INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)  
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025  
Terrestrial Biodiversity Data Processing and Sharing Architectural  
Model Based on IoT Technology for Sustainable Livelihoods: A  
Conceptual Review  
1Jeremiah Osida Onunga, 2Anselemo Ikoha Peters, 3Peter Edome Akwee  
1Lecturer/Research Fellow, Department of Renewable Energy and Technology, Turkana University  
College  
2Senior Lecturer, Department of Information Technology, Kibabii University  
3Professor, Department of Biological and Physical sciences, Turkana University College  
DOI: https://dx.doi.org/10.51244/IJRSI.2025.1210000270  
Received: 02 November 2025; Accepted: 08 November 2025; Published: 18 November 2025  
ABSTRACT  
This conceptual review examines the transformative potential of the Internet of Things (IoT) in processing,  
analyzing, and sharing terrestrial biodiversity data (TBD) to advance sustainable livelihoods in rural and arid  
environments. The review explores how IoT-based architectures can facilitate real-time data collection,  
integration, and dissemination to strengthen environmental monitoring, biodiversity management, and  
community decision-making. By linking technological innovation with ecosystem stewardship, IoT emerges as  
a key enabler for bridging the digital divide in biodiversity informatics and empowering marginalized  
populations to engage in adaptive and sustainable practices. The paper discusses how IoT systems; sensors,  
wireless networks, cloud computing, and mobile interfaces enable the acquisition and analysis of critical  
environmental parameters such as vegetation dynamics, soil moisture, and wildlife distribution. Through these  
systems, biodiversity data becomes a dynamic resource for guiding natural resource use, predicting  
environmental risks, and improving livelihood strategies. The integration of IoT technologies enhances  
transparency, accessibility, and the scalability of biodiversity information flows across institutional and  
community levels. Consequently, IoT-enabled data ecosystems contribute not only to the preservation of  
biodiversity but also to improved food security, income diversification, and resilience to climate shocks. Despite  
these advantages, challenges persist, including limited interoperability of IoT systems, concerns over data quality  
and privacy, inadequate infrastructure, and low digital literacy among rural users. Addressing these barriers  
requires coherent policy interventions, investment in IoT infrastructure, and inclusive capacity-building  
programs. The paper determines that integrating IoT-driven biodiversity architectures with sustainable  
development frameworks presents a viable pathway toward ecological resilience and socio-economic  
transformation. In operationalizing the intersection of technology, data, and livelihoods, IoT offers an innovative  
model for sustainable coexistence between people and nature. The paper concludes with policy implications,  
research gaps, and prospects for advancing IoT-driven biodiversity data ecosystems in Kenya and beyond.  
Keywords: Internet of Things (IoT), Terrestrial Biodiversity Data, Sustainable Livelihoods, Data Sharing  
Architecture, Rural Development.  
INTRODUCTION  
Biodiversity remains one of the most vital foundations of ecological balance, economic stability, and human  
survival. It encompasses the variety of living organisms, their genetic diversity, and the complex ecosystems  
they form (Díaz et al., 2020). Globally, terrestrial biodiversity provides essential ecosystem services such as  
nutrient cycling, soil fertility, water purification, carbon sequestration, and pollination. These ecological  
processes sustain agriculture, industry, health, and social well-being. However, despite decades of conservation  
initiatives, the world continues to witness unprecedented biodiversity loss due to deforestation, habitat  
degradation, pollution, and climate variability (Brooks et al., 2002). The consequences of this loss extend far  
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beyond environmental degradation, undermining food security, health outcomes, and economic opportunities,  
particularly for communities whose livelihoods depend directly on natural resources (Barrett et al., 2001).  
In Kenya, biodiversity is central to the country’s ecological integrity and socio-economic development. The  
nation is recognized as one of the world’s biodiversity-rich regions, hosting numerous species across ecosystems  
ranging from forests and wetlands to arid rangelands (UNDP, 2016). Yet, rapid land-use changes,  
overexploitation of natural resources, and human-induced pressures continue to erode terrestrial ecosystems  
(Turkana County Government, 2018–2022). The resulting biodiversity decline undermines ecological resilience  
and directly threatens the livelihoods of communities inhabiting arid and semi-arid lands (ASALs). Turkana  
County, located in northwestern Kenya, epitomizes these challenges. Frequent droughts, land degradation, and  
desertification have exacerbated resource scarcity, heightened vulnerability, and reduced adaptive capacity  
among pastoral populations.  
The effective management and utilization of terrestrial biodiversity data have become essential for reversing  
these trends. Biodiversity data encompassing information on species distribution, abundance, and environmental  
conditions provides the evidence base for ecosystem monitoring, conservation planning, and adaptive livelihood  
interventions (Díaz et al., 2020). However, current biodiversity data systems in Kenya are fragmented, poorly  
coordinated, and largely inaccessible to decision-makers and communities who need them most (Adera et al.,  
2014). Issues such as data silos, lack of interoperability, and limited real-time accessibility restrict the integration  
of scientific knowledge into local decision-making processes. Consequently, opportunities for early warning,  
resource optimization, and sustainable management are often missed.  
