Smart Grids and Internet of Things E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
SMART GRIDS AND INTERNET OF THINGS Smart grids and the Internet of Things (IoT) are rapidly changing and complicated subjects that are constantly changing and developing. This new volume addresses the current state-of-the-art concepts and technologies associated with the technologies and covers new ideas and emerging novel technologies and processes. Internet of Things (IoT) is a self-organized network that consists of sensors, software, and devices. The data is exchanged among them with the help of the internet. Smart Grids (SG) is a collection of devices deployed in larger areas to perform continuous monitoring and analysis in that region. It is responsible for balancing the flow of energy between the servers and consumers. SG also takes care of the transmission and distribution power to the components involved. The tracking of the devices present in SG is achieved by the IoT framework. Thus, assimilating IoT and SG will lead to developing solutions for many real-time problems. This exciting new volume covers all of these technologies, including the basic concepts and the problems and solutions involved with the practical applications in the real world. Whether for the veteran engineer or scientist, the student, or a manager or other technician working in the field, this volume is a must-have for any library. Smart Grids and Internet of Things: * Presents Internet of Things (IoT) and smart grid (SG)-integrated frameworks along with their components and technologies * Covers the challenges in energy harvesting and sustainable solutions for IoTSGs and their solutions for practical applications * Describes and demystifies the privacy and security issues while processing data in IoTSG * Includes case studies relating to IoTSG with cloud and fog computing machine learning and blockchain
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Table of Contents
Cover
Series Page
Title Page
Copyright Page
Preface
1 Introduction to the Internet of Things: Opportunities, Perspectives and Challenges
1.1 Introduction
1.2 IOT Platform
1.3 IOT Layers and its Protocols
1.4 Architecture and Future Problems for IOT Protection
1.5 Conclusion
References
2 Role of Battery Management System in IoT Devices
2.1 Introduction
2.2 Internet of Things
2.3 Power of IoT Devices in Battery Management System
2.4 Battery Life Estimation of IoT Devices
2.5 IoT Networking Technologies
2.6 Conclusion
References
3 Smart Grid - Overview, Challenges and Security Issues
3.1 Introduction to the Chapter
3.2 Smart Grid and Its Uses
3.3 The Grid as it Stands-What’s at Risk?
3.4 Creating the Platform for Smart Grid
3.5 Smart Grid in Power Plants
3.6 Google in Smart Grid
3.7 Smart Grid in Electric Cars
3.8 Revisit the Risk
3.9 Summary
References
4 IoT-Based Energy Management Strategies in Smart Grid
4.1 Introduction
4.2 Application of IoT for Energy Management in Smart Grids
4.3 Energy Management System
4.4 Types of EMS at Smart Grid
4.5 Participants of EMS
4.6 DER Scheduling
4.7 Important Factors for EMS Establishment
4.8 Optimization Approaches for EMS
4.9 Conclusion
References
5 Integrated Architecture for IoTSG: Internet of Things (IoT) and Smart Grid (SG)
5.1 Introduction
5.2 Introduction to Smart Grid
5.3 Integrated Architecture of IoT and Smart Grid
5.4 Smart Grid Security Services Based on IoT
References
6 Exploration of Assorted Modernizations in Forecasting Renewable Energy Using Low Power Wireless Technologies for IoTSG
6.1 Introduction to the Chapter
6.2 Intangible Architecture of Smart Grid (SG)
6.3 Internet of Things (IoT)
6.4 Renewable Energy Source (RES)- Key Technology for SG
6.5 Low Power Wireless Technologies for IoTSG
6.6 Conclusion
References
7 Effective Load Balance in IOTSG with Various Machine Learning Techniques
I. Introduction
II. IoT in Big Data
III. IoT in Machine Learning
IV. Machine Learning Methods in IoT
V. IoT with SG
VI. Deep Learning with IoT
VII. Challenges in IoT for SG
VIII. IoT Applications for SG
IX. Application of IoT in Various Domain
X. Conclusion
References
8 Fault and Delay Tolerant IoT Smart Grid
8.1 Introduction
8.2 Architecture
8.3 Opportunities and Challenges in Delay Tolerant Network for the Internet of Things
8.4 Energy Efficient IoT Enabled Smart Grid
8.5 Security in DTN IoT Smart Grid
8.6 Applications of DTN IoT Smart Grid
8.7 Conclusion
References
9 Significance of Block Chain in IoTSG - A Prominent and Reliable Solution
9.1 Introduction
9.2 Trustful Difficulties with Monetary Communications for IoT Forum
9.3 Privacy in Blockchain Related Work
9.4 Initial Preparations
9.5 In the IoT Power and Service Markets, Reliable Transactions and Billing
9.6 Potential Applications and Use Cases
9.7 Proposed Work Execution
9.8 Investigation of Secrecy and Trustworthy
9.9 Conclusion
References
10 IoTSG in Maintenance Management
10.1 Introduction to the Chapter
10.2 IoT in Smart Grid
10.3 IoT in the Generation Level, Transmission Level, Distribution Level
10.4 Challenges and Future Research Directions in SG
10.5 Components for Predictive Management
10.6 Data Management and Infrastructure of IoT for Predictive Management
10.7 Research Challenges in the Maintenance of Internet of Things
10.8 Summary
References
11 Intelligent Home Appliance Energy Monitoring with IoT
11.1 Introduction
11.2 Survey on Energy Monitoring
11.3 Internet of Things System Architecture
11.4 Proposed Energy Monitoring System with IoT
11.5 Energy Management Structure (Proposed)
11.6 Implementation of the System
11.7 Smart Home Automation Forecasts
11.8 Energy Reduction Based on IoT
11.9 Performance Evaluation
11.10 Benefits for Different User Categories
11.11 Results and Discussion with Benefits of User Categories
11.12 Summary
References
12 Applications of IoTSG in Smart Industrial Monitoring Environments
12.1 Introduction
12.2 Energy Management
12.3 Role of IoT and Smart Grid in the Banking Industry
12.4 Role of IoT and Smart Grid in the Automobile Industry
12.5 Role of IoT and SG in Healthcare Industry
12.6 IoT and Smart Grid in Energy Management - A Way Forward
12.7 Conclusion
References
13 Solar Energy Forecasting for Devices in IoT Smart Grid
13.1 Introduction
13.2 Role of IoT in Smart Grid
13.3 Clear Sky Models
13.4 Persistence Forecasts
13.5 Regressive Methods
13.6 Non-Linear Stationary Models
13.7 Linear Non-Stationary Models
13.8 Artificial Intelligence Techniques
13.9 Remote Sensing Model
13.10 Hybrid Models
13.11 Performance Metrics for Forecasting Techniques
13.12 Conclusion
References
14 Utilization of Wireless Technologies in IoTSG for Energy Monitoring in Smart Devices
14.1 Introduction to Internet of Things
14.2 IoT Working Principle
14.3 Benefits of IoT
14.4 IoT Applications
14.5 Introduction to Smart Home
14.6 Problem Statement
14.7 Introduction to Wireless Communication
14.8 How Modbus Communication Works
14.9 MQTT Protocol
14.10 System Architecture
14.11 IoT Based Electronic Energy Meter-eNtroL
14.12 AC Control System for Home Appliances – Switch2Smart
14.13 Scheduling Home Appliance Using Timer – Switch Binary
14.14 Hardware Design
14.15 Implementation of the Proposed System
14.16 Testing and Results
14.17 Conclusion
References
15 Smart Grid IoT: An Intelligent Energy Management in Emerging Smart Cities
15.1 Overview of Smart Grid and IoT
15.2 IoT Application in Smart Grid Technologies
15.3 Technical Challenges of Smart Grid
15.4 Energy Efficient Solutions for Smart Cities
15.5 Energy Conservation Based Algorithms
15.6 Conclusion
References
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 1
Table 1.1 The innovations of internet paradigm.
