The interpretability of deep neural networks has become a subject of great interest within the medical and healthcare domain. This attention stems from concerns regarding transparency, legal and ethical considerations, and the medical significance of predictions generated by these deep neural networks in clinical decision support systems. To address this matter, our study delves into the application of four well-established interpretability methods: Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive exPlanations (SHAP), Gradient-weighted Class Activation Mapping (Grad-CAM), and Layer-wise Relevance Propagation (LRP). Leveraging the approach of transfer learning with a multi-label-multi-class chest radiography dataset, we aim to interpret predictions pertaining to specific pathology classes. Our analysis encompasses both single-label and multi-label predictions, providing a comprehensive and unbiased assessment through quantitative and qualitative investigations, which are compared against human expert annotation. Notably, Grad-CAM demonstrates the most favorable performance in quantitative evaluation, while the LIME heatmap score segmentation visualization exhibits the highest level of medical significance. Our research underscores both the outcomes and the challenges faced in the holistic approach adopted for assessing these interpretability methods and suggests that a multimodal-based approach, incorporating diverse sources of information beyond chest radiography images, could offer additional insights for enhancing interpretability in the medical domain.

COVID-19 is a viral infectious disease that has created a global pandemic, resulting in millions of deaths and disrupting the world order. Different machine learning and deep learning approaches were considered to detect it utilizing different medical data. Thermal imaging is a promising option for detecting COVID-19 as it is low-cost, non-invasive, and can be maintained remotely. This work explores the COVID-19 detection issue using the thermal image and associated tabular medical data obtained from a publicly available dataset. We incorporate a multi-modal machine learning approach where we investigate the different combinations of medical and data type modalities to get an improved result. We use different machine learning and deep learning methods, namely random forests, Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN). Overall multi-modal results outperform any single modalities, and it is observed that the thermal image is a crucial factor in achieving it. XGBoost provided the best result with the area under the receiver operating characteristic curve (AUROC) score of 0.91 and the area under the precision-recall curve (AUPRC) score of 0.81. We also report the average of leave-one-positive-instance-out cross- validation evaluation scores. This average score is consistent with the test evaluation score for random forests and XGBoost methods. Our results suggest that utilizing thermal image with associated tabular medical data could be a viable option to detect COVID-19, and it should be explored further to create and test a real-time, secure, private, and remote COVID-19 detection application in the future.

The concept of the Internet of Medical Things brings a promising option to utilize various electronic health records stored in different medical devices and servers to create practical but secure clinical decision support systems. To achieve such a system, we need to focus on several aspects, most notably the usability aspect of deploying it using low-end devices. This study introduces one such application, namely FedSepsis, for the early detection of sepsis using electronic health records. We incorporate several cutting-edge deep learning techniques for the prediction and natural-language processing tasks. We also explore the multimodality aspect for the better use of electronic health records. A secure distributed machine learning mechanism is essential to building such a practical internet of medical things application. To address this, we analyze two federated learning techniques. Moreover, we use two different kinds of low-computational edge devices, namely Raspberry Pi and Jetson Nano, to address the challenges of using such a system in a practical setting and report the comparisons. We report several critical system-level information about the devices, namely CPU utilization, disk utilization, process CPU threads in use, process memory in use (non-swap), process memory available (non-swap), system memory utilization, temperature, and network traffic. We publish the prediction results with the evaluation metrics area under the receiver operating characteristic curve, the area under the precision–recall curve, and the earliness to predict sepsis in hours. Our results show that the performance is satisfactory, and with a moderate amount of devices, the federated learning setting results are similar to the single server-centric setting. Multimodality provides the best results compared to any single modality in the input features obtained from the electronic health records. Generative adversarial neural networks provide a clear superiority in handling the sparsity of electronic health records. Multimodality with the generative adversarial neural networks provides the best result: the area under the precision–recall curve is 96.55%, the area under the receiver operating characteristic curve is 99.35%, and earliness is 4.56 h. FedSepsis suggests that incorporating such a concept together with low-end computational devices could be beneficial for all the medical sector stakeholders and should be explored further.

The internet of medical things (IoMT) is a relatively new territory for the internet of things (IoT) platforms where we can obtain a significant amount of potential benefits with the combination of cognitive computing. Effective utilization of the healthcare data is the critical factor in achieving such potential, which can be a significant challenge as the medical data is extraordinarily heterogeneous and spread across different devices with different degrees of importance and authority. To address this issue, in this paper, we introduce a cognitive internet of medical things architecture with a use case of early sepsis detection using electronic health records. We discuss the various aspects of IoMT architecture. Based on the discussion, we posit that the proposed architecture could improve the overall performance and usability in the IoMT platforms in particular for different IoMT based services and applications. The use of an RNN-LSTM network for early prediction of sepsis according to Sepsis-3 criteria is evaluated with the empirical investigation using six different time window sizes. The best result is obtained from a model using a four-hour window with the assumption that data is missing-not-at-random. It is observed that when learning from heterogeneous sequences of sparse medical data for early prediction of sepsis, the size of the time window has a considerable impact on predictive performance.

Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.

The internet of medical things (IoMT) is relatively new territory for the internet of things (IoT) platforms where we can obtain a significant amount of potential benefits in terms of smart future network computing and intelligent health-care systems. Effective utilization of the health-care data is the key factor here in achieving such potential, which can be a significant challenge as the data is extraordinarily heterogeneous and spread across different devices with different degrees of importance and authority to access it. To address this issue, in this paper, we introduce an intelligent context-based metadata aggregator in the decentralized and distributed edge-based IoMT platform with a use case of early sepsis detection using clinical data. We thoroughly discuss the various aspects of the metadata aggregator and the overall IoMT architecture. Based on the discussion, we posit that the proposed architecture could improve the overall performance and usability in the IoMT platforms in particular for different IoMT based services and applications.

The rapid development of Internet-of-ings (IoT) techniques in G/ G deployments is witnessing the generation of massive amounts of data which are collected, stored, processed, and presented in an easily interpretable form. Analysis of IoT data helps provide smart services such as smart homes, smart energy, smart health, and smart environments through G and G technologies. At the same time, the threat of the cyberattacks and issues with mobile internet security is becoming increasingly severe, which introduces new challenges for the security of IoT systems and applications and the privacy of individuals thereby. Protecting IoT data privacy while enabling data availability is an urgent but difficult task.

Attribute-based encryption (ABE) has been used to provide data confidentiality and fine-grained access control in the Internet of Vehicles (IoV). However, the attributes of vehicles in IoV might change frequently due to the movements of vehicles. Thus, the invalid attributes need to be revoked in time and efficiently to ensure the security of the system. In this paper, we propose a data-sharing scheme based on ABE for IoV. By using a binary tree and attribute version keys, flexible revocation can be achieved for IoV. Moreover, the ciphertext can be stored on clouds, and the distribution and revocation of attribute keys can be realized by distributed attribute authorities. We performed the security analysis and proved the security of the proposed scheme. The results showed that the proposed scheme has lower average computing overhead in terms of attribute revocations compared with other schemes based on ABE, and can satisfy the performance requirement of data sharing for IoV.

This research investigates a number of selected mobile backhaul network (MBHN) deployments in a simulated environment and evaluates the feasibility of each deployment in terms of network failure, convergence and resilience. Four different scenarios from target network have been considered which includes single path route scenario, multi path route scenario, access bus network scenario and MPLS scenario, where three different use cases are experimented in each scenario. Research concludes that if backhaul network failure detection time is reduced, it help to reduce the overall network convergence time. Result shows that bidirectional forwarding detection (BFD) protocol works well to detect the failure in a network. The outcome and measurements from the simulated networks can be used as a guideline for configuration of real networks.

Edge-based distributed intelligence techniques, such as federated learning (FL), have recently been used in many research fields thanks, in part, to their decentralized model training process and privacy-preserving features. However, because of the absence of effective deployment models for the radio access network (RAN), only a tiny number of FL apps have been created for the latest generation of public mobile networks (e.g., 5G and 6G). There is an attempt, in new RAN paradigms, to move toward disaggregation, hierarchical, and distributed network function processing designs. Open RAN (O-RAN), as a cutting-edge RAN technology, claims to meet 5G services with high quality. It includes integrated, intelligent controllers to provide RAN with the power to make smart decisions. This paper proposes a methodology for deploying and optimizing FL tasks in O-RAN to deliver distributed intelligence for 5G applications. To accomplish model training in each round, we first present reinforcement learning (RL) for client selection for each FL task and resource allocation using RAN intelligence controllers (RIC). Then, a slice is allotted for training depending on the clients chosen for the task. Our simulation results show that the proposed method outperforms state-of-art FL methods, such as the federated averaging algorithm (FedAvg), in terms of convergence and number of communication rounds.

With the advent of edge computing, the Internet of Things (IoT) environment has the ability to process data locally. The complexity of the context reasoning process can be scattered across several edge nodes that physically placed at the source of the qualitative information by moving the processing and knowledge inference to the edge of the IoT network. This facilitates the real-time processing of a large range of rich data sources that would be less complex and expensive compare to the traditional centralized cloud system. In this paper, we propose a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge.

The introduction of distributed-reasoning through ubiquitous instrumentation within the distributed Internet of Things (IoT) leads to outstanding improvements in real-time monitoring, optimization, fault-tolerance, traffic, healthcare, so on. Using a ubiquitous controller to interconnect devices in the IoT, however monumental, is still in its embryonic stage, it has the potential to create distributed-intelligent IoT solutions that are more eclient and safer than centric intelligence. It is essential to step in a new direction for designing a distributed intelligent controller for task scheduling as a means to, first, dynamically interact with a smart environment in eclient real-time data processing and, second, react to flexible changes. To cope with these issues, we outline a two-level intelligence schema, using edge computing to enhance distributed IoT. The edge schema pushes the streaming processing capability from cloud to edge devices to better support timely and reliable streaming analytics to improve the performance of smart IoT applications. In this paper, in order to provide better, reliable, and flexible streaming analytics and overcome the data uncertainties, we proposed an IoT gateway controller to provide low-level intelligence by employing a fuzzy abductive reasoner. Numerical simulations support the feasibility of our proposed approaches.

