Data Storage Costs Research Articles

A LE-controlled 4D non-degenerate hyperchaotic system and STP-CS model based efficient image encryption algorithm

Abstract Restricted by the environment and hardware equipment resources, existing chaotic systems have shortcomings such as low complexity, low randomness, and chaotic degradation phenomena, which in turn cause the security risks of chaotic image encryption algorithms. To overcome these issues, this paper proposes a method for the construction of a LE-controlled four-dimensional (4D) non-degenerate discrete chaotic system. Numerical analysis has demonstrated that the developed system possess high complexity and unpredictability. Based on the developed chaotic system, an image compression encryption algorithm is proposed. Wherein, semi-tensor product compressed sensing is applied to allow data compression sampling in different dimensions resulting in reducing the data transfer load and storage cost. Subsequently, the positions and values of the image pixels are secretly altered during the algorithmic encryption process using two-dimensional cat confusion and finite field diffusion. The simulation results show that the proposed encryption algorithm effective enough to offer great encryption quality. The performance comparison analysis indicates that the proposed encryption algorithm has good furnishes better security, compression, as well as resistance to diverse data attacks.

Semantic segmentation network for mangrove tree species based on UAV remote sensing images

Mangroves are special vegetation that grows in the intertidal zone of the coast and has extremely high ecological and environmental value. Different mangrove species exhibit significant differences in ecological functions and environmental responses, so accurately identifying and distinguishing these species is crucial for ecological protection and monitoring. However, mangrove species recognition faces challenges, such as morphological similarity, environmental complexity, target size variability, and data scarcity. Traditional mangrove monitoring methods mainly rely on expensive and operationally complex multispectral or hyperspectral remote sensing sensors, which have high data processing and storage costs, hindering large-scale application and popularization. Although hyperspectral monitoring is still necessary in certain situations, the low identification accuracy in routine monitoring severely hinders ecological analysis. To address these issues, this paper proposes the UrmsNet segmentation network, aimed at improving identification accuracy in routine monitoring while reducing costs and complexity. It includes an improved lightweight convolution SCConv, an Adaptive Selective Attention Module (ASAM), and a Cross-Layer Feature Fusion Module (CLFFM). ASAM adaptively extracts and fuses features of different mangrove species, enhancing the network’s ability to characterize mangrove species with similar morphology and in complex environments. CLFFM combines shallow details and deep semantic information to ensure accurate segmentation of mangrove boundaries and small targets.Additionally, this paper constructs a high-quality RGB image dataset for mangrove species segmentation to address the data scarcity problem. Compared to traditional methods, our approach is more precise and efficient. While maintaining relatively low parameters and computational complexity (FLOPs), it achieves excellent performance with mIoU and mPA metrics of 92.21% and 95.98%, respectively. This performance is comparable to the latest methods using multispectral or hyperspectral data but significantly reduces cost and complexity. By combining periodic hyperspectral monitoring with UrmsNet-supported routine monitoring, a more comprehensive and efficient mangrove ecological monitoring can be achieved.These research findings provide a new technical approach for large-scale, low-cost monitoring of important ecosystems such as mangroves, with significant theoretical and practical value. Furthermore, UrmsNet also demonstrates excellent performance on LoveDA, Potsdam, and Vaihingen datasets, showing potential for wider application.

Response reconstruction based on measurement matrix optimization in compressed sensing for structural health monitoring

Structural health monitoring (SHM) data have a large volume, increasing the cost of data storage and transmission and the difficulties of structural parameter identification. The compressed sensing (CS) theory provides a signal acquisition and analysis strategy. Signal reconstruction using limited measurements and CS has attracted significant interest. However, the dynamic responses obtained from civil engineering structures contain noise, resulting in sparse samples and reducing the signal reconstruction accuracy. Therefore, we propose an optimization algorithm for the measurement matrix integrating the Karhunen-Loeve transform (KLT) and approximate QR decomposition (KLT-QR) to improve the accuracy of dynamic response reconstruction of SHM data. The KLT reduces the correlation between the measurement matrix and the sparse basis. The approximate QR decomposition is used to improve the independence between the column vectors of the measurement matrix, optimizing the measurement matrix. The experimental results for a laboratory steel beam indicate that the proposed KLT-QR algorithm outperforms three other algorithms regarding the accuracy of dynamic response reconstruction (acceleration, displacement, and strain), especially at high compression ratios. The acceleration responses from the Ji’an Bridge are utilized to verify the advantages of the proposed algorithm. The results demonstrate that the KLT-QR algorithm has the highest accuracy of reconstructing the vibration signals and yields better Fourier spectra than the conventional Gaussian measurement matrix.

