1,720,983 research outputs found
Leaving No One Behind:An Agenda for Voice-based Engagement of Remote Communities
Get PDFVoice-based digital platforms are increasingly recognized as powerful tools for fostering community participation and bridging digital divide. These platforms offer unique opportunities for engaging distributed communities, especially those with limited digital literacy and internet access. Despite their potential, the comprehensive impact of voice-based platforms is constrained by challenges concerning design inclusivity, sustainability, and stakeholder empowerment. This systematic review critically examines existing voice-based platforms, identifying key challenges and opportunities for enhancing their effectiveness. Our review reveals that innovative participation archetypes, when supported by inclusive design and robust policy frameworks, can significantly enhance community engagement. Additionally, sustainable business models and stakeholder empowerment are crucial for the long-term viability of these platforms. This study provides actionable insights and a research framework for ICTD researchers and practitioners, emphasizing the need for inclusive, sustainable, and contextually relevant design and development of voice-based solutions to drive sustainable social change and promote digital equity.</p
Open Access4D: Battle not won
No full textUnpublished PresentationThe trend is still: “transferring of Northern designs to Southern realities”
While 41% of the world’s household have access to the Internet, Africa is lagging far behind at 9%.
Africa has abysmal penetration rate for landline telephone, the number of fixed-broadband subscriptions Internet has increased the digital divide.... Africa is slow to take up technological innovation as most have to be imported from elsewhere..” Liam (2009
Artificial Neural Networks Models for Predicting Effective Drought Index: Factoring Effects of Rainfall Variability
No full textPublished ArticleThough most factors that trigger droughts cannot be prevented, accurate, relevant and timely forecasts can be used to mitigate their impacts. Drought forecasts must define the droughts severity, onset, cessation, duration and spatial distribution. Given the high probability of droughts occurrence in Kenya, her heavy reliance on rain-fed agriculture and lack of effective drought mitigation strategies, the country is highly vulnerable to impacts of droughts. Current drought forecasting approaches used in Kenya are not able to provide short and long term forecasts and they fall short of providing the severity of the drought. In this paper, a combination of Artificial Neural Networks and Effective Drought Index is presented as a potential candidate for addressing these drawbacks. This is demonstrated using forecasting models that were built using weather data for thirty years for four weather stations (representing 3 agro-ecological zones) in Kenya. Experiments varying various input/output combinations were carried out and drought forecasting network models were implemented in Matrix Laboratory's (MATLAB) Neural Network Toolbox. The models incorporate forecasted rainfall values in order to mitigate for unexpected extreme climate variations. With accuracies as high as 98 %, the solution is a great enhancement to the solutions currently in use in Kenya
ITIKI: bridge between African indigenous knowledge and modern science of drought prediction,
Get PDFPublished ArticleDroughts are the most common type of natural disaster in Africa and the problem is
compounded by their complexity. The agriculture sector still forms the backbone of
most economies in Africa, with 70% of output being derived from rain-fed smallscale
farming; this sector is the first casualty of droughts. Accurate, timely and relevant
drought predication information enables a community to anticipate and prepare
for droughts and hence minimize the negative impacts. Current weather forecasts are
still alien to African farmers, most of whom live in rural areas and struggle with illiteracy
and poor communications infrastructure. However, these farmers hold indigenous
knowledge not only on how to predict droughts, but also on unique coping strategies.