The emergence of the Internet of Things (IoT) presents a transformative opportunity to overcome these  
challenges. IoT technologies integrate sensors, devices, and communication networks to collect, transmit, and  
analyze environmental data in real time (Chiara, 2021). Wireless sensor networks (WSN), mobile applications,  
and cloud computing platforms enable continuous monitoring of variables such as temperature, soil moisture,  
vegetation cover, and wildlife movement (Aggrey, 2021). These technologies provide a cost-effective means to  
bridge data gaps, enhance accuracy, and ensure timely biodiversity information flows. In arid ecosystems like  
Turkana, where field-based monitoring is constrained by terrain and resources, IoT-based solutions offer new  
pathways for real-time environmental intelligence.  
Despite these advantages, the deployment of IoT technologies in biodiversity management remains limited in  
many developing countries. Structural barriers such as poor connectivity, high technology costs, and inadequate  
human capacity constrain IoT adoption (Waema & Okinda, 2011). In addition, institutional fragmentation and  
lack of harmonized data governance frameworks impede interoperability and collaborative data sharing (Ospina  
& Heeks, 2012). These constraints underscore the need for integrated architectural models that can harmonize  
IoT data flows across multiple stakeholders, enabling seamless data acquisition, processing, and dissemination  
for biodiversity conservation and sustainable livelihood support.  
An IoT-based architectural framework for terrestrial biodiversity data processing and sharing offers a structured  
solution to these constraints. Such a framework typically includes layered subsystems sensing, networking,  
processing, and application layers that work synergistically to support scalable, secure, and interoperable data  
management (Chiara, 2021). These layers facilitate communication between data sources and end users, allowing  
for real-time analysis and decision support. When integrated effectively, IoT architectures can connect local  
knowledge systems with global data infrastructures, enabling communities to respond adaptively to  
environmental change and resource stress.  
Beyond its technical applications, IoT-based biodiversity systems also have significant socio-economic  
implications. They can strengthen participatory environmental governance by involving communities in data  
collection, validation, and use. Access to near-real-time biodiversity information enhances local decision-  
making, enabling households to plan grazing routes, identify safe water sources, and anticipate climate-induced  
hazards (Ospina & Heeks, 2012). In doing so, IoT contributes to building social capital, improving food security,  
and fostering economic resilience core attributes of sustainable livelihoods (UNDP, 2016). Through such  
integration, biodiversity conservation becomes a driver of both ecological sustainability and human  
development.  
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This conceptual review therefore examines the role of IoT technologies in processing and sharing terrestrial  
biodiversity data for sustainable livelihoods. It synthesizes theoretical perspectives, architectural components,  
and empirical insights to illustrate how IoT-enabled biodiversity systems can enhance environmental monitoring,  
data accessibility, and socio-economic resilience. The review locates this discourse within the broader  
frameworks of sustainable development, digital transformation, and environmental governance, emphasizing the  
need for integrative models that connect technology, ecosystems, and human well-being. The paper provides a  
conceptual foundation for understanding how IoT-driven biodiversity data architectures can transform  
conservation practices and promote sustainable livelihoods in resource-constrained settings.  
Comparative Global Perspectives on IoT Biodiversity Models  
Globally, the integration of IoT technologies into biodiversity monitoring has gained momentum, offering  
valuable lessons for regions seeking to enhance environmental data systems and conservation decision-making.  
In India, the Wildlife Surveillance IoT Network uses RFID sensors and drone technologies to monitor tiger  
movements and habitat conditions in protected reserves, improving real-time wildlife tracking and conservation  
planning (Kumar et al., 2020). Similarly, Brazil’s Amazon IoT Watch initiative combines satellite and ground-  
based sensors to detect illegal logging and assess forest canopy health, providing timely data for enforcement  
and restoration interventions (Silva & dos Santos, 2021). These global models demonstrate how IoT can bridge  
critical biodiversity data gaps, improve the accuracy of ecological assessments, and support proactive  
environmental management.  
In Africa, South Africa’s Smart Savannahs initiative provides a relevant regional example of IoT integration in  
biodiversity monitoring. The program employs connected sensors, mobile applications, and community-driven  
data collection platforms to track wildlife migration and prevent poaching (Moyo et al., 2022). It also  
underscores the role of collaboration among local communities, researchers, and conservation authorities in  
ensuring inclusive and sustainable technological adoption. Collectively, these international and regional cases  
illustrate the potential for Kenya especially in arid regions such as Turkana to adapt scalable IoT-based  
biodiversity architectures that blend technology, indigenous knowledge, and policy frameworks to strengthen  
environmental governance and sustainable livelihoods.  
