Impact of Internet of Things (IOT) and Artificial Intelligence (AI) on National Development Through Science and Technology; A Systematic Review

1. Introduction

Background and Context

The past decade has witnessed the rapid evolution of the Internet of Things (IoT) and Artificial Intelligence (AI), both globally and within Africa, including Nigeria. These technologies have become foundational in modern science and technology landscapes, driving advancements across various sectors. IoT and AI have revolutionized industries by enabling enhanced connectivity, automation, and data analysis capabilities, which in turn have fueled innovation in smart cities, healthcare, and manufacturing. For instance, the integration of AI and IoT in smart cities has significantly improved energy management and environmental sustainability, particularly in regions where resource optimization is critical (Wu et al., 2022). In Nigeria, the adoption of these technologies is growing, with significant applications in areas such as agriculture, where IoT is used to optimize crop yields and AI is leveraged to improve healthcare outcomes through personalized treatments (Rejeb et al., 2022; Rawat, 2023).

The importance of IoT and AI in shaping national development cannot be overstated, particularly in driving industrial innovation, economic growth, and sustainability. Globally, these technologies are seen as key drivers of the Fourth Industrial Revolution, facilitating the development of smart cities, intelligent transportation systems, and advanced manufacturing processes (World Economic Forum, 2023). AI, on the other hand, which enables machines to perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving, has seen significant advancements globally, with an expected market value of $126 billion by 2025, with significant portions of this growth expected in developing regions such as Africa (Fraga-Lamas et al., 2021; Statista, 2023). In Nigeria, the government has initiated several policies to promote the integration of IoT and AI into various sectors, recognising their potential to spur industrial growth and enhance the country's competitiveness on a global scale (The Federal Ministry of Communications, Innovation and Digital Economy, 2024). The strategic implementation of these technologies is crucial for achieving sustainability goals, particularly in energy management and industrial efficiency. The integration of AI in smart manufacturing, for example, has the potential to drastically reduce waste and enhance resource utilization, thereby contributing to the broader goals of sustainable economic development (Nižetić et al., 2020).

Research Problem

The existing literature on the impact of IoT and AI on national development in Nigeria reveals a significant gap, particularly in studies that analyse the combined effects of these technologies. While various research efforts have explored the individual contributions of IoT and AI in sectors such as agriculture, healthcare, and finance, there is a paucity of studies that comprehensively examine their synergistic impact on national development. This gap is evident in the limited integration of IoT and AI within the policy frameworks and strategic initiatives aimed at fostering technological innovation and economic growth in Nigeria (Effoduh et al., 2021; Nahar et al., 2024). Moreover, existing studies often focus on the potential applications of these technologies rather than critically assessing their actual implementation and outcomes within the Nigerian context (Ejiyi et al., 2023; Shkalenko et al., 2024). This lack of comprehensive analysis hinders a full understanding of how IoT and AI can jointly drive national development, especially in addressing challenges related to infrastructure, governance, and social inclusion.

Given this gap, there is a pressing need for a comprehensive synthesis review that critically evaluates the contributions, challenges, and future prospects of IoT and AI in Nigeria's national development. Such a review would provide a holistic understanding of how these technologies are currently being utilised, identify key barriers to their effective implementation, and offer insights into potential strategies for overcoming these challenges (Humeau, 2024). Additionally, a synthesis review would enable policymakers, industry stakeholders, and researchers to better appreciate the interplay between IoT and AI, thereby facilitating the formulation of more robust and integrative national development policies (Alahi et al., 2023; Bibri et al., 2024). This approach is essential not only for maximising the benefits of these technologies but also for ensuring that their adoption aligns with broader development goals, including economic diversification, sustainability, and social equity in Nigeria.

Research Objectives

• To assess the effects of IoT and AI on various aspects of national development.
• To identify key developments and trends in IoT and AI technologies from 2017 to 2024.
• To explore challenges and opportunities associated with integrating IoT and AI into national development strategies.
• To provide strategic recommendations for policymakers and stakeholders.

Research Questions

• What are the key contributions of IoT and AI to national development through science and technology?
• How have IoT and AI technologies evolved between 2017 and 2024, and what trends are observable?
• What challenges and opportunities arise from the integration of IoT and AI into national development strategies?

2. Methodology

The methodology for this scoping review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) framework, as outlined by Tricco et al. (2018) and Peters et al. (2021). The PRISMA-ScR framework is crucial for ensuring methodological rigour and transparency, which enhances the comprehensiveness and reproducibility of the review process. By adopting PRISMA-ScR, this study systematically maps the existing literature on the impact of Internet of Things (IoT) and Artificial Intelligence (AI) on national development through science and technology, ensuring a thorough examination of the topic (Peters et al., 2021).

