This book discusses single-channel, device-to-device communication in the Internet of Things (IoT) at the signal encoding level and introduces a new family of encoding techniques that result in significant simplifications of the communication circuitry. These simplifications translate into lower power consumption, smaller form factors, and dynamic data rates that are tolerant to clock discrepancies between transmitter and receiver. Readers will be introduced to signal encoding that uses edge-coded signaling, based on the coding of binary data as counts of transmitted pulses. The authors fully explore the far-reaching implications of these novel signal-encoding techniques and illustrate how their usage can help minimize the need for complex circuitries for either clock and data recovery or duty-cycle correction. They also provide a detailed description of a complete ecosystem of hardware and firmware built around edge-code signaling. The ecosystem comprises an application-specific processor, automatic protocol configuration, power and data rate management, cryptographic primitives, and automatic failure recovery modes. The innovative IoT communication link and its associated ecosystem are fully in line with the standard IoT requirements on power, footprint, security, robustness, and reliability.

The recent developments in wireless communications, networking, and embedded systems have driven various innovative Internet of Things (IoT) applications, e.g., smart cities, mobile healthcare, autonomous driving and drones. A common feature of these applications is the stringent requirements for low-latency communications. Considering the typical small payload size of IoT applications, it is of critical importance to reduce the size of the overhead message, e.g., identification information, pilot symbols for channel estimation, and control data. Such low-overhead communications also help to improve the energy efficiency of IoT devices. Recently, structured signal processing techniques have been introduced and developed to reduce the overheads for key design problems in IoT networks, such as channel estimation, device identification, and message decoding. By utilizing underlying system structures, including sparsity and low rank, these methods can achieve significant performance gains. This book provides an overview of four general structured signal processing models: a sparse linear model, a blind demixing model, a sparse blind demixing model, and a shuffled linear model, and discusses their applications in enabling low-overhead communications in IoT networks. Further, it presents practical algorithms based on both convex and nonconvex optimization approaches, as well as theoretical analyses that use various mathematical tools.

This book presents state-of-the-art research on security and privacy- preserving for IoT and 5G networks and applications. The accepted book chapters covered many themes, including traceability and tamper detection in IoT enabled waste management networks, secure Healthcare IoT Systems, data transfer accomplished by trustworthy nodes in cognitive radio, DDoS Attack Detection in Vehicular Ad-hoc Network (VANET) for 5G Networks, Mobile Edge-Cloud Computing, biometric authentication systems for IoT applications, and many other applications It aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this particular area or those interested in grasping its diverse facets and exploring the latest advances on security and privacy- preserving for IoT and 5G networks.

Internet of Things (IoT), the newer generation of traditional wireless sensor network devices, offer wide variety of applications in various areas including military, medicine, home automation, remote monitoring, etc. Due to their wide usage and recent large-scale DDoS (distributed denial of service) attacks using millions of these devices, security of these devices have become an important aspect to address. Additionally, security implementation needs to be power efficient considering the limited power resource available to these wireless devices. Since users, as well as attackers, can control or access IoT devices remotely using smartphone or a computer, any attack on these devices can result in disasters. This thesis is directed towards development and implementation of a secure and power-efficient communication mechanism on these low-power devices. First, we performed a detailed analysis of the power consumption of these devices for different environment variables including temperature, lighting and location (in/outdoor), to understand effects of these parameters on device power consumption. Second, we proposed and implemented a novel security algorithm to detect and mitigate RPL (routing protocol layer) attacks in IoT networks. We evaluated changes in the behavior of IoT devices before and after the implementation of our proposed algorithm in terms of the change in battery life and power consumption. The proposed security implementation has the novel approach of using the RSSI (received signal strength indicator) tunneling to detect and mitigate RPL (routing protocol layer) attacks. Finally, we conducted experiments in simulation as well as on first generation real-world sensor nodes (Zolertia Z1 motes) to evaluate the power efficiency of our proposed algorithm. We conclude the thesis with insights on (a) the effect of interference present in the atmosphere on battery life, (b) security provided by the proposed algorithm, and (c) power-efficiency of the proposed security algorithm for IoT devices.

