SHAPEIoT: Secure Handshake Protocol for Autonomous IoT Device Discovery and Blacklisting using Physical Unclonable Functions and Machine Learning

This paper proposes a new lightweight handshake protocol implemented on top of the Constrained Application Protocol (CoAP) that can be used in device discovery and ensuring the IoT network security by autonomously managing devices of any computational complexity using whitelisting and blacklisting. A Physical Unclonable Function (PUF) is utilized for the session key generation in the proposed handshake protocol. The CoAP server performs real-time device discovery using the proposed handshake protocol, and anomaly detection using machinelearning algorithms to ensure the security of the IoT network. To the best of our knowledge, the presented PUF-based handshake protocol is the first to performs blacklisting and whitelisting. Whitelisted IoT devices not displaying anomalous behavior can join and remain in the IoT network. IoT devices that display anomalous behavior are autonomously blacklisted by the CoAP server and are either disallowed from joining the IoT network or are removed from the IoT network. Simulation results show that amongst the five machine learning algorithms studied, the stacking classifier displays the highest overall anomaly detection accuracy of 99.98%. Based on the results of the network simulation performed, the CoAP server is capable of blacklisting malicious IoT devices within the network with perfect accuracy.

Wireless Interference Analysis for Home IoT Security Vulnerability Detection

The integrity of wireless networks that make up the clear majority of IoT networks lack the inherent security of their wired counterparts. With the growth of the internet of things (IoT) and its pervasive nature in the modern home environment, it has caused a spike in security concerns over how the network infrastructure handles, transmits, and stores data. New wireless attacks such as KeySniffer and other attacks of this type cannot be tracked by traditional solutions. Therefore, this study investigates if wireless spectrum frequency monitoring using interference analysis tools can aid in the monitoring of device signals within a home IoT network. This could be used enhance the security compliance guidelines set forth by OWASP and NIST for these network types and the devices associated. Active and passive network scanning tools are used to provide analysis of device vulnerability and as comparison for device discovery purposes. The work shows the advantages and disadvantages of this signal pattern testing technique compared to traditional network scanning methods. The authors demonstrate how RF spectrum analysis is an effective way of monitoring network traffic over the air waves but also possesses limitations in that knowledge is needed to decipher these patterns. This article demonstrates alternative methods of interference analysis detection.

Clustering-based device-to-device discovery and content delivery in wireless networks

Device-to-device (D2D) cache placement and content delivery is a recent technology that exploits idle memory capacity of mobile devices and promises to fulfill users' content needs with very low delay and without using the scarce cellular network capacity. Recent literature on D2D caching mostly concentrated on optimal cache placement and mostly disregarded the required discovery mechanisms and the accompanying overhead. In this work we propose a clustering-based procedure that is used to cache content at the helper nodes along with advertisement and discovery procedures for content delivery. We implement a Long Term Evolution (LTE)-based simulation setup to evaluate the proposed schemes. Numerical results reveal that the clustering-based user and content discovery significantly improves cellular offloading and reduces the discovery overhead with a minimal sacrifice from delay.

WISE: MQTT-based Protocol for IP Device Provisioning and Abstraction in IoT Solutions

Although numerous consumer devices use Transmission Control Protocol/Internet Protocol (TCP/IP) protocol stack to connect and communicate over the Internet, their integration into a single Internet of Things (IoT) solution represents a challenge, primarily due to the lack of the standardized interoperability framework on the application layer. Devices produced by different manufacturers cannot operate together out of the box, thus raising the cost of system setup and maintenance. In this paper, a novel protocol is proposed that aims to bridge this gap. It is based on Message Queuing Telemetry Transport (MQTT) protocol and allows the seamless integration of different kinds of IP devices into the connected system. The proposed protocol is complete as it covers the aspects of device discovery and association in the IoT network. It provides mechanisms for IoT network maintenance and defines the abstract device model and communication patterns to enable system-wide device interoperability. The other goal of the protocol is to be portable to resource-constrained platforms. To validate the proposed protocol, it was integrated into the existing smart home hub, and for testing and validation purposes, prototype devices were developed.

