Real-time Traffics Research Articles

New Lightweight Cryptosystem for IoT in Smart City Environments

Internet of Things (IoT) devices, user interfaces (UI), software, as well as communication networks are all deployed within Smart Cities topology. The security approach designed for Internet of Things IoT should be able to prevent and detect both internal and external attacks. The problem in IoT network that not every linked node or device has an adequate amount of processing power. This means that data encryption and other related activities will be impossible and means that the security of any kind must be lightweight. A trustworthy security solution that stops illegal access to private data on the network is necessary for maintaining the privacy of information on the Internet of Things. Cryptographic processes need to be quicker and more compact without sacrificing security. The aim of this study is to reduce the execution time and power consumption of encryption processes without compromise the complexity of the encryption algorithm. This research presents a new lightweight cryptographic technique to protect various multimedia and real-time traffics across IoT network, by using two S-box in SubByte of encryption process, without affecting its performance. In this study, different audio samples will be used to test the new algorithm efficiency. Comparing the suggested method to the most advanced standard algorithm, it can reduce the cryptography process's execution time as well as energy consumption while maintaining the required security level. The outcomes demonstrate good performance in terms of power usage and delay. The new technique consumed a roughly 0.2 µJ for encryption process while the typical AES algorithm consumed 0.29 µJ, this mean the new algorithm achieved (33% power savings), while maintaining a good complexity level (security) within the process of encryption according to the results in tables I, II, and the comparison in table III. The novelty of this work can be showed by using dual XOR S-box technique which increased the complexity of SubByte process making it more secure without overload the processing performance, in addition to the reduction in encryption rounds which contribute to enhance the performance without compromise the security. Making it more suited for the Internet of Things (IoT) used in smart city environments.

Enhanced Algorithm for Reduction of Blackhole’s Re-Judgment Delay for Realtime Traffic in Mobile Adhoc Network

Mobile Ad-hoc Network (MANET) is a prominent technology in wireless communication in which mobile nodes operate in a distributed manner without having central control devices; This lack of control over network devices and over changing topology aspects of the network allows malicious nodes gain opportunities to join the network. The blackhole attack is one of the security risks that increases the network overhead. In this attack, the malicious node advertises itself as having the shortest path to the destination and falsely replies to the route requests, and drops all receiving packets. Therefore, the paper proposes a trust-level based re-judgment algorithm. Experiments were set up in the NS3 networking simulation tool. Simulations results showed that the proposed trust-level based re-judgment algorithm improves network throughput and packet delivery rate by 82.8% and 146.6 Kbps respectively. Also, reduces the end-to-end delay by an average of 24.17 milliseconds

Performance Evaluation of Congestion Control Algorithm in TCP/IP Differentiated Services During COVID-19 Pandemic Due to Heavy Surge in Real-Time Traffic

