Using Information Centric Networking in Internet of Things: A Survey

Information-Centric Networking (ICN) is a promising candidate architecture for the future internet that leverages the content name instead of the source of the content (host address). ICN decouples the content from its original location and owner, and enables in-network caching at the network layer. Similarly, the inception of ioT has enabled futuristic applications and services in almost every walk of life. Due to the nature of Internet of Things (IoT) applications, traditional TCP/IP-based internet architecture faces challenges such as addressing, heterogeneity, availability, scalability, and resource constraints. Therefore, ICN can be a suitable choice to address these challenges in IoT applications. Among other features of ICN, the in-network caching feature increases the availability and efficiency of content-sharing in IoT networks. Therefore, it is essential to have efficient cache placement strategies in ICN to increase its utility. in this regard, in this article, we propose an ICN cache placement strategy at the edge network for IoT applications. More precisely, we design a distributed cache placement scheme that aims to push the popular content to the edge network and keep the relatively less-popular content at the core. We also propose a collaborative mechanism to fetch content from the nearest neighbor's content-store as well as propose a cache replacement policy based on the content popularity. Through extensive simulations, we evaluate the proposed scheme and the obtained results show the efficiency and out-performance against similar strategies in the literature.

Information-centric networking (ICN) is a content-oriented network that uses content instead of traditional IP addresses, it facilitates the content retrieval and calculates downstream paths without the need of an optimal topology. ICN inherits mobility support features in its baseline design. Since the Internet of Things (IoT) faces major problems in supporting mobility management over IP, we believe that ICN would represent a major element in scalable IoT networks. This article discusses the use of ICN as a communication model in IoT environments, focusing on solving the mobility issue. Thus, we survey the relevant work that studies the merger between ICN and IoT, and describe the emergence, evolution, and state-of-the-art mobility approach. This article also presents discussions on major scalability challenges when using ICN as a communication enabler for mobile IoT and highlights key designs and guidelines.

In this paper, a Smart Device (SD) localization method, based on the Path Loss (PL) model of Macro Base Station (MBS) and femtocells, using the convex optimization method is discussed for an Internet of Things (IoT) networks. Localization plays a major role for smart city, smart agriculture, and smart health applications in IoT networks. Global Positioning System (GPS) works well for outdoor positioning but fails to provide accurate locations in an indoor environment and non-line-of-sight (NLOS) paths. We propose the Convex optimization (CO) method that uses the combined effects of the Received Signal Strength (RSS) from macrocells and femtocells. The method requires no additional infrastructure and localizes a Smart Device (SD) in an IoT environment. The Cramèr-Rao Lower Bound (CRLB) is also evaluated to analyze the performance of the estimator. Extensive simulations demonstrate that our proposed method provides an accurate location as compared to Least Square method.

A blockchain-enabled Model integrates blockchain technology with Intrusion Detection Systems to enhance the security of Internet of Things (IoT) networks. It ensures data integrity, decentralization, and tamper-proof logging of intrusion detection. The approach improves trust, transparency, and real-time threat detection in distributed IoT environments. The existing blockchain-based IDS approaches, Blockchain Enabled (BCE-IoT), uniquely integrate blockchain consensus with federated-style local training, lightweight cryptography, and Shapley Additive Explanations (SHAP)-based explainability, ensuring both security and interpretability in IoT environments. The proposed work combines Blockchain technology with explainable artificial intelligence solutions to create a new cybersecurity Model that strengthens intrusion detection within IoT networks. The proposed model enhances transparency in tracking cyberattacks by combining blockchain security storage capabilities with SHAP, an explainable AI. This research utilises machine learning and artificial intelligence to detect threats in real-time, countering Distributed Denial of Service (DDoS), Denial of Service (DoS), scanning, Cross-Site Scripting (XSS), injection, password, and backdoor attacks. BCE-IoT delivers more precise security by combining blockchain’s permanent data features and AI anomaly detectors, thereby reducing security alert mistakes. The performance effectiveness of Blockchain-Enabled IoT surpasses that of the Content Integrity Detection System. It combines Blockchain and Software-Defined Networking to enhance security in network environments, utilising blockchain-based mutual confirmation for software-defined networking to detect and block cyber threats. The evaluation establishes BCE-IoT as an effective IoT network security solution that delivers strong cybersecurity features, is adaptable to modern connected environments, and offers interpretable security solutions. The performance evaluations demonstrate that BCE-IoT provides a robust, flexible, and interpretable cybersecurity solution suitable for modern IoT environments.

