Overhead Analysis Research Articles

Stealthy Messaging: Leveraging Message Queuing Telemetry Transport for Covert Communication Channels

Covert channel methods are techniques for improving privacy and security in network communications. These methods consist of embedding secret data within normal network channels, making it more difficult for unauthorized parties to detect such data. This paper presents a new approach for creating covert channels using the Message Queuing Telemetry Transport (MQTT) protocol, widely used in the context of the Internet of Things (IoT). The proposed method exploits storage channels by altering the field length of MQTT messages. Our solution leverages well-known one-way mathematical functions to ensure that data remain hidden from third parties observing the MQTT stream. In this way, we ensure that not only the content of the communication is preserved but also that the communication itself takes place. We conducted a security analysis to show that our solution offers the above-mentioned property even against severe threats, such as an adversary being able to observe all the messages exchanged in the network (even in the clear). Finally, we conducted an overhead analysis of our solution both in terms of the time required to perform the required operations and of the bytes to send. Our study shows that our solution adds no significant time overhead, and the additional overhead in terms of transmitted bytes remains within acceptable limits.

A Low-Complexity Security Scheme for Drone Communication Based on PUF and LDPC

Due to the limited payload and power of drones, the computational overhead, storage overhead and communication overhead that can be used for secure communication are restricted, making it difficult to apply some complex but fairly secure authentication protocols on drones. In this paper, we propose a low-complexity protocol for storing identity information in a resource-unconstrained device that does not require the UAV to store the information, thereby enhancing the UAV’s resistance to capture. The protocol in this paper mainly consists of quasi-cyclic low-density parity-check (QC-LDPC) codes, physical unclonable functions (PUFs) based on random-access memory (RAM), “XOR” operations, and hash computation. The protocol in this paper is an authentication architecture in which the drone is guided by the ground station to read its identity information, and the drone does not store any identity information in advance. The protocol is divided into two phases: 1. fuzzy authentication of fingerprint PUF and 2. uniqueness authentication accomplished while guiding the recovery of identity PUF. Recovering identity PUF in this paper, QC-LDPC is used as the error control module, and the optimization of bit-flip decoding significantly reduces the probability of decoding failure. After the comparative security analysis and comparative overhead analysis of this paper’s protocol, it can be concluded that this paper’s protocol can withstand common attacks (including attacks attempting to pass authentication, attacks attempting to interfere with authentication, and physical capture attacks), and the storage and communication overhead is small in the case of large time overhead.

Data Communication Algorithm of HPDB Parallel Database System Based on Computer Network

High-Performance Database (HPDB) is designed to meet the demands of today’s modern data-driven world, as massive volumes of information need to be accessed and analyzed with minimal latency. A pivotal aspect of their operation lies in efficient data communication among nodes in a computer network, which is essential for parallel database systems. HPDBs may involve distributed architectures and parallel database systems to store and process data across multiple nodes or servers in a network. Hence, an algorithm called Hybridized Partitioning Strategy (HPS)-based Communication (C) for achieving HPDB has been proposed to facilitate data transmission and coordination across a computer network using the Message Passing Interface (MPI) protocol. The proposed HPS-C-HPDB technique includes partitioning and distributing data, query routing, and load balancing strategies to achieve high-performance levels. The (HPS) combines hash and range partitioning methods for effective processing and retrieval to balance data distribution, reduce communication overhead in parallel databases, and improve the system’s performance. In parallel database systems, query routing effectively routes requests into the optimal nodes or partitions based on the query’s conditions and the data’s placement and guarantees efficient data processing and retrieval. The proposed scheme is evaluated using various performance metrics like throughput, response time, speedup, and communication overhead analysis.

