Blockchain-Enabled Internet of Things for Unsupervised Structural Health Monitoring in Potential Building Structures

The integration of internet of things, artificial intelligence, and blockchain enabled the monitoring of structural health with unattended and automated means. Remote monitoring mandates intelligent automated decision-making capability, which is still absent in present solutions. The proposed solution in this chapter contemplates the architecture of smart sensors, customized for individual structures, to regulate the monitoring of structural health through stress, strain, and bolted joints looseness. Long range sensors are deployed for transmitting the messages a longer distance than existing techniques. From the simulated results, different sensors record the monitoring information and transmit to the blockchain platform in terms of pressure points, temperature, pre-tension force, and the architecture deems the criticality of transactions. Blockchain platform will also be responsible for storage and accessibility of information from a decentralized medium, automation, and security.

Cooperative Relaying Communication in IoT Applications for 5G Radio Networks

This chapter presents cooperative relaying networks that are helpful in Internet of Thing (IoT) applications for fifth-generation (5G) radio networks. It provides reliable connectivity as the wireless device is out of range from cellular network, high throughput gains and enhance the lifetime of wireless networks. These features can be achieved by designing the advanced protocols. The design of advanced protocols plays an important role to combat the effect of channel fading, data packet scheduling at the buffered relay, average delay, and traffic intensity. To achieve our goals, we consider two-way cooperative buffered relay networks and then investigate advanced protocols such as without channel state information (CSI) i.e., buffer state information (BSI) only and with partial transmit CSI i.e., BSI/CSI with the assistance of one dimensional Markov chain and transmission policies in fading environment. The outage probability of consecutive links and outage probability of multi-access and broadcast channels are provided in closed-form. Further, the buffered relay achieves maximum throughput gains in closed-form for all these protocols. The objective function of throughput of the buffered relay is evaluated in fractional programming that is transformed into linear program using standard CVX tool. Numerical results show that our proposed protocols performance better as compared to conventional method studied in the literature. Finally, this chapter provides possible future research directions.

Identification and Recognition of Speaker Voice Using a Neural Network-Based Algorithm

This chapter explains the speech signal in moving objects depending on the recognition field by retrieving the name of individual voice speech and speaker personality. The adequacy of precisely distinguishing a speaker is centred exclusively on vocal features, as voice contact with machines is getting more pervasive in errands like phone, banking exchanges, and the change of information from discourse data sets. This audit shows the location of text-subordinate speakers, which distinguishes a solitary speaker from a known populace. The highlights are eliminated; the discourse signal is enrolled for six speakers. Extraction of the capacity is accomplished utilizing LPC coefficients, AMDF computation, and DFT. By adding certain highlights as information, the neural organization is prepared. For additional correlation, the attributes are put away in models. The qualities that should be characterized for the speakers were acquired and dissected utilizing back propagation algorithm to a format picture.

Fine Tuning Smart Manufacturing Enterprise Systems

The workflow between business and manufacturing system level is changing leading to delay in exploring the context of innovative ideas and solutions. Smart manufacturing systems progress rapid growth in integrating the operational capabilities of networking functionality and communication services with cloud-based enterprise architectures through runtime environment. Fine tuning aims to process intelligent management, flexible monitoring, dynamic network services using internet of things (IoT)-based service oriented architecture (SOA) solutions in numerous enterprise systems. SOA is an architectural pattern for building software business systems based on loosely coupled enterprise infrastructure services and components. The IoT-based SOA enterprise systems incorporate data elicitation, integrating agile methodologies, orchestrate underlying black-box services by promoting growth in manufacturer enterprises workflow. This chapter proposes the integration of standard workflow model between business system level and manufacturing production level with an IoT-enabled SOA framework.

Estimation of Secured Wireless Sensor Networks and Its Significant Observation for Improving Energy Efficiency Using Cross-Learning Algorithms

Wireless sensor networks (WSNs) have as of late been created as a stage for various significant observation and control applications. WSNs are continuously utilized in different applications, for example, therapeutic, military, and mechanical segments. Since the WSN is helpless against assaults, refined security administrations are required for verifying the information correspondence between hubs. Because of the asset limitations, the symmetric key foundation is considered as the ideal worldview for verifying the key trade in WSN. The sensor hubs in the WSN course gathered data to the base station. Despite the fact that the specially appointed system is adaptable with the variable foundation, they are exposed to different security dangers. Grouping is a successful way to deal with vitality productivity in the system. In bunching, information accumulation is utilized to diminish the measure of information that streams in the system.