Data management solutions for real-time analytics in retail cloud environments

In today's rapidly evolving retail landscape, the importance of real-time data analytics has surged, driven by the need for businesses to make informed decisions and enhance customer experiences. This paper explores the critical role of effective data management solutions within cloud environments, focusing on retailers' unique challenges, such as data integration, latency, security, and storage costs. It presents practical solutions, including data partitioning, caching, AI-driven analytics, and the strategic use of data warehousing and data lakes. By adopting hybrid data management models, retailers can optimize their data processes and leverage real-time insights to drive operational efficiency and competitive advantage. This paper offers valuable recommendations for retail businesses and data architects to enhance their cloud-based data management strategies, ensuring they can navigate the complexities of real-time data effectively and sustainably. Keywords: Real-time analytics, Data management, Retail, Cloud environments, Data integration, AI-driven solutions.

Power Signal Histograms—A Method of Power Grid Data Compression on the Edge for Real-Time Incipient Fault Forensics

Across the power grid infrastructure, deployed power transmission systems are susceptible to incipient faults that interrupt standard operations. These incipient faults can range from being benign in impact to causing massive hardware damage and even loss of life. The power grid is continuously monitored, and incipient faults are recorded by Digital Fault Recorders (DFRs) to mitigate such outcomes. DFR-recorded data allow for power quality forensics and event analysis, but this ability comes at the cost of high data storage and data transmission requirements. It is common for data older than two weeks to be overwritten due to storage limitations, without being analyzed. This inhibits the creation of long-term data libraries that would enable incipient fault forensics and the characterization of behavior that precedes them, which limits the development and implementation of preventive measures; thus, there is a critical need to reduce DFR-recorded data’s storage requirements. This work addresses this critical need by leveraging the cyclic and residual histograms and introducing the frequency and Root Means Squared (RMS) histograms, which alleviate the current high data storage requirements and provide effective Incipient Fault Prediction (IFP). The residual, frequency, and RMS histograms are an extension of the cyclic histogram, reduce the data storage requirement by up to 99.58%, can be generated on the DFR without interrupting its normal operations, and are capable of predicting voltage arcing six hours before it is strong enough to trigger a DFR-recorded event.

Methodology for the automated selection of time-frequency representations

Data preprocessing is a key step in extracting useful information from sound and vibration data and often involves selecting a time-frequency representation. No single time-frequency representation is always optimal, and no standard method exists for selecting the appropriate time-frequency representation, so selecting the time-frequency representation requires expert knowledge and is susceptible to human bias. To address this, this work introduces a methodology to automate the selection of a time-frequency representation for a dataset using only a subset of the healthy, or normal, class of data. To select the parameters for each type of time-frequency representation, Bayesian optimization is used. With a candidate from each type of time-frequency representation, the average similarity is used to select the final candidate. Additionally, the use of multiple time-frequency representations within a single model is explored. Because there is currently no objective method to compare the selected time frequency representations against, the proposed methodology is evaluated in two case studies. In the case studies, the time frequency representations are used as inputs to a simple convolutional neural network that achieved 100% accuracy in classifying bearing faults and 94% accuracy in classifying the contact tip to workpiece distance in wire arc additive manufacturing. Additionally, the proposed methodology presents a 75% and 94% reduction in the data size for the two case studies. This offers further benefits for reducing costs of data transmission and storage in modern digital manufacturing architectures.

Recent advancement and human tissue applications of volume electron microscopy.

