Composite Identity of Things (CIDoT) on Permissioned Blockchain Network for Identity Management of IoT Devices

2021 ◽  
pp. 59-80
Author(s):  
Anang Hudaya Muhamad Amin ◽  
Fred N. Kiwanuka ◽  
Nabih T. J. Abdelmajid ◽  
Saif Hamad AlKaabi ◽  
Sultan Khalid Abdulqader Rashed Ahli
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Complex Systems ◽  
Identity Management ◽  
Autonomous Systems ◽  
Green Technology ◽  
Comprehensive Approach ◽  
Device Identification ◽  
Sensory Data ◽  
Iot Devices

Internet of things (IoT) is in the forefront of many existing smart applications, including autonomous systems and green technology. IoT devices have been commonly used in the monitoring of energy efficiency and process automation. As the application spreads across different kinds of applications and technology, a large number of IoT devices need to be managed and configured, as they are capable of generating massive amount of sensory data. Looking from this perspective, there is a need for a proper mechanism to identify each IoT devices within the system and their respective applications. Participation of these IoT devices in complex systems requires a tamper-proof identity to be generated and stored for the purpose of device identification and verification. This chapter presents a comprehensive approach on identity management of IoT devices using a composite identity of things (CIDoT) with permissioned blockchain implementation. The proposed approach described in this chapter takes into account both physical and logical domains in generating the composite identity.

Energy Aware Cluster Head Selection for Maximizing Lifetime Improvement in Internet of Things

2017 ◽  
Vol 9 (4) ◽  
pp. 71-91 ◽  
Author(s):  
Praveen Kumar Reddy Maddikunta ◽  
Rajasekhara Babu Madda
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Hybrid Algorithm ◽  
Artificial Bee Colony ◽  
Cluster Head ◽  
High Energy ◽  
Superior Performance ◽  
Cluster Head Selection ◽  
Bee Colony ◽  
Iot Devices

Energy efficiency is a major concern in Internet of Things (IoT) networks as the IoT devices are battery operated devices. One of the traditional approaches to improve the energy efficiency is through clustering. The authors propose a hybrid method of Gravitational Search Algorithm (GSA) and Artificial Bee Colony (ABC) algorithm to accomplish the efficient cluster head selection. The performance of the hybrid algorithm is evaluated using energy, delay, load, distance, and temperature of the IoT devices. Performance of the proposed method is analyzed by comparing with the conventional methods like Artificial Bee Colony (ABC), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and GSO algorithms. The performance of the hybrid algorithm is evaluated using of number of alive nodes, convergence estimation, normalized energy, load and temperature. The proposed algorithm exhibits high energy efficiency that improves the life time of IoT nodes. Analysis of the authors' implementation reveals the superior performance of the proposed method.

scholarly journals Smart Digital Forensic Readiness Model for Shadow IoT Devices

2022 ◽  
Vol 12 (2) ◽  
pp. 730
Author(s):  
Funmilola Ikeolu Fagbola ◽  
Hein Venter
Keyword(s):  
Information Technology ◽  
Internet Of Things ◽  
Conceptual Model ◽  
Criminal Activity ◽  
Forensic Evidence ◽  
Device Identification ◽  
Digital Forensic ◽  
Privacy Breach ◽  
Evidence Gathering ◽  
Iot Devices

Internet of Things (IoT) is the network of physical objects for communication and data sharing. However, these devices can become shadow IoT devices when they connect to an existing network without the knowledge of the organization’s Information Technology team. More often than not, when shadow devices connect to a network, their inherent vulnerabilities are easily exploited by an adversary and all traces are removed after the attack or criminal activity. Hence, shadow connections pose a challenge for both security and forensic investigations. In this respect, a forensic readiness model for shadow device-inclusive networks is sorely needed for the purposes of forensic evidence gathering and preparedness, should a security or privacy breach occur. However, the hidden nature of shadow IoT devices does not facilitate the effective adoption of the most conventional digital and IoT forensic methods for capturing and preserving potential forensic evidence that might emanate from shadow devices in a network. Therefore, this paper aims to develop a conceptual model for smart digital forensic readiness of organizations with shadow IoT devices. This model will serve as a prototype for IoT device identification, IoT device monitoring, as well as digital potential evidence capturing and preservation for forensic readiness.

scholarly journals Trust Assessment in Internet of Things Using Blockchain and Machine Learning

2020 ◽  
Author(s):  
Liming Wang ◽  
Hongqin Zhu ◽  
Jiawei Sun ◽  
Ran Dai ◽  
Qi Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Trust Management ◽  
Identity Management ◽  
Edge Computing ◽  
Management Model ◽  
Learning Methods ◽  
Transaction Model ◽  
Machine Learning Methods ◽  
Iot Devices

Abstract Since IoT devices are strengthened, edge computing with multi-center cooperation becomes a trend. Considering that edge nodes may belong to different center, they have different trust management model, it’s hard to assess trust among edge nodes. In this paper, we take blockchain to coordinate differences among centers, construct a trust environment for transactions in IoT. In detail, we propose a blockchain based identity management for IoT to ensure identity is credible, then design a transaction model to provide certification for IoT transactions. And, we take machine learning methods to analyze IoT transaction log, thus decide trust nodes or not. Experiment results show that our mechanism could effectively identify trustworthy edges in IoT.

