Computer Forensic Investigation in Cloud of Things

Digital data transformation is most challenging in developing countries. In recent days, all the applications are functioning with the support of internet of things (IoT). Wearable devices involve the most insightful information, which includes individual healthcare data. Health records of patients must be protected. IoT devices could be hacked, and criminals use this information. Smart cities with IoT use information technology to collect, analyze, and integrate information. Smart reduces the network traffic using the ground sensors, micro-radars, and drones monitor traffic to the traffic controller based on that signals are designed. The data collected includes the images and convey information to smart vehicles, which in turn, if data are hacked, may affect many people. Smart city includes important features such as smart buildings, smart technology, smart governance, smart citizen, and smart security. Cyber threat is a challenging problem, and usage of apps may increase malware that affects various customers.

Machine Learning Algorithms

Human intelligence is deeply involved in creating efficient and faster systems that can work independently. Creation of such smart systems requires efficient training algorithms. Thus, the aim of this chapter is to introduce the readers with the concept of machine learning and the commonly employed learning algorithm for developing efficient and intelligent systems. The chapter gives a clear distinction between supervised and unsupervised learning methods. Each algorithm is explained with the help of suitable example to give an insight to the learning process.

Comprehensive Overview of Neural Networks and Its Applications in Autonomous Vehicles

Deep learning and Artificial intelligence (AI) have been trending these days due to the capability and state-of-the-art results that they provide. They have replaced some highly skilled professionals with neural network-powered AI, also known as deep learning algorithms. Deep learning majorly works on neural networks. This chapter discusses about the working of a neuron, which is a unit component of neural network. There are numerous techniques that can be incorporated while designing a neural network, such as activation functions, training, etc. to improve its features, which will be explained in detail. It has some challenges such as overfitting, which are difficult to neglect but can be overcome using proper techniques and steps that have been discussed. The chapter will help the academician, researchers, and practitioners to further investigate the associated area of deep learning and its applications in the autonomous vehicle industry.

Deep Learning

The vast majority of the examination on profound neural systems so far has been centered on acquiring higher exactness levels by building progressively vast and profound structures. Preparing and assessing these models is just practical when a lot of assets; for example, handling power and memory are easy run of the mill applications that could profit by these models. The system starts handling the compelled gadget and depends on the remote part when the neighborhood part does not give a sufficiently precise outcome. The falling system takes into account a new ceasing component amid the review period of the system. This chapter empowers an entire assortment of independent frameworks where sensors, actuators, and registering hubs can cooperate and demonstrate that the falling design takes into account a free change in assessment speed on obliged gadgets while the misfortune in precision is kept to a base.

Distributed Computing for Internet of Things (IoT)

In recent years, internet of things (IoT) has expanded due to very good internet infrastructure everywhere. IoT has the ability to create a network of physical things that use embedded technologies in order to sense, converse, cooperate, and team up with other things. IoT-based applications require scalability and fault tolerance, which is very difficult to implement in centralized systems and computing environments. Distributed computing is an ideal solution to implement IoT-based applications. The chapter starts with the basics of distributed computing where difference with centralized computing, challenges, and types of distributed computing applications are discussed. The chapter deals with the role of distributed computing for IoT based on advantages, issues, and related IoT-based applications. The chapter discusses the recent topic of distributed computing—FOG computing—in connection with IoT-based applications. At last, the chapter addresses research and interest trends about distributed computing and IoT.

Machine Learning Applications for Anomaly Detection

The aim of this chapter is to describe and analyze the application of machine learning for anomaly detection. The study regarding the anomaly detection is a very important thing. The various phenomena often occur related to the anomaly study, such as the occurrence of an extreme climate change, the intrusion detection for the network security, the fraud detection for e-banking, the diagnosis for engines fault, the spacecraft anomaly detection, the vessel track, and the airline safety. This chapter is an attempt to provide a structured and a broad overview of extensive research on anomaly detection techniques spanning multiple research areas and application domains. Quantitative analysis meta-approach is used to see the development of the research concerned with those matters. The learning is done on the method side, the techniques utilized, the application development, the technology utilized, and the research trend, which is developed.

Rainfall Prediction Model Using Exponential Smoothing Seasonal Planting Index (ESSPI) For Determination of Crop Planting Pattern

The traditional crop farmers rely heavily on rain pattern to decide the time for planting crops. The emerging climate change has caused a shift in the rain pattern and consequently affected the crop yield. Therefore, providing a good rainfall prediction models would enable us to recommend best planting pattern (when to plant) in order to give maximum yield. The recent and widely used rainfall prediction model for determining the cropping patterns using exponential smoothing method recommended by the Food and Agriculture Organization (FAO) suffered from short-term forecasting inconsistencies and inaccuracies for long-term forecasting. In this study, the authors developed a new rainfall prediction model which applied exponential smoothing onto seasonal planting index as the basis for determining planting pattern. The results show that the model gives better accuracy than the original exponential smoothing model.

Network Security Approaches in Distributed Environment

Distributed computing is one of the thrust areas in the field of computer science, but when we are concerned about security a question arises, “Can it be secure?” From this note, the authors start this chapter. In the distributed environment, when the system is connected to a network, and the operating system firewall is active, it will take care of all the authentication and access control requests. There are several traditional cryptographic approaches which implement authentication and access control. The encryption algorithms such as Rijndael, RSA, A3, and A5 is used for providing data secrecy. Some of the key distribution techniques have been discussed such as Diffie Hellman key exchange for symmetric key, and random key generation (LCG) technique is used in red-black tree traversal which provides the security of the digital contents. The chapter deals with the advanced versions of the network security techniques and cryptographic algorithms for the security of multimedia contents over the internet.

Enhancing E-Government With Internet of Things

Internet of things (IoT) brings unprecedented changes to all contexts of our lives, as they can be informed by smart devices and real-time data. Among the various IoT application settings, e-government seems to be one that can be greatly benefited by the use of IoT, transforming and augmenting public services. This chapter aims to contribute to a better understanding of how IoT can be leveraged to enhance e-government. IoT adoption in e-government encompasses several challenges of technical as well as organizational, political, and legal nature, which should be addressed for developing efficient applications. With the application of IoT in e-government being at an early stage, it is imperative to investigate these challenges and the ways they could be tackled. The chapter provides an overview of IoT in e-government across several application domains and explores the aspects that should be considered and managed before it can reach its full potential.

Motion Planning Method for In-Pipe Walking Robots Using Height Maps and CNN-Based Pipe Branches Detector

In this chapter, the problem of motion planning for an in-pipe walking robot is studied. One of the key parts of motion planning for a walking robot is a step sequence generation. In the case of in-pipe walking robots it requires choosing a series of feasible contact locations for each of the robot's legs, avoiding regions on the inner surface of the pipe where the robot cannot step to, such as pipe branches. The chapter provides an approach to localization of pipe branches, based on deep convolutional neural networks. This allows including the information about the branches into the so-called height map of the pipeline and plan the step sequences accordingly. The chapter shows that it is possible to achieve prediction accuracy better than 0.5 mm for a network trained on a simulation-based dataset.