Keynote Speech 1: Leveraging Cloud and Fog Computing for Real-Time Applications: Resource Allocation and Scheduling Issues

Elderly population in the Asian countries is increasing at a very fast rate. Lack of healthcare resources and infrastructure in many countries makes the task of provding proper healthcare difficult. Internet of things (IoT) in healthcare can address the problem effectively. Patient care is possible at home using IoT devices. IoT devices are used to collect different types of data. Various algorithms may be used to analyse data. IoT devices are connected to the internet and all the data of the patients with various health reports are available online and hence security issues arise. IoT sensors, IoT communication technologies, IoT gadgets, components of IoT, IoT layers, cloud and fog computing, benefits of IoT, IoT-based algorithms, IoT security issues, and IoT challenges are discussed in the chapter. Nowadays global epidemic COVID19 has demolished the economy and health services of all the countries worldwide. Usefulness of IoT in COVID19-related issues is explained here.

This chapter describes how traditionally, Cloud Computing has been used for processing Internet of Things (IoT) data. This works fine for the analytical and batch processing jobs. But most of the IoT applications demand real-time response which cannot be achieved through Cloud Computing mainly because of inherent latency. Fog Computing solves this problem by offering cloud-like services at the edge of the network. The computationally powerful edge devices have enabled realising this idea. Witnessing the exponential rise of IoT applications, Fog Computing deserves an in-depth exploration. This chapter establishes the need for Fog Computing for processing IoT data. Readers will be able to gain a fair comprehension of the various aspects of Fog Computing. The benefits, challenges and applications of Fog Computing with respect to IoT have been mentioned elaboratively. An architecture for IoT data processing is presented. A thorough comparison between Cloud and Fog has been portrayed. Also, a detailed discussion has been depicted on how the IoT, Fog, and Cloud interact among them.

A Review on Fog Computing: Architecture, Fog with IoT, Algorithms and Research Challenges

Privacy preserving Internet of Things: From privacy techniques to a blueprint architecture and efficient implementation

With the continuous evolution of technology, the prevalence of the Internet of Things (IoT) has grown significantly. However, with this rise comesa number of challenges for integrated Cloud Computing (CC), including security, performance, latency, and network issues. Fortunately, Fog Computing is a solution that addresses these concerns by bringing CC closer to IoT devices. Essentially, fog serves as a data hub that processes and stores information locally on the fog node, rather than sending it to a cloud server. This results in faster response times and higher- quality services compared to those offered by traditional cloud servers. Fog Computing can optimize service delivery for multiple IoT clients by allowing the administrationof services and resource provisioning outside of CC, nearer to devices, or at specific locations for Service Level Agreements (SLAs). It's important to note that Fog Computing is not intended to replace CC, but rather to complement itas a critical component. In this paper, we explore various computing paradigms, examine the features and architecture of Fog Computing, analyze its relationship with IoT, and assess differentalgorithms used in Fog Computing systems. Furthermore, we tackle the distinctive challenges that emerge with Fog Computing as an intermediate layer between IoT sensors/devices and data centers. Keywords: Fog Computing, Internet of Things(IOT), Cloud Computing (CC).

A walkthrough of the emerging IoT paradigm: Visualizing inside functionalities, key features, and open issues

The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. IoT will lead to the development of a wide range of advanced information services that need to be processed in real-time and require data centers with large storage and computing power. The integration of IoT with Cloud and Fog Computing can bring not only the required computational power and storage capacity, but they enable IoT services to be pervasive, cost-effective, and can be accessed from anywhere using any device (mobile or stationary). However, IoT infrastructures and services will introduce grand security challenges due to the significant increase in the attack surface, complexity, heterogeneity and number of resources. In this paper, we present an IoT security framework for smart infrastructures such as Smart Homes (SH) and smart buildings (SB). We also present a general threat model that can be used to develop a security protection methodology for IoT services against cyber-attacks (known or unknown). Additionally, we show that Anomaly Behavior Analysis (ABA) Intrusion Detection System (ABA-IDS) can detect and classify a wide range of attacks against IoT sensors.

