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Machine-to-Machine Communication in Industry 4.0: A Digital Transformation
Abstract
In the era of Industry 4.0, marked by the integration of digital technologies into manufacturing processes, Machine-to-Machine (M2M) communication plays a pivotal role in driving the digital transformation of industries. This report explores the significance of M2M communication in the context of Industry 4.0, examining its impact on efficiency, productivity, and innovation. It delves into the key technologies underpinning M2M communication, the challenges and opportunities it presents, and the implications for various industries. Furthermore, the report explores real-world applications and case studies to illustrate the practical implementation of M2M communication. Through a comprehensive analysis, this report aims to provide insights into the current state and future trajectory of M2M communication in the landscape of Industry 4.0.
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In recent times, smart devices, smart homes, smart manufacturing, and even smart cities, all of which are connected to the Internet of Things (IoT) have become quite common. IoT technology heavily relies on Machine-to-Machine (M2M) communication in order to make all smart technologies behave in a smart way. Any type of connection between two devices that do not require human intervention is referred to as "machine-to-machine" communication technology. The IoT is a system of devices with unique identifiers that can transmit information over a network. There is no interaction between humans or machines in the IoT. The IoT is now widely used in a variety of sectors, including banking (connected branches), and industries where secrecy and privacy of data are the important components. The IoT allows multiple machines to design a connected data network, whereas M2M makes it possible for devices to communicate with one another. Therefore, this paper gives a brief review work on the security challenges faced in the connection between IoT and M2M communication and the significance of both technologies.
In Industry 5.0 paradigm, AI-based systems are the essential component for the Internet of Things. In most applications, Industry 5.0 showed a significant connection between intelligent systems and humans through accurate manufacturing automation with critical thinking skills. Also, Industry 5.0 brings several competent tools that help organizations work inexpensively and instantly change without any principal investment. In recent years, smart devices, wireless communication, and sensor nodes have made considerable advances, making the Internet of Things (IoT) ecosystems. The Internet of Things (IoT) is a network of interconnected, Internet-connected devices that can capture and transmit data without the need for human interaction over a wireless network. With the introduction of IoT, human beings take some relaxed and unburden feelings. IoT devices enable users to obtain information even in rural surroundings and prepare reports without restrictions. In addition, they accurately guide human beings with intelligent judgments via communication, as mentioned earlier, technologies. Numerous connected devices collect a considerable quantity of raw sensed data, where it needs pre-processing. However, only it turns into something valuable because of IoT devices’ adequate resources entailed to edge computing. Artificial intelligence-based algorithms are the necessary means for information inference in edge computing. Besides, the sensed data accumulated from IoT applications are generally unstructured and need further analysis, where AI-based models help extract relevant information.
With the rapid development of the Internet of Everything (IoE), the number of smart devices connected to the Internet is increasing, resulting in large-scale data, which has caused problems such as bandwidth load, slow response speed, poor security, and poor privacy in traditional cloud computing models. Traditional cloud computing is no longer sufficient to support the diverse needs of today’s intelligent society for data processing, so edge computing technologies have emerged. It is a new computing paradigm for performing calculations at the edge of the network. Unlike cloud computing, it emphasizes closer to the user and closer to the source of the data. At the edge of the network, it is lightweight for local, small-scale data storage and processing. This article mainly reviews the related research and results of edge computing. First, it summarizes the concept of edge computing and compares it with cloud computing. Then summarize the architecture of edge computing, keyword technology, security and privacy protection, and finally summarize the applications of edge computing.
The Internet of Things (IoT)-centric concepts like augmented reality, high-resolution video streaming, self-driven cars, smart environment, e-health care, etc have a ubiquitous presence now. These applications require higher data-rates, large bandwidth, increased capacity, low latency and high throughput. In light of these emerging concepts, IoT has revolutionized the world by providing seamless connectivity between heterogeneous networks (HetNets). The eventual aim of IoT is to introduce the plug and play technology providing the end-user, ease of operation, remotely access control and configurability. This paper presents the IoT technology from a bird’s eye view covering its statistical/architectural trends, use cases, challenges and future prospects. The paper also presents a detailed and extensive overview of the emerging 5G-IoT scenario. Fifth Generation (5G) cellular networks provide key enabling technologies for ubiquitous deployment of the IoT technology. These include carrier aggregation, multiple-input multiple-output (MIMO), massive-MIMO (M-MIMO), coordinated multipoint processing (CoMP), device-to-device (D2D) communications, centralized radio access network (CRAN), software-defined wireless sensor networking (SD-WSN), network function virtualization (NFV) and cognitive radios (CRs). This paper presents an exhaustive review for these key enabling technologies and also discusses the new emerging use cases of 5G-IoT driven by the advances in artificial intelligence, machine and deep learning, ongoing 5G initiatives, quality of service (QoS) requirements in 5G and its standardization issues. Finally, the paper discusses challenges in the implementation of 5G-IoT due to high data-rates requiring both cloud-based platforms and IoT devices based edge computing.