scholarly journals IPAssess: A Protocol-Based Fingerprinting Model for Device Identification in the IoT

2021 ◽  
Author(s):  
Siddhartha Bhattacharyya ◽  
Parth Ganeriwala ◽  
Shreya Nandanwar ◽  
Raja Muthalagu ◽  
anubhav gupta
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Internet Of Things ◽  
Classification Accuracy ◽  
Learning Models ◽  
Device Identification ◽  
Full Protocol ◽  
Iot Devices ◽  
Monitoring Technologies ◽  
Machine Learning Models

Internet of Things (IoT) are the most commonly used devices today, that provide services that have become widely prevalent. With their success and growing need, the number of threats and attacks against IoT devices and services have been increasing exponentially. With the increase in knowledge of IoT related threats and adequate monitoring technologies, the potential to detect these threats is becoming a reality. There have been various studies consisting of fingerprinting based approaches on device identification but none have taken into account the full protocol spectrum. IPAssess is a novel fingerprinting based model which takes a feature set based on the correlation between the device characteristics and the protocols and then applies various machine learning models to perform device identification and classification. We have also used aggregation and augmentation to enhance the algorithm. In our experimental study, IPAssess performs IoT device identification with a 99.6\% classification accuracy.

scholarly journals IPAssess: A Protocol-Based Fingerprinting Model for Device Identification in the IoT

2021 ◽  
Author(s):  
Siddhartha Bhattacharyya ◽  
Parth Ganeriwala ◽  
Shreya Nandanwar ◽  
Raja Muthalagu ◽  
anubhav gupta
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Internet Of Things ◽  
Classification Accuracy ◽  
Learning Models ◽  
Device Identification ◽  
Full Protocol ◽  
Iot Devices ◽  
Monitoring Technologies ◽  
Machine Learning Models

Internet of Things (IoT) are the most commonly used devices today, that provide services that have become widely prevalent. With their success and growing need, the number of threats and attacks against IoT devices and services have been increasing exponentially. With the increase in knowledge of IoT related threats and adequate monitoring technologies, the potential to detect these threats is becoming a reality. There have been various studies consisting of fingerprinting based approaches on device identification but none have taken into account the full protocol spectrum. IPAssess is a novel fingerprinting based model which takes a feature set based on the correlation between the device characteristics and the protocols and then applies various machine learning models to perform device identification and classification. We have also used aggregation and augmentation to enhance the algorithm. In our experimental study, IPAssess performs IoT device identification with a 99.6\% classification accuracy.

scholarly journals METHOD OF TRANSMITTING INFORMATION ON THE INTERNET OF THINGS

2021 ◽  
pp. 41-47
Author(s):  
Yaroslav Mikhnenko ◽  
Mariia Skulysh ◽  
Vasil Kurdecha ◽  
Galyna Mikhnenko
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Communication Systems ◽  
Main Idea ◽  
Threshold Value ◽  
Buffer Size ◽  
The Internet ◽  
Modified Method ◽  
Iot Devices ◽  
The Internet Of Things

Background. The IoT technology covers devices and appliances, such as thermostats, home security systems and cameras, lighting fixtures as well as other household appliances that support one or more shared ecosystems, and can be controlled by devices associated with that ecosystem, for example with smartphones and smart speakers. However, there are a lot of problems to be solved. One of these problems is the power supply of wireless sensors on the Internet of Things. Objective. The purpose of the study is to reduce energy consumption of IoT devices in the process of transmitting the collected data by regulating the number of transmission transactions. Methods. The analysis of the existing energy saving methods in IoT devices shows that the problem of choosing the optimal buffer size has not yet been solved. An optimization problem has been formulated, which allows considering the requirements for the quality of transmission of both information flows and communication systems that provide this transfer. Results. The article presents the modified method of information transmission to improve the energy efficiency of the network. The need to allocate a queue buffer at each of the nodes and explain the operation of the node using the queue buffer has been highlighted. The scheme of the project with the use of the modified Sleep / Wake algorithm has been created. Conclusions. The main idea of the method is to allocate a buffer at each node with a certain threshold value, and if the latter is exceeded, the transmission of information packets will begin. This increases the service life of WSN by 14.8… 20.6% compared to the IoT sensor networks that use an asynchronous queue cycle. Keywords: IoT; energy efficiency; life expectancy of the IoT network.