Table 1.2 Shows the some of the existing IOT platforms and its services, dra...
Table 1.3 IOT communication protocols.
Chapter 2
Table 2.1 Application of lithium batteries.
Table 2.2 Core current consumption of CC2650MODA.
Table 2.3 Peripheral current consumption of CC2650MODA.
Chapter 4
Table 4.1 Sections of IoT application in SG [11].
Table 4.2 Comparison among wired and wireless communication technologies [7]...
Table 4.3 Comparison of mathematical optimization approaches [7].
Table 4.4 Comparison of heuristic optimization approaches [7].
Table 4.5 Comparison of metaheuristic optimization approaches [7].
Table 4.6 Comparison of other optimization approaches [7].
Chapter 8
Table 8.1 Traditional grid vs. smart grid (intelligent network).
Chapter 9
Table 9.1 Data on energy consumption in the credit-distribution group (SuBC)...
Table 9.2 Data on power usage in the service community that has been anonymi...
Chapter 10
Table 10.2.1 Conceptual model of SG.
Table I. PHM algorithms outlook.
Chapter 11
Table 11.1 Typical energy consumption of a home.
Table 11.2 Hidden energy usage of household appliances.
Chapter 13
Table 13.1 Tradition grid vs smart grid.
Table 13.2 Comparison of regressive models.
Table 13.3 Comparison of deep learning models.
Chapter 14
Table 14.1 Function code table.
Table 14.2 Function code table.
Chapter 15
Table 15.1 Components of smart grid and its roles.
List of Illustrations
Chapter 1
Figure 1.1 Internet of Things environment.
Figure 1.2 IOT layers.
Figure 1.3 Data flow in IOT layers.
Figure 1.4 IOT protocols in each layer.
Figure 1.5 Taxonomy of technologies.
Figure 1.6 Open IOT domain network security issues.
Figure 1.7 IOT security threats.
Chapter 2
Figure 2.1 Batteries with nominal voltage and full charge voltage.
Figure 2.2 IoT - battery market by region.
Figure 2.3 IoT power calculator.
Figure 2.4 Capacity of CR2032 battery.
Figure 2.5 Pulse discharge of CR2032 battery.
Chapter 3
Figure 3.1 Uses of smart grid.
Figure 3.2 Electricity distribution to smart home.
Figure 3.3 Working principle of smart grid.
Figure 3.4 Uses of smart grid.
Figure 3.5 Challenges in smart grid.
Figure 3.6 Smart grid layered approach to security considerations.
Figure 3.7 Smart grid architecture.
Figure 3.8 Smart grid in power plants.
Figure 3.9 Communication network of smart grid.
Figure 3.10 Smart grid in vehicle to grid.
Figure 3.11 Electric car.
Figure 3.12 Blackouts in reliability.
Figure 3.13 Energy efficiency in smart grid.
Chapter 4
Figure 4.1 Main layers of IoT application in SGs [11].
Figure 4.2 Overview of EMS frameworks: (a) centralized; (b) decentralized; (...
Figure 4.3 Hierarchical management framework for MGs [18].
Figure 4.4 Smart home EMS architecture [21].
Figure 4.5 Architecture of smart building EMS.
Figure 4.6 Participants of EMS in SG environment.
Figure 4.7 Assignment and commitment of transmission and distribution system...
Figure 4.8 Wired and wireless communication technologies [7].
Figure 4.9 Supply- and demand-side aggregators [7].
Figure 4.10 EMS connections in the SG application in presence of the RES-bas...
Figure 4.11 The scheme of PEVs charging power control [34].
Figure 4.12 Uncertainty modeling overview.
Figure 4.13 Waveform demonstrating (a) harmonic distortion, (b) impulsive tr...
Figure 4.14 Three main schemes used in the DSM approaches [48].
Figure 4.15 Classifications of DR programs.
Figure 4.16 Procedure for solving an EMS problem [7].
Figure 4.17 Optimization approaches applied to the EMS [7].
Chapter 5
Figure 5.1 IoT architecture and its layers.
Figure 5.2 Phases of connectivity in IoT architecture.
Figure 5.3 Components of IoT architecture.
Figure 5.4 Components of smart grid architecture.
Figure 5.5 Smart grid technologies.
Figure 5.6 Smart grid architecture for smart metering application.
Figure 5.7 Integrated architecture of IoT and smart grid.
Figure 5.8 Safety concerns.
Figure 5.9 Smart grid security services based on IoT.
Figure 5.10 Stage by stage approach for effective IoT implementation.
Figure 5.11 Steps for implementation.
Figure 5.12 IoT base SG team.
Figure 5.13 Types of decision making.
Chapter 6
Figure 6.1 Model view of processes involved in smart grid model.
Figure 6.2 Different layers of IoT.
Figure 6.3 Total energy supply (TES) by source.
Figure 6.4 Electricity generation by renewable source.
Figure 6.5 Natural sources of renewable energy.
Figure 6.6 IoT stack.
Figure 6.7 IoT technologies for SG.
Chapter 7
Figure 7.1 Relationship between IoT and big data.
Figure 7.2 Workflow of machine learning with IoTSG.
Figure 7.3 Distributed clustering architecture.
