Storage Resources Research Articles

Temporal-Incremental Learning for Android Malware Detection

Malware classification is a specific and refined task within the broader malware detection problem. Effective classification aids in understanding attack techniques and developing robust defenses, ensuring application security and timely mitigation of software vulnerabilities. The dynamic nature of malware demands adaptive classification techniques that can handle the continuous emergence of new families. Traditionally, this is done by retraining models on all historical samples, which requires significant resources in terms of time and storage. An alternative approach is Class-Incremental Learning (CIL), which focuses on progressively learning new classes (malware families) while preserving knowledge from previous training steps. However, CIL assumes that each class appears only once in training and is not revisited, an assumption that does not hold for malware families, which often persist across multiple time intervals. This leads to shifts in the data distribution for the same family over time, a challenge that is not addressed by traditional CIL methods. We formulate this problem as Temporal-Incremental Malware Learning (TIML), which adapts to these shifts and effectively classifies new variants. To support this, we organize the MalNet dataset, consisting of over a million entries of Android malware data collected over a decade, in chronological order. We first adapt state-of-the-art CIL approaches to meet TIML's requirements, serving as baseline methods. Then, we propose a novel multimodal TIML approach that leverages multiple malware modalities for improved performance. Extensive evaluations show that our TIML approaches outperform traditional CIL methods and demonstrate the feasibility of periodically updating malware classifiers at a low cost. This process is efficient and requires minimal storage and computational resources, with only a slight dip in performance compared to full retraining with historical data.

Secure and Lightweight Cluster-Based User Authentication Protocol for IoMT Deployment

Authentication is considered one of the most critical technologies for the next generation of the Internet of Medical Things (IoMT) due to its ability to significantly improve the security of sensors. However, higher frequency cyber-attacks and more intrusion methods significantly increase the security risks of IoMT sensor devices, resulting in more and more patients’ privacy being threatened. Different from traditional IoT devices, sensors are generally considered to be based on low-cost hardware designs with limited storage resources; thus, authentication techniques for IoMT scenarios might not be applicable anymore. In this paper, we propose an efficient three-factor cluster-based user authentication protocol (3ECAP). Specifically, we establish the security association between the user and the sensor cluster through fine-grained access control based on Merkle, which perfectly achieves the segmentation of permission. We then demonstrate that 3ECAP can address the privilege escalation attack caused by permission segmentation. Moreover, we further analyze the security performance and communication cost using formal and non-formal security analysis, Proverif, and NS3. Simulation results demonstrated the robustness of 3ECAP against various cyber-attacks and its applicability in an IoMT environment with limited storage resources.

Cloud Network Collaborative SFC Deployment Under Heterogeneous Resource Of Power White Box Network

The construction of new electric power systems has led to a multiplication in the number of access nodes for electric power IoT information communication, accompanied by a deepening trend in business cloudification. However, due to the insufficient resource linkage and regulation capabilities of the electric power communication network on the cloud side, network side, and edge side, coupled with the lack of a convenient and unified cloud-network resource convergence and control mechanism, the existing electric power communication architecture is gradually finding it difficult to fully meet the demand for deterministic multi-service bearer. Therefore, it is necessary to evolve the original network equipment within the network toward a white box model, opening up interfaces for fine-grained sensing and full-area control, in order to provide more accurate and controllable network transmission services. In this paper, we first propose a cloud-network cooperative resource scheduling architecture for the electric power white box network. This architecture establishes a bearer network to achieve high consistency and cooperativeness of cloud-side end computing power through white box switches, uniformly schedules the global resources of the cloud-network convergence network, and improves the network service quality between cloud-side and side-side. Furthermore, we design a multi-dimensional re-source scheduling method for cloud-network synergy in the electric power white box network. This involves constructing a white box network virtualized heterogeneous resource (VHR) model and a heterogeneous resource control flow (HRCF) model. We transform the problem of merged deployment of service function chains (SFCs) into an SFC merger deployment problem by jointly controlling heterogeneous information, communication, storage, and arithmetic resources, along with the white box switch-specific pipelining resources in the white box network. We then carry out merger optimization of the same kind of function chains. Additionally, we propose a G-GS algorithm based on heuristic methods. Simulation results demonstrate that the method presented in this paper can significantly reduce the cost consumption of white box pipeline resources in the electric power white box network, thereby reducing the processing delay of SFC and improving the quality of electric power service bearing.

