Underwater Optical Communication Module : An Extension to the ns-3 Network Simulator

In the last decade, the field of wireless optical communication has gathered immense interest due to its adoption in growing bandwidth-hungry underwater applications. The expensive and non-standardized on-field research measurements call for a reliable simulation tool that allows researchers to realistically design and assess the performance of Underwater Optical Communication (UOC) systems before conducting actual underwater experiments. In this paper, we present a UOC module as an extension to the network simulator ns-3. The module can study the impact of different water conditions on underwater optical networks from the physical layer to the network layer. The proposed UOC module realizes physical layer models of the UOC channels where the added noise and interference effects are modeled as Additive White Gaussian Noise (AWGN). Results show the capability of our module to facilitate large underwater optical network design and optimization. Since ns-3 is an open-source software, the module has the flexibility and reusability to be further developed by the worldwide research community.acceptedVersionPeer reviewe

Dense Air Quality Sensor Networks: Validation, Analysis and Benefits

Air pollution is known to be harmful for human health and environments. The official air quality monitoring stations have been established across many smart cities around the world. Unfortunately, these monitoring stations are sparsely located and consequently do not provide high resolution spatio- temporal air quality information. This paper demonstrates how a dense sensor network deployment offers significant advantages in providing better and more detailed air quality information. We use data from a dense sensor network consisting of 126 low- cost sensors (LCSs) deployed in a highly populated district in Nanjing downtown, China. Using data obtained from 13 existing reference stations installed in the same district, we propose three LCSs validation methods to evaluate the performance of LCSs in the network. The methods assess the reliability, accuracy tests, and failure and anomaly detection performance. We also demonstrate how the reliable data generated from the sensor network provides deep insights into air pollution information at a higher spatio-temporal resolution. We further discuss potential improvements and applications derived from dense deployment of LCSs in cities.Peer reviewe

Tracking the Occluded Indoor Target with Scattered Millimeter Wave Signal

The popularity of mobile robots in factories, warehouses, and hospitals has raised safety concerns about human-machine collisions, particularly in non-line-of-sight (NLoS) scenarios such as corners. Developing a robot capable of locating and tracking humans behind the corners will greatly mitigate risk. However, most of them cannot work in complex environments or require a costly infrastructure. This paper introduces a solution that uses the reflected and diffracted Millimeter Wave (mmWave) radio signals to detect and locate targets behind the corner. Central to this solution is a localization convolutional neural network (L-CNN), which takes the angle-delay heatmap of the mmWave sensor as input and infers the potential target position. Furthermore, a Kalman filter is applied after L-CNN to improve the accuracy and robustness of estimated locations. A red-green-blue-depth (RGB-D) camera is attached to themmWave sensor as the annotation system to provide accurate position labels. The results of the experimental evaluation demonstrate that our data-driven approach can achieve remarkable positioning accuracy at the 10-centimeter level without extensive infrastructure. In particular, the approach effectively mitigates the adverse effects of diffraction and multi-bounce phenomena, making the system more resilient.Peer reviewe

Resource discovery for distributed computing systems: A comprehensive survey

Large-scale distributed computing environments provide a vast amount of heterogeneous computing resources from different sources for resource sharing and distributed computing. Discovering appropriate resources in such environments is a challenge which involves several different subjects. In this paper, we provide an investigation on the current state of resource discovery protocols, mechanisms, and platforms for large-scale distributed environments, focusing on the design aspects. We classify all related aspects, general steps, and requirements to construct a novel resource discovery solution in three categories consisting of structures, methods, and issues. Accordingly, we review the literature, analyzing various aspects for each category

Supporting the voice and choice of students : Promoting self-determination in the classroom: an observational study of teacher motivational behavior

Motivation is an important factor when trying to understand human behavior and well-being (Ryan & Deci, 2002), especially for teachers since the occupation involves making students perform certain behaviors (Reeve, 2002). Building on the Self-Determination Theory perspective of motivation the aim of this observational study was to describe and analyze the relation between teacher motivational behavior and student behaviors. The study was conducted over a three week period of observing one class in junior high school and their teachers. The results suggest that when teachers make students aware of themselves, their inner resources and support their choice, self-determined forms of motivation is increased. These findings are discussed as well as structured in a theoretical didactical model which describes how different conditions create different psychological responses

