Generative Boltzmann Adversarial Network in Manet Attack Detection and QOS Enhancement with Latency

Mobile Ad-Hoc Network (MANET) are considered as self-configured network those does not have any centralized base station for the network monitoring and control. MANET environment does not control architecture for routing for the frequent maintenance of topology. The drastic development of Internet leads to adverse effect of development in MANET for different multimedia application those are sensitive to latency. Upon the effective maintenance of the QoS routing route discovery is performed to calculate queue and contention delay. However, the MANET requirement comprises of the complex procedure to withstand the Quality of Service (QoS) with Artificial Intelligence (AI). In MANET it is necessary to compute the MANET attacks with improved QoS with the reduced latency as existing model leads to higher routing and increased latency.  In this paper proposed a Generative Boltzmann Networking Weighted Graph (GBNWG) model for the QoS improvement in MANET to reduce latency. With GBNWG model the MANET model network performance are computed with the weighted graph model. The developed weighted graph computes the routes in the MANET network for the estimation of the available path in the routing metrices. The proposed GBNWG model is comparatively estimated with the conventional QOD technique. Simulation analysis stated that GBNWG scheme exhibits the improved performance in the QoS parameters. The GBNWG scheme improves the PDR value by 12%, 41% reduced control packets and 45% improved throughput value

Contribución al desarrollo de técnicas avanzadas para la evaluación de prestaciones en la Internet de las Cosas

[SPA] Las nuevas tendencias tecnológicas apuntan hacia la agregación de tecnologías simplificando su uso y control, una mayor integración con el usuario, así como un aumento exponencial del número de dispositivos conectados. Todo se engloba bajo el concepto Internet of Things (IoT) entorno un gran abanico de aplicaciones como Industria 4.0 o Smart-City, donde el vínculo con el usuario es más estrecho. La tendencia actual pretende dotar estos dispositivos de capacidades cognitivas permitiendo el aprendizaje y la actuación entre el mundo físico y social con la mínima interacción del ser humano. Tradicionalmente se ha venido utilizando Quality of Service (QoS) como métrica de evaluación objetiva. El presente estudio muestra un modelo holístico que mejora el rendimiento en IoT a partir de métricas basadas en el dominio “coste-beneficio”. El dominio beneficio está compuesto por Quality of Data (QoD), Quality of Information (QoI) y Quality of user Experience (QoE). Y el dominio coste, queda constituido únicamente por Quality Cost (QC). Estas métricas efectúan evaluaciones objetivas y subjetivas en diferentes capas de la red siendo esenciales en dispositivos con recursos limitados para la optimización de estos. En este contexto, las tecnologías Low-Power Wide Area Network (LPWAN) como Long-Range (LoRa) y Long-Range Wide Area Network (LoRaWAN) permiten comunicaciones a grandes distancias con mínimo consumo de recursos. A su vez, es una tecnología muy versátil ya que permite ser embebidos en dispositivos estáticos o móviles como Unmanned Aerial Vehicles (UAVs). Para este estudio, el uso de técnicas de Artificial Intelligent (AI) es fundamental para predecir futuros fallos en las métricas y actuar de forma previa maximizando la disponibilidad de la red.[ENG] The new technology trends aim at technology aggregation, simplifying their use and control, greater integration with the user, and an exponential increase in the number of connected devices. Everything is encompassed under the Internet of Things (IoT) concept on a wide range of applications, such as Industry 4.0 or Smart-Cities, where the relationship with the user is closer. The current trend seeks to provide these devices with cognitive capabilities to learn and act between the physical and social world with minimal human interaction. Traditionally, Quality of Service (QoS) has been used as an objective evaluation metric. The present doctoral thesis proposes a holistic model capable of offering a measurement of the services provided in IoT from metrics based on the cost-benefit domains. The benefit domain is composed by three components, which are Quality of Data (QoD), Quality of Information (QoI), and Quality of user Experience (QoE). The cost domain is made up solely of the Quality Cost (QC) component. These quality components can measure, through the use of different metrics, the performance of a service in different layers of the architecture, being essential for optimization in devices with limited resources. In this context, Low-Power Wide Area Network (LPWAN) technologies such as Long-Range (LoRa) and Long-Range Wide Area Network (LoRaWAN) allow communications over long distances with minimum resource consumption. At the same time, it is a versatile technology since it can be embedded in static or mobile devices such as Unmanned Aerial Vehicles (UAVs). For this reason, LoRa/LoRaWAN and UAVs will be used as case studies. Finally, Artificial Intelligence (AI) techniques have become an extremely useful tool in different environments, including that of performance evaluation, and above all, for its predictive capacity. For this reason, they will also be a subject of study in this doctoral thesis.Escuela Internacional de Doctorado de la Universidad Politécnica de CartagenaUniversidad Politécnica de CartagenaPrograma de Doctorado en Tecnologías de la Información y las Comunicacione

