IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) and IPv6 Routing Protocol for Low-power and Lossy networks (RPL) have accelerated the integration of Wireless Sensor Networks (WSNs) and smart objects with the Internet. At the same time, the development of the Constrained Application Protocol (CoAP) has made it possible to provide resource constrained devices with web service functionalities. CoAP is an HTTP like web transfer protocol able to extend the REpresentational State Transfer (REST) architecture to LoWPANs. The major difference between CoAP and HTTP is the different transport layer protocol (UDP instead of TCP) and the header compression which makes the packet size significantly smaller. This work provides an evaluation of CoAP compared to HTTP. The performance is evaluated in terms of mote's energy consumption and response time. The aim of the paper is to demonstrate, with a quantitative and qualitative analysis, that CoAP is more suited to REST based WSNs compared to HTTP. The results have been obtained by means of simulation as well as tests on real sensor motes.

The use of Internet Protocol (IP) technology in Wireless Sensor Network (WSN) is a key prerequisite for the realization of the Internet of Things (IoT) vision. The IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) standard enables the use of IPv6 in networks of constrained devices. 6LowPAN enables the use of Service Oriented Architectures (SOAs) in WSN. The Internet Engineering Task Force (IETF) has defined the Constrained Application Protocol (CoAP), a web transfer protocol which provides several Hypertext Transfer Protocol (HTTP) functionalities, re-designed for constrained embedded devices. CoAP allows WSN applications to be built on top of Representational State Transfer (REST) architectures. This considerably eases the IoT application development and facilitates the integration of constrained devices with the Web. This work describes the prototype design and development of a Web platform which integrates a REST/CoAP WSN with a REST/HTTP Web application and allows a user to visualize WSN measurements in the Web browser. Since the WSN Web integration relies on a non-transparent gateway/server, we also show how to provide transparent cross-protocol resource access by means of an HTTP-CoAP proxy. The paper describes the major building blocks, functionalities and the implementation approach.

Intrusion detection and prevention are two of the most important issues to solve in network security infrastructure. Intrusion detection systems (IDSs) protect networks by using patterns to detect malicious traffic. As attackers have tried to dissimulate traffic in order to evade the rules applied, several machine learning-based IDSs have been developed. In this study, we focused on one such model involving several algorithms and used the NSL-KDD dataset as a benchmark to train and evaluate its performance. We demonstrate a way to create adversarial instances of network traffic that can be used to evade detection by a machine learning-based IDS. Moreover, this traffic can be used for training in order to improve performance in the case of new attacks. Thus, a generative adversarial network (GAN)—i.e., an architecture based on a deep-learning algorithm capable of creating generative models—was implemented. Furthermore, we tested the IDS performance using the generated adversarial traffic. The results showed that, even in the case of the GAN-generated traffic (which could successfully evade IDS detection), by using the adversarial traffic in the testing process, we could improve the machine learning-based IDS performance.

Managing the large volumes of IoT and M2M traffic requires the evaluation of the scalability and reliability for all the components in the end-to-end system. This includes connectivity, mobile network functions, and application or services receiving and processing the data from end devices. Firstly, this paper discusses the design of a containerized IoT and M2M application and the mechanisms for delivering automated scalability and high availability when deploying it in: (1) the edge using balenaCloud; (2) the Amazon Web Services cloud with EC2 instances; and (3) the dedicated Amazon Web Services IoT service. The experiments showed that there are no significant differences between edge and cloud deployments regarding resource consumption. Secondly, the solutions for scaling the 4G/5G network functions and mobile backhaul that provide the connectivity between devices and IoT/M2M applications are analyzed. In this case, the scalability and high availability of the 4G/5G components are provided by Kubernetes. The experiments showed that our proposed scaling algorithm for network slicing managed with SDN guarantees the necessary radio and network resources for end-to-end high availability.

The evolution from 5G to 6G networks is driven by the need to meet the stringent requirements, i.e., ultra-reliable, low-latency, and high-throughput communication. The new services are called Further-Enhanced Mobile Broadband (feMBB), Extremely Reliable and Low-Latency Communications (ERLLCs), Ultra-Massive Machine-Type Communications (umMTCs), Massive Ultra-Reliable Low-Latency Communications (mURLLCs), and Mobile Broadband Reliable Low-Latency Communications (MBRLLCs). Network slicing emerges as a critical enabler in 6G, providing virtualized, end-to-end network segments tailored to diverse application needs. Despite its significance, existing datasets for slice selection are limited to 5G or LTE-A contexts, lacking relevance to the enhanced requirements. In this work, we present a novel synthetic dataset tailored to 6G network slicing. By analyzing the emerging service requirements, we generated traffic parameters, including latency, packet loss, jitter, and transfer rates. Machine Learning (ML) models like Random Forest (RF), Decision Tree (DT), XGBoost, Support Vector Machine (SVM), and Feedforward Neural Network (FNN) were trained on this dataset, achieving over 99% accuracy in both slice classification and handover prediction. Our results highlight the potential of this dataset as a valuable tool for developing AI-assisted 6G network slicing mechanisms. While still in its early stages, the dataset lays a foundation for future research. As the 6G standardization advances, we aim to refine the dataset and models, ultimately enabling real-time, intelligent slicing solutions for next-generation networks.

Network traffic exhibits strong correlations which make it suitable for prediction. Real-time forecasting of network traffic load accurately and in a computationally efficient manner is the key element of proactive network management and congestion control. This paper compares predictions produced by different types of neural networks (NN) with forecasts from statistical time series models (ARMA, ARAR, HW). The novelty of our approach is to predict aggregated Ethernet traffic with NNs employing multiresolution learning (MRL) which is based on wavelet decomposition. In addition, we introduce a new NN training paradigm, namely the combination of multi-task learning with MRL. The experimental results show that nonlinear prediction based on NNs is better suited for traffic prediction purposes than linear forecasting models. Moreover, MRL helps to exploit the correlation structures at lower resolutions of the traffic trace and improves the generalization capability of NNs.