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Fog computing complements cloud computing by removing several limitations, such as delays and network bandwidth. It emerged to support Internet of Things (IoT) applications wherein its computations and tasks are carried out at the network’s edge. Heterogeneous IoT devices interact with different users throughout a network. However, data security is a crucial concern for IoT, fog and cloud network ecosystems. Since the number of anonymous users increases and new identity disclosures occur within the IoTs, it is becoming challenging to grow mesh networks to deliver end to end communications, as the extended IoT networks resemble a mesh architecture. To reinforce data security over IoTs, we deploy a microservice-based blockchain mechanism for fogs, which works as a decentralized client-server network medium (i.e., secured end device-based communication). We implement a blockchain equipped security scheme to be used with a fog-IoT hierarchical tree-based overlay mesh architecture to address and develop the network performance issues. In this study, we consider encryption and decryption delays from IoT and fog-integrated parts to monitor data records and compare them through the developed security scheme. The blocks of a blockchain offer the desired execution results mainly in terms of the algorithmic efficiency, which correlates with the existing algorithms, namely the Advanced Encryption Standard (AES), the Rivest Shamir Adleman (RSA), and the Data Encryption Standard (DES). Our ‘BFIM’ scheme has an enhanced task scheduling capacity and a more efficient throughput than the AES, DES, RSA resource deliverables (i.e., tasks). Our comprehensive performance evaluation implies that the Blockchain-based Fog IoT Microservice (i.e., BFIM) architecture provides a task delivery efficiency of 78.79% (i.e., task deliverable) and a service delivery efficiency of 83.24% (i.e., task scheduling). The ‘BFIM’ also has an overall process delivery efficiency of 75% (i.e., time delay, throughput) in the fog layer, rather than a central cloud layer running the AES, DES, and RSA algorithms.

Appropriate resource allocation and management in cloud datacenters has become a crucial consideration for the progress of cloud datacenters. Since allocating resources in a planned and convenient way has a prevailing impact in the issue of "Cloud computing" along these lines the cloud server farms must manage the complexities in making sense of and settling the strategies to control, dispense, utilize, work and move the resources in an enormously effective way. Maintaining Quality of Service (QoS) in cloud datacenters may also become tougher if resources (like- CPU, Hard disk, Memory, Networks etc.) are not properly allocated. Therefore, an extraordinarily efficient scheme should be pursued for resource allocation and management in cloud data centers after the analyzing, diagnosing and well-identifying of existing problems. In this research work, a resource allocation scheme is going to be proposed in the form of an algorithm named “Dynamic and Efficient Resource Allocation and Management (DERAM)” algorithm which will mainly take into consideration–managing the CPU, memory, hard disk and Networks as the resources of cloud computing., {"references":["Gao, Y., Xue, Y., & Li, J. (2013, July). Utilization-aware allocation for multi-tenant datacenters. In 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops (pp. 82-87). IEEE.","Ho, Y., Liu, P., & Wu, J. J. (2011, December). Server consolidation algorithms with bounded migration cost and performance guarantees in cloud computing. In 2011 Fourth IEEE International Conference on Utility and Cloud Computing (pp. 154-161). IEEE.","Gu, J., Hu, J., Zhao, T., & Sun, G. (2012). A new resource scheduling strategy based on genetic algorithm in cloud computing environment. Journal of computers, 7(1), 42-52.","Yuan, Y., & Liu, W. C. (2011, October). Efficient resource management for cloud computing. In 2011 International Conference on System science, Engineering design and Manufacturing informatization (Vol. 2, pp. 233-236). IEEE.","Younge, A. J., Von Laszewski, G., Wang, L., Lopez-Alarcon, S., & Carithers, W. (2010, August). Efficient resource management for cloud computing environments. In International conference on green computing (pp. 357-364). IEEE","Gulati, A., Shanmuganathan, G., Holler, A. M., & Ahmad, I. (2011). Cloud Scale Resource Management: Challenges and Techniques. HotCloud, 11, 3-3.","Rajarajeswari, C. S., & Aramudhan, M. (2013, December). Resource provisioning for SLA management. In 2013 International Conference on Advanced Computing and Communication Systems (pp. 1-6). IEEE.","Ngenzi, A., & Nair, S. R. (2015). Dynamic resource management in Cloud datacenters for Server consolidation. arXiv preprint arXiv:1505.00577.","Xia, B., & Tan, Z. (2010). Tighter bounds of the First Fit algorithm for the bin-packing problem. Discrete Applied Mathematics, 158(15), 1668-1675.","Song, Y., Wang, H., Li, Y., Feng, B., & Sun, Y. (2009, May). Multi-tiered on-demand resource scheduling for VM-based data center. In 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid (pp. 148-155). IEEE."]}