Enhancing Security and Flexibility in the Industrial Internet of Things: Blockchain-Based Data Sharing and Privacy Protection

To address the complexities, inflexibility, and security concerns in traditional data sharing models of the Industrial Internet of Things (IIoT), we propose a blockchain-based data sharing and privacy protection (BBDSPP) scheme for IIoT. Initially, we characterize and assign values to attributes, and employ a weighted threshold secret sharing scheme to refine the data sharing approach. This enables flexible combinations of permissions, ensuring the adaptability of data sharing. Subsequently, based on non-interactive zero-knowledge proof technology, we design a lightweight identity proof protocol using attribute values. This protocol pre-verifies the identity of data accessors, ensuring that only legitimate terminal members can access data within the system, while also protecting the privacy of the members. Finally, we utilize the InterPlanetary File System (IPFS) to store encrypted shared resources, effectively addressing the issue of low storage efficiency in traditional blockchain systems. Theoretical analysis and testing of the computational overhead of our scheme demonstrate that, while ensuring performance, our scheme has the smallest total computational load compared to the other five schemes. Experimental results indicate that our scheme effectively addresses the shortcomings of existing solutions in areas such as identity authentication, privacy protection, and flexible combination of permissions, demonstrating a good performance and strong feasibility.

Machine Learning-Based Security Evaluation and Overhead Analysis of Logic Locking

Machine Learning-Based Security Evaluation and Overhead Analysis of Logic Locking

A smart contract and IPFS-based framework for secure electric vehicles synchronization at charging station

The growth of electric vehicles (EVs) at a significant pace is a promising solution for reducing harmful greenhouse gas emissions compared to conventional vehicles. Nevertheless, optimal and efficient EV charging management is a serious concern that needs to be solved for the benefit of both charging stations (CSs) and EVs. In this regard, we proposed a smart contract and Interplanetary File System (IPFS)-based secure EV synchronization framework at the CS utilizing a 5G wireless network. Blockchain technology strengthens the security of the EV charging at the CS in a synchronized and coordinated manner. The IPFS and 5G wireless network ensure efficient and reliable EVs synchronization to charge vehicles from the available number of charging points (CPs) at a CS. Furthermore, we have coordinated and synchronized the EV charging, considering various scenarios based on the waiting queue time, energy demand, and EVs (they can arrive in an emergency). We have shown the implementation interface of the proposed model based on the execution of a smart contract using the Remix Integrated Development Environment (IDE). Moreover, we have performed the security analysis of the smart contract using Echidna fuzzing-based tool to ensure the secure EVs allocation to the corresponding CS. Finally, the performance analysis of the IPFS and smart-based proposed model is evaluated and simulated considering various metrics such as transaction storage cost analysis, computational and communication overhead analysis, utilized cost based on the smart contract functions, and the number of EVs.

Scalability of blockchain: Review of cross-sharding with high communication overhead

Sharding method is separates the network into smaller groups to reduce latency and enhance blockchain speed. To reduce storage cost, divide the network into separate segments, and allow nodes to maintain track of a portion of the blockchain's data ledger, it was initially employed in databases. This technology is an excellent choice for enhancing blockchain performance because of its practical requirements and the speed at which blockchain applications are developing. It has garnered a lot of interest. There are a number of unresolved issues regarding the review and analysis of sharding. In this paper, we examine current state-of-the-art sharding schemes by categorizing them according to blockchain type and sharding technique—more specifically, cross-sharding with low communication overhead and systematically and thoroughly analyzing the benefits and drawbacks of each. Sharding lowers communication overhead since the performance of blockchain apps that use it has significantly improved over the method that should be studied for reducing the communication cost of block consensus. We present various open addresses after doing a comprehensive review and analysis of the communication overhead.

FairShare: Blockchain Enabled Fair, Accountable and Secure Data Sharing for Industrial IoT

Industrial Internet of Things (IIoT) opens up a challenging research area towards improving secure data sharing which currently has several limitations. Primarily, the lack of inbuilt guarantees of honest behavior of participating, such as end-users or cloud behaving maliciously may result in disputes. Given such challenges, we propose a fair, accountable, and secure data sharing scheme, FairShare for IIoT. In this scheme, data collected from IoT devices are processed and stored in cloud servers with intermediate fog nodes facilitating computation. Authorized clients can access this data against some fee to make strategic decisions for improving the operational services of the IIoT system. By enabling blockchain, FairShare prevents fraudulent activities and thereby achieves fairness such that each party gets their rightful outcome in terms of data or penalty/rewards while simultaneously ensuring accountability of the services provided by the parties. Additionally, smart contracts are designed to act as a mediator during any dispute by enforcing payment settlement. Further, security and privacy of data are ensured by suitably applying cryptographic techniques like proxy re-encryption. We prove FairShare to be secure as long as at least one of the parties is honest. We validate FairShare with a theoretical overhead analysis. We also build a prototype in Ethereum to estimate performance and justify comparable results with a state-of-the-art scheme both via simulation and a realistic testbed setup. We observe an additional communication overhead of 256 bytes and a cost of deployment of 1.01 USD in Ethereum which are constant irrespective of file size.

