Research Articles

Permanent URI for this collection${dspace.ui.url}/handle/20.500.12479/35

Browse

Now showing 1 - 20 of 420

Augmented Reality-Based Outdoor Navigation System for Urban Firefighting: A Case of Dar es Salaam, Tanzania
(Indian Society for Education and Environment, 2025-08-11) Sammadile, Zulfa; Luwemba, Godfrey; Mgawe, Bonny; Nyambo, Devotha
Objective: This study aimed to develop an Augmented Reality (AR) based system for outdoor navigation to aid fire truck drivers in reaching incident areas during emergency response in Dar es Salaam, Tanzania. It involved developing three modules: an emergency incident reporting mobile application to improve incident localization, a route planning module to enable the visualization of routes and their conditions, and the AR navigation module to deliver real-time AR-based navigation instructions. Methods: Web AR technologies, specifically WebXR and Three.js, were employed to render AR navigation instructions based on a preselected route from the route planning module, which was developed using the Google Maps JavaScript, Places, and Routes APIs, and complemented with an added layer of house data by leveraging QGIS. Ionic Framework was used to develop the emergency incident reporting platform. The system’s effectiveness was evaluated through User Acceptance Testing (UAT) with 45 Dar es Salaam civilians and 5 fire brigade officers. Findings: The developed system improves incident localization through automatic location sharing via the emergency incident reporting platform and searchable address data via the custom geospatial dataset. It also improves the clarity of navigation instructions through its AR navigation module and routing decisions with its route planning module. During system validation, 91% of the participating civilians completed the emergency incident reporting task without prior training. Furthermore, all the participating officers reported clearer navigation instructions for the AR component, with particular appreciation for the route planning module. Although no direct comparison with existing methods was conducted, validation results revealed the system’s potential in improving emergency response in Dar es Salaam. Novelty: This study presents a context-aware AR navigation system integrated with a custom geospatial dataset that lacks in most previous studies and existing solutions. This integration enhances incident localization, route planning, and navigation, essential factors in improving emergency response.
A Response-by-Retrieval Chatbot for Enhancing Horticulture Extension Services in Tanzania
(ETASR, 2025-10) Lubawa, Amos; Nyambo, Devotha; Mduma, Neema; Sinde, Ramadhani
Horticulture, which encompasses the cultivation of flowers, fruits, herbs, and vegetables, is a key contributor to Tanzania’s export revenue generation. Smallholder farmers are the primary producers of these crops, and they rely heavily on extension services for critical information that shapes both their economic success and long-term sustainability. However, the delivery of such services from the government and other stakeholders faces challenges, including constraints in human capital, geographic barriers, misaligned information needs, as well as issues with the timeliness of information dissemination. To address these challenges, this study developed a Swahili-language chatbot designed to provide timely, context-specific information tailored to the needs of farmers. To ensure credibility and relevance, key private and public stakeholders were consulted, and comprehensive farming guides were collected to build a custom dataset. This dataset consisted of 307 passages and 2,231 question-answer pairs. Four multilingual models, Multilingual Bidirectional Encoder Representations from Transformers (mBERT), Cross-lingual Language Model Pretraining RoBERTa (XLM-R), Multilingual Decoding-Enhanced BERT with Disentangled Attention (mDeBERTa), and Afro Cross-lingual Language Model Pretraining RoBERTa (AfroXLMR), were finetuned on this dataset for a question-answering task. Among them, the mDeBERTa model achieved the strongest performance, with an Exact Match (EM) score of 62.69% and an F1 score of 75.35%. These results demonstrate the potential of adapting advanced language models for specialized, low-resource language tasks in agriculture. The deployment of mDeBERTa in a prototype chatbot highlights a promising pathway to bridge information gaps and enhance the accessibility of extension services for Tanzania’s smallholder farmers.
