Secure Routing Challenges for Opportunistic Internet of Things

Internet of Things(IoT) is a technical revolution of the internet where users, computing systems, and daily objects having sensing abilities, collaborate to provide innovative services in several application domains. Opportunistic IoT(OppIoT)is an extension of the opportunistic networks that exploits the interactions between the human-based communities and the IoT devices to increase the network connectivity and reliability. In this context, the security and privacy requirements play a crucial role as the collected information is exposed to a wide unknown audience. An adaptable infrastructure is required to handle the intrinsic vulnerabilities of OppIoT devices, with limited resources and heterogeneous technologies. This chapter elaborates the security requirements, the possible threats, and the current work conducted in the field of security in OppIoT networks.

AoSP-Based Secure Localization for Wireless Sensor Network

In wireless sensor network, node localization is helpful in reporting the event's origin, assisting querying of sensors, routing, and various cyber-physical system applications, where sensors are required to report geographically meaningful data for location-based applications. One of the accurate ways of localization is the use of anchor nodes which are generally equipped with global positioning system. However, in range-based approaches used in literature, like Angle of Arrival, the accuracy and precision decreases in case of multipath fading environment. Therefore, this chapter proposes an angle of signal propagation-based method where each node emits only two signals in a particular direction and knows its approximate position while receiving the second signal. Further, a method is proposed to define the coordinates of the nodes in reference to a local coordinate frame. The proposed method does the work with a smaller number of transmissions in the network even in the presence of malicious adversaries.

Middleware Approach to Enhance the Security and Privacy in the Internet of Things

Internet of things is one of the most rapidly growing research areas. Nowadays, IoT is applicable in various diverse areas because of its basic feature i.e., anything would be available to anyone at anytime. Further, IoT aims to provide service in a pervasive environment, although different problems crop up when the researchers move towards pervasiveness. Security and Privacy are the most intense problems in the field of IoT. There are various approaches available to handle these issues: Architectural security, Database security, Secure communication, and Middleware approaches. This chapter's authors concentrate on middleware approach from the security and privacy perceptive. Middleware can provide security by separating the end user from the actual complex system. Middleware also hides the actual complexity of the system from the user. So, the user will get the seamless services with no threats to security or privacy. This chapter provides a brief overview of secure middlewares and suggests the current research gaps as future directions.

Secure System Model for Replicated DRTDBS

Security in replicated distributed real time database system (RDRTDBS) is still explorative and, despite an increase in real-time applications, many issues and challenges remain in designing a more secure system model. However, very little research has been reported for maintaining security, timeliness, and mutual consistency. This chapter proposes the secure system model for RDRTDBS which secures the system from malicious attack. To prevent the request/response from malicious attack, authors have extended the system model with a cryptographic algorithm. In the cryptographic algorithm, a key must be secretly known only to the sender and receiver. Thus, in this chapter, authors have used the key generation algorithm to generate a key using an image. This secure system model maintains the confidentiality of the replicated data item and preserves its data integrity. It performs better in terms of malicious attack compared to other non-secure system models.

Blockchain Technology Integration in IoT and Applications

The Internet of Things (IoT) pictures an entire connected world, where things or devices are proficient to exchange a few measured data words and interrelate with additional things. This turns for a feasible digital demonstration of the existent world. Nonetheless, nearly all IoT things are simple to mistreat or compromise. Moreover, IoT devices are restricted in computation, power, and storage, so they are more vulnerable to bugs and attacks than endpoint devices like smartphones, tablets, and computers. Blockchain has remarkable interest from academics and industry because of its salient features including reduced dependencies on third parties, cryptographic security, immutability, decentralized nature, distributed nature, and anonymity. In the current scenario, blockchain with its features provides an anonymous framework for IoT. This chapter produces comprehensive knowledge of IoTs, Blockchain knowledge, security issues, Blockchain integration with IoT (BIoT), consensus, mining, message validation mechanisms, challenges, a solution, and future directions.

