Although several years have passed since its first introduction, the significance of Delay-Tolerant Networking (DTN) remains evident, particularly in challenging environments where traditional networks face operational limitations such as disrupted communication or high latency. This survey paper aims to explore the diverse array of applications where DTN technologies have proven successful, with a focus on emerging and novel application paradigms. In particular, we focus on the contributions of DTN in the Future Internet, including its contribution to space applications, smart cities and the Internet of Things, but also to underwater communications. We also discuss its potential to be used jointly with information-centric networks to change the internet communication paradigm in the future.

As the threat landscape advances and pressure to reduce the energy footprint grows, it is crucial to understand how security mechanisms affect the power consumption of cloud-native platforms. Although several studies in this domain have investigated the performance impact of security practices or the energy characteristics of containerized applications, their combined effect remains largely underexplored. This study examines how common Kubernetes (K8s) safeguards influence cluster energy use across varying security configurations and workload conditions. By employing runtime and network monitoring, encryption, and vulnerability-scanning tools under diverse workloads (idle, stressed, realistic application), we compare the baseline system behavior against the energy consumption introduced by each security configuration. Our findings reveal that always-on security mechanisms impose a persistent baseline energy cost—occasionally making an idle protected cluster comparable to a heavily loaded unprotected one, while security under load results in substantial incremental overhead. In particular, service meshes and full-tunnel encryption show the largest sustained overhead, while eBPF telemetry, network security monitoring, and vulnerability scans add modest or short-lived costs. These findings provide useful security-energy insights and trade-offs for configuring K8s in resource-constrained settings, including IoT/smart city deployments.

As the threat landscape advances and pressure to reduce the energy footprint grows, it is crucial to understand how security mechanisms affect the power consumption of cloud-native platforms. Although several studies in this domain have investigated the performance impact of security practices or the energy characteristics of containerized applications, their combined effect remains largely underexplored. This study examines how common Kubernetes (K8s) safeguards influence cluster energy use across varying security configurations and workload conditions. By employing runtime and network monitoring, encryption, and vulnerability scanning tools under diverse workloads (idle, stressed, and realistic application), we compare the baseline system behavior against the energy consumption introduced by each security configuration. Our findings reveal that always-on security mechanisms impose a persistent energy cost—occasionally making an idle protected cluster comparable to a heavily loaded unprotected one—while security under load results in substantial incremental overhead. In particular, service meshes and full-tunnel encryption show the largest sustained overhead, while Extended Berkeley Packet Filter (eBPF)-based telemetry, network security monitoring, and vulnerability scanning add modest or short-lived costs. These findings provide useful security–energy insights and trade-offs for configuring K8s in resource-constrained settings, including Internet of Things (IoT) and smart city deployments.

In recent years, the space industry has witnessed a resurgence, characterized by a notable proliferation of satellites operating at progressively lower altitudes, promising extensive global coverage and terrestrial-level data transfer speeds, while remaining cost-effective solutions. In particular, Wireless Sensor Networks (WSNs) can benefit from the wide coverage of space infrastructure due to their extensive deployment, disrupted communication nature, and the potential absence of terrestrial support. This study explored the utility of Low-Earth Orbit (LEO) satellite constellations as a communication infrastructure for interconnecting “smart” devices via ground stations in Internet of Things (IoT) scenarios. To this end, we designed and implemented a series of experiments conducted within the OMNeT++ simulator, utilizing an updated iteration of the original Open Source Satellite Simulator (OS3) framework. Our research encompassed an IoT Case Study, incorporating authentic sensor data sourced from the Smart Santander testbed. Throughout our experimentation, we investigated the impact of the constellation design parameters such as the number of satellites and orbital planes, as well as the inter-satellite link configuration on the obtained Round-Trip Time (RTT) and packet loss rates.

Anomaly detection (AD) has been recently employed in the context of edge cloud computing, e.g., for intrusion detection and identification of performance issues. However, state-of-the-art anomaly detection procedures do not systematically consider restrictions and performance requirements inherent to the edge, such as system responsiveness and resource consumption. In this paper, we attempt to investigate the performance of change-point based detectors, i.e., a class of lightweight and accurate AD methods, in relation to the requirements of edge cloud systems. Firstly, we review the theoretical properties of two major categories of change point approaches, i.e., Bayesian and cumulative sum (CUSUM), also discussing their suitability for edge systems. Secondly, we introduce a novel experimental methodology and apply it over two distinct edge cloud test-beds to evaluate the performance of such mechanisms in real-world edge environments. Our experimental results reveal important insights and trade-offs for the applicability and the online performance of the selected change point detectors.

IoT monitoring systems, from traffic and pedestrian sensing in smart cities to building energy metering and industrial robot telemetry, increasingly run as containerised microservices on Kubernetes (K8s) clusters that span constrained edge nodes and cloud servers. Cognitive orchestration layers extend K8s with network-, data- and energy-aware placement, running on the same nodes as the monitoring workloads they manage, with their own footprint rarely reported. This paper proposes a component-level cost-attribution methodology and an automation-footprint trade-off analysis that relates the manual interventions removed to deployment latency, CPU, memory and estimated power consumed by each of its functional roles. We apply it to Cognitive Decentralised Edge–Cloud Orchestration (CODECO), an open-source cognitive orchestration framework, across six infrastructures, including three operational IoT deployments, ARM and x86 hardware, and vanilla K8s, K3s and KinD. The automated CODECO and CODEF workflow reduces the number of manual interventions from 72 to 9 (87.5%) under our action-count definition, with a total of 4.57 GB memory overhead, the most significant observed trade-off. Decomposing these costs by architectural layer reveals that within the tested cluster sizes, the installation time of the scheduling component shows no measurable growth and that of the learning component grows slowly, while cross-layer network management contributes the largest share of installation time and idle CPU consumption. Results show that orchestration costs arise in different architectural layers, indicating they can be placed and optimised independently without sacrificing automation benefits.