Browsing by Author "Sinde, Ramadhani"

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AI for anglophone africa: unlocking its adoption for responsible solutions in academia-private sector
(Frontiers, 2023-04-11) Sinde, Ramadhani; Diwani, Salim; Leo, Judith; Kondo, Tabu; Elisa, Noe; Matogoro, Jabhera
In recent years, AI technologies have become indispensable in social and industrial development, yielding revolutionary results in improving labor efficiency, lowering labor costs, optimizing human resource structure, and creating new job demands. To reap the full benefits of responsible AI solutions in Africa, it is critical to investigate existing challenges and propose strategies, policies, and frameworks for overcoming and eliminating them. As a result, this study investigated the challenges of adopting responsible AI solutions in the Academia-Private sectors for Anglophone Africa through literature reviews, expert interviews, and then proposes solutions and framework for the sustainable and successful adoption of responsible AI.
Air Pollution Monitoring System based on Wireless Networks - Simulation
(Innovative Systems Design and Engineering, 2014) Swagarya, Godbless; Kaijage, Shubi; Sinde, Ramadhani
Air pollution is one of environmental issues that cannot be ignored. Industrial growth and urbanization results in the air pollutants concentrations in many areas. These pollutants can cause damages in human health and other living organisms. The available pollutant emission monitoring systems, such as Opsis, Codel, Urac and TAS-Air metrics are typically expensive. In addition, these systems have limitations to be installed on chimney due to their principle of operation. This causes other areas surrounding the factories being unmonitored and hence cause healthy issues. This paper proposes an industrial air pollution monitoring system based on the technology of wireless sensor networks (WSNs). This system is integrated with the global system for mobile communications (GSM) and its communication protocol used is zigbee. The system consists of sensor nodes, a control center and data base through which sensing data can be stored for history and future plans. The proposed system can be deployed to the industries for monitoring carbon monoxide (CO), sulfur dioxide (SO2) and dust concentration caused by industrial emissions due to process.
Available Bandwidth Estimation Techniques (ABETS) For An Efficient Telemedicine Content Transport Network
(IJERT, 2013-07) Kayange, Daniel; Sinde, Ramadhani; Sam, Anael
The ability to measure end-to-end Available Bandwidth (unused capacity) in the network path is useful for route selection in overlay networks, QoS verification, network management, traffic engineering and real-time resource provisioning, control flow and congestion, construction of overlay - routing infrastructure, topology building in peer to peer networks, and call admission control, dynamic encoding rate modification in streaming applications, capacity planning, intelligent routing systems, and design of transport layer protocols. This paper investigates at applying techniques and measurement of Available Bandwidth (AB) in the congestion control for the transmission of an efficient telemedicine content transport network by using an important ABETs tool like Pathload, IGI, and Pathchirp. This paper discusses measurement and simulation results of wired and wireless networks for the unused capacity in the network. The results can assist an organization or country in estimating the network bandwidth requirements depending on the ability of exchange multimedia data of an organization or country. The logistics could cater implementation of low cost telemedicine applications. The telemedicine systems could include wireless and wired medical interface and communication infrastructure. A simulation has been done to investigate the network quality of service.
A Blockchain-based Conceptual Model to Address Educational Certificate Verification Challenges in Tanzania
(Engineering, Technology & Applied Science Research (ETASR), 2023-08-08) Said, Said; Dida, Mussa; Kosia, Efraim; Sinde, Ramadhani
The proliferation of counterfeit educational certificates is an ongoing issue around the world, including Tanzania. The effect of this malpractice is detrimental to the credibility of education. Traditional strategies to prevent fake certificates are abortive, calling for a more sophisticated approach. Blockchain technology has recently emerged as an ideal solution to this problem due to its inherent attributes that ensure disintermediation, immutability, tamper proof, anonymity, transparency, consensus, security, and trust. However, most existing blockchain-based solutions lack crucial functionalities that are pertinent to the Tanzanian education system. This study unveiled the challenges faced by the current verification system in Tanzania and proposed a blockchain-based conceptual model to address them. The proposed model is based on blockchain, smart contracts, and the Interplanetary File System (IPFS). Quantitative and qualitative methods were used to investigate certification problems in Tanzania and modeling techniques were used to construct the conceptual model. The findings showed that the main challenges of the current verification system emanate from manual procedures, unverifiable credentials, susceptibility of centralized storage systems, disintegrated verification systems, revocation problems, difficulties in communication, and high dependency on the issuers. These challenges undermine certificate verification, impose a significant setback in the fight against forgeries, and create loopholes for forgeries to persist. It was conceptually demonstrated that these issues can be resolved through the proposed blockchain-based solution.
