Navigating the IoT landscape: Unraveling forensics, security issues, applications, research challenges, and future

Given the exponential expansion of the internet, the possibilities of security attacks and cybercrimes have increased accordingly. However, poorly implemented security mechanisms in the Internet of Things (IoT) devices make them susceptible to cyberattacks, which can directly affect users. IoT forensics is thus needed for investigating and mitigating such attacks. While many works have examined IoT applications and challenges, only a few have focused on both the forensic and security issues in IoT. Therefore, this paper reviews forensic and security issues associated with IoT in different fields. Future prospects and challenges in IoT research and development are also highlighted. As demonstrated in the literature, most IoT devices are vulnerable to attacks due to a lack of standardized security measures. Unauthorized users could get access, compromise data, and even benefit from control of critical infrastructure. To fulfil the security-conscious needs of consumers, IoT can be used to develop a smart home system by designing a FLIP-based system that is highly scalable and adaptable. Utilizing a blockchain-based authentication mechanism with a multi-chain structure can provide additional security protection between different trust domains. Deep learning can be utilized to develop a network forensics framework with a high-performing system for detecting and tracking cyberattack incidents. Moreover, researchers should consider limiting the amount of data created and delivered when using big data to develop IoT-based smart systems. The findings of this review will stimulate academics to seek potential solutions for the identified issues, thereby advancing the IoT field.Comment: 77 pages, 5 figures, 5 table

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

Towards fostering the role of 5G networks in the field of digital health

A typical healthcare system needs further participation with patient monitoring, vital signs sensors and other medical devices. Healthcare moved from a traditional central hospital to scattered patients. Healthcare systems receive help from emerging technology innovations such as fifth generation (5G) communication infrastructure: internet of things (IoT), machine learning (ML), and artificial intelligence (AI). Healthcare providers benefit from IoT capabilities to comfort patients by using smart appliances that improve the healthcare level they receive. These IoT smart healthcare gadgets produce massive data volume. It is crucial to use very high-speed communication networks such as 5G wireless technology with the increased communication bandwidth, data transmission efficiency and reduced communication delay and latency, thus leading to strengthen the precise requirements of healthcare big data utilities. The adaptation of 5G in smart healthcare networks allows increasing number of IoT devices that supplies an augmentation in network performance. This paper reviewed distinctive aspects of internet of medical things (IoMT) and 5G architectures with their future and present sides, which can lead to improve healthcare of patients in the near future

A Deep Learning-Based Privacy-Preserving Model for Smart Healthcare in Internet of Medical Things Using Fog Computing

With the emergence of COVID-19, smart healthcare, the Internet of Medical Things, and big data-driven medical applications have become even more important. The biomedical data produced is highly confidential and private. Unfortunately, conventional health systems cannot support such a colossal amount of biomedical data. Hence, data is typically stored and shared through the cloud. The shared data is then used for different purposes, such as research and discovery of unprecedented facts. Typically, biomedical data appear in textual form (e.g., test reports, prescriptions, and diagnosis). Unfortunately, such data is prone to several security threats and attacks, for example, privacy and confidentiality breach. Although significant progress has been made on securing biomedical data, most existing approaches yield long delays and cannot accommodate real-time responses. This paper proposes a novel fog-enabled privacy-preserving model called [Formula: see text] sanitizer, which uses deep learning to improve the healthcare system. The proposed model is based on a Convolutional Neural Network with Bidirectional-LSTM and effectively performs Medical Entity Recognition. The experimental results show that [Formula: see text] sanitizer outperforms the state-of-the-art models with 91.14% recall, 92.63% in precision, and 92% F1-score. The sanitization model shows 28.77% improved utility preservation as compared to the state-of-the-art

IoT Platform for COVID-19 Prevention and Control: A Survey

As a result of the worldwide transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has evolved into an unprecedented pandemic. Currently, with unavailable pharmaceutical treatments and vaccines, this novel coronavirus results in a great impact on public health, human society, and global economy, which is likely to last for many years. One of the lessons learned from the COVID-19 pandemic is that a long-term system with non-pharmaceutical interventions for preventing and controlling new infectious diseases is desirable to be implemented. Internet of things (IoT) platform is preferred to be utilized to achieve this goal, due to its ubiquitous sensing ability and seamless connectivity. IoT technology is changing our lives through smart healthcare, smart home, and smart city, which aims to build a more convenient and intelligent community. This paper presents how the IoT could be incorporated into the epidemic prevention and control system. Specifically, we demonstrate a potential fog-cloud combined IoT platform that can be used in the systematic and intelligent COVID-19 prevention and control, which involves five interventions including COVID-19 Symptom Diagnosis, Quarantine Monitoring, Contact Tracing & Social Distancing, COVID-19 Outbreak Forecasting, and SARS-CoV-2 Mutation Tracking. We investigate and review the state-of-the-art literatures of these five interventions to present the capabilities of IoT in countering against the current COVID-19 pandemic or future infectious disease epidemics.Comment: 12 pages; Submitted to IEEE Internet of Things Journa

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

A Fog and Blockchain Software Architecture for a Global Scale Vaccination Strategy

Nowadays, there are many fragmented records of patient’s health data in different locations like hospitals, clinics, and organizations all around the world. With the arrival of the COVID-19 pandemic, several governments and institutions struggled to have satisfactory, fast, and accurate decision-making in a wide, dispersed, and global environment. In the current literature, we found that the most common related challenges include delay (network latency), software scalability, health data privacy, and global patient identification. We propose to design, implement and evaluate a healthcare software architecture focused on a global vaccination strategy, considering healthcare privacy issues, latency mitigation, support of scalability, and the use of a global identification. We have designed and implemented a prototype of a healthcare software called Fog-Care, evaluating performance metrics like latency, throughput and send rate of a hypothetical scenario where a global integrated vaccination campaign is adopted in wide dispensed locations (Brazil, USA, and United Kingdom), with an approach based on blockchain, unique identity, and fog computing technologies. The evaluation results demonstrate that the minimum latency spends less than 1 second to run, and the average of this metric grows in a linear progression, showing that a decentralized infrastructure integrating blockchain, global unique identification, and fog computing are feasible to make a scalable solution for a global vaccination campaign within other hospitals, clinics, and research institutions around the world and its data-sharing issues of privacy, and identification.N/

Statistical Review of Health Monitoring Models for Real-Time Hospital Scenarios

Health Monitoring System Models (HMSMs) need speed, efficiency, and security to work. Cascading components ensure data collection, storage, communication, retrieval, and privacy in these models. Researchers propose many methods to design such models, varying in scalability, multidomain efficiency, flexibility, usage and deployment, computational complexity, cost of deployment, security level, feature usability, and other performance metrics. Thus, HMSM designers struggle to find the best models for their application-specific deployments. They must test and validate different models, which increases design time and cost, affecting deployment feasibility. This article discusses secure HMSMs' application-specific advantages, feature-specific limitations, context-specific nuances, and deployment-specific future research scopes to reduce model selection ambiguity. The models based on the Internet of Things (IoT), Machine Learning Models (MLMs), Blockchain Models, Hashing Methods, Encryption Methods, Distributed Computing Configurations, and Bioinspired Models have better Quality of Service (QoS) and security than their counterparts. Researchers can find application-specific models. This article compares the above models in deployment cost, attack mitigation performance, scalability, computational complexity, and monitoring applicability. This comparative analysis helps readers choose HMSMs for context-specific application deployments. This article also devises performance measuring metrics called Health Monitoring Model Metrics (HM3) to compare the performance of various models based on accuracy, precision, delay, scalability, computational complexity, energy consumption, and security