The Technology of Biotechnology and Big Data in Medicine Development

The rise of big data and digital health in medicine have been concurrent over the last two decades. Often confused, while virtually all digital health solutions, such as sensors wearable devices, and diagnostic algorithms, involve big data, not all big data in health care originates from digital health tools. Genomic sequencing data being one example of this. In this chapter, the role and importance of big data in medicines and medical device discovery and development are detailed with the specific focus of providing a detailed understanding of the product discovery, product development, clinical trials, regulatory authorization, and marketing processes. Concepts such as “dirty data,” regulatory decision-making, remote and virtualized clinical trials, and other key elements of digital health are discussed.

Device Discovery and Context Registration in Static Context Header Compression Networks

Due to the limited bandwidth of Low-Power Wide-Area Networks (LPWAN), the application layer is currently often tied straight above the link layer, limiting the evolution of sensor networks distributed over a large area. Consequently, the highly efficient Static Context Header Compression (SCHC) standard was introduced, where devices can compress the IPv6 and upper layer protocols down to a single byte. This approach, however, assumes that every compression context is distributed before deployment, again limiting the evolution of such networks. Therefore, this paper presents two context registration mechanisms leveraging on the SCHC adaptation layer. This is done by analyzing current registration solutions in order to find limitations and optimizations with regard to very constrained networks. Both solutions and the current State-of-The-Art (SoTA) are evaluated in a Lightweight Machine to Machine (LwM2M) environment. In such situation, both developed solutions decrease the energy consumption already after 25 transmissions, compared with the current SoTA. Furthermore, simulations show that Long Range (LoRa) devices still have a 80% chance to successfully complete the registration flow in a network with a 50% Packet Error Ratio. Briefly, the work presented in this paper delivers bootstrapping tools to constrained, SCHC-enabled networks while still being able to reduce energy consumption.

Energy-Efficient Device Discovery Mechanism for Device-to-Device Communications in 5G Networks

The conventional direct device discovery scheme which uses the random access protocol and encounters contentions or collisions is highly energy and time-consuming. To reduce the energy consumption of user equipments (UEs), this work proposes a two-phase hybrid device discovery mechanism for device-to-device (D2D) communications. In the first phase, the evolved packet core (EPC) or base station (BS) uses the location information of UEs to judge whether two UEs are able to establish a D2D link. In the second phase, UEs use Wi-Fi Direct to discover their target UEs. The BS directly assigns UEs’ states and allocates appropriate Wi-Fi channels to UEs. UEs no longer have to search or listen to all channels, thus reducing the discovery delay. The proposed mechanism saves cellular spectrum resources because it uses unlicensed bands for D2D discovery and communications. The performance of the proposed hybrid D2D discovery mechanism is also theoretically analyzed in this paper. Evaluation results show that the proposed D2D discovery mechanism has better performance in terms of energy consumption, discovery delay and discovery success rate, compared with the conventional direct D2D discovery scheme, especially in the network scenarios with smaller cells such as 5G networks. Additionally, the analytic results coincide with simulation results, demonstrating that our theoretic analysis is accurate.

An Introduction to IWoT

The major drawback of the industrial internet of things is the lack of interoperability across the plethora of IoT platforms. Cross-platform services often require the development of complex software components for protocol translation, device discovery, and thing lifecycle management. As a result, these systems are too expensive and hard to develop. The W3C Consortium launched the Web of Things Working Group to develop the standards for open interoperability in the internet of things. This chapter presents the web of things specifications for systems architecture and communication protocols and how they can be applied in industrial domains, building the industrial web of things. Finally, this chapter shows that this industrial web of things is built upon a network of systems and devices linked with universal open standards such as enterprise systems, which are nowadays communicated through the conventional “web of pages,” as a key player in the Industry 4.0 revolution.