Figure 7.4 Internet of Things with smart grid.
Chapter 8
Figure 8.1 IoT enabled smart grid technology.
Figure 8.2 Basic architecture of IoT enabled system.
Figure 8.3 NIST architecture.
Figure 8.4 IEEE architecture.
Chapter 9
Figure 9.1 Monetary association in energy of Internet-of-Things (IoT) and se...
Figure 9.2 Blockchain structure.
Figure 9.3 A primary blockchain for payment and billing payments and subnetw...
Figure 9.4 SuBC’s local schematic view for U2U interactions.
Figure 9.5 Handling of exchanges SuBC-x represents transaction improvement i...
Figure 9.6 Access of power and handling the transaction utilization.
Figure 9.7 SuBC and MaBC all have power consumption exchanges.
Figure 9.8 Anonymity degree curve for anonymity set in Table 9.2.
Figure 9.9 Memory percentages in Mixed-Bridge and the regular blockchain.
Figure 9.10 Percentages of CPU used by Mixed-Bridge and the regular blockcha...
Figure 9.11 Dealing out time for addition blocks to the standard blockchain ...
Figure 9.12 Dealing out period for block collateral in a traditional blockch...
Chapter 10
Figure 10.1.1 IoT cloud framework.
Figure 10.2.1 Architecture of smart grid.
Figure 10.2.2 Features smart grid.
Figure 10.2.3 Conceptual model of smart grid framework.
Figure 10.3.1 Real-time monitoring for generation from an IoT-oriented contr...
Figure 10.3.2 Different layers of integration of IoT in distribution level....
Figure 10.6.1 IoT data lifecycle and data management.
Chapter 11
Figure 11.1 IoT system architecture.
Figure 11.2 IoT energy management framework.
Figure 11.3 Hardware implementation of proposed system.
Figure 11.4 Functionalities of the IHAEM.
Figure 11.5 Typical energy consumption of a home.
Figure 11.6 IHAEM app sample view.
Chapter 12
Figure 12.1 Four-stage process of IoT mechanism.
Figure 12.2 Steps in energy management.
Figure 12.3 IoT applications for banking.
Figure 12.4 IoT for smart monitoring and control in automobile industry.
Figure 12.5 IoT beneficiaries in health care.
Chapter 13
Figure 13.1 Solar energy forecasting model flow.
Figure 13.2 Smart grid.
Figure 13.3 Simplified artificial neuron structure.
Figure 13.4 Artificial neural network.
Figure 13.5 ANN based prediction flow.
Figure 13.6 Deep learning models for forecasting problems.
Figure 13.7 Simple recurrent neural network.
Figure 13.8 A LSTM cell.
Figure 13.9 A CNN framework.
Chapter 14
Figure 14.1 IoT - Internet of Things.
Figure 14.2 Connecting multiple devices using IoT.
Figure 14.3 IoT architecture.
Figure 14.4 Applications of IoT.
Figure 14.5 Benefits of smart home.
Figure 14.6 Modbus proprieties and ISO/OSI model.
Figure 14.7 Format of MODBUS PDU and serial line PDU.
Figure 14.8 Pub/Sub architecture.
Figure 14.9 MQTT client to broker connection.
Figure 14.10 MQTT standard packet structure.
Figure 14.11 Control packet.
Figure 14.12 Proposed architecture.
Figure 14.13 Block diagram of the proposed system.
Figure 14.14 Block diagram of eNtroL.
Figure 14.15 PZEM-004T energy meter module.
Figure 14.16 ESP8266 module.
Figure 14.17 30A SPDT relay.
Figure 14.18 230V AC to 5V DC converter.
Figure 14.19 LM1117 IC- 5v to 3.3v converter.
Figure 14.20 Optocoupler-H11AA1 IC.
Figure 14.21 TRIAC driven opto isolator- MOC3021M IC.
Figure 14.22 TRIAC, BT136-600 IC.
Figure 14.23 Overview of Kaicad.
Figure 14.24 Schematic of IoT based energy meter (eNtroL).
Figure 14.25 Schematic of AC control system (switch2smart).
Figure 14.26 Schematic of Switching AC devices using timer (switch binary)....
Figure 14.27 eNtroL after soldering the components.
Figure 14.28 eNtroL end product with enclosure and sticker.
Figure 14.29 Web page for eNtroL.
Figure 14.30 Switch2Smart end product with enclosure and sticker.
Figure 14.31 Web page for switch2smart.
Figure 14.32 Web page for switch binary.
Chapter 15
Figure 15.1 Illustration of GA.
Figure 15.2 BFO-the stepwise illustration.
Figure 15.3 Illustration of binary particle swarm optimization.
Figure 15.4 Illustration of WDO.
Figure 15.5 Illustration of wind-driven optimization.
Figure 15.6 Representation of wind-driven bacterial foraging algorithm.
Guide
Cover
Series Page
Title Page
Copyright Page
Preface
Table of Contents
Begin Reading
Index
Also of Interest
Wiley End User License Agreement
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Smart Grids and Internet of Things
An Energy Perspective
Edited by
P. SanjeevikumarRajesh Kumar DhanarajMalathy SathyamoorthyJens Bo Holm-NielsenandBalamurugan Balusamy
This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2023 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 9781119812449
Front cover images supplied by Stockvault.comCover design by Russell Richardson
Preface
As editors, we feel privileged to have been asked to edit the 1st edition of “Smart Grids and Internet of Things: An energy perspective”. Internet of Things (IoT) is a self-organized network which consists of sensors, software and devices. The data is exchanged among them with the help of the internet. Smart Grids (SG) is a collection of devices deployed in a larger area to perform continuous monitoring and analysis in that region. It is highly responsible for balancing the flow of energy between the servers and consumers. SG also take care of transmission and distributing power to the components involved. The tracking of the devices present in SG is achieved by IoT framework. Thus, assimilating IoT and SG will lead to a betterment in developing solutions for many real time problems.
The book also presents information about various wireless communication protocol which helps in data transmission between the devices involved in IoTSG. The cloud and fog computing which helps to process the edge servers with low latency and avoiding congestion between the devices which are geographically dispersed is also discussed. The efficient big data analytics procedure which will avoid computation overhead and saves energy of the devices deployed in IoTSG is also presented in this book. Incorporation of Machine learning (ML) techniques which helps to automate the things in a sensible way for real time applications by using IoTSGs are discussed briefly. The variety of security threats, privacy issues, cyber-attacks which are very prevalent among the internet related applications are addressed in this book. Multifaceted block chain assimilated with IoTSG which will develop fault tolerant network with enhanced energy is also discussed.
