Isaac Olusegun Osunmakinde

Development of a Survivable Cloud Multi-robot Framework for Heterogeneous Environments

Cloud robotics is a paradigm that allows for robots to offload computationally intensive and data storage requirements into the cloud by providing a secure and customizable environment. The challenge for cloud robotics is the inherent problem of cloud disconnection. A major assumption made in the development of the current cloud robotics frameworks is that the connection between the cloud and the robot is always available. However, for multi-robots working in heterogeneous environments, the connection between the cloud and the robots cannot always be guaranteed. This work serves to assist with the challenge of disconnection in cloud robotics by proposing a survivable cloud multi-robotics (SCMR) framework for heterogeneous environments. The SCMR framework leverages the combination of a virtual ad hoc network formed by robot-to-robot communication and a physical cloud infrastructure formed by robot-to-cloud communications. The quality of service (QoS) on the SCMR framework was tested and validated by determining the optimal energy utilization and time of response (ToR) on drivability analysis with and without cloud connection. The design trade-off, including the result, is between the computation energy for the robot execution and the offloading energy for the cloud execution.

E-commerce assurance models and trustworthiness issues: an empirical study

Purpose – The internet provides a mechanism by which buyers and sellers meet in order to exchange goods and services online with the utmost convenience. However, there are many risks associated with the internet which, if left unattended, could continue deterring the adoption of e-commerce. These risks ultimately diminish online consumer trust in e-commerce. Web assurance models have been designed in an attempt to encourage online consumer trust through assurance. Unfortunately, many of these models have been inadequate in certain areas and this research aims to improve on them. Design/methodology/approach – It presents a comprehensive empirical survey on trustworthiness issues and e-commerce assurance models and proposes a new compliance-based e-commerce assurance model that integrates adaptive legislation, adaptive e-commerce-related standards and cooperative rating. The intelligent cooperative rating is based on the analytic hierarchy process and page-ranking techniques. Findings – Some findings of thi...

Software Project Profitability Analysis Using Temporal Probabilistic Reasoning

Providing viable estimates, understanding project requirements and doing proper risk management on software projects require extensive application and sophisticated techniques of analysis and interpretation. There is still a lack of informative techniques and feedback mechanisms that help to assess how well and efficiently a specific development methodology is performing. Analyzing project tasks would enhance how well individual tasks are estimated, how well they are defined, and whether items are completed on-time and on-budget. In this work, we propose a temporal probabilistic model that addresses feedback control mechanisms in project planning using the complex adaptive systems software engineering framework (CASSE). We have tested our approach in industry with a software development company in South Africa on two commercial project evaluations. Our preliminary results show that the temporal probabilistic model of the framework demonstrably enhances practitionerspsila understanding in managing software projects profitably - hence increasing business sustainability and management.

Bimodal biometrics for financial infrastructure security

This research examines whether the integration of facial and fingerprint biometrics can improve the performance in financial infrastructure security such as ATM protection. Fingerprint biometrics consider distorted and misaligned fingerprints caused by environmental noise such as oil, wrinkles, dry skin, dirt and displacement of the query fingerprint with the database fingerprint template during matching. The noisy, distorted and/or misaligned fingerprint produced as a 2-D on x-y image, is enhanced and optimized using a new hybrid Modified Gabor Filter-Hierarchal Structure Check (MGF-HSC) system model based on an MGF integrated with an HSC. However, in order to improve the accuracy of financial infrastructure, face biometrics are introduced using a fast principal component analysis algorithm, in which different face conditions such as lighting, blurriness, pose, head orientation and other conditions are addressed. The MGF-HSC approach minimizes false fingerprint matching and the dominant effect of distortion and misalignment of fingerprints to an acceptable level. The proposed bimodal biometrics increase the accuracy of the False Rejection Rate (FRR) to 98% when the False Acceptance Rate (FAR) is 0.1% in an experiment conducted with 1000 test cases. This result shows that facial biometrics can be used to support fingerprint biometrics for improving financial security based on with significant improvement in both FRR and FAR.