Enhancing Urban Surveillance with Fog Computing, Mobile Cloud, and Big Data Analytics in 5G Networks

The new emerging applications in 5G network, in the context of the Internet of Everything (IoE), will introduce high mobility, high scalability, real-time, and low latency requirements that raise new challenges on the services being provided to the users. Fortunately, Fog Computing and Cloud Computing, with their service orchestration mechanisms offer virtually unlimited dynamic resources for computation, storage and service provision, that will effectively cope with the requirements of the forthcoming services. 5G will use the benefits of centralized high performance computing cloud centers, cloud and fog RANs and distributed peer-to-peer mobile cloud that will create opportunities for companies to deploy many new real-time services that cannot be delivered over current mobile and wireless networks. This paper evaluates a model for fog and cloud hybrid environment service orchestration mechanisms for 5G network in terms of energy efficiency per user for different payloads.

Pressure Distributions and Flow Regimes: An Integrated Approach For CO2 Sequestration Project Design and Evaluation in Hydraulically Fractured Shale Reservoirs

This paper introduces an integrated approach for estimating the key parameters of CO2 storage projects in depleted hydraulically fractured shale reservoirs. The approach focuses deep insights into evaluating these projects based on the pressure distribution and flow regimes. The objective is reducing the uncertainties in the design and evaluation criteria of CO2 storage by better understanding the flow mechanisms in the porous media. This understanding helps in perfectly modeling CO2 distribution and characterizing the expected flow regimes.Several analytical models are developed for the pressure behavior of CO2 multi-size scale flow mechanisms. Diffusion flow, slip flow, adsorption flow, and Darcy and non-Darcy flow are all considered. The flow in the nano-size organic particles, the micro-size kerogen particles, and the macro-size matrix is analytically described. The flow inside natural fractures in the stimulated and unstimulated reservoir volume as well as the flow of CO2 in the hydraulic fractures are modeled. Several rectangular reservoir configurations, hydraulic fracture characteristics, and petrophysical properties of the matrix, kerogen, and organic matter are examined. An estimation model for the total volume of CO2 that could be stored in the depleted fractured reservoirs is presented. The contribution of the unstimulated and stimulated porous media as well as the hydraulic fractures to the total volume is determined by characterizing the flow regimes. Different models are proposed for the time at which CO2 reaches the hydraulic fracture tips, the borders between stimulated and unstimulated porous media, and the reservoir boundary. Profound explanations are introduced for the impact of the constrained pressure on the total capacity of the reservoirs for CO2 storage.The study has reached several conclusions. The characteristics of the organic matter, kerogen, and the micro-size scale matrix do not significantly impact the pressure distribution and flow regimes. Conversely, the pressure distribution and flow regimes are significantly impacted by the characteristics of the hydraulic and natural fractures. The hydraulic fractures may offer a reasonable capacity for CO2 storage, meanwhile, the capacity of stimulated and unstimulated reservoir volume is controlled by reservoir configuration and fracture spacing. The constrained pressure may strictly reduce the total capacity of CO2 storage in the reservoir. The total volume of the injected CO2 can be determined from the pressure point when the injection pulse has reached to reservoir boundary. Beyond this point, it is not recommended to inject CO2 as it could increase reservoir pressure more than initial pressure. The pressure of the depleted reservoir before injection is a key parameter in the design and evaluation of the CO2 storage in shale reservoirs.This study introduces a novel approach for the design and evaluation criteria of CO2 storage in depleted shale reservoirs. The approach uses the pressure distribution and flow regimes that could be observed in the multi-size scale components of the reservoirs. The study proposes several models for estimating CO2 geosequestration capacity in the hydraulic fractures, stimulated and unstimulated reservoir volume as well as the total storage capacity of the reservoirs. The approach confidently addresses the concerns regarding the applicability of unconventional resources for CO2 storage and the total capacity offered by these reservoirs for this purpose.

