Data-driven design of intelligent wireless networks: an overview and tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves

Mobile Ad Hoc Networks

Guiding readers through the basics of these rapidly emerging networks to more advanced concepts and future expectations, Mobile Ad hoc Networks: Current Status and Future Trends identifies and examines the most pressing research issues in Mobile Ad hoc Networks (MANETs). Containing the contributions of leading researchers, industry professionals, and academics, this forward-looking reference provides an authoritative perspective of the state of the art in MANETs. The book includes surveys of recent publications that investigate key areas of interest such as limited resources and the mobility of mobile nodes. It considers routing, multicast, energy, security, channel assignment, and ensuring quality of service. Also suitable as a text for graduate students, the book is organized into three sections: Fundamentals of MANET Modeling and Simulation—Describes how MANETs operate and perform through simulations and models Communication Protocols of MANETs—Presents cutting-edge research on key issues, including MAC layer issues and routing in high mobility Future Networks Inspired By MANETs—Tackles open research issues and emerging trends Illustrating the role MANETs are likely to play in future networks, this book supplies the foundation and insight you will need to make your own contributions to the field. It includes coverage of routing protocols, modeling and simulations tools, intelligent optimization techniques to multicriteria routing, security issues in FHAMIPv6, connecting moving smart objects to the Internet, underwater sensor networks, wireless mesh network architecture and protocols, adaptive routing provision using Bayesian inference, and adaptive flow control in transport layer using genetic algorithms

Mobile Ad Hoc Networks

Guiding readers through the basics of these rapidly emerging networks to more advanced concepts and future expectations, Mobile Ad hoc Networks: Current Status and Future Trends identifies and examines the most pressing research issues in Mobile Ad hoc Networks (MANETs). Containing the contributions of leading researchers, industry professionals, and academics, this forward-looking reference provides an authoritative perspective of the state of the art in MANETs. The book includes surveys of recent publications that investigate key areas of interest such as limited resources and the mobility of mobile nodes. It considers routing, multicast, energy, security, channel assignment, and ensuring quality of service. Also suitable as a text for graduate students, the book is organized into three sections: Fundamentals of MANET Modeling and Simulation—Describes how MANETs operate and perform through simulations and models Communication Protocols of MANETs—Presents cutting-edge research on key issues, including MAC layer issues and routing in high mobility Future Networks Inspired By MANETs—Tackles open research issues and emerging trends Illustrating the role MANETs are likely to play in future networks, this book supplies the foundation and insight you will need to make your own contributions to the field. It includes coverage of routing protocols, modeling and simulations tools, intelligent optimization techniques to multicriteria routing, security issues in FHAMIPv6, connecting moving smart objects to the Internet, underwater sensor networks, wireless mesh network architecture and protocols, adaptive routing provision using Bayesian inference, and adaptive flow control in transport layer using genetic algorithms

Securing IoT Applications through Decentralised and Distributed IoT-Blockchain Architectures

The integration of blockchain into IoT can provide reliable control of the IoT network's ability to distribute computation over a large number of devices. It also allows the AI system to use trusted data for analysis and forecasts while utilising the available IoT hardware to coordinate the execution of tasks in parallel, using a fully distributed approach. This thesis's  rst contribution is a practical implementation of a real world IoT- blockchain application, ood detection use case, is demonstrated using Ethereum proof of authority (PoA). This includes performance measurements of the transaction con-  rmation time, the system end-to-end latency, and the average power consumption. The study showed that blockchain can be integrated into IoT applications, and that Ethereum PoA can be used within IoT for permissioned implementation. This can be achieved while the average energy consumption of running the ood detection system including the Ethereum Geth client is small (around 0.3J). The second contribution is a novel IoT-centric consensus protocol called honesty- based distributed proof of authority (HDPoA) via scalable work. HDPoA was analysed and then deployed and tested. Performance measurements and evaluation along with the security analyses of HDPoA were conducted using a total of 30 di erent IoT de- vices comprising Raspberry Pis, ESP32, and ESP8266 devices. These measurements included energy consumption, the devices' hash power, and the transaction con rma- tion time. The measured values of hash per joule (h/J) for mining were 13.8Kh/J, 54Kh/J, and 22.4Kh/J when using the Raspberry Pi, the ESP32 devices, and the ESP8266 devices, respectively, this achieved while there is limited impact on each de- vice's power. In HDPoA the transaction con rmation time was reduced to only one block compared to up to six blocks in bitcoin. The third contribution is a novel, secure, distributed and decentralised architecture for supporting the implementation of distributed arti cial intelligence (DAI) using hardware platforms provided by IoT. A trained DAI system was implemented over the IoT, where each IoT device hosts one or more neurons within the DAI layers. This is accomplished through the utilisation of blockchain technology that allows trusted interaction and information exchange between distributed neurons. Three di erent datasets were tested and the system achieved a similar accuracy as when testing on a standalone system; both achieved accuracies of 92%-98%. The system accomplished that while ensuring an overall latency of as low as two minutes. This showed the secure architecture capabilities of facilitating the implementation of DAI within IoT while ensuring the accuracy of the system is preserved. The fourth contribution is a novel and secure architecture that integrates the ad- vantages o ered by edge computing, arti cial intelligence (AI), IoT end-devices, and blockchain. This new architecture has the ability to monitor the environment, collect data, analyse it, process it using an AI-expert engine, provide predictions and action- able outcomes, and  nally share it on a public blockchain platform. The pandemic caused by the wide and rapid spread of the novel coronavirus COVID-19 was used as a use-case implementation to test and evaluate the proposed system. While providing the AI-engine trusted data, the system achieved an accuracy of 95%,. This is achieved while the AI-engine only requires a 7% increase in power consumption. This demon- strate the system's ability to protect the data and support the AI system, and improves the IoT overall security with limited impact on the IoT devices. The  fth and  nal contribution is enhancing the security of the HDPoA through the integration of a hardware secure module (HSM) and a hardware wallet (HW). A performance evaluation regarding the energy consumption of nodes that are equipped with HSM and HW and a security analysis were conducted. In addition to enhancing the nodes' security, the HSM can be used to sign more than 120 bytes/joule and encrypt up to 100 bytes/joule, while the HW can be used to sign up to 90 bytes/joule and encrypt up to 80 bytes/joule. The result and analyses demonstrated that the HSM and HW enhance the security of HDPoA, and also can be utilised within IoT-blockchain applications while providing much needed security in terms of con dentiality, trust in devices, and attack deterrence. The above contributions showed that blockchain can be integrated into IoT systems. It showed that blockchain can successfully support the integration of other technolo- gies such as AI, IoT end devices, and edge computing into one system thus allowing organisations and users to bene t greatly from a resilient, distributed, decentralised, self-managed, robust, and secure systems