Theoretical Foundations  
The conceptual review of terrestrial biodiversity data processing and sharing is grounded on four interrelated  
theories that collectively explain the interaction between technology, information, and sustainable livelihoods.  
These theories are Innovation Diffusion Theory (IDT), Technology Adoption Theory (TAT), the Information  
Needs Assessment Model (INAM), and the Sustainable Livelihood Framework (SLF). Each theory contributes  
a distinct but complementary perspective on how IoT technologies can be integrated into biodiversity data  
systems to enhance accessibility, usability, and impact. Together, they form the theoretical foundation for  
understanding the mechanisms through which IoT-based architectures influence biodiversity information flow  
and sustainable livelihood outcomes.  
The Innovation Diffusion Theory (Rogers, 2003) explains how innovations such as IoT-based biodiversity  
systems are communicated through social networks over time and adopted by individuals or institutions.  
According to this theory, adoption depends on factors such as perceived relative advantage, compatibility,  
complexity, trial-ability, and observability of the innovation. Within the framework of biodiversity management,  
communities in arid and semi-arid regions are more likely to adopt IoT-enabled systems if they perceive them  
as useful, easy to use, and aligned with existing cultural and livelihood practices. The theory highlights the  
importance of social influence, communication channels, and institutional support in accelerating the diffusion  
of biodiversity technologies among users in low-resource settings (Adera et al., 2014).  
The Technology Adoption Theory, often referred to as the Technology Acceptance Model (Davis, 1989),  
complements IDT by focusing on individual attitudes and behavioral intentions toward technology use. The  
theory posits that perceived usefulness and perceived ease of use are primary determinants of technology  
acceptance. In biodiversity data management, this translates to the willingness of stakeholders such as local  
communities, conservation officers, and researchers to utilize IoT systems if they find them functionally relevant  
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and easy to operate. Simplified interfaces, localized language options, and low-cost IoT devices can therefore  
increase adoption rates. Empirical findings from biodiversity informatics research affirm that ease of access,  
affordability, and technical support significantly influence user engagement and long-term system sustainability  
(Waema & Okinda, 2011).  
The Information Needs Assessment Model (INAM) provides a framework for identifying, analyzing, and  
matching biodiversity information supply with user needs. The model emphasizes that the effectiveness of any  
information system depends on its responsiveness to user-specific contexts and decision-making requirements  
(Ospina & Heeks, 2012). In the case of IoT-based biodiversity data systems, INAM ensures that collected  
environmental data is relevant, timely, and formatted in a way that supports resource management decisions. It  
underscores the need for participatory information design—where users are actively involved in defining what  
data should be collected, how it should be processed, and how it can be delivered for maximum utility. This  
approach aligns closely with principles of inclusive knowledge systems and co-production of environmental  
information.  
Finally, the Sustainable Livelihood Framework (SLF) provides the overarching perspective that links technology  
adoption to livelihood outcomes. The framework, developed by the UK Department for International  
Development (DFID, 2000), conceptualizes livelihoods as a function of access to five key assets: natural, human,  
social, financial, and physical capital. The SLF posits that enhancing access to biodiversity data through IoT  
technologies can strengthen these assets by improving resource management, knowledge exchange, and income  
diversification (UNDP, 2016). For example, accurate biodiversity information can inform grazing strategies,  
crop selection, and water conservation practices thereby reducing vulnerability and fostering resilience in fragile  
ecosystems such as Turkana County.  
In synthesis, these four theories collectively explain the socio-technical dynamics underpinning IoT-based  
biodiversity architectures. Innovation Diffusion and Technology Adoption theories expound the behavioral and  
social processes that drive technological uptake, while the Information Needs Assessment Model ensures that  
biodiversity data systems are user-centered and relevant. The Sustainable Livelihood Framework then connects  
these technological and informational processes to tangible livelihood outcomes. This theoretical integration  
provides a holistic foundation for understanding how IoT technologies can transform biodiversity data  
management into a catalyst for sustainable development and environmental resilience in rural and semiarid  
regions.  
Conceptual Framework of the Terrestrial Biodiversity Data Architectural Model (TBDAM)  
The conceptual framework of the Terrestrial Biodiversity Data Architectural Model (TBDAM) presents an  
integrated structure for understanding how IoT technologies can be applied to enhance the collection, processing,  
and sharing of biodiversity data to support sustainable livelihoods. The framework is built upon the premise that  
biodiversity data, when efficiently captured and communicated, can significantly improve decision-making at  
community, institutional, and policy levels. It establishes the relationships among three core dimensions; access  
to IoT technologies, utilization of biodiversity data, and livelihood outcomes which are mediated through  
multiple technological and social subsystems. These relationships form a dynamic and iterative process in which  
data flows continuously between IoT devices, information systems, and users, creating feedback loops that  
strengthen adaptive capacity and resilience (Chiara, 2021).  