Eligibility Criteria

To ensure the selection of the most relevant literatures for evaluation, specific acceptance and rejection criteria were established for this review. Studies were included if they met the following criteria: (a) published between 2017 and 2024, to capture recent advancements in the impact of Internet of Things (IoT) and Artificial Intelligence (AI) on national development through science and technology (Elahi et al., 2023); (b) written in English, ensuring alignment with the research team's language proficiency and target audience (Peters et al., 2021); and (c) directly focused on the impact of IoT and AI on national development, ensuring relevance to the research questions. Exclusion criteria were applied to studies that: (a) focused on impacts of IoT and AI outside the field of science and technology; (b) lacked empirical data; or (c) did not substantially address the Nigerian context. These criteria were essential to ensure the review's precision and synthesise only relevant, evidence-based studies.

Information Sources

Various databases and search engines, such as Scopus, ScienceDirect, IEEE Xplore, Web of Science, and Google Scholar, offer extensive collections of publications for researchers to explore. This review utilised a range of databases to ensure a comprehensive capture of research. Specifically, ScienceDirect, IEEE Xplore and Google Scholar were selected to include local and regionally focused publications. IEEE Xplore and ScienceDirect were chosen for their emphasis on African-published scholarly journals, which are crucial for capturing studies on IoT and AI in Nigeria. Google Scholar was also incorporated to access journal publications from local Nigerian publishers and journals that may not be indexed in larger global databases. This approach was necessary due to the recognised limitation of focused databases specialising on IoT and AI impacts through science technology approach in African countries like Nigeria, making it challenging to obtain comprehensive literature on the region (Ahmetoglu et al., 2022; Elahi et al., 2023). The search process began on 22nd May, 2024, with the final search conducted on 10th Oct., 2024 to maintain transparency and ensure that the most recent studies were included.

Search Strategy

The search strategy was designed to capture comprehensive literature on the impact of IoT and AI on national development, with a particular focus on Africa, especially Nigeria. The review utilised databases such as ScienceDirect, IEEE Xplore, and Google Scholar, targeting peer-reviewed journal articles, conference proceedings, and reviews published between 2017 and 2024. Key search queries included terms like “Internet of Things,” “Artificial Intelligence,” “National Development,” and “Economic Growth,” ensuring a focus on technological impacts. Additional terms, such as “Nigeria” and “Africa,” were used to address region-specific challenges and opportunities. Boolean operators "AND" and "OR" were employed to enhance the search’s precision and breadth, ensuring a thorough capture of both global and region-specific insights, see (Table A1 Appendix) for the search strategy summary.

Selection of Sources

The selection of sources was conducted in accordance with the PRISMA framework (Sohrabi et al., 2021). Initially, a total of 598 records were identified from three databases: IEEE Xplore (n = 55), ScienceDirect (n = 73), and Google Scholar (n = 470). After removing 231 duplicate entries, 367 records were screened based on their titles and abstracts to assess relevance. During this phase, 302 records were excluded for not meeting the study criteria. The remaining 65 full-text articles were reviewed in detail, and a further 15 articles were excluded as they did not align with the inclusion criteria. Ultimately, 40 studies were selected for inclusion in the final review. The PRISMA flow diagram (Figure 1) illustrates the entire process of identifying, screening, and selecting studies.

Data Charting Process

Data extraction followed a manual approach using a pre-designed Excel sheet to systematically chart relevant data from each study. The charting process captured key variables such as IoT and AI adoption strategies, barriers, facilitators, and outcomes, ensuring consistency with the research objectives. A team of two reviewers performed the extraction, cross-checking each other’s work to maintain accuracy and avoid bias. Any discrepancies were resolved through team agreement, ensuring that critical data was not overlooked. This collaborative and rigorous approach ensured that the data charting process was thorough and reliable (Schmidt et al., 2021).

Data Items

The key variables extracted from the included studies focused on the impact of IoT and AI on national development. These variables included the types of IoT and AI technologies used, such as smart sensors, machine learning, and natural language processing, with specific applications across sectors like healthcare, agriculture, and manufacturing. Another essential variable was the adoption strategies employed, including government-led initiatives, private sector partnerships, and industry-driven innovations that facilitated the integration of these technologies. Additionally, the studies explored barriers to adoption, such as infrastructure limitations, data security concerns, and a lack of skilled workforce, which hindered the successful implementation of IoT and AI. Conversely, facilitators of adoption, including supportive government policies, available funding, and technological advancements, were identified as key enablers for the successful integration of these technologies. Lastly, the outcomes of IoT and AI adoption were examined, with studies highlighting their contributions to industrial innovation, economic growth, and environmental sustainability. These data items were crucial in providing a comprehensive understanding of how IoT and AI contribute to national development, the strategies that drive their adoption, and the challenges that need to be addressed for their full potential to be realised (Elahi et al., 2023).

