scholarly journals IoT Registration and Authentication in Smart City Applications with Blockchain

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1323
Author(s):  
Célio Márcio Soares Ferreira ◽  
Charles Tim Batista Garrocho ◽  
Ricardo Augusto Rabelo Oliveira ◽  
Jorge Sá Silva ◽  
Carlos Frederico Marcelo da Cunha Cavalcanti
Keyword(s):  
Time Management ◽  
Smart Cities ◽  
Fog Computing ◽  
Denial Of Service ◽  
Urban Environments ◽  
Edge Computing ◽  
Primary Data ◽  
Security Challenges ◽  
Security Information ◽  
Iot Devices

The advent of 5G will bring a massive adoption of IoT devices across our society. IoT Applications (IoT Apps) will be the primary data collection base. This scenario leads to unprecedented scalability and security challenges, with one of the first areas for these applications being Smart Cities (SC). IoT devices in new network paradigms, such as Edge Computing and Fog Computing, will collect data from urban environments, providing real-time management information. One of these challenges is ensuring that the data sent from Edge Computing are reliable. Blockchain has been a technology that has gained the spotlight in recent years, due to its robust security in fintech and cryptocurrencies. Its strong encryption and distributed and decentralized network make it potential for this challenge. Using Blockchain with IoT makes it possible for SC applications to have security information distributed, which makes it possible to shield against Distributed Denial of Service (DDOS). IoT devices in an SC can have a long life, which increases the chance of having security holes caused by outdated firmware. Adding a layer of identification and verification of attributes and signature of messages coming from IoT devices by Smart Contracts can bring confidence in the content. SC Apps that extract data from legacy and outdated appliances, installed in inaccessible, unknown, and often untrusted urban environments can benefit from this work. Our work’s main contribution is the development of API Gateways to be used in IoT devices and network gateway to sign, identify, and authorize messages. For this, keys and essential characteristics of the devices previously registered in Blockchain are used. We will discuss the importance of this implementation while considering the SC and present a testbed that is composed of Blockchain Ethereum and real IoT devices. We analyze the transfer time, memory, and CPU impacts during the sending and processing of these messages. The messages are signed, identified, and validated by our API Gateways and only then collected for an IoT data management application.

scholarly journals Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

2021 ◽  
Author(s):  
Karan Bajaj ◽  
Bhisham Sharma ◽  
Raman Singh
Keyword(s):  
High Speed ◽  
Smart Cities ◽  
Fog Computing ◽  
Edge Computing ◽  
Computation Offloading ◽  
Performance Parameters ◽  
Iot Applications ◽  
Time Sensitivity ◽  
Implementation Analysis ◽  
Increasing Demand

AbstractThe Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

scholarly journals A Parallel Meta-Heuristic Approach to Reduce Vehicle Travel Time in Smart Cities

2020 ◽  
Author(s):  
Hector Rico-Garcia ◽  
Jose-Luis Sanchez-Romero ◽  
Antonio Jimeno-Morenilla ◽  
Hector Migallon-Gomis
Keyword(s):  
Travel Time ◽  
Smart City ◽  
Smart Cities ◽  
Travelling Salesman Problem ◽  
Urban Environments ◽  
Departure Point ◽  
Classic Problem ◽  
Iot Devices ◽  
Vehicle Travel Time ◽  
Gpu Architecture

The development of the smart city concept and the inhabitants’ need to reduce travel time, as well as society’s awareness of the reduction of fuel consumption and respect for the environment, lead to a new approach to the classic problem of the Travelling Salesman Problem (TSP) applied to urban environments. This problem can be formulated as “Given a list of geographic points and the distances between each pair of points, what is the shortest possible route that visits each point and returns to the departure point?” Nowadays, with the development of IoT devices and the high sensoring capabilities, a large amount of data and measurements are available, allowing researchers to model accurately the routes to choose. In this work, the purpose is to give solution to the TSP in smart city environments using a modified version of the metaheuristic optimization algorithm TLBO (Teacher Learner Based Optimization). In addition, to improve performance, the solution is implemented using a parallel GPU architecture, specifically a CUDA implementation.

scholarly journals An Anomaly Mitigation Framework for IoT Using Fog Computing

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1565
Author(s):  
Muhammad Aminu Lawal ◽  
Riaz Ahmed Shaikh ◽  
Syed Raheel Hassan
Keyword(s):  
Smart Cities ◽  
Fog Computing ◽  
Multiclass Classification ◽  
Attack Detection ◽  
Gradient Boosting ◽  
Ip Address ◽  
Computing Paradigm ◽  
Security Challenges ◽  
Extreme Gradient Boosting ◽  
Boosting Algorithm

