Internet of Things–triggered and power-efficient smart pedometer algorithm for intelligent wearable devices
01 Jan 2020-pp 1-23
TL;DR: This chapter proposes an intelligent wearable device with a pedometer algorithm that detects and records physical movement such as walking that uses a smart algorithm for IWDs for step detection that analyzes the instantaneous acceleration versus time waveform and can detect the specific movements involved in stepping.
Abstract: Today wearable devices can be used to monitor the health of patients, among other uses. This chapter proposes an intelligent wearable device (IWD) with a pedometer algorithm that detects and records physical movement such as walking. This device uses a smart algorithm for IWDs for step detection that analyzes the instantaneous acceleration versus time waveform and can detect the specific movements involved in stepping. The results of the algorithm are discussed with different cases and threshold values considered. This algorithm uses a mobile app using the Internet of Things to keep track of steps, and the Gaussian noise is removed from the accelerometer data. This power-efficient pedometer algorithm can be used for IWDs.
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TL;DR: In this article , the authors implemented a UAV network based on IoT (Internet of Things) and a cloud server for the smart city for tracking the air quality of the landfill sites in real-time and alerting the UAV for capturing the visuals from the camera for detecting the exact cause of the pollutant.
24 citations
TL;DR: An overview of the IoT framework consisting of IoT architecture, protocols, and technologies is presented in this article, where the role of IoT forensics in cybercrime investigation in various domains like smart homes, smart cities, automated vehicles, and healthcare is also discussed.
Abstract: Internet of Things (IoT) is the utmost assuring framework to facilitate human life with quality and comfort. IoT has contributed significantly to numerous application areas. The stormy expansion of smart devices and their credence for data transfer using wireless mechanics boost their susceptibility to cyberattacks. Consequently, the cybercrime rate is increasing day by day. Hence, the study of IoT security threats and possible corrective measures can benefit researchers in identifying appropriate solutions to deal with various challenges in cybercrime investigation. IoT forensics plays a vital role in cybercrime investigations. This review paper presents an overview of the IoT framework consisting of IoT architecture, protocols, and technologies. Various security issues at each layer and corrective measures are also discussed in detail. This paper also presents the role of IoT forensics in cybercrime investigation in various domains like smart homes, smart cities, automated vehicles, and healthcare. The role of advanced technologies like artificial intelligence, machine learning, cloud computing, edge computing, fog computing, and blockchain technology in cybercrime investigation is also discussed. Lastly, various open research challenges in IoT to assist cybercrime investigation are explained to provide a new direction for further research.
21 citations
01 Jan 2022
TL;DR: In this paper, the authors presented an overview of fog computing along with the architectural framework of Industry 4.0 in detail and discussed the various problems faced in the implementation of Fog computing in industrial IoT.
Abstract: Expeditious technical developments have remodeled the industrial sector. These developments vary from mechanization of industrial tasks to autonomous industrial processes in which no human intervention is needed for regular working. An advanced concept i.e. Industrial Internet of Things (IIoT) evolved with the appliance of Internet of Things (IoT) in industrial processes; gave a new dimension to the technological advancements in the industrial sector by facilitating industrial processes with the support of Internet. Impeding the interpretation of IIoT to the production process supported another sub-domain of IoT, recognized as Industry 4.0. The concept of Industry 4.0 is realized using sensor networks, automated business processes, robots, smart equipment and machines, actuators, and people. Consequently, a huge volume of disparate data is initialized for analysis and processing. In industry, most of the processes are real-time. To avoid communication delays and ensure data security, the majority of the processes are completed locally and only necessary data is transferred over the Internet for cloud storage. To fulfill this objective, there is always a high requirement of a middleware amidst industrial processes/tools and cloud. In this connection, Fog is the most workable solution for distinct industrial scenarios. In the manufacturing industry, it can facilitate local processing along with tolerable communication delay to robots and actuators. Data gathered from various industrial processes is usually disorganized which needs pre-processing for refinement using Fog locally then communicated to the cloud. So, fog computing plays a vital role in various Industry 4.0 applications by resolving various issues. But the deployment of Fog computing in Industry 4.0 also faces a lot many challenges of different kinds related to programmability, security, heterogeneity, and interoperability. In this book chapter, we present an overview of Fog computing along with the architectural framework of Industry 4.0. We discussed the various applications of Fog computing in industry 4.0 in detail. Different problems faced in the implementation of fog computing in Industry 4.0 will be discussed. We have also introduced various research challenges to be dealt with for the efficient deployment of fog in Industry 4.0.