Numerous nations implemented countrywide lockdowns because of the COVID-19 epidemic, which forced almost all peoples to remain at home. The imposition of confinement restrictions led to an increase in residential users' Internet traffic demands, particularly for distant work, entertainment, commerce, and education. This resulted in changes to the traffic patterns within the Internet core. In this research, we investigate the impact of the countrywide lockdowns on internet traffic based on dscp values by collecting data from class-A, class-B and Class-C ISP.(Three Class-A ISP, Three Class-B ISP and Three Class-C ISP) which leads to heavy congestion in the network and finally the performance valuation is carried out. The overall rise is exponential in nature, we notice that the traffic volume climbed by 15-30% almost in a quarter, which is a significant gain in such a short amount of time. Direct examination of the traffic sources reveals that, although hypergiants continue to account for a sizeable portion of traffic, there has been a greater increase in non-hypergiant traffic as well as in traffic from home-use applications like Web conferencing, VPNs, and gaming. Academic networks show significant overall declines whereas many networks, particularly those serving residential users, pharmaceutical companies, online interaction platform increases in traffic demands. However, we can see significant gains in these networks when we look at applications related to lecturing and remote working. The changes in the nature of use by customers show that there is a sharp difference in application pertaining to usage. Previous to this period, most usage is on YouTube, Facebook and google search. However, after this period most traffic diverted to online platforms such as zoom, google meet, Teams, and others as well as OTT platform comes into the picture. Over the past two decades, there has been a notable shift in user profiles. In the early 2010s, we saw most traffic was inclined on stored video and audio data in continuation with conventional traffic [23, 49, 66], which was followed by content delivery and streaming applications [7, 24, 35, 37, 52], and mobile applications [32, 67] in the 2010s. Even though user profile modifications are a moving target, they usually take years to complete. So, it was possible to stay current, for example, by measurements. The impact of this pandemic created chaotic conditions as most traffic migrated to real time surge data. Thus, the congestion management becomes more complicated as of previous challenges. The ISP forced to provide more bandwidth to real-time traffic which leads to overprovisioning. Thus, an efficient algorithm is required for managing such surge traffic which may especially meet real time traffic as required by most customers. This paper thus focuses on impact of after corona-19 pandemic effect, the shift of traffic by various users and applications the behavior of some existing algorithms in terms of congestion management and finally suggested a balanced method for managing congestion in such heavy surge traffic.

Wireless Spy Camera Spotter System With Real-Time Traffic Similarity Analysis and WiFi Signal Tracing

Wireless Spy Camera Spotter System With Real-Time Traffic Similarity Analysis and WiFi Signal Tracing

Realtime Traffic Enhancement using Intelligent Route Optimization for Dynamic Logistics

Realtime Traffic Enhancement using Intelligent Route Optimization for Dynamic Logistics

Machine Learning Models for RealTime Traffic Flow Prediction

Machine Learning Models for RealTime Traffic Flow Prediction

Modeling of Freeway Real-Time Traffic Crash Risk Based on Dynamic Traffic Flow Considering Temporal Effect Difference

With the development of traffic detection facilities technology, it is currently possible to obtain high-resolution traffic flow data. Due to the particular driving characteristics of vehicles on freeways, once traffic crashes occur, they are generally with serious consequences, and hence traffic safety issues on freeways have always been popular topics. In order to better realize the change from static analysis after the crash to dynamic analysis before the crash toward freeway safety, as well as explore the relationship between dynamic traffic flow characteristics and real-time traffic crash risk under different temporal conditions, this research constructed a real-time traffic crash risk prediction model considering the temporal effect difference. First, traffic crash information and the matched big data of high-resolution traffic flow located on the section of milepost 100–130 of Interstate 5 (I-5) in Washington State, were extracted. In terms of temporal dimension, the research object was divided into weekdays and weekends, and the traffic state was divided into unsaturated and saturated. The random forest (RF) algorithm was introduced to identify the traffic flow variables of crash precursors, and support vector machine (SVM) was applied to build the traffic crash risk prediction model under the condition of temporal difference. A confusion matrix, receiver operating characteristic (ROC) curve, and area under curve (AUC) values were used to evaluate the accuracy of the model performance. Furthermore, the prediction performance of the proposed model was tested via constructing the risk model without consideration of temporal effect and traffic state difference. Finally, the rationality of variable screening was verified by inputting the data set without variable screening into the constructed model. The results showed that the occurrence mechanism of dynamic traffic crashes under different temporal effect conditions varies; the AUC values of the constructed prediction model were all between 0.7 and 0.9, indicating that the recommended model has good prediction accuracy. In conclusion, the real-time freeway traffic crash risk prediction model considering the temporal effect difference has certain advantages compared with the conventional model, and its performance is better than the prediction model without screening of important traffic flow variables. This approach can provide theoretical guidance for dynamic traffic safety management toward freeway under temporal difference conditions.

