Real-time Node Research Articles

Real-time fault detection for IIoT facilities using GA-Att-LSTM based on edge-cloud collaboration

With the rapid development of Industrial Internet of Things (IIoT) technology, various IIoT devices are generating large amounts of industrial sensor data that are spatiotemporally correlated and heterogeneous from multi-source and multi-domain. This poses a challenge to current detection algorithms. Therefore, this paper proposes an improved long short-term memory (LSTM) neural network model based on the genetic algorithm, attention mechanism and edge-cloud collaboration (GA-Att-LSTM) framework is proposed to detect anomalies of IIoT facilities. Firstly, an edge-cloud collaboration framework is established to real-time process a large amount of sensor data at the edge node in real time, which reduces the time of uploading sensor data to the cloud platform. Secondly, to overcome the problem of insufficient attention to important features in the input sequence in traditional LSTM algorithms, we introduce an attention mechanism to adaptively adjust the weights of important features in the model. Meanwhile, a genetic algorithm optimized hyperparameters of the LSTM neural network is proposed to transform anomaly detection into a classification problem and effectively extract the correlation of time-series data, which improves the recognition rate of fault detection. Finally, the proposed method has been evaluated on a publicly available fault database. The results indicate an accuracy of 99.6%, an F1-score of 84.2%, a precision of 89.8%, and a recall of 77.6%, all of which exceed the performance of five traditional machine learning methods.

Latency Reduction in Immersive Systems through Request Scheduling with Digital Twin Networks in Collaborative Edge Computing

Immersive systems, which offer immersive interactive experiences by tightly coupling the real and virtual worlds, encompassing Augmented Reality (AR) and Virtual Reality (VR), have undergone rapid development in recent years. The inherent latency issue of immersive systems poses considerable challenges for AR/VR applications that are highly sensitive to delay. To address this challenge, we propose utilizing edge collaboration as an efficient means to schedule AR/VR requests, owing to the inherent ability of edge nodes to seamlessly coordinate within the same geographical region and optimally allocate request resources to minimize latency. However, when it comes to edge nodes across different regions, a significant hurdle emerges. These nodes often lack real-time knowledge about cached resources across other regions, making cross-regional edge collaboration for AR/VR request scheduling an extremely challenging problem. We formally define and prove the NP-hardness of this problem. To tackle this complexity, we introduce a Federated Digital Twin Model Construction (FedDT), which establishes a global digital twin network connecting different edge nodes. This network simulates and reflects the resource status and availability of each edge node in real time. Subsequently, we devise an extended Dijkstra algorithm to determine the shortest path between AR/VR requests and edge nodes, taking into account current network conditions to optimize service delay. Both theoretical analysis and experimental results confirm that compared with baseline methods, our proposed solution consistently achieves an average delay reduction of 36.78% across various scenarios, effectively demonstrating its superior performance.

Collision Avoidance and Give Way of Heterogeneous and Variable-Sized Multiple Mobile Robots Based on Glued Nodes

Multiple mobile robot systems (MMRSs) play an important role in workshops, storage and other scenarios. A complex MMRS can contain heterogeneous robots, and even the robot sizes are not fixed, making it more difficult to avoid collisions. As a widely studied method, the zone-controlled method is not accurate enough for a system with heterogeneous and variable-sized robots, and it increases the difficulty of applying MMRS. Based on the concept of glued nodes, this paper proposes a more accurate calculation method than the zone-controlled method to avoid collisions among heterogeneous and variable-sized robots. In order to improve the efficiency of calculating glued nodes, this paper proposes a time-to-space method, which has a constant complexity after the system runs long enough. In addition to collision avoidance, this paper also studies the problem of giving way of idle robots. This paper proposes a method which can detect those idle robots blocking the working robots and plan paths for the idle robots to find avoidance nodes to give way to the working robots. Simulation experiments are carried out based on a real automated production line scenario, and the experimental results prove the effectiveness and efficiency of the proposed methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In a complex MMRS, the size of different robots can be different, and even the size of a robot is different when loaded and unloaded. One of the motivations of this paper is to avoid collisions in such a complex MMRS. A collision avoidance method is proposed to avoid collisions among robots by allocating nodes in real time. At the same time, this paper also addresses the problem of idle robots blocking working robots. If an idle robot blocks some working robots, an avoidance node will be found for the idle robot to give way to the working robots. The methods proposed in this paper can be applied to the roadmap of any structure. This means that in a scenario, regardless of the structure of roadmap and the different sizes of robots, the methods can be applied directly. The methods have been applied to many practical industrial projects in warehousing, manufacturing and other scenarios, and greatly reduces the difficulty of applying MMRS. In future research, there is a need to improve the efficiency of the methods so that they can be applied to a large-scale system.

