Olutayo Boyinbode

Bio: Olutayo Boyinbode is an academic researcher from Federal University of Technology Akure. The author has contributed to research in topics: Mobile device & Ubiquitous robot. The author has an hindex of 8, co-authored 30 publications receiving 1460 citations. Previous affiliations of Olutayo Boyinbode include University of Cape Town.

A survey on clustering algorithms for wireless sensor networks

Abstract: A wireless sensor network (WSN) consisting of a large number of tiny sensors can be an effective tool for gathering data in diverse kinds of environments. The data collected by each sensor is communicated to the base station, which forwards the data to the end user. Clustering is introduced to WSNs because it has proven to be an effective approach to provide better data aggregation and scalability for large WSNs. Clustering also conserves the limited energy resources of the sensors. This paper synthesises existing clustering algorithms in WSNs and highlights the challenges in clustering.

A Survey on Clustering Algorithms for Wireless Sensor Networks

Abstract: A wireless sensor network (WSN)consisting of a large number of tiny sensors can be an effective tool for gathering data in diverse kinds of environments. The data collected by each sensor is communicated to the base station, which forwards the data to the end user. Clustering is introduced to WSNs because it has proven to be an effective approach to provide better data aggregation and scalability for large WSNs. Clustering also conserves the limited energy resources of the sensors. This paper synthesises existing clustering algorithms news's and highlights the challenges in clustering.

Mobile learning: an application of mobile and wireless technologies in nigerian learning system.

Abstract: Mobile learning (M-Learning) is the point where mobile computing and e-learning intersect to produce an anytime, anywhere learning experiences. Advances in mobile technologies have enhanced M-learning tools at just the right moment to meet the need for more costeffective just in time training options-learning on the go. Electronic Learning offer methods, which decrease the limitations of traditional education but M-learning offers more. This paper discusses the existing devices and technologies appropriate to realize Mobile learning, its advantages over e-learning, and challenges to its adoption of in Nigeria.

Supporting Mobile Learning with WhatsApp based on Media Richness

Abstract: With the proliferation of mobile devices, mobile learning has become a learning paradigm in education. The aim of this paper is to evaluate the media richness of various message delivery methods in mobile learning (m-learning) environment. This study evaluates media richness in respect to content timeliness, content richness, content accuracy and content adaptability in WhatsApp, Email, SMS, Twitter and BBM. One-way ANOVA analysis and Post hoc analysis show that: (i) SMS has better performance than WhatsApp, Email, Twitter and BBM on content timeliness; this implies that SMS may be more appropriate for delivering real-time information such as notifying or reminding of some time-sensitive matters, (ii) WhatsApp has better performance than Email, SMS, Twitter and BBM on content richness and so may be applied in information delivery that is rich in images and videos, (iii) WhatsApp has better performance than Email, SMS, Twitter and BBM on content accuracy and content adaptability. WhatsApp due to its media richness is more appropriate for supporting learning activities in a mobile learning environment. This study suggests that developers and designers of an m-learning environment could adopt WhatsApp as a suitable information delivery medium to support corresponding learning activities in a mobile learning environment.

CloudeMR: A Cloud Based Electronic Medical Record System

Abstract: The utilization of modern information technology in the delivery of healthcare is to enhance the availability and reliability of improved healthcare services to patients at a reduced cost. The alternative in this context is to outsource the computing storage resources with the help of cloud infrastructure. The drastic reduction in the cost of healthcare services, utilization of resources, maintainability and the adoption of new technologies are some of the benefits that healthcare centers in rural areas can get from cloud-based medical information system. Also, new prospects such as easy and everpresent access to medical records and the chances to make use of services of physicians that are not readily available in the rural areas are some of the opportunities offered by a cloud-based medical information system. This paper proposes and implements a cloudbased electronic medical record (CloudeMR) system to improve the delivery of healthcare system in the rural communities of Nigeria.

A survey on routing protocols in Wireless Sensor Networks

Abstract: This paper presents a survey of state-of-the-art routing techniques in Wireless Sensor Networks (WSNs). Compared with traditional wireless networks, WSNs are characterized with denser levels of node deployment, higher unreliability of sensor nodes and severe power, computation and memory constraints. Various design challenges such as energy efficiency, data delivery models, quality of service, overheads etc., for routing protocols in WSNs are highlighted. We addressed most of the proposed routing methods along with scheme designs, benefits and result analysis wherever possible. The routing protocols discussed are classified into seven categories such as Data centric routing, Hierarchical routing, Location based routing, Negotiation based routing, Multipath based routing, Quality of Service (QoS) routing and Mobility based routing. This paper also compares the routing protocols against parameters such as power consumption, scalability, mobility, optimal routing and data aggregation. The paper concludes with possible open research issues in WSNs.

Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey

Abstract: The distributed nature and dynamic topology of Wireless Sensor Networks (WSNs) introduces very special requirements in routing protocols that should be met. The most important feature of a routing protocol, in order to be efficient for WSNs, is the energy consumption and the extension of the network's lifetime. During the recent years, many energy efficient routing protocols have been proposed for WSNs. In this paper, energy efficient routing protocols are classified into four main schemes: Network Structure, Communication Model, Topology Based and Reliable Routing. The routing protocols belonging to the first category can be further classified as flat or hierarchical. The routing protocols belonging to the second category can be further classified as Query-based or Coherent and non-coherent-based or Negotiation-based. The routing protocols belonging to the third category can be further classified as Location-based or Mobile Agent-based. The routing protocols belonging to the fourth category can be further classified as QoS-based or Multipath-based. Then, an analytical survey on energy efficient routing protocols for WSNs is provided. In this paper, the classification initially proposed by Al-Karaki, is expanded, in order to enhance all the proposed papers since 2004 and to better describe which issues/operations in each protocol illustrate/enhance the energy-efficiency issues.

A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis

Abstract: Clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. In particular, their main goal is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics. There is a vast body of knowledge in the area of clustering and there has been attempts to analyze and categorize them for a larger number of applications. However, one of the major issues in using clustering algorithms for big data that causes confusion amongst practitioners is the lack of consensus in the definition of their properties as well as a lack of formal categorization. With the intention of alleviating these problems, this paper introduces concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as providing a comparison, both from a theoretical and an empirical perspective. From a theoretical perspective, we developed a categorizing framework based on the main properties pointed out in previous studies. Empirically, we conducted extensive experiments where we compared the most representative algorithm from each of the categories using a large number of real (big) data sets. The effectiveness of the candidate clustering algorithms is measured through a number of internal and external validity metrics, stability, runtime, and scalability tests. In addition, we highlighted the set of clustering algorithms that are the best performing for big data.

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Abstract: Wireless sensor networks (WSNs) monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002–2013 of machine learning methods that were used to address common issues in WSNs. The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.

A Survey on Clustering Routing Protocols in Wireless Sensor Networks

Abstract: The past few years have witnessed increased interest in the potential use of wireless sensor networks (WSNs) in a wide range of applications and it has become a hot research area. Based on network structure, routing protocols in WSNs can be divided into two categories: flat routing and hierarchical or clustering routing. Owing to a variety of advantages, clustering is becoming an active branch of routing technology in WSNs. In this paper, we present a comprehensive and fine grained survey on clustering routing protocols proposed in the literature for WSNs. We outline the advantages and objectives of clustering for WSNs, and develop a novel taxonomy of WSN clustering routing methods based on complete and detailed clustering attributes. In particular, we systematically analyze a few prominent WSN clustering routing protocols and compare these different approaches according to our taxonomy and several significant metrics. Finally, we summarize and conclude the paper with some future directions.