Implementation of a Fuel Estimation Algorithm Using Approximated Computing

The rising concerns about global warming have motivated the international community to take remedial actions to lower greenhouse gas emissions. The transportation sector is believed to be one of the largest air polluters. The quantity of greenhouse gas emissions is directly linked to the fuel consumption of vehicles. Eco-driving is an emergent driving style that aims at improving gas mileage. Real-time fuel estimation is a critical feature of eco-driving and eco-routing. There are numerous approaches to fuel estimation. The first approach uses instantaneous values of speed and acceleration. This can be accomplished using either GPS data or direct reading through the OBDII interface. The second approach uses the average value of the speed and acceleration that can be measured using historical data or through web mapping. The former cannot be used for route planning. The latter can be used for eco-routing. This paper elaborates on a highly pipelined VLSI architecture for the fuel estimation algorithm. Several high-level transformation techniques have been exercised to reduce the complexity of the algorithm. Three competing architectures have been implemented on FPGA and compared. The first one uses a binary search algorithm, the second architecture employs a direct address table, and the last one uses approximation techniques. The complexity of the algorithm is further reduced by combining both approximated computing and precalculation. This approach helped reduce the floating-point operations by 30% compared with the state-of-the-art implementation

Blockchain-Based Access Control Techniques for IoT Applications

The Internet of Things is gaining more importance in the present era of Internet technology. It is considered as one of the most important technologies of everyday life. Moreover, IoT systems are ceaselessly growing with more and more devices. They are scalable, dynamic, and distributed, hence the origin of the crucial security requirements in IoT. One of the most challenging issues that the IoT community must handle recently is how to ensure an access control approach that manages the security requirements of such a system. Traditional access control technologies are not suitable for a large-scale and distributed network structure. Most of them are based on a centralized approach, where the use of a trusted third party (TTP) is obligatory. Furthermore, the emergence of blockchain technology has allowed researchers to come up with a solution for these security issues. This technology is highly used to record access control data. Additionally, it has great potential for managing access control requests. This paper proposed a blockchain-based access control taxonomy according to the access control nature: partially decentralized and fully decentralized. Furthermore, it presents an overview of blockchain-based access control solutions proposed in different IoT applications. Finally, the article analyzes the proposed works according to certain criteria that the authors deem important

A Low Latency Secure Communication Architecture for Microgrid Control

The availability of secure, efficient, and reliable communication systems is critical for the successful deployment and operations of new power systems such as microgrids. These systems provide a platform for implementing intelligent and autonomous algorithms that improve the power control process. However, building a secure communication system for microgrid purposes that is also efficient and reliable remains a challenge. Conventional security mechanisms introduce extra processing steps that affect performance by increasing the latency of microgrid communication beyond acceptable limits. They also do not scale well and can impact the reliability of power operations as the size of a microgrid grows. This paper proposes a low latency secure communication architecture for control operations in an islanded IoT-based microgrid that solves these problems. The architecture provides a secure platform that optimises the standard CoAP/DTLS implementation to reduce communication latency. It also introduces a traffic scheduler component that uses a fixed priority preemptive algorithm to ensure reliability as the microgrid scales up. The architecture is implemented on a lab-scale IoT-based microgrid prototype to test for performance and security. Results show that the proposed architecture can mitigate the main security threats and provide security services necessary for power control operations with minimal latency performance. Compared to other implementations using existing secure IoT protocols, our secure architecture was the only one to satisfy and maintain the recommended latency requirements for power control operations, i.e., 100 ms under all conditions.</p

Edge Devices for Internet of Medical Things: Technologies, Techniques, and Implementation

The health sector is currently experiencing a significant paradigm shift. The growing number of elderly people in several countries along with the need to reduce the healthcare cost result in a big need for intelligent devices that can monitor and diagnose the well-being of individuals in their daily life and provide necessary alarms. In this context, wearable computing technologies are gaining importance as edge devices for the Internet of Medical Things. Their enabling technologies are mainly related to biological sensors, computation in low-power processors, and communication technologies. Recently, energy harvesting techniques and circuits have been proposed to extend the operating time of wearable devices and to improve usability aspects. This survey paper aims at providing an overview of technologies, techniques, and algorithms for wearable devices in the context of the Internet of Medical Things. It also surveys the various transformation techniques used to implement those algorithms using fog computing and IoT devices

Real-Time Classification of Pain Level Using Zygomaticus and Corrugator EMG Features

The real-time recognition of pain level is required to perform an accurate pain assessment of patients in the intensive care unit, infants, and other subjects who may not be able to communicate verbally or even express the sensation of pain. Facial expression is a key pain-related behavior that may unlock the answer to an objective pain measurement tool. In this work, a machine learning-based pain level classification system using data collected from facial electromyograms (EMG) is presented. The dataset was acquired from part of the BioVid Heat Pain database to evaluate facial expression from an EMG corrugator and EMG zygomaticus and an EMG signal processing and data analysis flow is adapted for continuous pain estimation. The extracted pain-associated facial electromyography (fEMG) features classification is performed by K-nearest neighbor (KNN) by choosing the value of k which depends on the nonlinear models. The presentation of the accuracy estimation is performed, and considerable growth in classification accuracy is noticed when the subject matter from the features is omitted from the analysis. The ML algorithm for the classification of the amount of pain experienced by patients could deliver valuable evidence for health care providers and aid treatment assessment. The proposed classification algorithm has achieved a 99.4% accuracy for classifying the pain tolerance level from the baseline (P0 versus P4) without the influence of a subject bias. Moreover, the result on the classification accuracy clearly shows the relevance of the proposed approach.</p

