Modelling the Integrated QoS for Wireless Sensor Networks with Heterogeneous Data Traffic

The future of Internet of Things (IoT) is envisaged to consist of a high amount of wireless resource-constrained devices connected to the Internet. Moreover, a lot of novel real-world services offered by IoT devices are realized by wireless sensor networks (WSNs). Integrating WSN to the Internet has therefore brought forward the requirements of an end-to-end quality of service (QoS) guarantee. In this paper, the QoS requirements for the WSN-Internet integration are investigated by first distinguishing the Internet QoS from the WSN QoS. Next, this study emphasizes on WSN applications that involve traffic with different levels of importance, thus the way realtime traffic and delay-tolerant traffic are handled to guarantee QoS in the network is studied. Additionally, an overview of the integration strategies is given, and the delay-tolerant network (DTN) gateway, being one of the desirable approaches for integrating WSNs to the Internet, is discussed. Next, the implementation of the service model is presented, by considering both traffic prioritization and service differentiation. Based on the simulation results in OPNET Modeler, it is observed that real-time traffic achieve low bound delay while delay-tolerant traffic experience a lower packet dropped, hence indicating that the needs of real-time and delay-tolerant traffic can be better met by treating both packet types differently. Furthermore, a vehicular network is used as an example case to describe the applicability of the framework in a real IoT application environment, followed by a discussion on the future work of this research

An Expert System for Unit Commitment and Power Demand Prediction Using Fuzzy Logic and Neural Networks

The paper discusses the implementation of a fuzzy logic and artificial neural networks approach to providing a structural framework for the representation, manipulation and utilisation of data and information concerning prediction of power demand and generation commitments. An algorithm has been implemented and trained to predict the power demand at each load point on an hourly basis. The neural network is then implemented to supply the brute force necessary to accommodate the large amount of sensory data to provide the initial evaluation of the generation units to be committed. Results of the fuzzy model show a reasonable correspondence with the actual power demand. A standard deviation error for an hourly based prediction is limited to 4.4. Further refinement of the fuzzy model may produce further improvements. Implementation of artificial neural networks for scheduling an hourly unit commitment based on load demands is also discussed The backpropagation technique based on the I/O mapping method has been chosen for structuring the neural network. Geographically related load points and generating units are clustered into groups. Grouping has significantly reduced the number of inputs and outputs to the neural network and, hence, reduced the system complexity. As a result, both training requirements and running real time interaction are significantly improved. The expert system would replace and utilise the requirement for skilled dispatchers in scheduling the generators. It is anticipated that this facility is more accurate, dynamic, adaptive and more efficient than a skilled dispatcher. The overall cost of power generation is expected to be less if the new facility is used. Initial results have reflected a satisfactory correlation between predicted and actual results, with a standard deviation error of 1.71% and 1.96% in the base load units of HTPS and ATPS respectively

Vehicular Networks Dynamic Grouping and Re-Orchestration Scenarios

The topological structure in vehicular communication networks presents challenges for sustaining network connectivity on the road. Highway dynamics, for example, encourage the need for an adaptive and flexible structure to handle the rapid events of vehicles joining and leaving the road. Such demand aligns with the advancement made in software-defined networks and related dynamic network re-orchestration. This paper discusses the development of a virtual model that represents the operation of an autonomous vehicular network. It also investigates the ability to re-orchestrate the topology through software definition while running the various operational phases. Network self-formation, network expansion, retraction via vehicular members joining and leaving, and network self-healing when a topological rupture occurs as a result of a key member leaving the network are the key grouping phases. The communication approach is analyzed based on the status of network members and their ability to assume the various network roles. The concept is tested using both a Contiki–Cooja network simulator and a MATLAB analytical modeling tool to reflect the operation and performance of the grouping approach under various road scenarios. The outcome of the analysis reflects the ability of the group to be formulated within a measured latency considering the various network parameters such as communication message rate. The approach offers tools for managing the dynamic connectivity of vehicular groups and may also be extended to assume the function of an on-road network digital twin during the lifetime of a given group

Power Demand Prediction Using Fuzzy Logic

The paper discusses the implementation of a fuzzy-logic approach to provide a structural framework for the representation, manipulation and utilization of data and information concerning the prediction of power commitments. A neural network would then be implemented to accommodate and manipulate the large amount of sensor data involved. A training facility could allow the system to replace the requirement for skilled dispatchers in scheduling the generators. An algorithm has been implemented and trained to predict the total power demand on an hourly basis. The parameters taken into consideration cover environmental and weather-related conditions. Prediction of the power demand at each geographical load point, and hence the country-wide demand, has been tested in Jordan. Results concerning the daily prediction have been obtained. It is found to be very promising, especially in that the prediction is evaluated in a fuzzy environment

Internet of Things Gateway Edge for Movement Monitoring in a Smart Healthcare System

