Time Overhead Research Articles

M2STaR: A Multimode Spatio-Temporal Redundancy Design for Fault-Tolerant Coarse-Grained Reconfigurable Architectures

Coarse-grained reconfigurable architectures (CGRAs) can provide both energy efficiency and performance for embedded systems, and thus they are increasingly deployed in the areas of aerospace, automotive engineering, and security where reliability is also a main criterion. However, the state-of-the-art fault-tolerant strategies for CGRAs apply either temporal or spatial scheme, including redundancy, periodic detection, workload balancing, and reconfiguration, failing to exploit the feature of dynamic and partial reconfiguration of CGRAs. Also, vulnerable judging circuits and inflexible mode shifting bottleneck the reliability design of fault-tolerant CGRAs. This paper proposes a novel multi-mode fault-tolerant framework for CGRAs, which combines spatial-redundant data-paths with temporal-redundant voters and thus reduces the vulnerable judging circuits while balancing the performance and reliability. This framework can also enable a changing reliability level at runtime via an online configuration transformation method based on pre-compiled patterns. Within the proposed framework, we systematically searched the design space spanning various combinations of the mainstream schemes with a Markov process model to compare the effectiveness and accordingly selected five points as available modes in our design after comprehensive consideration of fault tolerance and time overhead on CGRA. The framework is comprehensively evaluated on a cycle-accurate CGRA simulator, considering both permanent and transient faults. The experimental results show that the fault coverage rate of single transient faults or permanent faults has increased from 71.74% to 93.84%, which means the fault tolerance of the system has been increased by 31.03% compared with the state-of-the-art methods. There is also a great improvement in mean-time-to-failure (MTTF) and reconfiguration latency over baseline designs.

An AI-Enabled Hybrid Lightweight Authentication Scheme for Intelligent IoMT Based Cyber-Physical Systems

In the era of smart healthcare, Internet of Medical Things (IoMT) based Cyber-Physical Systems (CPS) plays an important role, while accessing, monitoring, assessing, and prescribing patients ubiquitously. Efficient authentication and secure data transmission are the influential impediments of these networks to be addressed to maintain credence amidst clients, healthcare specialists, pharmacologists, and other associated entities. To address the authentication and data privacy issues in smart healthcare, in this paper, we proposed a hybrid lightweight authentication scheme leveraging supervised machine learning (SML) technique followed by Cryptographic Parameter Based Encryption and Decryption (CPBE&D) scheme to ensure the validation of legal patient wearable devices with secure transmission over the wireless communication channel. To achieve better results, we enabled the decentralized authentication of legitimate patient wearable devices to minimize computation cost, authentication time, and communication overhead with the help of SML technique to predicate and forward authentication attributes of patient wearable devices to the next concerned trusted authority, when it is shifted from region to another region. Simulation upshots of the SML and CPBE&D scheme exhibit extraordinary security features with cost-effective validation of legal patient wearable devices accompanied by worthwhile communication functionalities compared with the predecessor work.

A Novel Hybrid Model for Docker Container Workload Prediction

The emergence of containers dramatically simplifies and facilitates the development and deployment of applications. More and more enterprises deploy their applications on the container cloud platform. For cloud service providers, an effective container workload prediction method is a must to achieve efficient utilization of cloud resources. However, the existing methods are either rarely based on container load characteristics or cannot make accurate real-time predictions. In this paper, we propose a Docker container workload proactive prediction method using a hybrid model combining triple exponential smoothing and long short-term memory (LSTM), which not only can capture both short-term and long-term dependencies in container resource time series but also smooth the container resource utilization data. In order to improve the prediction accuracy of the hybrid model, those two single models are combined using the mean absolute percentage error (MAPE) method. Besides, we design a real-time Docker workload prediction system for the hybrid model. Our experiments show that the mean absolute percentage error of the hybrid model is decreased by an average of 3.24%, 12.18%, 13.42%, 43.45%, and 50.69% compared with the LSTM, the triple exponential smoothing, ES-ARIMA, Bayesian Ridge Regression and BiLSTM with an acceptable time and computational cost overhead.

