficiency. Also, new players are integrated into this grid such as smart meters with local control capabilities, electric vehicles that can act as mobile storage devices, and smart inverters that can provide auxiliary support. To achieve stable and safe operation, it is necessary to observe and coordinate all of these components in the smartgrid

Modelling and analysis of demand response implementation in the residential sector

Demand Response (DR) eliminates the need for expensive capital expenditure on the electricity distribution, transmission and the generation systems by encouraging consumers to alter their power usage through electricity pricing or incentive programs. However, modelling of DR programs for residential consumers is complicated due to the uncertain consumption behavious of consumers and the complexity of schedulling a large number of household appliances. This thesis has investigated the design and the implementation challenges of the two most commonly used DR components in the residential sector, i.e., time of use (TOU) and direct load control (DLC) programs for improving their effectiveness and implementation with innovative strategies to facilitate their acceptance by both consumers and utilities. In price-based DR programs, the TOU pricing scheme is one of the most attractive and simplest approaches for reducing peak electricity demand in the residential sector. This scheme has been adopted in many developed countries because it requires less communication infrastructure for its implementation. However, the implementation of TOU pricing in low and lower-middle income economies is less appealing, mainly due to a large number of low-income consumers, as traditional TOU pricing schemes may increase the cost of electricity for low income residential consumers and adversely affect their comfort levels. The research in this thesis proposes an alternative TOU pricing strategy for the residential sector in developing countries in order to manage peak demand problems while ensuring a low impact on consumers’ monthly energy bills and comfort levels. In this study, Bangladesh is used as an example of a lower-to-middle income developing country. The DLC program is becoming an increasingly attractive solution for utilities in developed countries due to advances in the construction of communication infrastructures as part of the smart grid concept deployment. One of the main challenges of the DLC program implementation is ensuring optimal control over a large number of different household appliances for managing both short and long intervals of voltage variation problems in distribution networks at both medium voltage (MV) and low voltage (LV) networks, while simultaneously enabling consumers to maintain their comfort levels. Another important challenge for DLC implementation is achieving a fair distribution of incentives among a large number of participating consumers. This thesis addresses these challenges by proposing a multi-layer load control algorithm which groups the household appliances based on the intervals of the voltage problems and coordinates with the reactive power from distributed generators (DGs) for the effective voltage management in MV networks. The proposed load controller takes into consideration the consumption preference of individual appliance, ensuring that the consumer’s comfort level is satisfied as well as fairly incentivising consumers based on their contributions in network voltage and power loss improvement. Another significant challenge with the existing DLC strategy as it applies to managing voltage in LV networks is that it does not take into account the network’s unbalance constraints in the load control algorithm. In LV distribution networks, voltage unbalance is prevalent and is one of the main power quality problems of concern. Unequal DR activation among the phases may cause excessive voltage unbalance in the network. In this thesis, a new load control algorithm is developed with the coordination of secondary on-load tap changer (OLTC) transformer for effective management of both voltage magnitude and unbalance in the LV networks. The proposed load control algorithm minimises the disturbance to consumers’ comfort levels by prioritising their consumption preferences. It motivates consumers to participate in DR program by providing flexibility to bid their participation prices dynamically in each DR event. The proposed DR programs are applicable for both developed and developing countries based on their available communication infrastructure for DR implementation. The main benefits of the proposed DR programs can be shared between consumers and their utilities. Consumers have flexibility in being able to prioritise their comfort levels and bid for their participation prices or receive fair incentives, while utilities effectively manage their network peak demand and power quality problems with minimum compensation costs. As a whole, consumers get the opportunity to minimise their electricity bills while utilities are able to defer or avoid the high cost of their investment in network reinforcements

Enabling sustainable power distribution networks by using smart grid communications

Smart grid modernization enables integration of computing, information and communications capabilities into the legacy electric power grid system, especially the low voltage distribution networks where various consumers are located. The evolutionary paradigm has initiated worldwide deployment of an enormous number of smart meters as well as renewable energy sources at end-user levels. The future distribution networks as part of advanced metering infrastructure (AMI) will involve decentralized power control operations under associated smart grid communications networks. This dissertation addresses three potential problems anticipated in the future distribution networks of smart grid: 1) local power congestion due to power surpluses produced by PV solar units in a neighborhood that demands disconnection/reconnection mechanisms to alleviate power overflow, 2) power balance associated with renewable energy utilization as well as data traffic across a multi-layered distribution network that requires decentralized designs to facilitate power control as well as communications, and 3) a breach of data integrity attributed to a typical false data injection attack in a smart metering network that calls for a hybrid intrusion detection system to detect anomalous/malicious activities. In the first problem, a model for the disconnection process via smart metering communications between smart meters and the utility control center is proposed. By modeling the power surplus congestion issue as a knapsack problem, greedy solutions for solving such problem are proposed. Simulation results and analysis show that computation time and data traffic under a disconnection stage in the network can be reduced. In the second problem, autonomous distribution networks are designed that take scalability into account by dividing the legacy distribution network into a set of subnetworks. A power-control method is proposed to tackle the power flow and power balance issues. Meanwhile, an overlay multi-tier communications infrastructure for the underlying power network is proposed to analyze the traffic of data information and control messages required for the associated power flow operations. Simulation results and analysis show that utilization of renewable energy production can be improved, and at the same time data traffic reduction under decentralized operations can be achieved as compared to legacy centralized management. In the third problem, an attack model is proposed that aims to minimize the number of compromised meters subject to the equality of an aggregated power load in order to bypass detection under the conventionally radial tree-like distribution network. A hybrid anomaly detection framework is developed, which incorporates the proposed grid sensor placement algorithm with the observability attribute. Simulation results and analysis show that the network observability as well as detection accuracy can be improved by utilizing grid-placed sensors. Conclusively, a number of future works have also been identified to furthering the associated problems and proposed solutions