At the end of each chapter, authors have clearly identified important research gaps and needs for future research related to IoTSG. This book includes chapters that provide the latest scientific knowledge on requirements for internet of things, smart grids, integrating IoT with smart grid, communications and security framework in IoTSG, utilizing blockchain technologies in IoTSG, along with their issues and challenges. Authors also have included case studies involving energy trading markets, and energy exchange platforms that will benefit the readers in understanding the application of IoTSG in energy sector. We believe the authors have done an outstanding job in presenting the latest information in their respective fields and hope this edition will bring about a prospective shift in energy market globally.
Sanjeevikumar PadmanabanEsbjerg, DenmarkJens Bo Holm-NielsenEsbjerg, DenmarkRajesh Kumar DhanarajDelhi, IndiaMalathy SathyamoorthyTamilnadu, IndiaBalamurugan BalusamyDelhi, India
1Introduction to the Internet of Things: Opportunities, Perspectives and Challenges
F. Leo John1*, D. Lakshmi2 and Manideep Kuncharam3
1 Department Computer Science and Information Technology, Prowess University, Delaware, Wilmington, USA
2 School of Computing Science and Engineering, VIT Bhopal University, Madhya Pradesh, India
3 Department of Information Technology, B.V. Raju Institute of Technology, Narasapur, Telangana, India
Abstract
The Internet of Things (IOT) and its security is an important role in the modern era of intelligent computing and its applications. The IOT advantages support the individuals and organizations from the remote regions to complete the tasks, operations and services and make their decisions in an effective manner. Providers of services and manufacturers of equipment primarily concentrate on the provision of information and pay little attention to the protection and privacy of the information provided. The IOT integrates a range of innovations and plans through standard communication protocols and special solution schemes to integrate a variety of smart artifacts. As the rising emphasis and major global investments show, green IOT, IOT security, self-configuration, self-adaptation and interoperable communication are the main topics for study. Sensors have been used by different industries to gather data, however their control systems are kept purposely isolated in order to prevent cyberattacks. The deployment of IOT security issues poses the entire evolution of smart objects. The capacity of IOT is extended to connecting devices, machines and applications to the Internet. IOT allows all the connected devices and things to exchange data or even control each other. The different types of current IOT platforms, IOT protocol threats, and IOT layers are discussed in this paper. Experts forecast that after the existence of 5G technology to the extent almost 50 billion devices or things are connected to the internet. This book chapter will be useful for developing IOT applications for organizations, with a better approach and provides a key factor in the decision-making process.
Keywords: IOT security, threats, privacy, IOT platforms
1.1 Introduction
It has now become a buzzword for everyone working in this field of research, with the rapid development of the Universal Object Interaction (UOI or IOT). IOT is the global “intelligent” versioned network for regular physical objects. Its ability to carry out its activities automatically by means of integrated computer hardware, cameras, sensors, actuators, control units and applications. Figure 1.1 illustrates the various layers and its protocols that get connected in the IOT environment. The 21st century is for IOT, which is seen as a physical devices network from electronic and software sensors. The network of around 27 billion physical devices on IOT is now available and the list is expanding. These devices (car, fridge, TV, etc.) can be uniquely recognized by an interconnected computing system and can be linked from anywhere to gain more services and value through effective information and communications technologies. The “THING” in IOT is everywhere around us, like health care equipment, houses, computers, mobiles, livestock, agriculture, humans, energy, industry, logistics etc. Today, intelligent health services, intelligent houses, intelligent traffic and intelligent home appliances are using this technology for greater digital use. Figure 1.1 shows the overview of the IOT environment.
It is possible to classify IOT applications into two categories:
IOT-based tracking systems:
These applications regularly capture and send data to the cloud from attached sensors or computers. Examples include home control, hospital security and intelligent measurements. They also provide online control and data analytics.
Applications for IOT control-oriented:
The program uses sensor data to track linked actuators in real time. For eg, autos, industrial robots and remote operation. The latency, accuracy and usability criteria which differ depending on the application situation. The most common application of IOT devices is data processing. For the calculation of such parameters, most IOT devices have single or multiple sensors. Every device involved in IOT represents a risk, and it is a major threat to an organization about the confidentiality of the data collected and the dataset integrity. Connecting low-cost IOT devices with minimal security mechanisms will face ever-increasing potential security threats. IOT applications pose a variety of security issues.
Figure 1.1 Internet of Things environment.
The major characteristics of IOT are:
Self-Adaptation: The capability of an IOT system needs to adapt with the operating conditions and changing contexts.
Self-Configuration: This feature enables several IOT or IOT devices to work along with larger numbers of devices simultaneously. IOT devices should have the capability of configuring the network, software upgrade with lesser or no manual intervention.
Interoperable Communication Protocols: This feature allows communication with all other devices within the infrastructure.
Unique Identity: Every IOT device has its own unique identifier. Integrated into Information Network: Data from the larger number of IOT nodes are connected to aggregate the data, analyze and predict or decision making.
1.1.1 The IOT Data Sources
Classification of Sensors
Data sources are growing enormously in terms of formats and volume. The scale of data will be incredibly high with the connectivity of numerous IOT connected devices and anticipated billions of sensors too in the near future too. A sensor is a device that detects changes in the environment. A sensor is worthless by itself, but it plays an essential part in using it in the electronic device.
The various types of data sources are given below:
Data from passive sources: Passive sensor data is less effective, low-power and needs to be allowed, generating data only when advised before data can be collected and transmitted. For example, when the readable machine is correctly invoked, only current statistics are provided by a sensor that measures ground-water saturation. These sensors are typically small, durable and used in hazardous, and remote places. Usually, these sensors are lightweight, rugged and used in hazardous and remote areas.
Real time data: Active sensor data streams the data continuously (Example: a jet engine). For easy receipt and extraction of insights from data sources, data capture, processing platforms and infrastructures must therefore be available.
Dynamic source (fog devices) Data: Here sensor data is collected from physical, mechanical, electrical and electronic components attached to the sensors. These sensors can be used to allow IOT devices to transmit data. These sensors possess the inherent resources and capacity to conduct communication with IOT applications based on business, the web and cloud using all types of IOT devices.
Features of Sensors
A node of the sensor, also called a mote, which is a node in a network of sensors that can process, collect sensory information and connect to other linked nodes in the network. A mote is a node, but a node is not a mote every time. The sensors deployed heavily can be the temperature, pressure removal, long-range communication, short-range communication.
