518 research outputs found
Comprehensive Performance Analysis of RPL Objective Functions in IoT Networks
As the movement for a vast implementation of IoT networks is rapidly accelerating, so many researchers are working to analyze the performance of RPL, the widely-used routing protocol for wireless sensor networks. The analysis usually involves a small number of metrics studied under a limited number of scenarios. In this paper however, we provide a comprehensive study for the performance of the two objective functions used in RPL; MRHOF and OF0, using the Cooja simulator in Contiki operating system. Using static-grid and mobile-random topologies with 25, 49, and 81 sender nodes including one sink node. Each topology was tested with three transmission ranges of 11, 20, and 50 meters to simulate sparse, moderate and dense networks. The selected metrics are convergence time, changes in DoDAG tree structures, average churn in the network, Average Power Consumption, Average Listen Duty Cycle, Average Transmit Duty Cycle, Average received packets, average lost packets, average duplicate packets, and average hop count. In fixed networks, the results show that OF0 usually perform better than MRHOF in terms of Energy Consumption, Convergence Time in the Static-Grid Topology, Listen Duty Cycle, and Transmit Duty Cycle
Evolving SDN for Low-Power IoT Networks
Software Defined Networking (SDN) offers a flexible and scalable architecture
that abstracts decision making away from individual devices and provides a
programmable network platform. However, implementing a centralized SDN
architecture within the constraints of a low-power wireless network faces
considerable challenges. Not only is controller traffic subject to jitter due
to unreliable links and network contention, but the overhead generated by SDN
can severely affect the performance of other traffic. This paper addresses the
challenge of bringing high-overhead SDN architecture to IEEE 802.15.4 networks.
We explore how traditional SDN needs to evolve in order to overcome the
constraints of low-power wireless networks, and discuss protocol and
architectural optimizations necessary to reduce SDN control overhead - the main
barrier to successful implementation. We argue that interoperability with the
existing protocol stack is necessary to provide a platform for controller
discovery and coexistence with legacy networks. We consequently introduce
{\mu}SDN, a lightweight SDN framework for Contiki, with both IPv6 and
underlying routing protocol interoperability, as well as optimizing a number of
elements within the SDN architecture to reduce control overhead to practical
levels. We evaluate {\mu}SDN in terms of latency, energy, and packet delivery.
Through this evaluation we show how the cost of SDN control overhead (both
bootstrapping and management) can be reduced to a point where comparable
performance and scalability is achieved against an IEEE 802.15.4-2012 RPL-based
network. Additionally, we demonstrate {\mu}SDN through simulation: providing a
use-case where the SDN configurability can be used to provide Quality of
Service (QoS) for critical network flows experiencing interference, and we
achieve considerable reductions in delay and jitter in comparison to a scenario
without SDN
IETF standardization in the field of the Internet of Things (IoT): a survey
Smart embedded objects will become an important part of what is called the Internet of Things. However, the integration of embedded devices into the Internet introduces several challenges, since many of the existing Internet technologies and protocols were not designed for this class of devices. In the past few years, there have been many efforts to enable the extension of Internet technologies to constrained devices. Initially, this resulted in proprietary protocols and architectures. Later, the integration of constrained devices into the Internet was embraced by IETF, moving towards standardized IP-based protocols. In this paper, we will briefly review the history of integrating constrained devices into the Internet, followed by an extensive overview of IETF standardization work in the 6LoWPAN, ROLL and CoRE working groups. This is complemented with a broad overview of related research results that illustrate how this work can be extended or used to tackle other problems and with a discussion on open issues and challenges. As such the aim of this paper is twofold: apart from giving readers solid insights in IETF standardization work on the Internet of Things, it also aims to encourage readers to further explore the world of Internet-connected objects, pointing to future research opportunities
A New Objective Function Based on Additive Combination of Node and Link Metrics as a Mechanism Path Selection for RPL Protocol
Since its development by IETF, the IPv6 routing protocol for low power and lossy networks (RPL) remains the subject of several researches. RPL is based on objective function as a mechanism selection of paths in the network. However, the default objective functions standardized selects the routes according to a single routing metric that leads to an unoptimized path selection and a lot of parent changes. Thus, we propose in this paper weighted combined metrics objective function (WCM-OF) and non-weighted combined metrics objective function (NWCM-OF) that are based both on additive link quality and energy metrics with equal weights or not to achieve a tradeoff between reliability and saved energy levels. The proposed objective functions were implemented in the core of Contiki operating system and evaluated with Cooja emulator. Results show that the proposed objective functions improved the network performances compared to default objective functions
Performance analysis of Routing Protocol for Low power and Lossy Networks (RPL) in large scale networks