A NOVEL FRAMEWORK FOR SOCIAL INTERNET OF THINGS: LEVERAGING THE FRIENDSHIPS AND THE SERVICES EXCHANGED BETWEEN SMART DEVICES

As humans, we tackle many problems in complex societies and manage the complexities of networked social systems. Cognition and sociability are two vital human capabilities that improve social life and complex social interactions. Adding these features to smart devices makes them capable of managing complex and networked Internet of Things (IoT) settings. Cognitive and social devices can improve their relationships and connections with other devices and people to better serve human needs. Nowadays, researchers are investigating two future generations of IoT: social IoT (SIoT) and cognitive IoT (CIoT). This study develops a new framework for IoT, called CSIoT, by using complexity science concepts and by integrating social and cognitive IoT concepts. This framework uses a new mechanism to leverage the friendships between devices to address service management, privacy, and security. The framework addresses network navigability, resilience, and heterogeneity between devices in IoT settings. This study uses a new simulation tool for evaluating the new CSIoT framework and evaluates the privacy-preserving ability of CSIoT using the new simulation tool. To address different CSIoT security and privacy issues, this study also proposes a blockchain-based CSIoT. The evaluation results show that CSIoT can effectively preserve the privacy and the blockchain-based CSIoT performs effectively in addressing different privacy and security issues

Enhancing the mechanical efficiency of skilled rowing through shortened feedback cycles

In elite level rowing competition, the average velocities of medallists differ by less than 1 % over 2000 m. Nations place sporting excellence in high regard and this magnifies the importance of success. As a result, sports science and technology is increasingly used to achieve marginal performance gains. This research considers how to advance biomechanical analysis and skills training provision with a particular focus on the technical and practical delivery of real-time feedback to coaches and athletes, thereby shortening the amount of time between feedback cycles. Underpinning any biomechanical feedback intervention, validated determinants of performance are required. Previous research revealed that, while gross biomechanical measures such as athlete power, stroke rate and stroke length have previously been used as key determinants of performance, elite athletes are nowadays performing within expected ranges and therefore it is no longer possible to easily differentiate crews using these measures alone. This thesis describes workshops held with elite coaches to investigate biomechanical efficiency where the outcomes led to a focus on how a boat accelerates and decelerates during a stroke and hence how the boat's velocity fluctuates. Novel metrics are proposed to quantify aspects of a stroke cycle and used to analyse an elite data set, collected using a standardised protocol. It is shown that individual elite rowers can be successfully differentiated and benchmark values of performance are presented. Consideration of previous research suggests that there is currently no suitably functional and flexible biomechanical real-time feedback system to deliver complex skills training in rowing. Therefore, this thesis describes the research that has led to the development and evaluation of new technology to deliver visual and audible interfaces that support the delivery of concurrent and terminal feedback in water and land-based environments. Coaches and athletes were involved throughout the design process to optimise system suitability and encourage adoption. The technology empowers a coach to intricately manipulate feedback provision, thereby promoting motor control and learning theory best practice. Novel insights relevant to designing interactive systems for use within an elite sporting population are also discussed. This research presents an end-to-end strategy for the applied delivery of real-time feedback to skilled rowers bringing together engineering and social science disciplines. A land-based case series reveals that while statistically significant skill learning was not achieved, participants acquired sport specific technical awareness and heightened motivation as a result of the skills training intervention. Existing motor learning literature was tested as part of the study with a key finding being the lack of support for audible display of stroke acceleration through frequency modulation. Study limitations were identified that explain the lack of an effect of skills training on rower efficiency. The study also acted as a validation of the use of a land-based simulator to monitor and manipulate stroke velocity and a validation of the candidate feedback interfaces that had been implemented. As of result of this work, rowing coaches are able to evaluate their athletes in a novel way, achieving a deeper appreciation of their biomechanical efficiency. Upon identifying athletes with a need for technical development, coaches can intervene with the proposed methodology of skill development making use of the new technologies developed to deliver performance gains. This methodology would achieve enhanced validity through a deeper understanding of the reliability of the new metrics and their relationship to boat speed. Future attempts to test for skill learning should build upon the findings made in this work and, in due course, technology and theory should combine to deliver terminal feedback training during water-based rowing

Evaluation of Trust in the Internet Of Things: Models, Mechanisms And Applications