The development of the Internet of Things over the last decade has led to large amounts of data being generated at the network edge. This highlights the importance of local data processing and reasoning. Machine learning is most commonly used to automate tasks and perform complex data processing and reasoning. Collecting such data in a centralized location has become increasingly problematic in recent years due to network bandwidth and data privacy concerns. The easy-to-change behavior of edge infrastructure enabled by software-defined networking (SDN) allows IoT data to be gathered on edge servers and gateways, where federated learning (FL) can be performed: creating a centralized model without uploading data to the cloud. In this paper, we analyze the use of edge computing and federated learning, a decentralized machine learning methodology that increases the amount and variety of data used to train deep learning models. To the best of our knowledge, this paper reports the first use of federated learning to help the Microgrid Energy Management System (EMS) predict load and obtain promising results. Simulations were performed using TensorFlow Federated with data from a modified version of the Dataport site

Over the past decade, the Internet of Things (IoT) has undergone a paradigm shift away from centralized cloud computing towards edge computing. Hundreds of billions of things are estimated to be deployed in the rapidly advancing IoT paradigm, resulting in enormous amounts of data. Transmitting all these data to the cloud has recently proven to be a performance bottleneck, as it causes many network issues relating to latency, power consumption, security, privacy, etc. However, existing paradigms do not use edge devices for decision making. The use of distributed intelligence could strengthen the IoT in several ways by distributing decision-making tasks to edge devices within the network, rather than sending all data to a central server. In this approach, all computational tasks and data are shared among edge devices. To achieve efficient distribution of IoT intelligence and utilization of network resources, it is necessary to integrate the transport network control with distributed edge and cloud resources to provide dynamic and efficient IoT services. This challenge can be overcome by equipping an edge IoT gateway with the intelligence required to extract information from raw data and to decide whether to actuate itself or to outsource complex tasks to the cloud. Distributed intelligence refers to a collaboration between the cloud and the edge. In this context, we first introduce a distributed SDN-based architecture for IoT that enables IoT gateways to perform IoT processing dynamically at the edge of the network, based on the current state of network resources. Next, we propose an algorithm for selecting clients for the training process of Federated Learning Applications, based on the context information of the network. In the last step, we deploy Federated Learning Applications in our distributed SDN-based architecture, using the gateways to provide distributed intelligence at the edge of the network, and conduct a comprehensive and detailed evaluation of the system from several perspectives.

The rapid advancement in sensors and communications has led to the expansion of the Internet of Things (IoT) services, where many devices need access to the transport network using fixed or wireless access technologies and mobile Radio Access Networks (RAN). However, supporting IoT in RAN is challenging as IoT services may produce many short and variable sessions, impacting the performance of mobile users sharing the same RAN. To address this issue, network slicing is a promising solution to support heterogeneous service segments sharing the same RAN, which is a crucial requirement of the upcoming fifth-generation (5G) mobile network. This paper proposes a two-level network slicing mechanism for enhanced mobile broadband (eMBB) and Ultra-Reliable and Low Latency communications (URLLC) in order to provide end-to-end slicing at the core and edge of the network with the aim of reducing latency for IoT services and mobile users sharing the same core and RAN using the O-RAN architecture. The problem is modeled at both levels as a Markov decision process (MDP) and solved using hierarchical reinforcement learning. At a high level, an SDN controller using an agent that has been trained by a Double Deep Q-network (DDQN) allocates radio resources to gNodeBs (next-generation NodeB, a 5G base station) based on the requirements of eMBB and URLLC services. At a low level, each gNodeB using an agent that has been trained by a DDQN allocates its pre-allocated resources to its end-users. The proposed approach has been demonstrated and validated through a real testbed. Notably, it surpasses the prevalent approaches in terms of end-to-end latency.

With recent advancements in communications and sensor technologies, the Internet of Things (IoT) has been experiencing rapid growth. It is estimated that billions of objects will be connected, which would create a vast amount of data. Cloud computing has been the predominant choice for monitoring connected objects and delivering data-based intelligence, but high response time and network load of cloud-based solutions are limiting factors for IoT deployment. In order to cope with this challenge, this paper proposes a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge

This paper focuses on a system of recognizing human’s emotion from a detected human’s face. The analyzed information is conveyed by the regions of the eye and the mouth into a merged new image in various facial expressions pertaining to six universal basic facial emotions. The output information obtained could be fed as an input to a machine capable to interact with social skills, in the context of building socially intelligent systems. The methodology uses a classification technique of information into a new fused image which is composed of two blocks integrated by the area of the eyes and mouth, very sensitive areas to changes human’s expression and that are particularly relevant for the decoding of emotional expressions. Finally we use the merged image as an input to a feed-forward neural network trained by back-propagation. Such analysis of merged images makes it possible, obtain relevant information through the combination of proper data in the same image and reduce the training set time while preserved classification rate. It is shown by experimental results that the proposed algorithm can detect emotion with good accuracy.

