Critical Systematic Review of the Transferability of IoT Solutions for Smart Waste Collection in Low-Resource Settings ()
1. Introduction
The adaptation of Internet of Things (IoT) technologies to waste management in developing countries is attracting growing attention [1] [2]. Rapid urbanization, population growth, and the persistent weakness of collection infrastructures reinforce the challenges associated with this field of research [3]-[7]. Various IoT applications have emerged in recent years, ranging from simple sensors to integrated systems combining automated management, geolocation, and data processing [7], [8]-[12]. These systems aim to improve operational efficiency, address public health issues, and reduce environmental impacts [13] [14]. However, such devices often rely on assumptions that are hardly compatible with the realities of cities in developing contexts: access to electricity and stable connectivity is limited, technical expertise is sometimes lacking, and the fragmented structure of public services complicates their deployment [15]-[18]. Few studies focus on African cities, while the existing literature remains largely centered on research conducted in Asia or in major cities of the Global North [19] [20]. This orientation prevents a nuanced understanding of local challenges and limits the transferability of proposed models.
In this paper, transferability is defined as the capacity of a technological solution initially designed in one geographical or socio-economic context to be effectively adapted and operationalized in another, while preserving both functionality and contextual relevance [21].
This review examines the adaptability of IoT systems designed for waste collection to low-resource urban environments, such as those observed in the Democratic Republic of Congo [22]-[25]. The analysis is based on a critical reading that integrates technical performance, structural constraints, community engagement, and economic realities [26] [27]. It provides a state of the art, identifies recurring obstacles, and highlights relevant practices in order to propose concrete pathways for context-appropriate development [28]-[30]. Through the examination of thirty-six empirical studies, the aim is to better align technological innovation with local feasibility in a perspective that is both pragmatic and grounded in field realities.
2. Methodology
2.1. Rationale for Choosing the PRISMA Protocol
To ensure the rigor, transparency, and reproducibility of this systematic review, the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) was adopted as the reference framework [31]. This internationally recognized standard structures the different stages of the process, from source identification to final analysis, while ensuring consistency and traceability of methodological decisions. This choice is fully justified in a field as cross-cutting as IoT-enabled smart waste management, which draws on research from engineering sciences, urban planning, and the social sciences. By clarifying inclusion and exclusion criteria, PRISMA helps reduce selection bias and ensures maximum representativeness of relevant studies.
2.2. Selection of Databases
Given the fundamentally interdisciplinary nature of smart waste management, this review relies on four scientific databases selected for the complementarity of their contributions. IEEE Xplore was used for its comprehensive coverage of Internet of Things technologies, sensor architectures, and microcontrollers. ScienceDirect provides essential insights into applied environmental engineering approaches. SpringerLink enriches the corpus with studies combining technology, urban planning, and social sciences, which are particularly relevant for analyzing waste management systems from an integrated urban perspective. Finally, Scopus was chosen for its broad coverage, advanced bibliometric tools, and ability to interconnect publications across diverse disciplines. This combination ensures a balanced coverage of the technical, ecological, and socio-institutional dimensions of the topic, while limiting biases linked to disciplinary silos.
2.3. Search Strategy
The search strategy was based on a combination of generic and specialized keywords, incorporating synonyms, terminological variants, and expressions commonly used in the field of IoT applied to waste management. The queries were formulated in English, the dominant language of scientific publications on this topic. Boolean operators (AND, OR) were used to cross-reference the main concepts: IoT technologies (“IoT”, “Internet of Things”), waste management (“waste collection”, “garbage monitoring”), logistics optimization (“route optimization”, “smart bins”, “sensor-based garbage”), and social or territorial dynamics (“community engagement”, “developing countries”, “Africa”, “Asia”).
The search period, set between 2016 and 2024, captures recent developments in the field, including the diffusion of low-cost solutions (such as Arduino and ESP32), the emergence of long-range networks (LoRaWAN), and the introduction of decentralized technologies such as blockchain into connected waste management systems.
The search strategy was conducted across four major databases (Scopus, IEEE Xplore, ScienceDirect, and SpringerLink), selected for their broad coverage of peer-reviewed literature in engineering, computer science, and environmental studies. These databases were prioritized to ensure access to the most relevant publications on IoT applications and smart waste management. Although other repositories such as Web of Science or certain regional databases may also contain relevant studies, the chosen sources already provide extensive disciplinary and geographical coverage.
2.4. Inclusion and Exclusion Criteria
A rigorous set of criteria was defined to ensure scientific relevance, contextual adequacy, and technological consistency with the objectives of this review. Priority was given to empirical research conducted in low-resource countries that effectively implemented IoT devices applied to waste collection. Publications that were purely theoretical, focused on industrialized countries, or addressed related domains such as recycling were automatically excluded. Table 1 summarizes the main inclusion and exclusion criteria used to filter the 329 publications initially identified. These criteria narrowed the final corpus to 36 studies relevant for analysis. This filtering framework ensured analytical coherence while maintaining a diversity of case studies useful for examining the transferability of solutions in low-resource contexts, particularly in sub-Saharan Africa.
Table 1. Summary of inclusion and exclusion criteria applied to the systematic review.
Category |
Inclusion Criteria |
Exclusion Criteria |
Geographical scope |
Studies conducted in developing countries or low-resource settings |
Studies focused on high-income countries or areas with high technological density |
Application domain |
Solid waste collection through IoT devices (sensors, networks, platforms) |
Studies limited to sorting, recycling, or general environmental management |
Type of technology |
Effective integration of sensors, microcontrollers, wireless networks,
or IoT cloud platforms |
Conceptual works without deployment or non-operational prototypes |
Methodology |
Empirical studies, pilot projects, or
field experiments |
Theoretical models without validation or purely abstract simulations |
Publication period |
Studies published between 2016 and 2024 |
Publications prior to 2016 or not
peer-reviewed |
Language of publication |
Articles written in English or French |
Works in other languages that are inaccessible or not translated |
2.5. Selection Process
The selection process of studies was conducted in accordance with the principles of the PRISMA protocol, ensuring transparency in the steps leading from the initial identification to the constitution of the final corpus. Only publications meeting the defined inclusion criteria (section 2.4) were retained, with a constant emphasis on traceability.
The literature search, carried out between March and May 2025, identified 329 publications from IEEE Xplore, ScienceDirect, SpringerLink, and Scopus. After removing duplicates and performing an initial screening based on titles and abstracts, 137 articles were selected for full-text reading. Of these, 101 were excluded due to lack of contextual relevance, purely theoretical approaches, or no direct connection to IoT-enabled waste collection. The final corpus thus consists of 36 empirical studies. Figure 1 illustrates this process through a simplified PRISMA flow diagram, showing the number of records at each stage and the main reasons for exclusion. This scheme reinforces the methodological robustness of the selection.
Figure 1. PRISMA flow diagram of study selection process (2016 - 2024).
2.6. Quality Appraisal
The methodological rigor of the 36 included studies was assessed using the Mixed Methods Appraisal Tool [32] across five dimensions: clarity of research questions, adequacy of data collection, appropriateness of study design, risk of bias and validity of measurements, and quality of analysis.
All studies obtained high scores (86% - 97%), indicating a solid and homogeneous evidence base. Even at the lower end of this range, the studies reflected satisfactory design and reporting, excluding any weak evidence. This homogeneity results from the rigorous PRISMA-guided inclusion and exclusion process and reinforces the internal validity of the synthesis, while also providing a benchmark for future comparative reviews on IoT-enabled waste collection.
2.7. Thematic Coding and Method of Analysis
The analysis of the 36 selected studies was based on an inductive thematic coding approach, allowing the identification of recurring dimensions, differentiating factors, and areas of tension related to the adaptation of IoT technologies in low-resource countries. This method enabled the structuring of extracted data into coherent analytical categories, combining technical, social, economic, and environmental dimensions.
The analysis process was carried out in two phases. First, a coding framework was developed around five main thematic axes, which also served as concrete criteria for assessing transferability: (i) technology, including type of sensors, microcontrollers, and energy compatibility; (ii) infrastructure and connectivity, i.e. network reliability and energy autonomy; (iii) community participation, such as citizen involvement and social acceptability; (iv) operational efficiency, including measurable gains in collection frequency or fuel savings; and (v) cost and scalability, covering deployment and maintenance feasibility. A solution was considered transferable when it achieved satisfactory performance across these dimensions while remaining compatible with the constraints of low-resource African urban settings.
Second, each article was read in full and manually coded using a matrix that cross-referenced these five axes (Table 2). Quantitative data (e.g., frequency of use of a sensor or network protocol) were aggregated to produce statistical visualizations. Qualitative data (narratives, contextual limitations, local successes) were synthesized as comparative observations. This dual reading, both qualitative and interpretative, provided a cross-cutting view of practices in smart waste collection and helped build a typology of approaches adapted to low-resource environments.
Data extraction was carried out using a structured synthesis table specifically designed for this review. This tool centralized key information from each study in a systematic and comparable manner, directly linked to the thematic coding axes. The table was developed in Microsoft Excel, which facilitated sorting, linking variables, and generating statistical graphs. This process proved essential for structuring the comparative analysis and highlighting trends, convergences, and divergences across geographical and technological contexts.
The coding framework was organized along thematic dimensions such as hardware (sensors, microcontrollers), network technologies, user/community participation, governance mechanisms, and contextual implementation factors. While the review included 36 studies, some of them reported multiple occurrences (e.g., different sensors, microcontrollers, or network types), meaning that a single study could contribute to several categories within a dimension. As a result, denominators vary across figures, which prevents totals from exceeding 100% and ensures a transparent representation of the coded distributions.
Table 2. General description of the 36 smart waste management initiatives.
Study |
Adaptation Techniques |
Connectivity Solutions |
Integration of Community Engagement |
Operational Efficiency Gains |
Cost and Scalability |
[33] |
Low-cost smart bins with remote
monitoring adapted for Nigerian urban
areas |
Sensor data
transmission via
Wi-Fi for real-time monitoring |
Limited direct community participation; focus on environmental justice |
Improved efficiency in terms of time and energy; reduced bin congestion |
Focus on accessibility and sustainability in low-income settings |
[1] |
Ultrasonic sensors and dynamic routing
algorithms adapted to Lahore’s infrastructure |
LoRaWAN and cellular networks for city-wide coverage |
No explicit community engagement;
system-centered
optimization |
32% improvement in route efficiency; 29% fuel reduction |
Demonstrated scalability through cloud analysis and pilot validation |
[34] |
Sensor models with regression and
classification for waste forecasting in Indian metropolises |
Internet-based data transmission with GPS tracking |
No direct
community
participation; focus on system
automation |
Dynamic route
optimization and waste prediction |
Focus on cost
reduction through sensor-based
automation |
[35] |
Arduino-based smart bins with
ultrasonic and
infrared sensors for Bangladesh |
Wi-Fi, GPS, and GSM modules for connectivity |
No community
engagement;
system alerts waste collectors |
Alerts reduce
overflow and save collection time |
Designed to be
cost-effective in
resource-limited
settings |
[36] |
Solar-powered smart bins with
intermittent cloud connectivity for Bangladesh |
Cloud-based data storage with
intermittent
connectivity |
Mobile apps for waste collectors;
indirect community interface |
Dynamic route
planning via
mobile apps |
Renewable energy
integration
enhances
sustainability and scalability |
[37] |
Blockchain-enabled VANET for
decentralized solid waste management in Pakistan |
UHF and
geofencing with blockchain for
secure
communication |
No direct
community
engagement; focus on system security |
Real-time vehicle tracking and waste collection
monitoring |
Pilot results suggest scalable and reliable deployment |
[38] |
Ultrasonic, weight, and gas sensors with Arduino for rapid waste collection |
Data sent to
municipal server via IoT |
No community
engagement;
system alerts
municipal
authorities |
Real-time
monitoring allows rapid waste
collection |
Focus on practical and low-cost
implementation for developing cities |
[39] |
Smart bins with
mobile app
integration for
Bangladeshi
municipalities |
IoT-based
communication
between bins and control centers |
Mobile app allows
citizens to report and provide
feedback |
Sensors and
human inputs
improve waste management
responsiveness |
Community
acceptance survey; system designed for scalability |
[40] |
IoT smart bins with semantic web and gamification for
Indonesia |
ESP32 Wi-Fi for data transmission; load cells and ultrasonic sensors |
Gamification
encourages user
participation and waste reduction |
Automated
updates of bin weight and
status improve
management |
Prototype tested; gamification
enhances
engagement and
sustainability |
[2] |
Low-cost smart bins with Wi-Fi sensors for Nigerian cities |
Wi-Fi network for remote bin level monitoring |
No direct
community
engagement; focus on operational
benefits |
Time, energy
efficiency, and
congestion
reduction |
Emphasis on
economic benefits for sustainable
urban
deployment |
[41] |
Theoretical analysis of challenges and
benefits of IoT
adoption in India |
Not specified;
focus on
communication framework |
Discusses producer acceptance and waste management |
Potential to
improve
monitoring and
management
efficiency |
Highlights initial
investment and
training costs as
barriers |
[17] |
Identification of IoT adoption barriers in India using Fuzzy
DEMATEL |
Not applicable;
focus on barrier analysis |
Highlights lack of awareness and
community
engagement |
Barriers include lack of planning and
dynamic routing |
Financial and
regulatory challenges limit scalability |
[42] |
Review of smart
collection and transport technologies for urban areas |
ICT-based
real-time
monitoring and tracking
technologies |
Notes lack of social awareness and public participation |
Stresses need for data-driven planning and route
optimization |
Cost-effectiveness and resource
constraints are
crucial in
developing
countries |
[16] |
MADM framework to evaluate IoT adoption barriers in Indian smart cities |
Not applicable;
decision-making framework for
barrier
prioritization |
Identifies
community
awareness and
service provider challenges |
Barriers impact
operational
efficiency and technology
adoption |
Supports policy
decisions for better scalability and cost management |
[20] |
Performance
analysis of
LoRaWAN and NB-IoT for
developing
countries |
LoRaWAN more
energy-efficient;
NB-IoT more
reliable |
No community
engagement goal; technical
performance study |
Network reliability and latency impact operational
efficiency |
Trade-offs between cost and energy
consumption affect scalability |
[43] |
IoT waste collection system adapted to
Parakou, Benin |
Sensor-equipped bins with real-time alerts |
No explicit
community
engagement;
system-centered
approach |
Optimizes
resource deployment and
reduces
operational costs |
Designed for urban, resource-limited
environments |
[44] |
Smart bin system with optimized routing for Ghana |
Bin-level detection via
microcontroller with GSM alerts |
Mobile app for
drivers; indirect
community
interface |
Real-time
monitoring
improves
collection
efficiency |
GSM latency noted; system designed for scalability |
[45] |
Smart bin with
ultrasonic sensor and cloud
communication for rural areas |
Cloud-based
remote
monitoring via IoT |
App monitors waste levels; no
direct
community role |
90% fill-level
detection accuracy improves
scheduling |
Cost-effective
design for
underdeveloped and rural
environments |
[46] |
ESP8266-based
system with
ultrasonic and soil moisture sensors |
Wi-Fi module for real-time data
transmission |
No community
engagement;
system alerts
authorities |
Dynamic route
adjustment
reduces fuel and operational costs |
Promotes circular economy and
environmental
sustainability |
[47] |
IoT-based waste
segregation with
machine learning for smart cities |
ESP32 and
ultrasonic sensors with IoT
communication |
No direct
community involvement; system alerts municipality |
Automated
segregation and timely collection
improve efficiency |
Focus on reducing human effort and
enhancing
scalability |
[26] |
Real-time monitoring and separation of dry and wet waste using IoT |
Sensor network with automatic data
logging and
notifications |
No explicit community engagement; focus on system automation |
Optimizes collection and route planning |
Suitable for both large and small urban environments |
[48] |
LoRa technology for real-time monitoring of bin fill level and air quality |
LoRa
communication for low-power,
long-distance data transfer |
No community
engagement;
technology-centered approach |
Data-driven resource allocation and
environmental
monitoring |
Cost-effective and scalable for residential and commercial areas |
[49] |
IoTBinCap algorithm for optimized
collection schedules in urban areas |
Sensor integration with IoT and
simulation-based validation |
No community
engagement; focus on operational efficiency |
Improved bin usage and reduced
overflows |
Algorithm allows
scalable management of urban waste |
[50] |
Smart waste
management based
on LoRa with
Floyd-Warshall
routing |
Low-cost sensors and LoRa for
real-time data
transmission |
No community
engagement;
system-centered
approach |
Shortest path routing saves time and
resources |
Affordable,
easy-to-replace
components enhance scalability |
[51] |
Dual-detection bins equipped with
ultrasonic and
methane sensors for decay detection |
ESP32 Wi-Fi
modules with cloud analytics and
machine learning |
No direct community engagement;
data-driven approach |
15% improvement in route efficiency;
decay monitoring |
Scalable AI-enhanced system for urban cleanliness |
[52] |
Smart bins with
machine learning for route
optimization |
Arduino MKR Wi-Fi and
ultrasonic
sensors for
real-time data |
No community engagement; focus on automation |
Reduced collection time, costs, and
environmental
impact |
Practical
implementation for smart urban waste management |
[53] |
Deep learning and IoT for autonomous waste sorting and route planning |
IoT sensors
combined with AI
algorithms for
classification |
User engagement features encourage responsible disposal |
Reduced operational costs and
environmental
footprint |
Scalability challenged by infrastructure and data needs |
[54] |
IoT smart bins with real-time
monitoring in
urban settings |
Sensor-equipped bins with machine learning and AI integration |
No direct
community
engagement;
system-centered |
50% increase in
collection
frequency; 20% fuel savings |
High initial costs
offset by
operational benefits |
[55] |
Multi-sensor IoT
system with cloud server for waste
monitoring |
Wi-Fi
communication for remote data access |
No explicit
community
engagement;
system alerts
authorities |
Real-time
monitoring
supports waste
reduction
strategies |
Cloud-based
system designed for urban scalability |
[56] |
IoT smart waste
system with RFID, sensors, and admin website |
Wi-Fi module for real-time bin
status notifications |
User website
informs public about nearest bin status |
Prevents overflow and reduces
financial costs |
Cement bin bodies improve safety and durability |
[57] |
IoT waste
management using RPL protocol for
optimized routing |
RPL routing
protocol for efficient,
low-power data transfer |
No community
engagement; focus on network
efficiency |
Reduces time, fuel, and labor for waste collection |
Protocol supports scalable IoT
network
deployment |
[13] |
Real-time
IoT-based
monitoring for
urban waste
management in
India |
Cloud platform with automated alerts for
municipal
response |
No direct
community
engagement;
system alerts
authorities |
Enables rapid
intervention and
reduces illegal
dumping |
Designed for
proactive, scalable
urban deployment |
[58] |
Multi-sensor IoT
system with
machine learning for waste
monitoring |
Arduino and ESP32 platforms with
real-time data alerts |
No direct community engagement; system automation |
Improves
efficiency of waste collection and
separation |
Cost-effective
solution suited for
urban
environments |
[59] |
IoT-based smart waste management
integrating all
stakeholders |
Android and web apps for
coordination
between
households and disposal sites |
High community
involvement through mobile and web
platforms |
Automated system streamlines entire waste management chain |
360-degree system
designed for full
automation and
scalability |
[8] |
IoT architecture based on LoRa for Bangalore solid waste
management |
LoRa for
long-distance,
low-power data transmission |
No community
engagement; focus on system
architecture |
Efficient data
collection and
monitoring via MQTT protocol |
Low-power,
long-range features enable urban
scalability |
[60] |
IoT-based
framework with two types of smart bins designed
specifically for COVID-19 affected households;
dynamic waste
collection
scheduling and route optimization |
Hybrid
architecture:
Wi-Fi for
residential bins + LoRaWAN for public bins;
integration with Blynk app, push
notifications, and Telegram alerts |
Indirect: special teams designated to collect from COVID
households to avoid contamination spread |
Improves packet
delivery ratio and throughput
compared to
existing systems; reduces
operational costs;
ensures timely waste collection from
sensitive areas |
Cost-effective due to hybrid
networking;
scalable to urban
areas; reduces
segregation costs since waste is
separated at source |
3. Results
3.1. Temporal and Geographical Evolution of Research
The annual distribution of publications identified in this review reveals a relatively stable dynamic between 2016 and 2022, with an average ranging from one to four articles per year. A clear inflection point, however, is observable from 2023 onward, marking a turning point in the attention given to the adaptation of IoT technologies to waste management in low-resource countries. This growth is linked to the democratization of low-cost microcontrollers and the wider adoption of low-power wide-area networks (LPWAN), particularly LoRa [8] [20] [48] [50].
This trend, illustrated in Figure 2, reflects renewed interest in more accessible technological solutions capable of addressing the structural constraints of precarious urban contexts. The dips observed in 2020 and 2022 may be associated both with the global disruption of research caused by the COVID-19 pandemic and with a stagnation in methodological approaches on this topic, due to the absence of significant renewal in analytical frameworks.
Figure 2. Temporal distribution of included studies by year of publication (2016 - 2024, n = 36).
Figure 3. Geographical distribution of included studies across regions (n = 36).
From a geographical perspective, Figure 3 highlights a strong concentration of studies in South Asia, particularly in India, which alone represents a large majority of the corpus, followed by Bangladesh and Pakistan. In Africa, Nigeria emerges as the main contributor, alongside a few West African countries such as Benin and Ghana. By contrast, Francophone Africa, and particularly the Democratic Republic of Congo, is almost absent from the experiments identified. This imbalance in scientific production raises questions about the representativeness of the existing results and limits the scope of the developed models in terms of transferability. It underscores the need to broaden study sites in order to better capture the diversity of contexts and local constraints when reflecting on the implementation of IoT in urban waste management.
3.2. Typologies of Technological Solutions
Figure 4. Distribution of microcontroller platforms (n = 38 coded occurrences across 36 studies).
Figure 5. Distribution of sensor types (n = 51 coded occurrences across 36 studies).
The majority of the devices identified rely on a homogeneous architecture centered on ultrasonic sensors, present in nearly 46% of cases (Figure 4). This choice reflects a compromise between cost, energy consumption, and ease of integration, but limits adaptability to complex or highly heterogeneous urban environments. Multi-sensor configurations, still in the minority (28%), introduce gains in precision but remain underexplored, particularly in low-infrastructure settings.
In terms of microcontrollers, Arduino dominates (42%), followed by ESP32 (30%) and Raspberry Pi (17%) (Figure 5). These platforms are favored for their low cost and technical accessibility, but their adoption often remains disconnected from contextual constraints such as energy stability or the need for embedded processing. The use of more advanced microcontrollers, or those specifically designed for constrained environments, remains marginal, highlighting a standardized approach with limited orientation toward frugal engineering or contextual resilience.
3.3. Communication Networks Used
Communication protocols play a key role in the viability of these systems. Figure 6 highlights the predominance of Wi-Fi–based systems, used in approximately 27% of the studies analyzed. This frequency of use can be attributed to the ease of integrating Wi-Fi modules into low-cost hardware architectures, particularly via microcontrollers such as Arduino or ESP8266. However, this technical option assumes the existence of a stable network, a condition rarely met in low-resource urban contexts, which raises questions about the actual transferability of these devices.
Figure 6. Distribution of network technologies (n = 41 coded occurrences across 36 studies).
In parallel, the growing use of LoRa and LoRaWAN networks, present in nearly 20% of cases, reflects an attempt to adapt to connectivity constraints. These long-range, low-power protocols offer significant potential in environments where telecommunication infrastructures are fragmented or absent. Nevertheless, their deployment often remains confined to experimental frameworks, with their long-term viability not systematically demonstrated.
Other communication modes, such as cloud-based solutions, GSM, or RFID, remain marginal, while nearly 30% of the studies do not specify the transmission modalities, thus limiting the critical assessment of technological choices. Taken together, these observations underscore the presence of a technological bias in documented approaches, where local reliability is often relegated to the background in favor of theoretical performance. This tension between technical sophistication and infrastructural realities deserves greater attention in future research.
3.4. Community Integration and Local User Participation
Figure 7 highlights the limited consideration of end-users in the systems studied. Nearly 60% of the solutions include no form of user interaction, confirming a largely technocentric approach. Interaction through mobile applications, present in 31% of cases, is often limited to notification functions without genuine feedback mechanisms. Only 9% of the studies incorporate direct participation, through co-design tools, acceptability surveys, or gamified mechanisms aimed at strengthening user ownership. Although relevant, these initiatives remain marginal. The widespread absence of community engagement raises questions about the sustainability, maintainability, and local anchoring of the proposed solutions.
Figure 7. Community participation approaches (n = 36 studies).
3.5. Functional Objectives and Scalability Constraints
The most frequently pursued technological objectives relate to collection monito-ring (Figure 8), improvements in operational efficiency, and route optimization. These priorities reflect a logistics-oriented approach, often at the expense of more systemic issues such as waste sorting or environmental analysis. While pragmatic, this functional concentration tends to reduce the transformative scope of the pro-posed solutions.
At the same time, the identified constraints reveal a persistent tension between technical ambitions and contextual feasibility (Figure 9). Although some soluti-ons claim demonstrated scalability, nearly half of the cases mention economic, technical, or regulatory limitations. Profitability in low-resource settings constitu-tes a recurrent design criterion but is rarely associated with mechanisms for susta-inable support. The integration of renewable energy remains marginal, highligh-ting a gap between sustainability discourse and actual technological choices. These trends point to the need to reorient system design toward architectures that are genuinely adapted to the constraints of low-resource territories.
Figure 8. Governance and institutional mechanisms (n = 36 studies).
Figure 9. Contextual implementation barriers and enablers (n = 55 coded occurrences across 36 studies).
4. Discussion
4.1. Critical Synthesis of Results
The cross-analysis of studies reveals a constant finding: IoT solutions are transferable to low-resource countries only if they are deeply redesigned, not merely transposed. The most viable configurations are those that combine frugal engineering with a systemic sobriety logic. This is reflected in the use of low-power open-source electronic components, low-bandwidth long-range protocols such as LoRaWAN, and decentralized architectures that minimize dependence on the cloud [12] [35] [43] [61]. These technical choices, modest in appearance, enable resilience in the face of power outages, weak network coverage, and the absence of structured technical support. They contrast with dominant approaches, often inspired by smart city standards from the Global North, whose sophistication becomes counterproductive in environments where service continuity is a luxury.
Beyond technical considerations, the few projects that incorporated contextual variables into their optimization logics (such as road conditions, topographic accessibility, and overflow frequency) achieved significantly more operational results. In these cases, waste collection was no longer designed solely around distance but prioritized according to the physical and social realities of the field. However, the near-total absence of longitudinal evaluation, social acceptability assessments, or post-deployment monitoring limits the scope of the identified experiments. This gap reflects a recurrent bias in IoT approaches in Africa: innovation too often remains technocentric, ignoring usage dynamics, economic trade-offs, and local capacities for appropriation. Truly transferable projects are those that combine the demand for ingenuity with field pragmatism, envisioning technology as an adjustable lever rather than an end in itself [1] [27] [43].
4.2. Structural Limitations Encountered
The limitations identified in the studies reviewed are not solely the result of inappropriate technological choices but stem from a deeper mismatch between the implicit requirements of the systems designed and the social and material realities of the contexts where they are deployed. Many projects rest on the tacit assumption of a minimum functional infrastructure: stable electricity, continuous network coverage, and trained human resources. Yet, in the peripheral neighborhoods of many African cities, these conditions are rarely met [62] [63]. A single sensor requiring regular recharging or a GSM module dependent on a stable signal can become inoperative within the first weeks. The problem lies not in the complexity of the technology but in its inability to withstand discontinuity, irregularity, and lived realities.
Another striking observation is the institutional isolation in which these projects are embedded. Very few are integrated into municipal or regional waste management policies. Most are the result of ad hoc initiatives led by research consortia or startups, often without lasting connections to local authorities. This lack of anchoring prevents the creation of governance, maintenance, and financing ecosystems capable of sustaining the solution beyond the experimental stage. In the absence of structured dialogue among engineers, policymakers, operators, and citizens, technology becomes an alien body, deployed in a territory that cannot support or repair it. This institutional vacuum is not only a technical obstacle; it also contributes to the invisibilization of communities, who remain excluded from decisions that directly concern them.
4.3. Observed Pathways of Innovation
In response to the structural limitations identified, some studies reveal forms of innovation that are discreet yet significant. These advances do not lie in the introduction of sophisticated technologies but in the capacity to adapt simple solutions to constrained environments. The recurring combination of minimalist sensors and LoRaWAN networks illustrates this logic. Unlike heavier and unstable protocols in Global South contexts, LoRa provides wide coverage with very low energy consumption, enabling systems that can operate for several months without recharging. The integration of microcontrollers such as ESP32 or Arduino, which are locally available and well documented, reinforces system autonomy while facilitating maintenance by non-specialist technicians. Here, innovation is not about power, but about robustness and continuity.
Other studies have proposed devices that go beyond the technical dimension alone by introducing a form of dialogue between the collection system and inhabitants. This link is established through accessible interfaces, SMS alerts, or community reporting systems. In some cases, sending notifications when bins are full or awarding reward points for punctual reports has helped strengthen user adherence. These approaches suggest that the intelligence of a system lies not only in its algorithms but also in its capacity to recognize citizens as co-actors. Finally, attempts to model waste collection routes contextually, by integrating variables such as road conditions, area usage, or risk levels, demonstrate a fine-grained understanding of the urban fabric. Where standardization fails, these approaches show that it is possible to design systems capable of embracing the shapes, ruptures, and urgencies of a living city.
4.4. Proposal for an Adapted Hybrid Model
The lessons drawn from this review suggest that a truly functional IoT system in low-resource contexts cannot be limited to superficial adaptation. It must be redesigned from the ground up, based on both vulnerabilities and often underestimated capacities [64]. The model proposed here does not aim to mimic Western standards reduced to their simplest form. On the contrary, it seeks to compose a sober yet intelligent architecture that combines technical robustness, social accessibility, and sensitivity to local dynamics. As illustrated in Figure 10, this hybrid architecture integrates both the technological components and the governance mechanisms required to adapt IoT-based waste collection to African urban contexts.
The device is based on a contextual priority index constructed from variables rooted in urban realities: physical accessibility of sites, road quality, frequency of overflows, and vulnerability of serviced areas such as markets, schools, or health centers. This index makes it possible to rank waste collection points according to a relevant order of treatment, not based on abstract metrics but on concrete criteria that directly affect residents’ quality of life. Once this prioritization is established, collection routes are optimized locally within each sector by an algorithm that takes into account terrain discontinuities.
On the hardware side, the system relies on ultra-low-power sensors powered by small solar modules, supported by microcontrollers capable of performing simple local computations. This reduces dependence on constant connectivity. Intelligence is distributed rather than centralized, protecting the system against network outages. On the user side, the interface is designed to work with tools already in common use: basic phones, SMS, and USSD menus. This approach is not about reducing technological ambitions but about cultivating an attentive form of ingenuity that turns constraints into drivers of innovation. In a country such as the DRC, it is less about importing technology than about claiming the right to a better organized city, where digital tools act as mediators rather than barriers.
Figure 10. Conceptual diagram of the adapted hybrid IoT model.
4.5. Limitations
While this review provides valuable insights into IoT-enabled waste collection in developing contexts, several limitations should be acknowledged. First, the search strategy relied on four major scientific databases, which, although comprehensive, may have excluded relevant studies indexed in other repositories, particularly regional databases. Second, only articles published in English and French were included, introducing a potential language bias and possibly overlooking contributions in other widely spoken languages in Africa and Asia. Third, this review synthesized findings descriptively without conducting a meta-analysis, which limits the ability to quantify effect sizes and statistically compare outcomes across studies.
Despite these limitations, the systematic PRISMA-guided approach, combined with the quality appraisal of included studies, ensures that the evidence base remains methodologically robust and relevant for informing future IoT-enabled waste management initiatives in African cities.
4.6. Implications for Research and Public Policy
The lessons drawn from this review extend beyond the technological field. They call for a broader reflection on how research is produced and how public policies are formulated in low-resource environments. On the academic side, it has become urgent to move beyond the paradigm of isolated technological demonstrations. Too many initiatives still focus on validating prototypes under ideal conditions, without considering the human, economic, and institutional variables that determine their viability. Research on IoT applied to waste management would gain in relevance if it adopted a transdisciplinary stance, bridging engineering sciences, urban studies, the sociology of practices, and the political economy of infrastructures. Such a shift in approach would also require a rethinking of performance evaluation criteria by integrating indicators of social sustainability, operational resilience, and citizen appropriation.
For public policymakers, this review highlights that useful innovation is not that which comes from outside, but that which is adjusted to the local fabric. An effective policy for modernizing waste management in the African context cannot be reduced to tenders for the import of turnkey technologies. It must begin with the establishment of flexible regulatory frameworks, open to experimentation, and capable of supporting local initiatives. This also involves financing pilot projects rooted in neighborhoods, training technicians capable of maintaining equipment, and fostering stronger articulation between municipalities, private operators, and communities. At the municipal level, it would be relevant to create urban innovation units able to test, adapt, and gradually scale up co-constructed solutions. For behind every uncollected waste item lies less a technical problem than a failure of collective organization. And this is precisely what public policies can address if they build on knowledge drawn from the field.
5. Conclusions
The results of this systematic review highlight a fundamental truth: in low-resource environments, it is not the technology itself that makes the difference, but its ability to adjust to reality. The direct export of IoT models designed for very different contexts has little impact, and may even reinforce inequalities in access to services. Adaptation, not importation, must therefore guide all technological innovation efforts in the field of urban waste management.
The most promising approaches identified in this review are not those that are the most technologically advanced, but those that combine three essential principles. Technical simplicity, which allows for local ownership and maintenance. Contextual logic, which grounds every choice in a fine-grained understanding of the constraints and opportunities of the territory. And finally, citizen participation, which transforms users into co-responsible actors within the system. It is at this intersection that truly transferable, effective, and sustainable solutions can be found.
In light of these findings, several recommendations can be formulated. First, technology should be conceived as a support tool rather than an end in itself, carefully adapted to local uses, skills, and expressed needs. Second, open, modular, and scalable architectures should be favored in order to enable progressive adjustments rather than uniform and rigid deployments. Finally, each experiment should be firmly anchored in a localized process that integrates evaluation, iteration, and collective learning before any national-scale extension.