Energy Aware Cluster Head Selection for Maximizing Lifetime Improvement in Internet of Things

2020 ◽  
pp. 1347-1367
Author(s):  
Praveen Kumar Reddy Maddikunta ◽  
Rajasekhara Babu Madda
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Hybrid Algorithm ◽  
Artificial Bee Colony ◽  
Gravitational Search Algorithm ◽  
Cluster Head ◽  
Cluster Head Selection ◽  
Energy Aware ◽  
Bee Colony ◽  
Iot Devices

Energy efficiency is a major concern in Internet of Things (IoT) networks as the IoT devices are battery operated devices. One of the traditional approaches to improve the energy efficiency is through clustering. The authors propose a hybrid method of Gravitational Search Algorithm (GSA) and Artificial Bee Colony (ABC) algorithm to accomplish the efficient cluster head selection. The performance of the hybrid algorithm is evaluated using energy, delay, load, distance, and temperature of the IoT devices. Performance of the proposed method is analyzed by comparing with the conventional methods like Artificial Bee Colony (ABC), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and GSO algorithms. The performance of the hybrid algorithm is evaluated using of number of alive nodes, convergence estimation, normalized energy, load and temperature. The proposed algorithm exhibits high energy efficiency that improves the life time of IoT nodes. Analysis of the authors' implementation reveals the superior performance of the proposed method.

scholarly journals Big Data and Its V’s with IoT to Develop Sustainability

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Xiao Chu ◽  
Shah Nazir ◽  
Kunhao Wang ◽  
Zeqi Leng ◽  
Wajeeha Khalil
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Information Technologies ◽  
Modern Society ◽  
Automatic Monitoring ◽  
Smart Devices ◽  
Comprehensive Overview ◽  
Sensory Data ◽  
Sustainable Environment ◽  
Iot Devices

Internet of Things (IoT) has been considered as one of the emerging network and information technologies that can comprehend automatic monitoring, identification, and management through a network of smart IoT devices. The effective use of IoT in different areas has improved efficiency and reduced errors. The rapid growth of smart devices such as actuators, sensors, and wearable devices has made the IoT enable for smart and sustainable developments in the area. Physical objects are interlinked with these smart devices for the progression to analyse, process, and manage the surroundings data. Such data can then be further utilised for smarter decisions and postanalysis for different purposes. However, with the limited IoT resources, the management of data is difficult due to the restrictions of transmission power place and energy consumption, and the processing can put pressure on these smart devices. The network of IoT is connected with big data through Internet for manipulating and storing huge bulk of data on cloud storage. The secure framework based on big data through IoT is the awful need of modern society which can be energy efficient in a sustainable environment. Due to the intrinsic characteristics of sensors nodes in the IoT, like data redundancy, constrained energy, computing capabilities, and limited communication range, the issues of data loss are becoming among the main issues which mostly depend on the completeness of data. Various approaches are in practice for the recovery problem of data, such as spatiotemporal correlation and interpolation. These are used for data correlation and characteristics of sensory data. Extracting correlation data became difficult specifically as the coupling degree between diverse perceptual attributes is low. The current study has presented a comprehensive overview on big data and its V’s with Internet of Things to describe the research into the area with in-depth review of existing literature.

Bringing Security to The Smallest Embedded Systems

2017 ◽  
Author(s):  
JOSEPH YIU
Keyword(s):  
Internet Of Things ◽  
Embedded Systems ◽  
Low Cost ◽  
Security Requirements ◽  
The Internet ◽  
Security Measures ◽  
Significant Challenge ◽  
Iot Devices

The increasing need for security in microcontrollers Security has long been a significant challenge in microcontroller applications(MCUs). Traditionally, many microcontroller systems did not have strong security measures against remote attacks as most of them are not connected to the Internet, and many microcontrollers are deemed to be cheap and simple. With the growth of IoT (Internet of Things), security in low cost microcontrollers moved toward the spotlight and the security requirements of these IoT devices are now just as critical as high-end systems due to:

Improving Energy Efficiency in Internet of Things using Artificial Bee Colony Algorithm

2020 ◽  
Vol 14 ◽  
Author(s):  
M. Sivaram ◽  
V. Porkodi ◽  
Amin Salih Mohammed ◽  
S. Anbu Karuppusamy
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Artificial Bee Colony ◽  
Trust Model ◽  
Sensor Nodes ◽  
Remote Areas ◽  
Bee Colony ◽  
Important Concern ◽  
Wide Range ◽  
Iot Devices

Background: With the advent of IoT, the deployment of batteries with a limited lifetime in remote areas is a major concern. In certain conditions, the network lifetime gets restricted due to limited battery constraints. Subsequently, the collaborative approaches for key facilities help to reduce the constraint demands of the current security protocols. Aim: This work covers and combines a wide range of concepts linked by IoT based on security and energy efficiency. Specifically, this study examines the WSN energy efficiency problem in IoT and security for the management of threats in IoT through collaborative approaches and finally outlines the future. The concept of energy-efficient key protocols which clearly cover heterogeneous IoT communications among peers with different resources has been developed. Because of the low capacity of sensor nodes, energy efficiency in WSNs has been an important concern. Methods: Hence, in this paper, we present an algorithm for Artificial Bee Colony (ABC) which reviews security and energy consumption to discuss their constraints in the IoT scenarios. Results: The results of a detailed experimental assessment are analyzed in terms of communication cost, energy consumption and security, which prove the relevance of a proposed ABC approach and a key establishment. Conclusion: The validation of DTLS-ABC consists of designing an inter-node cooperation trust model for the creation of a trusted community of elements that are mutually supportive. Initial attempts to design the key methods for management are appropriate individual IoT devices. This gives the system designers, an option that considers the question of scalability.

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