The advancement in IoT has prompted its application in areas such as smart homes, smart cities, etc., and this has aided its exponential growth. However, alongside this development, IoT networks are experiencing a rise in security challenges such as botnet attacks, which often appear as network anomalies. Similarly, providing security solutions has been challenging due to the low resources that characterize the devices in IoT networks. To overcome these challenges, the fog computing paradigm has provided an enabling environment that offers additional resources for deploying security solutions such as anomaly mitigation schemes. In this paper, we propose a hybrid anomaly mitigation framework for IoT using fog computing to ensure faster and accurate anomaly detection. The framework employs signature- and anomaly-based detection methodologies for its two modules, respectively. The signature-based module utilizes a database of attack sources (blacklisted IP addresses) to ensure faster detection when attacks are executed from the blacklisted IP address, while the anomaly-based module uses an extreme gradient boosting algorithm for accurate classification of network traffic flow into normal or abnormal. We evaluated the performance of both modules using an IoT-based dataset in terms response time for the signature-based module and accuracy in binary and multiclass classification for the anomaly-based module. The results show that the signature-based module achieves a fast attack detection of at least six times faster than the anomaly-based module in each number of instances evaluated. The anomaly-based module using the XGBoost classifier detects attacks with an accuracy of 99% and at least 97% for average recall, average precision, and average F1 score for binary and multiclass classification. Additionally, it recorded 0.05 in terms of false-positive rates.

scholarly journals Automatic Failure Recovery for Container-Based IoT Edge Applications

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3047
Author(s):  
Kolade Olorunnife ◽  
Kevin Lee ◽  
Jonathan Kua
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Smart Homes ◽  
Edge Computing ◽  
Failure Recovery ◽  
Software Failures ◽  
Iot Applications ◽  
Remote Management ◽  
Management Ability ◽  
Iot Devices

Recent years have seen the rapid adoption of Internet of Things (IoT) technologies, where billions of physical devices are interconnected to provide data sensing, computing and actuating capabilities. IoT-based systems have been extensively deployed across various sectors, such as smart homes, smart cities, smart transport, smart logistics and so forth. Newer paradigms such as edge computing are developed to facilitate computation and data intelligence to be performed closer to IoT devices, hence reducing latency for time-sensitive tasks. However, IoT applications are increasingly being deployed in remote and difficult to reach areas for edge computing scenarios. These deployment locations make upgrading application and dealing with software failures difficult. IoT applications are also increasingly being deployed as containers which offer increased remote management ability but are more complex to configure. This paper proposes an approach for effectively managing, updating and re-configuring container-based IoT software as efficiently, scalably and reliably as possible with minimal downtime upon the detection of software failures. The approach is evaluated using docker container-based IoT application deployments in an edge computing scenario.

scholarly journals Survey of DoS/DDoS attacks in IoT

2021 ◽  
Vol 3 (1) ◽  
pp. 23-28
Author(s):  
Rozan Khader ◽  
Derar Eleyan
Keyword(s):  
Smart Cities ◽  
Denial Of Service ◽  
Wearable Devices ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Iot Security ◽  
Counter Measures ◽  
Security Issues ◽  
Iot Devices ◽  
Internet Of Thing

The term internet of thing (IoT) has gained much popularity in the last decade. Which can be defined as various connected devices over the internet. IoT has rapidly  spread to include all aspects of our lives. For instance, smart houses, smart cities, and variant wearable devices. IoT devices work to do their desired goals, which is to develop a person life with his/her minimal involvement. At the same time, IoT devices have many weaknesses, which attackers exploit to affect these devices security. Denial of Service (DoS) and Distributed Denial of Service (DDoS) are considered the most common attacks that strike IoT security. The main aim of these attacks is to make victim systems down and inaccessible for legitimate users by malicious malware. This paper objective is to discuss and review security issues related to DoS/DDoS Attacks and their counter measures i.e. prevention based on IoT devices layers structure.

scholarly journals Fog Computing: An Overview of Big IoT Data Analytics

2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Muhammad Rizwan Anawar ◽  
Shangguang Wang ◽  
Muhammad Azam Zia ◽  
Ahmer Khan Jadoon ◽  
Umair Akram ◽  
...  
Keyword(s):  
Data Analytics ◽  
Smart Cities ◽  
Fog Computing ◽  
Computing Paradigm ◽  
Key Characteristics ◽  
Challenges And Opportunities ◽  
Cloud Framework ◽  
Iot Devices ◽  
Health Care Monitoring ◽  
Computing Structure

A huge amount of data, generated by Internet of Things (IoT), is growing up exponentially based on nonstop operational states. Those IoT devices are generating an avalanche of information that is disruptive for predictable data processing and analytics functionality, which is perfectly handled by the cloud before explosion growth of IoT. Fog computing structure confronts those disruptions, with powerful complement functionality of cloud framework, based on deployment of micro clouds (fog nodes) at proximity edge of data sources. Particularly big IoT data analytics by fog computing structure is on emerging phase and requires extensive research to produce more proficient knowledge and smart decisions. This survey summarizes the fog challenges and opportunities in the context of big IoT data analytics on fog networking. In addition, it emphasizes that the key characteristics in some proposed research works make the fog computing a suitable platform for new proliferating IoT devices, services, and applications. Most significant fog applications (e.g., health care monitoring, smart cities, connected vehicles, and smart grid) will be discussed here to create a well-organized green computing paradigm to support the next generation of IoT applications.

