Machine Learning Approach for Classifying Power Outage in Secondary Electric Distribution Network

Power outage is the problem that hinders social and economic development especially for developing countries like Tanzania. Frequent power outages damage electric equipment, and negatively affect the industrial production process. Power outages cannot be completely eradicated due to uncontrolled cause like natural calamities but technical challenges can be managed and hence reducing power outages. The existing manual methods used to locate power outage like customer calls is inefficient and time consuming. On the other hand, modern method like the Advanced Metering Infrastructure (AMI) still faces a challenge in effectively classifying power line outage due to the nature of imbalanced datasets. Therefore, there is a need to develop a Machine Learning (ML) model to accurately classify power line outage. In this study, machine learning models are constructed from ensemble algorithms and tested using outage AMI data from 2012 to 2019 with 2 hours interval records. We propose the following ensemble-based machine learning approach to enhance classification; data sampling, algorithm weighting and finally ensembling. Results show that the Hybrid Stacking Ensemble Classifier (HSEC) model outperforms the others by accuracy of 0.981 G-mean, followed by Extra tree with accuracy of 0.964 G-mean. This model can be used in power line outage classification in any Secondary Electrical Distribution Network (SEDN). This study can be extended to locate power outage to household

Design and Implementation of Distributed Identity and Access Management Framework for Internet of Things (IoT) Enabled Distribution Automation

The smart grid and Internet of Things (IoT) technologies play vital roles in improving the quality of services offered in traditional electrical grid. They open a room for the introduction of new services like distribution automation (DA) that has a significant advantage to both utility companies and final consumers. DA integrates sensors, actuators, intelligent electrical devices (IED) and information and communication technologies to monitor and control electrical grid. However, the integration of these technologies poses security threats to the electrical grid like Denial of Service (DoS) attacks, false data injection attacks, and masquerading attacks like system node impersonation that can transmit wrong readings, resulting in false alarm reports and hence leading to incorrect node actuation. To overcome these challenges, researchers have proposed a centralized public key infrastructure (PKI) with bridged certificate authority (CA) which is prone to DoS attacks. Moreover, the proposed blockchain based distributed identity and access management (DIAM) in IoT domain at the global scale is adding communicational and computational overheads. Also. It is imposing new security threats to the DA system by integrating it with online services like IoTEX and IoTA. For those reasons, this study proposes a DIAM security scheme to secure IoT-enabled distribution automation. The scheme divides areas into clusters and each cluster has a device registry and a registry controller. The registry controller is a command line tool to access and manage a device registry. The results show that the scheme can prevent impersonated and non-legitimate system nodes and users from accessing the system by imposing role-based access control (RBAC) at the cluster level. Keywords: Distributed Identity and Access Management; Electrical Secondary Distribution Network; Internet of Things; IoT Enabled Distribution Automation; Smart Grid Securit

Enhancing the classification accuracy of IP geolocation

Location aware applications such as confinement of online transactions within acceptable pre-established locations, targeted online advertising, enforcement of digital content and territory rights and cloud auditing can benefit from a more accurate yet robust IP geolocation framework. Various approaches to IP geolocation have been well documented. The most recent approach casts IP geolocation as a machine learning classification problem. Casting IP Geolocation as a machine learning classification problem makes it possible to incorporate more geolocation information to the classifier hence improve accuracy. To enhance the classification accuracy of the existing classification framework, we expand it to include 6 different types of geolocation information (only 5 have been implemented in this thesis). This improves the accuracy in terms of error distance from 253.34 miles to 155.74 miles. To implement this classifier we come up with 4 major subsystems; (1) Measurement subsystem (collects and formats measurements from PlanetLab and US census website), (2) Training subsystem (reads measurements and then develops probability densities as likelihood), (3) Testing subsystem (uses the probability densities of the training set to fit them to the measurements of the testing set. The county with the highest probability density is the estimated county of the target.), (4) Decision subsystem (compares the estimated location to the true location of the target and calculates the error distance between them. Decides if to reclassify target or not).

Towards infrastructure based software defined security

Any nation’s well-being relies upon secure and resilient critical infrastructure. Present day critical infrastructures are now increasingly exposed to cyber risks, which stem from growing integration of information and communications technologies such as Ethernet. Most existing security solutions can no longer contain ongoing cyber threats because the networked infrastructure systems were built to operate in static network configurations. These types of systems may give an attacker enough time to study specific system vulnerability, probe the network, collect network information and then launch an attack. Software-defined networking (SDN) is an approach to computer networking that allows control and forwarding elements in the network to be disassociated, allowing for a range of considerably more flexible and effective network management and threat mitigation solutions. In this dissertation, Software Defined Networks (SDN) is used to address security in two critical infrastructures; cloud infrastructures and smart grid infrastructures. Virtual Machine (VM) migration is the key player in Moving Target Defense (MTD) security in cloud infrastructures. To enhance the use of VM migrations as a security mechanism, this research explores to know the cost of VM Migrations in cloud infrastructures. This work addresses the cost of VM Migrations with Software Defined Networking (SDN) principles in a data center testbed characterized by wide-area network dynamics and realistic traffic scenarios. The results show that knowing the cost of VM Migration on the network ensures a successful VM Migration and improves the performance of competing flows in the network. Regarding to security in smart grid infrastructures, the dissertation quantitatively assesses security risks in smart grids in the perspectives of both the defender and the attacker. An existing security quantification model is improved to include criticality of every smart grid component. SDN principles together with the improved security quantification model are used to address DoS attacks (link flood attacks) in a smart grid environment. The results show that using SDN relieves the network of link flood threats, hence improving the performance of IEC 61850 applications, making them IEC 61850 time compliant