Efficient Big Data-Based Storage and Processing Model in Internet of Things for Improving Accuracy Fault Detection in Industrial Processes

As the lot of data is getting generated and captured in Internet of Things (IoT)—based industrial devices which is real time and unstructured in nature. The IoT technology—based sensors are the effective solution for monitoring these industrial processes in an efficient way. However, the real—time data storage and its processing in IoT applications is still a big challenge. This chapter proposes a new big data pipeline solution for storing and processing IoT sensor data. The proposed big data processing platform uses Apache Flume for efficiently collecting and transferring large amounts of IoT data from Cloud—based server into Hadoop Distributed File System for storage of IoT—based sensor data. Apache Storm is to be used for processing this real—time data. Next, the authors propose the use of hybrid prediction model of Density-based spatial clustering of applications with noise (DBSCAN) to remove sensor data outliers and provide better accuracy fault detection in IoT Industrial processes by using Support Vector Machine (SVM) machine learning classification technique.

Effect of Channel Modeling on Intercept Behavior of a Wireless BAN With Optimal Sensor Scheduling

Body area networks (BANs), a type of Personal Area Networks (PANs), form a significant part of health care applications. This chapter analyzes the effect of channel modeling on the intercept behavior of a wireless BAN while taking optimal sensor scheduling into account. A comparison is drawn between Lognormal and Weibull models for this case. Wireless BANs represent wireless networks of sensors allocated on, in, and around the human body. BANs are basically meant for health care applications where long-lasting and reliable operation is a must. Some healthcare applications carry sensitive information, therefore security is an important issue. A BAN with a sink node and various sensors is considered here along with an eavesdropper. Due to the radio wave propagation's broadcast nature, the wireless communication can be overheard by the eavesdropper. To safeguard the BAN, the propagation channels need to be characterized and modeled for designing reliable communication systems.

Industrial Automation Using Internet of Things

This chapter presents a comprehensive view of Industrial Automation using internet of things (IIoT). Advanced Industries are ushering in a new age of physical production backed by the information-based economy. The term Industrie 4.0 refers to the 4th paradigm shift in production, in which intelligent manufacturing technology is interconnected with physical machines. IIoT is basically a convergence of industrial systems with advanced, near-real-time computing and analytics, powered by low cost and low power sensing devices leveraging global internet connectivity. The key benefits of Industrial IoT systems are a) improved operational efficiency and productivity b) reduced maintenance costs c) improved asset utilization, monitoring and maintenance d) development of new business models e) product innovation and f) enhanced safety. Key parameters that impact Industrial Automation are a) Security b) Data Integrity c) Interoperability d) Latency e) Scalability, Reliability, and Availability f) Fault tolerance and Safety, and g) Maintainability, Serviceability, and Programmability.

DDoS Attacks and Defense Mechanisms Using Machine Learning Techniques for SDN

Distributed denial of service (DDoS) attack is one of the most disastrous attacks that compromises the resources and services of the server. DDoS attack makes the services unavailable for its legitimate users by flooding the network with illegitimate traffic. Most commonly, it targets the bandwidth and resources of the server. This chapter discusses various types of DDoS attacks with their behavior. It describes the state-of-the-art of DDoS attacks. An emerging technology named “Software-defined networking” (SDN) has been developed for new generation networks. It has become a trending way of networking. Due to the centralized networking technology, SDN suffers from DDoS attacks. SDN controller manages the functionality of the complete network. Therefore, it is the most vulnerable target of the attackers to be attacked. This work illustrates how DDoS attacks affect the whole working of SDN. The objective of this chapter is also to provide a better understanding of DDoS attacks and how machine learning approaches may be used for detecting DDoS attacks.

Machine Learning Techniques to Mitigate Security Attacks in IoT

Evolving technologies involve numerous IoT-enabled smart devices that are connected 24-7 to the internet. Existing surveys propose there are 6 billion devices on the internet and it will increase to 20 billion devices within a few years. Energy conservation, capacity, and computational speed plays an essential part in these smart devices, and they are vulnerable to a wide range of security attack challenges. Major concerns still lurk around the IoT ecosystem due to security threats. Major IoT security concerns are Denial of service(DoS), Sensitive Data Exposure, Unauthorized Device Access, etc. The main motivation of this chapter is to brief all the security issues existing in the internet of things (IoT) along with an analysis of the privacy issues. The chapter mainly focuses on the security loopholes arising from the information exchange technologies used in internet of things and discusses IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning, and reinforcement learning.