Over the past two decades, there has been a notable and swift advancement in the field of healthcare with regards to the Internet of Things (IoT). This progress has brought forth a substantial prospect for healthcare services to enhance performance, transparency, and cost effectiveness. Internet of Things gateways, such as local computational facilities, mobile devices, or custom miniature computational embedded electronics like the Raspberry Pi (RPi), are crucial in facilitating the required processing and data compression tasks as well as serving as front-end event detectors. Numerous home-based healthcare monitoring systems are currently accessible; however, they have several limitations. This paper examines the role of the Raspberry Pi gateway in the healthcare system, specifically in the context of pre-operative prehabilitation programs (PoPPs). The IoT remote monitoring system employed a Microduino integrated with various supporting boards as a wearable device. Additionally, a Raspberry Pi was utilised as a base station or mobile gateway, while ThingSpeak served as the cloud platform. The monitoring system was developed with the purpose of assisting healthcare personnel in real time, remotely monitoring patients while engaging in one or more of the nine typical physical activities that are often prescribed to individuals participating in a prehabilitation program. Furthermore, an alert notification system was designed to notify the clinician and patient if the values were abnormal (i.e., the patient had not been active for many days). The integration of IOT and Raspberry Pi technology into a pre-operative prehabilitation program yielded a promising outcome with a success rate of 78%. Consequently, this intervention is expected to facilitate the resolution of challenges encountered by healthcare providers and patients, including extended waiting periods and constraints related to staffing and infrastructure

Activity Classification Feasibility Using Wearables: Considerations for Hip Fracture

Falls in the elderly are a common health issue that can involve severe injuries like hip fractures, requiring considerable medical attention, and subsequent care. Following surgery, physiotherapy is essential for strengthening muscles, mobilizing joints and fostering the return to physical activities. Ideally, physiotherapy programmes would benefit from active home-based monitoring of the elderly patients’ daily activities and exercises. This paper aims at providing a preliminary analysis addressing three key research questions. First, what are the key involved activities (at-hospital, home exercises, and activities of daily living) during the post-operative hip fracture rehabilitation process? Second, how can one monitor and identify a range of leg exercises accurately? Last, what is the most suitable sensor location that can categorize the majority of the physical activities thought to be important during the rehabilitation programme? During preliminary testing, it was noted that a standard deviation of the acceleration signal was suitable for classification of static activities like sitting, whereas classification of the ambulatory activities like walking, both the frequency content and related amplitude of the acceleration signal, plays a significant role. The research findings suggest that the ankle is an appropriate location for monitoring most of the leg movement physical activities

Human Movement Monitoring and Analysis for Prehabilitation Process Management

Cancer patients assigned for abdominal surgery are often given exercise programmes (prehabilitation) prior to surgery, which aim to improve fitness in order to reduce pre-operative risk. However, only a small proportion of patients are able to partake in supervised hospital-based prehabilitation because of inaccessibility and a lack of resources, which often makes it difficult for health professionals to accurately monitor and provide feedback on exercise and activity levels. The development of a simple tool to detect the type and intensity of physical activity undertaken outside the hospital setting would be beneficial to both patients and clinicians. This paper aims to describe the key exercises of a prehabilitation programme and to determine whether the types and intensity of various prehabilitation exercises could be accurately identified using Fourier analysis of 3D accelerometer sensor data. A wearable sensor with an inbuilt 3D accelerometer was placed on both the ankle and wrist of five volunteer participants during nine prehabilitation exercises which were performed at low to high intensity. Here, the 3D accelerometer data are analysed using fast Fourier analysis, where the dominant frequency and amplitude components are extracted for each activity performed at low, moderate, and high intensity. The findings indicate that the 3D accelerometer located at the ankle is suitable for detecting activities such as cycling and rowing at low, moderate, and high exercise intensities. However, there is some overlap in the frequency and acceleration amplitude components for overland and treadmill walking at a moderate intensity

Ambient Intelligence Context-Based Cross-Layer Design in Wireless Sensor Networks

By exchanging information directly between non-adjacent protocol layers, cross-layer (CL) interaction can significantly improve and optimize network performances such as energy efficiency and delay. This is particularly important for wireless sensor networks (WSNs) where sensor devices are energy-constrained and deployed for real-time monitoring applications. Existing CL schemes mainly exploit information exchange between physical, medium access control (MAC), and routing layers, with only a handful involving application layer. For the first time, we proposed a framework for CL optimization based on user context of ambient intelligence (AmI) application and an ontology-based context modeling and reasoning mechanism. We applied the proposed framework to jointly optimize MAC and network (NET) layer protocols for WSNs. Extensive evaluations show that the resulting optimization through context awareness and CL interaction for both MAC and NET layer protocols can yield substantial improvements in terms of throughput, packet delivery, delay, and energy performances