Wildfires pose a serious hazard to both life and the environment, necessitating accurate and timely detection methods to minimize their impact. However, traditional approaches have limitations in precision, response time, and adaptability to environmental factors. To address this gap, machine learning (ML) and deep learning (DL) approaches are increasingly being adopted for fire detection. This study proposes a hybrid approach that combines deep feature extraction using Transfer Learning (TL) with EfficientNetB0 and traditional Gradient Boosting Models (GBM), specifically LightGBM, XGBoost, and CatBoost, for wildfire detection using UAV imagery. EfficientNetB0 is used to extract features, which are then dimensionally reduced using Principal Component Analysis (PCA). Optuna framework is used for hyperparameter tuning, to maximize the performance of the model. The results show that the GBM models enhanced with TL outperform their baseline counterparts, with CatBoost + TL achieving the highest precision (92. 37%). These findings confirm the role of TL in improving model precision, efficiency, and computational resource utilization, making it ideal for real-time wildfire detection.

In recent years, the use of the Internet of Things (IoT) has grown rapidly in smart cities, households, and smart industries environments due to the benefits of convenience and easy implementation. The Long-Range Wide Area Network (LoRaWAN) is a key technology driving this growth, which has seen significant adoption due to its scalability, long-range capabilities, low power consumption, lost cost and wide area coverage. As the use of LoRaWAN-enabled IoT environment has increased, the cyber-attacks related to it have also increased. About 95 % of the cyber-attacks in IoT environment occurs due to the security vulnerabilities caused by the user's errors. However, much research in the LoRaWAN environ-ment is highly focused on the security vulnerabilities due to technical errors, leaving gaps in identifying the security vulnerabilities that occur due to user errors. Therefore, this paper addresses these gaps by identifying user-centric errors which may lead to cyber-attacks in an LoRAWAN IoT environment. It builds a taxonomy based on a comprehensive literature review and maps these user errors to potential security vulnerabilities within LoRa WAN- enabled IoT environments.

Bushfire severity prediction plays a vital role in mitigating the catastrophic effects of bushfires on ecosystems, human life, and property. Accurate prediction models enable timely interventions and informed decision-making, which are crucial for effective fire prevention and response strategies. This study aims to develop an efficient bushfire severity prediction model by leveraging satellite-derived data, specifically from Landsat 8, which includes spectral indices, topographical and climatic features. The primary objective of this study is to assess the impact of different feature combinations on model performance using the XGBoost regression model with dNBR as the target variable. Eighteen tests were conducted, ranging from single-feature indices like NDVI to more complex multi-feature sets. The findings revealed that a 21-feature combination achieved the highest accuracy of 90.09%, with a MSE of 0.0008, a computational time of 1201.86 seconds, and a memory usage of 106.28 MB. However, adding more features did not always result in proportional improvements and often led to increased computational demands. In contrast, tests with fewer features demonstrated that high precision could be achieved with reduced resource consumption, offering a favourable trade-off between performance and efficiency. The results underscore the importance of strategic feature selection and optimization to enhance model accuracy while minimizing computational over-head. This study provides valuable insights for creating effective, high-performance fire severity prediction models, especially in resource-constrained settings.

The dramatic recent increase of the smart Internet of Everything (IoE) in Industry 4.0 has significantly\nincreased energy consumption, carbon emissions, and global warming. IoE applications in Industry\n4.0 face many challenges, including energy efficiency, heterogeneity, security, interoperability, and\ncentralization. Therefore, Industry 4.0 in Beyond the Sixth-Generation (6G) networks demands moving\nto sustainable, green IoE and identifying efficient and emerging technologies to overcome sustainability\nchallenges. Many advanced technologies and strategies efficiently solve issues by enhancing\nconnectivity, interoperability, security, decentralization, and reliability. Greening IoE is a promising\napproach that focuses on improving energy efficiency, providing a high Quality of Service (QoS), and\nreducing carbon emissions to enhance the quality of life at a low cost. This survey provides a comprehensive\noverview of how advanced technologies can contribute to green IoE in the 6G network of\nIndustry 4.0 applications. This survey provides a comprehensive overview of advanced technologies,\nincluding Blockchain, Digital Twins (DTs), Unmanned Aerial Vehicles (UAVs, a.k.a. drones), and\nMachine Learning (ML), to improve connectivity, QoS, and energy efficiency for green IoE in 6G\nnetworks. We evaluate the capability of each technology in greening IoE in Industry 4.0 applications\nand analyze the challenges and opportunities to make IoE greener using the discussed technologies.