Abstract Internet of Things (IoT) is a new paradigm that has changed the traditional way of living into a high tech life style. Smart city, smart homes, pollution control, energy saving, smart transportation, smart industries are such transformations due to IoT. A lot of crucial research studies and investigations have been done in order to enhance the technology through IoT. However, there are still a lot of challenges and issues that need to be addressed to achieve the full potential of IoT. These challenges and issues must be considered from various aspects of IoT such as applications, challenges, enabling technologies, social and environmental impacts etc. The main goal of this review article is to provide a detailed discussion from both technological and social perspective. The article discusses different challenges and key issues of IoT, architecture and important application domains. Also, the article bring into light the existing literature and illustrated their contribution in different aspects of IoT. Moreover, the importance of big data and its analysis with respect to IoT has been discussed. This article would help the readers and researcher to understand the IoT and its applicability to the real world.
The majority of marathon deaths are caused by sudden cardiac arrest (SCA), which occur in approximately 1 in 57,000 runners. Such deaths are more common among older males and usually occur in the last 4 miles of the racecourse. Although prompt resuscitation, including early use of an automated external defibrillator (AED), improves survival, the deployment of enough trained medical staff and AEDs is difficult due to increased cost. Moreover, most victims of exercise-related SCA have no premonitory symptoms. Therefore, we tried to use a novel approach to prevent sudden cardiac deaths (SCD) related to SCA using real-time electrocardiographic tele-monitoring system, as an initial trial to assess operative possibility in a full marathon. As a result, 3 out of 5 runners had reasonable measurement results and sufficient tele-monitoring without complications related to this trial was possible. However, many investigations and improvements, such as improving cost-effectiveness, reducing noise, and automating the monitoring system, are needed for practical application of these devices for athletes. As a next step, we would establish a novel strategy to reduce SCDs in athletes using next-generation devices, which include an alarm system associated with early application of AED. <Learning objectives: Sudden cardiac arrest (SCA) is a major problem in sports cardiology. Here we investigated a novel approach using a real-time tele-monitoring system of electrocardiogram (ECG) to prevent sudden cardiac deaths by making use of an advanced alarm system which responds to SCA risk. Three out of five cases we monitored showed reasonable measurement of ECG with centralized observation in full marathon. This is the first report of this method, which may lead to the effective application of automated external defibrillator in athletes.>.
With the burgeoning of the Internet of Everything, the amount of data generated by edge devices increases dramatically, resulting in higher network bandwidth requirements. In the meanwhile, the emergence of novel applications calls for the lower latency of the network. It is an unprecedented challenge to guarantee the quality of service while dealing with a massive amount of data for cloud computing, which has pushed the horizon of edge computing. Edge computing calls for processing the data at the edge of the network and develops rapidly from 2014 as it has the potential to reduce latency and bandwidth charges, address the limitation of computing capability of cloud data center, increase availability as well as protect data privacy and security. This paper mainly discusses three questions about edge computing: where does it come from, what is the current status and where is it going? This paper first sorts out the development process of edge computing and divides it into three periods: technology preparation period, rapid growth period and steady development period. This paper then summarizes the seven essential technologies that drive the rapid development of edge computing. After that, six typical applications that have been widely used in edge computing are illustrated. Finally, this paper proposes six open problems that need to be solved urgently in future development.
This paper presents an automatic wearable electrocardiogram (ECG) classification and monitoring system with stacked denoising autoencoder (SDAE). We use a wearable device with wireless sensors to obtain the ECG data, and send these ECG data to a computer with Bluetooth 4.2. Then these ECG data are classified by the automatic cardiac arrhythmia classification system. Firstly, the ECG feature representation is learned by the SDAE with sparsity constraint. Then the softmax regression is used to classify the ECG beats. In the fine tuning phase, active learning is added to improve the performance. In the active learning phase, we use the method that relies on the deep neural networks (DNN) posterior probabilities to associate confidence measures to select the most informative samples. Breaking-Ties (BT) and modified Breaking-Ties (MBT) methods are used to select the most informative samples. We validate the proposed method on the well-known MIT-BIH arrhythmia database and ECG data obtained from the wearable device. We follow the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) for class labeling and results presentation. The results show that the classification performance of our proposed approach outperforms the most of state-of-the-art methods.
