A Comparative Analysis of Machine Learning based Routing Protocols for Energy Optimization in IoT

In today’s competitive landscape, achieving customer-centricity is paramount for the sustainable growth and success of organisations. This research is dedicated to understanding customer preferences in the context of the Internet of things (IoT) and employs a two-part modeling approach tailored to this digital era. In the first phase, we leverage the power of the self-organizing map (SOM) algorithm to segment IoT customers based on their connected device usage patterns. This segmentation approach reveals three distinct customer clusters, with the second cluster demonstrating the highest propensity for IoT device adoption and usage. In the second phase, we introduce a robust decision tree methodology designed to prioritize various factors influencing customer satisfaction in the IoT ecosystem. We employ the classification and regression tree (CART) technique to analyze 17 key questions that assess the significance of factors impacting IoT device purchase decisions. By aligning these factors with the identified IoT customer clusters, we gain profound insights into customer behaviour and preferences in the rapidly evolving world of connected devices. This comprehensive analysis delves into the factors contributing to customer retention in the IoT space, with a strong emphasis on crafting logical marketing strategies, enhancing customer satisfaction, and fostering customer loyalty in the digital realm. Our research methodology involves surveys and questionnaires distributed to 207 IoT users, categorizing them into three distinct IoT customer groups. Leveraging analytical statistical methods, regression analysis, and IoT-specific tools and software, this study rigorously evaluates the factors influencing IoT device purchases. Importantly, this approach not only effectively clusters the IoT customer relationship management (IoT-CRM) dataset but also provides valuable visualisations that are essential for understanding the complex dynamics of the IoT customer landscape. Our findings underscore the critical role of logical marketing strategies, customer satisfaction, and customer loyalty in enhancing customer retention in the IoT era. This research offers a significant contribution to businesses seeking to optimize their IoT-CRM strategies and capitalize on the opportunities presented by the IoT ecosystem.

Internet of Things (IoT) ecosystems are becoming increasingly ubiquitous and heterogeneous, adding extra layers of complexity to secure communication and resource allocation. IoT computing resources are often located at the network edge and distributed across many heterogeneous sensors, actuators, and controller devices. This makes it challenging to provide the proper security mechanisms to IoT ecosystems in terms of manageability and maintainability. In an IoT ecosystem, computational resources are naturally distributed and shareable among their constituency, which creates an opportunity to distribute heavy tasks to them. However, resource allocation in IoT requires secure and complex communication and coordination mechanisms, which existing ones do not adequately support. In this paper, we present Secure Actor-based Model for IoT Communication (SecIoTComm), a model for representing secure IoT communication. SecIoTComm aims to represent secure IoT communication properties and design and implement novel mechanisms to improve their programmability and performance. SecIoTComm separates the communication and computation concerns, achieving design modularity in building IoT ecosystems. First, this paper presents the syntax and operational semantics of SecIoTComm. Then, we present an IoT framework implementing the key concepts of the model. Finally, we evaluate the developed framework using various performance and scalability metrics.

Analogous to the way humans use the Internet, devices will be the main users in the Internet of Things (IoT) ecosystem. Therefore, device-to-device (D2D) communication is expected to be an intrinsic part of the IoT. Devices will communicate with each other autonomously without any centralized control and collaborate to gather, share, and forward information in a multihop manner. The ability to gather relevant information in real time is key to leveraging the value of the IoT as such information will be transformed into intelligence, which will facilitate the creation of an intelligent environment. Ultimately, the quality of the information gathered depends on how smart the devices are. In addition, these communicating devices will operate with different networking standards, may experience intermittent connectivity with each other, and many of them will be resource constrained. These characteristics open up several networking challenges that traditional routing protocols cannot solve. Consequently, devices will require intelligent routing protocols in order to achieve intelligent D2D communication. We present an overview of how intelligent D2D communication can be achieved in the IoT ecosystem. In particular, we focus on how state-of-the-art routing algorithms can achieve intelligent D2D communication in the IoT.

