Smart Multimodal In-Bed Pose Estimation Framework Incorporating Generative Adversarial Neural Network

Monitoring in-bed pose estimation based on the Internet of Medical Things (IoMT) and ambient technology has a significant impact on many applications such as sleep-related disorders including obstructive sleep apnea syndrome, assessment of sleep quality, and health risk of pressure ulcers. In this research, a new multimodal in-bed pose estimation has been proposed using a deep learning framework. The Simultaneously-collected multimodal Lying Pose (SLP) dataset has been used for performance evaluation of the proposed framework where two modalities including long wave infrared (LWIR) and depth images are used to train the proposed model. The main contribution of this research is the feature fusion network and the use of a generative model to generate RGB images having similar poses to other modalities (LWIR/depth). The inclusion of a generative model helps to improve the overall accuracy of the pose estimation algorithm. Moreover, the method can be generalized for situations to recover human pose both in home and hospital settings under various cover thickness levels. The proposed model is compared with other fusion-based models and shows an improved performance of 97.8% at [email protected]. In addition, performance has been evaluated for different cover conditions, and under home and hospital environments which present improvements using our proposed model

An accurate RSS/AoA-based localization method for internet of underwater things

Localization is an important issue for Internet of Underwater Things (IoUT) since the performance of a large number of underwater applications highly relies on the position information of underwater sensors. In this paper, we propose a hybrid localization approach based on angle-of-arrival (AoA) and received signal strength (RSS) for IoUT. We consider a smart fishing scenario in which using the proposed approach fishers can find fishes’ locations effectively. The proposed method collects the RSS observation and estimates the AoA based on error variance. To have a more realistic deployment, we assume that the perfect noise information is not available. Thus, a minimax approach is provided in order to optimize the worst-case performance and enhance the estimation accuracy under the unknown parameters. Furthermore, we analyze the mismatch of the proposed estimator using mean-square error (MSE). We then develop semidefinite programming (SDP) based method which relaxes the non-convex constraints into the convex constraints to solve the localization problem in an efficient way. Finally, the Cramer–Rao lower bounds (CRLBs) are derived to bound the performance of the RSS-based estimator. In comparison with other localization schemes, the proposed method increases localization accuracy by more than 13%. Our method can localize 96% of sensor nodes with less than 5% positioning error when there exist 25% anchors

Intrusion Detection in Critical SD-IoT Ecosystem

The Internet of Things (IoT) connects physical objects with intelligent decision-making support to exchange information and enable various critical applications. The IoT enables billions of devices to connect to the Internet, thereby collecting and exchanging real-time data for intelligent services. The complexity of IoT management makes it difficult to deploy and manage services dynamically. Thus, in recent times, Software Defined Network (SDN) has been widely adopted in IoT service management to provide dynamic and adaptive capabilities to the traditional IoT ecosystem. This has resulted in the evolution of a new paradigm known as Software-defined IoT (SD-IoT). Although there are several benefits of SD-IoT, it also opens new frontiers for attackers to introduce attacks and intrusions. Specifically, it becomes challenging working in a critical IoT environment where any delay or disruption caused by an intruder can be life-threatening or can cause significant destruction. However, given the flexibility of SDN, it is easier to deploy different intrusion detection systems that can detect attacks or anomalies promptly. Thus, in this paper, we have deployed a hybrid architecture that allows monitoring, analysis, and detection of attacks and anomalies in the SD-IoT ecosystem. In this work, we have considered three scenarios, a) denial of services, b) distributed denial of service, and c) packet fragmentation. The work is validated using simulated experiments performed using SNORT deployed on the Mininet platform for three scenarios

A Simulation-based Decision Support System for Urban Traffic Management

One common challenge faced by smart cities is traffic congestion, caused also unintentionally by city planners, which significantly impacts urban life and the environment. To address these issues, various strategies and approaches are explored, including advanced traffic management methods, communication systems, and predictive modeling. In this study, we introduce a Digital Twin decision support system that focuses on accurately modeling a city’s road network and replicating traffic patterns for specific time frames. Central to this project is SUMO, a microscopic vehicular traffic simulator used to create the reference road model. Our research presents a flexible and viable methodology for generating SUMO-compliant simulations that leverage real vehicular data from the city to analyze urban traffic and its conditions. To illustrate our approach, we apply it to a specific case study centered on examining environmental emissions in the city of Bologna, Italy. This approach shows promise in enhancing traffic management and overall urban efficiency within the broader context of smart cities

A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control

Traffic congestion is one of the growing urban problem with associated problems like fuel wastage, loss of lives, and slow productivity. The existing traffic system uses programming logic control (PLC) with round-robin scheduling algorithm. Recent works have proposed IoT-based frameworks that use traffic density of each lane to control traffic movement, but they suffer from low accuracy due to lack of emergency vehicle image datasets for training deep neural networks. In this paper, we propose a novel distributed IoT framework that is based on two observations. The first observation is major structural changes to road are rare. This observation is exploited by proposing a novel two stage vehicle detector that is able to achieve 77% vehicle detection accuracy on UA-DETRAC dataset. The second observation is emergency vehicle have distinct siren sound that is detected using a novel acoustic detection algorithm on an edge device. The proposed system is able to detect emergency vehicles with an average accuracy of 99.4%