A two-echelon multi-trip vehicle routing problem with synchronization for an integrated water- and land-based transportation system

This study focuses on two-echelon synchronized logistics problems in the context of integrated water- and land-based transportation (IWLT) systems. The aim is to meet the increasing demand in city logistics as a result of the growth in transport activities, including parcel delivery, food delivery, and waste collection. We propose two models, a novel mixed integer linear joint model, and a logic-based Benders’ decomposition (LBBD) model, for a two-echelon problem under realistic settings such as multi-trips, time windows, and synchronization at the satellites with no storage and limited resource capacities. The objective is to optimize transfers and satellite assignments, thereby reducing overall logistics costs for street vehicles and vessels. Computational experiments demonstrate that the LBBD model is more robust in terms of solution quality and solution time on average while the added value of the LBBD is more evident when solving large-scale instances with 100 customers, reducing the overall costs by 10.6% on average and significantly reducing the fleet costs on both networks. Furthermore, we assess the effect of changing cost parameters and satellite locations in the proposed IWLT system–analyzing system behavior and suggesting potential improvements–and evaluate several system alternatives in city logistics–consisting of different transportation network designs (single- and two-echelon), vehicle types, and operational constraints. On average, the proposed two-echelon IWLT system reduces the number of kilometers traveled by vehicles at street level by ranging from 20% to 30% compared to a typical single-echelon service design that relies solely on trucks.

An intelligent system for determining the degree of tree bark beetle damage based on the use of generative-adversarial neural networks.

The increasing prevalence of tree bark beetle infestations poses a significant threat to forest ecosystems, leading to detrimental impacts on biodiversity, carbon storage, and timber resources. This study addresses the pressing need for innovative solutions to enhance forest management practices through early detection and assessment of tree health. We developed an intelligent system leveraging multispectral images and generative-adversarial networks (GANs) to accurately determine the extent of bark beetle damage. Recognizing the challenges posed by traditional neural networks, which require vast amounts of labeled training data, we proposed a novel approach that utilizes a GAN architecture. In this system, a discriminator functions as a classifier, effectively trained on both real and synthetically generated data. Our methodology not only reduces the dependency on extensive labeled datasets but also enhances the robustness of the classification process. The results indicate a classification accuracy of 87.5%, demonstrating significant promise for improving detection capabilities even with limited training resources. The implications of this research are profound, offering potential benefits for the forestry industry through optimized management strategies and economic gains. Furthermore, our findings contribute to the preservation of critical ecosystem services by providing a means to monitor forest health effectively. However, the responsible implementation of this technology is paramount. Continuous refinement of the model, integration with traditional ecological knowledge, and ensuring transparency and equitable access to the developed system are essential for maximizing societal benefits. Additionally, addressing risks such as misinterpretation of data, overreliance on technology, and privacy concerns is crucial to minimize unintended consequences. Overall, this research presents a significant advancement in the field of forest health monitoring and establishes a foundation for future developments in intelligent ecological management systems.

An anti-freezing hydrogel electrolyte based on hydroxyethyl urea for dendrite-free Zn ion batteries