Supporting the voice and choice of students : Promoting self-determination in the classroom: an observational study of teacher motivational behavior

Motivation is an important factor when trying to understand human behavior and well-being (Ryan & Deci, 2002), especially for teachers since the occupation involves making students perform certain behaviors (Reeve, 2002). Building on the Self-Determination Theory perspective of motivation the aim of this observational study was to describe and analyze the relation between teacher motivational behavior and student behaviors. The study was conducted over a three week period of observing one class in junior high school and their teachers. The results suggest that when teachers make students aware of themselves, their inner resources and support their choice, self-determined forms of motivation is increased. These findings are discussed as well as structured in a theoretical didactical model which describes how different conditions create different psychological responses

SPHERE-DNA : Privacy-Preserving Federated Learning for eHealth

The rapid growth of chronic diseases and medical conditions (e.g. obesity, depression, diabetes, respiratory and musculoskeletal diseases) in many OECD countries has become one of the most significant wellbeing problems, which also poses pressure to the sustainability of healthcare and economies. Thus, it is important to promote early diagnosis, intervention, and healthier lifestyles. One partial solution to the problem is extending long-term health monitoring from hospitals to natural living environments. It has been shown in laboratory settings and practical trials that sensor data, such as camera images, radio samples, acoustics signals, infrared etc., can be used for accurately modelling activity patterns that are related to different medical conditions. However, due to the rising concern related to private data leaks and, consequently, stricter personal data regulations, the growth of pervasive residential sensing for healthcare applications has been slow. To mitigate public concern and meet the regulatory requirements, our national multi-partner SPHERE-DNA project aims to combine pervasive sensing tech-nology with secured and privacy-preserving distributed privacy frameworks for healthcare applications. The project leverages local differential privacy federated learning (LDP-FL) to achieve resilience against active and passive attacks, as well as edge computing to avoid transmitting sensitive data over networks. Combinations of sensor data modalities and security architectures are explored by a machine learning architecture for finding the most viable technology combinations, relying on metrics that allow balancing between computational cost and accuracy for a desired level of privacy. We also consider realistic edge computing platforms and develop hardware acceleration and approximate computing techniques to facilitate the adoption of LDP-FL and privacy preserving signal processing to lightweight edge processors. A proof-of-concept (PoC) multimodal sensing system will be developed and a novel multimodal dataset will be collected during the project to verify the concept.©2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Effects of multipath attenuation in the optical communication-based internet of underwater things

The propagation of light underwater is tied closely to the optical properties of water. In particular, the underwater channel imposes attenuation on the optical signal in the form of scattering, absorption, and turbulence. These attenuation factors can lead to severe spatial and temporal dispersion, which restricts communication to a limited range and bandwidth. In this paper, we propose a statistical model to estimate the probability density function of the temporal dispersion in underwater wireless optical communication (UWOC) based Internet of Underwater Things (IoUTs) using discrete histograms. The underwater optical channel is modeled using Monte Carlo simulations, and the effects of temporal dispersion are presented by measuring the magnitude response of the channel in terms of received power. The temporal response analysis is followed by an extensive performance evaluation in terms of bit error rate (BER). To facilitate in-depth theoretical analysis, we have measured and presented magnitude response and BER of the channel under different field-of-views (FoVs), apertures, and water types. The three main areas under study are (i) BER versus link distance behavior, (ii) temporal response of the channel, and (iii) effect of scattering on photon travel. Our study shows the two main factors that contribute to beam spreading and temporal dispersion are (i) diffusivity of the optical source and (ii) multiple scattering. Furthermore, our results suggest that temporal dispersion caused due to multiple scattering cannot be mitigated completely; however, it can be minimized by optimizing the receiver aperture.Peer reviewe