As a promising branch of Internet of Things, Internet of Vehicles (IoV) is envisioned to serve as an essential data sensing and processing platform for intelligent transportation systems. In this paper, we aim to address location privacy issues in IoV. In traditional pseudonym systems, the pseudonym management is carried out by a centralized way resulting in big latency and high cost. Therefore, we present a new paradigm named Fog computing supported IoV (F-IoV) to exploit resources at the network edge for effective pseudonym management. By utilizing abundant edge resources, a privacy-preserved pseudonym (P#x00B3;) scheme is proposed in F-IoV. The pseudonym management in this scheme is shifted to specialized fogs at the network edge named pseudonym fogs, which are composed of roadside infrastructures and deployed in close proximity of vehicles. P#x00B3; scheme has following advantages: 1) context-aware pseudonym changing; 2) timely pseudonym distribution; and 3) reduced pseudonym management overhead. Moreover, a hierarchical architecture for P#x00B3; scheme is introduced in F-IoV. Enabled by the architecture, a context-aware pseudonym changing game and secure pseudonym management communication protocols are proposed. The security analysis shows that P#x00B3; scheme provides secure communication and privacy preservation for vehicles. Numerical results indicate that P#x00B3; scheme effectively enhances location privacy and reduces communication overhead for the vehicles.
Social media has revolutionized our societies. It has made fundamental impact on the way we work and live. More importantly, social media is gradually becoming a key pulse of smart societies by sensing the information about the people and their spatio-temporal experiences around the living spaces. Big data and computational intelligence technologies are helping us to manage and analyze large amounts of data generated by the social media, such as twitter, and make informed decisions about us and the living spaces. This paper reports our preliminary work on the use of social media for the detection of spatio-temporal events related to logistics and planning. Specifically, we use big data and AI platforms including Hadoop, Spark, and Tableau, to study twitter data about London. Moreover, we use the Google Maps Geocoding API to locate the tweeters and make additional analysis. We find and locate congestion around the London city. We also discover that, during a certain period, top third tweeted words were about job and hiring, leading us to locate the source of the tweets which happened to be originating from around the Canary Wharf area, UK's major financial center. The results presented in the paper have been obtained using 500,000 tweets.
Unquestionably, communicating entities (object, or things) in the Internet of Things (IoT) context are playing an active role in human activities, systems and processes. The high connectivity of intelligent objects and their severe constraints lead to many security challenges, which are not included in the classical formulation of security problems and solutions. The Security Shield for Internet of Things has been identified by DARPA (Defense Advanced Research Projects Agency) as one of the four projects with a potential impact broader than the Internet itself. To help interested researchers contribute to this research area, an overview of the IoT security roadmap overview is presented in this paper based on a novel cognitive and systemic approach. The role of each component of the approach is explained and interactions with the other main components, and their impact on the overall. A case study is presented to highlight the components and interactions of the systemic and cognitive approach. Then, security questions about privacy, trust, identification, and access control are discussed. According to the novel taxonomy of the IoT framework, different research challenges are highlighted, important solutions and research activities are revealed, and interesting research directions are proposed. In addition, current standardization activities are surveyed and discussed to the ensure the security of IoT components and applications.
The inherent characteristics of Internet of Things (IoT) devices, such as limited storage and computational power, require a new platform to efficiently process data. The concept of fog computing has been introduced as a technology to bridge the gap between remote data centers and IoT devices. Fog computing enables a wide range of benefits, including enhanced security, decreased bandwidth, and reduced latency. These benefits make the fog an appropriate paradigm for many IoT services in various applications such as connected vehicles and smart grids. Nevertheless, fog devices (located at the edge of the Internet) obviously face many security and privacy threats, much the same as those faced by traditional data centers. In this article, the authors discuss the security and privacy issues in IoT environments and propose a mechanism that employs fog to improve the distribution of certificate revocation information among IoT devices for security enhancement. They also present potential research directions aimed at using fog computing to enhance the security and privacy issues in IoT environments.
Nowadays Internet of Things (IoT) gained a great attention form researchers, since it becomes
an important technology that promises a smart human being life, by allowing a communications
between objects, machines and every things together with peoples. IoT represents a system which
consists a things in the real world, and sensors attached to or combined to these things, connected to
the Internet via wired and wireless network structure. The IoT sensors can use various types of
connections such as RFID, Wi-Fi, Bluetooth, and ZigBee, in addition to allowing wide area
connectivity using many technologies such as GSM, GPRS, 3G, and LTE. IoT-enabled things will
share information about the condition of things and the surrounding environment with people,
software systems and other machines. by the technology of the IoT , the world will becomes smart in
every aspects , since the IoT will provides a means of smart cities , smart healthcare , smart homes
and building , in addition to many important applications such as smart energy , grid , transportation ,
waste management and monitoring . In this paper we review a concept of many IoT applications and
future possibilities for new related technologies in addition to the challenges that facing the
implementation of the IoT.