A transformative revolution has been triggered by the inclusion of Internet of Things (IoT) sensors in healthcare’s real-time tracking capabilities. Recent advancements in miniaturization and low-power designs, wireless connectivity, and sensor fusion (integration of information from multiple sensors) continue to have a profound impact on the monitoring of health. Intelligent embeddability and superior sensing capabilities facilitate seamless patient data tracking, and strong security protects data against breaches. The role of IoT sensors in healthcare is to connect devices, such as biometric and temperature sensors or imaging and environmental sensors. IoT sensors are used in multiple applications such as fitness and vital signs tracking information, and patient care in smart healthcare facilities. Such technologies enable IoT-based medication adherence systems so that patients follow prescribed treatments, thus improving outcomes and reducing the possibility of hospital readmissions. Compared to regular monitoring devices, IoT sensors provide continuous data capture, increased accuracy, improved patient mobility and personalized healthcare solutions. Healthcare IoT adoption faces challenges in data privacy, regulation, and the complexity of technical integration of healthcare IoT in existing infrastructures. Ethical considerations must also be addressed to ensure equitable access and unbiased use of IoT technology. However, despite these obstacles, the future of IoT-based healthcare technologies remains an attractive proposition with continuous technology evolution likely to improve the quality of care, optimal resource allocation, and decision-making in real-time. This chapter explores the evolution of healthcare monitoring through IoT sensors, detailing their technological mechanisms, applications, benefits, and the potential for transforming the healthcare landscape.

Fog computing is now becoming prominent among users due to its on‐demand services and dynamic resource allocations. Resource allocation and scheduling is a critical task in cloud computing. Cloud resource management is based upon two main parts, which are: (1) state information collection and (2) scheduling decision making. In state information collection, a machine is responsible to collect information related to the current utilization of cloud resources. Based on the state information, the cloud resources are allocated according to scheduling decision making. The key purpose of scheduling is to attain good computing performance and to reduce the system cost. Security and privacy are becoming the key requirements with the huge influx of these millions of devices. The presence of security, privacy, and accountability measures establish trust among its stakeholders. It is dependent on the measures taken for the availability, accountability of security, and privacy of users' data and services in the fog, as well as sharing these statistics with the stakeholders. Any type of lapses in measures for security and privacy shatters user trust.This chapter provides a comprehensive review for trust management in machine to machine communications that lays the ground for trust establishment in fog computing. Comparative studies of authentication, authorization, and privacy measures are carried out while the role of service layer agreements (SLA) is discussed as well, followed by implementation issues and discussion based on a healthcare scenario. A taxonomy is presented that outlines the strategies used for trust management in fog. We have provided a detailed reference table of research work performed in this context. We also described the merits and demerits of each technique. This work will be equally helpful for researchers to have a global overview of existing fog trust management techniques.

With the popularisation and development of the internet of things, big data and cloud computing, the search of data in cloud-based internet of things becomes a hot research topic. However, the sensitive data, such as the medical data collected by wearable devices, is inevitable to be stored in the cloud server. Homomorphic encryption has the ability to calculate the ciphertext without decryption. We separate the calculating and the decryption into different security domains to preserve the privacy of sensitive data, so the original plaintext would not be exposed in the cloud. Hence, we can compare two ciphertexts to get the difference of them in a privacy preserving way. In order to accelerate the search process of range query, we build an encrypted self-balancing binary index tree. Based on oblivious RAM, the searching scheme can hide the access patterns of the node of tree. The actual nodes and logic relation of tree are stored on different servers. A sample implementation of the proposed scheme is given, and the experimental results and analysis are presented to illustrate the scheme's effectiveness and security.

The Internet of Things (IoT) is an interconnected network of computing nodes that can send and receive data without human participation. Software and communication technology have advanced tremendously in the last couple of decades, resulting in a considerable increase in IoT devices. IoT gadgets have practically infiltrated every aspect of human well-being, ushering in a new era of intelligent devices. However, the rapid expansion has raised security concerns. Another challenge with the basic approach of processing IoT data on the cloud is scalability. A cloud-centric strategy results from network congestion, data bottlenecks, and longer response times to security threats. Fog computing addresses these difficulties by bringing computation to the network edge. The current research provides a comprehensive review of the IoT evolution, Fog computation, and artificial-intelligence-inspired machine learning (ML) strategies. It examines ML techniques for identifying anomalies and attacks, showcases IoT data growth solutions, and delves into Fog computing security concerns. Additionally, it covers future research objectives in the crucial field of IoT security.