At its foundation, the TBDAM integrates four functional layers: the data collection layer, the processing layer,  
the communication layer, and the application or user interface layer. The data collection layer comprises sensing  
devices such as wireless sensor networks (WSN), radio frequency identification (RFID) tags, and LoRa-enabled  
devices that gather environmental data including soil moisture, vegetation density, and temperature variations  
(Aggrey, 2021). The processing layer employs cloud computing, middleware, and analytic algorithms to store,  
aggregate, and interpret data from diverse sources, ensuring reliability and standardization. The communication  
layer facilitates data transmission across devices and systems using protocols such as HTTP and MQTT, while  
the application layer provides interfaces such as mobile dashboards and radio broadcasts that enable end-users  
to access and interpret biodiversity information. Together, these layers form a modular, scalable, and  
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interoperable architecture designed to address the challenges of fragmented data systems and limited  
accessibility in remote environments.  
The conceptual framework further recognizes that technological access alone does not guarantee effective  
utilization of biodiversity data. Therefore, the model emphasizes human, institutional, and socio-economic  
enablers as integral to the functioning of the system. Factors such as digital literacy, affordability, trust in  
technology, and institutional collaboration influence how users adopt and apply IoT-generated data (Waema &  
Okinda, 2011). Community participation is particularly critical, users must perceive biodiversity data as relevant  
and actionable within their local context (Ospina & Heeks, 2012). In this sense, the TBDAM is both a  
technological and social construct, aligning with the principles of participatory information design and inclusive  
innovation. It provides not only the technical infrastructure for data sharing but also the socio-organizational  
mechanisms that facilitate knowledge exchange, feedback, and collective action.  
The framework ultimately connects IoT-enabled biodiversity data systems to the sustainable livelihood  
outcomes envisioned under the Sustainable Livelihood Framework (SLF). Through improving data accessibility  
and utilization, the model enhances the five livelihood assets; natural, human, social, physical, and financial  
capital thereby promoting resilience and adaptive capacity (UNDP, 2016). For instance, accurate real-time  
biodiversity information supports better grazing management, crop selection, and water resource allocation,  
reducing vulnerability to drought and land degradation. In essence, the TBDAM conceptual framework provides  
a holistic understanding of how IoT technologies can operationalize biodiversity informatics to foster sustainable  
environmental governance and socio-economic transformation in data-scarce regions like Turkana County.  
Figure 1: Terrestrial Biodiversity and Architectural Model  
IoT and Terrestrial Biodiversity Data Processing  
The Internet of Things (IoT) has emerged as a pivotal technology in transforming biodiversity monitoring, data  
processing, and decision-making systems. In the context of terrestrial biodiversity management, IoT provides  
the infrastructure for automated data collection and real-time analysis of environmental parameters such as soil  
moisture, temperature, vegetation cover, and species distribution (Chiara, 2021). These technologies allow for  
continuous, spatially distributed monitoring that far exceeds the limitations of traditional, manual methods.  
Through the integration of sensors, communication networks, and cloud-based processing platforms, IoT  
facilitates the generation of dynamic biodiversity data that can be accessed and utilized by multiple stakeholders,  
including policymakers, researchers, and local communities (Aggrey, 2021). This interconnectivity enhances  
data accuracy, timeliness, and relevance, providing the foundation for evidence-based conservation and  
sustainable resource management.  
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Central to IoT-based biodiversity data processing is the interaction between wireless sensor networks (WSN),  
radio frequency identification (RFID), and LoRa-enabled devices that capture field-level environmental data.  
These sensors act as the “nervous system” of the ecosystem, continuously transmitting data through low-power,  
wide-area communication protocols to cloud computing platforms for analysis and storage (Díaz et al., 2020).  
The processing layer of the Terrestrial Biodiversity Data Architectural Model (TBDAM) utilizes these inputs to  
generate analytical insights through data aggregation, filtering, and visualization techniques. Cloud computing  
and middleware tools enable the seamless integration of heterogeneous datasets, ensuring that biodiversity data  
from multiple locations and devices are standardized and interoperable. This enhances system scalability and  
allows for the inclusion of additional devices or data sources without major redesigns of the architecture (Chiara,  
2021).  
The communication protocols underpinning the TBDAM; Hypertext Transfer Protocol (HTTP) and Message  
Queuing Telemetry Transport (MQTT) play a crucial role in ensuring reliable, low-latency data transmission  
between IoT devices and end users (Aggrey, 2021). These protocols facilitate both request–response and  
publish–subscribe data exchanges, enabling real-time interaction between biodiversity databases, mobile  
applications, and user dashboards. The MQTT protocol, optimized for constrained networks, supports efficient  
transmission in areas with limited bandwidth, such as Turkana’s remote landscapes. Together, these  
communication mechanisms guarantee that biodiversity data can be accessed instantly by decision-makers and  
community members, fostering proactive management of environmental resources. The result is a seamless  
integration of technical processes that support efficient data flow from sensors to decision-support systems.  