Cluster based wireless sensor network for forests environmental monitoring
(International Journal of Advanced Technology and Engineering Exploration, 2020-02) Sinde, Ramadhani; Kaijage, Shubi; Njau, Karoli
Monitoring the forest’s weather has been essential to living things over the years. Currently, there is a shortage of information on real-time temporal and spatial environmental conditions of the forest that drive forest health condition. This work focuses on the sensing of humidity and temperature as weather data from the forest. Unlike the traditional systems used to collect weather information, the use of wireless sensor network (WSN) gives real-time data capture from every point of the forest. However, the WSN faces, the number of challenges, including low bandwidth, low power, and short battery lifespan. In this situation, batteries cannot be replaced since nodes are deployed in an inaccessible area. In order to prolong the network lifetime and reduce the network delay, we propose Zone based Clustering (ZbC) scheme and efficient routing to find the best path between source and cluster head. Initially, we deploy sensor nodes in three coronas namely C1, C2 and C3. We place the sink node at the center of the coronas. Based on the center point of the corona, we split each corona into four partitions each with three zones. Our work composed of two phases such as ZbC and Routing. In the first phase, we reduce energy consumption in data aggregation via ZbC scheme. In ZbC scheme, the hybrid Particle Swarm Optimization (PSO) and Affinity Propagation (AP) algorithm are utilized. Network delay is reduced in the second phase using Ant Colony Optimization (ACO) and FireFly Algorithm (FFA). Simulation results confirm that our proposed solution achieves a higher network lifetime up to 30%, reduces delay up to 35% and enhances throughput compared to the existing cooperative Time Division Multiple Access (cTDMA), Dynamic Random Allocation (DRA) and improved Artificial bee colony (iABC) methods.
A Comprehensive Blockchain-Based System for Educational Qualifications Management and Verification to Counter Forgery
(IEEE Access, 2025-02-17) Said, Said; Sinde, Ramadhani; Kosia, Efraim; Dida, Mussa; Ulomi, George
The prevalence of fake educational credentials poses a threat to the meritocratic nature of the education system and job markets. Verification of certificates to combat forgery has been a challenging endeavor due to the weaknesses of the current methods. Blockchain, capitalizing on its unique attributes, can provide an optimal solution to certification and verification problems by ensuring disintermediation, immutability, tamper-proof, efficiency, and security. Efforts to explore its potential in addressing these problems continue to gain momentum. However, the existing blockchain-based initiatives do not offer a holistic solution to the forgery problem, as they solely focus on a single education level or institution. Furthermore, these initiatives lack the essential features required to fully address this problem. This paper proposes a comprehensive blockchain-enabled system for issuing certificates from different educational levels and institutions in the country, providing a one-stop center for verifiers, such as employers, to verify all certificates a candidate possesses. As a proof of concept, a decentralized application (DApp), ElimuChain, has been developed, utilizing smart contracts and the InterPlanetary File System (IPFS). The system is deployed on the Binance Smart Chain (BSC) blockchain to evaluate its applicability in addressing the problem in the Tanzanian context. The results demonstrate that the proposed solution successfully manages the certification and verification process, and it is cost-effective, scalable, and efficient. Moreover, its performance was compared with the previous solutions in terms of latency and throughput. The comparison results show that it performs better than the counterpart for transactional operations.