Aqueous zinc ion batteries are considered as a promising energy storage resource due to their high security, abundant resources and low price. However, the development of aqueous zinc ion batteries has been severely hindered by compulsive dendrite generation, serious side reactions and poor temperature adaptability. Herein, we used hydroxyethyl urea as a hydrogel electrolyte additive to address above-mentioned challenges. Hydroxyethyl urea can break the hydrogen bonds (HBs) of water and enhancing the freezing-tolerance ability of the electrolyte. Meanwhile, hydroxyethyl urea can prevent the corrosion issue and inhibition of Zn dendrites. Consequently, the Zn//Zn symmetric battery can sustain stable cycling for over 3000 h at 1 mA cm−2, and it achieves a high coulombic efficiency of 99.6 %. Even at −40 ℃ the batteries show excellent cycling stability, the Zn//Zn symmetry battery can achieve steadily cycles over 3000 h. The Zn//NVO battery equipped with the altered electrolyte exhibits enhanced capacity retention compared to the one without additives.It demonstrates not only robust stability at room temperature but also maintains commendable functionality down to −40 ℃, validating the method’s efficacy. This work provides a simple strategy for enhancing low temperature performance of zinc ion batteries.

A novel recursive sub-tensor hyperspectral compressive sensing of plant leaves based on multiple arbitrary-shape regions of interest

Plant hyperspectral images (HSIs) contain valuable information for agricultural disaster prediction, biomass estimation, and other applications. However, they also include a lot of irrelevant background information, which wastes storage resources. In this paper, we propose a novel recursive sub-tensor hyperspectral compressive sensing method for plant leaves. This method uses recursive sub-tensor compressive sensing to compress and reconstruct each arbitrary-shape leaf region, discarding a large amount of background information to achieve the best possible reconstruction performance of the leaf region and significantly reduce storage space. The proposed method involves several key steps. Firstly, the optimal band is determined using the spatial spectral decorrelation criterion, and its corresponding mask image is used to extract the leaf regions from the background. Secondly, the recursive maximum inscribed rectangle algorithm is applied to obtain rectangular sub-tensors of leaves recursively. Each sub-tensor is then individually compressed and reconstructed. Finally, all sub-tensors can be reconstructed to form complete leaf HSIs without background information. Experimental results demonstrate that the proposed method achieves superior image reconstruction quality at extremely low sampling rates compared to other methods. The proposed method can improve average Peak Signal-to-Noise Ratio (PSNR) values by about 3.04% and 0.74% compared to Tensor Compressive Sensing (TCS) at the sampling rate of 2%. In the spectral domain, the proposed method can achieve significantly smaller Spectral Angle Mapper (SAM) values and relatively lower spectral indices errors for Double Difference, Triangular Vegetation Index, Leaf Chlorophyll Index, and Modified Normalized Difference 680 than those of TCS. Therefore, the proposed method achieves better compression performance for reconstructed plant leaf HSIs than the other methods.

Lightweight Implementation of the Signal Enhancement Model for Early Wood-Boring Pest Monitoring

Wood-boring pests are one of the most destructive forest pests. However, the early detection of wood-boring pests is extremely difficult because their larvae live in tree trunks and have high invisibility. Borehole listening technology is a new and effective method to detect the larvae of insect pests. It identifies infested trees by analyzing wood-boring vibration signals. However, the collected wood-boring vibration signals are often disturbed by various noises existing in the field environment, which reduces the accuracy of pest detection. Therefore, it is necessary to filter out the noise and enhance the wood-boring vibration signals to facilitate the subsequent identification of pests. The current signal enhancement models are all designed based on deep learning models, which have complex scales, a large number of parameters, high demands for storage resources, large computational complexity, and high time costs. They often run on resource-rich computers or servers, and they are difficult to deploy to resource-limited field environments to realize the real-time monitoring of pests; as well, they have low practicability. Therefore, this study designs and implements two model lightweight optimization algorithms, one is a pre-training pruning algorithm based on masks, and the other is a knowledge distillation algorithm based on the separate transfer of vibration signal knowledge and noise signal knowledge. We apply the two lightweight optimization algorithms to the signal enhancement model T-CENV with good performance outcomes and conduct a series of ablation experiments. The experimental results show that the proposed methods effectively reduce the volume of the T-CENV model, which make them useful for the deployment of signal enhancement models on embedded devices, improve the usability of the model, and help to realize the real-time monitoring of wood-boring pest larvae.