Structural observations are essential for the advancement of life science. Volume electron microscopy has recently realized remarkable progress in the three-dimensional analyses of biological specimens for elucidating complex ultrastructures in several fields of life science. The advancements in volume electron microscopy technologies have led to improvements, including higher resolution, more stability, and the ability to handle larger volumes. Although human applications of volume electron microscopy remain limited, the reported applications in various organs have already provided previously unrecognized features of human tissues and also novel insights of human diseases. Simultaneously, the application of volume electron microscopy to human studies faces challenges, including ethical and clinical hurdles, costs of data storage and analysis, and efficient and automated imaging methods for larger volume. Solutions including the use of residual clinical specimens and data analysis based on artificial intelligence would address those issues and establish the role of volume electron microscopy in human structural research. Future advancements in volume electron microscopy are anticipated to lead to transformative discoveries in basic research and clinical practice, deepening our understanding of human health and diseases for better diagnostic and therapeutic strategies.

Design, development and optimization of blockchain-based secure data management for nanotechnology in healthcare

The integration of nanotechnology in healthcare has brought about revolutionary advancements, particularly in diagnostics and therapeutics. However, managing the vast and sensitive data generated by these applications poses significant challenges, particularly concerning security, privacy, and regulatory compliance. This study explores the design, development, and optimization of a blockchain-based secure data management system tailored for nanotechnology applications in the United States healthcare sector. The proposed system leverages blockchain's decentralized and immutable ledger technology to enhance data security and privacy, ensure regulatory compliance, and improve overall system performance. Our implementation showed substantial improvements in data security, with an 86.67% reduction in data breaches and an 85% decrease in unauthorized access cases. Additionally, compliance with major data privacy standards like HIPAA, GDPR, and CCPA increased by over 20%. Performance metrics demonstrated robust scalability, maintaining high transaction speeds and manageable latency across various load conditions. Cost analysis revealed significant reductions in data storage, security, and compliance costs, highlighting the economic benefits of adopting blockchain technology in healthcare. Moreover, the system exhibited lower energy consumption compared to traditional data management systems, addressing environmental concerns. Correlation and scatter plot analyses provided insights into the scalability and performance dynamics of the blockchain system. These findings underscore the transformative potential of blockchain technology in addressing the data management challenges of nanotechnology in healthcare. The study concludes with recommendations for adopting blockchain to enhance data security, regulatory compliance, and economic efficiency in the U.S. healthcare sector.

Typification of Projects for the Transition to Cloud Services

Introduction. Digital solutions make the operation of the company clearer, reduce staff costs, and provide data security. Various aspects of automation, digitalization and cloud technologies are described in the literature. The question is raised about the quality of the methodological basis for such transformations. Global and narrow technical approaches are presented. As a rule, materials are presented from the perspective of experts in the implementation of digital technologies. In this paper, for the first time, the author's scheme is proposed that can allow not only providers, but also their customers to navigate the upcoming transition to the cloud. The latter will receive systematic information on how to select a contractor and the most economically feasible option of cooperation.Materials and Methods. The information model was based on the semantic network as a system of nodes, their characteristics and connections. Management of the cloud migration project and the migration itself were visualized. The contraction practice between providers and their customers was summarized. The specifics of the tasks of such projects were taken into account. The part of the subject area related to the implementation of a cloud service is algorithmic — a step-by-step transition to the cloud, a generalized scheme of the process taking into account the hierarchy of elements are presented.Results. For the first time, a method of self-preparation of a company for the implementation of cloud solutions is proposed. The algorithm systematizes the cloud migration processes. The activities related to goal setting, audit, selection of cloud environment and services, calculation of the economic efficiency of the project, planning and implementation of migration, technical support and scaling of processes are described. The possibilities of determining the economic feasibility of measures for the transition to the cloud are shown. The costs of equipment, data storage and processing, software licenses, salaries, information security, etc., are taken into account. The amount received is compared to the providers' offers. For the final decision, the costs of infrastructure support are taken into account — by the customer or the outsourcer. The best option is selected. As a result, the customer gets the opportunity to work with better profitability and scale the project. Feedback is provided, and processes are adjusted, starting with IT reaudit.Discussion and Conclusion. The proposed solution will give the customer's management a system view of the execution sequence when migrating to the cloud, the issues and tasks to discuss with a potential outsourcer. Providers can use the algorithm to typify and unify projects, which can eventually simplify the coordination of the list of services and the migration procedure with customers. In this way, the parties can free up significant resources in terms of time, labor and other costs. In addition, customers and providers can partially use the described semantic network to develop not only the organizational, but also the technical aspect of the project.