Models, Algorithms and Applications of DynasTIM Real-Time Traffic Simulation System

Intelligent Transportation Systems (ITS) have the potential to improve traffic conditions and reduce travel delays. As a decision support software system for ITS, DynasTIM is based on the principle of dynamic traffic assignment and developed for real-time online simulation, prediction and optimization of dynamic traffic flows in urban or expressway networks. This paper introduces the models, algorithms and some typical applications of DynasTIM. The main contents include: the functional architecture; the application architecture of the system; dynamic OD (Origin-Destination) flows estimation method with novel formula for assignment matrix computation; mesoscopic traffic model using variable-length speed influence region and calibrating speed online based on connected vehicles data; and parallel SPSA algorithm based urban area signal optimization method. The functions of DynasTIM are implemented basically through three main modules: state estimation (ES), state prediction and control strategy optimization (PS&CSO), and guidance strategy optimization (GSO). The case study is aimed at the populated Futian Central Business District (CBD) road network in Shenzhen, China, which has an area of about 7 square kilometers. Based on the archived turning counts collected from 359 video traffic detection locations, DynasTIM was calibrated offline for this network, in order to validate the capability of simulating actual traffic conditions, and to set up basic conditions for testing signal optimization methods. The results show that the simulation output flows of DynasTIM have fairly good matching accuracy with the real surveillance flows in the field. Furthermore, for the CBD network with 38 signalized intersections, the signal optimization method is evaluated and better signal timing plans are found which can reduce about 13% average travel delay, compared with the signal plans currently implemented in the field.

Deep-Deterministic Policy Gradient Based Multi-Resource Allocation in Edge-Cloud System: A Distributed Approach

Edge Cloud (EC) empowers the beyond 5G (B5G) wireless networks to cope with large-scale and real-time traffics of Internet-of-Things (IoT) by minimizing the latency and providing compute power at the edge of the network. Due to a limited amount of resources at the EC compared to the back-end cloud (BC), intelligent resource management techniques become imperative. This paper studies the problem of multi-resource allocation (MRA) in terms of compute and wireless resources in an integrated EC and BC environment. Machine learning-based approaches are emerging to solve such optimization problems. However, it is challenging to adopt traditional discrete action space-based methods due to their high dimensionality issue. To this end, we propose a deep-deterministic policy gradient (DDPG) based temporal feature learning attentional network (TFLAN) model to address the MRA problem. TFLAN combines convolution, gated recurrent unit and attention layers together to mine local and long term temporal information from the task sequences for excellent function approximation. A novel heuristic-based priority experience replay (hPER) method is formulated to accelerate the convergence speed. Further, a pruning principle helps the TFLAN agent to significantly reduce the computational complexity and balance the load among base stations and servers to minimize the rejection-rate. Lastly, data parallelism technique is adopted for distributed training to meet the needs of a high-volume of IoT traffic in the EC environment. Experimental results demonstrate that the distributed training approach suites well to the problem scale and can magnify the speed of the learning process. We validate the proposed framework by comparing with five state-of-the-art RL agents. Our proposed agent converges fast and achieves up to 28% and 72% reduction in operational cost and rejection-rate, and achieves up to 32% gain in the quality of experience on average, compared to the most advanced DDPG agent.

Smart Preliminary Channel Access to Support Real-Time Traffic in Wi-Fi Networks

Real-time applications (RTA) are an important use case for IEEE 802.11be, a new amendment to the Wi-Fi standard. This amendment introduces new complicated mechanisms to provide low delay and high reliability for RTA, but many of them are not supported by legacy devices that may be present in future Wi-Fi networks. In contrast, the preliminary channel access (PCA) method is designed to satisfy strict RTA requirements even in the presence of legacy devices and does not require significant changes to the Wi-Fi protocol. However, it significantly reduces the capacity for non-RTA traffic. This paper introduces a Smart PCA method, which improves the performance of all the stations in scenarios with multiple RTA stations. Extensive simulation shows that the Smart PCA method guarantees low delays for intensive RTA traffic in these scenarios. Moreover, it doubles the network capacity for the stations with non-RTA traffic.

