Data Communication Networks Research Articles

Event-Triggered Stabilizing Bit Rate Conditions for an n-Dimensional Linear System With i.i.d. Feedback Dropouts.

This article aims to stabilize an n -dimensional linear time-invariant (LTI) system, whose feedback packets are transmitted through a digital communication network. The digital network suffers from network delay and independent and identically distributed (i.i.d.) feedback dropouts, which may destabilize the system. The coupling among multiple state variables may further harm the stability of the system. In order to deal with these issues and save the occupied bandwidth of the feedback network, we propose a periodic event-triggering strategy. In our strategy, the state is measured periodically, but only quantized and transmitted when a certain condition is triggered. By well balancing the state coupling and making full use of both the information inside transmitted feedback packets and the one carried by sampling time instants, our strategy can maintain the desired mean square stability at a lower bit rate than conventional periodic sampling policies. The obtained stabilizing bit rate conditions are determined by the processing and network delays, the dropout rate, and the unstable eigenvalues of the system matrix, but independent of the process noise. Moreover, the lack of the direct state access does not incur any additional stabilizing bit rate. Simulations are done to confirm the effectiveness of the obtained stabilizing bit rate conditions.

An Efficient Variable Neighborhood Search for Generalized Regenerator Location Problems

Optical networks are one of the fastest data communication networks, which use optical fiber cables for transmitting data in the form of light pulses between sender and receiver nodes. As the quality of optical signal deteriorates when increasing the distance among terminal nodes, expensive electronic devices (regenerators) must be deployed in the network to recover the signal quality. The objective of Generalized Regenerator Location Problem (GRLP) is to minimize the number of installed regenerators, while weighted variant (WGRLP) assumes different installation costs (weights) of potential regenerator locations and minimizes the sum of regenerator installation costs. In this study, an improved variant of Basic Variable Neighborhood Search (iBVNS) was developed as a metaheuristic solution approach to both GRLP and WGRLP. Computational results provided by the proposed iBVNS on all available data sets from the literature are compared with the results of existing solution methods for GRLP and WGRLP. The obtained results show that the proposed iBVNS quickly reaches optimal or near-optimal solutions and in many cases improves upper bounds provided by exact method. In addition, the conducted non-parametric statistical tests indicate that iBVNS outperforms all heuristic approaches proposed earlier in the literature for the considered problems.

Application of ATM Asynchronous Transfer Mode Intellectualization in Civil Aviation System

The so-called ATM technology refers to asynchronous transmission technology, which is a highly efficient communication transmission technology. At present, it is widely used in many communication technology fields. Now this paper mainly analyzes its application in the field of civil aviation communication. At first, the paper analyzes and introduces ATM technology and civil aviation communication environment, points out the application advantages of ATM technology in civil aviation communication field, and finally makes a simple discussion on its application direction and mode. With the development of civil aviation communication business, the requirements for the traffic and reliability of data communication network are increasing. Civil aviation relies on ATM technology to build a basic network platform that can cover all airports of civil aviation and has carrier level reliability and availability. This paper introduces the working principle, system composition and workflow of ATM, improves the business level of maintenance and repair technicians, and ensures the safe operation of civil aviation air traffic control equipment.

UAV Communication Network Modeling and Energy Consumption Optimization Based on Routing Algorithm.

Topological information is provided, and research on the design of routing protocols for UAV self-assembling networks is conducted, in order to enable fleet communication transfer between UAVs and UAVs and enhance their communication transmission rate in the self-assembling network. A new routing protocol is proposed through greedy forwarding and peripheral forwarding of UAV self-assembling network communication data, UAV self-assembling network planarization processing, dynamic adjustment of routing mode based on topological information, and routing protocol decision content generation. The proposed network is described using stochastic geometry theory, with the UAV and building locations modeled as two independently distributed Poisson point processes and the building shape modeled as a rectangular body with height obeying the Rayleigh distribution. An estimated equation for typical user coverage is produced using this model. The simulation results show that the approximate expression matches with the simulation results with reduced computational complexity, which verifies the validity of the approximate analysis. By comparing it with the clustering-based routing protocol, it is concluded that the new routing protocol conditions for UAV self-assembly network can realize the communication transmission between UAVs and drones and further promote their communication transmission rate.