Optimizing healthcare data quality with optimal features driven mutual entropy gain

AbstractIn the dynamic domain of healthcare data management, safeguarding sensitive information while ensuring data efficiency is always of the highest priority. Healthcare data are frequently mishandled, posing significant risks. This research offers a new network that assesses the quality of visual data using robust features‐driven Mutual Entropy Gain (MEG). The proposed network addresses a critical gap in healthcare data management, significantly enhancing patient data security and operational efficiency in medical institutions. Our method begins with a thorough empirical investigation to find the optimal intermediate features for network input. We incorporate both distance entropy and probability entropy adopted and normalized in MEG, resulting in a comprehensive healthcare data quality evaluation. The results show that the network can distinguish between high‐quality and low‐quality data based on information content. Furthermore, our assessment reveals a large performance discrepancy between high and low‐quality data, even with variable datasets. Notably, using only half of the data achieves commendable accuracy when compared with using the complete dataset, demonstrating possible efficiency gains. This breakthrough has far‐reaching implications for healthcare providers, potentially reducing data storage costs, accelerating data processing times, and minimizing the risk of data breaches. In essence, our proposed network enhances efficiency and security in healthcare data and adapts to the evolving landscape of convergence ICT, paving the way for more robust, cost‐effective, and secure healthcare information systems that can significantly improve patient care and operational outcomes.

Алгоритмы оценивания показателей качества функционирования распределенной системы хранения конфиденциальных данных

The article presents algorithms for assessing the quality indicators of distributed data storage systems used to accumulate and process data from automated information systems for various purposes, serving a large number of geographically distributed clients. Mathematical expressions for indicators of confidentiality, availability and cost of data storage are given. The proposed algorithms make it possible to analyze the distribution of information resources among the elements of the data storage system in order to select rational solutions for organizing the storage of confidential information.

Social Media and Cloud Computing Impact on Human Resource Management in Multinational Enterprises

To address the issues of high storage costs for human resource data, resource management of human resource systems, and employee social interaction in human resource management, this study proposes an adaptive intelligent HR data placement strategy based on genetic algorithms. Additionally, a fitness model and an employee social interaction model are presented. The fitness model utilizes genetic algorithms to optimize data storage strategy and server load balancing, resulting in reduced overall costs and efficient resource utilization. This is achieved by comprehensively considering data transmission latency and storage costs. The employee social interaction model analyzes social media blog post topics and employee ranks, and employs a co-training method to predict employee interactions. The experimental results indicated that the placement strategy can improve the HR data transfer by nearly 50% and effectively reduce the standard deviation of server load by about 10-15%. The accuracy, recall, F1 value, and precision of the employee social interaction model were 77.89%, 63.97%, 70.32%, and 71.78%, respectively. The proposed strategy and model demonstrated higher accuracy and robustness in predicting and analyzing employee social interactions, thus providing more reliable decision support for human resource management.

Incorporation of adaptive compression into a GPU parallel computing framework for analyzing large-scale vessel trajectories

Automatic Identification System (AIS) offers a wealth of vessel navigation data, which underpins research in maritime data mining, situational awareness, and knowledge discovery within the realm of intelligent transportation systems. The flourishing marine industry has prompted AIS satellites and base stations to generate massive amounts of vessel trajectory data, escalating both data storage and calculation costs. The conventional Douglas-Peucker (DP) algorithm used for trajectory compression sets a uniform threshold, which hampers effective compression. Additionally, compressing and accelerating the computation of large datasets poses a significant challenge in real-world applications. To address these limitations, this paper aims to develop a new Graphics Processing Unit (GPU) parallel computing and compression framework that enables the acceleration of the optimal threshold calculation for each trajectory automatically in maritime big data mining. It achieves this by incorporating a new Adaptive DP with Speed and Course (ADPSC) algorithm, which utilizes the dynamic navigation characteristics of different vessels. It can effectively solve the associated computational time cost concern when using the ADPSC algorithm to compress vast trajectory datasets in the real world. Additionally, this paper proposes a novel evaluation metric for assessing compression efficacy based on the Dynamic Time Warping (DTW) method. Comprehensive experiments encompass vessel trajectory datasets from three representative research areas: Tianjin Port, Chengshan Jiao Promontory, and Caofeidian Port. The experimental results demonstrate that 1) the newly developed ADPSC method outperforms in terms of compression, and 2) the designed GPU parallel computing framework can significantly shorten the compression time for extensive datasets. The GPU-accelerated compression methodology not only minimizes storage and transmission costs for data from both manned and unmanned vessels but also enhances data processing speed, supporting real-time decision-making. From a theoretical perspective, it provides the key to the puzzle of realizing the real-time anti-collision of manned and unmanned ships, particularly in complex waters. It hence makes significant contributions to maritime safety in the autonomous shipping era.

