Browsing by Author "Okeyo, George"

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    Adaptive Cluster Head Selection Scheme for High Mobility Based IEEE 802.15.6 Wireless Body Area Networks
    (Scientific Research Publishing, 2018-06-14) Mile, Anthony; Okeyo, George; Kibe, Anne
    Due to the development in the field of Wireless Sensor Networks (WSNs), its major application, Wireless Body Area Network (WBAN) has presently become a major area of interest for the developers and researchers. Efficient sensor nodes data collection is the key feature of any effective wireless body area network. Prioritizing nodes and cluster head selection schemes plays an important role in WBAN. Human body exhibits postural mobility which affects distances and connections between different sensor nodes. In this context, we propose maximum consensus based cluster head selection scheme, which allows cluster head selection by using Link State. Nodal priority through transmission power is also introduced to make WBAN more effective. This scheme results in reduced mean power consumption and also reduces network delay. A comparison with IEEE 802.15.6 based CSMA/CA protocol with different locations of cluster head is presented in this paper. These results show that our proposed scheme outperforms Random Cluster head selection, Fixed Cluster head at head, Foot and Belly positions in terms of mean power consumption, network delay, network throughput and bandwidth efficiency.
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    An Agent-mediated Ontology-based Approach for Composite Activity Recognition in Smart Homes
    (JOURNAL OF UNIVERSAL COMPUTER SCIENCE, 2013-11-01) Okeyo, George; Chen, Liming; Wang, H.
    Activity recognition enables ambient assisted living applications to provide activity-aware services to users in smart homes. Despite significant progress being made in activity recognition research, the focus has been on simple activity recognition leaving composite activity recognition an open problem. For instance, knowledge-driven activity recognition has recently attracted increasing attention but mainly focused on simple activities. This paper extends previous work by introducing a knowledge-driven approach to recognition of composite activities such as interleaved and concurrent activities. The approach combines the recognition of single and composite activities into a unified framework. To support composite activity modelling, it combines ontological and temporal knowledge modelling formalisms. In addition, it exploits ontological reasoning for simple activity recognition and qualitative temporal inference to support composite activity recognition. The approach is organized as a multi-agent system to enable multiple activities to be simultaneously monitored and tracked. The presented approach has been implemented in a prototype system and evaluated in a number of experiments. The experimental results have shown that average recognition accuracy for composite activities is 88.26%.
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    Combining ontological and temporal formalisms for composite activity modelling and recognition in smart homes
    (Elsevier, 2014-03-05) Okeyo, George; Chen, Liming; Wang, H.
    Activity recognition is essential in providing activity assistance for users in smart homes. While significant progress has been made for single-user single-activity recognition, it still remains a challenge to carry out real-time progressive composite activity recognition. This paper introduces a hybrid ontological and temporal approach to composite activity modelling and recognition by extending existing ontology-based knowledge-driven approach. The compelling feature of the approach is that it combines ontological and temporal knowledge representation formalisms to provide powerful representation capabilities for activity modelling. The paper describes in detail ontological activity modelling which establishes relationships between activities and their involved entities, and temporal activity modelling which defines relationships between constituent activities of a composite activity. As an essential part of the model, the paper also presents methods for developing temporal entailment rules to support the interpretation and inference of composite activities. In addition, this paper outlines an integrated architecture for composite activity recognition and elaborated a unified activity recognition algorithm which can support the recognition of simple and composite activities. The approach has been implemented in a feature-rich prototype system upon which testing and evaluation have been conducted. Initial experimental results have shown average recognition accuracy of 100% and 88.26% for simple and composite activities, respectively.
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    Dynamic Sensor Data Segmentation for Real time Activity Recognition
    (Elsevier, 2012-12-03) Okeyo, George; Chen, Liming; Wang, H.; Sterritt, Roy
    Approaches and algorithms for activity recognition have recently made substantial progress due to advancements in pervasive and mobile computing, smart environments and ambient assisted living. Nevertheless, it is still difficult to achieve real-time continuous activity recognition as sensor data segmentation remains a challenge. This paper presents a novel approach to real-time sensor data segmentation for continuous activity recognition. Central to the approach is a dynamic segmentation model, based on the notion of varied time windows, which can shrink and expand the segmentation window size by using temporal information of sensor data and activities as well as the state of activity recognition. The paper first analyzes the characteristics of activities of daily living from which the segmentation model that is applicable to a wide range of activity recognition scenarios is motivated and developed. It then describes the working mechanism and relevant algorithms of the model in the context of knowledge-driven activity recognition based on ontologies. The presented approach has been implemented in a prototype system and evaluated in a number of experiments. Results have shown average recognition accuracy above 83% in all experiments for real time activity recognition, which proves the approach and the underlying model.