A fast temporal multiple sparse Bayesian learning-based channel estimation method for time-varying underwater acoustic OFDM systems

In this paper, a fast temporal multiple sparse Bayesian learning (FTMSBL)-based channel estimation method for underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) systems is proposed, which is optimized using the fast marginalized likelihood maximization method. The algorithm fully uses the consistent sparse structure and time-domain correlation properties of channels to improve the reconstruction performance and computational efficiency, offering better performance and higher computational efficiency than the traditional Bayesian learning algorithms. At the same time, the FTMSBL algorithm does not require computing the inverse of large matrices and consumes very little storage resources in the operation, making it suitable for hardware implementation. Simulation and sea trial results show that the FTMSBL-based underwater channel estimation algorithm achieves higher channel estimation accuracy than the orthogonal matching tracking algorithm, and the system bit error rate (BER) is significantly reduced; specifically, the FTMSBL algorithm can achieve optimal performance in strong time-dependent channels.

Coordinated Planning for Multiarea Wind-Solar-Energy Storage Systems That Considers Multiple Uncertainties

As the scale of renewable energy sources (RESs) expands, it is essential to optimize the configuration of wind, solar, and storage resources across different areas. Nevertheless, the unavoidable uncertainties associated with both energy supply and demand present significant challenges for planners. This study aims to address the challenge of coordinated planning for multiarea wind-solar-energy storage systems considering multiple uncertainties. First, uncertainties related to future peak demand, thermal generation output boundaries, demand variability, and stochastic unit production are analyzed and modeled on the basis of robust optimization and stochastic programming techniques. Then, a hierarchical coordinated planning model that incorporates both system-wide (SW) and local area (LA) planning models is proposed. The SW planning model is designed to manage the optimal capacity configuration of RESs and energy storage systems (ESSs) within each LA, as well as the operational boundary of LAs. The LA planning models aim to further optimize the capacities of RESs and ESSs and minimize the economic cost within each LA on the basis of local resource characteristics. To achieve the optimal solution, the analytical target cascading (ATC) algorithm is integrated with the column-and-constraint generation (C&CG) algorithm. The simulation results validate the effectiveness and reasonableness of the proposed coordinated planning model, which not only outperforms independent planning approaches but also effectively manages the uncertainties.

Optimal Flexibility Dispatching of Multi-Pumped Hydro Storage Stations Considering the Uncertainty of Renewable Energy

With the continuous increase in the penetration rate of renewable energy, the randomness and flexibility demand in the power system continues to increase. The main grid side of the power system vigorously develops pumped hydro storage (PHS) resources. However, the current PHS station scheduling method of a fixed time period and fixed power has lost a certain flexibility supply. In this paper, an optimal dispatching model of multi-pumped hydro storage stations is proposed to supply flexibility for different regions of the state grid in east China. Firstly, the credible predictable power (CPP) of renewable energy is calculated and the definition of flexibility demand of a power system is given. The calculation model for flexibility demand is established. Secondly, considering the regional allocation constraint in the state grid in east China, a non-centralized model of multi-PHS within the dispatch scope is established. In the model, the constraints of storage capacity of different hydropower conversion coefficients of each PHS station is considered. The flexibility supply model of PHS stations to each region of the state grid in east China is established to realize reasonable flexibility allocation. Then, by combining the PHS station models and the flexibility demand calculation model, the optimal dispatching model for the flexibility supply of multi-PHS stations is established. Finally, based on the network dispatching example, the effectiveness and superiority of the proposed strategy are verified by a case study.