Federated Multiple Tensor-on-Tensor Regression (FedMTOT) for Multimodal Data Under Data-Sharing Constraints

In recent years, diversified measurements reflect the system dynamics from a more comprehensive perspective in system modeling and analysis, such as scalars, waveform signals, images, and structured point clouds. To handle such multimodal structured high-dimensional (SHD) data, combining a large amount of data from multiple sites is necessary (i) to reduce the inherent population bias from a single site and (ii) to increase the model accuracy. However, impeded by data management policies and storage costs, data could not be easily shared or directly exchanged among different sites. Instead of simplifying or facilitating the data query process, we propose a federated multiple tensor-on-tensor regression (FedMTOT) framework to train the individual system model locally using (i) its own data and (ii) data features (not data itself) from other sites. Specifically, federated computation is executed based on alternating direction method of multipliers (ADMM) to satisfy data-sharing requirements, while the individual model at each site can still benefit from feature knowledge from other sites to improve its own model accuracy. Finally, two simulations and two case studies validate the superiority of the proposed FedMTOT framework.

A certificateless signcryption with proxy-encryption for securing agricultural data in the cloud

Precision agriculture (PA) involves collecting, processing, and analyzing datasets in agriculture for an informed decision. Due to the high data storage and application maintenance costs, farmers usually outsource their agricultural data obtained from PA to cloud service providers to leverage cloud services. Nonetheless, serious security concerns arise from using cloud services for farmers. For instance, an attacker can intercept agricultural data and run comprehensive statistical analyses to adjudicate farmers’ financial status, extort money, commit identity theft, etc. As a result, compelling data security schemes have become crucial for secure precision farming, where only legitimate users are required to access the agricultural data outsourced to the cloud. This article presents a certificateless signcryption scheme with proxy re-encryption (CLS-PRE) for secure access control in PA. An in-depth security analysis proves that the CLS-PRE scheme is secure in the Random Oracle Model. Detailed performance evaluation also shows that the scheme can reduce the time required to signcrypt and unsigncrypt messages and lower communication overhead.

The Transformative Impact of AI in Finance and Banking

The transformative impact of artificial intelligence (AI) in finance and banking is pro7 found, revolutionizing the way financial institutions operate, interact with customers, and make 8 decisions. This article explores the technological landscape that has paved the way for AI adoption, including falling data storage costs, data availability, advancements in machine learning, cost reduction, regulatory compliance, competitive advantage, risk management, customer experience, fraud detection, increased connectivity, and rapid advances in AI technologies. It discusses how AI is enhancing customer support, improving security through fraud detection algorithms, and enhancing credit scoring accuracy through machine learning. The value creation potential of AI in banking includes unlocking up to $1 trillion of incremental value annually through personalized services, cost reduction via automation, and uncovering new opportunities. Additionally, it provides examples of AI applications in banking, including personalization, automation, and insight extraction, showcasing how AI is transforming the industry. Despite these advancements, the article acknowledges challenges such as a lack of clear strategy, legacy systems, and fragmented data assets and proposes solutions like promoting a growth mindset and responsible AI deployment. It also addresses current issues in the banking sector, such as cyberattacks, voice cloning, and fraud, emphasizing the importance of AI in addressing these challenges. Overall, the article highlights the transformative potential of AI in finance and banking, urging banks to embrace an AI-first mindset for sustained growth and innovation.

Linking fast and slow: The case for generative models.

A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain calls for new analysis methods that link temporal scales: from the order of milliseconds over which neuronal dynamics evolve, to the order of minutes, days, or even years over which experimental observations unfold. This review article demonstrates how hierarchical generative models and Bayesian inference help to characterise neuronal activity across different time scales. Crucially, these methods go beyond describing statistical associations among observations and enable inference about underlying mechanisms. We offer an overview of fundamental concepts in state-space modeling and suggest a taxonomy for these methods. Additionally, we introduce key mathematical principles that underscore a separation of temporal scales, such as the slaving principle, and review Bayesian methods that are being used to test hypotheses about the brain with multiscale data. We hope that this review will serve as a useful primer for experimental and computational neuroscientists on the state of the art and current directions of travel in the complex systems modelling literature.