The three characteristics for a strong sensor is as follows:
It should be sensitive to the condition or phenomenon that it measures.
It should not be vulnerable to other physical conditions or environment.
During the measuring process, it should not alter the calculated phenomenon or condition.
Properties of Sensor
The most important thing is that a sensor can be defined by different properties:
Range: The initial and final values of the phenomenon or condition that the sensor can measure.
Sensitivity: the minimum parameter change that induces a measurable signal change.
Resolution: The minimal change in the sensor’s phenomenon or condition.
There are a wide variety of sensors that we can use to monitor almost any physical aspect surrounding us. Below are some common sensors commonly used in daily life:
Electronic Sensors
Temperature Sensor: Used to measure temperature in the physical environment. For example: thermocouple.
IR Sensor: It is used to detect obstacles and controls direction in the robotic vehicle. Eg.- Device having photo chips with photocell, Tv remote.
Ultrasonic Sensor: It is used to detect high frequency sound waves and measure the distant object. Ex: Transducers, SONAR, and Radar.
Touch Sensor: Touch Sensors are nothing but switches used in electric stove.
IOT Sensors
Proximity Sensor: It is used to find the properties of the existing or non-existing objects. It is mainly used in retail to track the number of particular products sold.
Chemical Sensors: Used to detect any changes in the liquid or air. Eg.-chemi resistor.
Gas Sensor: It is very similar to gas sensors but used in multiple domains such as agriculture, health, manufacturing industries and so on. Ex: Ozone Monitoring Type.
Humidity Sensor: Used to measure vapor in the atmosphere.
Robotics Sensor
Acceleration Sensor: For acceleration measurement an accelerometer is primarily used. These sensors can be used in many configurations. The selection form depends on the industry’s requirements.
Sound Sensor: These sensors are typically microphones used to detect the sound and to deliver the required voltage level based on the sound level detected.
Light Sensor: Light sensors are kinds of transducer systems used to detect light and create a voltage shift identical to the light intensity of the light sensors.
Tactile Sensor: This is a sensor form that specifies the interaction between the sensor and the target. In everyday scenarios including in lamps, touch sensors are most likely implemented with dim or improve luminosity by pressing their base and lifting buttons.
1.1.2 IOT Revolution
In comparison with the current global Internet, the benefits of exploring IOT technology are immense. But because several devices can interact with one another using IOT services independently of human interference, there are enough safety problems linked to this innovative communication process. IOT environment devices are exposed to higher degree external attackers analyzed on the Internet. In the future as a result of technological revolution, everything is a service. There is a drastic change in technology space, process space, infrastructure space and architecture space.
Space for Technology: There are a variety of interruptive, super- important and new technologies such as integration of micro services, nanotechnology and devices, as well as real time analyzes that lead to interfacing, knowledge engineering, actionable insights, dissemination, etc.
Space for Process: With the aid of advanced technology, services and software, big data, converging, scalable and immediate infrastructure, and trendy smartphones entering common IT, new process restructuring and integration, management frameworks, process revolution, process law, and regulation and re-engineering are emerging and evolving. Space for Infrastructure: The newly-seen technology, which integrates, centralizes, federates, automates and methods for sharing, explicitly demonstrates that in the days to come, the highly-critical and undesirable infrastructure environment will hit higher and green. The system for measuring, contacting, organizing, evaluating and displaying is making a great deal of improvements. The physical infrastructure has been transformed and built so that a transparent and discoverable network can be connected, and that the programmable, virtual and well-structured infrastructure can be controlled remotely.
Space for Architecture: Event-driven architecture (EDA), model-driven architecture and Service-oriented architecture (SOA) are the key architectural trends that simplify and streamline enterprise, embedded, mobile and cloud IT (MDA). Considering the unparalleled and rising demand from the service-orientation model, both products are perceived and prescribed as a product. This ensures that all is provided with its logical interface to allow other systems and resources to identify, bind and benefit from various skills and competencies. In this group, IT inventions and revolutions are shown in Table 1.1.
Table 1.1 The innovations of internet paradigm.
In terms of access and technology
In terms of content and operation
Computer’s Internet
The Device’s Internet
Services Internet
The Internet for Things
Energy’s Internet
Internet 1.0 - (The easy online world read only)
Internet 2.0 - (Workshops for social media)
Site 3.0 - (Web Information discovery and distribution of correct human perception information)
Internet 4.0 - (The intelligent Web-Entertainment and distribution of realistic perspectives into human use)
Literature Review
In the literature on IOT security threats a small spectrum of work has been investigated. This section addresses the works of numerous researchers to the whole of IOT protection. The security related issues and privacy issues and the relevant methods of resolution, as taken from the work of numerous researchers in this area, were discussed in [1]. As IOT or “IOT” (Internet of Things) implementations are rising, there would be a huge demand for the IOT sensors and actuators in the market. In [2], various IOT platforms are discussed. Usage of ThingSpeak platform was presented in [3]. IBM Watson platform pros and cons are discussed in [4].
In [5], various cloud IOT platforms are discussed. Limitations of the exiting platform and various new IOT platforms with enhanced features are discussed in [6, 7] In [8], the authors conducted a survey of various issues related to IOT protection and user privacy. IOT standards and communication protocol comparisons are discussed [9–13]. In [14], various IOT security considerations are presented in the design of the IOT framework. The authors presented in [15] and [16] the different communication protocol schemes, the need for IOT and the mechanism to be used to reduce the different IOT threats.
1.2 IOT Platform
An IOT device is linked to multiple devices by an Internet transmission protocol. Every IOT infrastructure has two key divisions: front end and back end. Sensors and drives (actuators) will be on the front end. The communication network and IOT platforms are at the back end [17]. The bulk of IOT processes are used for sensors. There are a large number of IOT platforms available for business and the markets [18].
Both platforms’ implementations and requirements differ slightly. The IOT platform needs to be easy, safe and stable, because if the platform fails, it could cost millions or even fails [19]. For users, on the other hand, an IOT platform failure can be an inconvenience. The new IOT channels described below in Table 1.2 are the popular IOT cloud platform created by the software giants. The demand for sensors expands as IOT applications, but for an IOT application it is important to choose the right sensor and the right IOT platform.
Table 1.2 Shows the some of the existing IOT platforms and its services, drawbacks and possible threats.
S. no.
IOT platform
Service
Drawbacks
Possible threats
1.