Recently, IETF (Internet Engineering Task Force) presented CoAP (Constrained Application Protocol) for the communication between sensor and actuator nodes in a constrained environment such as small amount of memory and low power. IETF CoAP protocol can convert easily, and can use to monitor or control infrastructure utilities through low-power sensor and actuator networks in IoT (Internet of Things) and M2M (Machine-to-Machine) environment. IETF CoAP protocol provides the special requirements of this constrained environment, especially considering energy, automation, and other IoT applications. In this paper, we design and implement a node registration and discovering based CoAP in IoT environment. The node is a CoAP node which is designed for working in constrained environment. For node registration in IoT, we used CoRE RD (Resource Directory) functionalities to register CoAP node’s information which works in IoT environment.

Internet of Things (IoT) networks are being designed to connect billions of sensors/devices for an application-specific environment to provide services and carry out special tasks. It enables the virtual connection of the real-world physical devices with Internet cyberspace. While, there is an exponentially growing trend in the use of IoT technology for diverse applications such as smart agriculture, smart industries, smart hospitals, smart homes, etc., there are pressing design issues which affect the IoT network performance in the long run, for example, replacement of batteries for increasing the lifetime of devices to maintain the network longevity. To tackle this challenge, various wireless powered-based energy transfer paradigms were proposed over time. Backscatter communication (BackCom) is likewise one of the energy-efficient solutions proposed, which can satisfy the stringent green communication aspects of IoT networks. To achieve massive connectivity in BackCom-enabled IoT network, selecting a suitable multiple access scheme is also a crucial part. Most multiple access schemes proposed for BackCom systems are based on frequency or timeslots sharing techniques. Multiple access techniques proposed for BackCom IoT system can reap the gain of non-orthogonal multiple access (NOMA) principle. In NOMA, multiple end nodes (ENs) of sensors network is served in the same resource block, thereby, increasing the spectrum efficiency of BackCom IoT system. In this chapter, we discuss the prospects of backscatter technique with application of NOMA for green communication in IoT network.

The internet of things (IoT) is a huge network of heterogeneous devices; sensors and actuators, which provide a better world of services such as smart cities, smart grid, health care, smart agriculture and so on. This novel concept represents a rich area of research challenges. Instead, the most important aspects of the IoT networks are security and privacy. Because of the potential augmentation of IoT devices number and their limitations in terms of computational power, energy and memory, those aspects become more and more difficult to be established. This chapter consists of an exhaustive study about IoT applications and security in such environment. Hence, we present IoT deployment issues and the existent attacks on IoT environment considering the IoT layers. Then, we discuss the requirement of IoT security as well as the actual solutions for remediation of the compromised security, such as authentication protocols, intrusion detection systems and machine learning.

Internet of Things (IoT) is a technology that allows various physical devices to connect to each other and exchange data via the internet network. The application of IoT is increasingly widespread in various sectors such as smart homes, manufacturing industries, smart agriculture, and healthcare. However, along with the increasing number of devices and the volume of data traffic sent, the potential risk of cybersecurity threats is also increasing. The large number of IoT devices that have limited computing capabilities makes the system more vulnerable to various attacks, including intrusion, exploitation of system weaknesses, and Distributed Denial of Service (DDoS) attacks. Therefore, early detection of anomalies in network traffic is a crucial aspect to maintain the security and stability of IoT systems. This study aims to develop and implement a Support Vector Machine (SVM)-based architecture as a classification method in an anomaly detection system on an IoT network. SVM was chosen because of its ability to handle high-dimensional data and non-linear classification effectively. The methodology used includes the process of extracting features from IoT network traffic datasets, data normalization, model training using the SVM algorithm, and evaluating model performance in distinguishing between normal and anomalous traffic. Thus, the implementation of SVM architecture can be an effective and efficient solution in intrusion detection systems for IoT networks. This research also opens up opportunities for the development of more adaptive security systems by integrating machine learning-based detection models into large-scale IoT infrastructures.