Beyond the technological infrastructure, IoT-based biodiversity data processing has significant implications for  
community empowerment and sustainable development. By enabling real-time access to environmental  
information, IoT technologies strengthen local capacities for adaptation, disaster preparedness, and natural  
resource planning (Ospina & Heeks, 2012). Pastoral communities, for instance, can use biodiversity data to track  
vegetation patterns and adjust grazing routes, while local governments can employ data analytics for drought  
forecasting and resource allocation (UNDP, 2016). Such applications demonstrate that IoT does more than  
collect data, it transforms information into actionable knowledge that supports sustainable livelihoods. The  
integration of IoT into biodiversity management thus represents not only a technological innovation but also a  
paradigm shift toward inclusive, data-driven environmental governance.  
Figure II: IoT and Terrestrial Biodiversity and Sharing Architecture  
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METHODOLOGY  
This conceptual review adopted a mixed-methods orientation that integrated both qualitative and quantitative  
perspectives to explore the application of IoT in terrestrial biodiversity data processing and sharing. The  
approach combined conceptual synthesis with contextual insights to ensure that the resulting framework is both  
theoretically robust and practically grounded. A systematic desk review was conducted to identify, select, and  
analyze scholarly works, policy documents, and institutional reports relevant to IoT-enabled biodiversity  
systems. The review emphasized literature that links digital innovation with biodiversity conservation,  
sustainable livelihoods, and environmental governance, particularly within arid and semi-arid regions.  
Complementary empirical insights were drawn from Turkana County, where secondary data and documented  
experiences of IoT utilization in environmental monitoring provided contextual grounding. Sources included the  
Turkana County Integrated Development Plans (CIDPs), reports from the Kenya National Environment  
Management Authority (NEMA), and other development partners engaged in natural resource management.  
Analytical rigor was achieved through content analysis, thematic synthesis, and cross-comparison of findings  
from both global and local studies. Quantitative evidence was used descriptively to illustrate trends and  
relationships, while qualitative perspectives enriched the interpretive understanding of socio-technical dynamics  
and community engagement.  
Overall, this blended methodological framework strengthens the transparency, reliability, and relevance of the  
conceptual review. It not only provides a coherent linkage between theory and practice but also establishes a  
foundation for future empirical testing of the Terrestrial Biodiversity Data Architectural Model (TBDAM). By  
combining global literature with locally grounded insights, the review delivers a balanced, evidence-informed  
perspective that enhances understanding of how IoT can drive biodiversity data sharing, ecosystem management,  
and sustainable livelihoods in data-scarce regions such as Turkana County.  
Architectural Model Design and Subsystems  
The Terrestrial Biodiversity Data Architectural Model (TBDAM) is designed as a multi-layered system that  
integrates sensing, networking, processing, and application components to facilitate efficient biodiversity data  
collection, processing, and sharing. The architecture is modular and scalable, allowing for interoperability among  
different devices and platforms. It employs a micro-service design that separates system functions into  
independent yet interconnected modules to enhance maintainability, flexibility, and fault tolerance (Chiara,  
2021). Each subsystem performs specific roles while contributing to the overall functionality of the model. The  
architectural design ensures that biodiversity data flows seamlessly from the point of collection to end-users,  
promoting real-time decision-making and improved environmental management. The TBDAM design also  
accommodates data heterogeneity by supporting multiple data formats and communication standards, thereby  
enabling the integration of information from various IoT devices and networks (Aggrey, 2021).  
The architecture comprises five major subsystems: the sensing subsystem, network subsystem, processing  
subsystem, service subsystem, and application subsystem. The sensing subsystem is responsible for collecting  
biodiversity-related data through devices such as wireless sensor networks (WSN), RFID tags, and LoRa-  
enabled nodes, which monitor environmental indicators like temperature, soil moisture, and vegetation cover  
(Díaz et al., 2020). The network subsystem handles communication and connectivity among devices, employing  
technologies such as Wi-Fi, ZigBee, and LTE-M to ensure stable and secure data transmission. The processing  
subsystem manages data aggregation, analytics, and storage through cloud computing and middleware platforms,  
enabling efficient handling of large datasets. The service subsystem facilitates data management functions such  
as classification, indexing, and retrieval, while the application subsystem provides user interfaces through mobile  
applications, dashboards, and radio communication channels for real-time visualization and decision support  
(Ospina & Heeks, 2012).  