A Negotiation-Based Scheme for Interconnecting Heterogeneous Networks With Enhanced Services

Although the TCP/IP protocols have made great contributions to the development of the Internet, there is a consensus that the Internet needs improvement to solve the address space exhausted, content-oriented service, and so on. The academic community has proposed lots of valuable but heterogeneous network protocols, which provide better network services but split the Internet. To connect these heterogeneous networks, many middleboxes (e.g.,NAT and Tunnel) are deployed on the Internet, but these middleboxes mask the communication peers and shield various network services that are provided by these heterogeneous networks. In this article, we propose the HNN (heterogeneous networks negotiation), which provides transparent end-to-end communication and connects heterogeneous networks stateless in the data plane. What's more, HNN enables the host to perceive the available network service provided by heterogeneous networks and helps the host structure appropriate packet header and packet forwarding path to take advantage of these heterogeneous network services. We implement and evaluate an HNN prototype with commodity Barefoot Tofino programmable switch S9180-32X. The evaluation results demonstrate that HNN introduces little processing time overhead (no more than 3%) compared to well-known heterogeneous connectivity techniques (i.e.,NAT and Tunnel) and allows hosts to utilize heterogeneous network services.

A layer-based random SGD for distributed training of RNNs

Since frequent data communication leads to increased time consumption in distributed training, compression of communication data is one of the effective ways to speed up the whole training process. Many existing compression methods introduce extra time overhead in data compression and decompression, for instance, Top-K and Random-k. There are also methods that can reduce the extra time overhead but suffer from the shortcoming of insufficient generalization. For example, the layer-based compression LR-SGD (Layer-based Random Stochastic Gradient Descent) cannot be applied to the training of recurrent neural networks. In this paper, an improved method based on LR-SGD is proposed. By being divided into virtual layers for compression, this improved method is able to train various networks efficiently. To validate the proposed method, experiments were conducted using different neural networks on a stimulative cluster. The results demonstrate that the proposed method applies well on different networks with low communication overhead and a nice convergence effect.

Cluster-based test vector re-ordering for reduced power dissipation in digital circuits

Optimizing testing power is a paramount concern in modern digital circuit design, particularly as the intricacy of circuits continues to rise. This issue becomes even more pronounced with the scaling down of feature sizes due to advancements in process technology. The increase in testing power dissipation, beyond the regular operational state, raises red flags for both designers and test engineers. This paper introduces a novel cluster-based approach, shedding light on the efficient reordering of test vectors to mitigate the heightened switching activity. This technique aims to reduce the power consumption during testing, while still ensuring efficient error detection, maintaining the test period, and preserving the original order of the scan chain. Importantly, this reordering method does not introduce any additional area or test time overhead, minimizing the risks associated with these factors. The potential impact of this approach is demonstrated through concrete examples drawn from ISCAS’89 benchmark circuits. The results showcase a notable 11% reduction in switching activity, all while preserving fault coverage levels.

Fog‐Sec: Secure end‐to‐end communication in fog‐enabled IoT network using permissioned blockchain system