Smart Parking System with Dynamic Pricing, Edge-Cloud Computing and LoRa

A rapidly growing number of vehicles in recent years cause long traffic jams and difficulty in the management of traffic in cities. One of the most significant reasons for increased traffic jams on the road is random parking in unauthorized and non-permitted places. In addition, managing of available parking places cannot achieve the expected reduction in traffic congestion related problems due to mismanagement, lack of real-time parking guidance to the drivers, and general ignorance. As the number of roads, highways and related resources has not increased significantly, a rising need for a smart, dynamic and effective parking solution is observed. Accordingly, with the use of multiple sensors, appropriate communication network and advanced processing capabilities of edge and cloud computing, a smart parking system can help manage parking effectively and make it easier for the vehicle owners. In this paper, we propose a multi-layer architecture for smart parking system consisting of multi-parametric parking slot sensor nodes, latest long-range low-power wireless communication technology and Edge-Cloud computation. The proposed system enables dynamic management of parking for large areas while providing useful information to the drivers about available parking locations and related services through near real-time monitoring of vehicles. Furthermore, we propose a dynamic pricing algorithm to yield maximum possible revenue for the parking authority and optimum parking slot availability for the drivers.</p

Design of a Partially Grid-Connected Photovoltaic Microgrid Using IoT Technology

This study describes the design and control algorithms of an IoT-connected photovoltaic microgrid operating in a partially grid-connected mode. The proposed architecture and control design aim to connect or disconnect non-critical loads between the microgrid and utility grid. Different components of the microgrid, such as photovoltaic arrays, energy storage elements, inverters, solid-state transfer switches, smart-meters, and communication networks were modeled and simulated. The communication between smart meters and the microgrid controller is designed using LoRa communication protocol for the control and monitoring of loads in residential buildings. An IoT-enabled smart meter has been designed using ZigBee communication protocol to evaluate data transmission requirements in the microgrid. The loads were managed by a proposed under-voltage load-shedding algorithm that selects suitable loads to be disconnected from the microgrid and transferred to the utility grid. The simulation results showed that the duty cycle of LoRa and its bit rate can handle the communication requirements in the proposed PV microgrid architecture

Requirements for Energy-Harvesting-Driven Edge Devices Using Task-Offloading Approaches

Energy limitations remain a key concern in the development of Internet of Medical Things (IoMT) devices since most of them have limited energy sources, mainly from batteries. Therefore, providing a sustainable and autonomous power supply is essential as it allows continuous energy sensing, flexible positioning, less human intervention, and easy maintenance. In the last few years, extensive investigations have been conducted to develop energy-autonomous systems for the IoMT by implementing energy-harvesting (EH) technologies as a feasible and economically practical alternative to batteries. To this end, various EH-solutions have been developed for wearables to enhance power extraction efficiency, such as integrating resonant energy extraction circuits such as SSHI, S-SSHI, and P-SSHI connected to common energy-storage units to maintain a stable output for charge loads. These circuits enable an increase in the harvested power by 174% compared to the SEH circuit. Although IoMT devices are becoming increasingly powerful and more affordable, some tasks, such as machine-learning algorithms, still require intensive computational resources, leading to higher energy consumption. Offloading computing-intensive tasks from resource-limited user devices to resource-rich fog or cloud layers can effectively address these issues and manage energy consumption. Reinforcement learning, in particular, employs the Q-algorithm, which is an efficient technique for hardware implementation, as well as offloading tasks from wearables to edge devices. For example, the lowest reported power consumption using FPGA technology is 37 mW. Furthermore, the communication cost from wearables to fog devices should not offset the energy savings gained from task migration. This paper provides a comprehensive review of joint energy-harvesting technologies and computation-offloading strategies for the IoMT. Moreover, power supply strategies for wearables, energy-storage techniques, and hardware implementation of the task migration were provided

Traffic Safety Factors in the Qassim Region of Saudi Arabia

This study investigates the factors that affect traffic safety in the Qassim region. A questionnaire was developed on the basis of the Handbook of road safety and consisted of 85 items measuring seven dimensions: area-wide traffic calming (22 items), vehicle design and protective devices (26 items), road design (24 items), road maintenance (three items), traffic education (four items), police campaigns and sanctions (three items), and post-accident care (three items). A sample encompassing 1,500 Qassim University students, and visitors was randomly selected to collect data. A total of 1,500 questionnaires were distributed to students, and visitors of which 1,053 were retrieved. The elimination of data outliers resulted in a sample of 909 subjects. The results pointed out a moderate level of traffic safety in the Qassim region. Furthermore, 10 leading causes of road traffic accidents emerged, namely, excess speed, irregular bypasses, irregular rotations, lack of prioritization of other drivers, irregular stops, lack of road readiness, driver carelessness, use of a mobile phone while driving, noncompliance with traffic signals, and, finally, nonuse of seat belts. On the basis of these results, conclusions and policy implications were provided