The integration of these subsystems enables a seamless data value chain from sensing to decision-making. Data  
captured in the field is transmitted to cloud servers for processing, after which it is transformed into meaningful  
insights accessible to users across institutional and community levels. This design not only promotes efficient  
data flow but also ensures scalability, reliability, and sustainability of the biodiversity information system  
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(UNDP, 2016). The TBDAM’s interoperability allows it to interact with external data repositories, national  
biodiversity information systems, and other IoT frameworks, strengthening collaborative conservation and data-  
driven policy formulation. Ultimately, the architectural model design reflects a balance between technical  
innovation and social inclusivity, making it adaptable to the unique environmental and socio-economic  
environments of regions like Turkana County.  
Figure III: Architectural Model Design and Subsystems (TBDAM).  
Graphical Representation of TBDAM  
The following figure provides a visual mapping of the Terrestrial Biodiversity Data Architectural Model  
(TBDAM):  
Figure 1V: Conceptual Mapping of the Terrestrial Biodiversity Data Architectural Model (TBDAM).  
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Empirical Insights from Turkana County  
Empirical findings from Turkana County provide valuable evidence on how IoT-based biodiversity data systems  
can enhance environmental management and sustainable livelihoods. The study revealed that access to IoT  
technologies such as mobile phones, radios, and sensor-based tools significantly influenced biodiversity data  
utilization and livelihood outcomes. Approximately 92% of respondents reported ownership of mobile phones,  
84% had access to radio, and a smaller proportion (23%) were aware of sensor-based monitoring systems for  
tracking vegetation and water resources. These figures demonstrate a high level of basic ICT penetration, which  
forms a strong foundation for IoT adoption (Waema & Okinda, 2011). The results further showed that the use  
of IoT-enabled biodiversity data improved decision-making among pastoralists, enabling them to identify  
suitable grazing areas, anticipate drought conditions, and manage livestock more efficiently. This illustrates that  
technological access translates into tangible livelihood benefits when aligned with user needs and environmental  
realities (Adera et al., 2014).  
Qualitative insights from key informant interviews and focus group discussions underscored the importance of  
data relevance, trust, and technical support in determining long-term IoT utilization. Respondents emphasized  
that biodiversity data must be timely, localized, and easily interpretable to be useful at the community level  
(Ospina & Heeks, 2012). Many participants associated IoT-based data dissemination particularly through radio  
and mobile platforms with improved early warning systems and more accurate information on water and pasture  
availability. However, limited awareness of IoT sensor technologies, high device costs, and low digital literacy  
were identified as major barriers to wider adoption. Institutional challenges such as lack of technical expertise,  
poor inter-agency coordination, and limited infrastructure investment further constrained scalability (UNDP,  
2016). These findings highlight that the success of IoT-driven biodiversity management systems depends not  
only on technology availability but also on social, economic, and institutional readiness.  
The empirical results also established a significant positive correlation between IoT access and improved  
livelihood assets; natural, human, and financial capital. Access to real-time biodiversity data strengthened  
adaptive capacities, reduced resource-based conflicts, and supported diversification of income-generating  
activities. Communities with greater exposure to IoT-based information systems reported better preparedness  
for drought and more sustainable resource utilization practices (Díaz et al., 2020). At the policy level, the  
integration of IoT into biodiversity management frameworks was seen as instrumental in advancing Kenya’s  
environmental sustainability goals, within arid and semi-arid regions like Turkana. These insights confirm that  
the Terrestrial Biodiversity Data Architectural Model (TBDAM) is not merely a technological innovation but a  
transformative framework that aligns digital inclusion with ecological resilience and sustainable livelihoods.  
Constructs for Future Empirical Validation  
To enable empirical testing of the Terrestrial Biodiversity Data Architectural Model (TBDAM), it is important  
to identify measurable constructs that translate conceptual relationships into testable variables. The proposed  
constructs include: Technological Access, referring to the availability, affordability, and usability of IoT devices  
and connectivity infrastructure (Waema & Okinda, 2011); Data Utilization, which captures how communities  
and institutions employ biodiversity information for planning and decision-making (Ospina & Heeks, 2012);  
and Institutional Collaboration, representing the degree of coordination and data-sharing among government  
agencies, research institutions, and local organizations (Adera et al., 2014). These dimensions collectively define  
the operational environment that determines the effectiveness and sustainability of IoT-driven biodiversity  
systems.  
Additional constructs such as Socio-cultural Readiness and Livelihood Impact are equally critical for  
contextualizing IoT adoption in biodiversity management. Socio-cultural readiness encompasses factors like  
trust in technology, community attitudes, gender inclusivity, and levels of digital literacy, which influence the  
pace and depth of technological diffusion (Rogers, 2003). Livelihood impact reflects changes across the five  
livelihood capitals; natural, human, social, physical, and financial as outlined in the Sustainable Livelihood  
Framework (DFID, 2000). These constructs can be assessed using Likert-scale instruments and validated through  
statistical modeling approaches such as confirmatory factor analysis or structural equation modeling.  