In the blooming era of the Internet of Things (IoT), trust has become a vital factor for provisioning reliable smart services without human intervention by reducing risk in autonomous decision making. However, the merging of physical objects, cyber components and humans in the IoT infrastructure has introduced new concerns for the evaluation of trust. Consequently, a large number of trust-related challenges have been unsolved yet due to the ambiguity of the concept of trust and the variety of divergent trust models and management mechanisms in different IoT scenarios. In this PhD thesis, my ultimate goal is to propose an efficient and practical trust evaluation mechanisms for any two entities in the IoT. To achieve this goal, the first important objective is to augment the generic trust concept and provide a conceptual model of trust in order to come up with a comprehensive understanding of trust, influencing factors and possible Trust Indicators (TI) in the context of IoT. Following the catalyst, as the second objective, a trust model called REK comprised of the triad Reputation, Experience and Knowledge TIs is proposed which covers multi-dimensional aspects of trust by incorporating heterogeneous information from direct observation, personal experiences to global opinions. The mathematical models and evaluation mechanisms for the three TIs in the REK trust model are proposed. Knowledge TI is as “direct trust” rendering a trustor’s understanding of a trustee in respective scenarios that can be obtained based on limited available information about characteristics of the trustee, environment and the trustor’s perspective using a variety of techniques. Experience and Reputation TIs are originated from social features and extracted based on previous interactions among entities in IoT. The mathematical models and calculation mechanisms for the Experience and Reputation TIs also proposed leveraging sociological behaviours of humans in the real-world; and being inspired by the Google PageRank in the web-ranking area, respectively. The REK Trust Model is also applied in variety of IoT scenarios such as Mobile Crowd-Sensing (MCS), Car Sharing service, Data Sharing and Exchange platform in Smart Cities and in Vehicular Networks; and for empowering Blockchain-based systems. The feasibility and effectiveness of the REK model and associated evaluation mechanisms are proved not only by the theoretical analysis but also by real-world applications deployed in our ongoing TII and Wise-IoT projects

Hybrid intelligent decision support system for distributed detection based on ad hoc integrated WSN & RFID

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe real time monitoring of environment context aware activities, based on distributed detection, is becoming a standard in public safety and service delivery in a wide range of domains (child and elderly care and supervision, logistics, circulation, and other). The safety of people, goods and premises depends on the prompt immediate reaction to potential hazards identified in real time, at an early stage to engage appropriate control actions. Effective emergency response can be supported only by available and acquired expertise or elaborate collaborative knowledge in the domain of distributed detection that include indoor sensing, tracking and localizing. This research proposes a hybrid conceptual multi-agent framework for the acquisition of collaborative knowledge in dynamic complex context aware environments for distributed detection. This framework has been applied for the design and development of a hybrid intelligent multi-agent decision system (HIDSS) that supports a decentralized active sensing, tracking and localizing strategy, and the deployment and configuration of smart detection devices associated to active sensor nodes wirelessly connected in a network topology to configure, deploy and control ad hoc wireless sensor networks (WSNs). This system, which is based on the interactive use of data, models and knowledge base, has been implemented to support fire detection and control access fusion functions aimed at elaborating: An integrated data model, grouping the building information data and WSN-RFID database, composed of the network configuration and captured data, A virtual layout configuration of the controlled premises, based on using a building information model, A knowledge-based support for the design of generic detection devices, A multi-criteria decision making model for generic detection devices distribution, ad hoc WSNs configuration, clustering and deployment, and Predictive data models for evacuation planning, and fire and evacuation simulation. An evaluation of the system prototype has been carried out to enrich information and knowledge fusion requirements and show the scope of the concepts used in data and process modelling. It has shown the practicability of hybrid solutions grouping generic homogeneous smart detection devices enhanced by heterogeneous support devices in their deployment, forming ad hoc networks that integrate WSNs and radio frequency identification (RFID) technology. The novelty in this work is the web-based support system architecture proposed in this framework that is based on the use of intelligent agent modelling and multi-agent systems, and the decoupling of the processes supporting the multi-sensor data fusion from those supporting different context applications. Although this decoupling is essential to appropriately distribute the different fusion functions, the integration of several dimensions of policy settings for the modelling of knowledge processes, and intelligent and pro-active decision making activities, requires the organisation of interactive fusion functions deployed upstream to a safety and emergency response.Saudi government, represented by the Ministry of Interior and General Directorate of Civil Defenc

Fog computing for sustainable smart cities: a survey

The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, specially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g. network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build an sustainable IoT infrastructure for smart cities. In this paper, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges towards implementing them, so as to shed light on future research directions on realizing Fog computing for building sustainable smart cities