Adaptive Recovery Mechanism for SDN Controllers in Edge-Cloud supported FinTech Applications

Financial Technology have revolutionized the delivery and usage of the autonomous operations and processes to improve the financial services. However, the massive amount of data (often called as big data) generated seamlessly across different geographic locations can end end up as a bottleneck for the underlying network infrastructure. To mitigate this challenge, software-defined network (SDN) has been leveraged in the proposed approach to provide scalability and resilience in multi-controller environment. However, in case if one of these controllers fail or cannot work as per desired requirements, then either the network load of that controller has to be migrated to another suitable controller or it has to be divided or balanced among other available controllers. For this purpose, the proposed approach provides an adaptive recovery mechanism in a multi-controller SDN setup using support vector machine-based classification approach. The proposed work defines a recovery pool based on the three vital parameters, reliability, energy, and latency. A utility matrix is then computed based on these parameters, on the basis of which the recovery controllers are selected. The results obtained prove that it is able to perform well in terms of considered evaluation parameters

A Heuristic-Based Appliance Scheduling Scheme for Smart Homes

The ever-growing demand for electricity in the residential sector results in creating a severe burden on electric grids. However, with the emergence of smart homes (SHs) and smart grids (SGs), this burden can be reduced to some extent. To address this issue, we propose an energy management system in this paper which manages the power requirements of SHs automatically according to the utility constraints and user priorities. The proposed system is based on a heuristic technique which considers the users priority and power available from the grid as well as distributed energy resources (DERs) for scheduling of appliances. It works by dividing the appliance scheduling problem in an SH into sub-problems for different time-slots. Results show that the proposed scheme efficiently manages the load demand of the SH with respect to power available from the utility, battery energy storage system, and user preferences

Sustainable Edge Node Computing Deployments in Distributed Manufacturing Systems

The advancement of mobile internet technology has created opportunities for integrating the Industrial Internet of Things (IIoT) and edge computing in smart manufacturing. These sustainable technologies enable intelligent devices to achieve high-performance computing with minimal latency. This paper introduces a novel approach to deploy edge computing nodes in smart manufacturing environments at a low cost. However, the intricate interactions among network sensors, equipment, service levels, and network topologies in smart manufacturing systems pose challenges to node deployment. To address this, the proposed sustainable game theory method identifies the optimal edge computing node for deployment to attain the desired outcome. Additionally, the standard design of Software Defined Network (SDN) in conjunction with edge computing serves as forwarding switches to enhance overall computing services. Simulations demonstrate the effectiveness of this approach in reducing network delay and deployment costs associated with computing resources. Given the significance of sustainability, cost efficiency plays a critical role in establishing resilient edge networks. Our numerical and simulation results validate that the proposed scheme surpasses existing techniques like shortest estimated latency first (SELF), shortest estimated buffer first (SEBF), and random deployment (RD) in minimizing the total cost of deploying edge nodes, network delay, packet loss, and energy consumption

Technologies for the diagnosis of angle closure glaucoma (ACE): protocol of a prospective, multicentre, cross-sectional diagnostic study

INTRODUCTION: Angle-closure is responsible for half of all glaucoma blindness globally. Patients with suspected glaucoma require assessment of the drainage angle by an experienced clinician. The goal of this study is to evaluate the diagnostic performance and cost-effectiveness of two non-contact tests, anterior segment OCT (Optical Coherence Tomography) (AS-OCT) and limbal anterior chamber depth for patients referred to hospital with suspected angle closure compared with gonioscopy by ophthalmologist. METHODS AND ANALYSIS: Study design: prospective, multicentre, cross-sectional diagnostic accuracy study. INCLUSION CRITERIA: adults referred from community optometry to hospital with suspected angle closure. PRIMARY OUTCOME: Sensitivity and specificity. SECONDARY OUTCOMES: Positive/negative likelihood ratios, concordance, cost-effectiveness, proportion of patients requiring subsequent clinical assessment by ophthalmologist. SAMPLE SIZE: 600 individuals who have been referred with suspected angle closure from primary care (community optometry). We will have a 95% probability of detecting the true sensitivity of either test to within ±3.5% based on a sensitivity of 90%. The study would also have a 95% probability of detecting the true specificity of either test to within ±5%, assuming a specificity of 75%. ETHICS AND DISSEMINATION: Ethical Review Board approval was obtained. REC reference: 22/LO/0885. Our findings will be disseminated to those involved in eye care services. We will have a knowledge exchange event at the end of the study, published via the Health Technology Assessment web page and in specialist journals. The results will be presented at professional conferences and directly to patients via patient group meetings and the Glaucoma UK charity. TRIAL REGISTRATION NUMBER: ISRCTN15115867