A Secured Framework to Identify and Mitigate Attack

2020 ◽  
Vol 5 (8) ◽  
pp. 4-7
Keyword(s):  
Denial Of Service ◽  
Edge Computing ◽  
The Internet ◽  
Valuable Data ◽  
Block Chain ◽  
Iot Devices ◽  
Screen Play

Nowadays security is major concern for any user connected to the internet. Various types of attacks are to be performed by intruders to obtaining user information as man- inmiddle attack, denial of service, malware attacks etc. Malware attacks specifically ransomware attack become very famous recently. Ransomware attack threaten the users by encrypting their most valuable data, lock the user screen, play some random videos and by various more means. Finally attacker takes benefits by users through paid ransom. In this paper, we propose a framework which prevents the ransomware attack more appropriately using various techniques as block chain, honeypot, cloud & edge computing. This framework is analyzed mainly through the IoT devices and generalized to the any malware attack.

Virtual Mobile Edge Computing Based on IoT Devices Resources in Smart Cities

2020 ◽  
Author(s):  
Mohammed Laroui ◽  
Hatem Ibn Khedher ◽  
Hassine Moungla ◽  
Hossam Afifi ◽  
Ahmed E. Kamal
Keyword(s):  
Smart Cities ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Iot Devices

scholarly journals Features-Aware DDoS Detection in Heterogeneous Smart Environments based on Fog and Cloud Computing

2021 ◽  
Vol 13 (3) ◽  
Author(s):  
Wanderson L Costa ◽  
Ariel L. C Portela ◽  
Rafael Lopes Gomes
Keyword(s):  
Cloud Computing ◽  
Intelligent System ◽  
Denial Of Service ◽  
Urban Environments ◽  
Smart Environments ◽  
Existing Problems ◽  
Segmentation Approach ◽  
Ddos Detection ◽  
Iot Devices ◽  
Personal Devices

Nowadays, urban environments are deploying smart environments (SEs) to evolve infrastructures, resources, and services. SEs are composed of a huge amount of heterogeneous devices, i.e., the SEs have both personal devices (smartphones, notebooks, tablets, etc) and Internet of Things (IoT) devices (sensors, actuators, and others). One of the existing problems of the SEs is the detection of Distributed Denial of Service (DDoS) attacks, due to the vulnerabilities of IoT devices. In this way, it is necessary to deploy solutions that can detect DDoS in SEs, dealing with issues like scalability, adaptability, and heterogeneity (distinct protocols, hardware capacity, and running applications). Within this context, this article presents an Intelligent System for DDoS detection in SEs, applying Machine Learning (ML), Fog, and Cloud computing approaches. Additionally, the article presents a study about the most important traffic features for detecting DDoS in SEs, as well as a traffic segmentation approach to improve the accuracy of the system. The experiments performed, using real network traffic, suggest that the proposed system reaches 99% of accuracy, while reduces the volume of data exchanged and the detection time.

Reliability and Security Challenges in Electrical/Optical On-Chip Interconnects for IoT Applications

2020 ◽  
pp. 218-246
Author(s):  
Muhammad Rehan Yahya ◽  
Ning Wu ◽  
Zain Anwar Ali
Keyword(s):  
Communication Systems ◽  
Data Transfer ◽  
Smart Cities ◽  
Security Issues ◽  
Iot Applications ◽  
Security Challenges ◽  
Chip Processing ◽  
Share Data ◽  
Iot Devices ◽  
On Chip

The evolution of internet of things (IoT) applications, cloud computing, smart cities, and 4G/5G wireless communication systems have significantly increased the demands for on chip processing. Network on chip (NoC) is a viable alternative that can provide higher processing and bandwidth for increasing demands. NoC offers better performance and more flexibility with lower communication latency and higher throughput. However, use of NoC-based IoT devices have raised concerns on security and reliability of integrated chips (IC), which is used in almost every application. IoT devices share data that becomes vulnerable to attack and can be compromised during the data transfer. Keeping in view these security challenges, a detailed survey is presented that covers the security issues and challenges focusing on NoCs along with proposed countermeasures to secure on-chip communication. This study includes on-chip security issues for electrical as well as optical on-chip interconnects.

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