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Establishing these measurable indicators provides a robust empirical foundation for future research and helps  
link the conceptual elements of the TBDAM to tangible development outcomes.  
Implications for Sustainable Livelihoods  
The integration of IoT technologies into terrestrial biodiversity data systems has profound implications for  
sustainable livelihoods, particularly in arid and semi-arid regions such as Turkana County. Through improving  
access to real-time environmental information, IoT strengthens the five core livelihood assets; natural, human,  
social, physical, and financial capital outlined in the Sustainable Livelihood Framework (DFID, 2000). Enhanced  
access to biodiversity data enables households to make informed decisions on grazing routes, water sourcing,  
and land use planning, reducing vulnerability to drought and resource scarcity. The availability of timely,  
accurate data also supports diversification of income streams through improved agricultural planning, fisheries  
management, and small-scale trade in biodiversity products (UNDP, 2016). Moreover, IoT-facilitated  
communication networks empower communities to collaborate on resource management, fostering stronger  
social cohesion and knowledge sharing (Ospina & Heeks, 2012). As a result, biodiversity conservation becomes  
both an environmental and socio-economic process driven by data-informed practices and community  
participation.  
Beyond individual and community-level impacts, IoT-based biodiversity data systems contribute to broader  
institutional and policy-level transformations. The Terrestrial Biodiversity Data Architectural Model (TBDAM)  
demonstrates that integrating IoT into environmental governance can bridge information gaps between scientific  
institutions, government agencies, and local communities (Chiara, 2021). Such integration supports the design  
of adaptive policies that respond to localized ecological realities while promoting transparency and  
accountability in biodiversity management. Furthermore, IoT-enabled data analytics enhance monitoring and  
evaluation mechanisms for sustainable development initiatives, aligning local practices with national and global  
conservation frameworks such as the Convention on Biological Diversity and the Sustainable Development  
Goals (Díaz et al., 2020). The implications of IoT adoption extend beyond technology itself, it provides a  
foundation for inclusive, knowledge-driven, and climate-resilient development that empowers vulnerable  
communities to thrive within changing environmental conditions.  
Socio-Cultural Dimensions of IoT Adoption  
Beyond technological and infrastructural limitations, socio-cultural factors play a defining role in shaping the  
adoption and sustainability of IoT-based biodiversity systems in Turkana County. Community perceptions of  
technology, levels of trust in digital data collection, and cultural attitudes toward environmental monitoring all  
influence the degree of local engagement (Ospina & Heeks, 2012). Gender disparities in access to digital tools  
and training further constrain participation, often leaving women and marginalized groups underrepresented in  
data-driven decision-making processes (UNDP, 2016). The absence of culturally responsive designs and  
communication channels can lead to skepticism or resistance, undermining the intended benefits of IoT  
interventions (Waema & Okinda, 2011).  
Equally important is the limited incorporation of indigenous knowledge systems that have long guided natural  
resource management and environmental stewardship in Turkana and similar arid regions. When IoT  
architectures fail to integrate local ecological wisdom, traditional practices, and linguistic diversity, they risk  
being perceived as externally imposed technologies (Adera et al., 2014). Effective adoption therefore requires  
participatory co-design processes where communities are involved in defining data needs, interpretation  
frameworks, and dissemination mechanisms. Embedding cultural inclusivity and local ownership within IoT  
projects not only enhances user acceptance but also ensures long-term sustainability and equitable access to  
biodiversity information (Rogers, 2003)  
IoT Biodiversity Programs in Africa  
Across Africa, several emerging pilot initiatives demonstrate the growing relevance of IoT technologies in  
biodiversity conservation and environmental monitoring. In Kenya, the Mara Smart Parks initiative employs  
drones, GPS trackers, and environmental sensors to monitor wildlife movements and vegetation dynamics in  
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real time, supporting ecosystem management and anti-poaching efforts (Ndiritu et al., 2021). In Uganda, the IoT  
for Wetlands project integrates low-cost water quality sensors with cloud-based data platforms to map aquatic  
biodiversity and detect pollution levels in the Lake Victoria Basin, enabling timely interventions by  
environmental authorities (Okello & Namaganda, 2020). These projects illustrate how IoT can bridge critical  
data gaps, foster collaboration among stakeholders, and enhance ecological decision-making across varying  
environmental contexts.  
In West Africa, Ghana’s GreenIoT pilot provides another notable example of IoT integration into forest  
ecosystem restoration. The program applies sensor networks and remote data collection systems to monitor tree  
growth, soil moisture, and microclimatic conditions, contributing to reforestation and biodiversity recovery  
efforts (Mensah et al., 2022). Beyond their technological innovation, these African initiatives highlight the  
importance of local partnerships, community engagement, and capacity development in ensuring sustainability.  
