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.

In-body medical devices can play an important role in clinical monitoring and diagnosis of diseases. Wireless devices implanted within a patient have to be physically small, and must overcome the challenges of having a little or no onboard electrical power and the highly attenuating electromagnetic propagation environment which is the human body. In this paper, we investigate the use of biodegradable implant to monitor the healing of soft-tissue trauma and to allow early stage diagnosis of infection. The implantable tag is designed to degrade in a predetermined and controlled method, the stage of which can be measured from outside the body without the need for further surgical intervention. The speed of degradation of the tag depends on the temperature and acidity of the subcutaneous tissue in which the tag is implanted. We show that as the electrical length of the tag pattern increases due to degradation, the resonant frequency changes significantly, and this change in resonant frequency can be detected from outside the patient. Results are presented showing the tag's performance at normal and oblique incidence, and techniques for miniaturizing and enhancing the tag's response sensitivity are given. As the entire tag is biodegradable, there is no need for further postoperative surgery to remove it from the patient at the end of its useful life.

To deploy effective communication systems in vehicle cavities, it is critical to understand the time variation of the in-vehicle channel. Initially, rapid channel variation is addressed, which is characterized in the frequency domain as a Doppler spread. It is then shown that, for typical Doppler spreads, the in-vehicle channel is underspread, and therefore, the information capacity approaches the capacity achieved with perfect receiver channel state information in the infinite bandwidth limit. Measurements are performed for a number of channel variation scenarios (e.g., absorptive motion, reflective motion, one antenna moving, and both antennas moving) at a number of carrier frequencies and for a number of cavity loading scenarios. It is found that the Doppler spread increases with carrier frequency; however, the type of channel variation and loading appear to have little effect. Channel variation over a longer time period is also measured to characterize the slower channel variation. Channel variation is a function of the cavity occupant motion, which is difficult to model theoretically; therefore, an empirical model for the slow channel variation is proposed, which leads to an improved estimate of the channel state.

This paper presents a concept of an advanced method of controlling coverage for indoor wireless networks. The concept is based on the fact that electromagnetic wave propagation in indoor scenarios is mainly influenced by the propagation environment, e.g. walls, floors, ceilings, furniture etc. and such a propagation environment can be adjusted dynamically according to the current demands of the network by deploying what we call Active Frequency Selective Surface (AFFS) Walls. Using these walls, we are able to control the signal propagation at various frequencies, control the shape of covered area and limit interference. An example two-storey scenario acting as a small conference center is presented. The concept is verified by simulating simple Wi-Fi network and system of AFSS Walls. Advantages as well as potential problems of the proposed concept are discussed.

In cellular networks, balancing the throughput among users is important to achieve a uniform Quality-of-Service (QoS). This can be accomplished using a variety of cross-layer techniques. In this paper, the authors investigate how the down-tilt of base-station (BS) antennas can be adjusted to maximize the user throughput fairness in a heterogeneous network, considering the impact of both a dynamic user distribution and capacity saturation of different transmission techniques. Finding the optimal down-tilt in a multi-cell interference-limited network is a complex problem, where stochastic channel effects and irregular antenna patterns has yielded no explicit solutions and is computationally expensive. The investigation first demonstrates that a fixed tilt strategy yields good performances for homogeneous networks, but the introduction of HetNet elements adds a high level of sensitivity to the tilt dependent performance. This means that a HetNet must have network-wide knowledge of where BSs, access-points and users are. The paper also demonstrates that transmission techniques that can achieve a higher level of capacity saturation increases the optimal down-tilt angle. A distributed reinforcement learning algorithm is proposed, where BSs do not need knowledge of location data. The algorithm can achieve convergence to a near-optimal solution rapidly (6-15 iterations) and improve the throughput fairness by 45-56% and the energy efficiency by 21-47%, as compared to fixed strategies. Furthermore, the paper shows that a tradeoff between the optimal solution convergence rate and asymptotic performance exists for the self-learning algorithm.