Adoption of wireless sensor networks and mobile phones to provide a bridge
between scientific and indigenous knowledge of weather forecasting methods is one
way of ensuring that the content of forecasts and the dissemination formats meet local
needs. A framework for achieving this integration is presented in this paper. A system
prototype to implement this framework is also presented
Investigating the adoption of indigenous knowledge in mitigating climate-linked challenges: a case study of Vhembe District in South Africa
Get PDFIndigenous knowledge (IK) plays a crucial role in rural African communities by contributing to the development of mitigation and adaptation strategies, enhancing resilience to climate-related challenges. However, there is limited documentation of its application in South Africa. This study investigates the adoption of IK in the Vhembe district, focusing on general use, disease prediction and farming practices. Results indicate that the respondents possessed a rich reservoir of IK, with 76.1% affirming its use for different purposes. Most respondents (75.3%) use IK for farming, with 63.1% using it for disease prediction. Most participants (74.3%) reported adequate confidence levels in their use of IK for general purposes. Respondents who had stayed longer in Vhembe reported higher confidence levels in using IK, as did those above 66 years old in using this knowledge system for disease prediction. The use of IK indicators for early warning of malaria outbreaks was also documented. Investigating and documenting IK use in communities could inform the basis for preservation and hence, enhance IK recognition. The integration of IK in the development of early warning systems may enhance their relevance and effectiveness in combating the effects of climate change and infectious diseases
Framework for Predicting Droughts in Developing Countries Using Sensor Networks and Mobile Phones
Get PDFDrought is the most complex and least understood of all natural disasters and it affects more people than any other hazard. Droughts have become synonymous with the developing countries and in particular the Sub-Saharan Africa where the hazard is chronic. Effects of droughts can be mitigated if accurate and timely drought predications were to be done. Unfortunately, despite the enormous advancements in science, predictions only provide indications of trends. A major weakness of the existing tools is the emphasis on macro/international level information. The tools also tend to ignore the at risk community who happen to be host to very crucial traditional knowledge on droughts. In this paper, we propose an integrated drought predication framework that considers both scientific and traditional knowledge and combines the use of mobile phones with wireless sensor networks to be able to capture and relay micro drought parameters. The framework is an enhancement of ITU’s Ubiquitous Sensor Network (USN) Layers. In order to accommodate the diverse roles mobile phones play in our framework, Layer 2 (USN Access Networking) is implemented using three sub-layers composed of heterogeneous gateways
IKON-OWL: Using Ontologies for Knowledge Representation of Local Indigenous Knowledge on Drought
Get PDFSemantic modeling and integration of local indigenous knowledge have become fundamental to improving the degree of accuracy of drought forecasting systems due to the variability of currently used environmental parameters. This research aims to acquire, organize and model natural indicators, behavior and ecological interactions of local indigenous knowledge with a focus on drought forecasting. The data are gathered using qualitative interpretative methodology from the interviews with local farmers and indigenous knowledge expert focus groups. The knowledge is formalised into semantic structure using an ontology for machine readability, reusability, integration, and interoperability with heterogeneous intelligent systems. In this paper, we present the design and development of a domain ontology for the indigenous knowledge on drought. The ontological model is an integral component of the research framework towards the development of semantics-based data integration middleware for local indigenous knowledge and modern knowledge on drought. This research contributes to modeling South African indigenous knowledge on drought into ontological models for use in drought forecasting systems, decision support systems, and expert systems
Ubiquitous Traffic Management with Fuzzy Logic - Case Study of Maseru, Lesotho
Get PDFConference ProceedingsMaseru is the capital city of Lesotho and is a relatively small city with roughly
67 vehicles registered each day. Traffic lights are used with the intension of effectively
managing vehicular traffic at junctions. These traffic lights follow a predetermined
sequence usually based on historic data. As a result of this design, they inherently fail to
efficaciously manage traffic flow when it is abnormal. Vehicles on one side have to wait
even though there are no cars on other sides of the road. The consequences of this include
increased congestion and atmospheric air pollution. Technological advancements have
resulted in the now widely researched Internet of Things paradigm with one of its
applications being vehicular traffic management. The focus of this paper is the design of a
prototype reactive system based on Internet of Things whose functionality includes traffic
lights that are capable of reacting to prevailing conditions. The system makes use of Radio
Frequency IDentifier technology and mobile tools to ubiquitously collect traffic data and
disseminate value added traffic information
A Security Algorithm for Wireless Sensor Networks in the Internet of Things Paradigm
Get PDFConference ProceedingsIn this paper we explore the possibilities of having an algorithm that can
protect Zigbee wireless sensor networks from intrusion; this is done from the Internet
of Things paradigm. This algorithm is then realised as part of an intrusion detection
system for Zigbee sensors used in wireless networks. The paper describes the
algorithm used, the programming process, and the architecture of the system
developed as well as the results achieved
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