Abstract The development of the Internet of Things (IoT) technology and their integration in smart cities have changed the way we work and live, and enriched our society. However, IoT technologies present several challenges such as increases in energy consumption, and produces toxic pollution as well as E-waste in smart cities. Smart city applications must be environmentally-friendly, hence require a move towards green IoT. Green IoT leads to an eco-friendly environment, which is more sustainable for smart cities. Therefore, it is essential to address the techniques and strategies for reducing pollution hazards, traffic waste, resource usage, energy consumption, providing public safety, life quality, and sustaining the environment and cost management. This survey focuses on providing a comprehensive review of the techniques and strategies for making cities smarter, sustainable, and eco-friendly. Furthermore, the survey focuses on IoT and its capabilities to merge into aspects of potential to address the needs of smart cities. Finally, we discuss challenges and opportunities for future research in smart city applications.

Australian retail sector has fallen victim to the recent lockdowns due to COVID-19 pandemic, which forced retailers to adopt new technologies to conduct business online. As a result, several businesses are attempting to use Blockchain technology to enhance security and promote transparency. Prior studies indicated the importance of human factors in technology adoption decisions. However, there is limited research on how human factors play out in blockchain technology adoption decisions in the Australian retail sector. Therefore, this study identifies the human factors and presents a research model to investigate their influence on technology adoption decisions in the Australian retail sector. The proposed study will use a quantitative approach and collects data using online survey questionnaires. This study will test the derived hypotheses using the structural equation modelling technique. This study is expected to help develop appropriate policies to enhance blockchain technology adoption in the Australian retail sector.

Next generation mobile networks have proposed the integration of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BS) to serve ground nodes. Despite the advantages of UAV-BSs, their dependence on the on-board, limited-capacity battery hinders their service continuity. Shorter trajectories can save flying energy, however UAV-BSs must also serve nodes based on their service priority since nodes' service requirements are not always the same. In this paper, we present an energy-efficient trajectory optimization for a UAV assisted IoT system in which the UAV-BS considers the IoT nodes' service priorities in making its movement decisions. We solve the trajectory optimization problem using Double Q- Learning algorithm. Simulation results reveal that the Q-Learning based optimized trajectory outperforms a benchmark algorithm, namely Greedily served algorithm, in terms of reducing the average energy consumption of the UAV-BS as well as the service delay for high priority nodes.

Next generation mobile networks have proposed the integration of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BS) to serve ground nodes with potentially varying QoS requirements. However, the dependence on the on-board, limited-capacity battery of the UAV-BS limits their service continuity. While conserving energy is important, meeting the QoS requirements of the ground nodes is equally important. We present an energy-efficient trajectory optimization for the UAV-BS while satisfying QoS requirements. We model the trajectory optimization as an MDP problem and solve it using Q-Learning. Simulation results reveal that our proposed algorithm decreases the average energy consumption by nearly 55% compared to a randomly-served algorithm.

