Hlabishi Isaac Kobo

Bio: Hlabishi Isaac Kobo is an academic researcher from Council for Scientific and Industrial Research. The author has contributed to research in topics: Software-defined networking & Wireless sensor network. The author has an hindex of 5, co-authored 13 publications receiving 395 citations. Previous affiliations of Hlabishi Isaac Kobo include Council of Scientific and Industrial Research & University of Pretoria.

A Survey on Software-Defined Wireless Sensor Networks: Challenges and Design Requirements

Abstract: Software defined networking (SDN) brings about innovation, simplicity in network management, and configuration in network computing. Traditional networks often lack the flexibility to bring into effect instant changes because of the rigidity of the network and also the over dependence on proprietary services. SDN decouples the control plane from the data plane, thus moving the control logic from the node to a central controller. A wireless sensor network (WSN) is a great platform for low-rate wireless personal area networks with little resources and short communication ranges. However, as the scale of WSN expands, it faces several challenges, such as network management and heterogeneous-node networks. The SDN approach to WSNs seeks to alleviate most of the challenges and ultimately foster efficiency and sustainability in WSNs. The fusion of these two models gives rise to a new paradigm: Software defined wireless sensor networks (SDWSN). The SDWSN model is also envisioned to play a critical role in the looming Internet of Things paradigm. This paper presents a comprehensive review of the SDWSN literature. Moreover, it delves into some of the challenges facing this paradigm, as well as the major SDWSN design requirements that need to be considered to address these challenges.

Fragmentation-Based Distributed Control System for Software-Defined Wireless Sensor Networks

Abstract: Software-defined wireless sensor networks (WSNs) are a new and emerging network paradigm that seeks to address the impending issues in WSNs. It is formed by applying software-defined networking to WSNs whose basic tenet is the centralization of control intelligence of the network. The centralization of the controller rouses many challenges such as security, reliability, scalability, and performance. A distributed control system is proposed in this paper to address issues arising from and pertaining to the centralized controller. Fragmentation is proposed as a method of distribution, which entails a two-level control structure consisting of local controllers closer to the infrastructure elements and a global controller, which has a global view of the entire network. A distributed controller system brings several advantages and the experiments carried out show that it performs better than a central controller. Furthermore, the results also show that fragmentation improves the performance and thus have a potential to have major impact in the Internet of things.

Towards a distributed control system for software defined Wireless Sensor Networks

Abstract: Software Defined Networking (SDN) is a developing networking paradigm that advocates a complete overhaul of the conventional networking. SDN decouples the control logic from the data forwarding functionality; which traditionally are coupled on the network device. The coupling stifles innovation and evolution because the network often becomes rigid. Software Defined Wireless Sensor Networks (SDWSN) is also an emerging network paradigm that infuses the SDN model into Wireless Sensor Networks (WSNs). WSNs have inherent constraints such as energy, memory etc. which have been a major hindrance of their progress. The application of SDN model in WSN is set to cultivate the potential of WSNs in modern communication and to bring about the efficiency that the WSNs have not yet achieved due to their inherent constraints. SDN based networks are anchored on the central controller for functionality. As the network scale up, issues of scalability, reliability and congestion arises and for that a distributed controllers are proposed. This paper investigates the viability of a distributed control system for SDWSN. The test results conducted show that it is viable to deploy a distributed control system for SDWSN; however an improvement is needed on the efficiency.

Efficient controller placement and reelection mechanism in distributed control system for software defined wireless sensor networks

Abstract: Copyright: 2019 Wiley. Due to copyright restrictions, the attached PDF file contains the pre-print version of the published item. For access to the published version, please consult the publisher's website: https://doi.org/10.1002/ett.3588

Multi-Objective Optimization Approach for Task Scheduling in Fog Computing

Abstract: The Fog computing paradigm allow applications to be processed at the edge of a network. This paradigm is designed to mitigate high latency and the burden of task requests sent to centralized cloud servers by end devices. Fog computing permits different portions of applications to be scheduled to Fog nodes available at the edge. These Fog nodes offer cloud processing services and have appeared as a feasible technique for real time applications. However, scheduling a task among available Fog nodes must be effective, meaning it must not over consume available resources because of limited resources at the edge. Consuming extra amount of energy than available on the Fog nodes can lead to network breakdown or application failure which is not acceptable for real-time applications. Therefore, to address this challenge, this paper presents an application scheduling technique based on virtualization technology to find an efficient algorithm that can optimize energy consumption and average delay of real-time applications in Fog computing networks. This is achieved by implementing four task scheduling policies in a Fog node scheduler to assess their performance and efficiency. Simulations were conducted using the iFogSim tool and the results demonstrate that the FCFS scheduling policy achieved improvement in energy consumption by 11 %, average task delay 7.78 %, 4.4 % network usage and execution time 15.1 % better than other algorithms.