Executing CNN-LSTM Algorithm for Recognizable Proof of Cervical Spondylosis Infection on Spinal Cord MRI Image

Various estimating mechanisms are present for evaluating the regional agony, neck torment, neurologic deficiencies of the sphincters at the stage midlevel of cervical spondylosis. It is necessary for the cervical spondylosis that the survey necessitates wide range of learning skills about the systemized life, experience, and ability of the expertise for learning the capability, life system, and experience. Doctors check the analysis of situation through MRI and CT scan, but additional interesting facts have been discovered in the physical test. For this, a programming approach is not available. The authors thereby propose a novel framework that accordingly inspects and investigates the cervical spondylosis employing computation of CNN-LSTM. Machine learning methods such as long short-term memory (LSTM) in fusion with convolution neural networks (CNNs), a kind of neural network (NN), are applied to this strategy to evaluate for making the systematization in various applications.

An Advanced Wireless Sensor Networks Design for Energy-Efficient Applications Using Condition-Based Access Protocol

A modern wireless sensor and its development majorly depend on distributed condition maintenance protocol. The medium access and its computing have been handled by multi hope sensor mechanism. In this investigation, WSN networks maintenance is balanced through condition-based access (CBA) protocol. The CBA is most useful for real-time 4G and 5G communication to handle internet assistance devices. The following CBA mechanism is energy efficient to increase the battery lifetime. Due to sleep mode and backup mode mechanism, this protocol maintains its energy efficiency as well as network throughput. Finally, 76% of the energy consumption and 42.8% of the speed of operation have been attained using CBI WSN protocol.

Machine Learning for Industrial IoT Systems

The use of AI algorithms in the IoT enhances the ability to analyse big data and various platforms for a number of IoT applications, including industrial applications. AI provides unique solutions in support of managing each of the different types of data for the IoT in terms of identification, classification, and decision making. In industrial IoT (IIoT), sensors, and other intelligence can be added to new or existing plants in order to monitor exterior parameters like energy consumption and other industrial parameters levels. In addition, smart devices designed as factory robots, specialized decision-making systems, and other online auxiliary systems are used in the industries IoT. Industrial IoT systems need smart operations management methods. The use of machine learning achieves methods that analyse big data developed for decision-making purposes. Machine learning drives efficient and effective decision making, particularly in the field of data flow and real-time analytics associated with advanced industrial computing networks.

Intelligent Speech Processing Technique for Suspicious Voice Call Identification Using Adaptive Machine Learning Approach

With recent advances in the field of data, there are many advantages of speedy growth of internet and mobile phones in the society, and people are taking full advantage of them. On the other hand, there are a lot of fraudulent happenings everyday by stealing the personal information/credentials through spam calls. Unknowingly, we provide such confidential information to the untrusted callers. Existing applications for detecting such calls give alert as spam to all the unsaved numbers. But all calls might not be spam. To detect and identify such spam calls and telecommunication frauds, the authors developed the application for suspicious call identification using intelligent speech processing. When an incoming call is answered, the application will dynamically analyze the contents of the call in order to identify frauds. This system alerts such suspicious calls to the user by detecting the keywords from the speech by comparing the words from the pre-defined data set provided to the software by using intelligent algorithms and natural language processing.

The Rise of IoT and Big Data Analytics for Disaster Management Systems

Uninvited disasters wreak havoc on society, both economically and psychologically. These losses can be minimized if events can be anticipated ahead of time. The majority of large cities in developing countries with increasing populations are highly vulnerable disaster areas around the world. This is due to a lack of situational information in their authorities in the event of a crisis, which is due to a scarcity of resources. Both natural and human-induced disasters need to be pre-planned and reactive to minimize the risk of causalities and environmental/infrastructural disruption. Disaster recovery systems must also effectively obtain relevant information. The developments in big data and the internet of things (IoT) have made a greater contribution to accuracy and timely decision-making in the disaster management system (DMS). The chapter explains why IoT and big data are needed to cope with disasters, as well as how these technologies work to solve the problem.