Data Synthesis Technique for Categorical Pestes Des Petits Ruminants (PPR) Data Using CTGAN Model
(Pre prints,org, 2023-05-11) Nyambo, Devotha; Mduma, Neema; Sinde, Ramadhani; Lyimo, Tumaini
Data scarcity is a significant challenge in the field of Machine Learning (ML), as data collection can be expensive, time‐consuming, and difficult, particularly in developing countries. This challenge is exaggerated on the need to use dataset for livestock disease predictions for early intervention and surveillance. To address this challenge, this paper presents a data synthesis method that has been used to accurately generate new data samples from few real‐world data. With much data available to train the ML models, overfitting is eliminated. We present the use of Generative Adversarial Networks mainly the Conditional Tabular Generative Adversarial Network to synthesize categorical data for training machine learning models for prediction of the Pestes des Petits Ruminants (PPR) disease. The results showed that training score became 0.89 and the cross‐ validation score was 0.87 after synthesized data was used with Random Forest algorithm. The resulting dataset can be used to support the prediction and surveillance of the Pestes des Petits Ruminants (PPR) disease. The proposed method can also be applied to any domain with categorical data, and has the potential to improve the performance of machine learning models with increased data availability.
Development and Testing of Road Signs Alert System Using a Smart Mobile Phone
(Hindawi, 2022-04-21) Masatu, Eric; Sinde, Ramadhani; Sam, Anael
Road traffic accident is a major problem worldwide resulting in significant morbidity and mortality. Advanced driver assistance systems are one of the salient features of intelligent systems in transportation. They improve vehicle safety by providing real-time traffic information to the driver. Road signs play an important role in road safety. To be effective, road signs must be visible at a distance that enables drivers to take the necessary actions. However, static road signs are often seen too late for a driver to respond accordingly. In this study, a system for alerting drivers about road signs has been developed and tested using a smart mobile phone. The study was carried out in Tanzania along an 80 km highway stretch from Arusha to Moshi town. The Haversine formula was used to measure and estimate the distance between two pairs of coordinates using the smartphone-based navigation application, Google Map. The application provides a voice alert to a needed action that enhances driver’s attention. We propose an alternative method that identifies and modifies a specific class of energy inefficiencies. According to the experimental results, the proposed methodology has the benefits of high accuracy within a user radius of 10 meters, minimum bandwidth, and low-cost application. Furthermore, the system application was secured by limiting access to the application program interface key to avoid unauthorized access to sensitive information.
Development of Self Speaking Body Weight Scale for Visually Impaired People in Tanzania
(International Journal of Advances in Scientific Research and Engineering, 2021-07) Nzasangamariya, Gloriose; Sinde, Ramadhani; Kaijage, Shubi
Speaking weight scale is an important low vision health aid that measures and announces the measured weight. It is valuable in numerous applications such as Bathroom scale, Kitchen scale, and more. Different talking scales have been developed for the blindcommunity. Many talking scales have language options for English, German, French, or Spanish. However, only limited work exists for Selfspeaking visually impaired community in EAC given the fact that no talking scale can announce weight in Selfwhich is the common language in EAC. Therefore, this project aims to develop a Self-speaking weighing machine to assist visually impaired people in Tanzania. The developed device is divided into two major parts. On the front end of the design, sensors are used to capture weight parameters. The captured values are mapped onto a sequence of voice patterns. The back-end consists of transferring a sequence of voice patterns to a loudspeaker whereby the voice patterns are stored on an SD card. Finally, the developed device has been evaluated on several objects with known weights. The results show that the developed device accurately measures weight, displays weight, and announces it in the Selflanguage. However, blind people still need assistance from sightedpersons to be directed to the scale’s platform. The developed device has great potential as a low vision health aid for Selfspeakers. Moreover, the features of this device can be further improved by integrating iBeacon technology to increase the autonomy of blind people to use the scale and navigate to the device’s location safely.
Development of the RFID Based Library Management and Anti-Theft System:A Case of East African Community (EAC) Region
(International Journal of Advances in Scientific Research and Engineering, 2021-05) Irankunda, Deo; Sinde, Ramadhani; Mduma, Neema; Dida, Mussa
Radio Frequency Identification (RFID) Systems are becoming very useful in our daily life due to its advantages such as reduction of human error, theft prevention, time consuming reduction, the auto identification of targeted objects, business processes automation etc. RFID systems has been applied in library to manage items and library operations. Different approaches have been adopted in library management system in the East African region unfortunately some challenges including theft, pages removal, non-customer satisfaction, high cost of used system etc. are still persisting. To address these challenges, an RFID based library management and anti-theft system has been developed to East African Community (EAC) library. It focused on the use of Ultra High Frequency (UHF) band which enable readers and tags to transmit and receive data at longrange. The developed system facilitates users to borrowand return library items using RFID modules and enable librarians to monitor, record library activities and prevent no issued item to cross the library entrance or exit.
