The Internet of Things (IoT) is increasingly being utilized, by both businesses and individuals, for many applications. This utilization means increases in the smart devices that are connected to the Internet of Things, which will significantly increase the challenges related to devices' interconnectivity and management, data and user privacy, and network, data, and device security. At the same time, blockchain approaches provide a decentralized, immutable, and peer-to-peer ledger technology that could be the right answer to these challenges. Significant challenges, however, accompany the integration of blockchain into the Internet of Things, since IoT smart devices may suffer from resource and power constraints and blockchain is associated with scalability and delay issues. In this paper, a practical incorporation of blockchain into the Internet of Things is demonstrated using Ethereum Proof of Authority (PoA). This provides performance analyses, which include measurement of the transaction arrival time, the system end-to-end latency for different network implementations over cellular and Wi-Fi, and the average power consumption. This includes the study of the effect of network bandwidth on the stability and synchronization of all nodes on the blockchain network.

In this study, a new blockchain protocol and a novel architecture that integrates the advantages offered by edge computing, artificial intelligence (AI), IoT end-devices, and blockchain were designed, developed, and validated. This new architecture has the ability to monitor the environment, collect data, analyze it, process it using an AI-expert engine, provide predictions and actionable outcomes, and finally share it on a public blockchain platform. For the use-case implementation, the pandemic caused by the wide and rapid spread of the novel coronavirus COVID-19 was used to test and evaluate the proposed system. Recently, various authors have traced the virus spread in sewage water and studied how it can be used as a tracking system. Early warning notifications can allow governments and organizations to take appropriate actions at the earliest stages possible. The system was validated experimentally using 14 Raspberry Pis, and the results and analyses proved that the system is able to utilize low-cost and low-power flexible IoT hardware at the processing layer to detect COVID-19 and predict its spread using the AI engine, with an accuracy of 95%, and share the outcome over the blockchain platform. This is accomplished when the platform is secured by the honesty-based distributed proof of authority (HDPoA) and without any substantial impact on the devices’ power sources, as there was only a power consumption increase of 7% when the Raspberry Pi is used for blockchain mining and 14% when used to produce an AI prediction.

Automatic Modulation Classification (AMC) is a rapidly evolving technology, which can be employed in software defined radio structures, especially in 5G and 6G technology. Machine Learning (ML) can provide novel and efficient technology for modulation classification, especially for systems working in low signal to noise ratio (SNR). In this article, two dynamic systems not reliant on received signal phase lock and frequency lock are presented, with both employing ML to classify the modulation types for different received SNR. The first model is developed from the previous existing literatures, which utilises constellation images (CI) and image classification technology. Here, modulation types can be detected in a dynamic way without phase lock and frequency lock. In the second model, a new method named Graphic Representation of Features (GRF) is proposed, which represents the statistical features as a spider graph for ML. The concepts are tested and verified using simulations and RF data using a lab software defined radio (SDR). The results from the two models are compared. With the GRF techniques an overall classification accuracy of 59% is observed for 0 dB SNR and 86% at 10 dB SNR, compared to a random guess accuracy of 25%.

This article presents a distributed and decentralized architecture for the implementation of distributed artificial intelligence (DAI) using hardware platforms provided by the Internet of Things (IoT). A trained DAI system has been implemented over IoT, where each IoT device acts as one or more of the neurons within the DAI layers. This is accomplished by the utilization of decentralized, self-managed blockchain technologies that allow trusted interactions and information to be exchanged between distributed neurons. The platform was built and customized to be used within the IoT system, and it is capable of handling DAI-related tasks. A new consensus mechanism based on Proof of Authority (PoA) and Proof of Work (PoW) has been designed and implemented, along with bespoke block and transaction formats. The proposed architecture was analyzed, implemented, and tested using a dedicated testbed with low-cost IoT devices. A quantitative measurement and performance evaluation of the system based on a real-world IoT application was conducted. The implemented DAI is found to have an accuracy of 92%–98%, with an energy cost of 0.12 joules (J) when utilizing a Raspberry Pi to run one neuron. The measured hash per joule (h/J) when using a Raspberry Pi for mining is 13.8 Kh/J compared to 54 Kh/J using an ESP32. The results showed that it is feasible to implement a DAI system utilizing the IoT hardware platform while maintaining the system’s accuracy. The integration of the blockchain has added an element of security and trust to the data and the interaction between system components.

This paper presents a millimeter wave series-fed microstrip line antenna array with low side lobes. For millimeter-wave beam forming applications, compact series fed arrays with very small horizontal width and low side lobes are very much required. The proposed array consists of a microstrip line with periodically placed stepped inverted-cone stub sections at a guided wavelength spacing. Surface current cancellation at each guided wavelength in an ideal microstrip line can be altered by symmetrically introducing the inverted-cone stub sections on two edges with a half wavelength offset. By controlling the dimensions and location of the inverted-cone stub sections, the equivalent surface currents can be altered and focused on the cone stub sections, resulting in an effective array radiation. The fabricated antenna array is resonating at 28 GHz and has an impedance bandwidth of 1.3 GHz. Peak realized gain of 10.2 dBi is achieved at 28 GHz and the measured side lobe levels are better than -15 dB in the E and H plane. The proposed microstrip line antenna array with low side lobes also benefits from simple fabrication and high integration ability, which makes it attractive for compact beamforming scenarios in millimeter-wave bands.

Software Defined Radio (SDR) instruments can be used to replace bulky and expensive spectrum analyzers for RF field measurements. Using commercial off the shelf (COTS) equipment a low cost, portable SDR instrument for measuring RF propagation has been created and tested. In the UK, parts of the VHF spectrum have been re-purposed for the use of Internet of Things devices, the instrument developed here is designed to meet the use-case of performing an urban propagation study in VHF and UHF Short Range Devices bands in a fast and low cost manner. Design of the hardware and software is discussed, as well as the calibration of the instrument. Results of a test propagation study are given for the completed instrument. It is shown that the SDR instrument is capable of performing the study to a high degree of agreement with a commercial spectrum analyzer, thus validating the approach. Readings of received power taken by the instrument are shown to agree with readings taken at the same locations with a commercial spectrum analyzer to within an average of 1.4dB at 71MHz and 1.1dB at 869.525MHz. From the measurements taken log distance models were able to be produced with a path loss exponent of 2.44 and log normal shadowing standard deviation of 8.5dB at 71MHZ and a path loss exponent of 4.06 and log normal shadowing standard deviation of 8.8dB at 869.525MHz.