Research on obstacle avoidance motion planning method of manipulator in complex multi scene

In order to improve the efficiency and success rate of obstacle avoidance motion planning of industrial manipulator in complex multi scenes, a collision detection model between manipulator and obstacles based on cylinder and sphere bounding box is established, and an improved RRT* algorithm based on heuristic probability fusion artificial potential field method(P-artificial potential field RRT*, PAPF-RRT*) is proposed. The probability target bias and random sampling point optimization strategy are introduced into the sampling, and the location optimization constraints are applied to the sampling points to enhance the sampling guidance and quality. In order to change the expansion direction of the traditional new node and the local optimization problem in special environment, the target gravity, obstacle repulsion and adaptive step size of the artificial potential field method are combined, so that the algorithm can guide the expansion direction and step size of the new node in real time within the resultant force range generated by APF, reducing excessive exploration and the expansion of the collision region. The Cubic B-spline is used to interpolate and optimize the planned path to reduce the complexity of the path and improve the flexibility of the path. The simulation results in two-dimensional and three-dimensional multi scenes show that the present algorithm reduces the average path search time by 31.22% and shortens the path length by 17.32% comparing with the traditional RRT* algorithm. The visual simulation results show that the present algorithm can make the manipulator successfully avoid obstacles and run to the target point quickly and smoothly.

An Opposition-Based Learning Black Hole Algorithm for Localization of Mobile Sensor Network.

The mobile node location method can find unknown nodes in real time and capture the movement trajectory of unknown nodes in time, which has attracted more and more attention from researchers. Due to their advantages of simplicity and efficiency, intelligent optimization algorithms are receiving increasing attention. Compared with other algorithms, the black hole algorithm has fewer parameters and a simple structure, which is more suitable for node location in wireless sensor networks. To address the problems of weak merit-seeking ability and slow convergence of the black hole algorithm, this paper proposed an opposition-based learning black hole (OBH) algorithm and utilized it to improve the accuracy of the mobile wireless sensor network (MWSN) localization. To verify the performance of the proposed algorithm, this paper tests it on the CEC2013 test function set. The results indicate that among the several algorithms tested, the OBH algorithm performed the best. In this paper, several optimization algorithms are applied to the Monte Carlo localization algorithm, and the experimental results show that the OBH algorithm can achieve the best optimization effect in advance.

Heterogeneous communication network planning based on ant colony algorithm in smart grid

With the development of smart grids, highly automated power systems increasingly rely on communication networks, and research on the reliability, safety, and economy of heterogeneous communication networks is particularly important. Traditional network planning methods do not take into account network load problems, resulting in serious network congestion and long delays. Aiming at the above problems, this paper proposes a heterogeneous communication network planning method for a smart grid based on an improved ant colony algorithm. This method improves the original ant colony algorithm by updating the pheromone concentration of the network node in real time and realizes the planning of heterogeneous communication networks in the smart grid. Simulation results show that the proposed method has the advantages of avoiding local network congestion, reducing the average delay, and improving network stability.

