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In the internet of things (IoT) domain, there has currently been a growing interest, leading to the idea of the IoT ecosystem. But the standards, technology, and structures of the conventional IoT framework do not provide the necessary QoS for today's massive data. Thus, for today's IoT ecosystem, a framework called SD-DRFC (software-defined dew, roof, fog, and cloud computing) is suggested in this article. The framework delivers facilities from the closest possible position of end-user gadgets and thus increases the QoS in an IoT system. Clear description about the role and features of each tier is also presented. The path to a multi-tier computational architecture assisted by SDN can be realized from the given detailed literature review. Using the iFogSim simulator, a use case based on the architecture provided is then given and evaluated. This article considers four QoS parameters (latency, network use, cost, and energy consumption). When compared the findings of the simulation, the proposed framework execution performs much better than cloud-only execution.

The internet of things (IoT) offers a range of benefits for its users, ranging from quicker and more precise perception of our ecosystem to more cost-effective monitoring of manufacturing applications, by taking internet access to the things. Due to the ubiquitous existence of the internet, there's been an increasing pace in the IoT. Such a growing pace has brought about the term of IoT ecosystem. This exponential growing IoT ecosystem will encounter several challenges in its path. Computing domains were used from very initial stage to assist the IoT ecosystem and mitigate those challenges. To understand the impact of computing domains in IoT ecosystem, this paper performs the elaborative study on cloud, fog, roof, and dew computing including their interaction, benefits, and limitations in IoT ecosystem. The brief comparative analysis on these four computing domains are then performed. The impact of internet and offline computing on these computing domains are then analyzed in depth. Finally, this paper presents the suggestions of potential appropriate computing domain strategies for IoT ecosystems.

Stock market price prediction is now a prominent and significant issue in financial and academic studies as the stock market plays a vital role in the economy. The process of attempting to anticipate the future valuation of a company's share is known as stock market price prediction. Share prices are time-series information, and artificial neural networks (ANNs) can uncover non-linear associations among time-series information. This makes ANN the best method for predicting stock market values. Many researchers are working on this topic and trying to find the best algorithm which is suitable for predicting the stock price. But significant improvement in prediction is still not achieved. Therefore, in this work, an ANN model is proposed and implemented by using a multilayer feedforward backpropagation method. In this work, data of fifteen companies over a six years span have been analyzed. To predict the specific result, the proposed model has been trained with four different algorithms: Levenberg Marquardt (LM), Bayesian regularization (BR), scaled conjugate gradient (SCG) and Quasi Newton) by changing their parameters. Number of hidden layers, hidden neurons and percentage of training data have been changed to get better output. The split ratio of training, testing and validation data sets is 70:15:15. The projected results are then compared to the actual data after the training and testing procedure to determine the accuracy. The accuracy of LM is 95.64%, BR is 91.26%, SCG accuracy is 88.91% and Quasi Newton is 84.20%. The result showed that, LM algorithm provides better accuracy than other models. In addition, less error has been found from the LM algorithm, making it the best algorithm for prediction in our proposed model.

The liver is one of the most vital organs of the human body. Even when partially injured, it functions normally. Therefore, detecting liver diseases at the early stages is challenging. Early detection of liver problems can improve patient survival rates. This research enlightens on several Artificial Intelligence techniques, including the Bagged Tree, Support Vector Machine, K-Nearest Neighbor, and Fine Tree classifier, to predict the presence of liver disease in a patient at an early stage. This study compares those models and selects the best technique to detect liver disease at an early stage. The classification performance is measured using the confusion matrix, True Positive Rate (TPR), False Positive Rate (FPR), ROC curve, and accuracy. The result shows that the Bagged Tree classifier achieves the highest classification accuracy (81.30%), which is very promising compared to the other algorithms. The proposed system also performs sensitivity analysis on the dataset to investigate the impact of each attribute on the model’s performance. It has been demonstrated that Alanine Aminotransferase (sgpt) attribute has the most significant impact on the prediction of liver disease. The proposed method could be used as an assistant framework for liver disease detection at an early stage.

The internet of things (IoT) creates immense volume of objects online. But cloud computing isn't suited to environmental demands. Hence, fog computing (FC) emerged which shifts the computation load into edge fog devices. However, FC also faces some obstacles which can be mitigated by software-defined networking (SDN). By combining SDN and FC, the network form can overcome almost all cloud limitations and can boost QoS. Within this article, architecture is proposed by combining SDN and FC to improve QoS for IoT ecosystem. With the architecture, an algorithm is propounded based on virtual partition. Then a use case is presented and evaluated through iFogSim simulator. The result shows a significant improvement of several QoS parameters in the execution of fog with SDN compared to the cloud-only execution. The results also show better results for energy consumption, network use (212.21% reduction), and latency (275.9% reduction) compared with previous similar use case.

The IEEE 802.16 technology (WiMAX) facing great challenges due to owning high mobility of mobile nodes, limited radio coverage of wireless devices, time varying nature of wireless medium. The transmission range of base station (BS) has a vital influence and must achieve the most economic case of energy in wireless networks. This paper investigates the effects of transmission range of BSs for two prominent routing protocols- Destination Sequenced Distance Vector (DSDV) and Ad-hoc On-demand Distance Vector (AODV) respectively over WiMAX networks. The NIST WiMAX module is used to configure WiMAX environment and performance differentials are analyzed using NS-2. The QoS metrics used to evaluate the performance are packet delivery ratio (PDR), throughput, routing overhead (RO) and RTR packet loss. Simulation results reveal that performance increases with increasing the transmission range of the BSs. Although both protocols shows almost similar PDR and Throughput results, AODV is more sensitive to the transmission range that is correlated to transmission power of BS than DSDV. Unlike Ad hoc network here DSDV outperforms AODV in terms of RO and RTR routing packet loss probability.

The Internet of Things (IoT) has become one of the most known terms in present times, reaching new levels and setting a trend in the world. Evidently, it is the future of connectivity which has turned physical objects into intelligent objects. Therefore, there has been a growing curiosity in the IoT field and this leads to the concept of IoT ecosystem. But the contemporary fragmented ecosystem of regulations, technologies, and systems slows IoT deployments. Thus, we consider multi-tiered computational infrastructure which would be feasible to provide services from the nearest possible location of end devices. To mitigate multi-tier infrastructure issues, Software-Defined Networking (SDN) steps in. The journey behind SDN-supported multi-tier computational infrastructure can be understood from this paper’s elaborate study. Next, a comparative analysis on dew, roof, fog and cloud is conducted and the impact of internet on these computing paradigms is briefly explained. Then a novel framework termed “SD-DRFC (Software-Defined Dew, Roof, Fog and Cloud computing)” is proposed for today's IoT ecosystem. The role and functionality of each tier of SD-DRFC framework are adequately explained. A use case focused on the SD-DRFC framework is then presented and simulated by utilizing iFogSim simulator. To evaluate the efficiency of the presented SD-DRFC framework, four QoS parameters (Latency, Network Usage, Cost, and Energy Consumption) are considered in this paper. When comparing the simulation results, the presented SD-DRFC framework performs much better than cloud-only implementation. The advantages and suitability of utilizing this proposed framework have been demonstrated by multiple use-cases which range from conceptual visions to existing running systems.