Thing Speak [
4
,
9
]
Storage service
Limited Uploading Data
Insider Threat, Lack of Confidentiality
2
IBM Watson [
5
,
6
,
9
]
Exchange, collection of data and service of the weather data
Cost, Integration and difficult to maintain
Key management
3
Amazon web services IOT [
7
–
9
]
Sensors can be conveniently connected to different applications from vehicles to turbine and intelligent home lights
Complex usage of services less secure for hosting critical enterprise applications
Advanced Intrusion Detection
4
Microsoft Azure IOT [
8
,
9
]
Faster connection between device and cloud
Log Security Events are not available
Denial of Service, false data injection
5
Google Cloud Platform [
8
,
9
]
Fully managed cloud services
Monitoring is not implemented to track sensor data
Side channel attacks, malicioussensor commands
6
Cisco IOT Cloud
Mobile Cloud based service
Does not support heterogeneous IOT devices
Location inference
7
Thingworx 8 IOT Platform
Industrial companies and faster connectivity
Hard to manage complex system
Hello flood attack and wormhole attack
8
Salesforce IOT
Customer service by gathering data from websites, devices and applications etc.,
Flexibility is limited. Security liability
Denial of Service, Unauthorized access
9
Oracle IOT
Fast Messaging Service in user devices
User Interface is difficult
Malware attacks
10
Kaa IOT
A complete middleware end to end IOT development smart devices
Less hardware modules supported
Black hole attack, wormhole attack
1.3 IOT Layers and its Protocols
The Internet of Things (IOT) keeps changing everything. Sadly, at the height of a safety nightmare several industries, consumer and industrial device owners and infrastructure operators are fast identified. Therefore, IOT security is not the subject of a single platform, a static collection of meta-security regimes as implemented in networked applications and hosts. Every IOT computer participating system and system, it requires a unique application IOT security depends on the application of the device to the affected or controlled device’s physical process or state and sensitivities. Figure 1.2 depicts the IOT layers. The architecture of IOT Security comprise of three distinct layers:
Sensing or Physical or Perception Layer
Networking or Gateway Layer
Data Processing Layer
Application or Session Layer
Figure 1.2 IOT layers.
Flow of Data in IOT Layers
Raw sensory data may be viewed as the lowest stages in the process of information hierarchy, where many IOT sensors accumulate a significant volume of data in terms of Exabyte or more than that in the course of time. The following layer processes the raw data in order to receive organized, filtered and comprehensible system knowledge ready for processing. The third layer gives us the intelligence by exposing the occult information from the organized data for smart intervention. Figure 1.3 shows the data row in IOT layers.
Figure 1.3 Data flow in IOT layers.
Perception or physical or Device or Sensing Layer
This layer involves wireless sensor networks (WSN) as an independent node category to include wireless communication with a restricted frequency and bandwidth. It acts as a central base station that operates a relay device with multiple hub links between the base and source.
Components of WSN Network Module: Communication Stack, Hardware, Software and Secure Data Collection.
Elements of WSN: The various components of WSN are Sensor, Memory, Transceivers and Batteries, and detector controllers
Limitations of WSN: Network discovery, power management, collaborative signal and information processing. Control and routing, tasking, queering and security.
Common Attacks in WSN: Fake node, node replication (Sybil Attack), confidentiality, integrity, black whole routing attacks, Denial of Service (DoS), physical damage/unauthorized application, availability, freshness, autonomy and authentication of an organization or enterprise.
DoS attacks on various layers: WSN devices can be targeted (DoS) on various network layers, including:
Physical layer attacks are jamming, node tampering.
Link layer Attacks are Battery exhaustion, collision, unfairness.
Network layer Attacks are hello flood, homing, spoofing, black hole, sybil, wormhole acknowledgement flooding. Transport layer threats are flooding, de-synchronization Application layer threats are traffic congestion generation
Counter Measures of WSN: The most appropriate technique for sensors and small electronic devices is the lightweight encryption technique. Therefore, lightweight encryption has allowed sensors to improve the safety and privacy of data stored in the sensor. Most of the issues caused by unauthorized users in the system can be solved by the node authentication mechanism.
Middleware/Processing Layer
In IOT, middleware is often used to connect with “cloud technology, federated overlays, or distributed systems.” The list of providers for middleware is as follows:
Event-based: Events where events function under unique parameters which relate to state change all components of middleware communicate among themselves. Security capabilities are offered in some middleware applications.
Service-based: Middleware Service-based implementations are the same as in a service-based computer system (SOC), which depends more on service- based architecture (SOA). They have security attributes and are vulnerable to security threats at the same time.
Virtual (VM) driven machine: Middleware applications that use virtual networks for secure results are referred to as Virtual machine (VM) applications. The individual modules that interact with VM on a network node are included in each device. In order to block malicious network propagation programs, Mate, a middleware system, includes a special security function that is managed.
Agent-based: Agent-based middleware provides a security risk analysis for responsible mobile operators.
Tuple spaces: Tuple space middleware includes modules with a registry called tuple spatial components that, as such, do not support a security framework.
Relevant application: As required by the application, this middleware concentrates on resource management for various applications.
Communication/Networking/Transport Layer
The transmitting layer transmits the information of a sensor to the perception layer through networks such as 3G, LAN, Bluetooth, Wireless, RFID, and NFC. It operates or stores, tests and executes enormous chunks of transport layer files. A multitude of tools can be managed and used in the lower layers. It utilizes diverse infrastructure such as databases, cloud computing and computer processing units. The business Intelligence layers operates the IOT framework as a whole, including games, business models and benefit and privacy for users. Hence, we do not discuss it further.
Application layer: It provides global governance of the applications provided, taking into account knowledge about objects handled in the layer of middleware.
Transport Security
Datagram Transport Layer (DTLS) can share information in a timely manner to avoid eavesdropping, tampering or forging datagram-based applications. The DTLS type depends mostly on the TLS safety rules and is built to provide the same security guarantees. This implementation has to do with a packet reordering, depletion of the data graph and data of larger scale than that of a datagram network packet in the DTLS protocol datagram.
Figure 1.4 gives an outline of some of the most well-known protocols which can be used to form a full communication stack by IOT devices. A broad variety of protocols can be used to facilitate the transmission and coordination of messages within an IOT system and its host network. Based on the operating cases and security specifications of any particular device, the required message stack and communication protocol selection would depend. The following are the few list of protocols in various layers and its features.
Figure 1.4 IOT protocols in each layer.
Transfer Control Protocol (TCP)
Layer: Transport/Network
Model/Architecture: Request/Response
Working Procedure: TCP is the most common protocol for the underlying secure transport of today’s web-based communications.
Limitations: In restricted network environments with high latencies or limited bandwidth, TCP is often not suitable for use.
