Software Defined Networks based Smart Grid Communication: A Comprehensive Survey

The current power grid is no longer a feasible solution due to ever-increasing user demand of electricity, old infrastructure, and reliability issues and thus require transformation to a better grid a.k.a., smart grid (SG). The key features that distinguish SG from the conventional electrical power grid are its capability to perform two-way communication, demand side management, and real time pricing. Despite all these advantages that SG will bring, there are certain issues which are specific to SG communication system. For instance, network management of current SG systems is complex, time consuming, and done manually. Moreover, SG communication (SGC) system is built on different vendor specific devices and protocols. Therefore, the current SG systems are not protocol independent, thus leading to interoperability issue. Software defined network (SDN) has been proposed to monitor and manage the communication networks globally. This article serves as a comprehensive survey on SDN-based SGC. In this article, we first discuss taxonomy of advantages of SDNbased SGC.We then discuss SDN-based SGC architectures, along with case studies. Our article provides an in-depth discussion on routing schemes for SDN-based SGC. We also provide detailed survey of security and privacy schemes applied to SDN-based SGC. We furthermore present challenges, open issues, and future research directions related to SDN-based SGC.Comment: Accepte

On the Integration of Unmanned Aerial Vehicles into Public Airspace

Unmanned Aerial Vehicles will soon be integrated in the airspace and start serving us in various capacities such as package delivery, surveillance, search and rescue missions, inspection of infrastructure, precision agriculture, and cinematography. In this thesis, motivated by the challenges this new era brings about, we design a layered architecture called Internet of Drones (IoD). In this architecture, we propose a structure for the traffic in the airspace as well as the interaction between the components of our system such as unmanned aerial vehicles and service providers. We envision the minimal features that need to be implemented in various layers of the architecture, both on the Unmanned Aerial Vehicle (UAV)'s side and on the service providers' side. We compare and contrast various approaches in three existing networks, namely the Internet, the cellular network, and the air traffic control network and discuss how they relate to IoD. As a tool to aid in enabling integration of drones in the airspace, we create a traffic flow model. This model will assign velocities to drones according to the traffic conditions in a stable way as well as help to study the formation of congestion in the airspace. We take the novel problem posed by the 3D nature of UAV flights as opposed to the 2D nature of road vehicles movements and create a fitting traffic flow model. In this model, instead of structuring our model in terms of roads and lanes as is customary for ground vehicles, we structure it in terms of channels, density and capacities. The congestion is formulated as the perceived density given the capacity and the velocity of vehicles will be set accordingly. This view removes the need for a lane changing model and its complexity which we believe should be abstracted away even for the ground vehicles as it is not fundamentally related to the longitudinal movements of vehicles. Our model uses a scalar capacity parameter and can exhibit both passing and blocking behaviors. Furthermore, our model can be solved analytically in the blocking regime and piece-wise analytically solved when in the passing regime. Finally, it is not possible to integrate UAVs into the airspace without some mechanism for coordination or in other words scheduling. We define a new scheduling problem in this regard that we call Vehicle Scheduling Problem (VSP). We prove NP-hardness for all the commonly used objective functions in the context of Job Shop Scheduling Problem (JSP). Then for the number of missed deadlines as our objective function, we give a Mixed Integer Programming (MIP) formulation of VSP. We design a heuristic algorithm and compare the quality of the schedules created for small instances with the exact solution to the MIP instance. For larger instances, these comparisons are made with a baseline algorithm

LunaNet: a Flexible and Extensible Lunar Exploration Communications and Navigation Infrastructure

NASA has set the ambitious goal of establishing a sustainable human presence on the Moon. Diverse commercial and international partners are engaged in this effort to catalyze scientific discovery, lunar resource utilization and economic development on both the Earth and at the Moon. Lunar development will serve as a critical proving ground for deeper exploration into the solar system. Space communications and navigation infrastructure will play an integral part in realizing this goal. This paper provides a high-level description of an extensible and scalable lunar communications and navigation architecture, known as LunaNet. LunaNet is a services network to enable lunar operations. Three LunaNet service types are defined: networking services, position, navigation and timing services, and science utilization services. The LunaNet architecture encompasses a wide variety of topology implementations, including surface and orbiting provider nodes. In this paper several systems engineering considerations within the service architecture are highlighted. Additionally, several alternative LunaNet instantiations are presented. Extensibility of the LunaNet architecture to the solar system internet is discussed