The Internet of Things (IoT) and Wireless Sensor Networks (WSN) have gained enough popularity within one and a half decades by obtaining viable solutions in multiple domains by proving one of the best technology duos in the Internet of Things ecosystem. Recently, Wireless sensor networks blended with the Internet of Things opening enough proximity to solve critical technological problems. In both IoT and WSN, sensors are the primary hardware of the entire system which demands high sensitivity and selectivity. Typically, these sensors are electronic devices that are equipped with limited battery power sources. So, the utilization of this power source in an economical manner with optimum use is the primary concern of the research and implementation of these technologies. Smart energy-concerned routing schemes are introduced, which play the principal role in achieving energy-efficient wireless communication between the inter-sensor nodes and between nodes to the base station. This paper focuses on the brief study and analysis of the implementation of a WSN-based IoT system in every aspect. This study covers the WSN-based IoT in different aspects and also discusses the Software Defined Network (SDN) and how this software technology works with WSN and IoT and make thing easier to control and helpful to these technologies. 

Internet of things (IoT) paradigm is becoming ubiquitous in everyday life. IoT-based systems are increasingly getting deployed in generic household, healthcare, public utilities, etc., related application. The reason for such rapid acceptance is in majority due to the internet's feature of connect from anywhere. However, due to some weaknesses inherent with the internet itself, IoT deployments also face certain challenges. Though there is a growing number of IoT-related reference architectures, frameworks, guidelines, platforms and standards, majority of them are yet to address the concerns due to internet outage. Furthermore, such growing numbers of architectures, frameworks, etc., also means bountiful amounts of both opportunities and risks for IoT vendors and original equipment manufacturers (OEMs). Of course, the IoT-device consumers also run the risks if the purchased products frameworks incompatible. We present One IoT version 2 (v2), an IoT framework and protocol that addresses the internet outage scenario. Similar to version 1 (v1), One IoT v2 is also forward compatible.

Chapter 19 - Security for the Internet of Things

Recently, extensive research into healthcare services and their technological advancements is being carried out globally. The Internet of Things (IoT) has proven to be a predominantly potential candidate in these advancements. IoT supports infrastructure development in connecting a wide range of medical devices, sensors, and health practitioners in order to ensure the best medical treatment for patients in both cities and remote places. This has enabled the IoT to provide major support in medical application, giving rise to assisted healthcare facilities. The IoT ecosystem has a complicated architecture, with several components interacting and an evolving variety of applications for users. This is a comprehensive and interdependent system that involves real-time data analysis, interconnections of device, data transfers, and analysis. The IoT creates a connected ecosystem developed from several intelligent systems that integrates respondents with machines and allows for statistics decision making. A network of interconnected devices that perceive vital data related to medical conditions of patients in real time is referred to as the Internet of Medical Things (IoMT), a healthcare application of IoT technology. Sensors used for sensing real-time data related to monitoring temperature, blood pressure levels, heartbeat levels, specialized signal converters, and communication interfaces can be added to existing medical facilities to turn them into IoMT devices. IoMT can be used to create wearable devices, home-care and personal-care medical devices, and mobile healthcare apps, along with creating communication with medical specialists in a given area. The IoMT system’s challenges and ecosystem interface requirements are also a matter of concern considering the complexity of the systems. This study offers a comprehensive evaluation of the various domains of use of IoMT, as well as its future scope and challenges. Recent advances in sensor technology, networks, cloud storage and processing, mobility, and big data analytics have made it possible to create affordable smart medical devices and a connected healthcare environment.