AI-federated novel delay-aware link-scheduling for Industry 4.0 applications in IoT networks

PurposeWith the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network and by reducing the latency of transmitted data. The communications in IIoT and Industry 4.0 requires handshaking of multiple technologies for supporting heterogeneous networks and diverse protocols. IIoT applications may gather and analyse sensor data, allowing operators to monitor and manage production systems, resulting in considerable performance gains in automated processes. All IIoT applications are responsible for generating a vast set of data based on diverse characteristics. To obtain an optimum throughput in an IIoT environment requires efficiently processing of IIoT applications over communication channels. Because computing resources in the IIoT are limited, equitable resource allocation with the least amount of delay is the need of the IIoT applications. Although some existing scheduling strategies address delay concerns, faster transmission of data and optimal throughput should also be addressed along with the handling of transmission delay. Hence, this study aims to focus on a fair mechanism to handle throughput, transmission delay and faster transmission of data. The proposed work provides a link-scheduling algorithm termed as delay-aware resource allocation that allocates computing resources to computational-sensitive tasks by reducing overall latency and by increasing the overall throughput of the network. First of all, a multi-hop delay model is developed with multistep delay prediction using AI-federated neural network long–short-term memory (LSTM), which serves as a foundation for future design. Then, link-scheduling algorithm is designed for data routing in an efficient manner. The extensive experimental results reveal that the average end-to-end delay by considering processing, propagation, queueing and transmission delays is minimized with the proposed strategy. Experiments show that advances in machine learning have led to developing a smart, collaborative link scheduling algorithm for fairness-driven resource allocation with minimal delay and optimal throughput. The prediction performance of AI-federated LSTM is compared with the existing approaches and it outperforms over other techniques by achieving 98.2% accuracy.Design/methodology/approachWith an increase of IoT devices, the demand for more IoT gateways has increased, which increases the cost of network infrastructure. As a result, the proposed system uses low-cost intermediate gateways in this study. Each gateway may use a different communication technology for data transmission within an IoT network. As a result, gateways are heterogeneous, with hardware support limited to the technologies associated with the wireless sensor networks. Data communication fairness at each gateway is achieved in an IoT network by considering dynamic IoT traffic and link-scheduling problems to achieve effective resource allocation in an IoT network. The two-phased solution is provided to solve these problems for improved data communication in heterogeneous networks achieving fairness. In the first phase, traffic is predicted using the LSTM network model to predict the dynamic traffic. In the second phase, efficient link selection per technology and link scheduling are achieved based on predicted load, the distance between gateways, link capacity and time required as per different technologies supported such as Bluetooth, Wi-Fi and Zigbee. It enhances data transmission fairness for all gateways, resulting in more data transmission achieving maximum throughput. Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation.FindingsOur proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. It also shows that AI- and IoT-federated devices can communicate seamlessly over IoT networks in Industry 4.0.Originality/valueThe concept is a part of the original research work and can be adopted by Industry 4.0 for easy and seamless connectivity of AI and IoT-federated devices.

Optimal Relay Angle-Based Clustering Routing Protocol for Wireless Sensor Networks

In this paper, a novel optimal relay angle-based clustering routing protocol called RACR is proposed to balance nodes’ energy consumption and prolong the network lifespan of wireless sensor networks (WSNs). In RACR, each node considers the parameters residual energy, number of neighbors, and average distance with neighbors as a criteria to determine whether to become a cluster head (CH) which cooperates with its neighbors to form a cluster. Afterward, the optimal relay angle is calculated for each CH to find its best relay node according to a target function considering the least energy consumption, which is applied to reduce the search range for finding routing paths. Consequently, all the CHs can find their best next-hop nodes within the determined field according to their residual energy, distance to the next-hop CH, and loads. Iteratively, the CHs obtain their best routing paths to the BS in the end. Simulation results demonstrate its effectiveness of RACR in terms of energy consumption, standard deviation of residual energy, data communication delay, network throughput, and lifespan.