We model the user's quality of experience (QoE) in a wireless voice over IP (VoIP) service as a function of the amount of effort the user has to put to continue her conversation. We assume that users would quit or terminate an ongoing call if they have to put more efforts than they could tolerate. Not knowing the tolerance threshold of each individual user, the service provider faces a decision dilemma of whether to fix the network problem immediately whenever he detects a user effort in the VoIP system, or ignore it with the hope that the user may still continue the call anyway. In this paper, we formulate the provider's dilemma as a non-cooperative game between the provider and the VoIP user experiencing a deteriorating QoE. We demonstrate that providers implementing the equilibrium solutions can expect to not only increase their revenues, but also reduce the number of cases when users quit out of frustration thus minimizing potential churning. We also discuss conditions under which a sophisticated user may or may not benefit from faking unwarranted efforts with a goal of receiving a better service from the provider. Finally, we conduct a subjective experiment of VoIP over WiFi, which verifies the key model assumption that perceptual quality is negatively correlated to the amount of effort the user has to put to continue the call.

Wireless access using contributed access points is fast becoming a reality. Technological means are now available for realizing this “anywhere anytime” low-cost wireless access whereby un-utilized wireless resources can be harnessed to form a collaborative network for public access. The success of such access facilities depend largely on the provisioning of fast authentication among the access points. This is crucial for supporting real-time applications such as Voice Over IP (VoIP). In our earlier work, we had developed an inter-domain fast authentication scheme based on the concept of “trust-clouds”. A trust-cloud is formed among neighbouring access points based on a relationship among the owners of the access points. Fast authentication among these inter-domain sites is achieved through cooperation among the trust-cloud members. In previous work, we theoretically formulated the concept of trust-clouds. In this paper we report on the implementation and experimental results of the “trust-clouds” fast authentication concept on legacy hardware. The experimental results verify that the trust-cloud framework can yield significant delay gains for the authentication process when handing off between administrative domains. It is also shown that no special computing platform is needed to achieve these benefits, indeed our testbed was implemented on low end hardware.

Mobile handset manufacturers are introducing new features that allow a user to configure the same handset for seamless operation with multiple wireless network providers. As the competitiveness in the wireless network service market intensifies, such products will deliver greater freedom for the mobile users to switch providers dynamically for a better price or quality of experience. For example, when faced with an unexpected wireless link quality problem, the user could choose to physically switch the provider, or she could be more strategic and use her freedom of switching provider as a ‘psychological weapon’ to force the current provider upgrading the link quality without delay. In this paper, we explore the latter option where users threaten to quit the current provider unless he (the provider) takes immediate actions to improve the link quality. By threatening the provider, the user will have to accept the risk of having to disconnect from the current provider and reconnect to another in the middle of a communication session, should the provider defies the threat. The user therefore will have to carefully assess the merit of issuing such threats. To analyze the dynamics of this scenario, we formulate the problem as a brinkmanship game theory model. As a function of user's and provider's payoff or utility values, we derive conditions under which the user could expect to gain from adopting the brinkmanship strategy. The effect of uncertainties in payoff values are analyzed using Monte Carlo simulation, which confirms that brinkmanship can be an effective strategy under a wide range of scenarios. Since user threats must be credible to the provider for the brinkmanship model to work, we discuss possible avenues in achieving threat credibility in the context of mobile communications.

Wireless voice over IP (VoIP) is subject to unpredictable link conditions which directly contribute to user irritation. Standard technological means available to the network provider to remedy wireless link problems require additional radio resources to be allocated. Given that radio resource is limited, it is not clear whether such allocation strategies to reduce user irritation are economically rewarding for the wireless providers. We model the resource allocation dilemma to reduce user irritation as a noncooperative game between the provider and the VoIP user suffering from a link quality problem. We demonstrate that providers implementing Nash equilibrium can expect to optimize their revenues and avoid potential churning.