User Datagram Protocol (UDP)
Layer: Transport/Network
Model/Architecture: Request/Response
Working Procedure: The UDP provides a lightweight transport mechanism for connectivity-free communication. For real-time services, such as computer games, voice and video communication, UDPs and many IOT sensor devices, live conferences are very limited.
Benefits: For both throughput and bandwidth UDP is much more effi-cient. It also supports multiple transmissions.
Limitations: Possibility of Data Corruption, no compensation for lost packets, no congestion control.
Message Queuing Telemetry Transport (MQTT)
Layer: Application
Model/Architecture: Publish/Subscribe
Working Mechanism: Three key components are included in the working mechanism structure: marketers, subscribers and brokers. From an IOT standpoint, publishers are simply linked to the broker’s lightweight sensors to return to sleep where possible. Subscribers are services that include a certain subject or sensory data in such a way that link to notify brokers before fresh information is obtained. Brokers are responsible for sorting and sending subscribers sensory information.
Uses: Protocol to collect and relay system data to servers. It is most suited for TCP networks. It targets large networks of small devices controlled from the cloud. Examples: Stock Price Tickers, Temperature updates, mobile notifications, oil pressure feeds
Benefits: Works in low latency device and unreliable network
Limitations: Scalability and security is not guaranteed along with transmission delay and packet overhead.
Constrained Application Protocol (CoAP)
Layer: Application
Model/Architecture: Request/Response
Working Mechanism: The architecture of the CoAP divides into two major sub layers: signaling and request or response. The sub layer of messaging is important for the accuracy and replication of communications while the sub layer of Request or Response is in-charge for communication. Four types of communications or notifications are available at CoAP: piggyback, authenticated, unconfirmed and confirmed. Confirmable and non-confirmable modes represent precise and inconsistent transmissions while other modes of request or response are used. Piggyback is used for direct communication between client and server in which the sends the server response immediately after receipt of the request. CoAP uses PUSH, DELETE, GET, Place and generating, updating, extracting and deleting message programs.
At CoAP, there are four types of communications or notifications: piggyback, authenticated, unconfirmed and independent. Confirmable and non-confirmable modes are transmissions that are reliable and unreliable, the other one is request or response modes.
The CoAP supports both confirmable and non-confirmable modes. represent precise and inconsistent transmissions while other modes of request or response are used. The CON message is sent from one node to another for reliable communication, and the sender needs an ACK response. Whereas unreliable communication uses NON messages that do not require any receiver ACK. Piggyback is used to communicate directly between the client and the server, where the server sends its response immediately after the request has been sent. CoAP uses PUSH, Edit, GET, Position, and programs to generate, update, extract and delete messages.
Uses: It is an optimized protocol for server-communicated UDP clients. In particular for sensors, valves and other related components for minor power reduction that have to be remotely controlled and tracked via the same restricted network. In machine to machine communication, it is commonly used.
Benefits: Reduces packet overhead and provides reliability along with multicast support.
Limitations: Does not have guaranteed authentication and data protection.
Secure Message Queuing Telemetry Transport (SMQTT) Layer: Application
Model/Architecture: Publish/Subscribe
Working Mechanism: An enhancement to MQTT is a stable MQTT (SMQTT) that uses lightweight encryption based on attributes. The broadcast encryption function, which encodes and transmits one message to several other nodes, is the main benefit of doing this. Usually, the algorithm consists of four main initialization, encoding, publishing and decryption levels. Customers and service providers register with the broker during the configuration process and get a master secret key. The data is encrypted, published and shipped by a courier to subscribers when the content is posted, and then decrypted using the same master key.
Uses: A protocol used by secured communication devices
Benefits: Works in low latency device and unreliable network Limitations: Encryption and Key generation algorithms are not norm. Advanced Message Queuing Telemetry Transport (AMQP)
Layer: Application
Model/Architecture: Publish/Subscribe
Working Mechanism: It’s all MQTT-like. The greatest contrast is that the broker has two essential parts: trade and queues. Based upon predefined roles and conditions, the exchange accepts and distributes messages to a publisher. Tails are mainly subjects and subscribed by subscribers who, as soon as they are in the queue, receive sensory information.
Uses: A queuing structure is designed to link servers to each other. AMQP is a dual protocol for wire that is intended for data communication or data exchange between two separate vendors. Companies such as JP Morgan use it to handle thousands of messages a day. NASA achieves this for Nebula cloud computing. It is used by Google to process complex events. Here are a few more AMQP cases and connections:
It is being used in India’s Aadhar project, one of the world’s largest biometric databases, home to 1.2 billion identities.
The project gathers eight terabytes per day of data from the architecture of ocean observers.
Everything about the queues at AMQP. It transmits messages to servers about transactions. It can manage thousands of right-wing queued transactions as a message based middleware from the banking sector.
Benefits: It has better security, reliability and interoperability. Limitations: If most of the smart devices are mobile. Owing to complex network conditions in various areas. It is difficult to communicate with cloud servers because of their changing location. Mobility issue arises with scalability.
Extensible Messaging and Presence Protocol (XMPP)
Layer: Application
Model/Architecture: It supports both publish/subscribe and Request/ Response.
Working Procedure: XMPP uses XML text in its native format, which naturally facilitates contact between individuals. It works like MQTT over TCP. XMPP enables a device to be quickly accessed. This is incredibly useful if such information runs between distant and largely irrelevant points, including person-to-person.
Uses: It is a message protocol originally developed to chat and transfer messages. Most systems only use polling or tracking on demand for notifications. Benefits: Its strength lies in the management, security and scalability of the IOT framework for customers.
Limitations: Packet overhead, High Power Consumption and does not support device to device communication
Data Distribution Service (DDS)
Layer: Application
Model/Architecture: Publish/Subscribe
Working Procedure: This method is a fast bus for intelligent system integration. The DDS aims at machines using machine data specifically. The primary objective of the DDS system is to bind devices to other machines. It distributes information. This is a middleware quality, high-performance, data-centered, rooted in manufacturing and embedded applications. The DDS provides multiple parallel receivers with millions of messages per second. The pub or sub-pattern will be enforced by DDS. It integrates system components together, provides low-latency, very stable access to data and a scalable architecture that IOT requires to be marketed and mission-critical.