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    A Hybrid Ontological and Temporal Approach for Composite Activity Modelling
    (IEEE, 2012-06) Okeyo, George; Chen, Liming; Wang, H.; Sterritt, Roy
    Activity modelling is required to support activity recognition and further to provide activity assistance for users in smart homes. Current research in knowledge-driven activity modelling has mainly focused on single activities with little attention being paid to the modelling of composite activities such as interleaved and concurrent activities. This paper presents a hybrid approach to composite activity modelling by combining ontological and temporal knowledge modelling formalisms. Ontological modelling constructors, i.e. concepts and properties for describing composite activities, have been developed and temporal modelling operators have been introduced. As such, the resulting approach is able to model both static and dynamic characteristics of activities. Several composite activity models have been created based on the proposed approach. In addition, a set of inference rules has been provided for use in composite activity recognition. A concurrent meal preparation scenario is used to illustrate both the proposed approach and associated reasoning mechanisms for composite activity recognition.
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    Jumping Finite Automata for Tweet Comprehension
    (IEEE, 2019-11-19) Obare, Stephen; Ade-Ibijola, Abejide; Okeyo, George
    Every day, over one billion social media text messages are generated worldwide, which provides abundant information that can lead to improvements in lives of people through evidence-based decision making. Twitter is rich in such data but there are a number of technical challenges in comprehending tweets including ambiguity of the language used in tweets which is exacerbated in under resourced languages. This paper presents an approach based on Jumping Finite Automata for automatic comprehension of tweets. We construct a WordNet for the language of Kenya (WoLK) based on analysis of tweet structure, formalize the space of tweet variation and abstract the space on a Finite Automata. In addition, we present a software tool called Automata-Aided Tweet Comprehension (ATC) tool that takes raw tweets as input, preprocesses, recognise the syntax and extracts semantic information to 86% success rate.
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    A Knowledge-driven Approach to Composite Activity Recognition in Smart Environments
    (Springer, 2012-12) Chen, Liming; Wang, H.; Sterritt, Roy; Okeyo, George
    Knowledge-driven activity recognition has recently attracted increasing attention but mainly focused on simple activities. This paper extends previous work to introduce a knowledge-driven approach to recognition of composite activities such as interleaved and concurrent activities. The approach combines ontological and temporal knowledge modelling formalisms for composite activity modelling. It exploits ontological reasoning for simple activity recognition and rule-based temporal inference to support composite activity recognition. The presented approach has been implemented in a prototype system and evaluated in a number of experiments. The initial experimental results have shown that average recognition accuracy for simple and composite activities is 100% and 88.26%, respectively.
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    Lexicon‐pointed hybrid N‐gram Features Extraction Model (LeNFEM) for sentence level sentiment analysis
    (Wiley, 2021-02-07) Mutinda, James; Mwangi, Waweru; Okeyo, George
    Sentiment analysis of social media textual posts can provide information and knowledge that is applicable in social settings, business intelligence, evaluation of citizens' opinions in governance, and in mood triggered devices in the Internet of Things. Feature extraction and selection is a key determinant of accuracy and computational cost of machine learning models for such analysis. Most feature extraction and selection techniques utilize bag of words, N‐grams, and frequency‐based algorithms especially Term Frequency‐Inverse Document Frequency. However, these approaches do not consider relationships between words, they ignore words' characteristics and they suffer high feature dimensionality. In this paper we propose and evaluate a feature extraction and selection approach that utilizes a fixed hybrid N‐gram window for feature extraction and minimum redundancy maximum relevance feature selection algorithm for sentence level sentiment analysis. The approach improves the existing features extraction techniques, specifically the N‐gram by generating a hybrid vector from words, Part of Speech (POS) tags, and word semantic orientation. The vector is extracted by using a static trigram window identified by a lexicon where a sentiment word appears in a sentence. A blend of the words, POS tags, and the sentiment orientations of the static trigram are used to build the feature vector. The optimal features from the vector are then selected using minimum redundancy maximum relevance (MRMR) algorithm. Experiments were carried out using the public Yelp dataset to compare the performance of the proposed model and existing feature extraction models (BOW, normal N‐grams and lexicon‐based bag of words semantic orientations). Using supervised machine learning classifiers the experimental results showed that the proposed model had the highest F‐measure (88.64%) compared to the highest (83.55%) from baseline approaches. Wilcoxon test carried out ascertained that the proposed approach performed significantly better than the baseline approaches. Comparative performance analysis with other datasets further affirmed that the proposed approach is generalizable.
