Transparent Access to Heterogeneous IoT Based on Virtual Resources

The Internet of Things(IoT)inspires industries to deploy a massive number of connected devices to provide smart and ubiquitous services to influence our daily life.Edge computing leverages sufficient computation and storage at the edge of the network to enable deploying complex functions closer to the environment using Internet-connected devices.According to the purpose of the environment including privacy level,domain functionality,network scale and service quality,various environment-specific services can be provided through heterogeneous applications with sensors and actuators based on edge computing.However,for providing user-friendly service scenarios based on the transparent access to heterogeneous devices in edge computing,a consistent interface shall be provided to deliver services from edge computing to clients.In this paper,we propose transparent computing based on virtual resources to access heterogeneous IoT devices without considering the underlying network configuration at the edge of the networks.For supporting transparent access to different edge computing environments through a consistent interface,the virtual resource of edge gateway is proposed to bridge the Internet and devices which are deployed on the edge of the network.The proposed edge gateway exposes the services of the Internet of Things devices to the Internet using virtual resources that represent the resources of physical devices.The virtual resources provide a consistent interface to enable clients to access devices in edge computing without considering underlying protocols.The virtual resource is generated by the resource directory in the edge gateway through the registration of a device.Based on the device registration,the device information is stored in the gateway to link virtual resources and devices for translating messages according to the destination protocols and identifying physical devices that are represented by virtual resources.Moreover,through collaboration with the service provider,the function of device discovery and monitoring is provided to clients.

Enhanced Water Quality Control Based on Predictive Optimization for Smart Fish Farming

The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource limitations.Internet of Things(IoT)based Modern fish farming systems can significantly optimize seafood production by minimizing resource utilization and improving healthy fish production.This objective requires intensive monitoring,prediction,and control by optimizing leading factors that impact fish growth,including temperature,the potential of hydrogen(pH),water level,and feeding rate.This paper proposes the IoT based predictive optimization approach for efficient control and energy utilization in smart fish farming.The proposed fish farm control mechanism has a predictive optimization to deal with water quality control and efficient energy consumption problems.Fish farm indoor and outdoor values are applied to predict the water quality parameters,whereas a novel objective function is proposed to achieve an optimal fish growth environment based on predicted parameters.Fuzzy logic control is utilized to calculate control parameters for IoT actuators based on predictive optimal water quality parameters by minimizing energy consumption.To evaluate the efficiency of the proposed system,the overall approach has been deployed to the fish tank as a case study,and a number of experiments have been carried out.The results show that the predictive optimization module allowed the water quality parameters to be maintained at the optimal level with nearly 30%of energy efficiency at the maximum actuator control rate compared with other control levels.

End-to-End 2D Convolutional Neural Network Architecture for Lung Nodule Identification and Abnormal Detection in Cloud

The extent of the peril associated with cancer can be perceivedfrom the lack of treatment, ineffective early diagnosis techniques, and mostimportantly its fatality rate. Globally, cancer is the second leading cause ofdeath and among over a hundred types of cancer;lung cancer is the secondmost common type of cancer as well as the leading cause of cancer-relateddeaths. Anyhow, an accurate lung cancer diagnosis in a timely manner canelevate the likelihood of survival by a noticeable margin and medical imagingis a prevalent manner of cancer diagnosis since it is easily accessible to peoplearound the globe. Nonetheless, this is not eminently efficacious consideringhuman inspection of medical images can yield a high false positive rate. Ineffectiveand inefficient diagnosis is a crucial reason for such a high mortalityrate for this malady. However, the conspicuous advancements in deep learningand artificial intelligence have stimulated the development of exceedinglyprecise diagnosis systems. The development and performance of these systemsrely prominently on the data that is used to train these systems. A standardproblem witnessed in publicly available medical image datasets is the severeimbalance of data between different classes. This grave imbalance of data canmake a deep learning model biased towards the dominant class and unableto generalize. This study aims to present an end-to-end convolutional neuralnetwork that can accurately differentiate lung nodules from non-nodules andreduce the false positive rate to a bare minimum. To tackle the problem ofdata imbalance, we oversampled the data by transforming available images inthe minority class. The average false positive rate in the proposed method isa mere 1.5 percent. However, the average false negative rate is 31.76 percent.The proposed neural network has 68.66 percent sensitivity and 98.42 percentspecificity.

Earthquake Risk Assessment Approach Using Multiple Spatial Parameters for Shelter Demands

The earthquake is considered one of the most devastating disasters in any area of the world due to its potentially destructive force.Based on the various earthquake-related parameters,the risk assessment is enabled in advance to prevent future earthquake disasters.In this paper,for providing the shelter space demands to reduce the damage level and prevention costs,an earthquake risk assessment approach is proposed for deriving the risk index based on multiple spatial parameters in the gridded map.The proposed assessment approach is comprised of pre-processing,methodologymodel,and data visualization.The risk index model derives the earthquake risk index by multiple spatial parameters including indexes of earthquake,danger,shelter,and building for blocks in the quantitative gridded map.The parameters are provided based onmathematicalmodels and combinedwith the risk index that presents the earthquake risk assessment result for each block.Therefore,the gridding approach is proposed to provide the elements of the risk assessment area that are used in the spatial parameters.The gridded map is developed for the selected area to visualize risk index parameters associated with each risk zone.Based on the derived result of the proposed earthquake risk indexmodel,emergency shelter requirements are provided according to the risk index for each location,which supports safety measures in advance to prevent future earthquake disasters.

