Sensing has become a fundamental component of modern network infrastructures, powering applications from environmental monitoring to industrial automation, and bridging the gap between digital systems and the physical world. Given the large amount of data generated by these systems, it is important to find strategies that are able to intelligently manage the flow of data between all interconnected devices, in order to reduce the utilized bandwidth to a minimum. In this paper, we study the use of Wireless Edge Caching (WEC) techniques to reduce latency and optimize bandwidth and energy consumption of sensing applications. We study a specific sensing scenario where a network of wireless sensors spread over a vast territory continuously collects data, part of which must be transmitted to a central base station. To aid and enhance the performance of this application, we employ the use of WEC techniques. Since the frequency of data queries is related to the sensors’ location, we introduce a novel caching algorithm, Closest In Farthest Out (CIFO), tailored to this scenario. CIFO is able to capture the characteristics of the sensing application and perform cache eviction decisions accordingly. We demonstrate the performance of our solution by implementing it in a simulated environment and comparing it to traditional caching strategies, showing how our solution is able to outperform the other strategies under multiple settings and different metrics.

In this demo, we showcase the collaborative effort that was put into bringing an offloading protocol for UAVs, Stop & Offload [1], on real-world hardware. The process includes multiple stages, from hardware configuration and testing in a simulated environment, to the final deployment in the field. The protocol enhances patrolling missions by improving coordination and data offloading among drones. We address the challenges of translating this protocol into a real hardware implementation using DJI Drones, and provide a detailed walkthrough of the process, from simulation to deployment. Our solution utilizes the DJI PSDK [2] libraries, ROS2 [5], and the Gazebo [4] simulator to ensure a secure and strong implementation.

Large-scale IT infrastructures are highly energy-intensive systems. To mitigate the environmental impact of networks, it is crucial to design energy-aware traffic engineering strategies that make the best use of a network’s redundancy in order to maximize energy savings. The Software-Defined Networking (SDN) paradigm is a powerful tool that eases network management by decoupling the control and data planes. Because of its flexibility and controllability, SDN is nowadays widely adopted in data centers and enterprise networks. In this paper, we provide energy-aware traffic solutions for SDNs. We conducted extensive experiments on real switches for a thorough power consumption characterisation. Thanks to this preliminary study, we could fully characterize the solution space of a performance-constrained energy optimization problem. We formulate two optimization problems for the selective activation of switches and ports under hard constraints on traffic demand, considering both a static and dynamic traffic scenario. We show that the proposed problems are NP-hard and provide two polynomial-time heuristics for the static and dynamic case, respectively. Through simulations, we show that our solution outperforms previous approaches in all the considered settings.

Boolean Network Tomography (BNT) aims at identifying failures of internal network components by means of end-to-end monitoring paths. However, when the number of failures is not known a priori, failure identification may require a huge number of monitoring paths. We address this problem by designing a Bayesian approach that progressively selects the next path to probe on the basis of its expected information utility, conditioned on prior observations. As the complexity of the computation of posterior probabilities of node failures is exponential in the number of failed paths, we propose a polynomial-time greedy strategy which approximates these values. To consider aging of information in dynamic failure scenarios where node states can change during a monitoring period, we propose a monitoring technique based on a sliding observation window of adaptive length. By means of numerical experiments conducted on real network topologies we demonstrate the practical applicability of our approach, and the superiority of our algorithms with respect to state of the art solutions based on classic BNT as well as sequential group testing.

Boolean Network Tomography (BNT) allows to localize network failures by means of end-to-end monitoring paths. Nevertheless, it falls short of providing efficient failure identification in real scenarios, due to the large combinatorial size of the solution space, especially when multiple failures occur concurrently. We aim at maximizing the identification capabilities of a bounded number of monitoring probes. To tackle this problem we propose a progressive approach to failure localization based on stochastic optimization, whose solution is the optimal sequence of monitoring paths to probe. We address the complexity of the problem by proposing a greedy strategy in two variants: one considers exact calculation of posterior probabilities of node failures given the observation, whereas the other approximates these values through a novel failure centrality metric. We discuss the approximation of the proposed approaches. Then, by means of numerical experiments conducted on real network topologies, we demonstrate the practical applicability of our approach. The performance evaluation evidences the superiority of our algorithms with respect to state of the art solutions based on classic Boolean Network Tomography as well as approaches based on sequential group testing.

Boolean network tomography is a powerful tool to infer the state (working/failed) of individual nodes from path-level measurements obtained by edge-nodes. We consider the problem of optimizing the capability of identifying network failures through the design of monitoring schemes. Finding an optimal solution is NP-hard and a large body of work has been devoted to heuristic approaches providing lower bounds. Unlike previous works, we provide upper bounds on the maximum number of identifiable nodes, given the number of monitoring paths and different constraints on the network topology, the routing scheme, and the maximum path length. These upper bounds represent a fundamental limit on identifiability of failures via Boolean network tomography. Our analysis provides insights on how to design topologies and related monitoring schemes to achieve the maximum identifiability under various network settings. Through analysis and experiments we demonstrate the tightness of the bounds and efficacy of the design insights for engineered as well as real networks.