A real-time demand response pricing model for the smart grid

Submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of Philosophy (PhD)This thesis contributes to a novel model for Real-Time Price Suggestions (RTPS) of the Smart Grid (SG), which is the next generation modern bi-directional grid, particularly with respect to the pricing model. The research employs an experiment-based methodology which includes the use of a simulation technique. The research developed a Demand Response (DR) pricing model. Energy users are keen to reduce their bills, and Energy Providers (EP) is also keen on reducing their industrial costs. The DR model would benefit them both. The model has been tested with the UK-based traditional price value using real-time usage data. Energy users significantly reduced their bill and EP reduced their industrial cost due to load shifting. The Price Control Unit (PCU) and Price Suggestion Unit (PSU) utilise a set of embedded algorithms to vary price based upon demand. This model makes suggestions based on an energy threshold and makes use of Simultaneous Perturbation Stochastic Approximation Methods to produce prices. The results show that bill and peak load reductions benefit both the energy provider and users. The tests on a daily basis and monthly basis both benefit energy users and energy provider. The model has been validated by building a hardware prototype. This model also addresses users’ preferences; if users are non-responsive, they can still reduce their bills. The model contributes significantly to the existing models, and the novel contribution is the PSU which uniquely benefits energy users and provider. Therefore, there is a number of fundamental aspect of contributions to the model RTPS constitutes the final thesis of the PhD. The Real-Time Pricing is a better pricing system, algorithm developed on a daily basis and monthly basis and finally building a hardware prototype

Edge/Fog Computing Technologies for IoT Infrastructure

The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices. Moreover, emerging IoT applications, such as augmented and virtual reality (AR/VR), intelligent transportation systems, and smart factories require ultra-low latency for data communication and processing. Fog/edge computing is a new computing paradigm where fully distributed fog/edge nodes located nearby end devices provide computing resources. By analyzing, filtering, and processing at local fog/edge resources instead of transferring tremendous data to the centralized cloud servers, fog/edge computing can reduce the processing delay and network traffic significantly. With these advantages, fog/edge computing is expected to be one of the key enabling technologies for building the IoT infrastructure. Aiming to explore the recent research and development on fog/edge computing technologies for building an IoT infrastructure, this book collected 10 articles. The selected articles cover diverse topics such as resource management, service provisioning, task offloading and scheduling, container orchestration, and security on edge/fog computing infrastructure, which can help to grasp recent trends, as well as state-of-the-art algorithms of fog/edge computing technologies

Design and development of an SDN robotic system with intelligent openflow IOT testbeds for power assessment, prediction and fault management

This thesis was submitted for the award of Docctor of Philosophy and was awarded by Brunel University LondonCurrent wind turbine and power grid industry have relatively little research and development with regards to implementing novel communication network and intel- ligent system to overcome issues that pertain to network failure and lack of monitor- ing. Wind turbine location could be a big concern when it comes to identifying an efficient location for future wind turbine and the impact of a site with non-efficient meteorological parameters can result in relocation of a wind turbine and revenue- loss. Unplanned wind turbine shutdowns that are considered to be one of the major revenue-loss factors of a modern wind farm business. Typically, the unplanned wind turbine shutdown is a result of sensors fail due to harsh environment challenges that prevent hardware status from being available on the monitoring system. The above mentioned research problems pertain to wind turbine site assessment and predic- tion of power. In this thesis, a novel programmable software-defined robotics and IoT testbeds are proposed with the fusion of Artificial Intelligence and optimiza- tion methods to solve specific problems related to wind turbine site assessment and fault management. The site selection process is implemented using proposed aerial and ground robotic systems that are incorporated with Software-Defined Networks and OpenFlow switching capabilities. A second stage development of the system is proposing a prediction platform that run on the aerial robot cluster using neural net- works optimization regression techniques. To overcome the unplanned wind turbine network outage, an IoT micro cloud cluster system is proposed that act as immedi- ate fail-over platform to provide continuous health readings of the wind turbine to ensure the turbine in question will not get shutdown unnecessarily. The proposed system help in minimizing revenue-loss caused by stopping a wind turbine from op- eration and help maintain generated power stability on the grid. Additionally, since large wind farms require an agile and scalable management of selecting the most efficient wind turbine location install. Thus, a softwarized cognitive routing proto- col is proposed. The group of quadcopters is a redundant failover Software-Defined Network/OpenFlow system that can cover every single way point of the farm land. Although, power consumption is essential for the continuity the service, a Software- Defined charging system testbed is proposed that uses inductive power transfer wit