Applicability of WSN and Biometric Models in the Field of Healthcare

Health is considered as the most important ingredient in human life. Health is wealth is the most frequent used proverb. A healthy person can perform its entire task with full enthusiasm and great energy and can solve all problems as mind is a powerful weapon, which controls all our functioning. But now due to change in our lifestyles, we are becoming prone to all kinds of health hazards. Due to unhealthy mind, we are not able to perform any tasks. Humans are becoming victims of many diseases and one of the most common reason for our degradation in health is stress. In this chapter, the authors present role of WSN and biometric models such as two factor remote authentication, verifying fingerprint operations for enhancing security, privacy preserving in healthcare, healthcare data by cloud technology with biometric application, and validation built hybrid trust computing perspective for confirmation of contributor profiles in online healthcare data. A comparison table is formulated listing all the advantages and disadvantages of various biometric-based models used in healthcare.

Modelling a Deep Learning-Based Wireless Sensor Network Task Assignment Algorithm

The 21st century is witnessing the emergence of a wide variety of wireless sensor network (WSN) applications ranging from simple environmental monitoring to complex satellite monitoring applications. The advent of complex WSN applications has led to a massive transition in the development, functioning, and capabilities of wireless sensor nodes. The contemporary nodes have multi-functional capabilities enabling the heterogeneous WSN applications. The future of WSN task assignment envisions WSN to be heterogeneous network with minimal human interaction. This led to the investigative model of a deep learning-based task assignment algorithm. The algorithm employs a multilayer feed forward neural network (MLFFNN) trained by particle swarm optimization (PSO) for solving task assignment problem in a dynamic centralized heterogeneous WSN. The analyses include the study of hidden layers and effectiveness of the task assignment algorithms. The chapter would be highly beneficial to a wide range of audiences employing the machine and deep learning in WSN.

Security and Privacy Challenges of Deep Learning

Deep learning is the buzz word in recent times in the research field due to its various advantages in the fields of healthcare, medicine, automobiles, etc. A huge amount of data is required for deep learning to achieve better accuracy; thus, it is important to protect the data from security and privacy breaches. In this chapter, a comprehensive survey of security and privacy challenges in deep learning is presented. The security attacks such as poisoning attacks, evasion attacks, and black-box attacks are explored with its prevention and defence techniques. A comparative analysis is done on various techniques to prevent the data from such security attacks. Privacy is another major challenge in deep learning. In this chapter, the authors presented an in-depth survey on various privacy-preserving techniques for deep learning such as differential privacy, homomorphic encryption, secret sharing, and secure multi-party computation. A detailed comparison table to compare the various privacy-preserving techniques and approaches is also presented.

Artificial Intelligence and SHGs

For inclusive growth and sustainable development of SHG and women empowerment, there is a need to provide an environment to access quality services from financial and non-financial agencies. While banks cannot reach all people through a ‘brick and mortar' model, new and advanced banking technology has enabled financial inclusion through branchless banking. By using artificial intelligence in banking, banks have a cost-effective and efficient solution to provide access to services to the financially excluded. Digital technology improves the accessibility and affordability of financial services for the previously unbanked or underbanked individuals and MSMEs. A big data-driven model can also be helpful for psychometric evaluations. Several psychometric tools help evaluate the applicant's answers which aid to capture information that can help to predict loan repayment behavior, comprising applicants' beliefs, performance, attitudes, and integrity.

Security in Rail IoT Systems

Indra Sistemas S.A. have designed and developed a safety and secure solution system for the rail transportation environment based on a distributed architecture under the domain of the Industrial IoT that enables V2V, V2I, and I2I communications, allowing peer-to-peer data sharing. UPM has designed and implemented a HW-based security infrastructure for extreme edge devices in IoT. The implementation takes advantage of HW accelerator to enhance security in low resources devices with a very low overhead in cost and memory footprint. Current security solutions are problematic due to centralized control entity. The complexity of this kind of system resides in the management, in a decentralized way, of the security at each point of the distributed architecture. This chapter describes how the system secures all the infrastructure based on a distributed architecture without affecting the throughput and the high availability of the data in order to get a top-performance, in compliance with the strengthen safety and security constrains of the rail environment's regulations.