Leukemia is usually characterized as a potentially fatal disease with different types and subtypes. It pretends as one of the major reason for death in developed and developing nations like Bangladesh, almost a hundred thousand people around the world are losing the fight against leukemia in every decade. An early prediction and diagnosis of a leukemia type have become inexorable in treatment, as it can reduce deadly risk to the valuable life of affected patients. This paper's objective is to design and development a smart decision support prediction system of Acute Lymphoblastic Leukemia Subtypes using Artificial Neural Network (i.e. ANN) and Adaptive Neuro-Fuzzy Inference System (i.e. ANFIS). For this prediction system, 500 datasets (main database) of different subtypes of Acute Lymphoblastic Leukemia patients are used. Training and testing are the two main steps of ANN and ANFIS, where 445 and 55 datasets are elected for training and testing respectively. According to the experimental result, it is apparent that the Adaptive Neuro-Fuzzy Inference System (i.e. ANFIS) approach performs better than the Artificial Neural Network (i.e. ANN) in both training and testing. Analysis reveals that a simple and well ergonomic back-propagation learning technique of Artificial Neural Network (i.e. ANN) and a hybrid optimization learning technique of the Adaptive Neuro-Fuzzy Inference System (i.e. ANFIS) can easily predict blood disorder efficiently and effectively in terms of hematological parameters.

Authentication is a challenging and emerging issue for Fog-IoT security paradigms. The fog nodes toward large-scale end-users offer various interacted IoT services. The authentication process usually involves expressing users' personal information such as username, email, and password to the Authentication Server (AS). However, users are not intended to express their identities or information over the fog or cloud servers. Hence, we have proposed an Anonymous User Authentication Scheme for Fog-IoT (AUASF) to keep the anonymity existence of the IoT users and detect the intruders. To provide anonymity, the user can send encrypted credentials such as username, email, and mobile number through the Cloud Service Provider (CSP) for registration. IoT user receives the response with a default password and a secret Id from the CSP. After that, the IoT user submits the default password for first-time access to Fog Service Provider (FSP). The FSP assigns a One Time Password (OTP) to each user for further access. The developed scheme is equipped with hash functions, symmetric encryptions, and decryptions for security perceptions across fog that serves better than the existing anonymity schemes.

In internet based life ‘Sentiment’ is a popular expression of the present patterns as there are heaps of work is being finished closing information or data mining. Utilizing sentiment examination different envision of events can be found, for example; human trafficking, burglary, ambiguous feelings and so on. Rapid miner, a social media mining stage which checks in the middle of sentiment relations through cloud mining techniques with a view to indicating the yield of a gross aftereffect of thought states. In this work, the authors have utilized rapid miner programming for investigating the continuous twitter cloud information mining methodology. Aftermath of this process, separation of human contemplation can be anticipated all through the varieties of their thinking sorts on each time based tweeting. The principle intention of this work is to demonstrate the performance execution between KNN and LR algorithms. Moreover, KNN performs superior to LR with 98% precision of genuine positivity upon individuals thinking examples or changes from constant data.