Complex Problems Solution as a Service Based on Predictive Optimization and Tasks Orchestration in Smart Cities

Smart cities have different contradicting goals having no apparent solution.The selection of the appropriate solution,which is considered the best compromise among the candidates,is known as complex problem-solving.Smart city administrators face different problems of complex nature,such as optimal energy trading in microgrids and optimal comfort index in smart homes,to mention a few.This paper proposes a novel architecture to offer complex problem solutions as a service(CPSaaS)based on predictive model optimization and optimal task orchestration to offer solutions to different problems in a smart city.Predictive model optimization uses a machine learning module and optimization objective to compute the given problem’s solutions.The task orchestration module helps decompose the complex problem in small tasks and deploy them on real-world physical sensors and actuators.The proposed architecture is hierarchical and modular,making it robust against faults and easy to maintain.The proposed architecture’s evaluation results highlight its strengths in fault tolerance,accuracy,and processing speed.

Blockchain Based Secured Load Balanced Task Scheduling Approach for Fitness Service

In recent times,the evolution of blockchain technology has got huge attention from the research community due to its versatile applications and unique security features.The IoT has shown wide adoption in various applications including smart cities,healthcare,trade,business,etc.Among these applications,fitness applications have been widely considered for smart fitness systems.The users of the fitness system are increasing at a high rate thus the gym providers are constantly extending the fitness facilities.Thus,scheduling such a huge number of requests for fitness exercise is a big challenge.Secondly,the user fitness data is critical thus securing the user fitness data from unauthorized access is also challenging.To overcome these issues,this work proposed a blockchain-based load-balanced task scheduling approach.A thorough analysis has been performed to investigate the applications of IoT in the fitness industry and various scheduling approaches.The proposed scheduling approach aims to schedule the requests of the fitness users in a load-balanced way that maximize the acceptance rate of the users’requests and improve resource utilization.The performance of the proposed task scheduling approach is compared with the state-of-the-art approaches concerning the average resource utilization and task rejection ratio.The obtained results confirm the efficiency of the proposed scheduling approach.For investigating the performance of the blockchain,various experiments are performed using the Hyperledger Caliper concerning latency,throughput,resource utilization.The Solo approach has shown an improvement of 32%and 26%in throughput as compared to Raft and Solo-Raft approaches respectively.The obtained results assert that the proposed architecture is applicable for resource-constrained IoT applications and is extensible for different IoT applications.

SDN Based DDos Mitigating Approach Using Traffic Entropy for IoT Network

The Internet of Things(IoT)has been widely adopted in various domains including smart cities,healthcare,smart factories,etc.In the last few years,the fitness industry has been reshaped by the introduction of smart fitness solutions for individuals as well as fitness gyms.The IoT fitness devices collect trainee data that is being used for various decision-making.However,it will face numerous security and privacy issues towards its realization.This work focuses on IoT security,especially DoS/DDoS attacks.In this paper,we have proposed a novel blockchain-enabled protocol(BEP)that uses the notion of a self-exposing node(SEN)approach for securing fitness IoT applications.The blockchain and SDN architectures are employed to enhance IoT security by a highly preventive security monitoring,analysis and response system.The proposed approach helps in detecting the DoS/DDoS attacks on the IoT fitness system and then mitigating the attacks.The BEP is used for handling Blockchain-related activities and SEN could be a sensor or actuator node within the fitness IoT system.SEN provides information about the inbound and outbound traffic to the BEP which is used to analyze the DoS/DDoS attacks on the fitness IoT system.The SENcalculates the inbound and outbound traffic features’entropies and transmits them to the Blockchain in the form of transaction blocks.The BEP picks the whole mined blocks’transactions and transfers them to the SDN controller node.The controller node correlates the entropies data of SENs and decides about the DoS or DDoS attack.So,there are two decision points,one is SEN,and another is the controller.To evaluate the performance of our proposed system,several experiments are performed and results concerning the entropy values and attack detection rate are obtained.The proposed approach has outperformed the other two approaches concerning the attack detection rate by an increase of 11%and 18%against Approach 1 and Approach 2 respectively.

Towards a Dynamic Virtual IoT Network Based on User Requirements

The data being generated by the Internet of Things needs to be stored,monitored,and analyzed for maximum IoT resource utilization.Software Defined Networking has been extensively utilized to address issues such as heterogeneity and scalability.However,for small-scale IoT application,sometimes it is considered an inefficient approach.This paper proposes an alternate lightweight mechanism to the design and implementation of a dynamic virtual network based on user requirements.The key idea is to provide users a virtual interface that enables them to reconfigure the communication flow between the sensors and actuators at runtime.The throughput of the communication flow depends on the data traffic load and optimal routing.Users can reconfigure the communication flow,and virtual agents find the optimal route to handle the traffic load.The virtual network provides a user-friendly interface to allow physical devices to be mapped with the corresponding virtual agents.The proposed network is applicable for all systems that lie in the Internet of Things domain.Results conclude that the proposed network is efficient,reliable,and responsive to network reconfiguration at runtime.