Recent Trends in Channel Assignment Techniques in Wireless Mesh Networks

Wireless mesh networks have numerous advantages in terms of connectivity as well as reliability. Traditionally, the nodes in wireless mesh networks are equipped with a single radio, but the limitations are lower throughput and limited use of the available wireless channel. To overcome this, the recent advances in wireless mesh networks are based on a multi-channel multi-radio approach. Channel assignment is a technique that selects the best channel for a node or to the entire network just to increase the network capacity. To maximize the throughput and the capacity of the network, multiple channels with multiple radios were introduced in these networks. In this work, algorithms are developed to improve throughput, minimize delay, reduce average energy consumption, and increase the residual energy for multi-radio multi-channel wireless mesh networks.

Secured Energy-Efficient Routing in Wireless Sensor Networks Using Machine Learning Algorithm

Wireless sensor networks consist of unattended small sensor nodes having low energy and low range of communication. It has been observed that if there is any system to periodically start and stop the sensors sensing activities, then it saves some energy, and thus, the network lifetime gets extended. According to the current literature, security and energy efficiency are the two main concerns to improve the quality of service during transmission of data in wireless sensor networks. Machine learning has proved its efficiency in developing efficient processes to handle complex problems in various network aspects. Routing in wireless sensor network is the process of finding the route for transmitting data among different sensor nodes according to the requirement. Machine learning has been used in a broad way for designing energy efficient routing protocols, and this chapter reviews the existing works in the said domain, which can be the guide to someone who wants to explore the area further.

Attacks, Vulnerabilities, and Their Countermeasures in Wireless Sensor Networks

Wireless sensor network (WSN) comprises sensor nodes that have the capability to sense and compute. Due to their availability and minimal cost compared to traditional networks, WSN is used broadly. The need for sensor networks increases quickly as they are more likely to experience security attacks. There are many attacks and vulnerabilities in WSN. The sensor nodes have issues like limited resources of memory and power and undependable communication medium, which is further complicated in unattended environments, secure communication, and data transmission issues. Due to the complexity in establishing and maintaining the wireless sensor networks, the traditional security solutions if implemented will prove to be inefficient for the dynamic nature of the wireless sensor networks. Since recent times, the advance of smart cities and everything smart, wireless sensor nodes have become an integral part of the internet of things and their related paradigms. This chapter discusses the known attacks, vulnerabilities, and countermeasures existing in wireless sensor networks.

The Threat of Intelligent Attackers Using Deep Learning

The significant increase in the number of interconnected devices has brought new services and applications, as well as new network vulnerabilities. The increasing hardware capacities of these devices and the developments in the artificial intelligence field mean that new and complex attack methods are being developed. This chapter focuses on the backoff attack in a wireless network using CSMA/CA multiple access, and it shows that an intelligent attacker, making use of control theory, can successfully exploit a sequential probability ratio test-based defense mechanism. Also, recent developments in the deep reinforcement learning field allows that attackers that do not have full knowledge of the defense mechanism are able to successfully learn to attack it. Thus, this chapter illustrates by means of the backoff attack, the possibilities that the recent advances in the artificial intelligence field bring to intelligent attackers, and highlights the importance of researching in intelligent defense methods able to cope with such attackers.

Introducing Machine Learning to Wireless Sensor Networks

From its advent in mid-20th century, machine learning constantly improves the user experience of existing systems. It can be used in almost every field such as weather, sports, business, IoT, medical care, etc. Wireless sensor networks are often placed in hostile environments to observe change in surroundings. Since these communicate wirelessly, many problems such as localisation of nodes, security of data being routed create barriers for proper functioning of system. Extending the horizon of machine learning to WSN creates wonders and adds credibility to the system. This chapter aids to present various machine learning aspects applied on wireless sensor networks and the benefits and drawbacks of applying machine learning to WSN. It also describes various data aggregation and clustering techniques that aim to reduce power consumption and ensure confidentiality, authentication, integrity, and availability amongst sensor nodes. This could contribute to design and alter pre-existing ML algorithms to improve overall performance of wireless sensor networks.