Electric Energy Monitoring Node Design Based on NodeMCU and MQTT Protocol

Intelligent electricity management systems can be widely used for monitoring the regional power consumption of enterprises, factories, campuses, properties, etc. To meet the current demand of electricity consumers for monitoring the power data of each node, a node design method based on Modbus and MQTT protocols for monitoring power consumption has been proposed. It can monitor the power, current, voltage, electricity, and other related parameters of the power nodes so that the electricity consumers can control the electric energy data of each power node in real time. The test results show that the node scheme can effectively monitor the data of each power node and realize the real-time monitoring of power consumption data, which shows that the design scheme is effective and feasible.

A system for biomedical audio signal processing based on high performance computing techniques

In this paper, a noninvasive portable prototype is presented for biomedical audio signal processing. The proposed prototype is suitable for monitoring the health of patients. The proposed hardware setup consists of a cost-effective microphone, multipurpose microcontroller and computing node that could be a mobile phone or general-purpose computer. Using parallel and high-performance techniques, this setup allows one to register and wirelessly multicast the recorded biomedical signals to computing nodes in real time. The developed prototype was used as a case study to estimate the heart rate (HR) from the captured biomedical audio signal. In this regard, the developed algorithm for estimating HR comprises three stages: preprocessing, separation, and HR estimation. In the first stage, the signal captured by the microphone is adapted for processing. Subsequently, a separation stage was proposed to alleviate the acoustic interference between the lungs and heart. The separation is performed by combining a non-negative matrix factorization algorithm, clustering approach, and soft-filter strategy. Finally, HR estimation was obtained using a novel and efficient method based on the autocorrelation function. The developed prototype could be used not only for the estimation of the HR, but also for the retrieval of other biomedical information related to the recording of cardiac or respiratory audio signals. The proposed method was evaluated using well-known datasets and compared with state-of-the-art algorithms for source-separation. The results showed that it is possible to obtain an accurate separation and reliable real-time estimation in terms of source separation metrics and relative error in the tested scenarios by combining multi-core architectures with parallel and high-performance techniques. Finally, the proposed prototype was validated in a real-world scenario.

A Software-Defined Directional Q-Learning Grid-Based Routing Platform and Its Two-Hop Trajectory-Based Routing Algorithm for Vehicular Ad Hoc Networks.

Dealing with the packet-routing problem is challenging in the V2X (Vehicle-to-Everything) network environment, where it suffers from the high mobility of vehicles and varied vehicle density at different times. Many related studies have been proposed to apply artificial intelligence models, such as Q-learning, which is a well-known reinforcement learning model, to analyze the historical trajectory data of vehicles and to further design an efficient packet-routing algorithm for V2X. In order to reduce the number of Q-tables generated by Q-learning, grid-based routing algorithms such as the QGrid have been proposed accordingly to divide the entire network environment into equal grids. This paper focuses on improving the defects of these grid-based routing algorithms, which only consider the vehicle density of each grid in Q-learning. Hence, we propose a Software-Defined Directional QGrid (SD-QGrid) routing platform in this paper. By deploying an SDN Control Node (CN) to perform centralized control for V2X, the SD-QGrid considers the directionality from the source to the destination, real-time positions and historical trajectory records between the adjacent grids of all vehicles. The SD-QGrid further proposes the flows of the offline Q-learning training process and the online routing decision process. The two-hop trajectory-based routing (THTR) algorithm, which depends on the source-destination directionality and the movement direction of the vehicle for the next two grids, is proposed as a vehicle node to forward its packets to the best next-hop neighbor node in real time. Finally, we use the real vehicle trajectory data of Taipei City to conduct extensive simulation experiments with respect to four transmission parameters. The simulation results prove that the SD-QGrid achieved an over 10% improvement in the average packet delivery ratio and an over 25% reduction in the average end-to-end delay at the cost of less than 2% in average overhead, compared with two well-known Q-learning grid-based routing algorithms.