Knowledge Graph Embedding for Hierarchical Entities Based on Auto-Embedding Size

Knowledge graph embedding represents entities and relations as low-dimensional continuous vectors. Recently, researchers have attempted to leverage the potential semantic connections between entities with hierarchical relationships in the knowledge graph. However, existing knowledge embedding methods that model entity hierarchical structures face the following challenges: (1) Setting the same embedding dimension for entities at different hierarchical levels can lead to overfitting issues and the waste of storage and computational resources; (2) Manually searching the embedding dimension in discrete space makes it easy to miss the optimal parameters and increases training costs. Therefore, we propose a knowledge embedding method, HEAES, that automatically learns the embedding dimensions for entities based on their hierarchical structure. Specifically, we first propose a new modeling method to capture the hierarchical relationships of entities, and we then train the model on a triple classification task. Then, we adaptively learn the pruning threshold to trim the embedding vectors, automatically learning different embedding dimensions for entities at different hierarchical levels. Experiments on the YAGO26K and DB111K datasets verify that introducing entity hierarchical information helps boost the model performance.

Wood nutrients: Underexplored traits with functional and biogeochemical consequences.

Resource storage is a critical component of plant life history. While the storage of nonstructural carbohydrates in wood has been studied extensively, the multiple functions of mineral nutrient storage have received much less attention. Here, we highlight the size of wood nutrient pools, a primary determinant of whole-plant nutrient use efficiency, and a substantial fraction of ecosystem nutrient budgets, particularly tropical forests. Wood nutrient concentrations also show exceptional interspecific variation, even among co-occurring plant species, yet how they align with other plant functional traits and fit into existing trait economic spectra is unclear. We review the chemical forms and location of nutrient pools in bark and sapwood, and the evidence that nutrient remobilization from sapwood is associated with mast reproduction, seasonal leaf flush, and the capacity to resprout following damage. We also emphasize the role wood nutrients are likely to play in determining decomposition rates. Given the magnitude of wood nutrient stocks, and the importance of tissue stoichiometry to forest productivity, a key unresolved question is whether investment in wood nutrients is a relatively fixed trait, or conversely whether under global change plants will adjust nutrient allocation to wood depending on carbon gain and nutrient supply.

New routing method based on sticky bacteria algorithm and link stability for VANET

With the rapid development of Telematics, the role of edge computing (Mobile Edge Computing, MEC) is becoming more and more significant. Mobile users can obtain massive computing and storage resources in a local way, thus effectively solving the congestion problem of the core network. However, in general urban edge network scenarios of VANET (Vehicular Ad-hoc Network), due to low node density and poor connectivity, there are few chances to encounter suitable forwarding nodes. In order to avoid the above situation and adapt to sparse scenarios, we propose new link-stabilized routing method & protocol based on sticky bacteria algorithm in this paper. The new idea and the significant findings of this proposed algorithm in enhancing the routing protocol is as follows: five factors affecting routing decision are identified: evaluation distance, deflection angle, number of neighboring nodes, rate difference, and the traffic of the road section at which it is located, and then these five factors are comprehensively evaluated by using the our designed Analytic Hierarchy Process (AHP) strategy, so as to determine the score of each node and the candidate routing paths; And the best routing path between two communicating nodes is found through the stronger global exploration capability of the sticky bacteria algorithm. Our experiments (that are based on our developed C++ programs) have proved that the method proposed in this paper has stronger link stability and the highest packet delivery rate. And the experiments and their results has enhanced our new method credibility in the field of ad hoc networks.

In vitro antimalarial susceptibility profile of Plasmodium falciparum isolates in the BEI Resources repository.

BEI Resources, a National Institute of Allergy and Infectious Diseases-funded program managed by the American Type Culture Collection, serves researchers worldwide through the provision of a centralized repository for the acquisition, production, characterization, preservation, storage, and distribution of standardized biological resources targeting National Institutes of Health priority pathogens including bacteria, viruses, pathogenic fungi, and parasitic protozoa. These reference materials are critical for the development of diagnostics, vaccines, and therapeutics and are available to qualified registered investigators and institutions worldwide. Bioresources within BEI include well-characterized malaria isolates as part of the Malaria Research and Reference Reagent Resource Center (MR4). These isolates are critical for screening antimalarial compounds, conducting drug resistance studies, and for resistance surveillance and management. In our efforts to enhance the characterization of MR4 P. falciparum isolates, we measured antimalarial susceptibility of >100 isolates against a panel of standard antimalarial compounds. Our results provide valuable information to assist current and prospective users of the BEI Resources repository in making data-driven requests of isolates to meet their research needs.

An adaptive binary classifier for highly imbalanced datasets on the Edge

Edge machine learning brings intelligence to low-power devices at the periphery of a network. By running machine learning algorithms on the Edge, classification can be performed faster without the need to transmit large data volumes across a network. However, on-device training is often not feasible since Edge devices have limited computing and storage resources. Improved, Scalable, Efficient, and Fast classifieR (iSEFR) is a classifier that performs both training and testing on low-power devices using linearly separable balanced datasets. The novelty of this work is the improvement of the iSEFR accuracy by fine-tuning the algorithm with datasets having an uneven class distribution. Three adaptive linear function transformation techniques were proposed to improve the decision threshold which is in the form of a linear function. Experiments using stratified sampling with 5-fold cross-validation demonstrate that one of the proposed techniques significantly improved F1-score, Recall and Matthews Correlation Coefficient (MCC) by an average of 23%, 35% and 21% compared to iSEFR. Further evaluation of this technique in a Fog environment using highly imbalanced datasets such as credit card fraud, network intrusion and diabetic retinopathy also showed a significant increase of 38%, 44% and 30% in F1-score, Recall and MCC with a Precision of 97%. The adaptive binary classifier maintained the time complexity of iSEFR without altering the class imbalance.

CLOUD DATA PROTECTION USING WEIBULL DISTRIBUTED RECURRENT NEURAL ERGODIC SIGNCRYPTION

Cloud computing has become an integral part of modern computing, offering scalable storage and processing resources. However, the security of data stored in the cloud remains a major concern, especially when dealing with sensitive information. Traditional encryption schemes, while effective, often face limitations in terms of computational overhead and vulnerability to advanced attacks. To address these challenges, we propose a novel Weibull Distributed Recurrent Neural Ergodic Skewed Certificateless Signcryption scheme aimed at enhancing data protection in cloud environments. The key problem addressed by this work is the inherent inefficiency of existing cryptographic solutions that either rely on certificate-based systems or suffer from high computational and communication costs. This is especially crucial in cloud systems where real-time data processing is essential. Our approach integrates Weibull distribution for key management and optimization, recurrent neural networks (RNNs) for secure data transmission prediction, and ergodic skewed signcryption to eliminate the need for certificate authorities. This results in improved security, reduced computational overhead, and efficient communication, ensuring that the data remains secure even in dynamic cloud environments. The proposed scheme was tested using various metrics, including encryption/decryption time, data throughput, and attack resistance. Results demonstrate a significant reduction in computational cost by approximately 28% compared to traditional certificateless encryption. Furthermore, encryption times decreased from an average of 1.8 ms to 1.2 ms, and the scheme showed robustness against man-in-the-middle and chosen-ciphertext attacks with a detection accuracy of 98.6%. These results confirm the efficacy of the proposed system for enhancing security in cloud computing environments while maintaining high performance.

Cost optimization in edge computing: a survey

The edge computing paradigm is becoming increasingly commercialized due to the widespread adoption of wireless communication technologies and the growing demand for compute-intensive mobile applications. Edge computing complements the cloud computing model by deploying computation, storage, and network resources to the edge locations of wireless access networks, empowering end devices to run resource-intensive applications. In order to promote the commercialization of edge computing, it is important to explore effective ways to reduce the cost of edge computing networks. This paper provides a comprehensive review of the research findings in recent years, offering a clear perspective on the research dynamics. This paper first recalls the architectural framework of edge computing. Then, the main optimization objectives and optimization methods are comprehensively described. Mainstream mathematical models for cost reduction are then shown in depth. The paper also discusses the methods used to evaluate the effectiveness. Then, typical examples of typical application scenarios for edge computing networks are examined in depth. Finally, the paper identifies some unresolved issues. We expect future research to make more attempts in these directions.