Efficient spatio-temporal modeling for sign language recognition using CNN and RNN architectures
(Frontiers, 2025-08-25) Myagila, Kasian; Nyambo, Devotha; Dida, Mussa
Computer vision has been identified as one of the solutions to bridge communication barriers between speech-impaired populations and those without impairment as most people are unaware of the sign language used by speech-impaired individuals. Numerous studies have been conducted to address this challenge. However, recognizing word signs, which are usually dynamic and involve more than one frame per sign, remains a challenge. This study used Tanzania Sign Language datasets collected using mobile phone selfie cameras to investigate the performance of deep learning algorithms that capture spatial and temporal relationships features of video frames. The study used CNN-LSTM and CNN-GRU architectures, where CNN-GRU with an ELU activation function is proposed to enhance learning efficiency and performance. The findings indicate that the proposed CNN-GRU model with ELU activation achieved an accuracy of 94%, compared to 93% for the standard CNN-GRU model and CNN-LSTM. In addition, the study evaluated performance of the proposed model in a signer-independent setting, where the results varied significantly across individual signers, with the highest accuracy reaching 66%. These results show that more effort is required to improve signer independence performance, including the challenges of hand dominance by optimizing spatial features.
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.
A decentralized IoT and blockchain-based architecture for general-purpose vehicle speed data collection: a case study of Tanzania’s highways
(Sciendo, 2025-06-09) Njuu, Kevin; Dida, Mussa; Runyoro, Angela-Aida
This study addresses the limitations of Tanzania’s current vehiclespeed detection systems, which are mostly manual, costly, andvulnerable to corruption. Existing automated systems are limited inscope and depend on centralized architectures, making them prone to data manipulation. To overcome these issues, this study proposes a novel four-layer decentralized architecture that integrates Internet of things (IoT) and blockchain technologies, offering a secure, transparent, and automated solution tailored to the Tanzanian context. The system supports the collection of general-purpose speed data and utilizes hyperledger fabric (HLF) channels to ensure confidentiality and privacy. A proof-of-concept was tested in parallel mode, achieving a latency of 1 s, throughput of 1 record/s, and an error rate of 0.07% under varying record loads. When scaling the number of nodes (NN), it maintained 3 s latency, 0.4 records/s throughput, and 7.5% error rate, demonstrating a 50% latency reduction and over double the throughput compared with sequential and asynchronous modes. Future research should explore the integration of 5G for further improvements.
AI Ethical Policy in Africa
(Digital Government: Research and Practice, 2025-10-10) Leo, Judith; Madu, Nanko; Ezenwa, Lydia; Ramkissoon, Lavina; Adebayo, Philip; Mugume, Timothy; nalwooga, Samiiha
Artificial intelligence (AI) is a rapidly growing sector within the African innovation ecosystem. Amidst rapid technological advancements in the development of AI solutions in African countries, it has been noted that AI technologies pose ethical quandaries. As a result, there is a need to identify these AI ethical policies in Africa and explore their benefits and detrimental in the AI ecosystem. Therefore, this study utilized both qualitative and quantitative methodologies using desk research and AI stakeholdersǯ engagement through key information interviews and focus group discussions, case studies, online surveys, and webinar sessions. A total of 300 publications and 165 participants contributed to the study. The result shows that about 12% of the collected responses indicate that the adoption, development, and use of AI ethical policies in African countries are in the initial stage. The AI ethical policies are beneficial; however, the following challenges hinder their adoption and development, which include limited understanding of AI, funding, lack of access to data, inadequate infrastructure, such as internet connectivity, and skills shortage. The study recommends the enhancement of collaborative networks for resource sharing within the AI stakeholdersǯ ecosystem and the development of adequate infrastructure that enhances the adoption of AI ethical policy.