Uses: The DDS provides the flexibility, reliability and speed that are required for the development of complex, real-time applications for high-performance embedded computer systems. The deployments include defense systems, wind turbines, healthcare integration, diagnostic imaging, vehicle monitoring and protection and property surveillance systems. Benefits: The preferred UDP is. Message broker and data center elimination simplifies implementation, minimizes latency, maximizes scalability, increases efficiency, decreases costs and complexity. It also has rigorous quality management of operation, multi-packages, configuration reliability and robust redundancy.
Table 1.3 IOT communication protocols.
Protocol
Layer
Signal spread
Network
Security
Benefits
Limitations
IEEE 802.15.4
Data Link
DSSS (Direct Sequence Spread Spectrum)
WPAN
AES Encryption
Low cost with better security and reliability
Low speed vulnerable to eavesdropping, jamming [
11
].
IEEE 802.11AH
Data Link
MIMO-OFDM (Multiple-input, multiple-output orthogonal frequency-division multiplexing)
WLAN
-
Uses low frequency spectra with less bandwidth and also less cost
Communication range is not specified vulnerable to impersonation attacks [
11
].
WirelessHART(Highway Addressable Remote Transducer Protocol)
Data Link
FHSS and DSSS
WPAN
AES Encryption
Cost effective and high performance best suited for greenfield projects and operates on low battery without wires
It lacks in security such as non repudiation, accounting, secure multicast and so on [
18
]
Z-wave
Data Link
FHSS(Frequency-hopping spread spectrum)
WPAN
AES Encryption
Low power consumption, easy to control from remote
Tampering the hard coded key in the device prone to packet injection attacks [
11
]
Bluetooth LE
Data Link
FHSS
WPAN
AES Encryption
Cheap, Free to use
Short range communication a only and it connects only two devices at a time
ZigBee smart Energy
Data Link
DSSS
WPAN
AES Block cipher
Mesh network, Direct communication, low power consumption
Costly, works with low speed in small distance
HomePlug
Data Link
Binary Phase Shift Keying(BPSK), Quadrature Phase Shift Keying(QPSK), 16Quadrature- Amplitude- Modulation(QAM), 64 QAM, 128QAM, 256 QAM
WPAN
AES Encryption
Low power and cost-reliable communications
Not compatible with certain power strips and vulnerable to entry of outer signals
Wireless Fidelity Wi-Fi
Data Link
DSSS, Complimentary Coding Key(CCK), OFDM
WPAN/ P2P
RC4 Stream Cipher and AES Block Cipher
Easy to add or remove Wi-Fi clients, data connection is fast up to 300Mbps
Scalability and security still needs to be improved
Long Term Evolution Advanced (LTE-A)
Data Link
MIMO, 64 QAM
WWAN
AES Encryption
Efficient delivering of data
Costly, DDOS and DOS attacks are possible
Long Range Radio Wide Area Network (LoRaWAN)
Data Link
Chirp spread spectrum or depends on end device
WWAN
End to end encryption
Low cost, mobility, security and reduced power consumption
Suitable for sending only small packets of data every couple of minutes
Routing Protocol for Lo-Power and Lossy Networks (RPL)
Network Layer Routing Protocols
WWAN
No Encryption
Low power consumption and High Scalability, low memory usage
Security
IPv6 over Low Power Wireless Personal Area Networks (6LoWPAN)
Network Encapsulation protocols
DSSS
WPAN
AES Encryption
Offers interoperability, security and management
scalability
Limitations: Mobility and maintainability is always hard to overcome for any heterogeneous IOT environment.
The above table indicates very few protocols commonly used to fulfill the internet’s requirements. The network layer contains two distinct sub-layers: a routing layer to relay data to base station and a packet encapsulation layer.
A variety of exciting innovations can support the IOT vision for multiple forms of applications. This section seeks to present and compile the most relevant IOT technologies. The Figure 1.5 illustrates the gathered IOT technologies which rely on an architectural perspective to present the aspects and qualifications of each technology.
The demands of the CIA. This ensures that security, honesty and availability must adhere to IOT traffic. Privacy is designed to ensure safe IOT flow. Only open to unique users from Confident Users. Honesty focuses primarily on stopping or modifying IOT traffic. Integrity in IOT communication can be enforced via end-to-end encryption. IOT traffic can also be handled by firewalls, but it does not ensure security high computing power endpoints on IOT computers. IOT consists of data exchange between users and objects or between objects. Such protection standards should be enforced in this setting.
Figure 1.5 Taxonomy of technologies.
IOT security is further classified into three layers. They are sensing layer security, communication security and service layer security.
Sensing Layer Security
The layer of devices contains products, sites, and stuff, such as light bulbs or devices such as medical equipment that are complicated. During the design, IOT protection should be considered and proper encryption should be done to ensure integrity and safety. Devices should be manipulated and the requisite software upgrades should continuously be performed. Some of the open challenges in sensing layer are listed below:
The IOT system has exposed a pervasive security issue.
Web device unreliable
Failure to authenticate/authorize
Network vulnerability
Failure to encrypt transport
Concerns over privacy
Smartphone apps are insecure
Cloud interface is insecure
Configuration with inadequate defense
Unclear Firmware/Software
Personal Layer Protection
Communication Layer Security
Protection of the gateway layers means messages between the devices and other networks enabled by the Internet. The gateway layer must take into account protection in protocol correspondence to maintain secrecy and honesty. Gateway or communication layer security open challenges are shown in Figure 1.6.
Service Layer Security
The protection of service layers reflects operations in IOT management such as policy and rules and system automation. It must concentrate on the role-based management of access and trail of system or user changes. In the case of an abnormal activity, data management should be carried out to detect infected computers.
Common Security Issues in IOT
In order to guarantee that customer’s interactions are handled in a secure environment, IOT should be installed. IOT faces various security problems to deter an unauthorized person from accessing private data.
Figure 1.6 Open IOT domain network security issues.
The following problems should be addressed by IOT research:
Intelligent/self-conscious event-driven agents should be made available.
Networked computer behavior.
Heterogeneous system sets privacy-preserving technologies.
Decentralized authentication and confidence templates.
Energy-efficient encryption and technology for data safety.
Cloud storage reliability and faith.
Property of records.
Questions of legislation and accountability.
Data storage archive.
Access and usage privileges, value-added sharing rules.
IOT solutions for artificial immune systems.
Protected inexpensive equipment.
Management of Privacy Policy.
Attacks on Different Layers
Shift is the only constant thing and end users are trying to grow. Software to satisfy their specifications. Threats have grown and increased security precautions which must be taken into account. Various attacks on four layers of IOT are discussed below.
Perception or Sensing Layer
The main issues in the data perception stage are data leakage, sovereignty, violation and authentication.
Data Leakage:
