Drone Empowered Small Cellular Disaster Recovery Networks for Resilient Smart Cities

Resilient communication networks, which can continue operations even after a calamity, will be a central feature of future smart cities. Recent proliferation of drones propelled by the availability of cheap commodity hardware presents a new avenue for provisioning such networks. In particular, with the advent of Google’s Sky Bender and Facebook’s internet drone, drone empowered small cellular networks (DSCNs) are no longer fantasy. DSCNs are attractive solution for public safety networks because of swift deployment capability and intrinsic network reconfigurability. While DSCNs have received some attention in the recent past, the design space of such networks has not been extensively traversed. In particular, co-existence of such networks with an operational ground cellular network in a post-disaster situation has not been investigated. Moreover, design parameters such as optimal altitude and number of drone base stations, etc., as a function of destroyed base stations, propagation conditions, etc., have not been explored. In order to address these design issues, we present a comprehensive statistical framework which is developed from stochastic geometric perspective. We then employ the developed framework to investigate the impact of several parametric variations on the performance of the DSCNs. Without loss of any generality, in this article, the performance metric employed is coverage probability of a down-link mobile user. It is demonstrated that by intelligently selecting the number of drones and their corresponding altitudes, ground users coverage can be significantly enhanced. This is attained without incurring significant performance penalty to the mobile users which continue to be served from operating ground infrastructure

Teaching Nursing Practice at Jordanian Universities

This research examined factors affecting the effectiveness of clinical teaching of nursing in Jordan for the first time. Three methods were used, a questionnaire administered to students and teachers, direct observation of ten teachers and critical incidents elicited from students. The technique of critical incidents asked students to give events which were examples of good and bad teaching practice and a large pool of incidents was developed. It was found that the questionnaire method did not give useful results because of a large response bias, suggesting that questionnaires, at least in English, may be inappropriate for use in Jordanian student evaluations. The critical incidents were classified by the researcher with a jury and the types of effective and ineffective behaviour identified were generally similar to those found in the previous literature. They were also supported both by the researcher's direct observations and by students' and teachers' suggestions for improving teaching, made in the questionnaire. Five categories of issue were identified, the interpersonal abilities of the teacher, summative evaluation, formative evaluation, professional competence and motivational factors. Some special problems were identified which reflected the difficulties of applying nursing practices learned from Anglo-American textbooks to Jordanian society. Other problems included teachers failing to have sufficient interaction with the students or with ward staff, teachers not being involved in clinical procedures themselves and teachers not interacting appropriately with students. There were also problems with evaluation; formative evaluation was often absent or confused with summative evaluation and the criteria for summative evaluation were not clear. These findings are discussed with reference to the previous literature and a number of suggestions are made for improving clinical teaching of nursing in Jordan

Coverage Analysis of Drone-Assisted Backscatter Communication for IoT Sensor Network

In this article, we develop a comprehensive framework to characterize the performance of drone assisted Backscatter communication based Internet of things (IoT) sensor network. We consider a scenario such where drone transmits RF carrier which is modulated by IoT sensor node (SN) to transmit its data. The SN implements load modulation which results in amplitude shift keying (ASK) type modulation for the impinging RF carrier. In order to quantify the performance of considered network, we characterize the coverage probability for the ground based SN node. The statistical framework developed to quantify the coverage probability explicitly accommodates dyadic backscatter channel which experiences deeper fades than that of the oneway Rayleigh channel. Our model also incorporates Line of Sight (LoS) and Non-LoS (NLoS) propogation states for accurately modeling large-scale path-loss between drone and SN. We consider spatially distributed SNs which can be modelled using spatial Binomial Point process (BPP). We practically implement the proposed system using Software Defined Radio (SDR) and a custom designed SN tag. The measurements of parameters such as noise figure, tag reflection coefficient etc., are used to parametrize the developed framework. Lastly, we demonstrate that there exists an optimal set of parameters which maximizes the coverage probability for the SN