AbstractThe technological integration of the Internet of Things (IoT)‐Cloud paradigm has enabled intelligent linkages of things, data, processes, and people for efficient decision making without human intervention. However, it poses various challenges for IoT networks that cannot handle large amounts of operation technology (OT) data due to physical storage shortages, excessive latency, higher transfer costs, a lack of context awareness, impractical resiliency, and so on. As a result, the fog network emerged as a new computing model for providing computing capacity closer to IoT edge devices. The IoT‐Fog‐Cloud network, on the other hand, is more vulnerable to multiple security flaws, such as missing key management problems, inappropriate access control, inadequate software update mechanism, insecure configuration files and default passwords, missing communication security, and secure key exchange algorithms over unsecured channels. Therefore, these networks cannot make good security decisions, which are significantly easier to hack than to defend the fog‐enabled IoT environment. This paper proposes the cooperative flow for securing edge devices in fog‐enabled IoT networks using a permissioned blockchain system (pBCS). The proposed fog‐enabled IoT network provides efficient security solutions for key management issues, communication security, and secure key exchange mechanism using a blockchain system. To secure the fog‐based IoT network, we proposed a mechanism for identification and authentication among fog, gateway, and edge nodes that should register with the blockchain network. The fog nodes maintain the blockchain system and hold a shared smart contract for validating edge devices. The participating fog nodes serve as validators and maintain a distributed ledger/blockchain to authenticate and validate the request of the edge nodes. The network services can only be accessed by nodes that have been authenticated against the blockchain system. We implemented the proposed pBCS network using the private Ethereum 2.0 that enables secure device‐to‐device communication and demonstrated performance metrics such as throughput, transaction delay, block creation response time, communication, and computation overhead using state‐of‐the‐art techniques. Finally, we conducted a security analysis of the communication network to protect the IoT edge devices from unauthorized malicious nodes without data loss.

Finding Taint-Style Vulnerabilities in Lua Application of IoT Firmware with Progressive Static Analysis

With the rapid growth of IoT devices, ensuring the security of embedded firmware has become a critical concern. Despite advances in existing vulnerability discovery methods, previous research has been limited to vulnerabilities occurring in binary programs. Although an increasing number of vendors are utilizing Lua scripting language in firmware development, no automated method is currently available to discover vulnerabilities in Lua-based programs. To fill this gap, in this paper, we propose FLuaScan, a novel progressive static analysis approach specifically designed to detect taint-style vulnerabilities in Lua applications in IoT firmware. FLuaScan first heuristically locates the code that handles user input, then divides the code into different segments to conduct a progressive taint analysis. Finally, a graph-based search method is applied to identify vulnerable code that satisfies the conditions of taint propagation. To comprehensively compare FLuaScan with state-of-the-art tool Tscancode, we conducted various experiments on a dataset consisting of 13 real-world firmware samples from different vendors. The results demonstrate the superior performance of FLuaScan in terms of accuracy (increased TP rate from 0% to 42.50%), effectiveness (discovered 21 vulnerabilities, of which 7 are unknown), and practicality (acceptable time overhead and visual output to assist in manual analysis).

Bidirectional Jump Point Search Path-Planning Algorithm Based on Electricity-Guided Navigation Behavior of Electric Eels and Map Preprocessing.

The electric eel has an organ made up of hundreds of electrocytes, which is called the electric organ. This organ is used to sense and detect weak electric field signals. By sensing electric field signals, the electric eel can identify changes in their surroundings, detect potential prey or other electric eels, and use it for navigation and orientation. Path-finding algorithms are currently facing optimality challenges such as the shortest path, shortest time, and minimum memory overhead. In order to improve the search performance of a traditional A* algorithm, this paper proposes a bidirectional jump point search algorithm (BJPS+) based on the electricity-guided navigation behavior of electric eels and map preprocessing. Firstly, a heuristic strategy based on the electrically induced navigation behavior of electric eels is proposed to speed up the node search. Secondly, an improved jump point search strategy is proposed to reduce the complexity of jump point screening. Then, a new map preprocessing strategy is proposed to construct the relationship between map nodes. Finally, path planning is performed based on the processed map information. In addition, a rewiring strategy is proposed to reduce the number of path inflection points and path length. The simulation results show that the proposed BJPS+ algorithm can generate optimal paths quickly and with less search time when the map is known.