A compliant assurance model for assessing the trustworthiness of cloud-based e-commerce systems

Many cloud-based e-commerce stores aim to attract and retain customers in order to be competitive. However, they are all faced with a challenge regarding gaining and maintaining consumer trust in a volatile cloud-based e-commerce environment where risks pertaining to information security, privacy of information and inadequate monitoring of compliance to applicable laws are prevalent. The pervasiveness of these risks has indirectly propelled the development of web assurance models, which were designed in an attempt to encourage online consumer trust. Regrettably, many of these models have been inadequate in certain areas, such as being unable to provide online real-time assurance on a comprehensive set of attributes, which include a check of compliance to the applicable e-commerce legislation or standards in a cloud-based environment. The aim of this research was to examine whether the integration of the attributes of adaptive legislation, adaptive ISO standards, policies, advanced user security and website availability can be used to develop a compliant assurance model. The model uses an intelligent cooperative rating based on the analytical hierarchy process and page ranking techniques to improve the level of cloud-based trustworthiness. We illustrated in an empirical explanatory survey conducted with 15 test samples from IEEE, Science Direct databases and real life data captured from E-commerce sites that the proposed compliant model strongly contributes to the improvement of cloud-based sites, as well as enhancing the trustworthiness of these websites. The findings of this research study can be used as a reference guide to understand the effectiveness of cloud-based e-commerce assurance models, as well as to enhance the trustworthiness of these models.

An intelligent integrative assistive system for dyslexic learners

Purpose – The objective of this paper is to present a comprehensive literature survey on dyslexic learners and to propose a model for integrated assistive technology of dyslexic learners. Design/methodology/approach – The use of the proposed model through real-life scenarios categorized as “phonological, reading and writing scenarios”. We have also surveyed some systems for use with dyslexic learners currently in use and have compared them on the basis of number of barriers, technological innovation, age group and fostering. Findings – Dyslexic learners are characterized by slow learning, poor handwriting, poor spelling skills and difficulties in planning, organizing, revising and editing texts; technology plays a major role in the educational environment; it has become crucial in impacting knowledge across the globe; and open research issues and challenges that have to be addressed in the design of the current dyslexic system have been presented in detail. Research limitations/implications – Full impleme...

Investigating the Use of Bayesian Network and k-NN Models to Develop Behaviours for Autonomous Robots

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Collision Avoidance in Unstructured Environments for Autonomous Robots: A Behavioural Modelling Approach

Collision avoidance is one of the important safety key operations that needs attention in the navigation system of an autonomous robot. In this paper, a Behavioural Bayesian Network approach is proposed as a collision avoidance strategy for autonomous robots in an unstructured environment with static obstacles. In our approach, an unstructured environment was simulated and the information of the obstacles generated was used to build the Behavioural Bayesian Network Model (BBNM). This model captures uncertainties from the unstructured environment in terms of probabilities, and allows reasoning with the probabilities. This reasoning ability enables autonomous robots to navigate in any unstructured environment with a higher degree of belief that there will be no collision with obstacles. Experimental evaluations of the BBNM show that when the robot navigates in the same unstructured environment where knowledge of the obstacles is captured, there is certainty in the degree of belief that the robot can navigate freely without any collision. When the same model was tested for navigation in a new unstructured environment with uncertainties, the results showed a higher assurance or degrees of belief that the robot will not collide with obstacles. The results of our modelling approach show that Bayesian Networks (BNs) have good potential for guiding the behaviour of robots when avoiding obstacles in any unstructured environment.

On the Relevance of Using Bayesian Belief Networks in Wireless Sensor Networks Situation Recognition

Achieving situation recognition in ubiquitous sensor networks (USNs) is an important issue that has been poorly addressed by both the research and practitioner communities. This paper describes some steps taken to address this issue by effecting USN middleware intelligence using an emerging situation awareness (ESA) technology. We propose a situation recognition framework where temporal probabilistic reasoning is used to derive and emerge situation awareness in ubiquitous sensor networks. Using data collected from an outdoor environment monitoring in the city of Cape Town, we illustrate the use of the ESA technology in terms of sensor system operating conditions and environmental situation recognition.

Cooperative Behaviours with Swarm Intelligence in Multirobot Systems for Safety Inspections in Underground Terrains

Underground mining operations are carried out in hazardous environments. To prevent disasters from occurring, as often as they do in underground mines, and to prevent safety routine checkers from disasters during safety inspection checks, multirobots are suggested to do the job of safety inspection rather than human beings and single robots. Multirobots are preferred because the inspection task will be done in the minimum amount of time. This paper proposes a cooperative behaviour for a multirobot system (MRS) to achieve a preentry safety inspection in underground terrains. A hybrid QLACS swarm intelligent model based on Q-Learning (QL) and the Ant Colony System (ACS) was proposed to achieve this cooperative behaviour in MRS. The intelligent model was developed by harnessing the strengths of both QL and ACS algorithms. The ACS optimizes the routes used for each robot while the QL algorithm enhances the cooperation between the autonomous robots. A description of a communicating variation within the QLACS model for cooperative behavioural purposes is presented. The performance of the algorithms in terms of without communication, with communication, computation time, path costs, and the number of robots used was evaluated by using a simulation approach. Simulation results show achieved cooperative behaviour between robots.