Building an Information-Centric and LoRa-Based Sensing Platform for IoT

A Lightweight Forwarding Strategy for Named Data Networking in Low-end IoT

Internet of Things (IoT) is changing our life for the better. IoT covers various heterogeneous devices which are sensing the environment and/or monitoring a special event in the network. These devices are mostly mobile and are wirelessly connected either to each other or to the Internet. Additionally, IoT devices run many different applications and services which provide the consumers with data and information. This paper focuses on the design and implementation of an Application Programming Interface (API) for IoT services using the Named Data Networking (NDN). NDN provides an efficient and intelligent communication paradigm for constrained devices like in IoT and uses named content to transmit data in the network. We extend the IoT-NDN architecture and add a new layer for IoT services which will effectively improve the performance of communication between the heterogeneous devices and the services integrated in IoT-NDN. Furthermore, we show the usage of our API and give a small example of network time synchronisation on IoT devices. This illustrates how flexible and lightweight is our approach for several services implemented on heterogeneous devices in IoT.

The vision of the Internet of Things (IoT) is that it connects all kinds of things by leveraging the creation of increasingly affordable and small devices that can be embedded for sensing, processing, wireless communication, and actuation. Named data networking (NDN) is a newly emerging Internet paradigm that may replace the current Internet architecture and that fulfills most of the expectations of the IoT. Software-defined networking (SDN) is an emerging paradigm of technology that is highly capable of managing overall networks efficiently and transforming complex network architectures into manageable, simple ones. The combination of the SDN controller, NDN, and IoT can be lethal in the overall performance of the network. Broadcast storms, due to the flooding nature of NDN’s wireless channel, are a serious issue when it comes to forwarding interest and data packets. Energy consumption of sensor nodes in dense IoT scenarios causes problems in forwarding as well as unnecessary delays, decreases network performance, and increases the cost and packet delay for important packets. We took these problems as our baseline and proposed an energy-efficient, priority-based forwarding (EPF) in SDN-enabled NDN–IoT. Our scheme EPF used the efficient flow management of the SDN controller to control the broadcast storm and efficiently forward the priority-based packets. A defer timer mechanism was used to prioritized the packet upon its arrival to the node. An energy threshold mechanism was used to control energy consumption and improve overall energy efficiency. We compared our scheme with the traditional flooding mechanism and geographic interest forwarding; EPF outclassed the other schemes and produced the best results in terms of total number of interests and retransmissions, content retrieval time, total number of priority interests, energy consumption, and network lifetime.

The Internet of Things (IoT) gives a strong structure for connecting things to the internet to facilitate Machine to Machine (M2M) communication and data transmission through basic network protocols such as TCP/IP. IoT is growing at a fast pace, and billions of devices are now associated, with the amount expected to reach trillions in the coming years. Many fields, including the army, farming, manufacturing, healthcare, robotics, and biotechnology, are adopting IoT for advanced solutions as technology advances. This paper offers a detailed view of the current IoT paradigm, specifically proposed for robots, namely the Internet of Robotic Things (IoRT). IoRT is a collection of various developments such as Cloud Computing, Artificial Intelligence (AI), Machine Learning, and the (IoT). This paper also goes over architecture, which would be essential in the design of Multi-Role Robotic Systems for IoRT. Furthermore, includes systems underlying IoRT, as well as IoRT implementations. The paper provides the foundation for researchers to imagine the idea of IoRT and to look beyond the frame while designing and implementing IoRT-based robotic systems in real-world implementations.

Named data networking (NDN) is an emerging information-centric networking paradigm, in which the Internet of Things (IoT) achieves excellent scalability. Recent literature proposes the concept of NDN-IoT, which maximizes the expansion of IoT applications by deploying NDN in the IoT. In the NDN, the security is built into the network by embedding a public signature in each data package to verify the authenticity and integrity of the content. However, signature schemes in the NDN-IoT environment are facing several challenges, such as signing security challenge for resource-constrained IoT end devices (EDs) and verification efficiency challenge for NDN routers. This article mainly studies the data package authentication scheme in the package-level security mechanism. Based on mobile edge computing (MEC), an efficient certificateless group signature scheme featured with anonymity, unforgeability, traceability, and key escrow resilience is proposed. The regional and edge architecture is utilized to solve the device management problem of IoT, reducing the risks of content pollution attacks from the data source. By offloading signature pressure to MEC servers, the contradiction between heavy overhead and shortage of ED resources is avoided. Moreover, the verification efficiency in NDN router is much improved via batch verification in the proposed scheme. Both security analysis and experimental simulations show that the proposed MEC-based certificateless group signature scheme is provably secure and practical.