In this paper, we analyze the state of the art in distributed ledger technologies and blockchains and investigate potential applications in the Internet of Things (IoT) domain. Afterwards, we discuss interoperability of blockchains, and their use in smart contracts, and artificial intelligence.

This paper analyzes the integration of neural networks and linear systems for the identification, state estimation and output feedback control of weakly nonlinear systems. Considering previous knowledge about the system given by approximated linear state-space models, linear observers and linear controllers, training algorithms for the neuro-identification, state neuro-estimation and output feedback neuro-control were derived considering the dynamics of the nonlinear system. It was found that the integrated linear-neuro model can identify the dynamics of the system much more accurately than a purely linear model or a purely neuro model. It was also found that the state estimation and vibration isolation performance of the system with integrated linear-neuro output feedback control is better than the system with linear control or neuro-control.

The point of view of Isaac Asimov is unlikely in a close future, but machines that develop tasks in a sensible manner are already a fact. In light of this remark, recent research tries to understand the requirements and design options that imply providing an autonomous agent with means for detecting emotions. If we think about of exporting this model to machines, it is possible that they become capable to evolve emotionally according to such models and would take part in the society more or less cooperatively, according to the perceived emotional state. The main purpose of this research is the implementation of a decision model affected by emotional feedback in a cognitive robotic assistant that can capture information about the world around it. The robot will use multi-modal communication to assist the societal participation of persons deprived of conventional modes of communication. The aim is a machine that can predict what the user will do next and be ready to give the best possible assistance, taking in account the emotional factor. The results indicate the benefits and importance of emotional feedback in the closed loop human-robot interaction framework. Cognitive agents are shown to be capable of adapting to emotional information from humans.

In the beginning of 2000, researchers started to see the potential of using Augmented Reality (AR) in educational and foresaw that further research within the field. Since then, AR research have taken many different approaches. This is also true for AR in relation to pedagogical purposes. This study is to investigate what has been studied within the AR field related to mobile augmented reality. It attempts to make systematic review of how learning and pedagogical aspects have been approached in the articles. In recent years, mobile augmented reality has become increasingly interesting due to the mobile devices small form factors and their ability to let the students move around freely while learning. The aim of this study is to make a systematic review of pedagogical uses of mobile augmented reality. Based on a review of previous literature of mobile AR systems for pedagogical purposes, published between 2000-2017, make it possible to see in which direction mobile AR systems for education are heading and how future mobile AR systems should be designed to best fit the needs of future students so they can more effectively improve their learning.

Mobile devices and virtualized appliances in the Internet of Things can be end nodes on varying networks owned by different parties over time, while still seamlessly participating in licit or illicit activities. Digital Forensics and Incident Response (DFIR) tools today struggle to perform digital investigations in such loosely controlled networked environments as they face several challenges including: scarcity of resources, availability, trust, privacy, data volumes, velocity and variety. In this paper we analyze the state of research in DFIR in networked environments, identifying the challenges facing DFIR tools particularly in loosely controlled network environments. We present the requirements for a system to address these challenges at the various steps of the typical digital investigation methodology. From this we identify the need for support from Peer to Peer (P2P) overlays and discuss their relative merits and drawbacks in order to identify those that would best support DFIR in loosely controlled networked environments. Finally we incorporate both structured and unstructured P2P overlays in various capacities in our architecture in order to organize devices in loosely controlled networks, using context information, thus enabling efficient capture, analysis and reporting of artifacts of use in digital investigations.

Simulation plays an important role on studing the Internet of Things (IoT) traffic, which has an increasing impact on network infrastructures.The existing simulation tools and mechanisms on IoT mainly focus on simulating large-scale deployment of IoT and heterogeneous IoT applications. This paper concentrates on how to simulate the lengthy, burthty and multi-level time-scale IoT applications quickly. A time-scale data generation (TSDG) method is proposed to reduce the used simulation time of different IoT scenarios while keeping the real world characteristics of the IoT applications. Implementations of TSDG in ns-3 and simulation experiments of smart home and smart shopping centre are described in the paper. The evaluations results show that the proposed TSDG method can effectively reduce the time used to simulation IoT applications in ns-3 while reflect the IoT application traffic in the real world.