Collectively, they provide scalable and context-sensitive models that Kenya can adapt for regions like Turkana,  
where IoT-driven biodiversity data systems could transform conservation strategies and strengthen climate  
resilience.  
Knowledge Gaps and Future Directions  
Despite the promising outcomes demonstrated by the Terrestrial Biodiversity Data Architectural Model  
(TBDAM), several knowledge and implementation gaps remain. Limited interoperability among IoT devices,  
inadequate data governance frameworks, and concerns over data security and privacy continue to hinder large-  
scale adoption (Chiara, 2021). Financial constraints, low digital literacy, and insufficient institutional capacity  
further restrict deployment in rural contexts such as Turkana County (Waema & Okinda, 2011). Future research  
should explore the integration of emerging technologies such as artificial intelligence for predictive analytics,  
block-chain for data integrity, and edge computing for real-time processing to enhance scalability and  
sustainability. Additionally, participatory policy frameworks that align IoT innovations with local ecological  
knowledge systems and national biodiversity strategies are essential for ensuring inclusive and long-term impact  
(Díaz et al., 2020).  
Enhanced Policy and Strategy Recommendations  
For the effective adoption and long-term sustainability of IoT-driven biodiversity data systems, Kenya requires  
coherent and multi-level policy frameworks that align technology with environmental sustainability goals. This  
involves active collaboration among national and county governments, research institutions, private sector  
innovators, and community-based organizations. Such partnerships should co-create data standards,  
interoperability protocols, and governance mechanisms to ensure the ethical and transparent use of biodiversity  
data. Integrating IoT into national biodiversity information systems can further strengthen data coordination  
across key institutions such as the National Environment Management Authority (NEMA), the Kenya Wildlife  
Service (KWS), and county governments, enhancing information sharing and evidence-based environmental  
planning (UNDP, 2016).  
At the operational level, capacity-building initiatives must be institutionalized to strengthen both technical and  
community-level competencies. Training programs targeting conservation officers, extension workers, and local  
community members can enhance digital literacy, technical proficiency, and trust in IoT systems. Public-private  
partnerships (PPPs) offer a viable mechanism for mobilizing resources, fostering innovation, and facilitating  
technology transfer. Donor agencies, universities, and ICT enterprises should collaborate to design blended  
financing models that support IoT infrastructure, cloud-based data management, and continuous technical  
support (Adera et al., 2014). Establishing open-data frameworks would further democratize access to  
biodiversity information, allowing diverse stakeholders including researchers, policymakers, and local  
communities to contribute to adaptive management and collaborative conservation.  
Investment in IoT infrastructure within arid and semi-arid lands (ASALs) is equally critical for ensuring  
equitable technological diffusion and data accessibility. Building local innovation hubs can promote  
entrepreneurship in biodiversity-related technologies and generate employment opportunities while advancing  
environmental stewardship. Moreover, policy interventions must address issues of data privacy, interoperability,  
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and ethical governance to safeguard biodiversity information and foster public confidence in digital systems.  
Through these integrated and inclusive policy measures, Kenya can promote resilience, knowledge-driven  
decision-making, and sustainable livelihoods particularly in ecologically sensitive regions such as Turkana  
County.  
CONCLUSION  
IoT-enabled biodiversity data architectures represent a transformative approach to environmental management  
and livelihood improvement. By converting raw environmental data into actionable knowledge, IoT systems  
enhance the ability of communities and institutions to make informed decisions regarding resource use,  
conservation, and adaptation to climate variability (Chiara, 2021). The Terrestrial Biodiversity Data  
Architectural Model (TBDAM) exemplifies how integrated technological frameworks can overcome traditional  
data limitations, ensuring that biodiversity information is accessible, relevant, and usable across multiple  
contexts.  
The TBDAM reinforces inclusivity by linking technological innovation with social participation. Through  
mobile applications, cloud platforms, and radio dissemination, biodiversity data reaches diverse user groups,  
including marginalized rural communities. This democratization of data strengthens local governance, fosters  
trust in technology, and promotes collaboration among stakeholders in biodiversity management (Ospina &  
Heeks, 2012). The model’s design also enhances resilience by improving early warning systems, supporting  
adaptive livelihood strategies, and fostering long-term environmental sustainability.  
In essence, IoT-based biodiversity systems such as the TBDAM bridge the gap between digital transformation  
and sustainable development. They create an enabling environment where information becomes a resource for  
empowerment and resilience rather than exclusion. Moving forward, aligning IoT-driven biodiversity  
frameworks with national policy priorities and community knowledge systems will be essential for ensuring  
ecological integrity, technological inclusiveness, and sustainable livelihoods in regions facing climate and  
resource pressures (UNDP, 2016).  
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