Wireless Technology is Easy to Use

Wireless networking is the connection of computers, digital communication devices, network equipment, and various other devices via radio waves. It is applied in places where the wired infrastructure cannot be installed or the price of introducing such a structure is too high. In addition, it has some features that are a great advantage over wired networking, such as customer mobility, easy expandability, and fast and low-cost temporary networking. Wireless technology allows us mobility and ease of use, but most users do not think about security. Users are insufficiently informed about the dangers of the Internet. Many of them do not pay attention to that and access important data such as bank accounts, e-mail, and any other contents that must be preserved and hidden. Today, there are more and more malicious actions, where hackers use various methods and technologies to attack users' accounts, bypassing all protections. Today, the issue of security is one of the priorities for every Internet user. Due to its characteristics, wireless communication is exposed to attacks due to the way they are sent, and there is a possibility of intercepting information.

Data communications network for real-time industrial control systems

The advancements in network technologies and the evolution of the Internet of Things (IoT) have made supporting industrial control systems over probabilistic data networks promising. However, control systems’ communication over the traditional data networks is faced with problems of instability in feedback control and poor quality of performance due to time-varying data propagation delay. This paper presents two approaches that can enable real-time industrial control over non-deterministic computer networks allowing control system designers to take advantage of the existing communication infrastructures. The first approach is based on system-level interaction over two wires network called the collaboration network. The second approach is based on the implementation of the virtual local area network (VLAN). This method allows real-time control of industrial equipment or systems over IP-based networks while other computers are connected. Nodes providing real-time control services have the same PortID on the VLAN switch. This approach minimizes data traffic and reduces time-varying delay in system control over IP networks. The first approach was modeled and simulated using Proteus ISIS software. Two PIC16F877A microcontrollers were used to represent two nodes. CISCO packet tracer was used in the second approach to model and simulate IP-based control system communications over the traditional data network. Results indicate that the use of a two-wire collaborative network approach to a real-time control system is effective but requires an additional network alongside the main data traffic channel. VLAN, therefore, presents a more flexible approach that relies on the same infrastructures.

Event-based state estimation under constrained bit rate: An encoding–decoding approach

This paper is concerned with the state estimation problem for a class of linear continuous-time systems under bit rate constraints. The data transmission between the sensors and the estimator is implemented through a digital communication network with limited bandwidth. By resorting to the singular value decomposition technique, an event-based encoding strategy is developed to encode the measurement signals into 1-bit codewords so as to reduce network resource consumption. The Zeno phenomenon is first proven to be excluded, and then a sufficient condition is established under which the decoding error is ultimately bounded. Subsequently, a necessary condition is proposed to derive a lower bound of the bit rate below which the decoding error diverges. Moreover, the explicit expression of the desired state estimator is parameterized and the boundedness of the estimation errors is also analyzed. Finally, two simulation examples are presented to validate the effectiveness of the proposed theoretical results.

Asymptotic behavior of a critical fluid model for bandwidth sharing with general file size distributions

This work concerns the asymptotic behavior of solutions to a critical fluid model for a data communication network, where file sizes are generally distributed and the network operates under a fair bandwidth sharing policy, chosen from the family of (weighted) α-fair policies introduced by Mo and Walrand (IEEE/ACM Trans. Netw. 8 (2000) 556–567). Solutions of the fluid model are measure-valued functions of time. Under law of large numbers scaling, Gromoll and Williams (Ann. Appl. Probab. 19 (2009) 243–280) proved that these solutions approximate dynamic solutions of a flow level model for congestion control in data communication networks, introduced by Massoulié and Roberts (Telecommun. Syst. 15 (2000) 185–201). In a recent work (Stoch. Syst. 10 (2020) 251–273), we proved stability of the strictly subcritical version of this fluid model under mild assumptions. In the current work, we study the asymptotic behavior (as time goes to infinity) of solutions of the critical fluid model, in which the nominal load on each network resource is less than or equal to its capacity and at least one resource is fully loaded. For this we introduce a new Lyapunov function, inspired by the work of Kelly and Williams (Ann. Appl. Probab. 14 (2004) 1055–1083), Mulvany, Puha and Williams (Queueing Syst. 93 (2019) 351–397) and Paganini et al. (IEEE Trans. Automat. Control 57 (2012) 579–591). Using this, under moderate conditions on the file size distributions, we prove that critical fluid model solutions converge uniformly to the set of invariant states as time goes to infinity, when started in suitable relatively compact sets. We expect that this result will play a key role in developing a diffusion approximation for the critically loaded flow level model of Massoulié and Roberts (Telecommun. Syst. 15 (2000) 185–201). Furthermore, the techniques developed here may be useful for studying other stochastic network models with resource sharing.