In this publication we present Immersive Networking as a novel framework for connecting people and places and things in virtual environments with augmented reality to be used in eg. Virtual training environments. The research is mandated by technology advances in internetworking underpinned by 5G networks and Internet-of-Things. These advances present new possibilities and challenges to integrate people, places and things in virtual environments. Existing frameworks such as MPEG-V possess representational capabilities but have insufficient support for integrating entities from the real world via heterogeneous infrastructure. MPEG-V for instance makes no statements about distributed control. Seamless experiences in virtual environment require self-organization of connectivity between people, places and things via heterogeneous 5G and Internet-of-Things infrastructures. A second important aspect of the quality of our experience is the immediacy of responses. Both aspects of seamless and self-organizing connections between entities require that we push control to the end-devices co-located with the entities themselves. These end-devices may incorporate sensing gateways and interaction devices, which include both local and non-local information from the virtual environment in the interaction. Thus delegation of control to end-devices requires means for the organizing or relations and clustering by relevance. This capability is particularly important as the projected number of devices and sensors to be connected via the Internet-of-Things is projected to be in the order of 50 billion by 2020. Immersive Networking supported by MediaSense constitutes a scalable self-organizing means for connecting people and places and things in virtual environments with augmented reality. MediaSense moves control to the edge enabling immediacy in experiences based on seamless self-organization through clustering of entities in relations organized by relevance. We conclude by validating our approach in several scenarios evaluating the relevance and application in human decision-making..

A rapidly increasing and vast number of “things” will be connected to the Internet, in all sectors of society (people, places, sensors, homes, industry, government services, etc.). The urgency of finding sustainable solutions requires “things” and services of the overall system to display autonomic intelligent behavior. The ability of cloud infrastructure to orchestrate the fine-grained and agile control of “things” is limited. This mandates an alternative approach intelligently moving control to the “things”. Thereby we minimize the reliance on cloud infrastructure, and are able to build more agile, intelligent and effective solutions in various application areas, such as Health, Transport and through Automation. We provide examples of such novel solutions tested in “Urban ICT Arena”, a largescale smart city testbed in Stockholm.

This paper presents a system and mechanisms enabling real-time awareness and interaction among vehicles connected via heterogeneous mobile networks. Information obtained by vehicles is considered as the centre in our system. Vehicles are organized dynamically in overlaid clusters. In each cluster, vehicle-related information is pushed in time. As a network node, each vehicle has the function of content abstraction and distribution. Through processing and abstracting the sensed data, various vehicle-related information are organized and denoted in hierarchical names at each node. The data are transmitted and forwarded using protocols accordant with the characteristics of the content. In this way, large-scale and real-time information exchanges among vehicles are realized. Part of our system has been implemented and tested. An open source platform providing standard sensor and actuator API can be provided.

A novel conceptual framework is presented in this paper with an aim to standardize and virtualize Internet of Things’ (IoT) infrastructure through deploying OpenFlow technology. The framework can receive e-services based on context information leaving the current infrastructure unchanged. This framework allows the active collaboration of heterogeneous devices and protocols. Moreover it is capable to model placement of physical objects, manage the system and to collect information for services deployed on an IoT infrastructure. Our proposed IoT virtualization is applicable to a random topology scenario which makes it possible to 1) share flow-sensors’ resources, 2) establish multi-operational sensor networks, and 3) extend reachability within the framework without establishing any further physical networks. Flow-sensors achieve better results comparable to the typi-cal-sensors with respect to packet generation, reacha-bility, simulation time, throughput, energy consump-tion point of view. Even better results are possible through utilizing multicast groups in large scale net-works.

This article investigates new forms for creating and enabling massive and scalable participatory immersive experiences in live cultural events, characterized by processes, involving pervasive objects, places and people. The multi-disciplinary research outlines a new paradigm for collaborative creation and participation towards technological and social innovation, tapping into crowd-sensing. The approach promotes user-driven content-creation and offsets economic models thereby rewarding creators and performers. In response to these challenges, we propose a framework for bringing about massive and real-time presence and awareness on the Internet through an Internet-of-Things infrastructure to connect artifacts, performers, participants and places. Equally importantly, we enable the in-situ creation of collaborative experiences building on relevant existing and stored content, based on decisions leveraging multi-criteria clustering and proximity of pervasive information, objects, people and places. Finally, we investigate some new ways for immersive experiences via distributed computing but pointing forward to the necessity to do more with regard to collaborative creation.

Sharing data among vehicles is one of the most important ways to provide safety-related and value-added services to connected vehicles. Nevertheless, access to the shared data must be controlled to prevent the exposure of users' privacy and data leakage or corruption. Attribute-based encryption (ABE) can provide data confidentiality and fine-grained access control. However, the complex and dynamic driving environment of vehicles may cause the attributes of vehicles to change frequently, and thus put a huge burden on the attribute management of the system or degrade the security of the system. In this paper, we propose a secure data sharing method by using ABE and blockchain for Internet of Vehicles. By using ABE, the data owner can stipulate the policy of the data access control based on the attributes of vehicles. The trusted authority is replaced by blockchain, which reduces the burden and solved the problem of single point failure of the trusted authority and increases the transparency of the whole system. An adaptive attribute revocation method is used to balance the revocation time and system cost. Moreover, the shared data are stored in a distributed Inter-Planetary File System (IPFS) to improve the efficiency and security of the data sharing system. The test results show that the proposed method can well satisfy the performance requirement of secure data sharing for Internet of Vehicles.

