Low Earth orbit (LEO) mega-constellations enable global, low-latency connectivity but challenge the security function chain (SFC) orchestration due to fast-changing visibility, resource volatility, and heterogeneous quality of service (QoS) requirements. To tackle this issue, we present in this paper SATStab, a stability-aware orchestration framework that co-designs: (i) stability-regularized objectives penalizing configuration churn across time windows; (ii) temporal decoupling with visibility-aware repair and warm starts; and (iii) hierarchical decoupling that separates fast association decisions from slower SFC placement and resource allocation. We formulate SATStab as a mixed-integer non-linear programming (MINLP) problem and evaluate it with realistic LEO dynamics. We solve it through a two-stage approach, where in the first stage, we optimize satellite-user associations while in the second stage, we optimize SFC placements. Through extensive simulations, we show that, relative to a monolithic MINLP, SATStab reduces user-satellite handovers by 54.3%, satellite-ground station (GS) re-associations by 59.3%, and SFC migrations by 32.7%, while cutting solution time by 56.8% without degrading end-to-end (E2E) delay. These results prove the efficiency of SATStab in terms of resource orchestration and resilience.

Low Earth orbit (LEO) satellite networks present critical challenges for security function chain (SFC) orchestration and associations due to rapid topology changes, resource volatility, and heterogeneous service requirements that render conventional SFC optimization approaches ineffective. To tackle this issue, we introduce here STARS, an optimization framework that fundamentally transforms the sequential time-window optimization for SFC orchestration and satellite association through three techniques: (1) Stability-aware regularization that penalizes configuration changes across time windows, thus reducing handovers by 54% and security function migrations by 33%; (2) Temporal decoupling that leverages solutions from prior time windows as warm-start seeds and dynamic repairing using real-time visibility constraints; and (3) Hierarchical decoupling that separates satellite association and SFC placement into computationally efficient stages, thus reducing time complexity. Through rigorous formulation as a mixed-integer non-linear programming (MINLP) and simulation-based evaluation, STARS achieves a 57% reduction in optimization solution time, a 7% reduction in the load of deployed security function instances, and efficient CPU utilization (9.81% increase) compared to benchmark schemes. STARS delivers these substantial benefits without any degradation in end-to-end delay. Note that the reported performance values are based on our specific system parameter choices and simulation setup and may not be universally representative. The co-design of stability mechanisms and decoupling strategies establishes STARS as a new paradigm for resilient satellite network optimization, balancing optimality, continuity, and computational tractability under high LEO satellite dynamicity.

The low Earth orbit (LEO) satellite megaconstellation can provide ubiquitous coverage and high-performance connectivity, supporting multi-slice applications with various key performance indicator (KPI) requirements. However, due to the dynamic nature of LEO satellites, limited resources, and the diverse demands of different slices, managing user association (UA) and resource allocation becomes an increasingly challenging task in areas with overlapping satellite coverage. This paper proposes a joint optimization model for UA and resource allocation in satellite networks (SLSNs). Based on mixed-integer non-linear programming (MILP), our model minimizes the total propagation delay and optimizes the demand satisfaction ratio (DSR) using a Max-Min approach to ensure each slice meets its unique throughput requirements. In addition, a visibility-aware component is incorporated to prioritize longer satellite visibility, reduce handovers, and improve network stability. Due to the computational complexity of the MILP model, we propose a heuristic-based balanced association with delay-aware bandwidth distribution (B-DAD) approach. B-DAD operates in two phases: the initial UA phase selects satellites based on a combined metric of delay, load, and visibility duration, while the residual bandwidth distribution phase reallocates unused bandwidth among associated users proportionally. Extensive simulations demonstrate that our approaches significantly improve DSR, propagation delays, transmission delays, and network stability compared to the widely adopted benchmark maximum sum of data rate (Max-SR) and Greedy methods under varying elevation angles. Our findings highlight the effectiveness of the MILP model in achieving optimal solutions and the efficiency of B-DAD as a scalable alternative for large-scale scenarios.

The IPv6 Routing Protocol for LLNs (RPL) is fundamental to the Internet of Things (IoT), providing essential routing capabilities for devices operating in constrained environments. At the heart of RPL’s efficiency is the Trickle Algorithm (TA), designed to optimize the dissemination of control messages across networks, balancing timely updates with the imperative to conserve bandwidth and energy. This paper focuses on validating the implementation of the TA within the Latest version of Contiki-OS (Contiki-NG), a leading experimental platform for IoT networks. By comparing the results of theoretical models with practical simulations of Contiki-NG, we aim to determine the reliability of the Contiki-NG representation of the TA behaviour. Our analysis reveals a high degree of agreement between theoretical expectations and simulated results, underscoring Contiki-NG’s robustness as a simulation tool for IoT research. The findings affirm the precision of Contiki-NG implementation and highlight the platform’s value in facilitating the development and testing of IoT protocols. By ensuring the reliability of these simulations, our work strengthens the confidence of the IoT research community in using Contiki-NG as a foundation for exploring innovative solutions to the unique challenges of IoT networks.