Security and Privacy Protection of Internet of Vehicles Consensus Algorithm Based on Wireless Sensors

Due to its large network scale, open communication environment, unstable wireless network, and other characteristics, it is extremely vulnerable to attacks and causes security problems, resulting in the collapse of the Internet of Vehicles system. The application of the Internet of Vehicles is becoming more and more extensive, but there are still problems such as information security and privacy leakage in the Internet of Vehicles. Through the analysis of the security threats and privacy protection requirements faced by the Internet of Vehicles system, this paper mainly studies information security, vehicle identity privacy, and location privacy in the process of Internet of Vehicles wireless communication. Therefore, it is urgent to conduct research on the information security and privacy protection issues of the Internet of Vehicles. This paper discusses the research on the security and privacy protection of the consensus algorithm for the Internet of Vehicles based on wireless sensors, compares and analyzes the wireless sensor data privacy protection protocols based on sharding technology, Tongtai encryption technology, and perturbation technology, and selects an optimized Kalman consensus filter. The algorithm is applied to the node information exchange of the sensor network, and two filters (low pass and band pass) are used to unify the observations and covariance of the network. Estimation of the sensor network state with and without data packet loss, the effect of system estimation error under different packet loss rates, data privacy protection algorithm performance, vehicle network data communication volume, and confusion factors on algorithm efficiency and the node energy consumption was compared and analyzed. Based on the application of wireless sensors, the estimation error and inconsistency estimation error of the algorithm in this paper finally converge to about 0.5, and both can maintain good stability and have good robustness. In addition, the communication volume of the algorithm in this paper is about 30% of the SCPDA algorithm. The Kalman consensus filtering algorithm reduces the amount of confusing data sent, improves privacy protection, and also achieves lower communication overhead.

Critical evedenciarcy analysis of 26/11 attacks with reference to cyber-terrorism

By thorough analysis of Mumbai terrorist attacks of 26/11, it was observed that the attacks were form of Cyber terrorism as it was done to convey particular destructive message to the government, sending of threatening emails, defacing of government websites, hacking and cracking of crucial government systems. 'Protected systems' were compromised and civil amenities were disrupted. Digital information systems computer networks which not only affected governing system but also the population of a target area was affected to create a panic or situation of threat. Computer and digital communication networks were main tools of extremists in this case. Also, Supreme Court judgement on Mumbai Attacks case, in para number 57 specifies about use of Google Earth, tampering with telecommunication networks and digital networking shows that the Attack was done by was done by Hi-Tech offenders. Although electronic eavesdropping often yields valuable data, even tantalizing clues can be missed if the technology is not closely monitored, the intelligence gleaned from it is not linked with other information, or analysis does not sift incriminating activity from the ocean of digital data as traced by Inteligence agencies of US, UK and India during investigation of attacks.

A Classifier to Detect Profit and Non Profit Websites Upon Textual Metrics for Security Purposes

Currently, most organizations have a defense system to protect their digital communication network against cyberattacks. However, these defense systems deal with all network traffic regardless if it is from profit or non-profit websites. This leads to enforcing more security policies, which negatively affects network speed. Since most dangerous cyberattacks are aimed at commercial websites, because they contain more critical data such as credit card numbers, it is better to set up the defense system priorities towards actual attacks that come from profit websites. This study evaluated the effect of textual website metrics in determining the type of website as profit or nonprofit for security purposes. Classifiers were built to predict the type of website as profit or non-profit by applying machine learning techniques on a dataset. The corpus used for this research included profit and non-profit websites. Both traditional and deep machine learning techniques were applied. The results showed that J48 performed best in terms of accuracy according to its outcomes in all cases. The newly built models can be a significant tool for defense systems of organizations, as they will help them to implement the necessary security policies associated with attacks that come from both profit and non-profit websites. This will have a positive impact on the security and efficiency of the network.