Prediction of SACCOS Failure in Tanzania using Machine Learning Models
(Engineering, Technology and Applied Science Research, 2024-02-08) Magashi, Cosmas; Agbinya, Johnson; Sam, Anael; Mbelwa, Jimmy
Savings and Credit Co-Operative Societies (SACCOS) are seen as viable opportunities to promote financial inclusion and overall socioeconomic development. Despite the positive outlook for socioeconomic progress, recent observations have highlighted instances of SACCOS failures. For example, the number of SACCOS decreased from 4,177 in 2018 to 3,714 in 2019, and the value of shares held by SACCOS members in Tanzania dropped from Tshs 57.06 billion to 53.63 billion in 2018. In particular, there is limited focus on predicting SACCOS failures in Tanzania using predictive models. In this study, data were collected using a questionnaire from 880 members of SACCOS, using a stratified random sampling technique. The collected data was analyzed using machine learning models, including Random Forest (RF), Logistic Regression (LR), K Nearest Neighbors (KNN), and Support Vector Machine (SVM). The results showed that RF was the most effective model to classify and predict failures, followed by LR and KNN, while the results of SVM were not satisfactory. The findings show that RF is the most suitable model to predict SACCOS failures in Tanzania, challenging the common use of regression models in microfinance institutions. Consequently, the RF model could be considered when formulating policies related to SACCOS performance evaluation.
Centralized Admission System for Advanced Level Private Schools: Case of Kilimanjaro Region, Tanzania
(International Association for Digital Transformation and Technological Innovation, 2019-03-24) Fujo, Mwapashua; Dida, Mussa
Globally, it is desirable to have fair and transparent student admissions into both public and private universities, colleges and schools. A case in point, in Tanzania 35% of students enrolled each year in higher level learning institutions and technical education are from Advanced Level (A-level) private schools. Of concern is that, this paper confirms and quantify that 93.5% of admissions into A-level schools were performed on paper based. Such admissions were characterized by multiple admissions, being costly, inconsistency, inaccuracy, and difficulties in following admission procedures. On the other hand, existing manual admission systems were considered unfair and not transparent. To mitigate these challenges a centralized web-based solution named Tanzania Central Processing Admission System (TCPAS) has been conceptualized to resolve the identified admission challenges. This paper presents on an ongoing research work aimed to address the challenges facing the current admission procedures of A-Level private schools in Tanzania. The proposed TCPAS is designed to be a web-mobile solution. The TCPAS tool is intended to reduce admission costs by reducing turnaround time for entire admission processes; encourage the use of paperless admission; control forgery over entry qualifications (certificates) during the admission process; has a centralized data handling capability; saves admission vacancies; and reach many geographically scattered applicants. Moreover, questionnaires were used to gather requirements from 150 respondents from the case study (Kilimanjaro region).
A Web-based Data Visualization Tool Regarding School Dropouts and User Asssesment
(Engineering, Technology and Applied Science Research, 2020-08) Kayanda, Angelika; Machuve, Dina
Data visualization is important for understanding the enormous amount of data generated daily. The education domain generates and owns huge amounts of data. Presentation of these data in a way that gives users quick and meaningful insights is very important. One of the biggest challenges in education is school dropouts, which is observed from basic education levels to colleges and universities. This paper presents a web-based data visualization tool for school dropouts in Tanzania targeting primary and secondary schools, together with the users’ feedback regarding the developed tool. We collected data from the United Republic of Tanzania Government Open Data Portal and the President’s Office - Regional Administration and Local Government (PO-RALG). Python was then used to preprocess the data, and finally, with JavaScript, a web-based tool was developed for data visualization. User acceptance testing was conducted and the majority agreed that data visualization is very helpful for quickly understanding data, reporting, and decision making. It was also noted that the developed tool could be useful not only in the education domain but it could also be adopted by other departments and organizations of the government.
