Accurate energy consumption forecasting is critical for optimizing energy usage, lowering operational costs, and encouraging sustainability in smart buildings. Machine learning (ML) has developed as an effective method for energy forecasting, using sensor data to anticipate consumption trends and increase efficiency. However, due to regulations such as GDPR and growing privacy concerns, sharing sensitive energy data with third parties is often prohibited, providing issues for traditional centralized ML techniques. Federated Learning (FL) provides a feasible alternative by allowing for decentralized model training across several buildings without explicitly exchanging raw data. This privacy-preserving strategy enables organizations to jointly train reliable models while retaining data sovereignty. Our experimental results demonstrate that by using the CU-BEMS dataset, both FL and centralized forecasting models perform similarly, with an R2score of ≈87%. Furthermore, FL decreases bandwidth use by limiting data transfers, making it a scalable and economical energy management solution for smart buildings. These findings demonstrate FL’s ability to ensure safe, data-driven decision-making for sustainable energy utilization.

Cooperative intelligent transport systems (C-ITS) enable information to be shared wirelessly between vehicles and infrastructure in order to improve transport safety and efficiency. Delivering C-ITS services using existing cellular networks offers both financial and technological advantages, not least since these networks already offer many of the features needed by C-ITS, and since many vehicles on our roads are already connected to cellular networks. Still, C-ITS pose stringent requirements in terms of availability and latency on the underlying communication system; requirements that will be hard to meet for currently deployed 3G, LTE, and early-generation 5G systems. Through a series of experiments in the MONROE testbed (a cross-national, mobile broadband testbed), the present study demonstrates how cellular multi-access selection algorithms can provide close to 100 percent availability, and significantly reduce C-ITS transaction times. The study also proposes and evaluates a number of low-complexity, low-overhead single-access selection algorithms, and shows that it is possible to design such solutions so that they offer transaction times and availability levels that rival those of multi-access solutions.

ANIARA (https://www.celticnext.eu/project-ai-net) attempts to enhance edge architectures for smart manufacturing and cities. AI automation, orchestrated lightweight containers, and efficient power usage are key components of this three-year project. Edge infrastructure, virtualization, and containerization in future telecom systems enable new and more demanding use cases for telecom operators and industrial verticals. Increased service flexibility adds complexity that must be addressed with novel management and orchestration systems. To address this, ANIARA will provide en-ablers and solutions for services in the domains of smart cities and manufacturing deployed and operated at the network edge(s).

Information-centric networking (ICN) with its design around named-based forwarding and in-network caching holds great promises to become a key architecture for the future Internet. Many proposed ICN hop-by-hop congestion control schemes assume a fixed and known link capacity, which rarely - if ever - holds true for wireless links. Firstly, we demonstrate that although these congestion control schemes are able to fairly well utilise the available wireless link capacity, they greatly fail to keep the delay low. In fact, they essentially offer the same delay as in the case with no hop-by-hop, only end-to-end, congestion control. Secondly, we show that by complementing these schemes with an easy-to-implement, packet-train capacity estimator, we reduce the delay to a level significantly lower than what is obtained with only end-to-end congestion control, while still being able to keep the link utilisation at a high level.

Large network operators have thousands or tens of thousands of access aggregation links that they need to manage and dimension properly. Measuring and understanding the traffic characteristics on these type of links are therefore essential. What do the traffic intensity characteristics look like on different timescales from days down to milliseconds? How do the characteristics differ if we compare links with the same capacity but with different type of clients and access technologies? How do the traffic characteristics differ from that on core network links? These are the type of questions we set out to investigate in this paper. We present the results of packet level measurements on three different 1Gbit/s aggregation links in an operational IP network. We see large differences in traffic characteristics between the three links. We observe highly skewed link load probability densities on timescales relevant for buffering (i.e. 10-milliseconds). We demonstrate the existence of large traffic spikes on short timescales (10-100ms) and show their impact on link delay. We also found that these traffic bursts often are caused by only one or a few IP flows.

Today increasingly large volumes of TV and video are distributed over IP-networks and over the Internet. It is therefore essential for traffic and cache management to understand TV program popularity and access patterns in real networks.

Internet traffic volumes continue to grow at a great rate, now pushed by video and TV distribution in the networks. This brings up the need for traffic engineering mechanisms to better control the traffic. The objective of traffic engineering is to avoid congestion in the network and make good use o

We model a Web client using empirical probability distributions for user clicks and transferred data sizes. By using a heuristic threshold value to distinguish user clicks in a packet trace we get a simple method for analyzing large packet traces in order to get information about user off times and amount of data transferred due to a user click. We derive the empirical probability distributions from the analysis of the packet trace. The heuristic is not perfect, but we believe it is good enough to produce a useful Web client model. We use the empirical model to implement a Web client traffic generator. The characteristics of the generated traffic is very close to the original packet trace, including self-similar properties.