ENF-S: An Evolutionary-Neuro-Fuzzy Multi-Objective Task Scheduler for Heterogeneous Multi-Core Processors

In this paper, an evolutionary-neuro-fuzzy-based task scheduling approach (ENF-S) to jointly optimize the main critical parameters of heterogeneous multi-core systems is proposed. This approach has two phases: first, the fuzzy neural network (FNN) is trained using a non-dominated sorting genetic algorithm (NSGA-II), considering the critical parameters of heterogeneous multi-core systems on a training data set consisting of different application graphs. These critical parameters are execution time, temperature, failure rate, and power consumption. The output of the trained FNN determines the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">criticality degree</i> for various processing cores based on the system's current state. Next, the trained FNN is employed as an online scheduler to jointly optimize the critical objectives of multi-core systems at runtime. Due to the uncertainty in sensor measurements and the difference between computational models and reality, applying the fuzzy neural network is advantageous. The efficiency of ENF-S is investigated in various aspects including its joint optimization capability, appropriateness of generated fuzzy rules, comparison with related research, and its overhead analysis through several experiments on real-world and synthetic application graphs. Based on these experiments, our ENF-S outperforms the related studies in optimizing all design criteria. Its improvements over related methods are estimated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$19.21\%$</tex-math></inline-formula> in execution time, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$13.07\%$</tex-math></inline-formula> in temperature, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$25.09\%$</tex-math></inline-formula> in failure rate, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$13.16\%$</tex-math></inline-formula> in power consumption, averagely.

COAC: Cross-Layer Optimization of Accelerator Configurability for Efficient CNN Processing

To achieve high accuracy, convolutional neural networks (CNNs) are increasingly growing in complexity and diversity in layer types and topologies. This makes it very challenging to efficiently deploy such networks on custom processor architectures for resource-scarce edge devices. Existing mapping exploration frameworks enable searching for the optimal execution schedules or hardware mappings of individual network layers, by optimizing each layer’s spatial (dataflow parallelization) and temporal unrolling (TU, execution order). However, these tools fail to take into account the overhead of supporting different unrolling schemes within a common hardware architecture. Using a fixed unrolling scheme across all layers is also not ideal, as this misses significant opportunities for energy and latency savings from optimizing the mapping of diverse layer types. A balanced approach assesses the right amount of mapping flexibility needed across target neural networks, while taking into account the overhead to support multiple unrollings. This article, therefore, presents cross-layer optimization of accelerator configurability (COAC), a cross-layer design space exploration and mapping framework to optimize the flexibility of neural processing architectures by balancing configurability overhead against resulting energy and latency savings for end-to-end inference. COAC does not only provide a systematical analysis of the architectural overhead in function of the supported spatial unrollings (SUs), but also builds an automated flow to find the best unrolling combination(s) for efficient end-to-end inference with limited hardware overhead. Results demonstrate that architectures with carefully optimized flexibility can achieve up to 38% energy-delay-product (EDP) savings for a set of six neural networks at the expense of a relative area increase of 9.5%.

Active 3D Double-RIS-Aided Multi-User Communications: Two-Timescale-Based Separate Channel Estimation via Bayesian Learning