The integration of low Earth orbit (LEO) satellite communication into 6G networks promises a transformative impact on global connectivity by expanding coverage to remote regions and enhancing service reliability. However, this new infrastructure also introduces significant security challenges due to its expansive attack surface. To address this concern, we propose a dynamic security functions allocation (SFA) model that optimizes the allocation of security functions (SFs) across satellites while considering computational resource limitations, dynamic topology changes, and the visibility constraints of satellite constellations. Our model leverages the flexibility of 6G network slicing (NS) to share non-critical SFs between slices, reducing resource overhead while maintaining essential security demands. To minimize the risk of sharing highly sensitive SFs between slices, our model employs a nonlinear penalty, which prioritizes minimizing risk by aggressively penalizing high-risk SFs sharing. This dynamic risk management framework assesses the probability and impact of security breaches, ensuring that SFs are shared only when the security risk is acceptable, balancing resource efficiency and security. By dynamically adapting to the network’s operational conditions, our approach provides a robust framework for efficient and secure satellite communication in 6G networks. Simulation results demonstrate the model’s flexibility in managing trade-offs across key network performance metrics.

Many organizations were forced to quickly transition to the work-from-anywhere (WFA) model as a necessity to continue with their operations and remain in business despite the restrictions imposed during the COVID-19 pandemic. Many decisions were made in a rush, and cybersecurity decency tools were not in place to support this transition. In this article, we first attempt to uncover some challenges and implications related to the cybersecurity of the WFA model. Second, we conducted an online user study to investigate the readiness and cybersecurity awareness of employers and their employees who shifted to work remotely from anywhere. The user study questionnaire addressed different resilience perspectives of individuals and organizations. The collected data includes 45 responses from remotely working employees of different organizational types: Universities, government, private, and nonprofit organizations. Despite the importance of security training and guidelines, it was surprising that many participants had not received them. A robust communication strategy is necessary to ensure that employees are informed and updated on security incidents that the organization encounters. In addition, there is an increased need to pay attention to the security-related attributes of employees, such as their behavior, awareness, and compliance. Finally, we outlined best practice recommendations and mitigation tips guided by the study results to help individuals and organizations resist cybercrime and fraud and mitigate WFA-related cybersecurity risks.

The United States is the second largest producer of apples in the world with an estimated $21 billion downstream revenue. Since agriculture in the USA is highly mechanized, it is critical that latest advancements in technology are always integrated to the agricultural sector to not only improve efficiency but also improve quality, quantity, and to ensure faster distribution. Crop disease hampers the overall agricultural productivity and for a temperature-controlled crop like apple trees, identification of diseases at beginning stage is of paramount importance. There are two ways to identify and rectify issues relating to apple tree diseases, firstly by engaging expert biologists and secondly via automated identification through image processing. The biggest challenges with identification of diseases via biologist are accuracy, time constraints in case of bigger farms and budgetary limits. This research proposes the use of Machine Learning (ML) technique to aid and assist in automated disease detection and identification, and hence, making it affordable. It proposes the use of an ensemble (via weighted average) over single models, thereby improving performance and robustness by utilizing augmentations (positional and colour) which were not present in earlier studies. The proposed work surely creates an impact on the current plant disease diagnosis field by making the classification mode accurate and robust since it reaches accuracy of ~95% for all the classes.

Daily retail sales are impacted by a lot of external factors, holidays and special events are one such category. In general, retail sales are largely impacted by fluctuations in demand hence, it is common for a retailer to run out of stock for some items and overstock the other items and this happens due to the lack of understanding of the actual number of items in demand for a particular item at a particular time of the month. This research work examines the impact of holidays or special events on the sales of a wide category of items using predictive analytics. It is done by performing exploratory data analysis and pre-processing methods followed by feature engineering and information extraction to extract the optimal input parameters to be fed into the model. The dataset is time-series data however, advanced machine learning algorithms are also used along with time-series methods to see if time-series data works well with non-time-series algorithms. Different time-series methods along with gradient boosting and the Facebook prophet model are evaluated in this work, achieving 92.83 % forecast accuracy with the Facebook prophet model. The gradient boosting model performs well with a MAPE value of 22.25% and time-series Holt Winters' additive method provides a MAPE value of 12.84 %. Each of the algorithms provides a good score with this time-series data and an appropriate algorithm can be chosen as per the business need.