Synthesis of Results

The results of this study were synthesised using a narrative approach, which is well-suited for synthesising data from diverse studies with varying methodologies, as commonly done in systematic reviews (Lisy et al., 2016). This method enabled the identification of recurring themes, patterns, and significant trends concerning the adoption of IoT and AI in national development. The narrative synthesis was particularly effective given the heterogeneity of the studies, which ranged from quantitative assessments of technological impacts to qualitative insights into policy frameworks and challenges (Elahi et al., 2023). By employing this approach, the review provided a comprehensive understanding of how IoT and AI have been integrated into national development strategies, highlighted gaps in existing research, and offered actionable recommendations for policymakers to address barriers and maximise the potential of these technologies for sustainable growth.

3. Results

Overview of Selected Studies

Quantitative Approaches in IoT and AI Adoption

Many of the studies reviewed employed quantitative methods, utilising surveys and structured questionnaires to evaluate IoT and AI adoption across sectors. For example, Adelami et al. (2024) and Oke et al. (2020) focused on the adoption of IoT in agriculture and construction, measuring relationships between IoT usage and improvements in productivity, security, and efficiency. These studies used inferential statistics, such as regression analysis, to quantify the operational benefits of IoT, providing empirical data on the effects of these technologies in specific sectors. Similarly, Elegunde and Osagie (2020) and Omotayo et al. (2023) adopted cross-sectional designs to investigate AI's impact on employee performance in banking and supply chain management, providing insights into how these technologies enhance operational efficiency, see (table A2 Appendix)

Narrative and Qualitative Reviews

Several studies took a narrative or qualitative review approach to assess the broader theoretical implications of IoT and AI adoption. Gbaden et al. (2024) and Onayinka et al. (2024) focused on AI's integration into journalism and education, offering critical insights into the challenges and opportunities these technologies present. Bello et al. (2023) and Mustapha et al. (2019) also explored the application of IoT in the oil and gas and automobile sectors, respectively, addressing infrastructure limitations and security concerns as major barriers to wider adoption. These studies provided valuable theoretical perspectives on how IoT and AI could transform industries if properly supported by policy.

Experimental and Simulation-Based Designs

Some studies, like those by Abayomi and Usman (2021) and Ajayi and Oloyede (2021), used experimental and simulation-based designs to explore IoT’s role in improving operational efficiency. Abayomi and Usman (2021) conducted field trials in agriculture, finding that IoT applications in precision farming increased crop yields by 30%, while Ajayi and Oloyede (2021) used simulation models to assess urban traffic management, reporting a 40% reduction in congestion. These studies provided practical examples of how IoT can be leveraged to enhance resource management and operational efficiency in both rural and urban settings.

Impact of AI in Manufacturing and Service Delivery

Several cross-sectional studies, such as those by Emeh et al. (2024) and Alheadary (2024), examined AI adoption in manufacturing and its impact on operational performance. Emeh et al. (2024) reported a 30% increase in operational efficiency among firms adopting AI, while Alheadary (2024) found a 32% improvement in productivity linked to AI and IoT technologies. These studies demonstrated the critical role of AI in enhancing competitiveness and innovation in the manufacturing sector, with findings supported by descriptive and inferential statistical methods, such as regression analysis.

IoT in Healthcare and Education

IoT's application in healthcare and education was explored in studies like Ojo et al. (2023) and Amuda et al. (2023). Ojo et al. (2023) highlighted how IoT integration in healthcare improved patient outcomes and service delivery, though issues like data privacy and infrastructure challenges were noted. Similarly, Amuda et al. (2023) found that IoT enhanced classroom management and teaching efficiency among pre-service teachers, but infrastructure and data security concerns remained significant barriers to full adoption.

IoT and AI for National Development

Studies like Ojuawo and Ogunseye (2023) and Ndubuaku and Okereafor (2015) provided broader analyses of IoT's role in national development. Ojuawo and Ogunseye (2023) examined how IoT can drive economic growth in agriculture, healthcare, and security, stressing the need for government policies to support infrastructure development and IoT deployment. Similarly, Ndubuaku and Okereafor (2015) reviewed the state of IoT deployment in Africa, highlighting the barriers to IoT integration, such as poor infrastructure and digital illiteracy, while recognizing IoT’s potential to boost productivity and social development.

