Building detection in satellite images has been considered an essential field of research in remote sensing and computer vision. There are currently numerous techniques and algorithms used to achieve building detection performance. Different algorithms have been proposed to extract building objects from high-resolution satellite images with standard contrast. However, building detection from low-contrast satellite images to predict symmetrical findings as of past studies using normal contrast images is considered a challenging task and may play an integral role in a wide range of applications. Having received significant attention in recent years, this manuscript proposes a methodology to detect buildings from low-contrast satellite images. In an effort to enhance visualization of satellite images, in this study, first, the contrast of an image is optimized to represent all the information using singular value decomposition (SVD) based on the discrete wavelet transform (DWT). Second, a line-segment detection scheme is applied to accurately detect building line segments. Third, the detected line segments are hierarchically grouped to recognize the relationship of identified line segments, and the complete contours of the building are attained to obtain candidate rectangular buildings. In this paper, the results from the method above are compared with existing approaches based on high-resolution images with reasonable contrast. The proposed method achieves high performance thus yields more diversified and insightful results over conventional techniques.

Computer networks are facing threats of ever-increasing frequency and sophistication. Encryption is becoming the norm in both legitimate and malicious network traffic. Therefore, intrusion detection systems (IDSs) are now required to work efficiently regardless of the encryption. In this study, we propose two new methods to improve the efficacy of the Cisco Cognitive Threat Analytics (CTA) system. In the first method, the efficacy of CTA is improved by sharing of intelligence information across a large number of enterprise networks. In the second method, a four variant-based global reputation model (GRM) is designed by employing an outlier ensemble normalization algorithm in the presence of missing data. Intelligence sharing provides additional information in the intrusion detection process, which is much needed, particularly for analysis of encrypted traffic with inherently low information content. Robustness of the novel outlier ensemble normalization algorithm is also demonstrated. These improvements are measured using both encrypted and non-encrypted network traffic. Results show that the proposed information sharing methods greatly improve the anomaly detection efficacy of malicious network behavior with bad base-line detection efficacy and slightly improve upon the average case.

Mobile edge computing (MEC) reduces the latency for end users to access applications deployed at the edge by offloading tasks to the edge. With the popularity of e-commerce and the expansion of business scale, server load continues to increase, and energy efficiency issues gradually become more prominent. Computation offloading has received widespread attention as a technology that effectively reduces server load. However, how to improve energy efficiency while ensuring computing requirements is an important challenge facing computation offloading. To solve this problem, using non-orthogonal multiple access (NOMA) to increase the efficiency of multi-access wireless transmission, MEC supporting NOMA is investigated in the research. Computing resources will be divided into separate sub-computing that will be handled via e-commerce terminals or transferred to edge sides by reutilizing radio resources, we put forward a Group Switching Matching Algorithm Based on Resource Unit Allocation (GSM-RUA) algorithm that is multi-dimensional. To this end, we first formulate this task allocation problem as a long-term stochastic optimization problem, which we then convert to three short-term deterministic sub-programming problems using Lyapunov optimization, namely, radio resource allocation in a large timescale, computation resource allocating and splitting in a small-time frame. Of the 3 short-term deterministic sub-programming problems, the first sub-programming problem can be remodeled into a 1 to n matching problem, which can be solved using the block-shift-matching-based radio resource allocation method. The latter two sub-programming problems are then transformed into two continuous convex problems by relaxation and then solved easily. We then use simulations to prove that our GSM-RUA algorithm is superior to the state-of-the-art resource management algorithms in terms of energy consumption, efficiency and complexity for e-commerce scenarios.

To address the challenges of data privacy protection and collaborative management in agricultural consumer electronics (ACE) devices, this study proposes a distributed data processing framework based on federated learning (FL). The framework employs a three-tier system architecture comprising a base station (BS), edge devices that train models using local data, and a BS that aggregates these models to generate a global one. This iterative process establishes a paradigm that balances privacy preservation and performance optimization. Additionally, we design an incentive mechanism based on auction theory to simulate the buyer-seller interaction between BSs and edge devices. In this mechanism, devices submit bids according to their minimum energy requirements. To maximize resource allocation utility, we propose a greedy auction algorithm that satisfies multiple economic properties. Simulation results demonstrate that the algorithm not only guarantees these properties but also enhances system utility and efficiency.

Real-time health data monitoring is pivotal for bolstering road services’ safety, intelligence, and efficiency within the Internet of Health Things (IoHT) framework. Yet, delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems. We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this. This strategy is devised to streamline the data retrieval path, subsequently diminishing network strain. Crafting an adept cache processing scheme poses its own set of challenges, especially given the transient nature of monitoring data and the imperative for swift data transmission, intertwined with resource allocation tactics. This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach, facilitating nuanced health monitoring via edge devices. The system capitalizes on cloud computing for intricate health data analytics, especially in pinpointing health anomalies. Given the dynamic locational shifts and possible connection disruptions, we have architected a hierarchical detection system, particularly during crises. This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times. Furthermore, we introduce the Cache-Assisted Real-Time Detection (CARD) model, crafted to optimize utility. Addressing the inherent complexity of the NP-hard CARD model, we have championed a greedy algorithm as a solution. Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio (CHR) and data freshness, outshining its contemporaneous benchmark algorithms. The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution. To encapsulate, this paper tackles the nuances of real-time health data monitoring in the IoHT landscape, presenting a joint edge-cloud caching strategy paired with a hierarchical detection system. Our methodology yields enhanced cache efficiency and data freshness. The corroborative numerical data accentuates the feasibility and relevance of our model, casting a beacon for the future trajectory of real-time health data monitoring systems.

Bus passenger profiling is a critical task for optimizing urban transportation, but it is hindered by three key challenges: the heterogeneity of passenger behaviors, complex station-level interactions, and the prevalence of sparse, noisy transit data. Conventional end-to-end models that operate on aggregated traffic flow often fail to address these issues systematically. To overcome these limitations, this paper proposes GRASP, a novel two-stage paradigm for passenger profiling and flow prediction. In the first stage, GRASP acts as a disentangling module, constructing a passenger-centric graph to cluster individuals into distinct behavioral profiles based on their co-occurrence patterns. In the second stage, it performs profile-aware forecasting by learning group-specific, dynamic spatio-temporal dependencies using an adaptive station graph. This station-level model is further enhanced by a contrastive learning objective to ensure robustness against data imperfections. Extensive experiments on three real-world datasets demonstrate that GRASP not only achieves significantly superior flow prediction accuracy but also uncovers actionable passenger profiles. By structurally decoupling passenger behavior from station-level dynamics, GRASP offers a more interpretable and effective solution for data-driven public transportation management.