The features of information dissemination in vehicular networks make it necessary to introduce Information-Centric Networking (ICN) technique to vehicular networks. However, some design principles of ICN must be implemented and extended in order to realize efficient data dissemination in vehicular networks. This paper proposes an approach for realizing ICN-based data dissemination in vehicular networks. An architecture for organizing and disseminating information in a vehicle is suggested. The concept of cluster of common interest is proposed, with which groups of vehicles can be established dynamically according to the information they are interested in. Within each cluster, information are disseminated to the vehicles in a way decided by the features of the information. To show the feasibility of our approach, the approach has been implemented and tested in the simulation environment.

Context information can add more meaning and value to the sensor data in Internet of Things. However, due to the large amount of sources of contexts and sensor data, the exchange of contexts through the networks may cause more and more data traffic. This paper proposes a novel method for maintaining and exchanging contexts through the Internet more efficiently. Contexts are classified into different levels. Each level of contexts is maintained and exchanged using different methods. A new protocol for exchanging the contexts is designed. Through the independent transmitting of contexts and sensor data, the context-awareness in Internet of Things can be realized, and the network bandwidth usage can be greatly reduced and thus the energy of node can also be saved.

Smart Grid represents an efficient power transmission, distribution and management system. However, solutions for Smart Grid have raised security and privacy problems. Moreover, with the introduction of renewable energy resources, such as rooftop solar panels and small biogas plants, more and more electricity consumers are involved in the energy generation system. This may cause the power system unstable and/or the waste of the energy. Blockchain is a promising technology for solving these problems in the future energy system on account of its distributed trust, anonymity, data integrity and availability. In this paper, we propose a Blockchain-based architecture for Smart Grid. By using the proposed architecture, electricity consumers can be fully involved in the energy system and tracing the details of the energy they have consumed or generated. At the same time, the stability of the energy system can be kept, reducing the waste of the energy and potential hazard to the electrical equipment.

The Internet of Things (IoT) connects smart devices to enable various intelligent services. The deployment of IoT encounters several challenges, such as difficulties in controlling and managing IoT applications and networks, problems in programming existing IoT devices, long service provisioning time, underused resources, as well as complexity, isolation and scalability, among others. One fundamental concern is that current IoT networks lack flexibility and intelligence. A network-wide flexible control and management are missing in IoT networks. In addition, huge numbers of devices and large amounts of data are involved in IoT, but none of them have been tuned for supporting network management and control. In this paper, we argue that Software-defined Networking (SDN) together with the data generated by IoT applications can enhance the control and management of IoT in terms of flexibility and intelligence. We present a review for the evolution of SDN and IoT and analyze the benefits and challenges brought by the integration of SDN and IoT with the help of IoT data. We discuss the perspectives of knowledge-driven SDN for IoT through a new IoT architecture and illustrate how to realize Industry IoT by using the architecture. We also highlight the challenges and future research works toward realizing IoT with the knowledge-driven SDN.

Being able to obtain various environmental and driving data from vehicles is becoming more and more important for the current and future intelligent transportation systems (ITSs) to operate efficiently and economically. However, the limitations of privacy protection and security of the current ITSs are hindering users and vehicles providing data. In this paper, we propose a new ITS architecture by using blockchain technology solving the privacy protection and security problems and promoting users and vehicles to provide data to ITSs. The proposed architecture uses blockchain as a trust infrastructure to protect users’ privacy and provide trustworthy services to users. It is also compatible with the legacy ITS infrastructure and services. In addition, the hierarchical organization of chains enables the scalability of the system, and the use of smart contracts provides a flexible way for introducing new services in the ITS. The proposed architecture is demonstrated by a proof of concept implementation based on Ethereum. The test results show that the proposed architecture is feasible.

The Internet of Things (IoT) architecture is expected to evolve into a horizontal model containing various open systems, integrated environments, and platforms. However, not much research effort has been devoted to developing architectures for horizontal IoT solutions so far. This paper presents an IoT architecture based on Software-Defined Networking (SDN). In this architecture, devices, gateways, and data are open and programmable to IoT application developers and service operators. Moreover, IoT data provision and interoperability are supported at different levels. We present an implementation of the proposed architecture. Our implementation shows that the proposed architecture enables rapid creation of IoT applications by reusing ready applications and data. The measurement and evaluation results demonstrate the feasibility of the proposed architecture.