Double-reconfigurable intelligent surface (RIS) is a promising technique, achieving a substantial gain improvement compared to single-RIS techniques. However, in double-RIS-aided systems, accurate channel estimation is more challenging than in single-RIS-aided systems. This work solves the problem of double-RIS-based channel estimation based on active RIS architectures with only one radio frequency (RF) chain. Since the slow time-varying channels, i.e., the BS-RIS 1, BS-RIS 2, and RIS 1-RIS 2 channels, can be obtained with active RIS architectures, a novel multi-user two-timescale channel estimation protocol is proposed to minimize the pilot overhead. First, we propose an uplink training scheme for slow time-varying channel estimation, which can effectively address the double-reflection channel estimation problem. With channels’ sparisty, a low-complexity Singular Value Decomposition Multiple Measurement Vector-Based Compressive Sensing (SVD-MMV-CS) framework with the line-of-sight (LoS)-aided off-grid MMV expectation maximization-based generalized approximate message passing (M-EM-GAMP) algorithm is proposed for channel parameter recovery. For fast time-varying channel estimation, based on the estimated large-timescale channels, a measurements-augmentation-estimate (MAE) framework is developed to decrease the pilot overhead. Additionally, a comprehensive analysis of pilot overhead and computing complexity is conducted. Finally, the simulation results demonstrate the effectiveness of our proposed multi-user two-timescale estimation strategy and the low-complexity Bayesian CS framework.

Naïve Bayes Supervised Learning based Physical Layer Authentication: Anti-Spoofing techniques for Industrial Radio Systems

Physical Layer Security (PLS) based authentication schemes are an alternative to conventional security schemes such as e.g. certificates or Message Authentication Codes (MACs). They can provide a more lightweight solution compared to traditional cryptography in order to meet the requirement of secure data transmission. However, errors in Physical Layer Authentication (PLA) techniques can occur due to adverse influences resulting from PLS schemes such as receiver noise or fading channels. Skillfull methods are therefore required in order to detect anormal system behaviour. One promising solution are supervised classification schemes. The application of Naïve Bayes (NB) classifiers for PLA is therefore proposed and evaluated within this work. Prior to that, we analyse the resource efficiency within typical Ultra Reliable Low Latency Communication (URLLC) applications and conduct a security overhead analysis. We propose strategies in order to overcome the problem of missing training data from either the URLLC user or attacker node. A real world Software Defined Radio (SDR) based testbed using OFDM (Orthogonal Frequency Division Multiplexing) is implemented in order to evaluate the performance of NB based PLA. The measurements are conducted within the german campus network frequency band (LTE band 43). Further, we conduct a hyperparameter optimization (HPO) based on random search. The investigated classifiers show promising results in terms of authentication accuracy and Receiver Operating Characteristic (ROC) curve performance.

Review Paper on the uses of Digital Signature in MQTT Protocol

The Message Queue Telemetry Transport (MQTT) protocol for publish/subscribe middleware is proposed in this paper as a way to secure messages. In which the end-to-end method employs the Advanced Encryption System (AES) and Secure Hash Algorithm (SHA), and analyses the overhead associated with the usage of digital signatures Because there is no encryption method applied to the payload, MQTT has this drawback. Which enables one to discover the payload content that results in no data privacy. MQTT also has issues with data integrity. This digital signature's function is to confirm the payload's authenticity, that it doesn't alter during transmission, and that the payload is secret.The proposed solution can be evaluated and tested after which the programme can secure the MQTT payload. The addition of a security mechanism to MQTT, such as the encryption and decryption processes and verification outcomes, results in overhead in many areas. The overhead employed in this study is used to calculate the payload size, message sending time, process of digital signature security mechanism, memory consumption, and CPU utilisation. In an overhead analysis, overhead is performed by looking at many AES key types and numerous SHA key types. Upon closer inspection, it is seen that the digital signature system has resulted in a size increase for a number of the previously listed elements.