Internet of Things Security in Cloud: A Review on Fog Layer Security
(IEEE AFRICON, 2023-10-31) Kimeu, Japheth; Mtoi, Mary; Riwa, Janeth; Sinde, Ramadhani
Cloud computing in IoT systems enables flexible design with distributed data, infrastructure, and resources accessible from diverse industrial settings. The tremendous rise of the Internet of Things (IoT) has posed numerous issues to the centralized cloud computing architecture which are solved by fog computing. A passive rogue fog node acting as a man-in-the-middle attack poses a significant security vulnerability in the cloud fog layer, compromising data confidentiality and making identification difficult. This survey paper proposes an Intrusion Detection System (IDS) to protect the fog layer from the Man-in-the-Middle Attack (MitM/MITM/MiTM) which is present in the rogue node. Literature review methodology is employed to study various scientific articles providing a comprehensive survey of the existing security and privacy concerns in cloud computing.
Lifetime improved WSN using enhanced-LEACH and angle sector-based energy-aware TDMA scheduling
(Cogent Engineering, 2020-07-20) Sinde, Ramadhani; Begum, Feroza; Njau, Karoli; Kaijage, Shubi
Network lifetime remains as a significant requirement in Wireless Sensor Network (WSN) exploited to prolong network processing. Deployment of low power sensor nodes in WSN is essential to utilize the energy efficiently. Clustering and sleep scheduling are the two major processes involved in improving network lifetime. However, abrupt and energy unaware selection of cluster head (CH) is nonoptimal in WSN which reflects in the drop of energy among sensor nodes. This paper addresses the twofold as utilization of sensor nodes to prolong the node’s energy and network lifetime by LEACH-based cluster formation and Time Division Multiple Access scheduling (TDMA). Clusters are constructed by the design of an EnhancedLow-Energy adaptive Clustering Hierarchy protocol (E-LEACH) that uses parallel operating optimization (Grey Wolf Optimization (GWO) and Discrete Particle Swarm Optimization (D-PSO)) for selecting an optimal CH and helper CH. The fitness values estimation from GWO and D-PSO is concatenated to prefer the best optimal CH. E-LEACH also manages the cluster size which is one of the conventional disadvantages in LEACH. CHs are responsible to perform energy-aware TDMA scheduling which segregates the coverage area into 24 sectors. Alternate sectors are assigned
Machine learning model for predicting Peste des Petits Ruminants
(IEEE, 2023-11-16) Nyambo, Devotha; Ngulumbi, Nguse; Mduma, Neema; Sinde, Ramadhani; Lyimo, Tumaini
Peste des petits ruminants (PPR) is a viral disease that affects small ruminants and is prevalent in many developing countries, particularly in Africa and Asia. It can spread through direct contact, air, and contaminated feed and water. PPR can result in significant economic losses and has a detrimental impact on small ruminant production and trade. Clinical signs include fever, respiratory distress, and diarrhoea, and prevention is primarily through vaccination with a live attenuated vaccine. In this study, 24 samples were selected, pre-processed and synthesized using the Conditional Tabular Generative Adversarial Networks (CTGAN) model. Feature extraction was performed, revealing difficult_breathing as the most important feature in predicting PPR in ruminants. The study used Random Forest Classifier which was fine-tuned using Bayesian Optimization to attain an accuracy of 91%.
A Novel Mechanism for Detection of Address Resolution Protocol Spoofing Attacks in Large-Scale Software-Defined Networks
(IEEE, 2024-06) Patrice, Laurent; Sinde, Ramadhani; Leo, Judith
Address Resolution Protocol (ARP) spoofing has been a long-standing problem with no clear remedy until now. The attacks can be launched easily utilizing an enormous number of publicly available tools on the web; however, they are extremely tough to counterattack due to ARP’s stateless nature for not authenticating ARP replies for a subsequent request. Previous studies have demonstrated significant efforts to counterattack these assaults in Software-Defined Networks (SDN); however, much effort has been focused solely on detecting the assaults, with little effort being made to address performance bottlenecks, scalability, and Single Point of Failure (SPOF) issues in large-scale networks. In this study, we focus on developing ARP spoofing attacks detection mechanism in large-scale SDN that is immune to SPOF and provides enhanced network performance and scalability. The main purpose is to enable controllers to intercept and analyze all incoming ARP packets, learn address mappings, and store them in the application’s memory to be used as a basis for ongoing ARP cache comparisons while maintaining a global cache in a controller. To achieve the goal of this study, a simulation experiment in a closed network environment was undertaken to precisely monitor network traffic and result patterns. Mininet and the Open Network Operating System were used to implement the data plane and OpenFlow controllers. The results show that, the proposed solution is resistant to ARP spoofing attacks, with an average detection and mitigation time of 4.3 and 26.19 milliseconds, respectively. Further significant improvements have been observed in alleviating SPOF and performance bottlenecks.