14 citations
TL;DR: The possibility of realizing such systems for so-called Tech-Business-Analytics for different real-world applications of predictive business decisions has been addressed in this paper.
Abstract: This study examines the emerging fields of data analytics and decision prediction using data collected across different systems using Internet of Things technology. The Internet of Things (IoT) is a collection of interrelated computing devices, mechanical and digital machines, objects, animals, or people that are provided with unique identifiers and the ability to transmit data across a network without needing human-to-human or human-to-computer interaction. A specified aim of predicting the future, along with the explanation of the problem using another high-tech system and model should be used to process the enormous and continuous data produced. The possibility of realizing (design and development) such systems for so-called Tech-Business-Analytics for different real-world applications of predictive business decisions has been addressed in this paper.
2 citations
01 Jan 2022
TL;DR: In this article, a Hierarchical Peer Connected Fog Architecture (HPCFA) is proposed to lower latency time and computational overhead, where the fog nodes are organized in a hierarchy where the peer fog nodes present at the same level are also interconnected with each other.
Abstract: The ever-growing number of vehicles brings forth challenges in traffic management. This causes various traffic management issues in urban cities around the world. Some of the issues are: delay in emergency/alarming situations, non-deterministic waiting time of local transport, increased fuel consumption, etc. To help the people travelling by local transport in the cities by knowing the position of the bus, at a specific time, would ease them from indefinite wait or pass over of bus. In this chapter, our focus is to provide a trouble free, smart and innovative IoT-based traffic assistant that can solve real time transport related problems. A Hierarchical Peer Connected Fog Architecture (HPCFA) is proposed to lower latency time and computational overhead. In HPCFA, the fog nodes are organized in a hierarchy where the peer fog nodes present at the same level are also interconnected with each other. The data from IoT devices equipped on the roads will capture the position of the vehicle which is then transmitted to the nearest fog node. This fog node will further transmit the information through HPCFA to the user. Using HPCFA, the total energy consumption is also reduced to some extent. The proposed architecture is very flexible, as it works both with fog nodes or without fog nodes and directly with the cloud. Further, an android application is also developed for the proposed architecture. The simulations and results are also displayed.
1 citations
References
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01 Jan 2010
TL;DR: In this paper, a reference design using the 3-axis ADXL345 accelerometer in a full-featured pedometer that can recognize and count steps, as well as measure distance, speed, and to an extent, calories burned is described.
Abstract: This article, based on a study of the characteristics of each step a person takes, describes a reference design using the 3-axis ADXL345 accelerometer in a full-featured pedometer that can recognize and count steps, as well as measure distance, speed, and—to an extent—calories burned. The ADXL345’s proprietary (patent pending), on-chip, 32-level first-in, first-out (FIFO) buffer can store data and operate on it for pedometer applications to minimize host processor intervention, thus saving system power—a big concern for portable devices. Its 13-bit resolution (4 mg/LSB) allows pedometers to even measure low-speed walking (where each step represents about 55 mg of acceleration change) with reasonable accuracy. Understanding the Model From the characteristics that can be used to analyze running or walking, we choose acceleration as the relevant parameter. The three components of motion for an individual (and their related axes) are forward (roll), vertical ( yaw), and side (pitch), as shown in Figure 1. The ADXL345 senses acceleration along its three axes: x, y, and z. The pedometer will be in an unknown orientation,
120 citations
TL;DR: The current work is focused on improving one of the most crucial criteria that appear to exert an enormous impact on the WSNs performance, namely the area coverage, and proposes two nature-based algorithms, namely Improved Cuckoo Search and Chaotic Flower Pollination algorithm.