BSKM-FC: Blockchain-based secured key management in a fog computing environment

With an increase in the number of devices in the edge layer connected to the fog server of the fog computing environment, it is found that vulnerable and unauthorized activities are also increasing relatively. Thus, there is a requirement for authorized access control in such an environment that is governed by secured and efficient key management between the communicating devices. Many researchers have presented conventional solutions for key management depending on a third party. The third party operated for a key generation or key distribution in a fog computing environment based on a centralized architecture which leads to the drawbacks like a single-point failure and inconsistency of key management. So, effective and secured key management between the edge devices which are willing to communicate is the major concern to be addressed in the current digital world to achieve access control. This paper proposes a BSKM-FC (Blockchain-based Secured Key Management in a Fog Computing Environment) which is a decentralized system in a fog computing environment without using a third party. The BSKM-FC makes use of a one-way hash chain for the generation of private and public key pairs and ECC (Elliptic Curve Cryptography) for secured sharing. Upon successful authentication, the session key generation at both edge devices is based on the key pair provided by the fog server and stored securely in the blockchain. The BSKM-FC system uses private blockchain technology in the fog layer to provide secured storage and management. The work is implemented in the Truffle Blockchain and found that BSKM-FC performs better in terms of overall block preparation time. The security analysis of the proposed scheme is carried out based on the ROR model and also verified using AVISPA for some known attacks. Informal security analysis of the proposed work is performed by considering some of the known attacks where we observe that the proposed scheme overcomes such attacks. Performance overhead analysis is demonstrated using MIRACL considering computation cost, communication cost, and storage cost. The results show that the proposed scheme meets security requirements and performs effectively. The computation overhead, communication overhead, storage overhead, and block preparation time of the proposed scheme were improved by 7% to 14%, 9% to 18%, 7% to 17%, and 14% to 28%, respectively, as compared to existing schemes.

Oblivious Statistic Collection With Local Differential Privacy in Mutual Distrust

Location data is valuable for various applications such as epidemiology, natural disasters, and urban planning but causes exposure of sensitive information, e.g., home or work place, from collected data in a datastore. Local Differential Privacy (LDP)-based data collection is a promising technology to protect sensitive information. A mobile device modify data to make each piece of data indistinguishable from others but keep its intrinsic value for statistical characteristics in data. Although LDP fundamentally protects the privacy exposure from a data store, a datastore suffer a shortcomings on it; as a datastore can never validate the modified data due to concealed raw data, that allows anyone to tamper with one’s data or inject any amount of data, and thus manipulate the statistics of the whole data in a datastore, called data poisoning attack. As a device does not disclose raw data and a datastore cannot collaborate to validate data with a device who may be an adversary on this mutual distrust relationship, data collection needs an ability to avoid the effect of data poisoning.. The cause of data poisoning is the direct relationship between data volume and statistic; the more data a device sends gives more statistical changes on merged data in a datastore. In this paper, we propose to decouple statistical characteristics from data volumes on LDP-based data collection process to minimize the effect of poisoned data on a datastore. We utilize Oblivious Transfer (OT) protocol to retrieve only statistic characteristics of receiving data at a datastore. As OT protocol inevitably strengthen privacy protection on LDP-based data collection and accordingly drops statistic characteristics of data, We adjust LDP processing to collaboratively work with OT protocol. The proposed adjustment method adapts the protection strength of LDP to OT protocol behavior so that a data store receives data containing sufficient statistical characteristics. We conduct qualitative and experimental overhead analysis and show that our method decouples the relationship between statistical characteristics from data volume. Our experimental result also prove that the overhead can be acceptable on devices such as smartphones and IoT.

Computational Overhead Analysis for Mitigation of Content Poisoning Attack in NDN-based IoT Body Area Networks

The Body Sensors Network has developed into a significant element of the Medical IoT. Massive data gathering from a resource-constrained network represents a laborious procedure in IoT equipped BSN/WBAN, creating a number of difficult problems. Conventional communication protocols may be used on IoT platforms without any alterations and provide impractical techniques for large-scale networks. The new architecture known as ICN has the potential to overcome these difficulties in huge data collection. According to this perspective, the proposed work indicates a CPA mitigation strategy for information centric WBANs.

Improved malicious node detection method for detecting a bait in an extensive network for getting the maximum throughput

AbstractMobile Ad‐hoc Network (MANET) is an ad hoc Wireless subset with a unique dynamic geometry of the system and movable nodes. The MANETs are auto‐organized networks that permit mobility without infrastructure. Specific protocols for MANET routing are provided with these attributes. The benefits such as infinite stability and the essential organizational characteristics of MANET are advantageous in circumstances where crisis and military use arise. In the light of malicious nodes, this necessity can lead to real safety threats and interfere in routing. In this specific case, it is a challenge to avoid or identify malicious nodes that initiate grey hole attacks or collective blackhole attacks. This paper addresses and analyzes the risks and security policies of MANET. This method is mainly directed at the Dynamic Source Routing Protocol (DSR). MANET's malicious nodes have been used with schematics such as Control, Two Acknowledgement IDS (TWOACK), Adaptive Acknowledgement IDS(AACK), and Enhanced Adaptive Acknowledgement IDS (EAACK). This research aims to identify trends, difficulties, and possible recommendations for use with MANET using the malicious node detection tool. To fix efficiency, monitoring, and overhead analysis limitations and resolve existing security problems related to MANET. The goal is to offer an Improved Cooperative Malicious Node Detection System (ICMNDS) to detect malicious nodes, which keeps overhead minimum.