Mobile-based system for cost-effective e-learning contents delivery in resource and bandwidth constrained learning environments
(Hong Kong Bao Long Accounting And Secretarial Limited, 2014-12-01) Mahenge, Michael; Mwangoka, Joseph
The advancement in Information and Communication Technologies (ICTs) has brought opportunities for the development of Smart Cities. The Smart City uses ICT to enhance performance and wellbeing, to reduce costs and resource consumption, and to engage more effectively and actively with its citizens. In particular, the education sector is adopting new ways of learning in Higher Education Institutions (HEIs) through e-learning systems. While these opportunities exist, e-learning content delivery and accessibility in third world countries like Tanzania is still a challenge due to resource and network constrained environments. The challenges include: high cost of bandwidth connection and usage; high dependency on the Internet; limited mobility and portability features; inaccessibility during the offline period and shortage of ICT facilities. In this paper, we investigate the use of mobile technology to sustainably support education and skills development particularly in developing countries. Specifically, we propose a Cost-effective Mobile Based Learning Content Delivery system for resource and network constrained environments. This system can be applied to cost-effectively broaden and support education in many cities around the world, which are approaching the 'Smart City' concept in their own way, even with less available technology infrastructure. Therefore, the proposed solution has the potential to reduce the cost of the bandwidth usage, and cut down the server workload and the Internet usage overhead by synchronizing learning contents from some remote server to a local database in the userâ€™s device for offline use. It will also improve the quality of experience and participation of learners as well as facilitate mobility and portability in learning activities, which also supports the all-compassing learning experience in a Smart City.
IoT-powered system for environmental conditions monitoring in poultry house: A case of Tanzania
(Taylor and Francis Online, 2021-07-07) Lufyagila, Beston; Machuve, Dina; Clemen, Thomas
Poultry health is imperative for the continued growth of poultry and increased production. Environmental conditions such as temperature, humidity, and ammonia gas have an impact on poultry health. They affect the respiratory system and eventually cause death. In Tanzania, most smallholder farmers use charcoal and kerosene stoves to control the environmental parameters since they have limited access to low-cost, secure, and user-friendly poultry house monitoring systems. However, these traditional methods are unreliable, difficult to manage, not environmentally friendly, and inaccurate. Data were collected from 120 poultry farmers in Arusha and Kilimanjaro regions using convenience and snowball sampling techniques. Of the respondents, 43% revealed that smallholder farmers do not adopt automated systems despite being available because they are expensive. In this study, a system based on the Internet of Things (IoT) was developed for environmental conditions monitoring in poultry houses for low-resourced smallholder farmers in Tanzania. The system saves time (84%) and labour costs (66.7%) compared to the traditional system, as the farmer can monitor and control the conditions securely, reliably, and remotely. The study also proposes an algorithm for the system to work online and offline (i.e.,, synchronizing with the cloud server when internet access is available).
Usefulness and Usability of a Web-Based System for Disseminating Maize Production Knowledge in the Manyara Region, Tanzania
(African Journals Online (AJOL), 2025-04-30) Ngessa, Victor; Mtei, Kelvin; Michael, Kisangiri; Magesa, Mawazo
This study designed and evaluated the usefulness and usability of a web-based system for disseminating maize production knowledge to farmers in Tanzania’s Manyara region. The system covered all stages of maize production, from farm preparation to harvesting and storage, and provided knowledge in multimedia formats with interactive features. Eighty-one smallholder maize farmers from Hanang, Mbulu, and Babati districts evaluated the system's usefulness and usability (satisfaction) via questionnaires, while an additional 10 respondents assessed its usability in terms of effectiveness and efficiency. Researchers observed respondents using the system to assess its effectiveness and efficiency. Data were analysed both quantitatively and qualitatively. Results showed that most smallholders found the system’s content and features useful (x̅=4.5). With an average System Usability Scale score of 87.5 and all users successfully completing tasks, the system proved easy to use. The study highlights the need for designing useful and user-friendly web-based systems, like the one under study, to help smallholders in developing countries access crop production knowledge, enhancing their productivity and decision-making capabilities.
Modeling the effects of contaminated environments on the transmission dynamics of avian influenza in humans and domestic birds
(Elsevier, 2025-09) Soka, Serapia; Mayengo, Maranya; Kgosimore, Moatlhodi
Avian influenza is a viral infection that affects birds and can spread to humans and other animals, causing severe illness and high mortality in both populations. Migratory birds are the primary transmitters of the virus, shedding it into the environment. This study investigates the effects of contaminated environments in the transmission dynamics of avian influenza. We suggest a deterministic mathematical model to capture the interactions between humans, domestic birds, and contaminated environments. A model takes the form of a system of non-linear ordinary differential equations. The next-generation matrix technique calculates the basic reproduction number (). The stability of both the disease-free and endemic equilibrium points is analyzed. When, the avian influenza free equilibrium is globally asymptotically stable, whereas when, the endemic equilibrium is globally asymptotically stable. The Latin Hypercube Sampling (LHS) and partial rank correlation coefficients (PRCC) methods are employed to assess the sensitivity of the model parameters. A numerical simulation is performed to investigate the effects of different model parameters associated with environmental contamination towards. The results indicate that the transmission rates of the avian influenza virus by humans and domestic birds are directly proportional.