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    Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey
    (Canadian Center of Science and Education, 2018-01-24) Abdi, Mohamed Hussein; Okeyo, George; Mwangi, Ronald Waweru
    Collaborative Filtering Recommender Systems predict user preferences for online information, products or services by learning from past user-item relationships. A predominant approach to Collaborative Filtering is Neighborhood-based, where a user-item preference rating is computed from ratings of similar items and/or users. This approach encounters data sparsity and scalability limitations as the volume of accessible information and the active users continue to grow leading to performance degradation, poor quality recommendations and inaccurate predictions. Despite these drawbacks, the problem of information overload has led to great interests in personalization techniques. The incorporation of context information and Matrix and Tensor Factorization techniques have proved to be a promising solution to some of these challenges. We conducted a focused review of literature in the areas of Context-aware Recommender Systems utilizing Matrix Factorization approaches. This survey paper presents a detailed literature review of Context-aware Recommender Systems and approaches to improving performance for large scale datasets and the impact of incorporating contextual information on the quality and accuracy of the recommendation. The results of this survey can be used as a basic reference for improving and optimizing existing Context-aware Collaborative Filtering based Recommender Systems. The main contribution of this paper is a survey of Matrix Factorization techniques for Context-aware Collaborative Filtering Recommender Systems.
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    Modeling the risk of invasion and spread of Tuta absoluta in Africa
    (Elsevier, 2016-08-08) Guimapi, Ritter Y.A.; Mohamed, Samira A.; Okeyo, George; Ndjomatchoua, Frank T.; Ekesi, Sunday; Tonnang, Henri E.Z.
    Tuta absoluta is an invasive insect that originated from South America and has spread to Europe Africa and Asia. Since its detection in Spain in 2006, the pest is continuing to expand its geographical range, including the recent detection in several Sub-Saharan African countries. The present study proposed a model based on cellular automata to predict year-to-year the risk of the invasion and spread of T. absoluta across Africa. Using, land vegetation cover, temperature, relative humidity and yield of tomato production as key driving factors, we were able to mimic the spreading behavior of the pest, and to understand the role that each of these factors play in the process of propagation of invasion. Simulations by inferring the pest’s natural ability to fly long distance revealed that T. absoluta could reach South of Africa ten years after being detected in Spain (Europe). Findings also reveal that relative humidity and the presence of T. absoluta host plants are important factors for improving the accuracy of the prediction. The study aims to inform stakeholders in plant health, plant quarantine, and pest management on the risks that T. absoluta may cause at local, regional and event global scales. It is suggested that adequate measures should be put in place to stop, control and contain the process used by this pest to expand its range.
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    An Ontology-Based Hybrid Approach to Activity Modeling for Smart Homes
    (IEEE, 2013-12-19) Chen, Liming; Nugent, Chris; Okeyo, George
    Activity models play a critical role for activity recognition and assistance in ambient assisted living. Existing approaches to activity modeling suffer from a number of problems, e.g., cold-start, model reusability, and incompleteness. In an effort to address these problems, we introduce an ontology-based hybrid approach to activity modeling that combines domain knowledge based model specification and data-driven model learning. Central to the approach is an iterative process that begins with “seed” activity models created by ontological engineering. The “seed” models are deployed, and subsequently evolved through incremental activity discovery and model update. While our previous work has detailed ontological activity modeling and activity recognition, this paper focuses on the systematic hybrid approach and associated methods and inference rules for learning new activities and user activity profiles. The approach has been implemented in a feature-rich assistive living system. Analysis of the experiments conducted has been undertaken in an effort to test and evaluate the activity learning algorithms and associated mechanisms.
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    Ontology-based Learning Framework for Activity Assistance in an Adaptive Smart Home
    (Atlantis Press, 2011-05) Okeyo, George; Chen, Liming; Wang, H.; Sterritt, Roy
    Activity and behaviour modelling are significant for activity recognition and personalized assistance, respectively, in smart home based assisted living. Ontology-based activity and behaviour modelling is able to leverage domain knowledge and heuristics to create Activities of Daily Living (ADL) and behaviour models with rich semantics. However, they suffer from incompleteness, inflexibility, and lack of adaptation. In this article, we propose a novel approach for learning and evolving activity and behaviour models. The approach uses predefined “seed” ADL ontologies to identify activities from sensor activation streams. Similarly, we provide predefined, but initially unpopulated behaviour ontologies to aid behaviour recognition. First, we develop algorithms that analyze logs of activity data to discover new activities as well as the conditions for evolving the seed ADL ontologies. Consequently, we provide an algorithm for learning and evolving behaviours (or life habits) from these logs. We illustrate our approach through scenarios. The first scenario shows how ADL models can be evolved to accommodate new ADL activities and peculiarities of individual smart home’s inhabitants. The second scenario describes how, subsequent to ADL learning and evolution, behaviours can be learned and evolved.