Abstract: The popularity of Wireless Sensor Networks (WSNs) is rapidly growing due to its wide-ranged applications such as industrial diagnostics, environment monitoring or surveillance. High-quality construction of WSNs is increasingly demanding due to the ubiquity of WSNs. The current work is focused on improving one of the most crucial criteria that appear to exert an enormous impact on the WSNs performance, namely the area coverage. The proposed model is involved with sensor nodes deployment which maximizes the area coverage. This problem is proved to be NP-hard. Although such algorithms to handle this problem with fairly acceptable solutions had been introduced, most of them still heavily suffer from several issues including the large computation time and solution instability. Hence, the existing work proposed ways to overcome such difficulties by proposing two nature-based algorithms, namely Improved Cuckoo Search (ICS) and Chaotic Flower Pollination algorithm (CFPA). By adopting the concept of calculating the adaptability and a well-designed local search in previous studies, those two algorithms are able to improve their performance. The experimental results on 15 instances established a huge enhancement in terms of computation time, solution quality and stability.
103 citations
TL;DR: Across all waking hours of 1 d, step counts differ between devices, and the SW, regardless of settings, was the most accurate method of counting steps.
Abstract: PurposeThe purpose of this study was to determine the accuracy of 14-step counting methods under free-living conditions.MethodsTwelve adults (mean ± SD age, 35 ± 13 yr) wore a chest harness that held a GoPro camera pointed down at the feet during all waking hours for 1 d. The GoPro continuou
103 citations
TL;DR: Pedometer use resulted in a greater increase in leisure walking without any impact on overall activity level, and on average, their blood pressure decreased over 12 months, although the clinical relevance is unknown.
Abstract: PURPOSE We compared the effectiveness of 2 physical activity prescriptions delivered in primary care—the standard time-based Green Prescription and a pedometer step-based Green Prescription—on physical activity, body mass index (BMI), blood pressure, and quality of life in low-active older adults. METHODS We undertook a randomized controlled trial involving 330 low-active older adults (aged ≥65 years) recruited through their primary care physicians' patient databases. Participants were randomized to either the pedometer step- based Green Prescription group (n = 165) or the standard Green Prescription group (n = 165). Both groups had a visit with the primary care practitioner and 3 telephone counseling sessions over 12 weeks aimed at increasing physical activity. Outcomes were the changes in physical activity (assessed with the Auckland Heart Study Physical Activity Questionnaire), blood pressure, BMI, quality of life (assessed with the 36-Item Short Form Health Survey), physical function status (assessed with the Short Physical Performance Battery), and falls over a 12-month period. RESULTS Of the patients invited to participate, 57% responded. At 12 months, leisure walking increased by 49.6 min/wk for the pedometer Green Prescription compared with 28.1 min/wk for the standard Green Prescription (P = .03). For both groups, there were signifi cant increases across all physical activity domains at 3 months (end of intervention) that were largely maintained after 12 months of follow-up. BMI did not change in either group. Signifi cant improvements in blood pressure were observed for both groups without any differences between them. CONCLUSIONS Pedometer use resulted in a greater increase in leisure walking without any impact on overall activity level. All participants increased physi- cal activity, and on average, their blood pressure decreased over 12 months, although the clinical relevance is unknown.
72 citations
26 Apr 2013
TL;DR: Results proved that gyroscope based step detection algorithm provide a high accuracy when performing different activities and at slow paced walking.
Abstract: Accuracy of step counting is one of the main problems that exist in current Pedometers, especially when walking slowly on flat lands and performing different activities, such as climbing up and down stairs and walking on inclined planes. Although accelerometer based pedometers provide a reasonable accuracy when walking at higher speeds, the accuracy of them are not sufficient at slow walking speeds and performing different activities. This paper proposes a novel algorithm to detect steps using single-point gyroscopic sensors embedded in mobile devices. Preliminary analysis of data collected in different environments with the involvement of male and female volunteers indicated that gyroscope alone provides sufficient information necessary for accurate step detection. Algorithm was developed based on the gyroscopic data in conjunction with zero crossing and threshold detection techniques. The results proved that gyroscope based step detection algorithm provide a high accuracy when performing different activities and at slow paced walking.
44 citations