Phishing website detection is the task of classifying websites as phishing or legitimate based on URL parameters and certain behaviour of the site. In today's world, dependency on websites has become inevitable. With the increase in website users population and the rise of the internet, cyber-attacks have become a common thing. Attackers across the globe target innocent users to steal their personal classified information such as login credentials, credit or debit card information, which may lead to serious monetary and identity damage for the users. One of the main challenges with this problem is the constant change in phishing URLs. Due to this, there is a constant need to update the detection mechanism, which may be extinct in a short period of time. Most of the current phishing detection tools utilise the black box method, where phishing URLs are stored and queried for verification. This may not be an efficient way due to the constant change in the URLs. In this study, a machine learning based approach is proposed along with a feature selection method to select the right set of features that may contribute to higher detection accuracy. The proposed model is also aimed at being simple, faster, and interpretable. Efficiency, accuracy, and model execution time will be evaluated against the final model.

Airbnb is known to be a home-sharing and rental platform which provides facilities to homeowners or renters (referred to as hosts) to offer their houses otherwise known as listings on an online platform for guests booking. It is the responsibility of the hosts to set the expected price of their items independently. Although Airbnb along with a few sites provides many advices, we are yet to have any free or accurate system. This became difficult for the hosts to correctly come up with a price for their listed properties due to many different factors in the system. There are a few pricing models available in the market, however, these are not free. It is the responsibility of the host to enter the appropriate basic price for each night for a particular property. The other challenge is dynamic pricing based on holidays, seasons, and weather. The host can't keep the same price for all the dates as this impacts the business significantly. It is extremely critical to ensure appropriate prices are listed during this competitive time. This study compares the performance of numerous machine learning algorithms and methodologies in Airbnb price prediction to identify the most accurate one. Linear Regression, XGBoost, Random Forest, ANN and KNN are among the machine learning models experimented in this study. Different performance measures are used to validate the results.

This research proposes a computer vision-based solutions to identify whether a patient is covid19/normal/Pneumonia infected with comparable or better state-of-the-art accuracy. Proposed solution is based on deep learning technique CNN (Convolutional Neural networks) with multiple approaches to cover all open issues. First approach is based on CNN models based on pre-trained models; second approach is to create CNN model from scratch. Experimentation and evaluation of multiple approaches helps in covering all open points and gaps left unattended in related work performed to solve this problem. Based on the experimentation results of both the approaches and study of related work done by other researchers, Both the approaches are equally effective can be recommended for multi-class classification of lung disease.

Sign Language Recognition is a form of action recognition problem. The purpose of such a system is to automatically translate sign words from one language to another. While much work has been done in the SLR domain, it is a broad area of study and numerous areas still need research attention. The work that we present in this paper aims to investigate the suitability of deep learning approaches in recognizing and classifying words from video frames in different sign languages. We consider three sign languages, namely Indian Sign Language, American Sign Language, and Turkish Sign Language. Our methodology employs five different deep learning models with increasing complexities. They are a shallow four-layer Convolutional Neural Network, a basic VGG16 model, a VGG16 model with Attention Mechanism, a VGG16 model with Transformer Encoder and Gated Recurrent Units-based Decoder, and an Inflated 3D model with the same. We trained and tested the models to recognize and classify words from videos in three different sign language datasets. From our experiment, we found that the performance of the models relates quite closely to the model's complexity with the Inflated 3D model performing the best. Furthermore, we also found that all models find it more difficult to recognize words in the American Sign Language dataset than the others.

Routing discovery is a pivotal component of the communication stack in low-power and lossy networks (LLNs). The IPv6 routing protocol for LLNs, termed RPL, has been recently standardized to provide routing discovery in a wide range of LLN-based deployments realizing the Internet-of-Things (IoT) vision. RPL was mainly designed with the assumption that the predominant traffic flow would be gathering data toward a single destination, typically the root node. However, node-to-node (N2N) communication, where the root is neither a source nor destination, is a prime requirement in most of the LLN-based applications, such as actuating, decision making, and controlling applications. RPL in its current form does not cater well to such applications where there is a good number of inward N2N traffic flows. In this article, we propose a hybrid routing mechanism based on RPL for high N2N communications in LLNs referred to by HRPL. HRPL is implemented on Contiki and evaluated through extensive simulations on Cooja. HRPL is fully backward compatible with RPL where HRPL and RPL nodes can work together seamlessly in a hybrid network. Results show that HRPL provides a considerable improvement in the packet delivery ratio relative to the standardized RPL-based modes and to the RPL-based opportunistic routing approach. While HRPL requires a slightly larger memory footprint more than that for RPL, HRPL manages to significantly reduce the control plane packets, number of hops, and MAC transmissions needed to successfully deliver N2N data packets. Accordingly, this is translated to lower packet delay and less energy consumption.

The article covers the methods of Deep Learning for education data mining (EDM) and learning analytics (LA). The authors have reviewed the researches on EDM and LA that have employed the classical approaches of artificial neural networks (ANNS). The authors come to conclusion that LA and EDM present insufficient volume of research on the application of a new and more profound technology of artificial neural networks - Deep Learning. The new methods are described and the research on LA and EDM that employs Deep Learning is studied.