Muscle strength is mostly measured by wearable devices. However, wearing such devices is a tedious, unpleasant, and sometimes impossible task for stroke patients. In this paper, a mathematical model is proposed to estimate the strength of the upper limb muscles of a stroke patient by using Microsoft Kinect sensor. A prototype exergame is designed and developed to mimic real post-stroke rehabilitation exercises. Least-square regression matrix is used to find the relation between the kinematics of the upper limb and the strength of the corresponding muscles. Kinect sensor is used along with a force sensing resistors (FSR) glove and two straps to collect both, real-time upper limb joints data and the strength of muscles of the subjects while they are performing the exercises. The prototype of this system is tested on five stroke patients and eight healthy subjects. Results show that there is no statistically significant difference between the measured and the estimated values of the upper-limb muscles of the stroke patients. Thus, the proposed method is useful in estimating the strength of the muscles of stroke patient without the need to wear any devices.
In order to overcome the scalability problem of the traditional Internet of Things architecture (i.e., data streams generated from distributed IoT devices are transmitted to the remote cloud via the Internet for further analysis), this article proposes a novel approach to mobile edge computing for the IoT architecture, edgeIoT, to handle the data streams at the mobile edge. Specifically , each BS is connected to a fog node, which provides computing resources locally. On the top of the fog nodes, the SDN-based cellular core is designed to facilitate packet forwarding among fog nodes. Meanwhile, we propose a hierarchical fog computing architecture in each fog node to provide flexible IoT services while maintaining user privacy: each user's IoT devices are associated with a proxy VM (located in a fog node), which collects, classifies, and analyzes the devices' raw data streams, converts them into metadata, and transmits the metadata to the corresponding application VMs (which are owned by IoT service providers). Each application VM receives the corresponding metadata from different proxy VMs and provides its service to users. In addition, a novel proxy VM migration scheme is proposed to minimize the traffic in the SDN-based core.
This paper presents a new eHealth platform incorporating humanoid robots to support an emerging multidimensional care approach for the treatment of diabetes. The architecture of the platform extends the Internet of Things (IoT) to a web-centric paradigm through utilizing existing web standards to access and control objects of the physical layer. This incorporates capillary networks, each of which encompasses a set of medical sensors linked wirelessly to a humanoid robot linked (via the Internet) to a web-centric disease management hub (DMH). This provides a set of services for both patients and their caregivers that support the full continuum of the multidimensional care approach of diabetes. The platform’s software architecture pattern enables the development of various applications without knowing low-level details of the platform. This is achieved through unifying the access interface and mechanism of handling service requests through a layered approach based on object virtualization and automatic service delivery. A fully functional prototype is developed and its end-toend functionality and acceptability are tested successfully through a clinician-led pilot study, providing evidence that both patients and caregivers are receptive to the introduction of the proposed platform.
Objective:
To assess the feasibility of conducting a randomised controlled trial of a home-based virtual reality system for rehabilitation of the arm following stroke.
Design:
Two group feasibility randomised controlled trial of intervention versus usual care.
Setting:
Patients' homes.
Participants:
Patients aged 18 or over, with residual arm dysfunction following stroke and no longer receiving any other intensive rehabilitation.
Interventions:
Eight weeks' use of a low cost home-based virtual reality system employing infra-red capture to translate the position of the hand into game play or usual care.
Main measures:
The primary objective was to collect information on the feasibility of a trial, including recruitment, collection of outcome measures and staff support required. Patients were assessed at three time points using the Wolf Motor Function Test, Nine-Hole Peg Test, Motor Activity Log and Nottingham Extended Activities of Daily Living.
Results:
Over 15 months only 47 people were referred to the team. Twenty seven were randomised and 18 (67%) of those completed final outcome measures. Sample size calculation based on data from the Wolf Motor Function Test indicated a requirement for 38 per group. There was a significantly greater change from baseline in the intervention group on midpoint Wolf Grip strength and two subscales of the final Motor Activity Log. Training in the use of the equipment took a median of 230 minutes per patient.
Conclusions:
To achieve the required sample size, a definitive home-based trial would require additional strategies to boost recruitment rates and adequate resources for patient support.
