With the exponential growth of the Industrial Internet of Things (IIoT), multiple outlets are constantly producing a vast volume of data. It is unwise to locally store all the raw data in the IIoT devices since the energy and storage spaces of the end devices are strictly constrained. self-organization and short-range Internet of Things (IoT) networking also support outsourced data and cloud computing, independent of the distinctive resource constraint properties. For the remainder of the findings, there is a sequence of unfamiliar safeguards for IoT and cloud integration problems. The delivery of cloud computing is highly efficient, storage is becoming more and more current, and some groups are now altering their data from in house records Cloud Computing Vendors' hubs. Intensive IoT applications for workloads and data are subject to challenges while utilizing cloud computing tools. In this report, we research IoT and cloud computing and address cloud-compatible problems and computing techniques to promote the stable transition of IoT programs to the cloud.

https://journal.qubahan.com/index.php/qaj/article/download/36/21

IoT and Cloud Computing Issues, Challenges and Opportunities: A Review

Knowledge growth and development: internet of things (IoT) research, 2006–2018

The convergence and interplay of edge, fog, and cloud in the AI-driven Internet of Things (IoT)

For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients' lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification.

/pdf/iomt-based-automated-detection-and-classification-of-4l3zs76odq.pdf

IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning.

In the cloud-based Internet of Things (IoT) environments, quantifying uncertainty is an important element input to keep the acceptable level of reliability in various configurations. In this paper, we aim to address the pricing model of delivering data over the cloud while taking into consideration the dynamic uncertainty factors such as network topology, transmission/reception energy, nodal charge and power, and computation capacity. These uncertainty factors are mapped to different nodes with varying capabilities to be processed using Artificial Intelligence (AI)-based algorithms. Accordingly, we aim to find a way to calculate and predict the price per big data service over the cloud using AI and deep learning. Therefore, in this paper, we propose a framework to address big data delivery issues in cloud-based IoT environments by considering uncertainty factors. We compare the performance of the framework using two AI-based techniques called Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA) in both centralized and distributed versions. The use of AI techniques can be applied in multilevel to provide a kind of deep learning to further improve the performance of the system under study. The results reveal that the distributed algorithm outperforms the centralized one. In addition, the results show that the GA has lower running time compared to the SAA in all the test cases such as 68% of improvement in the centralized version, and 66% of improvement in the distributed version in case when the size of uncertainty array is 256. Moreover, when the size of uncertainty array increases, the results show 60% speed up in the distributed GA compared to its centralized version. The improvements achieved would help the service providers to actually improve their profit using the proposed framework.

/pdf/quantifying-uncertainty-in-internet-of-medical-things-and-2yxuu2o6pe.pdf

Quantifying Uncertainty in Internet of Medical Things and Big-Data Services Using Intelligence and Deep Learning

Workload aware VM consolidation method in edge/cloud computing for IoT applications

Trustworthy transmission is a beneficial step toward the success of the new era of telecommunication technologies and online social networks (OSNs). Many sensitive applications can benefit from OSNs, e.g., eHealth and medical services. However, OSNs have always been prey to Sybil attacks where numerous fake nodes are being generated and propagated in social networks to mimic like real nodes for the purpose of achieving malicious goals. Thus, for security reasons and for the sensitivity of data used in eHealth applications, such fake nodes have to be detected and deactivated immediately. The emerging field of the Internet of Medical Things (IoMT) promotes trust management (TM) among various IoMT devices to provide accurate and reliable communications, which is quite essential in critical diseases such as COVID-19. TM provides a secure platform to IoMT devices using different security protocols in the IoMT network. Generically, if a device is not comfortable to connect with additional devices in a network, the motive of the communication process is not succeeded and leads to disappointment for one device toward others. To handle these types of situations, a TM mechanism, named fuzzy-based TM mechanism for preventing Sybil attacks in the Internet of Medical Things (FTM-IoMT), is proposed. The FTM-IoMT provides TM for the users of eHealth systems using IoMT infrastructures. It is an intelligent mechanism to recognize Sybil or untrustworthy nodes in the system. The proposed mechanism helps IoMT nodes to collect authentic and credible information from their neighboring nodes as well as to neglect Sybil nodes. The trust value of a node is evaluated using fuzzy logic processing followed by the trust attributes, such as integrity, receptivity, and compatibility of a node. The FTM-IoMT provides a double evaluation check based on fuzzy logic processing and fuzzy filter. The proposed scheme shows superior results when compared to the state-of-the-art approaches.

FTM-IoMT: Fuzzy-Based Trust Management for Preventing Sybil Attacks in Internet of Medical Things

Internet of Medical Things (IoMT) provides a diverse platform for healthcare to enhance the accuracy, reliability, and efficiency. In addition, it utilizes the productivity of available equipment to improve patients’ health. IoMT also provides distinct ways by which healthcare will be revolutionized as it provides numerous opportunities to handle operations with precision. However, numerous advantages have raised several security challenges, such as trust, data integrity, network constraints, and real-time processing among others. There is a requirement for a robust approach to maintain data integrity along with the behavior detection of nodes to completely maintain a secure environment. In the proposed approach, the mechanism is capable of maintaining a robust network by predicting and eliminating malicious nodes. The proposed NeuroTrust approach utilizes the trust parameters to evaluate the degree of trust that include reliability, compatibility, and packet delivery. This approach also lightens the two-way computation burden and uses a lightweight encryption mechanism to further enhance the security and integrity during data dissemination, which is required for the digital revolution in delivering efficient high quality healthcare. The performance of the proposed approach has been extensively evaluated against the absolute trust formulation, accuracy of trust computation, energy consumption, and several potential attacks. The simulation results show the effective performance to identify malicious and compromised nodes, and maintain resilience against various attacks.

NeuroTrust—Artificial-Neural-Network-Based Intelligent Trust Management Mechanism for Large-Scale Internet of Medical Things

Secure distributed adaptive bin packing algorithm for cloud storage

The Internet of Things is progressively turning into a pervasive computing service, needing enormous volumes of data storage and processing. However, due to the distinctive properties of resource constraints, self-organization, and short-range communication in Internet of Things (IoT), it always adopts to cloud for outsourced storage and computation. This integration of IoT with cloud has a row of unfamiliar security challenges for the data at rest. Cloud computing delivers highly scalable and flexible computing and storage resources on pay-per-use policy. Cloud computing services for computation and storage are getting increasingly popular and many organizations are now moving their data from in-house data centers to the Cloud Storage Providers (CSPs). Time varying workload and data intensive IoT applications are vulnerable to encounter challenges while using cloud computing services. Additionally, the encryption techniques and third-party auditors to maintain data integrity are still in their developing stage and therefore the data at rest is still a concern for IoT applications. In this paper, we perform an analysis study to investigate the challenges and strategies adapted by Cloud Computing to facilitate a safe transition of IoT applications to the Cloud.

Irfan Mohiuddin

Papers

Workload aware VM consolidation method in edge/cloud computing for IoT applications

FTM-IoMT: Fuzzy-Based Trust Management for Preventing Sybil Attacks in Internet of Medical Things

NeuroTrust—Artificial-Neural-Network-Based Intelligent Trust Management Mechanism for Large-Scale Internet of Medical Things

Secure distributed adaptive bin packing algorithm for cloud storage

Security Challenges and Strategies for the IoT in Cloud Computing