Load Balancing Optimization of In-Memory Database for Massive Information Processing of Internet of Things (IoTs)

Based on the analysis of the key technologies of the Internet of Things service platform architecture, a load balancing optimization scheme of in-memory database based on the massive information processing of the Internet of Things service platform is proposed. This scheme firstly proposes a system model that can satisfy the mass sensor information processing under the open platform environment and designs several functional unit modules of the system. By combining these functional units, the service can be configured for thousands of services and tenants. This paper presents an adaptive strategy selection method, which can automatically select the optimization strategy by dividing the position and querying the selection rate to improve the efficiency of the adaptive index algorithm. The index structure is initialized by parallel sorting algorithm, and the query statement is executed and the index structure is optimized by thread level parallel and radix sort methods. An elastic pipeline technique is proposed, which includes an elastic iterator model and a dynamic scheduler. The elastic iterator model is an upgrade of the traditional iterator model, adding the characteristics of dynamic multicore execution. In the process of query processing, dynamic scheduler monitors the load of each node in real time and dynamically adjusts the parallelism, so as to realize the load balance of in-memory database and maximize the utilization of hardware resources. The elastic pipeline realizes the isolation of parallelism from query compilation to avoid inappropriate parallelism allocation caused by missing and insufficient information during query compilation.

Decision support system for the load distribution on a computer network

Controlling the quality and speed of data transmission is an urgent and practically significant task in the field of information technology. Despite the fact that the computing power of software systems and the bandwidth of networks are increasing every year, at the same time, the volumes of processed, transmitted and stored information are growing. Therefore, difficulties continue to arise in processing the required amount of data, which significantly increases the time to work with them and reduces the quality of the work done. This problem is especially acute in an environment when it is required to make decisions on the basis of data - in decision support systems. Thus, we are faced with the task of distributing the load over communication channels from overloaded nodes to free nodes in real time. To solve it, the article proposes a mathematical apparatus that allows you to build the necessary computer network. In the course of the study, it was also found that for a more successful solution to the problem posed, a computer network must work with a neural network. Its development and integration into the proposed mathematical apparatus can become the basis for further research.

Autonomous Configuration of Communication Systems for IoT Smart Nodes Supported by Machine Learning

Machine Learning brings intelligence services to IoT systems, with Edge Computing contributing for edge nodes to be part of these services, allowing data to be processed directly in the nodes in real time. This paper introduces a new way of creating a self-configurable IoT node, in terms of communications, supported by machine learning and edge computing, in order to achieve a better efficiency in terms of power consumption, as well as a comparison between regression models and between deploying them in edge or cloud fashions, with a real case implementation. The correct choice of protocol and configuration parameters can make the difference between a device battery lasting 100 times more. The proposed method predicts the energy consumption and quality of signal using regressions based on node location, distance and obstacles and the transmission power used. With an accuracy of 99.88% and a margin of error of 1.504 mA for energy consumption and 98.68% and a margin of error of 1.9558 dBm for link quality, allowing the node to use the best transmission power values for reliability and energy efficiency. With this it is possible to achieve a network that can reduce up to 68% the energy consumption of nodes while only compromising in 7% the quality of the network. Besides that, edge computing proves to be a better solution when energy efficient nodes are needed, as less messages are exchanged, and the reduced latency allows nodes to be configured in less time.

The advancing assessment of power system stability using smart grid technology

The paper proposes new method for assessment of power system stability. New method based on advancing approach to determination of stability limits. The paper represents more accurate and reliable ways to determine limits for power transmission through the grid. Nowadays, the static stability limits formed by using special software which solves nonlinear equations of the electrical system. Power systems control power flows through the power lines for avoiding black outs by using automated control systems. Automation thresholds depend on the maximum power transmitted through the line or section, and the maximum transmitted power is calculated using the iterative methods. But the calculations in this way are not sufficiently accurate and flexible, since the calculations are done for the entire system, and not for each section individually. And it means the limits on certain sections and lines can vary. The proposed method differs from the existing one in a way it determines the limits using the angle shift between the voltage phasors in the nodes in real time. It makes possible to more deeply and adaptively manage. Also proposed new control algorithms which determines stability boundaries in advance. Algorithm was verified through experimental studies on physical model of the power system.