A Survey on 5G Networks for the Internet of Things: Communication Technologies and Challenges

Abstract: The Internet of Things (IoT) is a promising technology which tends to revolutionize and connect the global world via heterogeneous smart devices through seamless connectivity. The current demand for machine-type communications (MTC) has resulted in a variety of communication technologies with diverse service requirements to achieve the modern IoT vision. More recent cellular standards like long-term evolution (LTE) have been introduced for mobile devices but are not well suited for low-power and low data rate devices such as the IoT devices. To address this, there is a number of emerging IoT standards. Fifth generation (5G) mobile network, in particular, aims to address the limitations of previous cellular standards and be a potential key enabler for future IoT. In this paper, the state-of-the-art of the IoT application requirements along with their associated communication technologies are surveyed. In addition, the third generation partnership project cellular-based low-power wide area solutions to support and enable the new service requirements for Massive to Critical IoT use cases are discussed in detail, including extended coverage global system for mobile communications for the Internet of Things, enhanced machine-type communications, and narrowband-Internet of Things. Furthermore, 5G new radio enhancements for new service requirements and enabling technologies for the IoT are introduced. This paper presents a comprehensive review related to emerging and enabling technologies with main focus on 5G mobile networks that is envisaged to support the exponential traffic growth for enabling the IoT. The challenges and open research directions pertinent to the deployment of massive to critical IoT applications are also presented in coming up with an efficient context-aware congestion control mechanism.

Internet of Things (IoT) for Next-Generation Smart Systems: A Review of Current Challenges, Future Trends and Prospects for Emerging 5G-IoT Scenarios

Abstract: The Internet of Things (IoT)-centric concepts like augmented reality, high-resolution video streaming, self-driven cars, smart environment, e-health care, etc. have a ubiquitous presence now. These applications require higher data-rates, large bandwidth, increased capacity, low latency and high throughput. In light of these emerging concepts, IoT has revolutionized the world by providing seamless connectivity between heterogeneous networks (HetNets). The eventual aim of IoT is to introduce the plug and play technology providing the end-user, ease of operation, remotely access control and configurability. This paper presents the IoT technology from a bird’s eye view covering its statistical/architectural trends, use cases, challenges and future prospects. The paper also presents a detailed and extensive overview of the emerging 5G-IoT scenario. Fifth Generation (5G) cellular networks provide key enabling technologies for ubiquitous deployment of the IoT technology. These include carrier aggregation, multiple-input multiple-output (MIMO), massive-MIMO (M-MIMO), coordinated multipoint processing (CoMP), device-to-device (D2D) communications, centralized radio access network (CRAN), software-defined wireless sensor networking (SD-WSN), network function virtualization (NFV) and cognitive radios (CRs). This paper presents an exhaustive review for these key enabling technologies and also discusses the new emerging use cases of 5G-IoT driven by the advances in artificial intelligence, machine and deep learning, ongoing 5G initiatives, quality of service (QoS) requirements in 5G and its standardization issues. Finally, the paper discusses challenges in the implementation of 5G-IoT due to high data-rates requiring both cloud-based platforms and IoT devices based edge computing.

A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges

Abstract: In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to apply machine learning techniques. In this paper, we provide a comprehensive survey on the literature involving machine learning algorithms applied to SDN. First, the related works and background knowledge are introduced. Then, we present an overview of machine learning algorithms. In addition, we review how machine learning algorithms are applied in the realm of SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security. Finally, challenges and broader perspectives are discussed.

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

Abstract: Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas.

A Survey on Emerging SDN and NFV Security Mechanisms for IoT Systems

Abstract: The explosive rise of Internet of Things (IoT) systems have notably increased the potential attack surfaces for cybercriminals. Accounting for the features and constraints of IoT devices, traditional security countermeasures can be inefficient in dynamic IoT environments. In this vein, the advantages introduced by software defined networking (SDN) and network function virtualization (NFV) have the potential to reshape the landscape of cybersecurity for IoT systems. To this aim, we provide a comprehensive analysis of security features introduced by NFV and SDN, describing the manifold strategies able to monitor, protect, and react to IoT security threats. We also present lessons learned in the adoption of SDN/NFV-based protection approaches in IoT environments, comparing them with conventional security countermeasures. Finally, we deeply discuss the open challenges related to emerging SDN- and NFV-based security mechanisms, aiming to provide promising directives to conduct future research in this fervent area.