In the near future, i.e., beyond 4G, some of the prime objectives or demands that need to be addressed are increased capacity, improved data rate, decreased latency, and better quality of service. To meet these demands, drastic improvements need to be made in cellular network architecture. This paper presents the results of a detailed survey on the fifth generation (5G) cellular network architecture and some of the key emerging technologies that are helpful in improving the architecture and meeting the demands of users. In this detailed survey, the prime focus is on the 5G cellular network architecture, massive multiple input multiple output technology, and device-to-device communication (D2D). Along with this, some of the emerging technologies that are addressed in this paper include interference management, spectrum sharing with cognitive radio, ultra-dense networks, multi-radio access technology association, full duplex radios, millimeter wave solutions for 5G cellular networks, and cloud technologies for 5G radio access networks and software defined networks. In this paper, a general probable 5G cellular network architecture is proposed, which shows that D2D, small cell access points, network cloud, and the Internet of Things can be a part of 5G cellular network architecture. A detailed survey is included regarding current research projects being conducted in different countries by research groups and institutions that are working on 5G technologies.
Every moment counts in action recognition. A comprehensive understanding of
human activity in video requires labeling every frame according to the actions
occurring, placing multiple labels densely over a video sequence. To study this
problem we extend the existing THUMOS dataset and introduce MultiTHUMOS, a new
dataset of dense labels over unconstrained internet videos. Modeling multiple,
dense labels benefits from temporal relations within and across classes. We
define a novel variant of long short-term memory (LSTM) deep networks for
modeling these temporal relations via multiple input and output connections. We
show that this model improves action labeling accuracy and further enables
deeper understanding tasks ranging from structured retrieval to action
prediction.
The iBracelet and the Wireless Identification and Sensing Platform (WISP), RFID-based techniques, promise the ability to infer human activity directly from sensor readings. This family of techniques can help solve the longstanding problem of how to infer human activity. The WISP approach appears promising for a variety of sensing problems, in addition to human-activity inferencing, in which battery free operation or compatibility with RFID infrastructure is key. The iBracelet appears most promising for industrial or enterprise-context human activity inference applications in which a wearable reader is not a burdensome requirement.
For many industry contexts, the implementation of Artificial Intelligence (AI) has contributed to what has become known as the fourth industrial revolution or “Industry 4.0” and creates an opportunity to deliver significant benefit to both businesses and their stakeholders. Robot arms are one of the most common devices utilised in manufacturing and industrial processes, used for a wide variety of automation tasks on, for example, a factory floor but the effective use of these devices requires AI to be appropriately trained. One approach to support AI training of these devices is the use of a “Digital Twin”. There are, however, a number of challenges that exist within this domain, in particular, success depends upon the ability to collect data of what are considered as observations within the environment and the application of one or many trained AI policies to the task that is to be completed. This project presents a case-study of creating and training a Robot Arm Digital Twin as an approach for AI training in a virtual space and applying this simulation learning within physical space. A virtual space, created using Unity (a contemporary Game Engine), incorporating a virtual robot arm was linked to a physical space, being a 3D printed replica of the virtual space and robot arm. These linked environments were applied to solve a task and provide training for an AI model. The contribution of this work is to provide guidance on training protocols for a digital twin together with details of the necessary architecture to support effective simulation in a virtual space through the use of Tensorflow and hyperparameter tuning. It provides an approach to addressing the mapping of learning in the virtual domain to the physical robot twin.
The Industrial Internet of Things is expected to attract significant investments for Industry 4.0. In this new environment, blockchain have immediate potential in industrial applications, providing immutable, traceable and auditable communication. However, works on the blockchain literature targets environments that do not meet the reality and requirements of a factory, in addition to needing cloud infrastructure in which communication delay increases. In this context, this article presents a blockchain-based machine-to-machine communication architecture, which it is deployed directly to industrial devices. Evaluation results reveal the need for the evolution of the current stage of blockchain technologies to meet the strict requirements time demanded by machine-to-machine communication in the industrial mining environment.
Cybersecurity vulnerabilities of cardiac implantable electronic devices have been an area of increasing concern in the past 4 years. Physicians should be informed of these issues as they evolve so they can counsel their patients with the most accurate and up-to-date information.
With the developments of communication technologies and smart manufacturing, industrial Internet of Things (IIoT) has emerged. Software-defined networking (SDN), a promising paradigm shift, has provided a viable way to manage IIoT dynamically, called software-defined industrial Internet of Things (SDIIoT). In SDIIoT, lots of data and flows are generated by industrial devices, where a physically distributed but logically centralized control plane is necessary. However, one of the most intractable problems is how to reach consensus among multiple controllers under complex industrial environments. In this paper, we propose a blockchain-based consensus protocol in SDIIoT, along with detailed consensus steps and theoretical analysis, where blockchain works as a trusted third party to collect and synchronize network-wide views between different SDN controllers. Specially, it is a permissioned blockchain. In order to improve the throughput of this blockchain-based SDIIoT, we jointly consider the trust features of blockchain nodes and controllers, as well as the computational capability of the blockchain system. Accordingly, we formulate view change, access selection, and computational resources allocation as a joint optimization problem. We describe this problem as a Markov decision process by defining state space, action space, and reward function. Due to the fact that it is difficult to solve this joint problem by traditional methods, we propose a novel dueling deep Q-learning approach. Simulation results are presented to show the effectiveness of our proposed scheme.