Qualitative Case Studies in Operations Management: Trends, Research Outcomes, And Future Research Implications

Our study examines the state of qualitative case studies in operations management. Five main operations management journals are included for their impact on the field. They are in alphabetical order: Decision Sciences, International Journal of Operations and Production Management, Journal of Operations Management, Management Science, and Production and Operations Management. The qualitative case studies chosen were published between 1992 and 2007. With an increasing trend toward using more qualitative case studies, there have been meaningful and significant contributions to the field of operations management, especially in the area of theory building. However, in many of the qualitative case studies we reviewed, sufficient details in research design, data collection, and data analysis were missing. For instance, there are studies that do not offer sampling logic or a description of the analysis through which research out-comes are drawn. Further, research protocols for doing inductive case studies are much better developed compared to the research protocols for doing deductive case studies. Consequently, there is a lack of consistency in the way the case method has been applied. As qualitative researchers, we offer suggestions on how we can improve on what we have done and elevate the level of rigor and consistency

The pursuit of responsiveness in production environments: from flexibility to reconfigurability

Many production plants are pursuing responsiveness (i.e., timely purposeful change guided by external demands) as one of their main performance priorities and are looking for ways for their responsiveness to be improved. One of the ways that they are currently trying to do this is through the flexibility provided by production practices. On the other hand, other systems are also being now developed based on reconfigurability (such as reconfigurable manufacturing systems (RMSs)) which can enhance a company’s technological ability to respond to market requirements by reconfiguring its products and processes. This paper analyses how current production programmes can be a prior step to achieving reconfigurability. The analysis uses a holistic framework that considers a number of linkages or combinations of practices (technology, JIT, TQ, HR, TPM and production strategy) and how these enhance performance in terms of cost, quality and responsiveness. The framework is tested with data collected from a survey of 314 plants worldwide using a series of canonical correlation analyses. The results confirm not only the importance of practice linkages that do not only include technology as the launch pad for reconfigurability, but also that in their pursuit of responsiveness it is vital for plants to implement practices in the technology programme as well as to link them to organisational programmes. The framework presents a contribution to both theory and practice. It offers novel insights into the programme and production practices involved in transitioning from flexibility to reconfigurability in the pursuit of responsiveness and provides a basis for future research.Ministerio de Ciencia e Innovación DPI-2009-11148Junta de Andalucía P08-SEJ-0384

Learning Augmented Optimization for Network Softwarization in 5G

The rapid uptake of mobile devices and applications are posing unprecedented traffic burdens on the existing networking infrastructures. In order to maximize both user experience and investment return, the networking and communications systems are evolving to the next gen- eration – 5G, which is expected to support more flexibility, agility, and intelligence towards provisioned services and infrastructure management. Fulfilling these tasks is challenging, as nowadays networks are increasingly heterogeneous, dynamic and expanded with large sizes. Network softwarization is one of the critical enabling technologies to implement these requirements in 5G. In addition to these problems investigated in preliminary researches about this technology, many new emerging application requirements and advanced opti- mization & learning technologies are introducing more challenges & opportunities for its fully application in practical production environment. This motivates this thesis to develop a new learning augmented optimization technology, which merges both the advanced opti- mization and learning techniques to meet the distinct characteristics of the new application environment. To be more specific, the abstracts of the key contents in this thesis are listed as follows: • We first develop a stochastic solution to augment the optimization of the Network Function Virtualization (NFV) services in dynamical networks. In contrast to the dominant NFV solutions applied for the deterministic networking environments, the inherent network dynamics and uncertainties from 5G infrastructure are impeding the rollout of NFV in many emerging networking applications. Therefore, Chapter 3 investigates the issues of network utility degradation when implementing NFV in dynamical networks, and proposes a robust NFV solution with full respect to the underlying stochastic features. By exploiting the hierarchical decision structures in this problem, a distributed computing framework with two-level decomposition is designed to facilitate a distributed implementation of the proposed model in large-scale networks. • Next, Chapter 4 aims to intertwin the traditional optimization and learning technologies. In order to reap the merits of both optimization and learning technologies but avoid their limitations, promissing integrative approaches are investigated to combine the traditional optimization theories with advanced learning methods. Subsequently, an online optimization process is designed to learn the system dynamics for the network slicing problem, another critical challenge for network softwarization. Specifically, we first present a two-stage slicing optimization model with time-averaged constraints and objective to safeguard the network slicing operations in time-varying networks. Directly solving an off-line solution to this problem is intractable since the future system realizations are unknown before decisions. To address this, we combine the historical learning and Lyapunov stability theories, and develop a learning augmented online optimization approach. This facilitates the system to learn a safe slicing solution from both historical records and real-time observations. We prove that the proposed solution is always feasible and nearly optimal, up to a constant additive factor. Finally, simulation experiments are also provided to demonstrate the considerable improvement of the proposals. • The success of traditional solutions to optimizing the stochastic systems often requires solving a base optimization program repeatedly until convergence. For each iteration, the base program exhibits the same model structure, but only differing in their input data. Such properties of the stochastic optimization systems encourage the work of Chapter 5, in which we apply the latest deep learning technologies to abstract the core structures of an optimization model and then use the learned deep learning model to directly generate the solutions to the equivalent optimization model. In this respect, an encoder-decoder based learning model is developed in Chapter 5 to improve the optimization of network slices. In order to facilitate the solving of the constrained combinatorial optimization program in a deep learning manner, we design a problem-specific decoding process by integrating program constraints and problem context information into the training process. The deep learning model, once trained, can be used to directly generate the solution to any specific problem instance. This avoids the extensive computation in traditional approaches, which re-solve the whole combinatorial optimization problem for every instance from the scratch. With the help of the REINFORCE gradient estimator, the obtained deep learning model in the experiments achieves significantly reduced computation time and optimality loss