An overview of pipeline leak detection and location systems
(Pan African International Conference on Information Science, Computing and Telecommunications, 2014-07) Boaz, Lawrence; Kaijage, Shubi; Sinde, Ramadhani
Leak detection in transmission pipelines is crucially important for safe operation. Delay in detecting leaks leads to loss of property and human life in fire hazards and loss of valuable material. Leaking of methane and hydrocarbon gas causes negative impacts on the eco system such as global warming and air pollution. Pipeline leak detection systems play a key role in minimization of the probability of occurrence of leaks and hence their impacts. Today there are many available technologies in the domain of leak detection. This paper provides an overview on external and internal leak detection and location systems and a summary of comparison regarding performance of each system.
Pathload for available bandwidth esti-mation techniques (ABETs) for an efficient telemedicine content transport network
(ResearchGate, 2013-08) Kayange, Daniel; Sam, Anael; Sinde, Ramadhani
The ability to measure end-to-end Available Bandwidth (unused capacity) in the network path is useful for route selection in overlay networks, for QoS verification, network management and traffic engineering. This paper investigates at applying techniques and measurement of Available Bandwidth (AB) in the congestion control for the transmission of an efficient telemedicine content transport network by using an important ABETs tool such as Pathload. This paper discusses measurement and simulation results of wired and wireless networks for the unused capacity in the real telemedicine network path and normal network path. The results can assist an organization or country in estimating the network bandwidth requirements depending on the ability of exchange multimedia data of an organization or country. The logistics could cater implementation of low cost telemedicine applications. The telemedicine systems could include wireless and wired medical interface and communication infrastructure. A simulation has been done to investigate the network quality of service
A Practical Approach to Available Bandwidth Estimation Techniques (ABETs) for an Efficient Telemedicine Content Transport Network
(nternational Journal of Scientific & Engineering Research, 2013-09) Kayange, Daniel; Sam, Anael; Sinde, Ramadhani; Ochola, Elisha
The ability to measure end-to-end Available Bandwidth (unused capacity) in the network path is useful for route selection in overlay networks, QoS verification, network management, traffic engineering and real-time resource provisioning, control flow and congestion, construction of overlay - routing infrastructure, topology building in peer to peer networks, and call admission control, dynamic encoding rate modification in streaming applications, capacity planning, intelligent routing systems, and design of transport layer protocols. This paper investigates at applying a practical approach techniques for a measurement of Available Bandwidth (AB) in the congestion control for the transmission of an efficient telemedicine content transport network by using an important ABETs tool such as IGI and PTR. This paper discusses measurement and simulation results of wired and wireless networks for the unused capacity in the network. The results can assist an organization or country in estimating the network bandwidth requirements depending on the ability of exchange multimedia data of an organization or country. The logistics could cater implementation of low cost telemedicine applications. The telemedicine systems could include wireless and wired medical interface and communication infrastructure. A simulation has been done to investigate the network quality of service
Radio Frequency Energy Harvesting for Underground Sensor Nodes: Possibilities and Challenges
(IEE ACCESS, 2024-03-28) Mahenge, Emmanuel; Sinde, Ramadhani; Dida, Mussa; Sam, Nael
Radio frequency energy harvesting (RFEH) is considered as one of the possible and envi- ronmentally friendly solution for energizing sensor devices and prolonging the lifetime of wireless sensor networks (WSNs). Despite being studied and experimented in several environments where WSNs are used, studies and experiments related to RFEH in underground wireless systems are limited to near-field wireless power transfer (WPT), measurement of received signal strength, and current conduction. The goal of this study is to examine the possibilities and challenges of actualizing RFEH in wireless underground sensor networks (WUSNs). A radio-frequency (RF) spectral survey was conducted, and a comparison was performed with similar surveys conducted worldwide to determine the generally available ambient RF energy. Using the aboveground to underground (AG2UG) RF communication model, the signal path loss was analyzed under varying conditions. By relating the ambient RF power and AG2UG signal path loss, it was found nearly impossible to harvest ambient RF energy with the harvesting antenna buried within the soil, as the best-case environment will require a rectenna with sensitivity of at least −62.75dBm. However ambient RF energy can be harvested when the harvesting antenna is in free space, while the other components are underground and will require a high sensitivity of at least −40 dBm. Another possibility for underground RFEH is the use of a dedicated WPT device located 1m above the ground, transmitting at 20 dBm with the RF energy harvester 30 cm below the soil surface with a sensitivity of at least -28.5 dBm.