Task‐oriented resource prediction and adjustment algorithm for space‐based information network

SummaryThe tasks of a space‐based information network are complex and diverse, but the resources of a space‐based environment are minimal. The existing methods are challenging to match task demand to resource supply accurately. Aiming at the problem of accurate prediction from task to resource, we propose a resource prediction adjustment strategy. First, we propose a multidimensional resource prediction algorithm based on improved particle swarm optimization and back propagation (IPSO‐BP) neural network. The improved PSO is used to optimize the weight and threshold of BP neural network to make up for the defects that BP neural network is easy to fall into local minimum and the predicted output value is not unique. Second, to meet the quality of service (QoS) of tasks, we propose a density‐based performance evaluation algorithm (DPEA) to adjust resources. This method uses the idea of local sensitive hash to select the evaluation subset for the configuration task, then dynamically selects the nearest neighbors of the configuration task, and uses the idea of weighted average to evaluate the QoS performance index of the configuration task. Simulation results show that the proposed resource prediction and adjustment strategy effectively reduces the scheduling time overhead and improves resource utilization.

Blockchain-Based Licensed Spectrum Fair Distribution Method towards 6G-Envisioned Communications

Spectrum distribution is a classical licensed spectrum accessing method in mobile communication networks. The licensed idle spectrum resources are authorized and distributed from spectrum owners to mobile users. However, the exponential growth of user capacity brings excessive load pressure on the traditional centralized network architecture. With a lack of sufficient supervision and penalty measures, dishonest behaviors of spectrum owners and spectrum users will lead to an unfair status in the distribution process. As a result, the honest participants’ interest will be harmed. As an important supporting infrastructure of Internet of Things technology, 6G cannot completely follow the existing spectrum distribution method. Towards 6G network spectrum distribution, a blockchain-based licensed spectrum fair distribution method is proposed. A lightweight consensus mechanism named proof of trust (PoT) is applied to reduce computational power consumption and consensus time overhead. We deploy the method on the Ethereum test chain; a theoretical analysis and experimental results demonstrate the fairness, effectiveness and security of the method.

Multi-User Matching and Resource Allocation in Vision Aided Communications

Visual perception is an effective way to obtain the spatial characteristics of wireless channels and to reduce the overhead for communications system. A critical problem for the visual assistance is that the communications system needs to match the radio signal with the visual information of the corresponding user, i.e., to identify the visual user that corresponds to the target radio signal from all the environmental objects. In this paper, we propose a user matching method for environment with a variable number of objects. Specifically, we apply 3D detection to extract all the environmental objects from the images taken by multiple cameras. Then, we design a deep neural network (DNN) to estimate the location distribution of users by the images and beam pairs at multiple moments, and thereby identify the users from all the extracted environmental objects. Moreover, we present a resource allocation method based on the taken images to reduce the time and spectrum overhead compared to traditional resource allocation methods. Simulation results show that the proposed user matching method outperforms the existing methods, and the proposed resource allocation method can achieve 92% transmission rate of the traditional resource allocation method but with the time and spectrum overhead significantly reduced.

Bare-Metal Redundant Multi-Threading on Multicore SoCs Under Neutron Irradiation

A software technique is presented to protect commercial multi-core microprocessors against radiation-induced soft errors. Important time overheads associated with conventional software redundancy techniques limit the feasibility of advanced critical electronic systems. In our approach, redundant bare-metal threads are used, so that critical computation is distributed over the different micro-processor cores. In doing so, software redundancy can be applied to Commercial Off-The-Shelf (COTS) micro-processors without incurring high-performance penalties. The proposed technique was evaluated using a low-cost single board computer (Raspberry Pi 4) under neutron irradiation. The results showed that the Redundant Multi-Threading versions detected and recovered all the Silent Data Corruption (SDC) events, and only increased HANG sensitivity with respect to the unhardened original versions. In addition, higher Mean Work to Failure (MWTF) estimations are achieved with our bare-metal technique than with the state-of-the-art bare-metal software-based techniques that only implement temporal redundancy.