With the trend of Internet of Everything, the number of end devices (e.g. smartphones, smart glasses) has increased rapidly which makes the data produced by these devices have grown at rates far more than the growth rate of network bandwidth. At the same time, the emergence of novel applications demands lower latency of the network, such as augmented reality and manless driving. Edge computing integrates any computing, storage and network resources at the edge of the network into a unified platform that provides services for users. This new computing model gets around the bottleneck that is caused by network bandwidth and latency, has received widespread attention in both industrial and academic. In this survey, we first introduce the concepts of edge computing and provide the definition of it. To help the readers to better understand the characteristics of edge computing, we compare it with cloud computing. We then analyze the three representative instances of edge computing platform and make a systematic comparison of them. After that, we enumerate some typical applications based on the edge computing to describe the advantages of edge computing in mobile or Internet of Things applications. Finally, the paper lays out several grand challenges of edge computing.
This paper evaluates blockchain's roles in strengthening cybersecurity and protecting privacy. Since most of the data is currently stored in cloud data centers, it also compares how blockchain performs vis-vis the cloud in various aspects of security and privacy. Key underlying mechanisms related to the blockchain's impacts on the Internet of Things (IoT) security are also covered. From the security and privacy considerations, it highlights how blockchain-based solutions could possibly be, in many aspects, superior to the current IoT ecosystem, which mainly relies on centralized cloud servers through service providers. Using practical applications and real-world examples, the paper argues that blockchain's decentralized feature is likely to result in a low susceptibility to manipulation and forgery by malicious participants. Special consideration is also given to how blockchain-based identity and access management systems can address some of the key challenges associated with IoT security. The paper provides a detailed analysis and description of blockchain's roles in tracking the sources of insecurity in supply chains related to IoT devices. The paper also delves into how blockchain can make it possible to contain an IoT security breach in a targeted way after it is discovered. It discusses and evaluates initiatives of organizations, inter-organizational networks and industries on this front. A number of policy implications are discussed. First, in order to strengthen IoT, regulators can make it obligatory for firms to deploy blockchain in supply chain, especially in systems that are mission critical, and have substantial national security and economic benefits. Second, public policy efforts directed at protecting privacy using blockchain should focus on providing training to key stakeholders and increasing investment in this technology. Third, one way to enrich the blockchain ecosystem would be to turn attention to public-private partnerships. Finally, national governments should provide legal clarity and more information for parties to engage in smart contracts that are enforceable.
With a wealth of scientifically proven health benefits, meditation was enjoyed by about 18 million people in the U.S. alone, as of 2012. Yet, there remains a stunning lack of convenient tools for promoting long-term and effective meditation practice. In this paper, we present MindfulWatch, a practical smartwatch-based sensing system that monitors respiration in real-time during meditation -- offering essential biosignals that can potentially be used to empower various future applications such as tracking changes in breathing pattern, offering real-time guidance, and providing an accurate bio-marker for meditation research. To this end, MindfulWatch is designed to be convenient for everyday use with no training required. Operating solely on a smartwatch, MindfulWatch can immediately reach the growing population of smartwatch users, making it ideal for longitudinal data collection for meditation studies. Specifically, it utilizes motion sensors to sense the subtle “micro” wrist rotation (0.01 rad/s) induced by respiration. To accurately capture breathing, we developed a novel self-adaptive model that tracks changes in both breathing pattern and meditation posture over time. MindfulWatch was evaluated based on data from 36 real-world meditation sessions (8.7 hours, 11 subjects). The results suggest that MindfulWatch offers reliable real-time respiratory timing measurement (70% errors under 0.5 seconds).
M2M communication offers ubiquitous applications and is one of the leading facilitators of the Internet of Things paradigm. Unlike human-tohuman communication, distinct features of M2M traffic necessitates specialized and interoperable communication technologies. However, most existing solutions offering wired or wireless connectivity have limitations that hinder widespread horizons of M2M applications. To cope with the peculiar nature of M2M traffic, the evolving 5G system considers the integration of key enabling networking technologies for ubiquitous connectivity and guaranteed QoS. This study outlines the state-of-the-art architecture, recent advances, and open research challenges in communication technologies for M2M. Also, an overview of considerable architectural enhancements and novel techniques expected in 5G networks is presented, followed by the resultant services and benefits for M2M communications.
