In many African countries, mobile payments are vital for financial transactions. However, enabling peer-to-peer payments is challenging due to limited network infrastructure and resource-constrained devices. Companies like Ejara are working to bring accessible financial services to underserved communities. However, existing routing protocols for Opportunistic Networks (OppNets), like traditional epidemic routing, create high routing overhead and latency from indiscriminate packet flooding, putting significant pressure on network and device resources. This study addresses these issues by introducing an optimized epidemic routing protocol with Reinforcement Learning algorithms integration to enhance packet forwarding in OppNets. The protocol dynamically adapts forwarding decisions based on delivery probability, latency, and resource constraints. Emulation results demonstrate that this approach significantly reduces routing overhead and latency while improving packet delivery reliability. This solution has meaningful implications for enabling efficient mobile payments and peer-to-peer interactions, especially in resource-constrained environments with intermittent connectivity, supporting broader accessibility to financial services.

Assuring the trustworthiness of AI-based systems, especially in video surveillance, is critical in smart cities today. Large Language Models have emerged as the strongest tool for analyzing videos by offering human-readable descriptions of complex scenes. However, their effectiveness in sensitive tasks like abnormal behavior detection depends heavily on trust and system reliability. This paper proposes a new approach for enhancing trustworthiness in LLMs using a QoE-based framework. We propose a system that couples these Vid-LLMs with a real-time feedback loop, which incorporates both user corrections and validations to improve accuracy and user trust. We show how such a system can be trained and evaluated using the UCA Crime database and demonstrate that it is able to describe abnormal events in a highly reliable way. Our results indicate that the QoE-based trust model significantly improves user satisfaction, serving as a valued approach in real-world surveillance applications or other similar use cases.

In the past decades, the use of urban rivers for recreational and sporting activities has gained increasing interest. However, bathing in urban surface waters is not without health risks due to short-term pollution of fecal origin, which may have an important impact on the overall population health within a region where bathing in water streams is possible. Therefore, EU member states are required to lower the contamination risk of such areas through active water quality management, as defined by the Bathing Water directory (BWD, 2006/7/EC). This paper develops and evaluates a cost-effective IoT-based water quality monitoring system, based on low-cost water quality sensors coupled with machine-learning approaches. By monitoring spatiotemporal dynamics of several physical and chemical parameters correlated with bacterial indicators, managers can more easily decide if the water quality of a bathing site is enough for usage. To determine the water suitability at particular river sites, the system employs a convolutional neural network (CNN) deep learning classifier, integrating federated learning (FL) with knowledge distillation (FedKD) to streamline model architecture, reduce communication costs, and preserve data privacy. The system is tested on the Seine and the Marne rivers (Paris area, France) and results demonstrate that FedKD outperforms centralized knowledge distillation (KD) and FL algorithms such as FedAvg and UFedAVG. Using the current features, it achieves a satisfactory average accuracy of 90.74% at Marne station.

The monitoring of surface water in smart cities can be enhanced with the Internet of Things (IoT) and the use of transfer learning. The former allows the increase in the coverage area and to better exploit this data. The latter has the potential to reduce the need of data collection, which may be costly. In this work, we discuss the potential use of these two domains for the estimation of surface water quality in the Marne River (France). The assessment is made using physico-chemical data from sensors to predict the concentration of fecal indicator bacteria. The results show that the use of transfer learning has the potential to enhance water quality monitoring in smart cities.

In the context of large-scale Internet of Things (IoT), one of the main issues comes from the lack of an efficient routing protocol that could handle thousands of devices and provide a low-power forwarding mechanism for huge amounts of data. Furthermore, this routing protocol should cope with the intrinsic device-to-device communications paradigm of IoT, where nodes no longer need an intermediate station for communication and synchronizing, in order to exploit all options to deliver a better quality of service (QoS) for the network. Although many solutions have been proposed to meet QoS requirements for various applications based on IoT, they usually do not provide significant increase on a network performance when the number of nodes becomes too large. Therefore, in this work, we provide a new modelling paradigm, organized on a two-level control mechanism, to overcome this problem. For the first level, we propose a new Routing Protocol for Low-Power and Lossy Networks (RPL) approach based on multi-hop clustering technique (MHC-RPL). It is used as a local control to organize nodes in clusters, in order to reduce energy consumption in the IoT. The second level uses Software Defined Networking (SDN) with Q-routing algorithm for intelligent management of the global network. Our results show that the proposed model provides significant better results in terms of end-to-end delay, packet delivery ratio and energy-consumption than current state-of-the-art.

This paper proposes a new QoS-aware forwarding strategy for Named Data Networking. Borrowing techniques from the ant colony optimization, the proposed strategy, which is called Ant colony based QoS-aware forwarding strategy (AC-QoS-FS), makes full use of both forward and backward ants to rank interfaces. Forward and backward ants (Interest and Data packets) probe realtime network QoS parameters to update the interfaces ranking in order to select the best one for forwarding the incoming Interests. The effectiveness of AC-QoS-FS is validated through ndnSIM simulation.

In this paper, we follow up an existing modeling framework to analytically evaluate the performance of multi-hop flows along a wireless chain of four nodes. The proposed model accounts for a non-perfect physical layer, handles the hidden node problem, and is applicable under workload conditions ranging from flow(s) with low intensity to flow(s) causing the network to saturate. Its solution is easily and quickly obtained and delivers estimates for the expected throughput and for the datagram loss probability of the chain with a good accuracy.

IEEE 802.11 is implemented in many wireless networks, including multi-hop networks where communications between nodes are conveyed along a chain. We present a modeling framework to evaluate the performance of flows conveyed through such a chain. Our framework is based on a hierarchical modeling composed of two levels. The lower level is dedicated to the modeling of each node, while the upper level matches the actual topology of the chain. Our approach can handle different topologies, takes into account Bit Error Rate and can be applied to multi-hop flows with rates ranging from light to heavy workloads. We assess the ability of our model to evaluate loss rate, throughput, and end-to-end delay experienced by flows on a simple scenario, where the number of nodes is limited to three. Numerical results show that our model accurately approximates the performance of flows with a relative error typically less than 10%.