Underwater detector network is one amongst the foremost difficult and fascinating analysis arenas that open the door of pleasing plenty of researchers during this field of study. In several under water based sensor applications, nodes are square measured and through this the energy is affected. Thus, the mobility of each sensor nodes are measured through the water atmosphere from the water flow for sensor based protocol formations. Researchers have developed many routing protocols. However, those lost their charm with the time. This can be the demand of the age to supply associate degree upon energy-efficient and ascendable strong routing protocol for under water actuator networks. During this work, the authors tend to propose a customary routing protocol named level primarily based routing protocol (LBRP), reaching to offer strong, ascendable and energy economical routing. LBRP conjointly guarantees the most effective use of total energy consumption and ensures packet transmission which redirects as an additional reliability in compare to different routing protocols. In this work, the authors have used the level of forwarding node, residual energy and distance from the forwarding node to the causing node as a proof in multicasting technique comparisons. Throughout this work, the authors have got a recognition result concerning about 86.35% on the average in node multicasting performances. Simulation has been experienced each in a wheezy and quiet atmosphere which represents the endorsement of higher performance for the planned protocol

Fog computing is one of the most important emerging paradigms in recent technological development. It alleviates several limitations of cloud computing by bringing computation, communication, storage, and real-time services near to the end-users. However, with the rapid development of automation in smart cities, the number of task executions by fog nodes are increasing, requiring additional fog nodes. In this paper, we present a Scheduling-based Dynamic Fog Computing (SDFC) Framework to augment the utilization of existing resources rather than adding further fog resources. It includes an additional layer, Master Fog (MF), between the cloud and general-purpose fogs, which are addressed here as Citizen Fog (CF). The MF is responsible for deciding task execution in CFs and the cloud. We use the Comparative Attributes Algorithm (CAA) to schedule tasks based on their priority and a Linear Attribute Summarized Algorithm (LASA) to select the most available CF with the highest computational ability. Our empirical results validate our SDFC framework and show the dependency on the cloud reduces by 15%–20% and overall execution time decreases by 45%–50%.

Fog computing provides better integration facilities between cloud computing and Internet of things with real-time interactions. It delivers faster computing functionalities sitting close to user applications and has local storage options. Despite these potentials in fogging, maintaining secure data communication is still a challenging issue and needs further development. In this paper, we have presented an Identity-Based Encryption (IBE) scheme that ensures secure data transmission to authorized users. To provide data security, we have proposed a four-level Hierarchical Identity-Based Architecture for Fog Computing(HIBAF). In addition, we have evaluated our scheme to analyze the performance in terms of user load, memory utilization, response time, and delay time over different sizes of datasets. Finally, we have compared our results with other cryptography systems to figure out the effectiveness of our scheme and found overall 30% efficient.

Alzheimer’s is a catastrophic neuro-degenerative state in the elderly which reduces thinking skills and thereby hamper daily activity. Thus the management may be helpful for people with such condition. This work presents sensor based management system for Alzheimer’s patient. The main objective of this work is to report an early prototype of an eventual wearable system that can assist in managing the health of such patients and notify the caregivers in case of necessity. A brief case study is presented which showed that the proposed prototype can detect agitated and clam states of patients. As the ultimately developed assistive system will be packaged as a wearable device, the case study also investigated the usability of wearable devices on different age groups of Alzheimer’s patients. In addition, electro dermal activity for 4 patient of age group 55–60 and 60-7s years were also explored to assess the health condition of the patients.

The cooperative junction of mobile nodes consists of any centralized access point or a self-configuring network existing infrastructure without the required intervention is called ad hoc network. A well designed security protocol is a challenging task due to its unique characteristics such as, lack of central authority, frequent topology changes, rapid node mobility, shared radio channel and limited availability of resources for ad hoc network. However, most of these protocols are two kinds. They are proactive and reactive in terms. Both of these approaches contains their own limitations, for example, in maintaining the routing information the proactive protocols use excess bandwidth while, the reactive ones have long route request delay. In this paper, we proposed a secure hybrid ad hoc routing protocol, called Secure Zone Routing Protocol (SZRP). The proposed protocol is based on the concept of zone routing protocol (ZRP) with AES (Advanced Encryption System).