Real-Time IoT-Based Air Quality Monitoring and Health Hazards Indicator System for Mines Regions: A Case Study of Bulyanhulu Gold Mine
(International Journal of Computer Science and Mobile Computing, 2023-07) Flavian, Daudi; Sinde, Ramadhani; Kisangiri, Michael
Air quality in mining regions is a significant concern due to the release of pollutants from mining activities, posing health risks to nearby communities. However, limited information on air quality levels often leads to neglect of this issue. Inhaling pollutants like PM2.5/PM10, CO, CO2, SO2, and NO2 can result in chronic diseases such as respiratory issues, asthma, and cancer. To tackle this problem, a study suggests the implementation of a real-time Internet of Things (IoT)-based air quality monitoring and health hazards indicator system for mining regions. The proposed system utilizes a reliable wireless sensing system, incorporating sensors like MQ7, MQ135, MQ136, MiCS4514, PMS7003, and DHT22, along with ESP8266, STM32, ATmega328 microcontroller, LoRa shields, and the ThingSpeak IoT server. It ensures continuous operation with a self-contained design, including a solar charger shield connected to a photovoltaic solar panel and rechargeable battery. The smart sensing device continuously monitors air quality and uploads real-time data to the cloud through a coordinator node. The collected data is processed to calculate the Air Quality Index (AQI), which is analyzed to generate early warnings and indicate potential health hazards. The results are accessible through a web-based dashboard for easy visualization. This system simplifies monitoring and provides accurate pollutant data. It supports environmental stakeholders by aggregating and analyzing air quality data, generating reports, and facilitating public access to air quality information. Additionally, it helps identify health hazards, enabling informed decision-making, policy formulation, and mitigation strategies.
Refining Network Lifetime of Wireless Sensor Network Using Energy-Efficient Clustering and DRL-Based Sleep Scheduling.
(MDPI, 2020-03-10) Sinde, Ramadhani; Begum, Feroza; Njau, Karoli; Kaijage, Shubi
Over the recent era, Wireless Sensor Network (WSN) has attracted much attention among industrialists and researchers owing to its contribution to numerous applications including military, environmental monitoring and so on. However, reducing the network delay and improving the network lifetime are always big issues in the domain of WSN. To resolve these downsides, we propose an Energy-Efficient Scheduling using the Deep Reinforcement Learning (DRL) (ES-DRL) algorithm in WSN. ES-DRL contributes three phases to prolong network lifetime and to reduce network delay that is: the clustering phase, duty-cycling phase and routing phase. ES-DRL starts with the clustering phase where we reduce the energy consumption incurred during data aggregation. It is achieved through the Zone-based Clustering (ZbC) scheme. In the ZbC scheme, hybrid Particle Swarm Optimization (PSO) and Affinity Propagation (AP) algorithms are utilized. Duty cycling is adopted in the second phase by executing the DRL algorithm, from which, ES-DRL reduces the energy consumption of individual sensor nodes effectually. The transmission delay is mitigated in the third (routing) phase using Ant Colony Optimization (ACO) and the Firefly Algorithm (FFA). Our work is modeled in Network Simulator 3.26 (NS3). The results are valuable in provisions of upcoming metrics including network lifetime, energy consumption, throughput and delay. From this evaluation, it is proved that our ES-DRL reduces energy consumption, reduces delays by up to 40% and enhances throughput and network lifetime up to 35% compared to the existing cTDMA, DRA, LDC and iABC methods.