Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities

Optimization of energy consumption in future intelligent energy networks (or Smart Grids) will be based on grid-integrated near-real-time communications between various grid elements in generation, transmission, distribution and loads. This paper discusses some of the challenges and opportunities of communications research in the areas of smart grid and smart metering. In particular, we focus on some of the key communications challenges for realizing interoperable and future-proof smart grid/metering networks, smart grid security and privacy, and how some of the existing networking technologies can be applied to energy management. Finally, we also discuss the coordinated standardization efforts in Europe to harmonize communications standards and protocols.Comment: To be published in IEEE Communications Surveys and Tutorial

Australian Bank Credit Risk Management: A Regulatory Examination of Provisioning, Capital Adequacy & Stress-Testing

Recent widespread turmoil in the banking sector has renewed the notion that improper credit risk management continues to be the catalyst for serious banking problems. The Australian banking sector emerged relatively unscathed from recent global turmoil, providing an interesting setting for research. This thesis investigates the credit risk management practice of Australian banks’ loan portfolios from a regulatory perspective by examining regulatory loan-loss provisioning, regulatory capital, and regulatory stress-testing in Australia. Findings suggest the potential for Australian banks to under-provision when faced with pressure to raise capital ratios. IRB banks, on the other hand, report regulatory provisions in line with expected loss estimates as intended under the Basel II capital framework. Australian bank supervisors are encouraged to remain vigilant when assessing bank capital adequacy by paying particular attention to the sufficiency of bank regulatory provisioning practices as a reserve against expected credit losses. This thesis finds Australian banks target a level of regulatory capital above the imposed regulatory minimum, with quarterly speed of adjustment coefficients of 19 and 15 per cent for total and tier 1 capital ratios, respectively. Bank risk, size and ROE are found to be significant determinants of Australian bank capital buffers and Basel II is found to have increased bank capital buffers. Findings suggest a positive relationship exists between the business cycle and Australian bank capital buffers, interpreted to be a countercyclical effect and results indicate bank-specific regulatory imposed PCRs are having their intended effect on capital ratios. An investigation into current regulatory stress-testing practice reveals that variation in the performance between banks is widely disregarded. A simulation experiment suggests that once key stress-testing variables are decomposed from mean values into their empirically fitted distributions, the increase in average banking system losses is substantial. Both the average and worst performing group of banks suffer a significantly greater magnitude of loss. Under a severe stress scenario of a 30 per cent decline in the property index and a 10 per cent default rate, failing to decompose the mean results in an underestimation of average bank losses of 1.86 per cent of total assets. The worst performing group of banks have losses underestimated by a total of 2.32 per cent of total assets, a substantial amount of loss with significant implications for current regulatory stress-testing practice. This thesis provides insights into the credit risk management practices of a banking sector which, in recent times, has outperformed global counterparts. The final implication of the findings of this thesis is relevant for all authorities with vested interest in the resilience of worldwide banking systems. For proper management of credit risk, regulatory provisioning, regulatory capital and regulatory stress-testing cannot be assessed in isolation, a simultaneous assessment is essential. This thesis provides an essential step in promoting a greater awareness of the interrelations between these three components in the Australian banking system

Optimal deployments of defense mechanisms for the internet of things

Internet of Things (IoT) devices can be exploited by the attackers as entry points to break into the IoT networks without early detection. Little work has taken hybrid approaches that combine different defense mechanisms in an optimal way to increase the security of the IoT against sophisticated attacks. In this work, we propose a novel approach to generate the strategic deployment of adaptive deception technology and the patch management solution for the IoT under a budget constraint. We use a graphical security model along with three evaluation metrics to measure the effectiveness and efficiency of the proposed defense mechanisms. We apply the multi-objective genetic algorithm (GA) to compute the {\em Pareto optimal} deployments of defense mechanisms to maximize the security and minimize the deployment cost. We present a case study to show the feasibility of the proposed approach and to provide the defenders with various ways to choose optimal deployments of defense mechanisms for the IoT. We compare the GA with the exhaustive search algorithm (ESA) in terms of the runtime complexity and performance accuracy in optimality. Our results show that the GA is much more efficient in computing a good spread of the deployments than the ESA, in proportion to the increase of the IoT devices

Issues in Infrastructure Development Today: The Interlinkages

Brings out the issues in infrastructure development in India linking market failure, policy, regulation, and privatisation in India c. 2000. Is the lead article in the India Infrastructure ReportIndia; infrastructure; regulation; market-structure; coordination; state-failure; subsidies

Trustworthy autonomic architecture (TAArch): Implementation and empirical investigation