The Internet of Things (IoT) creates an ecosystem that connects people and objects through the internet. IoTenabled healthcare has revolutionized healthcare delivery by moving toward a more pervasive, patientcentered, and preventive care model. In the ongoing COVID-19 pandemic, it has also shown a great potential for effective remote patient health monitoring and management, which leads to preventing straining the healthcare system. Nevertheless, due to the heterogeneity of data sources and technologies, IoT-enabled healthcare systems often operate in vertical silos, hampering interoperability across different systems. Consequently, such sensory data are rarely shared nor integrated, which can undermine the full potential of IoT-enabled healthcare. Applying semantic technologies to IoT is a promising approach for fulfilling heterogeneity, contextualization, and situation-awareness requirements for real-time healthcare solutions. However, the enrichment of sensor streams has been under-explored in the existing literature. There is also a need for an ontology that enables effective patient health monitoring and management during infectious disease outbreaks. This study, therefore, aims to extend the existing ontology to allow patient health monitoring for the prevention, early detection, and mitigation of patient deterioration. We evaluated the extended ontology using competency questions and illustrated a proof-of-concept of ontology-based semantic representation of vital sign streams.

The health of individuals and populations requires concerted and collaborative efforts by healthcare, public health, social care, and personal health management. The inter-sectoral collaborations are more crucial than ever, especially when facing public health crises, including the ongoing pandemic of coronavirus disease-2019 (COVID-19). Although the capabilities of healthcare and public health systems have increased with a dramatic boost in the use of the Internet of Things (IoT), such IoT-enabled systems are often operating in silos. A pressing need, thus, is the seamless integration of those currently incompatible systems. A promising solution is to leverage semantic technologies to increase interoperability among such systems. Therefore, this article aims to: conduct a systematic review on the current state-of-the-art semantic IoT solutions used in health domain; identify the associated challenges; propose a federated edge-cloud semantic IoT architecture to facilitate the healthcare and public health (HC-PH) collaborations for the health and well-being of the individuals and populations.

A novel idea presented in this paper is to deploy the Open Flow technology in wireless sensor networks that can lead to a significant achievement in Internet of things and cloud computing arena through network virtualization. Two new abstract layers namely Common platform layer and virtualization layer can be added at the top and bottom of a preset Infrastructure as a Service architecture. Our proposed IOT virtualization can be applicable in a random topology scenario which makes possible of the flow-sensors' resources to be shared, establishment of multi operational sensor networks and escalation of the reach ability under the same platform without establishing any further physical networks. Flow-sensor achieved 39% more reach ability than the typical sensors in an ideal scenario and even better results are possible from the amount of packets generation and simulation time viewpoint for larger scale networks.

This paper studied communication between sensor groups-access points and a networking & communication model and packet transmission algorithm such that networks are divided into several multicast domains of stationary flow-sensors and received several packet-flows from mobile flow-sensors. The proposed approach to Internet of Things' virtualization is suitable for invoking and multi-tasking sensor networks within random network scenarios ranging from large single operational networks to distinct service networks within the involvement of multiple operational networks. The result implies larger multicast groups perform better than smaller ones whereas large flows of packets decrease the performances from the whole network viewpoint.

This paper analyzes the performance and practical implementation of fuzzy-neural networks for the autonomous motion of mobile robots. The designed fuzzy-neural controller is a refined version of a conventional fuzzy controller, and was trained to optimize a given cost function minimizing positioning error. It was found that the mobile robot with fuzzy-neural controller presents good positioning and tracking performance for different types of desired trajectories. It was verified by computer simulation as well as experimentally using a laboratory-scale car-like robot model.

Positioning and tracking control systems are an important component of autonomous robot applications. This paper presents the design method of tracking control systems based on H infinite preview control where the present and future desired positions of the robot are used to determine the control actions to be applied so that the robot describes the desired trajectory as close as possible. The performance improvements achieved with H infinite preview control have been examined in the frequency and time domains for different types of reference signals when applied to a one-dimensional positioning system. It was found that preview control improves the tracking performance by improving the phase response of the tracking system.

There is a raise in penetration of smart phone while using enterprise application, as most of them are downloaded from the public market, resulting in challenge for security framework, causing a threat to lose sensitive user data. To prevent this ARM introduces the virtualization technique in hardware level, which prevents processing of trusted application that is completely isolated from general processing. To improvise this, we need to understand ARM Architecture; however it is still black box for users and developers. In this article, we take a deep look at the hardware architecture of the ARM trust zone to study and analyze its implementation and also to create its replica in emulator. Moreover we describe feasibility of various designs, implementation of trust zone feature in android emulator; with sample trusted application called secure channeling and concludes with annotation of suitable design on future enhancement. The security domain for secure processing and utility in emulator is to benefit the user and developer community.

With the rise of the Internet of Things (IoT), this means recognizing the need for architectures to handle billions of devices and their interactions. A virtual world engine at the massively multiplayer scale is a massively multiplayer online world (MMOW); one thing virtual world engines realized when going into the scale of MMOs, is the cost of maintaining a potentially quadratic number of interactions between a massive number of objects, laid out in a spatial dimension. Research into IoT was fueled by research in wireless sensor networks, but rather than start from a device perspective, this article looks at how architectures deal with interacting entities at large scale. The domain of MMOWs is examined for properties that are affected by scale. Thereafter the domain of IoT is evaluated to see if each of those properties are found and how each is handled. By comparing the current state of the art of MMOWs and IoT, with respect to scalability, the problem of scaling IoT is explicated, as well as the problem of incorporating an MMOW with IoT into a pervasive platform. Three case studies of a MMOW interfacing with IoT are presented in closing.