A Novel PCB Defect Detection Method Based on Digital Image Processing

Printed circuit board (PCB) has a wide range of applications, and its automated quality inspection is a vital part of economic production. The time overhead of the current PCB defect detection method will increase with the increase in the number of detection targets. To ensure the stability of detection performance, this paper proposes a novel PCB detection method based on color threshold segmentation. Experiments show that this method can effectively detect and analyse various information about solder pads and solder pastes, among which the detection accuracy rate of solder pads is 99.4%, the missed detection rate is 0.4%, and the detection accuracy rate of solder paste is 99.3%, The missed detection rate is 0.03%. At the same time, the method in this paper has better robustness and stability than other PCB defect detection methods, and can better meet the needs of industrial production.

Efficient Self-learning Evolutionary Neural Architecture Search

The evolutionary algorithm has become a major method for neural architecture search recently. However, the fixed probability distribution employed by the traditional evolutionary algorithm may lead to structural complexity and redundancy due to its inability to control the size of individual architectures, and it cannot learn from empirical information gathered during the search process to guide the subsequent search more effectively and efficiently. Moreover, evaluating the performance of all the searched architectures requires significant computing resources and time overhead. To overcome these challenges, we present the Efficient Self-learning Evolutionary Neural Architecture Search (ESE-NAS) method. Firstly, we propose an Adaptive Learning Strategy for Mutation Sampling, composed of a Model Size Control module and a Credit Assignment method for Mutation Candidates, to guide the search process by learning from the model size information and evaluation results of the architectures and adjusting the probability distributions for evolution sampling accordingly. Additionally, we developed a neural architecture performance predictor to further improve the efficiency of NAS. Experiments on CIFAR-10 and CIFAR-100 datasets show that ESE-NAS significantly brings forward the first hitting time of the optimal architectures and reaches a competitive performance level with classic manual-designed and NAS models while maintaining structural simplicity and efficiency.

Spiking Neural P Systems for Basic Arithmetic Operations

As a novel biological computing device, the Spiking Neural P system (SNPS) has powerful computing potential. The application of SNPS in the field of arithmetic operation has been a hot research topic in recent years. Researchers have proposed methods and systems for implementing basic arithmetic operations using SNPS. This paper studies four basic arithmetic operations, improves the parallelization of addition and multiplication methods, and designs more effective natural number addition and multiplication SNPS, as well as SNPS for subtraction and for division of natural numbers based on multiple subtractions. The effectiveness of the proposed SNPS is verified by example. Compared with the same kind of SNPS, for the addition operation the number of neurons used in our system is reduced by 50% and the time overhead is reduced by 33%, while for the multiplication operation the number of neurons is reduced by 40%.

A Prediction-Based Route Guidance Method Toward Intelligent and Green Transportation System

For the application of intelligent and green transportation systems (e.g., autonomous driving), traffic congestion is a severe challenge. So far, when traffic congestion is perceived for a route, a common solution is searching for another congestion-free route. However, it is observed that not all congestion should be tackled with re-routing since the extra overhead (e.g., travel time, fuel consumption, and CO2 emission) caused by specific congestion might be lower than that of re-routing. Against this backdrop, a Prediction-based Route Guidance Method (PRGM) is proposed for intelligent and green transportation systems. To begin with, PRGM involves a novel hybrid and dynamic system architecture based on the collaboration of vehicle clusters and the cloud platform. Notably, a backup mechanism between adjacent cluster heads is designed to avoid the problem that the data might be lost during dynamic clustering. Furthermore, PRGM involves a novel traffic congestion control strategy, which is based on four procedures: perception about traffic congestion with three indexes (i.e., speed index, dense index, acceleration index); judgment about congestion type with four defined congestion types; prediction about congestion duration considering the formation of congestion (i.e., why and how the congestion is formed); route planning about vehicles considering congestion duration and the extra time overhead of re-routing. Simulations are performed, and they show that the proposed PRGM not only can perceive traffic congestion more precisely and timely but also can reduce the travel time, fuel consumption, and CO2 emission of vehicles.