Internet of Things (IoT) and named data network (NDN) are innovative technologies to meet up the future Internet requirements. NDN is considered as an enabling approach to improving data dissemination in IoT scenarios. NDN delivers in-network caching, which is the most prominent feature to provide fast data dissemination as compared to Internet protocol (IP)-based communication. The proper integration of caching placement strategies and replacement policies is the most suitable approach to support IoT networks. It can improve multicast communication which minimizes the delay in responding to IoT-based environments. Besides, these approaches are playing a most significant role in increasing the overall performance of NDN-based IoT networks. To this end, in this article, the challenges of NDN-IoT caching are identified with the aim to develop a new hybrid strategy for efficient data delivery. The proposed strategy is comparatively and extensively studied with NDN-IoT caching strategies through an extensive simulation in terms of average latency, cache hit ratio, and average stretch ratio. From the simulation findings, it is observed that the proposed hybrid strategy outperformed to achieve a higher caching performance of NDN-based IoT scenarios.

CoMap: An efficient virtual network re-mapping strategy based on coalitional matching theory

The Internet of Things (IoT) is a diverse technology that primarily utilizes TCP/IP protocols in IoT environments. However, it is often argued that the existing IP protocol stack is insufficient to support data sharing and storage capabilities for mobile IoT applications. Named Data Networking (NDN) has gained significant attention over time as a solution to the limitations of host-centric services in traditional IP networks. The key advantages of NDN include name-based routing and network caching, along with mobility support in wireless networks. The integration of NDN and IoT can address issues related to frequent node mobility, name-based routing, and storage constraints on resource-limited devices. IoT devices are frequently in mobility, creating routing challenges when transitioning from one domain to another. Interest packets remain unsatisfied until the producer reconnects to a new Point of Attachment (PoA) and updates its location. Despite these reconnections, challenges such as interest and data packet losses, increased latency, and suboptimal routing paths persist during communication. Consequently, managing producer mobility remains difficult during the frequent movement of nodes across different IoT domains. This paper proposes a novel Zone-Based Producer Mobility Management (ZBP-MM) protocol for NDN-based IoT environments. The proposed technique is evaluated and compared against the OPMSS scheme using the ndnSIM simulator. Results demonstrated that the proposed scheme achieves better hop count and cache hit ratio, with minimized interest satisfaction delay and handover delay, compared to the OPMSS technique during producer mobility scenarios in intra- and inter-zones.

O Livro de Minicursos do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC 2021) contém os minicursos selecionados para apresentação na edição do evento realizada online, no período de 16 a 20 de agosto de 2021, na cidade de Uberlândia/MG. O Livro dos Minicursos do SBRC tem sido tradicionalmente utilizado como material de estudo de alta qualidade por alunos de graduação e pós-graduação, bem como por profissionais da área. O principal objetivo dos Minicursos do SBRC é oferecer treinamento e atualização de curto prazo em temas normalmente não cobertos nas estruturas curriculares e que possuem grande interesse entre acadêmicos e profissionais. No livro são abordados temas atuais e de interesse da comunidade, como sistemas de software emergentes, mobilidade e segurança em redes centradas em informação, virtualização de funções de rede na Internet das Coisas (IoT), redes vestíveis e sistemas ciber-humanos e ainda aprendizado federado aplicado à IoT. No primeiro capítulo, intitulado “Emergent Software Systems: Theory and Practice”, os autores apresentam o conceito de Sistemas de Software Emergentes. A abordagem do Software Emergente visa reduzir o esforço inicial para criar soluções autônomas; suporta a criação de sistemas totalmente adaptáveis capazes de aprender autonomamente sobre a estrutura do sistema e seu ambiente operacional sem nenhum conhecimento pré-definido. No segundo capítulo, “Revisitando as ICNs: Mobilidade, Segurança e Aplicações Distribuídas através das Redes de Dados Nomeados”, os autores apresentam uma introdução e revisão dos fundamentos das Information-Centric Networks (ICN) e em seguida a arquitetura Named Data Networking (NDN). Os autores apresentam as principais questões relacionadas à mobilidade, segurança e aplicações distribuídas através por demonstrações e atividades práticas com ambientes previamente configurados. No terceiro capítulo, “Virtualização de Funções de Rede na IoT: Um Panorama do Gerenciamento de Desempenho x Segurança”, trata do uso de Network Function Virtualization (NFV) em IoT do ponto de vista de gerenciamento, desempenho e segurança da rede. O texto apresenta questões e o estado da arte relacionados ao desempenho do uso de NFV na detecção e mitigação de ameaças em redes específicas de IoT. O quarto capítulo, “Das Redes Vestíveis aos Sistemas Ciber-Humanos: Uma Perspectiva na Comunicação e Privacidade dos Dados”, aborda a rápida evolução que vem ocorrendo desde os primórdios das redes vestíveis até a pesquisa de ponta em nanorredes. Essa evolução rápida fundamenta a construção dos sistemas ciberfísicos e ciber-humanos que possuem aplicações em diversas áreas. Os autores descrevem a necessidade de explorar as vulnerabilidades e desafios que essas redes possuem em relação à privacidade dos dados e resiliência de seus serviços e apresentam uma discussão, demonstrando através de exemplos práticos essas fragilidades e um levantamento do estado da arte de propostas acadêmicas para a proteção da privacidade e resiliência de seus serviços. No quinto e último capítulo, “Aprendizado Federado aplicado à Internet das Coisas”, os autores apresentar os principais fundamentos do Aprendizado Federado (Federated Learning – FL) abrangendo as ferramentas e passos necessários para o desenvolvimento de aplicações e serviços voltadas à IoT. Os conceitos abordados incluem uma introdução à Aprendizagem de Máquina (centralizada e distribuída), o estado-da-arte em FL, uma visão geral dos trabalhos existentes, os desafios e as perspectivas futuras para o avanço da área. Os cinco capítulos abordam de forma ampla temas atuais da comunidade, sendo uma obra útil para pesquisadores e profissionais da área de redes de computadores e sistemas distribuídos.