Modern Energy Optimization Approach for Efficient Data Communication in IoT-Based Wireless Sensor Networks

Many researchers are drawn to mobile wireless sensor networks (WSN) and the Internet of Things (IoT) because of the significant challenges of power consumption and network connectivity. A technique that takes into consideration the characteristics such as network probability, the identified region of individual nodes, and the radius of the whole identified region is presented in this article. Free-space propagation is carried out in the region of interest. This approach assures network connection, long-term communication sustainability, and maximum energy efficiency. It was discovered that a mathematical network model can be built using the probability theory. It has been possible to examine and evaluate the changes in sensor nodes as a function of distance from the detection region using this approach. As a result, a correlation has been established between a network’s communication radius and the identified region. Additionally, a novel method has been developed to reduce energy consumption and sustain connectivity through boosting the connectivity feature. Notably, IoT-based WSN architectures require more energy optimization than any other network, because they have resource-limited nodes. Also, a simulation plot of the proposed approach’s mathematical network scheme is shown to show if it works. The proposed method consumes much less energy averagely 40% compared to the existing methods, which are LEACH, ZTR, and DSR when the radius is 100.

Machine-learning-based anomaly detection in optical fiber monitoring

Secure and reliable data communication in optical networks is critical for high-speed Internet. However, optical fibers, serving as the data transmission medium providing connectivity to billons of users worldwide, are prone to a variety of anomalies resulting from hard failures (e.g., fiber cuts) and malicious physical attacks [e.g., optical eavesdropping (fiber tapping)]. Such anomalies may cause network disruption, thereby inducing huge financial and data losses, compromising the confidentiality of optical networks by gaining unauthorized access to the carried data, or gradually degrading the network operations. Therefore, it is highly required to implement efficient anomaly detection, diagnosis, and localization schemes for enhancing the availability and reliability of optical networks. In this paper, we propose a data-driven approach to accurately and quickly detect, diagnose, and localize fiber fault anomalies, including fiber cuts and optical eavesdropping attacks. The proposed method combines an autoencoder-based anomaly detection and an attention-based bidirectional gated recurrent unit algorithm, whereby the former is used for fault detection and the latter is adopted for fault diagnosis and localization once an anomaly is detected by the autoencoder. We verify the efficiency of our proposed approach by experiments under various attack anomaly scenarios using real operational data. The experimental results demonstrate that (i) the autoencoder detects any fiber fault or anomaly with an F1 score of 96.86%, and (ii) the attention-based bidirectional gated recurrent unit algorithm identifies the detected anomalies with an average accuracy of 98.2% and localizes the faults with an average root mean square error of 0.19 m.

A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions

In this work, we an envision Home Energy Management System (HEMS) as a Cyber-Physical System (CPS) architecture including three stages: Data Acquisition, Communication Network, and Data Analytics. In this CPS, monitoring, forecasting, comfort, occupation, and other strategies are conceived to feed a control plane representing the decision-making process. We survey the main technologies and techniques implemented in the recent years for each of the stages, reviewing and identifying the cutting-edge challenges that the research community are currently facing. For the Acquisition part, we define a metering device according to the IEC TS 63297:2021 Standard. We analyze the communication infrastructure as part of beyond 2030 communication era (5G and 6G), and discuss the Analytics stage as the cyber part of the CPS-based HEMS. To conclude, we present a case study in which, using real data collected in an experimental environment, we validate proposed architecture of HEMS in monitoring tasks. Results revealed an accuracy of 99.2% in appliance recognition compared with the state-of-the-art proposals.

Implementation of network information security monitoring system based on adaptive deep detection

Abstract For a better detection in Network information security monitoring system, the author proposes a method based on adaptive depth detection. A deep belief network (DBN) was designed and implemented, and the intrusion detection system model was combined with a support vector machine (SVM). The data set adopts the NSL-KDD network communication data set, and this data set is authoritative in the security field. Redundant cleaning, data type conversion, normalization, and other processing operations are performed on the data set. Using the data conversion method based on the probability mass function probability mass function coding, a standard data set with low redundancy and low dimensionality can be obtained. Research indicates that when the batch size reaches 64, the accuracy of the test set reaches its maximum value. As the batch size increases, the accuracy first increases and then decreases. When the batch size continues to increase, the model will inevitably fall into the local optimal state, resulting in the degradation of the detection performance of the system. In terms of the false alarm rate, the DBN-SVM model is also the highest; however, it is only 10.73%. Under the premise of increasing the detection rate, the false alarm rate is improved; for the overall detection performance of the model, it is within an acceptable range. In terms of accuracy, the DBN-SVM model also scored the highest. The accuracy rate is the ratio of normal and correct classification for intrusion detection. It can explain the detection ability of the model. In summary, the overall detection ability of the DBN-SVM model is the best. The good classification ability to use SVM is proved, and the classification of low-dimensional features is expected to increase the detection rate of the system.

