Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Internet of Things (IoT) is one of the most rapidly used technologies in the last decade in various applications. The smart things are connected in wireless or wired for communication, processing, computing, and monitoring different real-time scenarios. The things are heterogeneous and have low memory, less processing power. The implementation of the IoT system comes with security and privacy challenges because traditional based existing security protocols do not suitable for IoT devices. In this survey, the authors initially described an overview of the IoT technology and the area of its application. The primary security issue CIA (confidentially, Integrity, Availability) and layer-wise issues are identified. Then the authors systematically study the three primary technology Machine learning(ML), Artificial intelligence (AI), and Blockchain for addressing the security issue in IoT. In the end, an analysis of this survey, security issues solved by the ML, AI, and Blockchain with research challenges are mention.

Survey on IoT security: Challenges and solution using machine learning, artificial intelligence and blockchain technology

Internet of Vehicles (IoV) is an emerging concept that is believed to help realize the vision of intelligent transportation systems (ITSs). IoV has become an important research area of impactful applications in recent years due to the rapid advancements in vehicular technologies, high throughput satellite communication, the Internet of Things, and cyber–physical systems. IoV enables the integration of smart vehicles with the Internet and system components attributing to their environments, such as public infrastructures, sensors, computing nodes, pedestrians, and other vehicles. By allowing the development of a common information exchange platform between vehicles and heterogeneous vehicular networks, this integration aims to create a better environment and public space for the people as well as to enhance safety for all road users. Being a participatory data exchange and storage, the underlying information exchange platform of IoV needs to be secure, transparent, and immutable in order to achieve the intended objectives of ITS. In this connection, the adoption of blockchain as a system platform for supporting the information exchange needs of IoV has been explored. Due to their decentralized and immutable nature, IoV applications enabled by blockchain are believed to have a number of desirable properties, such as decentralization, security, transparency, immutability, and automation. In this article, we present a contemporary survey on the latest advancement in blockchain for IoV. Particularly, we highlight the different application scenarios of IoV after carefully reviewing the recent literature. We also investigate several key challenges where blockchain is applied in IoV. Furthermore, we present the future opportunities and explore further research directions of IoV as a key enabler of ITS.

Blockchain for the Internet of Vehicles Towards Intelligent Transportation Systems: A Survey

In recent years, the Internet of Things (IoT) has made great progress. The interconnection between IoT and the Internet enables real-time information processing and transaction implementation through heterogeneous intelligent devices. But the security, the privacy, and the reliability of IoT are key challenges that limit its development. The features of the blockchain, such as decentralization, consensus mechanism, data encryption, and smart contracts, are suitable for building distributed IoT systems to prevent potential attacks and to reduce transaction costs. As a decentralized and transparent database platform, blockchain has the potential to raise the performance of IoT security to a higher level. This article systematically analyzes state of the art of IoT security based on the blockchain, paying special attention to the security features, issues, technologies, approaches, and related scenarios in blockchain-embedded IoT. The integration and interoperation of blockchain and IoT is an important and foreseeable development in the computational communication system.

Embedding Blockchain Technology Into IoT for Security: A Survey

Conventional Internet of Things (IoT) ecosystems involve data streaming from sensors, through Fog devices to a centralized Cloud server. Issues that arise include privacy concerns due to third party management of Cloud servers, single points of failure, a bottleneck in data flows and difficulties in regularly updating firmware for millions of smart devices from a point of security and maintenance perspective. Blockchain technologies avoid trusted third parties and safeguard against a single point of failure and other issues. This has inspired researchers to investigate blockchain’s adoption into IoT ecosystem. In this paper, recent state-of-the-arts advances in blockchain for IoT, blockchain for Cloud IoT and blockchain for Fog IoT in the context of eHealth, smart cities, intelligent transport and other applications are analyzed. Obstacles, research gaps and potential solutions are also presented.

A survey on the adoption of blockchain in IoT: challenges and solutions

Blockchain is the backbone technology behind crypto-currency and Bitcoin. By concept, Blockchain is a distributed database where transactions are recorded in an incorruptible and non-modifiable manner. Currently, Blockchain technology is envisioned as a powerful framework for open-access networks, decentralized information processing and sharing systems, etc. This review is motivated due to the lack of an extensive survey on the existing decentralized consensus mechanisms in Blockchain technology. So in this paper, an in-depth review of the distributed consensus mechanisms has been presented. In addition to this, a comparative analysis of the consensus protocols based on the type of Blockchain is also demonstrated.

Study of Blockchain Based Decentralized Consensus Algorithms

The Internet of Things (loT) is advantageous in many areas such as people, society, and industries as it interconnects a different kind of devices and information entities hence it originates a huge number of value-added services. Typically, IoT environments are comprised of many diversified devices but with limited resource. To establish an end-to-end authentication between the applications and monitoring devices in a smart environment is a tough task. Because of multifariousness in respect of topology, devices, different security and communication used in IoT, the authentication mechanisms which are existing in the current period are ill-protected to security threats and can be a disruption in the progress of IoT in realizing Smart Environment. Hence, the symmetric and asymmetric cryptosystems have to be convergent and to guarantee the complete security of IoT environment a lightweight cryptographic protocol is necessary. In this paper, we present a lightweight authentication scheme based on ECC which offers adequate security measures and also performance is up to the mark.

An ECC based Lightweight Authentication Protocol For Mobile Phone in Smart Home

Internet of Things (IoT) as per estimated will connect 50 billion devices by 2020. Since its evolution, IoT technology provides lots of flexibility to develop and implement any application. Most of the application improves the human living standard and also makes life easy to access and monitoring the things in real time. Though there exist some security and privacy issues in IoT system like authentication, computation, data modification, trust among users. In this paper, we have identified the IoT application like insurance, supply chain system, smart city and smart car where trust among associated users is an major issue. The current centralized system does not provide enough trust between users. Using Blockchain technology we have shown that trust issue among users can be managed in a decentralized way so that information can be traceable and identify/verify any time. Blockchain has properties like distributed, digitally share and immutable which enhance security. For Blockchain implementation, Ethereum platform is used.

Trustworthy Management in Decentralized IoT Application using Blockchain

In the era of the Internet of Things(loT) smart devices are connected with wire or wireless way. The IoT devices are capable of sensing the environment and has the ability to transmit that information to the next level. The application area of IoT is Smart city, Smart transportation, Healthcare sector, Agriculture, Monitoring environment. Each of these applications, lots of information are share or transmit among different devices. In the information sharing system among devices, lots of security and privacy challenges exist like data leakage, data modification, device identity. In this paper, authors firstly identify the communication protocols used in IoT application and given their working principle. Secondly, challenges exist in IoT and corresponding Blockchain solution approach are explained, Lastly, the authors proposed a secure architecture based on open Blockchain which can solve some of the challenges in IoT applications.

Utkalika Satapathy

Papers

Survey on IoT security: Challenges and solution using machine learning, artificial intelligence and blockchain technology

Study of Blockchain Based Decentralized Consensus Algorithms

An ECC based Lightweight Authentication Protocol For Mobile Phone in Smart Home

Trustworthy Management in Decentralized IoT Application using Blockchain

A Secure Framework for Communication in Internet of Things Application using Hyperledger based Blockchain