Information Centric Networking (ICN) is an emerging research topic aiming at shifting the Internet from its current host-centric paradigm towards an approach centred around content, which enables the direct retrieval of information objects in a secure, reliable, scalable, and efficient way. The exposure of ICN to scenarios other than static content distribution is a growing research topic, promising to extend the impact of ICN to a broader scale. In this context, particular attention has been given to the application of ICN in Internet of Things (IoT) environments. The current paper, by focusing on local domain IoT scenarios, such as multi-sensory Machine to Machine environments, discusses the challenges that ICN, particularly Interest-based solutions, impose to service discovery. This work proposes a service discovery mechanism for such scenarios, relying on an alternative forwarding pipeline for supporting its core operations. The proposed mechanism is validated through a proof-of-concept prototype, developed on top of the Named Data Networking ICN architecture, with results showcasing the benefits of our solution for discovering services within a collision domain.

A survey of Internet of Things communication using ICN: A use case perspective

International audience

A fuzzy based Co-Operative Blackmailing Attack detection scheme for Edge Computing nodes in MANET-IOT environment

The Internet of Medical Things (IoMT) is the network of medical devices, hardware infrastructure, and software applications used to connect the healthcare information technology. Massive traffic growth and user expectations cause challenges in the current exhausting models of IoMT data. To reduce the IoMT traffic, Information Centric Network (ICN) is a suitable technique. ICN uses persistent naming multicast communication that reduces the response time. ICN in IoMT provides a promising feature to reduce the overhead due to the distribution of commonly accessed contents. Some parameters such as energy consumption, communication cost, etc., influence the performance of sensors in the IoMT network. Excessive and unbalanced energy consumption degrades the network performance and lifetime. This article presents a framework called Dynamic Cache Scheme (DCS) that implements energy-efficient cache scheduling in IoMT over ICN to reduce network traffic. The proposed framework establishes a balance between the multi-hop traffic and data item freshness. The technique improves the freshness of data; thus, updated data are provided to the end-users via the effective utilization of caching in IoMT. The proposed framework is tested on important parameters, i.e., cache-hit-ratio, stretch, and content retrieval latency. The results obtained are compared with the state-of-the-art models. Results’ analysis shows that the proposed framework outperforms the compared models in terms of cache-hit-ratio, stretch, and content retrieval latency by 59.42%, 32.66%, and 18.8%, respectively. In the future, it is intended to explore the applicability of DCS in more scenarios and optimize further.

As devices for the Internet of Things (IoT) are typically battery-powered, energy efficiency is a major challenge for IoT networks. In this paper, we leverage the in-network caching of Information-Centric Networking (ICN) to propose a novel cooperative caching scheme, based on the IoT data lifetime and user request rate, to improve the energy efficiency of IoT networks. By caching IoT data at different nodes (such as content routers, base stations, etc.), IoT devices can stay in sleep mode for a larger portion of time and therefore reduce the overall energy consumption. With the help of an auto- configuration mechanism, the proposed IoT data Lifetime-based Cooperative Caching (LCC) scheme can dynamically adapt to the change of request rate. Extensive evaluations were performed and the simulation results show that LCC outperforms existing schemes in terms of total energy consumption reduction (up to 40%) and the reduction in the average number of hops traversed along the path (up to 20%), which is also directly related to the response time. Keywords- Internet of Things (IoT), Cooperative Caching, Information-Centric Network (ICN).

To develop access control mechanisms, particularly in terms of maintaining effective and secure access control within Internet of Things (IoT) networks. Whereas the sufficient use must be made of blockchain-based access control technology. This is because of the sheer volume of connected devices and the subsequent increase in transactions. This can negatively impact the performance and responsiveness of the networks. Thus, this article proposes a comprehensive approach that evaluates the requester’s reputation with respect to regulating access requests for IoT resources. The proposed approach combines fuzzy reputation with a decay algorithm. It then calculates the quantitative reputation value for each IoT user. This considers multiple variables, such as the Access Request Rate, frequency of requests, etc. This new reputation value serves as the basis for access-control decisions. Extensive simulations and experiments are conducted to evaluate the effectiveness of the proposed framework. For the simulation, we used a single-board Raspberry Pi. We also used a hybrid blockchain network environment comprising Geth and Hyperledger Fabric. We then analyzed and compared the performance of the proposed framework with that of the existing approaches. The results demonstrated that compared to the mathematical mechanism, the framework provides improved access control in IoT networks. The fuzzy-based reputation framework captures the dynamic nature of IoT environments, and effectively identifies trustworthy and malicious devices, whereas the decay algorithm reflects the most recent user behavior.