While interest in Internet of Things (IoT) applications has surged in recent years, the broad diversity in their constraints, such as power consumption, channel bandwidth, link robustness, and packet latency, still challenges state-of-the-art technologies to enable efficient and ubiquitous wireless connectivity for IoT devices in many practical scenarios. In this thesis, we study three sets of primary constraints in developing IoT networks: energy efficiency, spectral efficiency, and physical-layer security.First, this thesis introduces EE-IoT, an energy-efficient wireless communication scheme for IoT networks. The key enabler of EE-IoT is an asymmetric physical-layer design that allows low-complex and single-carrier IoT devices to communicate with multi-carrier-based wireless local-area network (WLAN) access point (AP) at a very low sampling rate, leading to a significant reduction of IoT devices' hardware complexity and power consumption. This thesis further introduces a practical design, termed WiFi-IoT, to enable a transparent coexistence of IoT devices and legacy Wi-Fi devices. WiFi-IoT design allows a multi-antenna AP to simultaneously serve Wi-Fi and IoT devices.℗ Second, to improve the spectral efficiency of dense IoT networks, this thesis presents a practical uplink distributed multiple-input multiple-output (MIMO) scheme, termed UD-MIMO, for WLANs that enables concurrent data transmissions from multiple users to multiple APs in the absence of fine-grained inter-node synchronization. UD-MIMO leverages new co-channel interference management and data decoding techniques that allow WLANs' APs to decode concurrent data packets from asynchronous users. This thesis further introduces a first-of-its-kind multi-antenna long-range (LoRa) gateway, termed MaLoRaGW, that enables multi-user MIMO LoRa communications, marking a significant advancement in LoRa network capabilities. MaLoRaGW features a joint design for uplink packet detection and downlink beamforming, enabling it to concurrently serve multiple LoRa user devices in both uplink and downlink. The key component of MaLoRaGW is a baseband signal design for uplink packet collision recovery, accurate channel estimation, and implicit beamforming.Third, this thesis proposes two jamming-resilient receiver architectures to secure delay-constrained IoT networks against jamming attacks. The proposed schemes leverage multi-antenna technology and new signal detection methods to suppress jamming signals and decode desired signals. It first introduces JammingBird, a multi-antenna receiver design for vehicular ad hoc networks that can tolerate high-power and in-band constant jamming attacks. JammingBird uses two MIMO-based modules, a jamming-resistant synchronizer and a jamming suppressor module, in its physical-layer design. Jamming-resistant synchronizer employs a spatial projection filter that alleviates the impact of jamming signal and allows JammingBird to conduct packet timing and frequency synchronizations. The jamming suppressor module leverages the spatial degrees of freedom provided by multiple antennas to cancel the jamming signal and recover the signal of interest. This thesis further proposes a MIMO-based receiver design to secure ZigBee communications against constant jamming attacks. The enabler of the proposed scheme is a learning-based jamming mitigation method, which can mitigate unknown interference using an optimized neural network.This thesis provides details on the system implementation, experimental setup, and performance evaluation of the proposed schemes in real-world environments. It further delves into an in-depth analysis of the lessons learned and highlights the open issues in the design of efficient and secure wireless IoT communications and networks.

TAGLINE Securing the Future of IoT with Advanced Edge Computing Solutions KEY FEATURES ● Tailored security protocols for edge computing, ensuring comprehensive protection against cyber threats. ● Master strategies for deploying, monitoring, and securing edge devices to maintain a resilient IoT ecosystem. ● Gain valuable insights from real-world examples, guiding you through the implementation of secure edge computing solutions across diverse industries. DESCRIPTION Embark on a journey into the cutting-edge world of secure edge computing. In this meticulously crafted handbook, delve deep into the intricacies of this transformative technology that is reshaping the landscape of computing. From its fundamental principles to advanced applications, this book leaves no stone unturned in demystifying the complexities of secure edge computing. Explore the architecture that underpins this paradigm shift, unraveling how it seamlessly integrates cloud resources with local devices to enhance efficiency and reliability. Dive into the nuances of security in edge computing, understanding the unique challenges posed by distributed networks and diverse endpoints. Learn essential strategies for safeguarding data integrity, confidentiality, and availability in this dynamic environment, ensuring robust protection against emerging threats. Discover real-world case studies and best practices from industry experts, gaining invaluable insights into deploying and managing secure edge computing solutions across various domains. With clear explanations, practical examples, and actionable advice, Secure Edge Computing For IoT empowers you to harness the full potential of this transformative technology while fortifying your digital infrastructure against evolving security risks. Prepare to embark on a journey of innovation and resilience at the edge of tomorrow’s computing landscape. WHAT WILL YOU LEARN ● Understand routing protocols and communication strategies tailored for edge environments. ● Implement measures to fortify edge infrastructure against cyber threats and safeguard sensitive data. ● Leverage real-time insights for informed decision-making and innovation. ● Integrate ML algorithms to enhance edge capabilities and optimize operations. ● Ensure reliability, scalability, and compliance with industry standards. ● Gain practical insights into the development process, from design to deployment. ● Protect edge infrastructure with encryption, authentication, and intrusion detection. ● Adhere to regulations and best practices in edge computing to ensure regulatory compliance and data privacy. ● Understand the components and architecture that underpin edge computing ecosystems. ● Explore practical examples and use cases from various industries to illustrate best practices and challenges in implementing secure edge computing solutions. WHO IS THIS BOOK FOR? This book is tailored for a diverse audience of IT professionals seeking to deepen their understanding of secure edge computing. It is particularly beneficial for DevOps engineers, system administrators, cloud architects, IoT developers, data analysts, and cybersecurity specialists. TABLE OF CONTENTS 1. Introduction to IoT and Edge Computing 2. Edge Computing Fundamentals and Use Cases 3. Edge Networking and Routing Protocols 4. IoT and Edge Computing Security 5. Data Analytics and Machine Learning at Edge 6. Secure Edge Design and Development 7. Secure Edge Penetration Testing and Incident Management 8. Edge Computing Cybersecurity and Cryptography 9. Cloud Computing in the Context of Edge Computing 10. Secure Edge Development and Implementation Index