Multi-agent-based decentralized residential energy management using Deep Reinforcement Learning

In smart grid, energy consumption has grown exponentially in residential houses, which necessitates the adoption of demand response management. To alleviate and handle the energy management in residential houses, an efficient residential energy management (REM) system can be employed to regulate the energy consumption of appliances for several energy loads such as non-shiftable, shiftable, and controllable loads. Many researchers have focused on the REM using machine learning and deep learning techniques which is not able to provide secure and optimal energy management procedure. Thus, in this paper, a multi-agent-based decentralized REM, i.e., MD-REM approach is proposed using Deep Reinforcement Learning (DRL) with the utilization of blockchain. Furthermore, The combinatorial model DQN, i.e., Q-learning and deep neural network (DNN) is employed, to gain the optimal price based on the reduced energy consumption by appliances associated with different energy loads utilizing the Markov Decision Process (MDP). Here, multiple agents are designed to handle different energy loads and consumption is controlled by the DQN agent, then reduced consumption data is securely shared among all stakeholders using blockchain-based smart contract. The performance evaluation of the proposed MD-REM approach seems to be efficient in terms of reduced energy consumption, optimal energy price, reward, and total profit analysis. Moreover, blockchain-based result is evaluated for the proposed MD-REM approach considering the performance metrics such as transaction efficiency, Interplanetary File System (IPFS) bandwidth utilization, and data storage cost comparison.

Blockchain-secure patient Digital Twin in healthcare using smart contracts.

Modern healthcare has a sharp focus on data aggregation and processing technologies. Consequently, from a data perspective, a patient may be regarded as a timestamped list of medical conditions and their corresponding corrective interventions. Technologies to securely aggregate and access data for individual patients in the quest for precision medicine have led to the adoption of Digital Twins in healthcare. Digital Twins are used in manufacturing and engineering to produce digital models of physical objects that capture the essence of device operation to enable and drive optimization. Thus, a patient's Digital Twin can significantly improve health data sharing. However, creating the Digital Twin from multiple data sources, such as the patient's electronic medical records (EMR) and personal health records (PHR) from wearable devices, presents some risks to the security of the model and the patient. The constituent data for the Digital Twin should be accessible only with permission from relevant entities and thus requires authentication, privacy, and provable provenance. This paper proposes a blockchain-secure patient Digital Twin that relies on smart contracts to automate the updating and communication processes that maintain the Digital Twin. The smart contracts govern the response the Digital Twin provides when queried, based on policies created for each patient. We highlight four research points: access control, interaction, privacy, and security of the Digital Twin and we evaluate the Digital Twin in terms of latency in the network, smart contract execution times, and data storage costs.

Energy optimization with authentication and cost effective storage in the wireless sensor IoTs using blockchain

AbstractIn this paper, a hybrid blockchain‐based authentication scheme is proposed that provides the mechanism to authenticate the randomly distributed sensor IoTs. These nodes are divided into three types: ordinary nodes, cluster heads and sink nodes. For authentication of these nodes in a Wireless Sensor IoTs (WSIoTs), a hybrid blockchain model is introduced. It consists of both private and public blockchains, which are used to authenticate ordinary nodes and cluster heads, respectively. Moreover, to handle the issue of cluster head failure due to inefficient energy consumption, Improved Heterogeneous Gateway‐based Energy‐Aware Multi‐hop Routing (I‐HMGEAR) protocol is proposed in combination with blockchain. It provides a mechanism to efficiently use the overall energy of the network. Besides, the processed data of subnetworks is stored on blockchain that causes the issue of increased monetary cost. To solve this issue, an external platform known as InterPlanetary File System (IPFS) is used, which distributively stores the data on different devices. The simulation results show that our proposed model outperforms existing clustering scheme in terms of network lifetime and data storage cost of the WSIoTs. Our proposed scheme increases the lifetime of the network as compared to existing trust management model, intrusion prevention and multi WSN authentication schemes by 17.5%, 24.2% and 19.6%, respectively.