Schistosomiasis remains a significant public health challenge in Africa, where it is considered endemic, particularly in areas with inadequate access to clean water. This study focuses on mitigating the spread of schistosomiasis by monitoring the physicochemical parameters of water sources to detect the proliferation of infected snails, which serve as the intermediate host for the parasite. We propose a system that leverages sensing technology to monitor water physicochemical parameters, e.g., temperature (Temp), pH, and electrical conductivity (EC) and uses AI to predict their future values, alongside the dynamics of the infected snail population, which are crucial for schistosomiasis transmission. By forecasting these trends, the system facilitates proactive interventions, such as water treatment and sanitation improvements. To accomplish this, we leverage FNet, a model that effectively captures temporal features through discrete Fourier transforms (DFT), delivering Transformer-level accuracy while being more resource-efficient. While FNet is originally tailored for language processing tasks, we adapt it for time series analysis by employing a quantization technique. This approach is particularly advantageous in contexts where computational and operational efficiency is critical, making it well-suited for environments with limited infrastructure or financial flexibility. Our evaluation demonstrates that the FNet model outperforms transformers in terms of resource efficiency while maintaining similar performance, making it a promising tool for real-time monitoring and early detection of schistosomiasis risk. The results highlight the potential of this approach to enhance disease control efforts and contribute to improved public health outcomes in endemic regions.

The dairy industry holds an important part of the food sector of Senegal. Nevertheless, milk collection is a difficult logistical problem that has long been of interest to researchers. In addition, some dairies notably those in Senegal's sylvo-pastoral zone (Ferlo), encounter many problems related to the impossibility of communicating with their surroundings because they are in white areas ( i.e areas where the services of telecommunications operators are not available). This affects their efficiency in the daily collection of milk from the production unit to the milk processing unit. In this paper, thanks to the ramp-up of LPWANs, especially Long Range(LoRa) technology, we build a communication system that facilitates the collection, manipulation and presentation of data related to milk collection in these areas. The system enables the creation of a system that helps in the decision making to support logistical management in the milk collection sector.

Reducing the energy consumed in cloud computing is becoming one of the most challenging research directions due to the overwhelming growth of services that are hosted and delivered by cloud computing. Indeed, the energy consumed by data transport represents a significant percentage according to the overall consumption of the cloud. Hence, by exploiting network and router virtualization technologies, we firstly propose a Green Cloud Architecture (GCA), where we can either shut down, or make in sleeping mode virtual routers; or migrate virtual routers towards another physical router according to energy-awareness. Secondly, we evaluate our green cloud architecture by proposing an energy-aware resource allocation algorithm. The mapping algorithm is evaluated through simulations and our green architecture significantly reduces the power consumption during data transport by up to 41%.

The main goal of Cluster-based sensor networks is to decrease system delay and reduce energy consumption. LEACH is a cluster-based protocol for micro sensor networks which achieves energy-efficient, scalable routing and fair media access for sensor nodes. However, the election of a malicious or compromised sensor node as the cluster head is one the most significant breaches in cluster-based wireless sensor networks. We propose a deterministic key management scheme, called DKS-LEACH, to secure LEACH protocol against malicious attacks. Our contributions are twofold. Firstly, we design and performed a theoretical evaluation of our security model which secures the setup and study phases of LEACH protocol. Secondly, using the TOSSIM simulator, we performed an evaluation of the power consumption of DKS-LEACH. The results indicate clear advantages of our approach in preventing the election of untrustworthy cluster head as well different kind of attacks from malicious sensor nodes.

This paper focuses on fault tolerance of super-nodes in P2P-SIP systems. The large-scale environments such as P2P-SIP networks are characterized by high volatility (i.e. a high frequency of failures of super-nodes). Most fault-tolerant proposed solutions are only for physical defects. They do not take into account the timing faults that are very important for multimedia applications such as telephony. We propose HP2P-SIP which is a timing and physical fault tolerant approach based on a hierarchical approach for P2P-SIP systems. Using the Oversim simulator, we demonstrate the feasibility and the efficiency of HP2P-SIP. The obtained results show that our proposition reduces significantly the localization time of nodes, and increases the probability to find the called nodes. This optimization allows to improve the efficiency of applications that have a strong time constraints such as VoIP systems in dynamic P2P networks.

We address the problem of finding indirect overlay paths that reduce the latency between pairs of nodes in an overlay. To this end we propose to rely on an Internet Coordinate System (ICS), namely Vivaldi, to estimate RTTs and help find these interesting detours. We define two initial criteria to illustrate our approach and assess their true/false positive rates.

Geolocation of Internet hosts enables a diverse and interesting new class of location-aware applications. Previous measurement-based approaches use reference hosts, called landmarks, with a well-known geographic location to provide the location estimation of a target host. This leads to a discrete space of answers, limiting the number of possible location estimates to the number of adopted landmarks. In contrast, we propose Constraint-Based Geolocation (CBG), which infers the geographic location of Internet hosts using multilateration with distance constraints, thus establishing a continuous space of answers instead of a discrete one. CBG accurately transforms delay measurements to geographic distance constraints, and then uses multilateration to infer the geolocation of the target host. Our experimental results show that CBG outperforms the previous measurement-based geolocation techniques. Moreover, in contrast to previous approaches, our method is able to assign a confidence region to each given location estimate. This allows a location-aware application to assess whether the location estimate is sufficiently accurate for its needs.