The Internet Protocol.

The use of the Internet is growing in this day and age, so another area has developed to use the Internet, called Internet of Things (IoT). It facilitates the machines and objects to communicate, compute and coordinate with each other. It is an enabler for the intelligence affixed to several essential features of the modern world, such as homes, hospitals, buildings, transports and cities. The security and privacy are some of the critical issues related to the wide application of IoT. Therefore, these issues prevent the wide adoption of the IoT. In this paper, we are presenting an overview about different layered architectures of IoT and attacks regarding security from the perspective of layers. In addition, a review of mechanisms that provide solutions to these issues is presented with their limitations. Furthermore, we have suggested a new secure layered architecture of IoT to overcome these issues.

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IoT Elements, Layered Architectures and Security Issues: A Comprehensive Survey.

The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends mainly on the IoT. Advanced machine learning (ML) techniques are being used to strengthen the STI smartness further. However, some decisions are very challenging due to the vast number of STI components and big data generated from STIs. Computation cost, communication overheads, and privacy issues are significant concerns for wide-scale ML adoption within STI. These issues can be addressed using Federated Learning (FL) and blockchain. FL can be used to address the issues of privacy preservation and handling big data generated in STI management and control. Blockchain is a distributed ledger that can store data while providing trust and integrity assurance. Blockchain can be a solution to data integrity and can add more security to the STI. This survey initially explores the vehicular network and STI in detail and sheds light on the blockchain and FL with real-world implementations. Then, FL and blockchain applications in the Vehicular Ad Hoc Network (VANET) environment from security and privacy perspectives are discussed in detail. In the end, the paper focuses on the current research challenges and future research directions related to integrating FL and blockchain for vehicular networks.

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Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey

This paper provides an overview on side-channel attacks with emphasis on vulnerabilities in the smart home. Smart homes are enabled by the latest developments in sensors, communication technologies, internet protocols, and cloud services. The goal of a smart home is to have smart household devices collaborate without involvement of residents to deliver the variety of services needed for a higher quality of life. However, security and privacy challenges of smart homes have to be overcome in order to fully realize the smart home. Side channel attacks assume data is always leaking, and leakage of data from a smart home reveals sensitive information. This paper starts by reviewing side-channel attack categories, then it gives an overview on recent attack studies on different layers of a smart home and their malicious goals.

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Side channel attacks on smart home systems: A short overview

Energy and security are major challenges in a wireless sensor network, and they work oppositely. As security complexity increases, battery drain will increase. Due to the limited power in wireless sensor networks, options to rely on the security of ordinary protocols embodied in encryption and key management are futile due to the nature of communication between sensors and the ever-changing network topology. Therefore, machine learning algorithms are one of the proposed solutions for providing security services in this type of network by including monitoring and decision intelligence. Machine learning algorithms present additional hurdles in terms of training and the amount of data required for training. This paper provides a convenient reference for wireless sensor network infrastructure and the security challenges it faces. It also discusses the possibility of benefiting from machine learning algorithms by reducing the security costs of wireless sensor networks in several domains; in addition to the challenges and proposed solutions to improving the ability of sensors to identify threats, attacks, risks, and malicious nodes through their ability to learn and self-development using machine learning algorithms. Furthermore, this paper discusses open issues related to adapting machine learning algorithms to the capabilities of sensors in this type of network.

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Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues

Risk monitoring strategy for confidentiality of healthcare information

Flying Ad-hoc networks are emergent area in Ad-hoc networks evolved from MANETs and VANETs. Small unmanned aerial vehicles (UAVs) are used in FANETs applications and these small UAVs have limited resources while efficiently utilization of these resources is most critical task in real time monitoring of FANETs application. Network consumes its resources in path selection process and data routing from source to destination. Selecting of efficient routing protocol to utilize all available resources played vital role in extending network life time. In this article fisheye state routing (FSR) protocol is implemented in FANET and compare networks performance in term of channel utilization, link utilization vs throughput and packet delivery ratio (PDR) with distance sequence distance vector (DSDV), optimized link state routing (OLSR), adhoc on demand distance vector (AODV), dynamic source routing (DSR) and temperary ordered routing protocol (TORA). Experimental analysis slows that FSR is good in term of PDR (16438 packets delivered), channel utilization (89%) and link vs throughput from the rest of routing protocols after addressing of these problems UAVs resources are efficiently utilized (energy).

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Energy efficient hierarchical based fish eye state routing protocol for flying Ad-hoc networks

Flying Ad-hoc Networks creates rapid topology changes that causes routing problems between Unmanned Aerial Vehicle and ground base station. Mobile Ad-hoc networks and Vehicular Ad-hoc network protocols are conventionally adopted to overcome routing issues. But Still, they do not fully address the routing problem in Flying Ad-hoc Networks. In this paper, Fisheye state routing protocol is implemented and evaluated to fully overcome routing issues in a Flying Ad-hoc Network and fully utilize the limited resource of Unmanned Aerial Vehicles. Performance evaluation is measured in terms of throughput, average end-to-end delay, packet drop analysis as congestion measure with Ad-Hoc On-Demand Distance Vector (AODV), Distance Sequence Distance Vector (DSDV), Optimized Link State Routing (OLSR), Temporary Ordered Routing Protocol (TORA) and Dynamic Source Routing (DSR). Fisheye state routing protocol showed promising results regarding throughput, packet drop rate, and average end-to-end delay compared with traditional protocols. Moreover, with the suggested improvement of the parameters, network lifetime is increased, and resources harvesting becomes under control.

Unmanned Aerial Vehicles Routing Formation Using Fisheye State Routing for Flying Ad-hoc Networks

https://www.techscience.com/cmc/v70n2/44722/pdf

Muhammad Abul Hassan

Papers

Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey

Risk monitoring strategy for confidentiality of healthcare information

Energy efficient hierarchical based fish eye state routing protocol for flying Ad-hoc networks

Unmanned Aerial Vehicles Routing Formation Using Fisheye State Routing for Flying Ad-hoc Networks

Improved Sequencing Heuristic DSDV Protocol Using Nomadic Mobility Model for FANETS