Enhanced Model for Predicting Student Dropouts in Developing Countries Using Automated Machine Learning Approach: A Case of Tanzanian’s Secondary Schools

The Sub-Saharan countries are leading in dropout rates in secondary schools by 37.5% followed by South Asia 15.5% and Middle East 11% in 2018. In Tanzania, student dropouts in secondary schools increased from 3.8% in 2018 to 4.2% in 2019. Different initiatives such as parent-workshops, parent-teacher meetings, community empowerment programs, school feed programs, and secondary education development program (SEDP) have been used to address student dropout but unfortunately, the dropout problem still persists. The persisting dropout problem especially in secondary schools is attributed to a lack of proper identification of root causes and unavailability of formal methods that can be used to project the severity of the problem. In addressing this problem, machine learning (ML) techniques have done a great job in predicting secondary school dropouts. However, most of the ML models suffer from processing features, and hyper-parameters tuning leads to poor prediction accuracy in identifying the root causes of the student dropout. In this study, the AutoML model has been used to improve prediction accuracy by selecting the corresponding hyper-parameters, features, and ML algorithm for the acquired dataset. The proposed model achieved a better prediction accuracy of DT = 99.8%, KNN = 99.6%, MLP = 99% and NB = 97%. The improved prediction score indicates an accurate selection of features that cause student dropout that can be looked in a close eye in the learning process for early intervention

Comparative Study of AutoML Approach, Conventional Ensemble Learning Method, and KNearest Oracle-AutoML Model for Predicting Student Dropouts in Sub-Saharan African Countries

Student dropout in secondary schools is a major issue in developing countries, particularly in Sub-Saharan Africa. Sub-Saharan African countries had the highest dropout rate (37.5%), followed by South Asia (15.5%), the Middle East (11%), East Asia (9.5%), Latin America (7%), and Central Asia (3.5%). Various initiatives such as the big results now initiatives, no child left behind, and secondary education development programme as well as machine learning prediction models have been used to reduce the severity of the problem in Sub-Saharan countries. The ongoing dropout problem, particularly in secondary schools is ascribed to improper root cause identification and the absence of formal procedures that can be used to estimate the severity of the issue. This study has compared the AutoML model, ensemble learning approach, and KNORA-AutoML to predict student dropout problems. The KNORA-AutoML model scored 97% of accuracy, precision = 71%, and AUC = 87% when compared to the conventional ensemble of optimized ML models with accuracy = 96%, precision = 70%, and AUC = 78%. KNORA-AutoML model performance increased by 0.6% accuracy, 0.8% precision, and 8.7% AUC. An optimized model draws a lot of attention to the findings related to student dropout rates in developing countries

The Role of Human Centered Design (HCD) and Challenge Driven Education in Enhancing the Innovation Capacity of Africa’s Young People: Case of Youth for Children (Y4C) Innovation Hub in Tanzania

Part 4: Sustainable ICT, Informatics, Education and Learning in a Turbulent World - “Doing the Safari Way”International audienceAs the world advances rapidly and economic conditions continue to change, issues such as globalization and changing labour markets are putting a huge pressure on youth worldwide. There is an urgent need for young people to be trained with flexible set of skills that will allow them to take the front seat and actively contribute to initiatives directly affect their development. The first step towards addressing the pressing concerns of today’s youth, especially in Africa, involves improving the existing education systems. This paper elaborates curriculum improvement in education systems by adapt a human centered design (HCD) and Challenge-Driven Education (CDE) approaches that encourages students to work on projects that address real challenges solicited from industry partners. Youth for Children (Y4C) innovation hub, a partnership between UDSM college of ICT (CoICT) and UNICEF Tanzania, was established in 2016 to promote child rights and provide the design skills and social context to support students in developing products and solutions with real social value. Y4C Hub provides unique value in its emphasis on HCD and CDE approaches where projects are undertaken for a period of one year and are directly linked to solving real challenges facing the society in collaboration with the challenge owners and mentors. In the year since launching the hub, 173 CoICT students, 36 supervisors, and 50 secondary school girls have been trained on HCD. A Final Year Project course curriculum has been reviewed to reflect a more challenge driven approach, offering a chance for promising projects to be incubated

Transforming African Education Systems in Science, Technology, Engineering, and Mathematics (STEM) Using ICTs: Challenges and Opportunities

This paper presents the role of ICTs in transforming Africa’s Education Systems (AES) in science, technology, engineering, and mathematics (STEM) subjects/courses. The paper highlights on a positive shift across Africa in using ICT to improve the quality of teaching and learning through activities such as intensive ICT skills training to teachers, increase in ICT equipments and applications in schools, and emergence of living labs (LLs) and innovation spaces/centres (InnoSpace). We first provide some of the challenges of integrating ICTs in education followed by a description of key past and current ICT initiatives supporting the adoption of ICTs in schools using a number of case studies in sub-Saharan Africa. We further present various ICT-based models for education, as a transformational approach towards integrating ICTs in AES. Moreover, we provide various ICT platforms deployed for education service delivery in disadvantaged African society (e.g., rural areas) including LLs and InnoSpace across the continent. Finally, we highlight our main findings and observations in terms of opportunities and future ICT for education research directions in Africa. Our aim is to provide some guidelines and ensure that Africa uniformly meet the 2030 United Nations Sustainable Development Goal number 4, which is to ensure inclusive and quality education for all and promote lifelong learning, particularly using ICTs