A virtual world engine at the massively multiplayer scale is a massively multiplayer online world (MMOW); one thing virtual world engines realized when going into the scale of MMOs, is the cost of maintaining a potentially quadratic number of interactions between a massive number of objects, laid out in a spatial dimension. With the rise of the Internet of Things (IoT), this means recognizing the need for architectures to handle billions of devices and their interactions. Research into IoT was fueled by research in wireless sensor networks, but rather than start from a device perspective, this article looks at how architectures deal with interacting entities at large scale. The domain of MMOWs is examined for properties that are affected by scale. Thereafter the domain of IoT is evaluated to see if each of those properties are found and how each is handled. By comparing the current state of the art of MMOWs and IoT, with respect to scalability, it is discussed how research from one domain can possibly be exapted to the other domain and vice versa. A case study of a MMOW interfacing with IoT is presented in closing.

Use of context has been prevailing in distributed computing with the emergence of mobile computing. An entity is said to be context-aware if it responds to context changes. These distributed context-aware entities share context information in order to make intelligent decisions or carry out important tasks. MediaSense, an Internet-of-Things (IoT) platform, offers such intelligent delivery of context information to any host- anywhere. However, such an IoT platform is facing challenge of discovering context entities. In view of this, this paper particularly addresses the challenge of discovering distributed context entities by extending the distributed protocol, Distributed Context eXchange Protocol (DCXP), for MediaSense platform. In particular, a publish/subscribe approach has been employed to overcome the challenge which enables fast context entity discovery.

One of the challenges for Autonomic Management in Future Internet is to bring about self-organization in a rapidly changing environment and enable participating nodes to be aware and respond to changes. The massive number of participating nodes in Internet-of-Things calls for a new approach in regard of Autonomic Management with dynamic self-organization and enabling awareness to context information changes in the nodes themselves. To this end, we present new algorithms to enable self-organization with logical-clustering, the goal of which is to ensure that logical-clustering evolves correctly in the dynamic environment. The focus of these algorithms is to structure logical-clustering topology in an organized way with minimal intervention from outside sources. The correctness of the proposed algorithm is demonstrated on a scalable IoT platform, MediaSense. Our algorithms sanction 10 nodes to organize themselves per second and high accuracy of nodes discovery. Finally, we outline future research challenges towards autonomic management of IoT.

The unprecedented prevalence of ubiquitous sensing will revolutionise the Future Internet where state-of-the-art Internet-of-Things (IoT) is believed to play the pivotal role. In the fast forwarding IoT paradigm, hundreds of billions of things are estimated to be deployed which would give rise to an enormous amount of data. Cloud computing has been the prevailing choice for controlling the connected things and the data, and providing intelligence based on the data. But response time and network load are on the higher side for cloud based solutions. Recently, edge computing is gaining growing attention to overcome this by employing rule-based intelligence. However, requirements of rules do not scale well with the proliferation of things. At the same time, rules fail in uncertain events and only offer pre-assumed intelligence. To counter this, this paper proposes a novel idea of leveraging the belief-network with the edge computing to utilize as an IoT edge-controller the aim of which is to offer low-level intelligence for IoT applications. This low-level intelligence along with cloud-based intelligence form the distributed intelligence in the IoT realm. Furthermore, a learning approach similar to reinforcement learning has been proposed. The approach, i.e. enabling a Future IoT Controller (FITC) has been verified with a simulated SmartHome scenario which proves the feasibility of the low-level intelligence in terms of reducing rules domination, faster response time and prediction through learning experiences at the edge.

Crowdsourcing was initially devised as a method for solving problems through soliciting contributions from a large online community. Crowdsourcing is facing new challenges to handle the increase of information in real-time from a vast number of sources in Internet-of-Things (IoT) scenarios. Thus we seek to leverage the power of social web, smart-devices, sensors, etc., fusing these heterogeneous sources into distributed context information in order to enable novel crowdsourcing scenarios. This mandates research in efficient management of heterogeneous and distributed context information through logical-clustering. Logical-clustering can efficiently filter out similar context information obtained from distributed sources based on context similarity. However, the efficiency of logical-clustering is challenged by the distribution of context information in crowdsourcing scenarios. Publish/Subscribe mechanism can counter this challenge. To this end, we propose a scalable publish/subscribe model, MediaSense, which is based on p2p technologies. This paper presents our approach to a scalable logical-clustering concept. The evaluation of our approach applied to MediaSense can achieve a rate of approximately 3530 messages/sec for publish/subscribe events. Moreover, this approach further achieves 99% increase for subscription matching and 163% improvement in memory requirements in comparison with other approaches.