With growing needs to better understand our environments, the Internet-of-Things (IoT) is gaining importance among information and communication technologies. IoT will enable billions of intelligent devices and networks, such as wireless sensor networks (WSNs), to be connected and integrated with computer networks. In order to support large scale networks, IETF has defined the Routing Protocol for Low power and Lossy Networks (RPL) to facilitate the multi-hop connectivity. In this paper, we provide an in-depth review of current research activities. Specifically, the large scale simulation development and performance evaluation under various objective functions and routing metrics are pioneering works in RPL study. The results are expected to serve as a reference for evaluating the effectiveness of routing solutions in large scale IoT use cases
TFUZZY-OF: a new method for routing protocol for low-power and lossy networks load balancing using multi-criteria decision-making
The internet of things (IoT) based on a network layer perspective includes low-power and lossy networks (LLN) that are limited in terms of power consumption, memory, and energy usage. The routing protocol used in these networks is called routing over low-power and lossy networks (RPL). Therefore, the IoT networks include smart objects that need multiple routing for their interconnections which makes traffic load balancing techniques indispensable to RPL routing protocol. In this paper, we propose a method based on fuzzy logic and the technique for the order of prioritization by similarity to the ideal solution (TOPSIS) as a well-known multi-criteria decision-making method to solve the load balancing problem by routing metrics composition. For this purpose, a combination of both link and node routing metrics namely hop count, expected transmission count, and received signal strength indicator is used. The results of simulations show that this method can increase the quality of services in terms of packet delivery ratio and average end-to-end delay
Performance Assessment of Routing Protocols for IoT/6LoWPAN Networks
The Internet of Things (IoT) proposes a disruptive communication paradigm that allows
smart objects to exchange data among themselves to reach a common goal. IoT application
scenarios are multiple and can range from a simple smart home lighting system to fully controlled
automated manufacturing chains. In the majority of IoT deployments, things are equipped with
small devices that can suffer from severe hardware and energy restrictions that are responsible
for performing data processing and wireless communication tasks. Thus, due to their features,
communication networks that are used by these devices are generally categorized as Low Power
and Lossy Networks (LLNs).
The considerable variation in IoT applications represents a critical issue to LLN networks,
which should offer support to different requirements as well as keeping reasonable
quality-of-service (QoS) levels. Based on this challenge, routing protocols represent a key issue
in IoT scenarios deployment. Routing protocols are responsible for creating paths among devices
and their interactions. Hence, network performance and features are highly dependent
on protocol behavior. Also, based on the adopted protocol, the support for some specific requirements
of IoT applications may or may not be provided. Thus, a routing protocol should be
projected to attend the needs of the applications considering the limitations of the device that
will execute them.
Looking to attend the demand of routing protocols for LLNs and, consequently, for IoT
networks, the Internet Engineering Task Force (IETF) has designed and standardized the IPv6
Routing Protocol for Low Power and Lossy Networks (RPL). This protocol, although being robust
and offering features to fulfill the need of several applications, still presents several faults and
weaknesses (mainly related to its high complexity and memory requirement), which limits its
adoption in IoT scenarios. An alternative to RPL, the Lightweight On-demand Ad Hoc Distancevector
Routing Protocol – Next Generation (LOADng) has emerged as a less complicated routing
solution for LLNs. However, the cost of its simplicity is paid for with the absence of adequate
support for a critical set of features required for many IoT environments. Thus, based on the
challenging open issues related to routing in IoT networks, this thesis aims to study and propose
contributions to better attend the network requirements of IoT scenarios. A comprehensive survey,
reviewing state-of-the-art routing protocols adopted for IoT, identified the strengths and
weaknesses of current solutions available in the literature. Based on the identified limitations,
a set of improvements is designed to overcome these issues and enhance IoT network performance.
The novel solutions are proposed to include reliable and efficient support to attend
the needs of IoT applications, such as mobility, heterogeneity, and different traffic patterns.
Moreover, mechanisms to improve the network performance in IoT scenarios, which integrate
devices with different communication technologies, are introduced.
The studies conducted to assess the performance of the proposed solutions showed
the high potential of the proposed solutions. When the approaches presented in this thesis
were compared with others available in the literature, they presented very promising results
considering the metrics related to the Quality of Service (QoS), network and energy efficiency,
and memory usage as well as adding new features to the base protocols. Hence, it is believed
that the proposed improvements contribute to the state-of-the-art of routing solutions for IoT
networks, increasing the performance and adoption of enhanced protocols.A Internet das Coisas, do inglês Internet of Things (IoT), propõe um paradigma de
comunicação disruptivo para possibilitar que dispositivos, que podem ser dotados de comportamentos
autónomos ou inteligentes, troquem dados entre eles buscando alcançar um objetivo
comum. Os cenários de aplicação do IoT são muito variados e podem abranger desde um simples
sistema de iluminação para casa até o controle total de uma linha de produção industrial. Na
maioria das instalações IoT, as “coisas” são equipadas com um pequeno dispositivo, responsável
por realizar as tarefas de comunicação e processamento de dados, que pode sofrer com severas
restrições de hardware e energia. Assim, devido às suas características, a rede de comunicação
criada por esses dispositivos é geralmente categorizada como uma Low Power and Lossy Network
(LLN).