In this study, we detail the use of a small, label-free optoelectronic biosensor for the detection of anti-dengue antibodies in human serum samples. The system consists of a vertical-cavity surface-emitting laser that can be tuned, a guided-mode resonant sensor surface, and two silicon pin detectors. After showing adequate sensitivity in a clinically relevant experiment, researchers hypothesized that this cutting-edge biosensor could serve as a novel platform for the development of efficient point-of-care diagnostic techniques for the identification of infectious diseases. Humans are susceptible to one of the world’s most common and contagious diseases, dengue fever, which is spread by the Aedes albopictis mosquito. Human deaths were also a direct outcome of the widespread increase in Dengue fever cases. A shortage of medical professionals and healthcare facilities only made matters worse. In order to achieve this goal, it will be necessary to employ archaic medical technologies. Recent innovations like Fog Computing and the success of remote healthcare in real time, Cloud Computing, and the Internet of Things have opened up new frontiers in technology (IoT). In this research, we proposed a new method for diagnosing dengue disease. Information gathered from those who have been afflicted by a disease will be shared with higher authorities. Various IoT devices compile comprehensive reports on each and every patient. Once medical history is gathered, the patient’s whereabouts must be ascertained. Sensors connected to the internet are used to gather information on the weather, the location, the effectiveness of medications, the safety of the surrounding environment, and the patients’ states of health. The fog computing layer is the bridge between IoT sensors and cloud servers. Two primary functions of the fog computing layer are the creation of notifications and the categorization of users’ health conditions. Dengue fevers are diagnosed using an Artificial Neural Network (ANN) trained with the Salp Swarm Optimization method Salp swarm algorithm (SSA). The dataset will be analyzed using an Internet of Things (IoT) scenario built with the Java simulator CupCarbon U-one 3.8.2. The proposed method achieves competitive or better outcomes than the state-of-the-art alternatives.

Multi-source Internet of Things (IoT) data, archived in institutions’ repositories, are becoming more and more widely open-sourced to make them publicly accessed by scientists, developers, and decision makers via web services to promote researches on geohazards prevention. In this paper, we design and implement a big data-turbocharged system for effective IoT data management following the data lake architecture. We first propose a multi-threading parallel data ingestion method to ingest IoT data from institutions’ data repositories in parallel. Next, we design storage strategies for both ingested IoT data and processed IoT data to store them in a scalable, reliable storage environment. We also build a distributed cache layer to enable fast access to IoT data. Then, we provide users with a unified, SQL-based interactive environment to enable IoT data exploration by leveraging the processing ability of Apache Spark. In addition, we design a standard-based metadata model to describe ingested IoT data and thus support IoT dataset discovery. Finally, we implement a prototype system and conduct experiments on real IoT data repositories to evaluate the efficiency of the proposed system.

Resource allocation in smart settings, more specifically in Internet of Things (IoT) transportation, is challenging due to the complexity and dynamic nature of fog computing. The demands of users may alter over time, necessitating more trustworthy resource allocation and administration. Effective resource allocation and management systems must be designed to accommodate changing user needs. Fog devices don’t just run fog-specific software. Resource and link failures could be brought on by the absence of centralised administration, device autonomy, and wireless communication in the fog environment. Resources must be allocated and managed effectively because the majority of fog devices are battery-powered. Latency-aware IoT applications, such as intelligent transportation, healthcare, and emergency response, are now pervasive as a result of the enormous growth of ubiquitous computing. These services generate a large amount of data, which requires edge processing. The flexibility and services on-demand for the cloud can successfully manage these applications. It’s not always advisable to manage IoT applications exclusively in the cloud, especially for latency-sensitive applications. Thus, fog computing has emerged as a bridge between the cloud and the devices it supports. This is typically how sensors and IoT devices are connected. These neighbouring Fog devices control storage and intermediary computation. In order to improve the Fog environment reliability in IoT-based systems, this paper suggests resource allocation and management strategy. When assigning resources, latency and energy efficiency are taken into account. Users may prioritise cost-effectiveness over speed in a fog. Simulation was performed in the iFogSim2 simulation tool, and performance was compared with one of the existing state-of-the-art strategy. A comparison of results shows that the proposed strategy reduced latency by 10.3% and energy consumption by 21.85% when compared with the existing strategy.

Fog computing is a new computational paradigm proposed by Cisco that subdues the shortcomings of cloud computing by transferring some of the core functions of cloud towards the network edge. Internet of Things (IOT) is a very promising technology, which are used heavily in many areas of life and with such a large and rapid spread produces a lot of challenges in IOT. As reported by Cisco, by the year 2020, the next generation Internet-connected devices would comprise of around 50 billion Internet-connected devices which are located at the edge of the network and require support for mobility, low latency, real-time, and location-aware services. The Cloud computing is used as a main component in IOT architecture to provide processing and storage services for IOT system but the conventional cloud computing architecture is completely centralized in nature, and therefore, fails to provide real-time and low latency services to billions of IOT devices at the edge of the network, simultaneously. The main objective of this research is to combine the capabilities of Fog Computing, Cognitive and Context-Aware IOT to build a cognitive management framework for Fog computing capable of adopting it's behavior in a context-aware fashion to maximize the number of functions that can be carried out in The Fog and to provide a solution for determining which functions should be carried out in the Fog and which should be carried out in the cloud and how the Fog should interact with the Cloud, The same function in some situations can be carried out in the fog while in other situations can be carried out in the cloud according to its context.