In multiowner wireless networks, access points (APs) are owned and operated by different administrations, leading to significant authentication delays during handoff between APs. We propose to exploit the trust between the owners of neighboring APs for reducing the authentication delay. In the proposed authentication scheme, neighboring APs that trust each other share the security key for the visiting node to avoid lengthy authentication routines each time the visiting node switches APs. The performance of the proposed trust-based authentication scheme is evaluated using a Markov model. Using numerical experiments, we first study a basic scenario where mobile nodes are not aware of the trust networks that exist in a given neighborhood. Subsequently, we consider an advanced scenario where a mobile node functionality is augmented to discover the trust network so as to minimize roaming beyond the trusted APs. We find that, even with the basic implementation, the average number of full authentications needed for a roaming mobile reduces linearly as the likelihood of two neighboring APs trusting each other increases. With the advanced implementation, our experiments show that quadratic reduction is achieved. The Markov model is validated using discrete event simulation.

In this paper, we propose a novel inter-domain fast-authentication scheme based on a concept of "trust clouds". A trust cloud is formed among neighbouring access points based on a relationship among the owners of the access points. Fast-authentication for inter-domain sites is achieved through cooperation among the trust cloud members. The approach is distributed without a central authentication server being involved in distributing the security master key to the access points belonging to the trust cloud. We propose two algorithms for mobile visiting nodes to perform authentication at handoff and show through a simulation study that the approach significantly reduces authentication delay compared with traditional server based schemes.

This paper introduces project TITAN, which investigates an alternative construct of residential broadband access. The aim of the project is to increase the utilisation of deployed broadband capacity such as xDSL and cable modem connections. In order to achieve this, we propose a Collaborative Community Network (CCN) where residential broadband users contribute their spare broadband capacity to other users over a wireless medium, to form a collaborative community wireless Internet access network. Our novel design has the strength that it can provide high data-rate wireless Internet access by utilising existing infrastructure, while utilising the community pool of network resources to provide a distributed network management. We build an autonomous high capacity wireless access network using a combination of cognitive wireless access mechanisms and a distributed processing platform to continuously optimise the configuration and utilisation of the network. A novel and interesting consequence of our architecture is a paradigm shift in terms of the boundary between service providers and consumers. In our model, consumers collaborate with the service providers to achieve the goals of high availability, high capacity wireless network access. This paper introduces the TITAN model and functional components and discuss the main research challenges associated with the model.

Expanding ring search (ERS) is a widely used technique to reduce broadcast overhead in multi-hop wireless networks (e.g., ad-hoc and sensor networks). ERS works by searching successively larger areas in the network centred around the source of broadcast. Network-wide broadcast is initiated only if L successive searches fail. This paper explores if there exists an optimal L that would minimise the broadcast cost of ERS. A theoretical model is developed to analyse the expected broadcast cost as a function of L. Using this model, we show that an optimal L exists for any random network topology. The analytical results are validated through extensive numerical experiments that consider a large number of random network topologies of varying sizes and hop lengths. By tuning the parameter L to the optimum value, broadcast cost can be reduced up to 52% depending on the topology.

In this paper, we propose and analyze a timer-based location caching scheme to reduce the signalling cost of on-demand location management in the context of a light-weight cellular wireless network architecture, called a cell-hopping network. The timer value is the most important control parameter of the proposed caching scheme. We derive a closed-form expression as a function of the message-to-mobility ratio (MMR), for the optimum timer value that minimizes signalling cost. Using numerical examples, we show that the signalling cost can be reduced by as much as 80% for reasonable MMR values, if the caching timer is set to the optimum value. Conversely, incorrectly selected timer values can nullify the use of the location cache.

The recent auctioning of wireless bandwidth in various countries indicates that the service provided by Telcos over these networks will be very expensive. A new type of architecture is currently evolving as a low cost alternative cellular wireless service by allowing the users to play the role of a wireless network operator. Some users of the current Internet may install a low cost base station based on their needs and interconnection between these base stations will form a grid of wireless Internet. This paper provides a survey on related work that either uses ad-hoc mobile networking or a combination of fixed and wireless networking. Finally we present an alternate architecture and describe new issues that arise from this new type of architecture.