Remote correspondence frameworks are exceedingly disposed to security perils. The critical usages of remote correspondence frameworks are in military, business, human administrations, retail, and transportations. These structures use wired, cell, or ad hoc frameworks. Remote sensor frameworks, actuator frameworks, and vehicular frameworks have gotten a fantastic thought in the open eye and industry. Of late, the Internet of things (IoT) has become broad research thought. The IoT is considered as possible destiny of the Web. In future, IoT will accept a vital activity and change our living styles, standards, similarly to game plans. The use of IoT in different applications is required to rise rapidly in the coming years. The IoT grants billions of devices, society, and organizations to connect with others and exchange information. Due to the extended usage of IoT contraptions, the IoT frameworks are slanted to various security strikes. The presence of successful security and assurance shows that IoT frameworks are incredibly expected to ensure protection, get the chance to control, and decency, among others. In this chapter, a complete assessment on security and insurance issues in IoT frameworks is provided.

The Internet of things (IoT) ecosystem has grown exponentially with the convergence of various technologies such as deep learning, sensor systems, and advances in computing platforms. With such a highly pervasive nature of “smart” devices, the nature of data being collected and processed can be increasingly private and require safeguards to ensure the data’s integrity and security. Physically unclonable functions (PUFs) have emerged as a lightweight, viable security protocol in the Internet of Things (IoT) framework. Malicious modeling of PUF architectures has proven to be difficult due to the inherently stochastic nature of PUF architectures. In this work, we show that knowledge of the underlying PUF structure is unnecessary to clone a PUF. We tackle the problem of cloning PUF-based edge nodes in different settings such as unencrypted, encrypted, and obfuscated challenges in an IoT framework. We present a novel non-invasive, architecture-independent, machine learning attack for robust PUF designs and can handle encryption and obfuscation-based security measures on the transmitted challenge response pairs (CRPs). We show that the proposed framework can successfully clone different PUF architectures, including those encrypted using two (2) different encryption protocols in DES and AES and with varying degrees of obfuscation. We also show that the proposed approach outperforms a two-stage brute force attack model. Finally, we offer a machine learning-based countermeasure, a discriminator, which can distinguish cloned PUF devices and authentic PUFs with an average accuracy of 96%. The proposed discriminator can be used for rapidly authenticating millions of IoT nodes remotely from the cloud server.

Background: Internet of Things (IoT) is the next emerging technique in which each and every thing in the world is getting connected to the internet by means of various communication techniques. By using the technique of multicasting, these things communicate with each other to transfer the data. So there is a need of an authentication scheme in order to prevent the data form getting corrupted while transmitting. As we know trillions of devices are connected in IoT, a secure communication should be implemented for the same. So a light weight authentication should be provided for IoT. Methods: The IoT consist of many objects, the objects may me mobile phones, refrigerator, air cooler etc. along with sensors, actuators, base station. The objects are mainly connected with sensors in order to retrieve data from each of them. Since sensor network is adhoc networks and the computational power consumed of sensor networks are also very low. Hence in the Wireless Sensor Network (WSN) many sensors are connected with one or more base station. The base station in wireless sensor network manages all the sensor nodes with the help of processor and memory. Since we are transforming from wireless sensor networks to IoT, the objects are connected to internet by providing address to individual object. So, the base station not having the capable to provide a secure communication between the objects along with sensors and internet.In this paper, we propose a new authentication scheme by means of two different approaches. Since IoT contains many numbers of objects, we connect certain objects which are in same area and provide a database for that object. The data related to particular node is stored and updated frequently in the database and maintained by Data Base Management System (DBMS) and it is connected to internet. Results: Whenever a user wants to access the data, the authentication is provided by means of login id with hashing password or with the help of MAC passwords. These two authentication scheme provide better when compared to existing method which are shown in results. The metrics that measures the performance of the proposed approach are the resistance against node compromise,computation overhead,communication overhead, robustness to packet loss and message entropy. Applications: This main application of mutual authentication scheme for IoT is to provide a authentication between the end user and the sensor network data. This method is also suitable for smart house application where the user can securely access the data from anywhere in the world.