A grande variedade de cenários IoT representam uma questão crucial para as LLNs,
que devem oferecer suporte aos diferentes requisitos das aplicações, além de manter níveis
de qualidade de serviço, do inglês Quality of Service (QoS), adequados. Baseado neste desafio,
os protocolos de encaminhamento constituem um aspecto chave na implementação de
cenários IoT. Os protocolos de encaminhamento são responsáveis por criar os caminhos entre
os dispositivos e permitir suas interações. Assim, o desempenho e as características da rede
são altamente dependentes do comportamento destes protocolos. Adicionalmente, com base
no protocolo adotado, o suporte a alguns requisitos específicos das aplicações de IoT podem
ou não ser fornecidos. Portanto, estes protocolos devem ser projetados para atender as necessidades
das aplicações assim como considerando as limitações do hardware no qual serão
executados.
Procurando atender às necessidades dos protocolos de encaminhamento em LLNs e,
consequentemente, das redes IoT, a Internet Engineering Task Force (IETF) desenvolveu e padronizou
o IPv6 Routing Protocol for Low Power and Lossy Networks (RPL). O protocolo, embora
seja robusto e ofereça recursos para atender às necessidades de diferentes aplicações, apresenta
algumas falhas e fraquezas (principalmente relacionadas com a sua alta complexidade e
necessidade de memória) que limitam sua adoção em cenários IoT. Em alternativa ao RPL, o
Lightweight On-demand Ad hoc Distance-vector Routing Protocol – Next Generation (LOADng)
emergiu como uma solução de encaminhamento menos complexa para as LLNs. Contudo, o
preço da simplicidade é pago com a falta de suporte adequado para um conjunto de recursos
essenciais necessários em muitos ambientes IoT. Assim, inspirado pelas desafiadoras questões
ainda em aberto relacionadas com o encaminhamento em redes IoT, esta tese tem como objetivo
estudar e propor contribuições para melhor atender os requisitos de rede em cenários IoT.
Uma profunda e abrangente revisão do estado da arte sobre os protocolos de encaminhamento
adotados em IoT identificou os pontos fortes e limitações das soluções atuais. Com base nas debilidades
encontradas, um conjunto de soluções de melhoria é proposto para superar carências
existentes e melhorar o desempenho das redes IoT. As novas soluções são propostas para incluir
um suporte confiável e eficiente capaz atender às necessidades das aplicações IoT relacionadas
com suporte à mobilidade, heterogeneidade dos dispositivos e diferentes padrões de tráfego.
Além disso, são introduzidos mecanismos para melhorar o desempenho da rede em cenários IoT
que integram dispositivos com diferentes tecnologias de comunicação.
Os vários estudos realizados para mensurar o desempenho das soluções propostas mostraram
o grande potencial do conjunto de melhorias introduzidas. Quando comparadas com
outras abordagens existentes na literatura, as soluções propostas nesta tese demonstraram um aumento do desempenho consistente para métricas relacionadas a qualidade de serviço, uso de
memória, eficiência energética e de rede, além de adicionar novas funcionalidades aos protocolos
base. Portanto, acredita-se que as melhorias propostas contribuiem para o avanço do estado
da arte em soluções de encaminhamento para redes IoT e aumentar a adoção e utilização dos
protocolos estudados
Network Intrusion Detection Using Autoencode Neural Network
In today's interconnected digital landscape, safeguarding computer networks against unauthorized access and cyber threats is of paramount importance. NIDS play a crucial role in identifying and mitigating potential security breaches. This research paper explores the application of autoencoder neural networks, a subset of deep learning techniques, in the realm of Network Intrusion Detection.Autoencoder neural networks are known for their ability to learn and represent data in a compressed, low-dimensional form. This study investigates their potential in modeling network traffic patterns and identifying anomalous activities. By training autoencoder networks on both normal and malicious network traffic data, we aim to create effective intrusion detection models that can distinguish between benign and malicious network behavior.The paper provides an in-depth analysis of the architecture and training methodologies of autoencoder neural networks for intrusion detection. It also explores various data preprocessing techniques and feature engineering approaches to enhance the model's performance. Additionally, the research evaluates the robustness and scalability of autoencoder-based NIDS in real-world network environments. Furthermore, ethical considerations in network intrusion detection, including privacy concerns and false positive rates, are discussed. It addresses the need for a balanced approach that ensures network security while respecting user privacy and minimizing disruptions. operation. This approach compresses the majority samples & increases the minority sample count in tough samples so that the IDS can achieve greater classification accuracy
- …