Quality management performance modelling for the South African contact centre industry.

Doctoral Degree. University of KwaZulu- Natal, Durban.Against the background of an extreme youth unemployment problem, South Africa seeks to identify and support industries that may offer substantial solutions. The employment potential of the contact centre industry was recognised by the South African government as far back as 2004. By capitalising on comparative advantages such as lower costs, South Africa has successfully claimed a place amongst the preferred international customer service destinations. While lower costs remain a key driver behind the outsourcing of services to offshore destinations like South Africa, a shift in focus towards the ‘quality of service’ is increasingly featured in outsourcing decisions. It follows that, in order to maintain the competitive momentum amidst intense international rivalry, it is imperative that contact centre managers understand the relationship between quality practices and business performance. While these relationships have been investigated across various industry sectors and in various locations globally from as far back as the early 90s, such relationships have not been empirically investigated in the contact centre environment and specifically not in the South African context. The primary objective of this study is to address this gap by developing a model that reveals the nature of the quality practice / performance relationships together with the moderating impact of contingency factors. This should serve as a valuable, context-specific, industry reference while academically contributing towards the development of quality management theory. Based on extensive academic and practice literature, a new industry-specific measurement instrument was developed that demonstrated very good reliability and validity. By initially exploring the extent and manner in which quality practices are deployed it was found that the South African contact centre industry are generally ‘high users’ of quality practices that are normally deployed as part of a more holistic quality program. The proposed quality practice / performance model was based on features of prominent models found in the literature where Path Analysis techniques were employed to test the relationships among variables. Regression analyses confirmed the importance of ‘Top Management Support’ where Leadership quality practices showed a strong, positive and significant impact on the deployment of ‘Core quality practices’ such as Customer, Human Resource, Operational, Infrastructure and Relationship practices. When the impact of each core group of quality practices was measured in isolation i.e. via directly related performance metrics, the results show that all groups have a strong, positive and significant impact on performance. Similar results were obtained when performance was measured at an organisational level for both operational and business performance. Further, synergistic value was found in the deployment of quality practices thus confirming the interdependent nature of such practices. The key implication is that although there are variations in the impact among the various quality practices, all contribute significantly to operational and business performance – thus supporting the deployment of full-blown quality programs. The results may however be used for piecemeal program implementations that focus on the practices that offer the highest impact on performance i.e. customer and human resource-related practices. Finally, the contingency factors that demonstrated the highest moderating impact on the practice / performance relationships included ‘Management Knowledge’, External Demand for Compliance’ and ‘Culture’ while demographic factors had no significant impact. The result partially supports both the universal and context driven approaches to quality management. Path analyses revealed a good fit of the model to the data