With the proliferation of Internet of things (IoT) and the burgeoning of 4G/5G network, we have seen the dawning of the IoE (Internet of everything) era, where there will be a huge volume of data generated by things that are immersed in our daily life, and hundreds of applications will be deployed at the edge to consume these data. Cloud computing as the de facto centralized big data processing platform is not efficient enough to support these applications emerging in IoE era, i.e., 1) the computing capacity available in the centralized cloud cannot keep up with the explosive growing computational needs of massive data generated at the edge of the network; 2) longer user-perceived latency caused by the data movement between the edge and the cloud; 3) privacy and security concerns from data owners in the edge; 4) energy constraints of edge devices. These issues in the centralized big data processing era have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Leveraging the power of cloud computing, edge computing has the potential to address the limitation of computing capability, the concerns of response time requirement, bandwidth cost saving, data safety and privacy, as well as battery life constraint. “Edge” in edge computing is defined as any computing and network resources along the path between data sources and cloud data centers. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.
This paper introduces the proposal of a remote sensory system for the detection of sleep disorders in geriatric outpatients. Although the most accurate solution would be an in-depth study in a sleep clinic, it is not a realistic environment for the elderly. The objective is that the patient stays at home, and without changing their daily routines, the clinicians get objective information in order to make a correct diagnosis of the sleep disorders. As a first step towards achieving a home remote monitory system, this work introduces a Body Sensor Network (BSN) to monitor various vital signals as Electrocardiogram (ECG) and Electromyogram (EMG) in order to collect enough information for sleep disorder diagnosis, focusing on the detection of obstructive sleep apnea. This work proposes an algorithm to infer obstructive sleep apnea (OSA) based on power spectral analysis of ECG signals from a single-lead electrocardiogram, demonstrating the feasibility of BSN to detect OSA with around 85% sensitivity.
Recent research and technology trends are shifting toward IoT and CRNs. However, we think that the things-oriented, Internet-oriented, and semantic-oriented versions of IoT are meaning- less if IoT objects are not equipped with cogni- tive radio capability. Equipping IoT objects with CR capability has lead to a new research dimen- sion of CR-based IoT. In this article, we present an overview of CR-based IoT systems. We high- light potential applications of CR-based IoT sys- tems. We survey architectures and frameworks of CR-based IoT systems. We furthermore discuss spectrum-related functionalities for CR-based IoT systems. Finally, we present open issues and research challenges faced by CR-based IoT net- works along with future directions.
Industry investment and research interest in edge computing, in which computing and storage nodes are placed at the Internet's edge in close proximity to mobile devices or sensors, have grown dramatically in recent years. This emerging technology promises to deliver highly responsive cloud services for mobile computing, scalability and privacy-policy enforcement for the Internet of Things, and the ability to mask transient cloud outages. The web extra at www.youtube.com/playlist?list=PLmrZVvFtthdP3fwHPy-4d61oDvQY-RBgS includes a five-video playlist demonstrating proof-of-concept implementations for three tasks: assembling 2D Lego models, freehand sketching, and playing Ping-Pong.
Elderly care is one of the many applications supported by real-time activity recognition systems. Traditional approaches use cameras, body sensor networks, or radio patterns from various sources for activity recognition. However, these approaches are limited due to ease-of-use, coverage, or privacy preserving issues. In this paper, we present a novel wearable Radio Frequency Identification (RFID) system aims at providing an easy-to-use solution with high detection coverage. Our system uses passive tags which are maintenance-free and can be embedded into the clothes to reduce the wearing and maintenance efforts. A small RFID reader is also worn on the user's body to extend the detection coverage as the user moves. We exploit RFID radio patterns and extract both spatial and temporal features to characterize various activities. We also address the issues of false negative of tag readings and tag/antenna calibration, and design a fast online recognition system. Antenna and tag selection is done automatically to explore the minimum number of devices required to achieve target accuracy. We develop a prototype system which consists of a wearable RFID system and a smartphone to demonstrate the working principles, and conduct experimental studies with four subjects over two weeks. The results show that our system achieves a high recognition accuracy of 93.6 percent with a latency of 5 seconds. Additionally, we show that the system only requires two antennas and four tagged body parts to achieve a high recognition accuracy of 85 percent.
Blockchain technology has the potential to revolutionize applications and redefine the digital economy.
Background:
Non-immersive virtual reality is an emerging strategy to enhance motor performance for stroke rehabilitation. There has been rapid adoption of non-immersive virtual reality as a rehabilitation strategy despite the limited evidence about its safety and effectiveness. Our aim was to compare the safety and efficacy of virtual reality with recreational therapy on motor recovery in patients after an acute ischaemic stroke.