Sector-Specific Case Studies

Sector-specific studies, such as Ogidiaka et al. (2017) and Udo et al. (2021), evaluated IoT adoption in organizations and heterogeneous networks, respectively. Ogidiaka et al. (2017) conducted a cross-sectional survey of organizations in Lagos, finding that while IoT adoption was still in its early stages, it held significant potential for improving internal operations and service delivery. Udo et al. (2021) analyzed IoT networks in heterogeneous environments, identifying challenges related to standardization and integration that could hinder the full potential of IoT technologies.

Theoretical and Policy-Driven Studies

Theoretical studies, such as those by Nwankwo and Ugwu (2023) and Odoh et al. (2018), examined the broader policy implications of IoT and AI adoption. Nwankwo and Ugwu (2023) focused on IoT’s role in agriculture and other sectors, recommending government policies to support IoT integration for enhanced productivity. Odoh et al. (2018) explored AI’s impact on accounting operations, finding significant effects of AI tools like expert systems on accounting performance, supported by regression analysis.

Challenges and Opportunities based on the reviewed studies in IoT and AI Integration

Several studies focused on identifying the challenges and opportunities associated with IoT and AI integration in key sectors. Amade and Nwakanma (2021) and Idris and Sani (2024) applied fuzzy decision-making models and simulations to assess IoT’s impact on construction project management, finding that while IoT improved decision-making and operational efficiency, infrastructural and technical skill shortages hindered broader implementation. Afolabi and Oduwoye (2023) explored the integration of AI and IoT in healthcare, showing significant improvements in service delivery but noting persistent challenges with infrastructure and technology adoption.

The evolutionary trends of IoT and AI technologies evolved between 2017 and 2024

Between 2017 and 2024, the evolution of IoT and AI technologies in Nigeria has shown significant growth, reflecting global trends. Key developments and trends observed during this period include:

• Increased Adoption Across Sectors: Early studies, such as Ogidiaka et al. (2017), indicated that IoT adoption was still in its nascent stages, particularly in industries like manufacturing and service delivery. By 2024, more advanced implementations of IoT and AI are evident, especially in precision agriculture (Adelami et al., 2024), construction (Oke et al., 2020), and healthcare (Ojo et al., 2023). This demonstrates a shift from exploratory use to more widespread operationalisation of these technologies across sectors.
• Improved Operational Efficiency: A major trend from 2017 to 2024 is the consistent improvement in operational efficiency enabled by IoT and AI. Studies such as Elegunde and Osagie (2020) in the banking sector and Alheadary (2024) in manufacturing firms highlight significant increases in productivity and operational performance, driven by AI-driven automation, predictive analytics, and real-time data processing from IoT devices.
• Integration in National Development Strategies: By 2023 and 2024, studies like those of Ojuawo and Ogunseye (2023) and Ndubuaku and Okereafor (2015) reflect an increasing recognition of IoT and AI as key drivers of national development. These technologies are now seen as essential in sectors like agriculture, healthcare, and security, aligning with national priorities for economic growth and sustainability.
• Sector-Specific Applications: While early implementations focused on basic operational improvements, more recent studies, such as those by Idris and Sani (2024) and Ajayi and Oloyede (2021), reveal that IoT and AI applications have become more sophisticated, focusing on areas like smart grids, urban traffic management, and construction project management. This evolution reflects an increasing reliance on IoT for decision-making and resource optimisation.
• Overcoming Infrastructure Challenges: One consistent challenge across the period has been the issue of infrastructure. However, the focus has shifted from merely identifying the problem to proposing specific solutions. For example, while early studies like Mustapha et al. (2019) highlighted infrastructure and political challenges as barriers to IoT adoption, by 2023, studies such as Afolabi and Oduwoye (2023) recommend the development of government policies and frameworks to enhance the necessary infrastructure for AI and IoT.
• Focus on Data Privacy and Security: With the growth of IoT and AI applications, concerns about data security and privacy have intensified, especially in industries like healthcare and public services. Studies like Omigie et al. (2023) reflect an increasing awareness of these risks, advocating for improved data security measures in AI-driven systems, such as records and archival management.
• Growing Role in Education and Healthcare: AI and IoT are increasingly applied in education and healthcare, as evidenced by studies such as Amuda et al. (2023) and Aboh et al. (2022). These technologies are being used to enhance service delivery in hospitals and improve learning outcomes in schools, indicating a shift toward more socially impactful applications.

The period from 2017 to 2024 reflects a growing maturity in the deployment of IoT and AI technologies across Nigeria, with clear advancements in operational efficiency, productivity, and strategic sectoral integration which is one of the research objectives of this study, despite ongoing challenges in infrastructure, data security, and policy support.

Impact of IoT and AI from the reviewed studies across various sectors in Nigeria

The general impact of IoT and AI from the reviewed studies across various sectors in Nigeria demonstrates significant improvements in operational efficiency, productivity, and service delivery. Across industries such as agriculture, manufacturing, education, healthcare, and urban management, IoT and AI have been shown to, see (table 3 Appendix)

• Increase Productivity and Efficiency: In sectors like agriculture and manufacturing, IoT and AI have improved resource management, crop yields, and operational performance by as much as 30-32%. Similarly, AI adoption in manufacturing has led to a 30% increase in operational efficiency, while in the financial sector, AI adoption has contributed to a 5% boost in productivity.
• Enhance Service Delivery: In the healthcare and education sectors, IoT and AI have improved patient outcomes, teaching efficiency, and administrative processes. For instance, IoT integration in healthcare has been linked to improved patient monitoring and service delivery, while AI in education has enhanced learning outcomes and personalised teaching methods.
• Optimise Decision-Making and Project Management: In construction and urban planning, IoT has facilitated better decision-making through data-driven project management and optimised traffic management, reducing congestion by up to 40%.
• Boost Innovation in Business Operations: In sectors like banking and journalism, AI has improved work processes, employee performance, content creation, and fact-checking, significantly enhancing the quality and speed of operations.
• Improve National Development Prospects: IoT has the potential to drive economic growth in critical areas like agriculture, health, and national security. Its use in smart grids and energy distribution has improved reliability and efficiency, contributing to broader economic and social development.

The overall impact of IoT and AI across various sectors in Nigeria has led to improvements in productivity and operational efficiency by 30-40% as reviewed from the studies, enhanced service delivery and decision-making, and contributed to economic growth, although challenges such as infrastructure and digital literacy remain barriers to full adoption.

However, based on the analysis of the studies, approximately 85% of them agree that IoT and AI can demonstrate significant improvements in operational efficiency, productivity, and service delivery across various sectors in Nigeria through the application of science and technology.

Challenges on IoT and AI Adoption in Nigeria as acknowledged by the studies

The challenges of IoT and AI adoption in Nigeria are multifaceted, primarily revolving around infrastructure, technical skills, security, and regulatory issues. Infrastructural limitations, such as poor power supply and unreliable internet connectivity, create significant obstacles to the integration of IoT systems across sectors like agriculture, healthcare, and education (Ndubuaku & Okereafor, 2015; Ojo et al., 2023). Additionally, the lack of skilled professionals capable of managing and maintaining IoT and AI technologies exacerbates these barriers, preventing full-scale implementation (Mustapha et al., 2019; Ogwo et al., 2023). Data security and privacy concerns are another critical issue, with many studies highlighting vulnerabilities in AI-driven systems, which can lead to misuse or unauthorized access to sensitive data (Omigie et al., 2023; Afolabi & Oduwoye, 2023).

Policy and regulatory gaps further compound these challenges, as the absence of clear guidelines for IoT and AI governance hampers adoption in sectors such as manufacturing and education (Emeh et al., 2024; Abiodun & Ganiyu, 2021). The high cost of implementation also poses a challenge, particularly for small and medium-sized enterprises (SMEs) that cannot afford the necessary technologies and infrastructure (Amuda et al., 2023; Gbaden et al., 2024). Additionally, socio-cultural resistance, driven by fears of job displacement and ethical concerns surrounding AI decision-making, adds another layer of complexity (Ajayi et al., 2023; Oke et al., 2024). The lack of standardization and interoperability between IoT systems also leads to inefficiencies, especially when integrating these technologies with legacy systems (Udo et al., 2021; Amade & Nwakanma, 2021).

Recommendations from the reviewed studies.

To address these challenges, governments and private sectors must prioritize investments in infrastructure improvements, such as enhancing power supply and internet connectivity, while fostering public-private partnerships to share the cost burden (Ndubuaku & Okereafor, 2015; Idris & Sani, 2024). Additionally, education and training programs should be developed to cultivate the technical expertise necessary to manage IoT and AI technologies (Elegunde & Osagie, 2020; Nwankwo & Ugwu, 2023). Clear regulatory frameworks must be established to ensure data security and provide ethical guidelines for AI usage (Gbaden et al., 2024; Omotayo et al., 2023). Finally, financial support and incentives should be targeted towards businesses, particularly SMEs, to facilitate technology adoption, while promoting standardization to enhance system interoperability (Emeh et al., 2024; Ogidiaka et al., 2017).