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Reasearch Interests | Back home |
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Leveraging IoT and Edge-AI for Detecting and Managing Schistosomiasis Project | Back home |
This project, funded by AFRIREC (2024-2025), proposes developing a prototype sensing device and an AI-based decision support system for
detecting and managing Schistosomiasis. The project will include sensing environment conditions for Schistosomiasis
parasites and information from health centers and traditional data collection methods and fusing these with different
machine learning models based on healthcare experts’ knowledge to detect favorable conditions within water points.
The far reaching goals of this project include reducing the effect of Schistosomiasis on the African economy,
training engineers in the important areas of IoT and machine learning, creating new knowledge that can be
commercialized in an area unique to the continent, among others.
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Smart agriculture, health, and livestock in developing countries in West Africa Project | Back home |
The Smart agriculture, health, and livestock in developing countries in West Africa, NICT Program of FY2023
International Collaboration and Deployment Fund project (2023-2024) proposes an intelligent water point management
solutions for precision agriculture and livestock farming based on technologies such as IoT, Edge computing as well
as NerveNet architecture. |
SAFeComNet Project | Back home |
The SAFeComNet (Safer Artisanal Fishing intEgrating a COMmunity NETwork) project (2021-2024) funded by ESP aims
to provide a network communication architecture.In Senegal, the fishery remains small-scale/artisanal
with more than 19,000 dugout canoes of different sizes. 600,000 Senegalese work in this sector with 400,000 fishermen.
Due to climate change and the European Union fisheries agreement with Senegal, fish are now far away from the coast
and thus fishermen should cross more distances, without cellular network connectivity, for fishing opportunities.
Therefore, a lot of marine accidents occur with roughly 100 deaths per year. Expected results of this project are:
develop and deploy a system that will directly be used by end-users in the fishermen community in Senegal; deploy a
radio network and computing infrastructure for fishermen, and other stakeholders, covering previously
unconnected regions through the use of TV white space and LoRa technologies. |
FerloNet Project | Back home |
The FerloNet project (2021-2023) funded by DRI/UCAD aims to sense, collect and disseminate information related to pastoral and other activities in Senegal.
In fact, pastoralism has an important socioeconomic role in Senegal, and in particular in the Ferlo region. Therefore, it is mandatory to support herders
in their efforts to adapt to climate change. As consequence, FerloNet proposes a holistic approach that addresses opportunistic delay-tolerant networks,
Internet of Things, and device-to-device communication for an efficient information delivery. Furthermore, it considers opportunistic networks which
include mobile nodes and enables communication even when no permanent route between source and destination node is available.
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COWShED: COmmunication within White Spots for brEeDers Project | Back home |
The COWShED projet (2017-2019) funded by ISOC proposes a geolocalization communication system based on long range radio frequency transmission
within white spots. In fact, the livestock transhumance in Senegal is done in several areas and more specifically in the sylvo-pastorale areas located in the Ferlo’s region where it is difficult or impossible to communicate with terrestrial communication systems. The main reason is due to the existence of white spots. A white spot is defined as a no cellular coverage area.
To overcome the presence of white spot satellite communications can be used. However, in underserved areas, satellite communications are very expensive for people.
Therefore, we propose a low-cost communication for rural population such as breeders, farmers based on long range radio
communication within ISM (Industrial, Science and Medical) bands. It is worth noticing that the ISM band is free.
The proposed autonomous mobile mesh network is developed by breeders to lead livestock's, in a safe manner, where
grass and water points are available. Our main goal is to encourage activities that can generate financial returns
for rural population by preserving the environment.
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Sensor-based Bilharzia Detection Project | Back home |
Sensor-based Bilharzia Detection project (2014-2016) funded by the IDRC aims to use Under Water Wireless Sensors Network (UWSN) in order to monitor water points. In fact, schistosomiasis (bilharzia), one of the most relevant parasitoses of humans, is confirmed by microscopic detection of eggs in stool, urine, or organ biopsies after the infection of patient.The proposed Sensor-based epidemiology uses data collected by sensors networks, that are deployed in water points, in order to develop more-sensitive disease-prediction and control-model. This tool may provide a potentially vital capability for use by disease control program managers, particularly in less-developed countries.
Sensor-based Bilharzia Detection detects contaminated water source, and thus enables to take proactive decisions
such as prohibit the insfected area, or prevent the miracidium to infect the mollusc or the parasitic larvae to enter through the
skin of humans. The main goal is to stop the life cycle of the bilharzia.
UQAM university is involved in this project. |
ANA Project | Back home |
The FP6-IST-27489 ANA (Autonomic Network Architecture) project (2006-2010) aims at exploring novel ways of organizing and using networks beyond legacy Internet technology. The ultimate goal is to design and develop a novel autonomic network architecture that enables flexible, dynamic, and fully autonomous formation of network nodes as well as whole networks. Universities and research institutes from Europe and Northern America are participating in this project. |
GeoLIM Project | Back home |
GeoLIM project (2004-2006) aims at providing the geographic location of an Internet host using solely its IP address. The key element of GeoLIM is its ability to transform delay measurements between landmarks (probe machines) and a target host, into geographic distance constraints. It uses multilateration -alike GPS- to estimate the geographic location of the target host. The code for GeoLim is here as a collection of routines in R (you can get R itself here.) The test is carried out on PlanetLab. |