Exploring the role of funding-driven vaccination in infection dynamics of TB: A mathematical modeling approach
(Elsevier B.V., 2025-08-26) Ruojaa, Chiganga; Mayengoa, Maranya; Nyerere, Nkuba; Nyabadza, Farai
This study explores the impact of funding-driven vaccination on the transmission and spread of tuberculosis within the human population. To accurately capture the human behavior, a deterministic mathematical model is formulated, incorporating attitudes of patients towards hospital treatment. The well-posedness of the model is examined using the Lipschitz condition. An effective funding reproduction number is derived via the next-generation matrix method. Model parameters for simulation reasons are estimated by fitting to real-world data. Both theoretical and numerical findings show that decreases with increased vaccination funding and a higher proportion of patients exhibiting positive attitudes towards hospital treatment. These findings underscore the importance for policymakers to prioritize health by ensuring sufficient funding for vaccination programs, which is crucial for reducing disease burden in the community.
Modeling the Effects of Human Awareness and Use of Insecticides on the Spread of Human African Trypanosomiasis: AFractional-Order Model Approach
(MDPI, 2025-09-25) Koga, Oscar; Mayengo, Maranya; Helikumi, Mlyashimbi; Mhlanga, Adquate
In this research work, we proposed and studied a fractional-order model for Human African Trypanosomiasis (HAT) disease transmission, incorporating three control strategies: health education campaigns, prevention measures, and use of insecticides. The theoretical analysis of the model was presented, including the computation of disease-free equilibrium and basic reproduction number. We performed the stability analysis of the model and the results showed that the disease-free equilibrium point was locally asymptotically stable whenever ℛ0<1 and unstable when ℛ0>1. Furthermore, we performed parameter estimation of the model using HAT-reported cases in Tanzania. The results showed that fractional-order model had a better fit to the real data compared to the classical integer-order model. Sensitivity analysis of the basic reproduction number was performed using computed partial rank correlation coefficients to assess the effects of parameters on HAT transmission. Additionally, we performed numerical simulations of the model to assess the impact of memory effects on the spread of HAT. Overall, we observed that the order of derivatives significantly influences the dynamics of HAT transmission in the population. Moreover, we simulated the model to assess the effectiveness of proposed control strategies. We observed that the use of insecticides and prevention measures have the potential to significantly reduce the spread of HAT within the population.
An in-depth analysis of female students enrollment dynamics in Tanzanian mathematics Master’s programs using mixed-methods
(Elsevier Ltd, 2025-09-09) Karunde, Safina; Mduma, Neema; Masanja, Verdiana
The under representation of female students in mathematics Master’s programs is a global challenge, including in Tanzania. Despite recognition, gaps persist in understanding historical enrollment patterns and contextual factors influencing female enrollment in Tanzanian mathematics Master’s programs. This research addresses these gaps through a mixed-methods approach. Quantitative analysis using an Auto regressive Integrated Moving Average (ARIMA) model for enrollment forecasting reveals nuanced dynamics in female enrollment, enhancing precision in predictions. Specifically, the ARIMA(0,1,1) model predicts the constant change of future values beyond the available data, while the prediction interval indicates enrollment fluctuations, gradually decreasing within the confidence intervals, and considering the simulated features. Qualitative document analysis provides contextual insights into socioeconomic and policy-related factors, such as cultural attitudes, societal norms, educational policies, availability of female role models, level of support, and gender biases. A Support Vector Machine (SVM) model comprehensively analyzes the impact of these (contextual) factors on the probability of female enrollment. Essentially, the prioritization of efforts should commence with addressing educational policies, promoting female role models, increasing support mechanisms, challenging cultural attitudes and societal norms, and addressing gender biases with nuanced strategies. Eventually, this study offers a cohesive under- standing of female enrollment dynamics, providing a foundation for targeted interventions and policies to foster gender diversity in Tanzanian mathematics Master’s programs.
