Who is the owner of your health documents? Although the answer to this question may seem straightforward and intuitive, today, we are far from the situation where you are the one who has the key to this critical information. Hospitals, health maintenance organizations (HMO), private doctors, and different medical institutions produce large quantities of medical information about each of us, and there is a need to maintain this information privately , synchronously and accessible to each one of us. In this paper, we propose the medical record chain ( MrC ) system, with its Blockchain architecture, as a tool to achieve this goal. The MrC system would be accessible to the patients so their medical data would be organized and available for viewing, regardless of where the information was produced. Furthermore, each patient can give a recognized medical service provider temporary permission to view and update his medical records. The architectural design of the MrC system ensures that no centralized authority controls the medical records. To enable the assimilation of the system in different institutions, a bridge to the system is proposed so that no change is required in the existing information systems of the medical body but a convenient and simple interface. Moreover, medical institutions would be incentivized to employ the system by allowing access to extensive medical datasets that have undergone de-identification. Implementing the MrC system would return ownership of the data to where it belongs- the patient. It would improve patients' health by allowing multiple medical institutions to access accurate information quickly. Finally, a by-product of the MrC system is improving public health by making comprehensive de-identified datasets available for medical research.

Radio resource management of the downlink of multi-user multiple-input multiple-output systems with multi-antenna users is considered to evaluate the performance of three zero-forcing precoding strategies: 1) Block Diagonalization (BD) where all streams are used for each selected user; 2) Coordinated-Transmit-Receive-1$(\mathbf{CTR}_{\mathbf{1}})$where only the strongest stream is used for each scheduled user; 3) Coordinated-Transmit-Receive-Flexible$(\mathbf{CTR}_{\mathbf{F}})$that allows a flexible stream allocation per selected user. Although the more complex$\mathbf{CTR}_{\mathbf{F}}$has the potential for better performance due to its flexibility, it might be difficult to implement it in practice. Hence, it is crucial to comprehend when the simpler CTR1or BD can be used instead of CT$\mathbf{R}_{\mathbf{F}}$. Our analysis compares the precoding strategies within$\mathbf{3GPP}$-compliant scenarios using realistic modulation and coding schemes in systems featuring large numbers of users and Base-Station (BS) antennas. We compare the performance of these precoding strategies under Sum-Rate (SR) maximization and Proportional Fairness (PF) and show that previous research conclusions carried out for SR maximization only hold for a small number of BS antennas. Indeed, for SR maximization, BD matches the performance of$\mathbf{CTR}_{\mathbf{F}}$when BS antenna arrays are sufficiently large. For PF, the results are more nuanced, depending significantly on system parameters.

In this paper, we consider the problem of prediction of Radio Link Failures (RLF) in flying ad hoc networks (FANETs).Many environmental factors that influence the quality of radio wave propagation are dynamic, and thus, drones must continually learn and update their radio link quality prediction model while they operate online.Online machine learning algorithms can be used to build adaptive RLF predictors without requiring a pre-deployment effort.To predict the RLF, we use an online machine learning algorithm and information gathering by message-passing from the neighbors.We propose an algorithm called ML-Net (Machine Learning and Network algorithm) to predict RLF.To the best of our knowledge, the combination of online machine learning algorithms together with the message-passing algorithm has not been used before.The proposed methodology outperforms the state-of-the-art online machine learning algorithms.

The objective of our work is to address the challenge of delivering time-sensitive data across a multi-hop wireless network. We aim to maximize the number of packets that reach their destination before the strict deadline. To achieve this, we introduce a deep reinforcement learning (DRL) approach that determines the optimal route, scheduling, and power allocation for each flow while complying with strict time constraints.

In this work, we study the connected version of the covering problem motivated by the coverage of ad-hoc drones’ swarm. We focus on the situation where the number of drones is given, and this number is not necessarily enough to cover all users. That is, we deal with a budget optimization problem, where the budget is the number of given drones. We assume that each ground user has different QoS requirements. Additionally, each ground user has a weight that corresponds to the importance (rank) of the user. Moreover, we consider the case when there is no third-party entity that provides connectivity to the drones. In this paper, we propose a 3D deployment scheme with the given number of drones such that the sum of the weights (ranks) of the ground users covered by drones is maximized (when the covering radii satisfy QoS of these users), and the drones form a connected graph. We present a number of approximate solutions with provable guaranteed performance evaluation that have been validated also through the simulation platform.

In this study, we consider a scenario where Unmanned Aerial Vehicles are deployed in an area of interest in order to monitor this area, and the data gathering process following the monitoring is applied towards a central UAV (sink) for analysis. Our objective is to find a minimum-length TDMA schedule by jointly determining the scheduled concurrent links at each slot, along with their used power levels and the established routes toward the sink, under SINR requirements. Since the problem is known to be NP-hard, we aim to provide solutions to real-world situations using efficient heuristic approach. Our research goal is to use deep neural network to approximate the solution of finding the minimum frame length for the TDMA frame. We propose the TDMA frame length minimization by the deep neural network approach (named MTFLet). We have performed extensive experimental evaluations of MTFLet and simulations showing that MTFLet outperforms other state-of-art methods.

In this paper, an unmanned aerial vehicles (UAVs) deployment framework based on machine learning is studied.It aims to maximize the sum of the weights of the ground users covered by UAVs while UAVs forming a connected communication graph.We focus on the case where the number of UAVs is not necessarily enough to cover all ground users.We develop an UAV Deployment Deep Neural network (UD-DNNet) as a UAV's deployment deep network method.Simulation results demonstrate that UDDNNet can serve as a computationally inexpensive replacement for traditionally expensive optimization algorithms in real-time tasks and outperform the state-of-the-art traditional algorithms.

In this work, we considered power allocation and request scheduling in mobile ad hoc networks (MANET) clusters, which are generally modeled as optimization problems with constraints. Optimization algorithms often incur significant time complexity, which creates significant discrepancies between theoretical results and real-time processing. We aim to provide a novel machine-learning-based perspective to address this challenge. We use a deep neural network (DNN) to approximate the nonlinear mapping between the inputs and outputs of an optimization algorithm. If a nonlinear mapping can be learned accurately by a DNN, then optimization tasks can be performed more efficiently. We propose SPCDNet as a scheduling and power control deep network mapping method. A key challenge in training a DNN for resource allocation problems is a lack of ground-truth data, meaning the optimal power allocation between the time slots of each transmitter is unknown. To address this issue, we designed an optimal solver based on linear programming optimization methods and used its solutions to train SPCDNet. Simulation results demonstrate that SPCDNet can serve as a computationally inexpensive replacement for traditionally expensive optimization algorithms in real-time tasks and provide very good approximation solutions, where the average run time of SPCDNet for each network size is very low compared to the hundreds of seconds used by an optimal solver, whose time complexity increases exponentially with the input size.

This paper presents an efficient method for data acquisition and automatic meter reading (AMR) system from smart electric meters (SM) using power-line communications (PLC) networks. A PLC network uses existing electrical wiring to simultaneously carry both data and electric power. However, since the transmission medium in a PLC network is not designed for high quality data transfer, its efficiency in carrying out AMR applications is limited. The PLC network's traffic load is significantly affected both by the routing algorithm and the polling ordering of the SM. This paper suggests two novel smart polling algorithms based on application layer approach. The proposed algorithms demonstrate efficient utilization of both proactive and reactive routing protocols for AMR applications in narrowband PLCs networks. The benefit of the proposed approach is demonstrated for the two most common protocols, the routing protocol for low-power and lossy networks and the lightweight on-demand ad hoc distance-vector routing protocol.