Secure integration of extremely resource-constrained nodes on distributed ROS2 applications

Background: modern robots employ artificial intelligence algorithms in a broad ange of applications. These robots acquire information about their surroundings and use these highly-specialized algorithms to reason about their next actions. Despite their effectiveness, artificial intelligence algorithms are highly susceptible to adversarial attacks. This work focuses on mitigating attacks aimed at tampering with the communication channel between nodes running micro-ROS, which is an adaptation of the Robot Operating System (ROS) for extremely resource-constrained devices (usually assigned to collect information), and more robust nodes running ROS2, typically in charge of executing computationally costly tasks, like processing artificial intelligence algorithms. Methods: we followed the instructions described in the Data Distribution Service for Extremely Resource Constrained Environments (DDS-XRCE) specification on how to secure the communication between micro-ROS and ROS2 nodes and developed a custom communication transport that combines the application programming interface (API) provided by eProsima and the implementation of the Transport Security Layer version 1.3 (TLS 1.3) protocol developed by wolfSSL. Results: first, we present the first open-source transport layer based on TLS 1.3 to secure the communication between micro-ROS and ROS2 nodes, providing initial benchmarks that measure its temporal overhead. Second, we demystify how the DDS-XRCE and DDS Security specifications interact from a cybersecurity point of view. Conclusions: by providing a custom encrypted transport for micro-ROS and ROS2 applications to communicate, extremely resource-constrained devices can now participate in DDS environments without compromising the security, privacy, and authenticity of their message exchanges with ROS2 nodes. Initial benchmarks show that encrypted single-value messages present around 20% time overhead compared to the default non-encrypted micro-ROS transport. Finally, we presented an analysis of how the DDS-XRCE and DDS Security specifications relate to each other, providing insights not present in the literature that are crucial for further investigating the security characteristics of combining these specifications.

Efficient fault monitoring in wastewater treatment processes with time stacked broad learning network

Process monitoring models play an increasingly indispensable role in promptly differentiating faults within the wastewater treatment process to maintain a safe state. The accuracy and time overhead are crucial indicators in judging whether models results are excellent or not. In terms of accuracy, deep learning methods excel, because of their generalized approximation and strong learning capabilities that enable them to infinitely approach an ideal level of monitoring accuracy. Meanwhile, they also have large computational loads that generates significant time overhead, which poses challenges for real-time monitoring of the wastewater treatment process. Hence, a novel process monitoring method called Time-Stacked Broad Learning System (Time-SBLS) is developed to alleviate these concerns in an effective and accurate manner. Time-SBLS leverages the efficiency of the BLS while incorporating the residual mechanism and time lag idea to capture necessary nonlinear features and time correlation associated with the fault detection accuracy better. Furthermore, another advantage of Time-SBLS is its inherited efficiency from BLS regarding time overhead, which is minimized by utilizing the pseudo-inverse technique to calculate weight parameters. Experimental findings on the Benchmark platform reveal that Time-SBLS offers considerable advantages in terms of accuracy and network time overhead compared to other state-of-the-art approaches.

A configurable control flow error detection method based on basic block repartition

Radiation-induced soft errors have emerged as a critical issue that must be addressed in fields such as aerospace, where control flow errors they cause could lead to catastrophic consequences. However, existing control flow errors detection methods based on signature analysis typically use a single type of signature to record predecessor–successor relationships, which has limited expressive power and struggles to balance program residual failure rate and time overhead. In response to this, we employ a basic block repartitioning technique to adjust the control flow graph of a program to conform to jump rules we set, and design a novel signature analysis scheme that combines node type signatures with predecessor–successor signatures to detect control flow errors inter basic block. We also improve the signatures mechanism to detect control flow errors intra basic block and inter procedures, thus providing the ability to detect these two types of control flow errors and allowing for flexible configuration based on the reliability requirements of the program. Experimental results show that compared to baselines, the proposed method can ensure a low residual failure rate while maintaining a low time overhead.