Invex Relaxation Based Cooperative Localization Using RSS Measurements

Received signal strength (RSS)-based localization techniques have attracted a lot of interest as they are easy to implement and do not require any localization-specific hardware. However, maximum likelihood (ML) formulation of RSS-based localization problem is non-convex, non-linear, and discontinuous, and cannot be solved using standard optimization techniques. We propose techniques that converts the ML objective function into an invex (invariant convex) function and solve them using gradient descent. We also employ coordinate descent to solve the invex problem in a completely distributed manner without any synchronization requirements. The coordinate descent-based technique can be implemented on the sensor nodes as it has low computational complexity and scales very well to large networks. We prove the convergence theoretically, derive the convergence rate, and provide a detailed computational complexity and communication overhead analysis of the techniques. We perform extensive performance analysis and compare our techniques with centralized and distributed localization methods, and demonstrate the superior performance of the proposed techniques in terms of convergence rate, localization accuracy, and execution time.

Towards a Secure and Scalable Maritime Monitoring System Using Blockchain and Low-Cost IoT Technology

Maritime Domain Awareness (MDA) is a strategic field of study that seeks to provide a coastal country with an effective monitoring of its maritime resources and its Exclusive Economic Zone (EEZ). In this scope, a Maritime Monitoring System (MMS) aims to leverage active surveillance of military and non-military activities at sea using sensing devices such as radars, optronics, automatic Identification Systems (AISs), and IoT, among others. However, deploying a nation-scale MMS imposes great challenges regarding the scalability and cybersecurity of this heterogeneous system. Aiming to address these challenges, this work explores the use of blockchain to leverage MMS cybersecurity and to ensure the integrity, authenticity, and availability of relevant navigation data. We propose a prototype built on a permissioned blockchain solution using HyperLedger Fabric—a robust, modular, and efficient open-source blockchain platform. We evaluate this solution’s performance through a practical experiment where the prototype receives sensing data from a Software-Defined-Radio (SDR)-based low-cost AIS receiver built with a Raspberry Pi. In order to reduce scalability attrition, we developed a dockerized blockchain client easily deployed on a large scale. Furthermore, we determined, through extensive experimentation, the client optimal hardware configuration, also aiming to reduce implementation and maintenance costs. The performance results provide a quantitative analysis of the blockchain technology overhead and its impact in terms of Quality of Service (QoS), demonstrating the feasibility and effectiveness of our solution in the scope of an MMS using AIS data.

Toward Energy-Efficient Multiple IRSs: Federated Learning-Based Configuration Optimization

Intelligent reflecting surface (IRS) can enhance the capacity and cost-effectiveness in future wireless networks substantially. However, the configuration optimization of IRS in an energy-efficient way is still a challenging work. In this paper, we propose a solution to the problem of maximizing the total throughput of a multiple IRSs assisted multi-user communication system. A federated deep learning (FDL) based algorithm is designed to obtain the optimal reflection configurations of all IRSs in parallel, where the model parameters are transmitted instead of the dataset itself as in deep learning (DL). Specifically, a deep neural network (DNN) is formulated to fit the coupling relationship between the coordinate information of users and the optimal reflecting vector of IRS. Meanwhile, the analysis of transmission and computation overhead is performed to establish an accurate energy consumption model. For performance evaluation, we conduct a series of simulations to verify the effectiveness of the FDL framework. The simulation results demonstrate that the test accuracy of the FDL framework is as high as 95.22% with only 1/36 of the transmission energy consumption compared with the DL. Moreover, the total throughput can achieve 93% of the theoretical performance.