Status of e-learning implementation to enhance learning in Tanzanian university institutions
(International Journal of Technology Enhanced Learning, 2025-07-10) Ambele, Raiton; Kaijage, Shubi; Dida, Mussa; Trojer, Lena
This study examines the current e-learning implementation in Tanzanian universities, highlighting the challenges and opportunities associated with its utilisation. Data was collected from 610 respondents, including e-learning support staff, lecturers, and students from five selected universities through questionnaires and interviews. The findings reveal that universities in Tanzania still face implementation challenges despite significant growth in e-learning adoption. These challenges encompass limited internet and technological infrastructure, inadequate access to high-quality e-learning materials, low digital literacy proficiency among students and educators, insufficient support systems, and resistance to change. Strategies such as improving internet connectivity, developing relevant educational materials, providing comprehensive training and support, establishing robust learner support systems, and fostering an innovative culture are recommended to address these issues. By tackling these challenges, Tanzanian higher education institutions can effectively integrate e-learning and enhance the educational experiences of their students.
Calling for synergized strategies to monitor and mitigate urban air and noise pollution in low and middle-income countries
(IOP publishing, 2025-08-01) Gani, Shahzad; Clark, Sierra; Agrawal, Girish; Arku, Raphael; Jejeola, Martins; Kubwimana, Jean; Kumari, Kajal; Rojas, Néstor
Modelling and analysis of hydraulic transients in water pipelines using physics-informed neural network
(Elsevier, 2025-03-20) Mushumbusi, Philbert; Leo, Judith; Chaudhari, Ashvinkumar; Masanja, Verdiana Grace
Hydraulic transients remain a challenge in fluid flow systems, spanning basic pipelines to complex networks. While advances in transient analysis methods have been made, most approaches require full boundary condition data or rely on computationally intensive mesh- based techniques. In response to these limitations, PINNs have emerged as a promising alternative for predicting pressure and flow rate transients in pipeline systems without requiring complete knowledge of boundary conditions. The PINN model was trained both with and without initial and boundary condition data, achieving results that matched the Method of Characteristics reference with remarkable accuracy. Notably, the model effectively captured pressure and flow rate traces, even when tested on data from unmonitored locations. This demonstrate the robustness of PINN in addressing incomplete data challenges, enhance mesh- free computation, and optimising transient analysis. These findings highlight PINN as a powerful tool for improving field data accuracy and computational efficiency in hydraulic systems, paving the way for their broader application in fluid flow networks.
A Secured Energy Saving With Federated Assisted Modified Actor-Critic Framework for 6G Networks
(IEEE, 2025-06) Abubakar, Attai; Mollel, Michael; Ozturk, Metin; Ramza, Naeem
Ultra-dense base station deployments must be operated in a way that allows the network's energy consumption to be adapted to the spatio-temporal traffic dynamics, thereby minimizing overall energy consumption. To achieve this goal, we leverage two artificial intelligence algorithms—federated learning and actor-critic—to develop a proactive and intelligent cell switching framework. This framework can learn the operating policy of small base stations in an ultra-dense heterogeneous network, resulting in maximum energy savings while respecting quality of service (QoS) constraints. Additionally, the use of federated learning enhances the security of the system as only the model parameters are shared among the entities, rather than the raw and potentially sensitive data. In other words, in the event of a data breach or eavesdropping, only the parameters of the developed artificial intelligence model would be compromised. This approach ensures that the network remains secure against attacks targeting confidential and sensitive data. The performance evaluation reveals that the proposed framework can achieve an energy saving that is about 77% more than that of the state-of-the-art solutions while respecting the QoS constraints of the network.

Browse

Recent Submissions