Fault-tolerant design for data efficient retransmission in WiNoC

There is a sharp decline in the network performance when the wireless link fails as a data path in the Wireless Network-on-Chip (WiNoC). To counteract this problem, we propose a fault-tolerance mechanism for the efficient retransmission of data in the WiNoC. When an error is detected in the data transmission process, this mechanism works to feed back the fault information to the source node in real time via fault signal lines. In the source node, the highest transmission priority is assigned to the backup retransmitted data, and the corresponding direct link is positioned to enable the data packet for its efficient retransmission to the destination node, thereby ensuring efficiency in fault tolerance. Additionally, we have improved the receiving port of the wireless router, added the corresponding redundant buffers and mux, and dynamically selected the retransmitted non-faulty data packets to be written to the local router in order to avoid the disorderly retransmission of the data packets. The evaluation results of this paper demonstrate that compared with the methods which are under different fault conditions, this fault-tolerant method drastically improves the data throughput rate, reduces the delay, effectively guarantees the reliability of the network, and improves the system performance.

A Service Delay Minimization Scheme for QoS-Constrained, Context-Aware Unified IoT Applications

With the unprecedented advancements in the field of Internet of Things (IoT), novel increasingly complex services are getting conceived and implemented with the day. A new trend in this landscape of next-generation IoT applications has been those orchestrated by the seamless integration of numerous vertical IoT applications, also known as, unified IoT services. However, such unified IoT applications are resource hungry (computation, storage, as well as network resources) and are usually characterized by real-time constraints. These render cloud and fog enactments insufficient. This article employs a context-aware computing approach that helps alleviating massive data transfers across fog nodes in real time. User requests are managed at fog nodes were depending upon their required contexts, availability of such contexts in the fog node and respective deadline, the requests are serviced either locally or at other fog nodes and Quality of Service (QoS) is fulfilled. To this end, a distributed service management algorithm is proposed that services the user requests at every fog node by either sharing contexts among context requests within a fog node, or by bringing in unavailable contexts from other fog nodes or by migrating service requests to remote fog nodes with available contexts in a deadline-aware fashion. Accordingly, this article proposes a novel smart multichannel queuing (SMCQ) model that schedules requests to virtual machines (VMs) at individual fog nodes by assigning them to specific priority groups (based on deadline vicinity) and thus minimizes service delay. The proposed algorithms for delay-tolerant service migration have been presented and simulation results carried out to demonstrate the efficacy of the proposed methodology.

Design and development of a wireless sensor network time synchronization system for photovoltaic module monitoring

Since the working situation of photovoltaic modules cannot be tracked in the real time and defective components cannot be located and controlled specifically, a remote monitoring system of photovoltaic modules based on wireless sensor networks is designed to improve the management efficiency of photovoltaic power plants. The monitoring data (current, voltage, and illumination, etc.) of photovoltaic modules are collected by the wireless sensor network nodes in real time, and then, there are orderly transmitted to the coordinator and server by the ZigBee communication protocol with the time synchronization algorithm. Time synchronization algorithm based on Gaussian delay model performs best in synchronization accuracy and energy-efficient reference broadcast synchronization algorithm is the most advantageous in synchronization energy consumption. Adaptive energy–efficient reference broadcasting synchronization algorithm is proposed with a balance between energy consumption and accuracy, which is applicable to large networks and thus conducive to the application of wireless sensor network in large-scale photovoltaic module monitoring. With the analysis and processing of the received data, the situation of the photovoltaic modules can be accordingly judged and thus the information management of photovoltaic power plant can be realized. For a photovoltaic power plant with a total power output of 100 kW and with 400 photovoltaic modules, experimental results show that the overall efficiency of the photovoltaic power plant can be increased by a wide margin.