Decentralized Federated Learning Over Slotted ALOHA Wireless Mesh Networking

Federated Learning (FL) presents a mechanism to allow decentralized training for machine learning (ML) models inherently enabling privacy preservation. The classical FL is implemented as a client-server system, which is known as Centralised Federated Learning (CFL). There are challenges inherent in CFL since all participants need to interact with a central server resulting in a potential communication bottleneck and a single point of failure. In addition, it is difficult to have a central server in some scenarios due to the implementation cost and complexity. This study aims to use Decentralized Federated learning (DFL) without a central server through one-hop neighbours. Such collaboration depends on the dynamics of communication networks, e.g., the topology of the network, the MAC protocol, and both large-scale and small-scale fading on links. In this paper, we employ stochastic geometry to model these dynamics explicitly, allowing us to quantify the performance of the DFL. The core objective is to achieve better classification without sacrificing privacy while accommodating for networking dynamics. In this paper, we are interested in how such topologies impact the performance of ML when deployed in practice. The proposed system is trained on a well-known MINST dataset for benchmarking, which contains labelled data samples of 60K images each with a size $28\times 28$ pixels, and 1000 random samples of this MNIST dataset are assigned for each participant’ device. The participants’ devices implement a CNN model as a classifier model. To evaluate the performance of the model, a number of participants are randomly selected from the network. Due to randomness in the communication process, these participants interact with the random number of nodes in the neighbourhood to exchange model parameters which are subsequently used to update the participants’ individual models. These participants connected successfully with a varying number of neighbours to exchange parameters and update their global models. The results show that the classification prediction system was able to achieve higher than 95% accuracy using the three different model optimizers in the training settings (i.e., SGD, ADAM, and RMSprop optimizers). Consequently, the DFL over mesh networking shows more flexibility in IoT systems, which reduces the communication cost and increases the convergence speed which can outperform CFL

Optimal Coverage and Rate in Downlink Cellular Networks: A SIR Meta-Distribution Based Approach

In this paper, we present a detailed analysis of the coverage and spectral efficiency of a downlink cellular network. Rather than relying on the first order statistics of received signal-to- interference-ratio (SIR) such as coverage probability, we focus on characterizing its meta- distribution. Our analysis is based on the alpha- beta-gamma (ABG) path-loss model which provides us with the flexibility to analyze urban macro (UMa) and urban micro (UMi) deployments. With the help of an analytical framework, we demonstrate that selection of underlying degrees-of-freedom such as BS height for optimization of first order statistics such as coverage probability is not optimal in the network-wide sense. Consequently, the SIR meta-distribution must be employed to select appropriate operational points which will ensure consistent user experiences across the network. Our design framework reveals that the traditional results which advocate lowering of BS heights or even optimal selection of BS height do not yield consistent service experience across users. By employing the developed framework we also demonstrate how available spectral resources in terms of time slots/channel partitions can be optimized by considering the meta-distribution of the SIR

Grasp Classification with Weft Knit Data Glove using a Convolutional Neural Network

Grasp classification using data gloves can enable therapists to monitor patients efficiently by providing concise information about the activities performed by these patients. Although, classical machine learning algorithms have been applied in grasp classification, they require manual feature extraction to achieve high accuracy. In contrast, convolutional neural networks (CNNs) have outperformed popular machine learning algorithms in several classification scenarios because of their ability to extract features automatically from raw data. However, they have not been implemented on grasp classification using a data glove. In this study, we apply a CNN in grasp classification using a piezoresistive textile data glove knitted from conductive yarn and an elastomeric yarn. The data glove was used to collect data from five participants who grasped thirty objects each following Schlesinger’s taxonomy. We investigate a CNN’s performance in two scenarios where the validation objects are known and unknown. Our results show that a simple CNN architecture outperformed k-nn, Gaussian SVM, and Decision Tree algorithms in both scenarios in terms of the classification accuracy

Dielectric and Double Debye Parameters of Artificial Normal Skin and Melanoma

The final publication is available at Springer via http://dx.doi.org/10.1007/s10762-019-00597-xThe aim of this study is to characterise the artificial normal skin and melanoma by testing samples with different fibroblast and metastatic melanoma cell densities using terahertz (THz) time-domain spectroscopy (TDS) attenuated total reflection (ATR) technique. Results show that melanoma samples have higher refractive index and absorption coefficient than artificial normal skin with the same fibroblast density in the frequency range between 0.4 and 1.6 THz, and this contrast increases with frequency. It is primarily because that the melanoma samples have higher water content than artificial normal skin, and the main reason to melanoma containing more water is that tumour cells degrade the contraction of the collagen lattice. In addition, complex refractive index and permittivity of the melanoma samples have larger variations than that of normal skin samples. For example, the refractive index of artificial normal skin at 0.5 THz increases 4.3% while that of melanoma samples increases 8.7% when the cell density rises from 0.1 to 1 M/ml. It indicates that cellular response of fibroblast and melanoma cells to THz radiation is significantly different. Furthermore, the extracted double Debye (DD) model parameters demonstrate that the static permittivity at low frequency and slow relaxation time can be reliable classifiers to differentiate melanoma from healthy skin regardless of the cell density. This study helps understand the complex response of skin tissues to THz radiation and the origin of the contrast between normal skin and cancerous tissues