Karina Gomez Chavez

Bio: Karina Gomez Chavez is an academic researcher from RMIT University. The author has contributed to research in topics: Base station & Software-defined networking. The author has an hindex of 6, co-authored 20 publications receiving 99 citations.

Hierarchical routing protocols for wireless sensor network: a compressive survey

Abstract: Wireless sensor networks (WSNs) are one of the key enabling technologies for the internet of things (IoT). WSNs play a major role in data communications in applications such as home, health care, environmental monitoring, smart grids, and transportation. WSNs are used in IoT applications and should be secured and energy efficient in order to provide highly reliable data communications. Because of the constraints of energy, memory and computational power of the WSN nodes, clustering algorithms are considered as energy efficient approaches for resource-constrained WSNs. In this paper, we present a survey of the state-of-the-art routing techniques in WSNs. We first present the most relevant previous work in routing protocols surveys then highlight our contribution. Next, we outline the background, robustness criteria, and constraints of WSNs. This is followed by a survey of different WSN routing techniques. Routing techniques are generally classified as flat, hierarchical, and location-based routing. This survey focuses on the deep analysis of WSN hierarchical routing protocols. We further classify hierarchical protocols based on their routing techniques. We carefully choose the most relevant state-of-the-art protocols in order to compare and highlight the advantages, disadvantage and performance issues of each routing technique. Finally, we conclude this survey by presenting a comprehensive survey of the recent improvements of low-energy adaptive clustering hierarchy routing protocols and a comparison of the different versions presented in the literature.

Machine Learning in Network Anomaly Detection: A Survey

Abstract: Anomalies could be the threats to the network that have ever/never happened. To protect networks against malicious access is always challenging even though it has been studied for a long time. Due to the evolution of network in both new technologies and fast growth of connected devices, network attacks are getting versatile as well. Comparing to the traditional detection approaches, machine learning is a novel and flexible method to detect intrusions in the network, it is applicable to any network structure. In this paper, we introduce the challenges of anomaly detection in the traditional network, as well as in the next generation network, and review the implementation of machine learning in the anomaly detection under different network contexts. The procedure of each machine learning category is explained, as well as the methodologies and advantages are presented. The comparison of using different machine learning models is also summarised.

SECO: SDN sEcure COntroller algorithm for detecting and defending denial of service attacks

Abstract: Software Defined Network (SDN) brings additional flexibility to the traditional network allowing the implementation of intelligent information processing. SDN introduces a new architecture, where the controller acts as the brain of the network controlling several tasks such as routing, load balancing and providing the required quality of service (QoS). However, having a centralized controller makes the network vulnerable in terms of security. This paper introduces SDN sEcure COntroller (SECO) a novel and simple detect and defense algorithm, running in the controller, for improving SDN security features under Denial of Service (DoS) attacks. The network performance during attack is tested with and without the SECO algorithm. In this paper we show by means of simulations that the DoS attacks can degrade the controller's performance and the proposed algorithm could significantly reduce the impact of such DoS attacks.

Free Spectrum for IoT: How Much Can It Take?

Abstract: Vast numbers of IoT devices will soon be deployed in a ubiquitous manner connecting billions of small sensors to the Internet. The means of connectivity will be primarily provided by wireless networks. This paper presents the results of a spectrum occupancy experiment conducted in the ISM-band in Melbourne, Australia. The focus of this experiment is on the spectrum window of the already highly utilized 915-928~MHz as a potential candidate for some IoT technologies. The aim of this paper is to quantify the feasibility of using this free spectrum band for present and future IoT applications. We classify the spectrum measurements according to the population densities and we formulate a relation between the current spectrum occupancy and the underlaying population density. Based on the current occupancy figures and by using the pure ALOHA access model due to its similarity to the access models currently deployed in class-licensed bands, we draw future paradigms on the available spectral room for additional IoT services in this band.

Evaluating Coverage Performance of NB-IoT in the ISM-band

Abstract: The Narrowband Internet of Things (NB-IoT) is one of the premier access technologies adopted by many telecom operators as the next-generation IoT solution. This technology is primarily designed to tackle the rapid growth of low-power IoT applications. However, NB-IoT runs in licensed frequency bands leased by the conventional telecom operators and is usually very expensive. In this paper, we present a framework for evaluating the link-level performance of NB-IoT in the license-free radio spectrum, namely in the prominent 915 MHz band. The evaluation is performed using extensive physical layer emulation, including the effect of existing high-level interference recorded in this band. We report the resulting packet error rate with respect to the level of interference and different NB-IoT configurations. The results show promising feasibility of using the license-free spectrum to support NB-IoT enabled by the packet-repetition feature.

A Review of Applications and Communication Technologies for Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) Based Sustainable Smart Farming

Abstract: To reach the goal of sustainable agriculture, smart farming is taking advantage of the Unmanned Aerial Vehicles (UAVs) and Internet of Things (IoT) paradigm. These smart farms are designed to be run by interconnected devices and vehicles. Some enormous potentials can be achieved by the integration of different IoT technologies to achieve automated operations with minimum supervision. This paper outlines some major applications of IoT and UAV in smart farming, explores the communication technologies, network functionalities and connectivity requirements for Smart farming. The connectivity limitations of smart agriculture and it’s solutions are analysed with two case studies. In case study-1, we propose and evaluate meshed Long Range Wide Area Network (LoRaWAN) gateways to address connectivity limitations of Smart Farming. While in case study-2, we explore satellite communication systems to provide connectivity to smart farms in remote areas of Australia. Finally, we conclude the paper by identifying future research challenges on this topic and outlining directions to address those challenges.

Privacy Protection and Energy Optimization for 5G-Aided Industrial Internet of Things

Abstract: The 5G is expected to revolutionize every sector of life by providing interconnectivity of everything everywhere at high speed. However, massively interconnected devices and fast data transmission will bring the challenge of privacy as well as energy deficiency. In today’s fast-paced economy, almost every sector of the economy is dependent on energy resources. On the other hand, the energy sector is mainly dependent on fossil fuels and is constituting about 80% of energy globally. This massive extraction and combustion of fossil fuels lead to a lot of adverse impacts on health, environment, and economy. The newly emerging 5G technology has changed the existing phenomenon of life by connecting everything everywhere using IoT devices. 5G enabled IIoT devices has transformed everything from traditional to smart, e.g. smart city, smart healthcare, smart industry, smart manufacturing etc. However, massive I/O technologies for providing D2D connection has also created the issue of privacy that need to be addressed. Privacy is the fundamental right of every individual. 5G industries and organizations need to preserve it for their stability and competency. Therefore, privacy at all three levels (data, identity and location) need to be maintained. Further, energy optimization is a big challenge that needs to be addressed for leveraging the potential benefits of 5G and 5G aided IIoT. Billions of IIoT devices that are expected to communicate using the 5G network will consume a considerable amount of energy while energy resources are limited. Therefore, energy optimization is a future challenge faced by 5G industries that need to be addressed. To fill these gaps, we have provided a comprehensive framework that will help energy researchers and practitioners in better understanding of 5G aided industry 4.0 infrastructure and energy resource optimization by improving privacy. The proposed framework is evaluated using case studies and mathematical modelling.