Internet of Things, cognitive systems, and blockchain technology are three fields which have created numerous revolutions in software development. It seems that a combination among these fields may results in emerging a high potential and interesting field. Therefore, in this paper, we propose a framework for Internet of Things based on cognitive systems and blockchain technology. To the best of our knowledge, there is no framework for Internet of Things based on cognitive systems and blockchain. In order to study the applicability of the proposed framework, a recommender system based on the proposed framework is suggested. Since the proposed framework is novel, the suggested recommender system is novel. The suggested recommender system is compared with the existing recommender systems. The results show that the suggested recommender system has several benefits which are not available in the existing recommender systems.

IoT ecosystems generates vast amount of data from interconnected devices, sensors, and networks. This vast amount of data poses significant challenges for traditional way of analysis. Deep learning comes into picture, with its ability to model complex pattern and unstructured data, addressing these challenges and elevating the IoT systems to new levels of intelligence. The integration of deep learning with Internet of Things (IoT) ecosystems provides an advancement in creating the intelligent network of connected devices. Key deep learning architectures like convolutional neural network (CNN) and Recurrent Neural Network (RNN) excel at processing sensor data for visual inspection and environmental monitoring. Looking forward, advancement in improvised learning, explainable AI, and next generation networks like 5G/6G will further enhance the capabilities of intelligent IoT ecosystems. This chapter explains how deep learning architecture facilitates the applications of IoT.

Undeniably, the Internet of Things (IoT) ecosystem continues to evolve at a breakneck pace, exceeding all growth expectations and ubiquity barriers. From sensor to cloud, this giant network keeps breaking technological bounds in several domains, and wireless sensor nodes (motes) are expected to be predominant as the number of IoT devices grows towards the trillions. However, their future in the IoT ecosystem still seems foggy, where several challenges, such as (i) device’s connectivity, (ii) intelligence at the edge, (iii) security and privacy concerns, and (iv) growing energy needs, keep pulling in opposite directions. This prospective paper offers a succinct and forward-looking review of recent trends, challenges, and state-of-the-art solutions of low-end IoT motes, where reconfigurable computing technology plays a key role in tomorrow’s IoT devices.

The rapid proliferation of Internet of Things (IoT) devices has led to a surge in energy consumption, necessitating innovative strategies to enhance energy efficiency. Machine Learning (ML) has emerged as a promising approach to optimize energy utilization in IoT networks by enabling intelligent data processing, adaptive resource allocation, and predictive maintenance. This paper explores various ML techniques, including supervised, unsupervised, and reinforcement learning, for optimizing energy consumption in IoT ecosystems. It highlights key challenges such as data heterogeneity, real-time processing constraints, and limited computational resources while discussing potential solutions like federated learning, edge computing, and energy-aware neural networks. The study also presents recent advancements and case studies that demonstrate the effectiveness of ML-driven energy-efficient IoT frameworks. By integrating ML-based approaches, IoT networks can achieve enhanced sustainability, prolonged device lifespan, and improved operational efficiency. Future research directions focus on lightweight ML models, decentralized learning paradigms, and AI-driven energy harvesting techniques to further advance energy-efficient IoT networks.

Internet of Things (IoT) allows different objects connect with each other through the Internet without any human's interaction. Wireless sensor network (WSN) is small sensor nodes that are densely deployed in places that are impossible to contain wired cables, it is considered as one of the IoT examples that focus on how sensors can monitor in several domains. The most WSN problems are focused on its battery power limitation that motivates the Hierarchical routing protocol to be used. Hierarchical routing protocol aims to reduce the usage of the network energy and to increase its lifetime. One of the most common Hierarchical routing protocols is the Low energy Adaptive Clustering Hierarchical routing protocol (LEACH) that deals with the security challenge. The security is one of the critical issues in WSN especially in the LEACH in term how to secure the content of the transmitted data and how to transmit these data in a secure manner to solve the attacks that it deals with, this survey focuses on how to secure LEACH using many methods that will also improve the LEACH itself.