The Short Courses Book of 39th Brazilian Symposium on Computer Networks and Distributed Systems (SBRC 2021) contains the short courses selected for presentation in the online edition of the event, from August 16 to 20, 2021, in the city of Uberlândia/MG. The main objective of the SBRC Short Courses is to offer short-term training and updating on topics not typically covered in curricular structures and which are of great interest among academics and professionals. The Book has traditionally been used as high-quality study material by undergraduate and graduate students and professionals in the field. The Short Courses covers current topics of interest to the community, such as emerging software systems; mobility, security in information-centric networks; virtualization of network functions in the Internet of Things (IoT); wearable networks, and cyber-human systems; and federated learning applied to IoT. In the first chapter, entitled “Emergent Software Systems: Theory and Practice,” the authors present the concept of Emerging Software Systems. The Emerging Software approach aims to reduce the initial effort to create standalone solutions; it supports fully adaptable systems capable of autonomously learning about the system structure and its operating environment without any pre-defined knowledge. In the second chapter, “Revisitando as ICNs: Mobilidade, Segurança e Aplicações Distribuídas através das Redes de Dados Nomeados”, the authors present an introduction and review of the fundamentals of Information-Centric Networks (ICN) and then the Named Data Networking (NDN) architecture. The authors present the main issues related to mobility, security, and distributed applications through demonstrations and practical activities with previously configured environments. The third chapter, “Virtualização de Funções de Rede na IoT: Um Panorama do Gerenciamento de Desempenho x Segurança”, deals with the use of Network Function Virtualization (NFV) in IoT from the point of view of network management, performance, and security. The text presents issues and state-of-the-art related to the performance of NFV in the detection and mitigation of threats in specific IoT networks. The fourth chapter, “Das Redes Vestíveis aos Sistemas Ciber-Humanos: Uma Perspectiva na Comunicação e Privacidade dos Dados”, addresses the rapid evolution that has been taking place from the beginnings of wearable networks to cutting-edge research in nanonetworks. This rapid evolution underlies the construction of cyber-physical and cyber-human systems that have applications in several areas. The authors describe the need to explore the vulnerabilities and challenges that these networks have concerning data privacy and the resilience of their services and present a discussion, demonstrating these weaknesses through practical examples and a survey of the state of the art of academic proposals for privacy protection and resiliency of your services. In the fifth and final chapter, “Aprendizado Federado aplicado à Internet das Coisas”, the authors present the main foundations of Federated Learning (FL), covering the tools and steps necessary for the development of IoT applications and services. The concepts covered include an introduction to Machine Learning (centralized and distributed), the state-of-the-art in FL, an overview of current work, challenges, and future perspectives for advancing the field. The five chapters broadly address current community issues, making them practical work for researchers and professionals in computer networks and distributed systems.

International audience

Internet of Things (IoT) evolution calls for stringent communication demands, including low delay and reliability. At the same time, wireless mesh technology is used to extend the communication range of IoT deployments, in a multi-hop manner. However, Wireless Mesh Networks (WMNs) are facing link failures due to unstable topologies, resulting in unsatisfied IoT requirements. Named-Data Networking (NDN) can enhance WMNs to meet such IoT requirements, thanks to the content naming scheme and in-network caching, but necessitates adaptability to the challenging conditions of WMNs.In this work, we argue that Software-Defined Networking (SDN) is an ideal solution to fill this gap and introduce an integrated SDN-NDN deployment over WMNs involving: (i) global view of the network in real-time; (ii) centralized decision making; and (iii) dynamic NDN adaptation to network changes. The proposed system is deployed and evaluated over the wiLab.1 Fed4FIRE+ test-bed. The proof-of-concept results validate that the centralized control of SDN effectively supports the NDN operation in unstable topologies with frequent dynamic changes, such as the WMNs.