Parallel Redundancy Protocol for Railway Wireless Data Communication Network

In order to solve the problem that the railway application services have strict requirements on the reliability, delay, and other indicators of mobile data communication, a parallel redundancy protocol for the railway wireless data communication network is proposed. By introducing a redundancy adaptation layer into the standard TCP/IP model, multi-link parallel data transmission is realized and transparent to the network layer and above. Transmitting multiple copies of the same packet through different paths makes the application systems have redundant communication capability without changing the existing data transmission mode. At the same time, according to the requirements of the receiving entity for redundant data elimination, an improved Bloom filter is proposed. By expanding the bit array and using the countdown mechanism, the elements in the data set can be dynamically inserted, deleted, and retrieved over time. Theoretical analysis and test results show that the proposed protocol can meet the requirements of data transmission between the on-board and trackside equipment and has the characteristics of simple deployment, high reliability, and high stability. Compared to using a single transmission link, the packet loss rate from field test can be reduced by 39.67% at the maximum, and the throughput and end-to-end data transmission delay are also significantly improved. The test in this paper also shows that the additional delay introduced is a maximum of 5.07 μs, which will not significantly affect the data transmission performance.

Communication MWSN Data Transmission Mechanism Based on a Wireless Sensor Network

A wireless sensor network (WSN) is considered to have a wide application prospect in a smart distribution network due to its unique low power consumption, fast self-organization, and superior coordination. In order to study the communication MWSN data transmission mechanism based on a wireless sensor network, the difficulty of applying a wireless sensor network to an intelligent distribution network communication is to meet the real-time and reliability requirements of power industry distribution network data communication specifications under the limited network resources. Firstly, the transmission probability is calculated by considering the path loss, the speed of the node, the direction of the node, and the residual energy of the node. In addition, the transmission and replacement order of the message is determined according to the survival time of the data message. Simulation experiments are carried out on MATLAB. The result shows the proposed routing algorithm, compared with the DT algorithm, flooding algorithm, and FAD algorithm, the delivery rate of data messages is increased by at least 6%, and the number of copies is reduced by at least 10% compared with the flooding algorithm and the FAD algorithm. On the basis of satisfying the mobility of sink nodes, the algorithm proposed in this paper can effectively save network energy consumption and prolong the network lifetime.

Smart Analysis of Learners Performance Using Learning Analytics for Improving Academic Progression: A Case Study Model

In the current COVID-19 pandemic era, Learning Management Systems (LMS) are commonly used in e-learning for various learning activities in Higher Education. Learning Analytics (LA) is an emerging area of LMS, which plays a vital role in tracking and storing learners’ activities in the online environment in Higher Education. LA treats the collections of students’ digital footprints and evaluates this data to improve teaching and learning quality. LA measures the analysis and reports learners’ data and their activities to predict decisions on every tier of the education system. This promising area, which both teachers and students can use during this pandemic outbreak, converges LA, Artificial Intelligence, and Human-Centered Design in data visualization techniques, semantic and educational data mining techniques, feature data extraction, etc. Different learning activities of learners for each course are analyzed with the help of LA plug-ins. The progression of learners can be monitored and predicted with the help of this intelligent analysis, which aids in improving the academic progress of each learner in a secured manner. The Object-Oriented Programming course and Data Communication Network are used to implement our case studies and to collect the analysis reports. Two plug-ins, local and log store plug-ins, are added to the sample course, and reports are observed. This research collected and monitored the data of the activities each students are involved in. This analysis provides the distribution of access to contents from which the number of active students and students’ activities can be inferred. This analysis provides insight into how many assignment submissions and quiz submissions were on time. The hits distribution is also provided in the analytical chart. Our findings show that teaching methods can be improved based on these inferences as it reflects the students’ learning preferences, especially during this COVID-19 era. Furthermore, each student’s academic progression can be marked and planned in the department.