This paper presents a new architecture for trustworthy autonomic systems. This trustworthy autonomic architecture is different from the traditional autonomic computing architecture and includes mechanisms and instrumentation to explicitly support run-time self-validation and trustworthiness. The state of practice does not lend itself robustly enough to support trustworthiness and system dependability. For example, despite validating system's decisions within a logical boundary set for the system, there’s the possibility of overall erratic behaviour or inconsistency in the system emerging for example, at a different logical level or on a different time scale. So a more thorough and holistic approach, with a higher level of check, is required to convincingly address the dependability and trustworthy concerns. Validation alone does not always guarantee trustworthiness as each individual decision could be correct (validated) but overall system may not be consistent and thus not dependable. A robust approach requires that validation and trustworthiness are designed in and integral at the architectural level, and not treated as add-ons as they cannot be reliably retro-fitted to systems. This paper analyses the current state of practice in autonomic architecture, presents a different architectural approach for trustworthy autonomic systems, and uses a datacentre scenario as the basis for empirical analysis of behaviour and performance. Results show that the proposed trustworthy autonomic architecture has significant performance improvement over existing architectures and can be relied upon to operate (or manage) almost all level of datacentre scale and complexity

Resource management for cost-effective cloud and edge systems

With the booming of Internet-based and cloud/edge computing applications and services,datacenters hosting these services have become ubiquitous in every sector of our economy which leads to tremendous research opportunities. Specifically, in cloud computing, all data are gathered and processed in centralized cloud datacenters whereas in edge computing, the frontier of data and services is pushed away from the centralized cloud to the edge of the network. By fusing edge computing with cloud computing, the Internet companies and end users can benefit from their respective merits, abundant computation and storage resources from cloud computing, and the data-gathering potential of edge computing. However, resource management in cloud and edge systems is complicated and challenging due to the large scale of cloud datacenters, diverse interconnected resource types, unpredictable generated workloads, and a range of performance objectives. It necessitates the systematic modeling of cloud and edge systems to achieve desired performance objectives.This dissertation presents a holistic system modeling and novel solution methodology to effectivelysolve the optimization problems formulated in three cloud and edge architectures: 1) cloud computing in colocation datacenters; 2) cloud computing in geographically distributed datacenters; 3) UAV-enabled mobile edge computing. First, we study resource management with the goal of overall cost minimization in the context of cloud computing systems. A cooperative game is formulated to model the scenario where a multi-tenant colocation datacenter collectively procures electricity in the wholesale electricity market. Then, a two-stage stochastic programming is formulated to model the scenario where geographically distributed datacenters dispatch workload and procure electricity in the multi-timescale electricity markets. Last, we extend our focus on joint task offloading and resource management with the goal of overall cost minimization in the context of edge computing systems, where edge nodes with computing capabilities are deployed in proximity to end users. A nonconvex optimization problem is formulated in the UAV-enabled mobile edge computing system with the goal of minimizing both energy consumption for computation and task offloading and system response delay. Furthermore, a novel hybrid algorithm that unifies differential evolution and successive convex approximation is proposed to efficiently solve the problem with improved performance.This dissertation addresses several fundamental issues related to resource management incloud and edge computing systems that will further in-depth investigations to improve costeffective performance. The advanced modeling and efficient algorithms developed in this research enable the system operator to make optimal and strategic decisions in resource allocation and task offloading for cost savings

Real-Time IoV Task Offloading through Dynamic Assignment of SDN Controllers: Algorithmic Approaches and Performance Evaluation

Task offloading in Internet of Vehicles (IoV) is very crucial. The widespread use of IoT applications frequently interacts with the cloud, thereby increasing the load on centralized cloud controllers. Centralized network management in cloud infrastructure is not feasible for the latest IoT trends. Decentralized and decoupled network management in Software Defined Networks (SDN) can enhance IoV services. SDN and IoV coupling can better handle task offloading in ubiquitous and dynamic IoV environments. However, appropriate SDN controller assignment and allotment strategies play a prominent role in IoV communication. In this study, we developed algorithms for SDN controller assignment and allotment namely 1) Next Fit Allotment and Assignment of SDN Controller in IoV (NFAAC), 2) Dynamic Bin Packing Allotment and Assignment of SDN Controller in IoV (DBPAAC), and 3) Dynamic Focused and Bidding Allotment and Assignment algorithm of SDN Controller in IoV (DFBAAC). These algorithms were simulated using open-flow switch controllers. The controllers were modeled as Road Side Units (RSU) that can allocate bandwidth and resource requirements to vehicles on the road. Our results show that our proposed algorithm works efficiently for SDN controller assignment and allocation, outperforming the existing work by a significant improvement of 13.5%. The working of the proposed algorithms are verified, tested, and analytically presented in this study