Methods:
In this randomised, controlled, single-blind, parallel-group trial we enrolled adults (aged 18-85 years) who had a first-ever ischaemic stroke and a motor deficit of the upper extremity score of 3 or more (measured with the Chedoke-McMaster scale) within 3 months of randomisation from 14 in-patient stroke rehabilitation units from four countries (Canada [11], Argentina [1], Peru [1], and Thailand [1]). Participants were randomly allocated (1:1) by a computer-generated assignment at enrolment to receive a programme of structured, task-oriented, upper extremity sessions (ten sessions, 60 min each) of either non-immersive virtual reality using the Nintendo Wii gaming system (VRWii) or simple recreational activities (playing cards, bingo, Jenga, or ball game) as add-on therapies to conventional rehabilitation over a 2 week period. All investigators assessing outcomes were masked to treatment assignment. The primary outcome was upper extremity motor performance measured by total time to complete the Wolf Motor Function Test (WMFT) at the end of the 2 week intervention period, analysed in the intention-to-treat population. This trial is registered with ClinicalTrials.gov, number NTC01406912.
Findings:
The study was done between May 12, 2012, and Oct 1, 2015. We randomly assigned 141 patients: 71 received VRWii therapy and 70 received recreational activity. 121 (86%) patients (59 in the VRWii group and 62 in the recreational activity group) completed the final assessment and were included in the primary analysis. Each group improved WMFT performance time relative to baseline (decrease in median time from 43·7 s [IQR 26·1-68·0] to 29·7 s [21·4-45·2], 32·0% reduction for VRWii vs 38·0 s [IQR 28·0-64·1] to 27·1 s [21·2-45·5], 28·7% reduction for recreational activity). Mean time of conventional rehabilitation during the trial was similar between groups (VRWii, 373 min [SD 322] vs recreational activity, 397 min [345]; p=0·70) as was the total duration of study intervention (VRWii, 528 min [SD 155] vs recreational activity, 541 min [142]; p=0·60). Multivariable analysis adjusted for baseline WMFT score, age, sex, baseline Chedoke-McMaster, and stroke severity revealed no significant difference between groups in the primary outcome (adjusted mean estimate of difference in WMFT: 4·1 s, 95% CI -14·4 to 22·6). There were three serious adverse events during the trial, all deemed to be unrelated to the interventions (seizure after discharge and intracerebral haemorrhage in the recreational activity group and heart attack in the VRWii group). Overall incidences of adverse events and serious adverse events were similar between treatment groups.
Interpretation:
In patients who had a stroke within the 3 months before enrolment and had mild-to-moderate upper extremity motor impairment, non-immersive virtual reality as an add-on therapy to conventional rehabilitation was not superior to a recreational activity intervention in improving motor function, as measured by WMFT. Our study suggests that the type of task used in motor rehabilitation post-stroke might be less relevant, as long as it is intensive enough and task-specific. Simple, low-cost, and widely available recreational activities might be as effective as innovative non-immersive virtual reality technologies.
Funding:
Heart and Stroke Foundation of Canada and Ontario Ministry of Health.
The proliferation of Internet of Things and the success of rich cloud services have pushed the horizon of a new computing paradigm, Edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of Edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative Edge to materialize the concept of Edge computing. Finally, we present several challenges and opportunities in the field of Edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.
This paper presents the development of an integrated decision-making framework for on-demand parcel delivery services that considers Just-In-Time delivery, fuel consumption and carbon emissions. Optimal policies based on the Markov decision process are established to allow for inclusion of parcel delivery requests. The framework’s integrated dynamic algorithm, based on a continuous variable feedback control, allows for unified processing of delivery requests and route scheduling. Computational experiments show that the integrated approach could increase revenue by 6.4% by reducing fuel and emission costs by 2.5%; however, the approach may incur more cost in terms of timeliness compared to a myopic approach.
Wireless control for the IoT: power spectrum and security challenges
- K Gatsis
- G J Pappas
Gatsis K, Pappas GJ. Wireless control for the IoT: power spectrum and security
challenges. In: Proc. 2017 IEEE/ACM second international conference on
internet-of-things design and implementation (IoTDI), Pittsburg, PA, USA, 18-21 April 2017. INSPEC Accession Number: 16964293
- H.-J Thomas
- Christian Uhlemann
- Rolf Lehmann
- Steinhilper
Thomas H.-J. Uhlemann, Christian Lehmann, Rolf Steinhilper, "The Digital
Twin: Realizing the Cyber-Physical Production System for Industry 4.0",
Procedia CIRP, Volume 61, 2017, Pages 335-340