Variable control chart for detecting black hole attack in vehicular ad-hoc networks

The nature of VANET networks makes the network vulnerable to several attacks such as the black hole attack which is part of Denial of Service attacks (DOS). The aim of these attacks is to prohibit users from using one or more services by making the communication unavailable. In this sense and during the routing process, the malicious node tries to acquire the route by forging routing information in order to receive the data and then drops it without transferring it to its destination. In this paper, we propose a novel detection method of such attack during communication in a VANET network. This method is based on a variable control chart which is widely used in the industrial field to monitor the quality of a given process. This method can identify malicious nodes in real time by deploying the monitoring system in each receiving node within the network. Our method is easy to implement and does not require any modification to the 802.11p standard or the routing protocol as we will demonstrate by NS-2 simulations and SUMO microscopic and continuous road traffic simulator using a real map.

A Blockchain-Based Node Selection Algorithm in Cognitive Wireless Networks

In cognitive wireless networks, multi-node cooperative spectrum sensing can effectively improve the accuracy of spectrum sensing, but there is a non-linear relationship between the number of nodes and sensing accuracy. Nodes with low reliability participate in cooperative sensing, which is not conducive to the improvement of sensing accuracy, and reduces the energy efficiency of spectrum sensing, which poses challenges to the normal operation of cognitive wireless networks. In order to improve energy efficiency and sensing performance, this paper proposes the node evaluation and scheduling (NES) algorithm and the Secure Spectrum Sensing based on Blockchain (SSSB) algorithm, which can evaluate the reliability of sensing nodes in real time, and obtain the trust value of the node. The nodes information is stored in the management center of blockchain. Blockchain encrypts nodes information to ensure that a node corresponds to its own trust value without confusion. Fusion Center of cognitive wireless networks select good performance nodes to participate in cooperative spectrum sensing. Which can reduce energy consumption while improving the sensing performance. The simulation experiment results show that the new algorithm in this paper is far superior to the traditional algorithm. Under the same other conditions, the detection probability is increased by 5%, and the energy consumption is reduced by 10%, and the safety index has also been greatly improved.

Energy-Efficient TDMA Scheduling for UVS Tactical MANET

In a tactical mobile ad-hoc network (MANET), unmanned vehicles are deployed for surveillance and reconnaissance. They send multimedia data to a center node in real time. In this letter, we propose a centralized TDMA slot and power scheduling schemes which maximize energy efficiency (EE) considering Quality-of-Service (QoS) for the tactical MANET. We formulate this problem as a non-concave ratio optimization, and propose the optimal slot allocation and power control algorithms based on the Dinkelbach method and the concave-convex procedure. The performance of the proposed algorithm is verified by numerical results, which show a fairly good energy efficiency.

A Model for Detecting Information Technology Infrastructure Policy Violations in a Cloud Environment

The pervasiveness of the internet and available connectivity solutions brought about by cloud computing has led to unprecedented increase in technologies built based on information technology infrastructures. Most organizations consider the deployment of different types of protection systems to curb the various malicious activities. Organizations offer sophisticated monitoring and reporting capabilities to identify attacks against cloud environment, while stopping multiple classes of attacks before they successful interfere with network activities. Users with ill intentions have increasingly used the cloud as an attack vector due to its ubiquity, scalability and open nature despite the existence of policy violation detection systems necessitating the need to strengthen access policies from time to time. Policy violation detection plays a major role in information security by providing a systematic way of detection and interpreting attacks. Some of the known weaknesses of most detection tools are the generation of false positives or false alerts and inability to perform analysis if traffic is encrypted as well as failure to detect and prevent attacks. This research paper was concerned with the investigation of weaknesses of firewall and Intrusion Detection system (IDS) which are supported by the cloud. The research design for the paper was based on the mixed methods. Experimental results revealed weakness in existing systems specifically IDS and firewall. Unlike the existing systems, new model designed to overcome the shortfall was able to detect both known and unknown attacks and signatures. Moreover, the model was capable of preventing the occurrence of false positives, and terminates suspicious nodes in real time without human intervention. Based on the tests carried out, it was recommended that Policy violation detection model be implemented to guarantee protection. An additional area of application such as migration from one cloud to another is not achievable, at this moment because of the heterogeneous nature of the cloud. This is a potential area for investigation in future.