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    Ontology-Enabled Activity Learning and Model Evolution in Smart Homes
    (Springer, Berlin, Heidelberg, 2010-10-26) Sterritt, Roy; Wang, H.; Chen, Liming; Okeyo, George
    Activity modelling plays a critical role in activity recognition and assistance in smart home based assisted living. Ontology-based activity modelling is able to leverage domain knowledge and heuristics to create Activities of Daily Living (ADL) models with rich semantics. However, they suffer from incompleteness, inflexibility, and lack of adaptation. In this paper, we propose a novel approach for learning and evolving activity models. The approach uses predefined ”seed” ADL ontologies to identify activities from sensor activation streams. We develop algorithms that analyze logs of activity data to discover new activities as well as the conditions for evolving the seed ADL ontologies. We illustrate our approach through a scenario that shows how ADL models can be evolved to accommodate new ADL activities and preferences of individual smart home’s inhabitants.
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    A Survey on Privacy and Security of Internet of Things
    (Elsevier, 2020-10-13) Ogonji, Mark Mbock; Okeyo, George; Wafula, Joseph Muliaro
    Internet of Things (IoT) has fundamentally changed the way information technology and communication environments work, with significant advantages derived from wireless sensors and nanotechnology, among others. While IoT is still a growing and expanding platform, the current research in privacy and security shows there is little integration and unification of security and privacy that may affect user adoption of the technology because of fear of personal data exposure. The surveys conducted so far focus on vulnerabilities based on information exchange technologies applicable to the Internet. None of the surveys has brought out the integrated privacy and security perspective centred on the user. The aim of this paper is to provide the reader with a comprehensive discussion on the current state of the art of IoT, with particular focus on what have been done in the areas of privacy and security threats, attack surface, vulnerabilities and countermeasures and to propose a threat taxonomy. IoT user requirements and challenges were identified and discussed to highlight the baseline security and privacy needs and concerns of the user. The paper also proposed threat taxonomy to address the security requirements in broader perspective. This survey of IoT Privacy and Security has been undertaken through a systematic literature review using online databases and other resources to search for all articles that meet certain criteria, entering information about each study into a personal database, and then drawing up tables summarizing the current state of literature. As a result, the paper distills the latest developments
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    A Systematic Approach to Adaptive Activity Modeling and Discovery in Smart Homes
    (IEEE, 2011-12-12) Chen, Liming; Okeyo, George; Wang, H.; Sterritt, Roy; Nugent, Chris
    Activity modelling and discovery plays a critical role in smart home based assisted living. Existing approaches to pattern recognition using data-intensive analysis suffers from various drawbacks. To address these shortcomings, this paper introduces a novel ontology-based approach to activity modelling, activity discovery and evolution. In this approach, activity modelling is undertaken through ontological engineering by leveraging domain knowledge and heuristics. The generated activity models evolve from the initial “seed” activity models through continuous activity discovery and learning. Activity discovery is performed through ontological reasoning. The paper describes the approach in the context of smart home with special emphases placed on activity discovery algorithms and evolution mechanism. The approach has been implemented in a feature-rich assistive living system in which new daily activities can be detected and further used to evolve the underlying activity models.
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    Time Handling for Real-time Progressive Activity Recognition
    (ACM, 2011-09-18) Sterritt, Roy; Wang, H.; Chen, Liming; Okeyo, George
    In a dense sensor-based smart home (SH), a significant challenge is to segment the sensor data stream in real-time to continuously support progressive activity recognition (AR). In this paper, we evaluate an approach that supports the segmentation of the sensor data stream used for knowledge-driven AR. The approach is based on the notion of dynamically varied sliding time windows, where data segments are formally modeled as time windows and ontological reasoning used to infer the ongoing activities of daily living (ADLs). We then present an algorithm that supports perpetual, real-time activity recognition and provide an implementation of both the proposed approach and the ADL ontology it uses. For evaluation, we developed a synthetic data generator and generated a set of synthetic ADLs. In addition, we implemented a real-time activity recognition system and a simple simulator that plays back a synthetic ADL as if the sensors are activated in real-time. We evaluated the real-time activity recognition algorithm, and obtained 99.2% average recognition accuracy on the synthetic ADLs tested. The ability of the algorithm to discriminate sensors that are activated in error was evaluated for selected ADLs and impressive results obtained.