Abstract5G and 6G promise to be catalysts for the Internet of Things (IoT), enabling ultra‐massive and mission‐critical IoT. The demands of new IoT applications and the large number of connected objects being announced will exacerbate the thorny issue of spectrum scarcity in wireless communications. Cognitive radio (CR) is a revolutionary technology that promises to mitigate the problem of spectrum scarcity through more efficient use of spectrum. Integrating CR into the IoT allows objects to opportunistically access spectrum resources already allocated to a Primary User (PU) without causing interference. Spectrum sensing (SS) allows objects to be aware of the PU's spectrum occupancy status. In radio environments where the PU signal is subject to multipath and shadowing effects that degrade the channel between the PU and objects, SS performed by a single object could be inaccurate and lead to incorrect decisions about the PU's status. Cooperative spectrum sensing (CSS) has been proposed to improve detection performance. However, this cooperation between objects has opened the way to a new form of attack known as the Spectrum Sensing Data Falsification (SSDF) or Byzantine attack. In a SSDF attack, attackers falsify their local sensing results before sharing them in the CSS. This attack is very harmful to the CSS and can lead to a loss of spectrum opportunities or interference with the PU. In this paper, from the attack point of view, a sophisticated Byzantine attack model that generalizes well the simple attack strategies has been proposed and allows an attacker to implement other attack strategies. From a defense point of view, a new and innovative Weighted Sequential Hypothesis Testing (WSPRT) scheme has been suggested. This ensures the security of the CSS while significantly reducing the average number of samples required for overall decision‐making in a very hostile IoT network. The results obtained from simulations carried out in various attacks scenarios show that the proposed secure CSS scheme requires at most six samples to detect the state of the PU without error when the proportion of attackers reaches 80%. This performance far exceeds that of other defense mechanisms such as classic WSPRT, SPRT, and majority rule, with which we have compared it under the same conditions. In general, for the classic WSPRT, SPRT, and majority rule mechanisms, the error rate starts to deteriorate at ratio 40% of attackers and the number of samples is greater than six and reaches 50.

Named data networking (NDN) and edge cloud computing (ECC) are emerging technologies that are considered as the most representative technologies for the future Internet. Both technologies are the promising enabler for the future Internet such as fifth generation (5G) and beyond which requires fast information response time. We believe that clear benefits can be achieved from the interplay of NDN and ECC and enables future network technology to be much more flexible, secure and efficient. In this paper, therefore, we integrate NDN with ECC in order to achieve fast information response time. Our framework is based on N-Tier architecture and comprises of three main Tiers. The NDN is located at the Tier1 (Things/end devices) and comprises of all the basic functionalities that connect Internet of Things (IoT) devices with Tier 2 (Edge Computing), where we have deployed our Edge node application. The Tier 2 is then further connected with Tier 3 (Cloud Computing), where our Cloud node application is deployed at multiple hops on the Microsoft Azure Cloud machine located in Virginia, WA, USA. We implement an NDN-based ECC framework and the outcomes are evaluated through testbed and simulations in terms of interest aggregation, round trip time (RTT), service lookup time with single query lookup time and with various traffic loads (load-based lookup time) from the IoT devices. Our measurements show that enabling NDN with edge computing is a promising approach to reduce latency and the backbone network traffic and capable of processing large amounts of data quickly and delivering the results to the users in real time.

This thesis collects a selective set of outcomes of a PhD course in Electronics, Telecommunications, and Information Technologies Engineering and it is focused on designing techniques to optimize computational resources in different wireless communication environments. Mobile Edge Computing (MEC) is a novel and distributed computational paradigm that has emerged to address the high users demand in 5G. In MEC, edge devices can share their resources to collaborate in terms of storage and computation. One of the computational sharing techniques is computation offloading, which brings a lot of advantages to the network edge, from lower communication, to lower energy consumption for computation. However, the communication among the devices should be managed such that the resources are exploited efficiently. To this aim, in this dissertation, computation offloading in different wireless environments with different number of users, network traffic, resource availability and devices' location are analyzed in order to optimize the resource allocation at the network edge. To better organize the dissertation, the studies are classified in four main sections. In the first section, an introduction on computational sharing technologies is given. Later, the problem of computation offloading is defined, and the challenges are introduced. In the second section, two partial offloading techniques are proposed. While in the first one, centralized and distributed architectures are proposed, in the second work, an Evolutionary Algorithm for task offloading is proposed. In the third section, the offloading problem is seen from a different perspective where the end users can harvest energy from either renewable sources of energy or through Wireless Power Transfer. In the fourth section, the MEC in vehicular environments is studied. In one work a heuristic is introduced in order to perform the computation offloading in Internet of Vehicles and in the other a learning-based approach based on bandit theory is proposed.