WISE Abstraction Framework for Wireless Networks

Current wireless networks commonly consist of nodes with different capabilities (e.g., laptops and PDAs). Link quality such as link error rate and data transmit rate can differ widely. For efficient operation, the design of wireless networks must take into account such heterogeneity among nodes and wireless links. We present systematic approaches to overcome problems due to heterogeneous node capability and link quality in wireless networks. We first present a general framework called WISE (Wireless Integration Sublayer Extension) that abstracts specific details of low-level wireless communication technologies (e.g., modulation or backoff scheme). WISE provides a set of common primitives, based on which upper-level protocols can operate efficiently without knowing the underlying details. We also present a number of protocol extensions that employ the WISE framework to enhance the performance of specific upper-level protocols while hiding lower-level heterogeneity (e.g., link error rate). Our multihop WLAN architecture improves system performance by allowing client nodes to use multihop paths via other clients to reach an AP. Our geographic routing extension considers both location and link quality in the next hop selection, which leads to optimal paths under certain conditions. To address heterogeneity in node capability, we consider virtual routing backbone construction in two settings: cooperative and selfish. In the cooperative setting, we present a protocol extension that constructs an optimal backbone composed of a small number of high-capability nodes, which can be generalized to a more resilient backbone. For the selfish case, we use game theory and design an incentive-compatible backbone construction scheme. We evaluate our work from multiple perspectives. We use theoretical analysis to prove that our extensions lead to optimal solutions. We use simulations to experiment with our schemes in various scenarios and real-world implementation to understand the performance in practice. Our experiment results show that our schemes significantly outperform existing schemes

An Exploration of Wireless Networking and the Management of Associated Security Risk

The rapid expansion of wireless information technology (IT) coupled with a dramatic increase in security breaches forces organizations to develop comprehensive strategies for managing security risks. The problem addressed was the identification of security risk management practices and human errors of IT administrators, putting the organization at risk for external security intrusion. The purpose of this non-experimental quantitative study was to investigate and determine the security risk assessment practices used by IT administrators to protect the confidentiality and integrity of the organization\u27s information. The research questions focused on whether the security risk management practices of IT administrators met or exceeded the minimally accepted practices and standards for wireless networking. The security risk assessment and management model established the theoretical framework. The sample was 114 participants from small to medium IT organizations comprised of security engineers, managers, and end users. Data collection was via an online survey. Data analysis included both descriptive and inferential statistical methods. The results revealed that greater than 80% of participants conducted appropriate risk management and review assessments. This study underscored the need for a more comprehensive approach to managing IT security risks. IT managers can use the outcome of this study as a benchmark for evaluating their current risk assessment procedures. Experiencing security breaches in organizations may be inevitable. However, when organizations and industry leaders can greatly reduce the cost of a data breach by developing effective risk management plans that lead to better security outcomes, positive social change can be realized

SUIDS : a resource-efficient intrusion detection system for ubiquitous computing environments

The background of the project is based on the notion of ubiquitous computing. Ubiquitous computing was introduced as a prospective view about future usage of computers. Smaller and cheaper computer chips will enable us to embed computing ability into any appliances. Along with the convenience brought by ubiquitous computing, its inherent features also exposed its weaknesses. It makes things too easy for a malicious user to spy on others. An Intrusion Detection System (IDS) is a tool used to protect computer resources against malicious activities. Existing IDSs have several weaknesses that hinder their direct application to ubiquitous networks. These shortcomings are caused by their lack of considerations about the heterogeneity, flexibility and resource constraints of ubiquitous networks. Thus the evolution towards ubiquitous computing demands a new generation of resource-efficient IDSs to provide sufficient protections against malicious activities. SUIDS is the first intrusion detection system proposed for ubiquitous computing environments. It keeps the special requirements of ubiquitous computing in mind throughout its design and implementation. SUIDS adopts a layered and distributed system architecture, a novel user-centric design and service-oriented detection method, a new resource-sensitive scheme, including protocols and strategies, and a novel hybrid metric based algorithm. These novel methods and techniques used in SUIDS set a new direction for future research and development. As the experiment results demonstrated, SUIDS is able to provide a robust and resource-efficient protection for ubiquitous computing networks. It ensures the feasibility of intrusion detection in ubiquitous computing environments

Enabling Deep Intelligence on Embedded Systems

As deep learning for resource-constrained systems become more popular, we see an increased number of intelligent embedded systems such as IoT devices, robots, autonomous vehicles, and the plethora of portable, wearable, and mobile devices that are feature-packed with a wide variety of machine learning tasks. However, the performance of DNNs (deep neural networks) running on an embedded system is significantly limited by the platform's CPU, memory, and battery-size; and their scope is limited to simplistic inference tasks only. This dissertation proposes on-device deep learning algorithms and supporting hardware designs, enabling embedded systems to efficiently perform deep intelligent tasks (i.e., deep neural networks) that are high-memory-footprint, compute-intensive, and energy-hungry beyond their limited computing resources. We name such on-device deep intelligence on embedded systems as Embedded Deep Intelligence. Specifically, we introduce resource-aware learning strategies devised to overcome the four fundamental constraints of embedded systems imposed on the way towards Embedded Deep Intelligence, i.e., in-memory multitask learning via introducing the concept of Neural Weight Virtualization, adaptive real-time learning via introducing the concept of SubFlow, opportunistic accelerated learning via introducing the concept of Neuro.ZERO, and energy-aware intermittent learning, which tackles the problems of the small size of memory, dynamic timing constraint, low-computing capability, and limited energy, respectively. Once deployed in the field with the proposed resource-aware learning strategies, embedded systems are not only able to perform deep inference tasks on sensor data but also update and re-train their learning models at run-time without requiring any help from any external system. Such an on-device learning capability of Embedded Deep Intelligence makes an embedded intelligent system real-time, privacy-aware, secure, autonomous, untethered, responsive, and adaptive without concern for its limited resources.Doctor of Philosoph

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems