Browsing by Author "Lyimo, Martine"

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Adaptive Antenna for Maritime LoRaWAN: A Systematic Review on Performance, Energy Efficiency, and Environmental Resilience
(MDPI, 2025-10-03) Lyimo, Martine; Mgawe, Bonny; Leo, Judith; Dida, Mussa; Michael, Kisangiri
Long Range Wide Area Network (LoRaWAN) has become an attractive option for maritime communication because it is low-cost, long-range, and energy-efficient. Yet its performance at sea is often limited by fading, interference, and the strict energy budgets of maritime Internet of Things (IoT) devices. This review, prepared in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, examines 23 peer-reviewed studies published between 2019 and 2025 that explore adaptive antenna solutions for LoRaWAN in marine environments. The work covered four main categories: switched-beam, phased array, reconfigurable, and Artificial Intelligence or Machine Learning (AI/ML)-enabled antennas. Results across studies show that adaptive approaches improve gain, beam agility, and signal reliability even under unstable conditions. Switched-beam antennas dominate the literature (45%), followed by phased arrays (30%), reconfigurable designs (20%), and AI/ML-enabled systems (5%). Unlike previous reviews, this study emphasizes maritime propagation, environmental resilience, and energy use. Despite encouraging results in signal-to-noise ratio (SNR), packet delivery, and coverage range, clear gaps remain in protocol-level integration, lightweight AI for constrained nodes, and large-scale trials at sea. Research on reconfigurable intelligent surfaces (RIS) in maritime environments remains limited. However, these technologies could play an important role in enhancing spectral efficiency, coverage, and the scalability of maritime IoT networks.
Dataset: Optimizing LoRaWAN Throughput in Maritime Environments Through Adaptive Coding and Modulation in Rayleigh Fading Channels
(Zenodo, 2025) Lyimo, Martine; Mgawe, Bonny; Leo, Judith; Dida, Mussa; Michael, Kisangiri
This dataset supports the article “Optimizing LoRaWAN Throughput in Maritime Environments through Adaptive Coding and Modulation under Rayleigh Fading”. It includes simulation outputs and MATLAB source code for reproducing all figures and results in the study. Files include throughput, PER, energy efficiency, spectral efficiency, and an ACM algorithm function.
An Integrated Deep Learning-based Lane Departure Warning and Blind Spot Detection System: A Case Study for the Kayoola Buses
(IEEE, 2023-11-16) Ziryawulawo, Ali; Mduma, Neema; Lyimo, Martine; Mbarebaki, Adonia; Madanda, Richard; Sam, Anael
Deep learning-based driver assistance systems (ADAS) have attracted interest from researchers due to their impact on improving vehicle safety and reducing road traffic accidents. In Uganda, road accidents have continued to soar with an increase of up to 42% in 2021 due to the growing road traffic density. To curb the high rates of road accidents, especially for heavy-duty vehicles, Kiira Motors Corporation a state-owned mobility solutions enterprise needs advanced driver assistance systems for improved safety of their market entry products- the Kayoola buses. This research presents an approach to vehicular safety enhancement through the integration of Lane Departure Warning (LDW) and Blind Spot Detection systems (BSD) using advanced deep learning algorithms. The resultant LDW and BSD system is realized on the Raspberry Pi platform, incorporating diverse sensors. By combining these advanced features, the study not only bridges an essential research void but also offers a practical resolution to pressing road safety concerns in the East African context. The integration of LDW and BSD systems through deep learning techniques marks a pivotal advancement in vehicular safety. The lane detection model was tested on DET and TuSimple datasets. Our model attained a mean F1 Score of 77.59% and a mean IoU of 65.26% on the Dataset for Lane Extraction (DET) and an overall accuracy of 97.96% on the TuSimple dataset. Our work presents an integrated lane departure warning and blind spot detection system that will be able to alert the driver using the graphical user interface, and auditory feedback. The anticipated real-world implementation is poised to substantiate the system’s effectiveness, thereby contributing to safer roads regionally and inspiring innovation in automotive engineering by leveraging artificial intelligence.
LSTM-based deep reinforcement learning for ISI mitigation in maritime LoRaWAN
(Elsevier B.V., 2026-02-28) Lyimo, Martine; Mgawe, Bonny; Leo, Judith; Dida, Mussa; Michael, Kisangiri
For reliable, long-range, low-power Maritime Internet of Things (MIoT) communication (e.g., vessel tracking, ocean monitoring, and offshore automation), LoRaWAN offers attractive coverage and energy efficiency. How ever, sea-surface reflections, wave motion, and platform mobility create time-varying multipath with large delay spread, which induces inter-symbol interference (ISI) and degrades packet delivery ratio (PDR) and energy performance. This paper proposes a Long Short-Term Memory (LSTM)-assisted Deep Reinforcement Learning (DRL) framework LSTM–DDPG Adaptive Modulation and Coding (LD-AMC) that proactively mitigates ISI by predicting short-term channel evolution and adapting the LoRaWAN physical-layer parameters. An LSTM pre dictor learns temporal correlations in observed link metrics (RSSI, SNR, PER, and RMS delay spread) and pro vides one-step-ahead forecasts, which are appended to the agent state. A Deep Deterministic Policy Gradient (DDPG) controller then selects the spreading factor (SF), coding rate (CR), bandwidth (BW), and transmit power (P ) within LoRaWAN constraints to maximize a reward that favors reliable delivery and throughput while penalizing energy cost and ISI severity. MATLAB/Simulink simulations under coastal and offshore two-ray maritime channels show that LD-AMC reduces ISI-induced symbol errors by up to 58%, improving PDR by up to 47% and reducing energy per delivered packet by up to 32% compared with standard and enhanced ADR baselines.
Mobile-based business-to-business platform for the pharmaceutical industry in Tanzania
(NM-AIST, 2021-07) Lyimo, Martine
The right to the uppermost attainable standard of health is a fundamental human right. In everyday life, mobile phones have become essential devices for most people in both developed and developing countries. Paper-based ordering of medicines in the pharmaceutical industry is time consuming and can enhance the spread of diseases such as COVID-19. Electronic ordering and stock management can solve these challenges, but while it has been widely adopted in developed countries, it remains underused in Tanzania. This study aimed to develop a mobile application (DawaFasta) and web application which links wholesale and retail pharmacies in Tanzania, to support electronic ordering and stock management. System and user requirements were collected through questionnaires, interviews and observations from 105 wholesale and retail pharmacies in Arusha, Dar es salaam and Kilimanjaro regions of Tanzania. The developed applications were evaluated for acceptability and usability by 9 wholesale and 15 retail pharmacy personnel to assess serviceability and usefulness. The results show that 96% found it very useful. The main reason is to bridge the gap between wholesale and retail pharmacies and provide easy access to medications. The 4% were not sure because they were unable to distinguish between legitimate and illegitimate online pharmacies. The application was assessed as having good usability for online pharmacy business purposes by wholesale and retail pharmacies. This application would need further development in order to raise awareness and add more features
Optimizing LoRaWAN throughput in maritime environments through adaptive coding and modulation in Rayleigh fading channels
(Taylor & Fransic Online, 2026-06-01) Lyimo, Martine; Mgawe, Bonny; Leo, Judith; Dida, Mussa; Michael, Kisangiri
This article proposes an Adaptive Coding and Modulation (ACM) scheme to improve the throughput of Long-Range Wide Area Network (LoRaWAN) in a maritime environment where wireless communication faces severe challenges from multipath propagation, interference, and Rayleigh fading. Unlike conventional LoRaWAN systems that use fixed physical layer configurations, dynamic adjustment of spreading factor (SF), coding rate (CR), and bandwidth (BW), according to channel conditions based on real-time Signal-to-Noise Ratio (SNR), is another solution to respond to communication challenges in the maritime environment. Simulations using a Rayleigh fading model with six different configuration levels demonstrate that the proposed ACM can achieve a gain of up to 103 times the throughput improvement compared to fixed SF7 under low SNR conditions while maintaining link reliability. This work establishes the foundation for implementing efficient and reliable LoRaWAN networks in maritime Internet of Things (IoT) applications.
Secure and Efficient Teller Desk Transactions Through Biometric and RFID Technology
(Springer Nature, 2026-04-01) Niyongabire, Pacifique; Michael, Kisangiri; Leo, Judith; Lyimo, Martine
This paper introduces a biometric banking system, a technological innovation that is set to transform cash withdrawals at CRDB Bank Burundi by eliminating paper-based transactions. The system’s key features include biometric authentication methods, such as fingerprint and RFID card scanning, integrated with advanced encryption protocols for robust data security. It effectively addresses common banking inefficiencies, such as lengthy transaction times and security vulnerabilities associated with manual processes. Additionally, the system offers real-time transaction notifications via SMS, WhatsApp, or email, enhancing transparency, and customer satisfaction. Extensive testing with CRDB staff, customers, and other interested individuals has demonstrated the system’s efficiency, reliability, and user acceptance. A statistical survey conducted with CRDB staff, customers, and other stakeholders revealed an 85% reduction in transaction times, a 90% increase in operational efficiency, and a significant improvement in security measures. The findings highlight significant reductions in transaction times, increased operational efficiency, and heightened security measures. This paper discusses the system's design, implementation, and testing phases, emphasizing its potential to revolutionize banking operations and set a precedent for digital transformation in the banking sector.