DeepNR: An adaptive deep reinforcement learning based NoC routing algorithm

Network-on-Chip (NoC) has become a cost-effective communication interconnect for Tiled Chip Multicore Processor systems. The communication between cores is done through packet exchange. As the computational intensity of applications increases, the amount of packet exchange between cores will also increase. The improper routing of these packets will result in high congestion thereby degrading the system performance. This marks the need for congestion-aware routing in NoC. In the real world, the applications running in NoC create diverse traffic, which in turn creates challenges in routing. Such challenges have resulted in more researchers relying on machine learning algorithms to tackle them. However, the issues pertaining to storage overhead and packet latency prevail in such methodologies. This paper presents an adaptive routing algorithm DeepNR, which uses a deep reinforcement learning approach. The proposed approach considers network information for state representation, routing directions for actions, and queuing delay for reward function. Experiments carried out on synthetic as well as real-time traffics to demonstrate the effectiveness and efficiency of DeepNR using the Gem5 simulator. The results obtained for DeepNR indicate a reduction of up to 21.25% and 44% in overall packet latency under high traffic conditions on real and synthetic traffic respectively, as compared to the existing approaches. Also, DeepNR achieves a throughput of above 90% in both the traffic scenarios.

Development of a Dynamic Calibration Method of Traffic Simulation Parameters Based on Bayes Theorem and Real-Time Traffic Flow Data

Development of a Dynamic Calibration Method of Traffic Simulation Parameters Based on Bayes Theorem and Real-Time Traffic Flow Data

Crosstalk aware transient error correction coding technique for NoC links

In Deep Sub Micron (DSM) technology, the critical issues in the NoC interconnect design are to meet the performance, power consumption requirements of the SoC and to address reliability simultaneously, interconnect delay, power consumption and crosstalk noise. It is essential to select an optimal coding technique to improve communication reliability and reduce self and coupling switching activities. In this paper, Crosstalk Aware Transient Error Correction (CATEC) coding technique is proposed to handle the reliability issues and flit dependent switching activities. The achievable reliability and reduction of switching activities are evaluated using real-time traffics. It is proved that the CATEC technique corrects all the error patterns with one or two transient error bits and few error patterns with six to nine transient error bits. Furthermore, 99% of the error patterns with three, four and five transient error bits are too corrected for a 32 bit flit. This technique considerably reduces flit dependent self and coupling transitions. This results in low power NoC link and even avoids crosstalk to a greater extent.

Delay distributions on encrypted real-time traffics

Real-time streams are common for nowadays communication for both data and video. Real-time data communication such as chat application and telemetry, as well as real-time video communication such as video call, video conference, or monitoring video are prone to the third party attacked. End to end encryption has been considered as the most realistic solution such as applied by WhatsApp so that the transmitted data is difficult to be revealed by the attacker. Since encryption and decryption processes injected additional delay, the algorithm should be fast enough to avoid inconvenience to users. This paper examines delay distribution for encrypted real-time traffics. The simulation shows that the decryption contributed the most to the end to end delay. Decryption delay is about 4.58 times higher than encryption and transmission delay. Although assessment involves only two links, the complex network has relatively lower than the obtained decryption delay. Meanwhile, encryption contributed only about 0.2% to the overall end to end delay.

Evaluating spatial configuration of fire stations based on real-time traffic

Fire stations play a key role in controlling typical thermal runaway phenomena (fires). Based on real-time traffic, we develop a data crawling and processing algorithm for travel times, and propose an evaluation model for the spatial configuration of fire stations. In the model, the fire service levels are divided into four levels based on the total fire service score (TFSS). The present work gathered 16,448 fire incidents in Changsha, China from 2013 to 2017 as fire service demand points, and the minimum travel times for each fire incident point in the four evaluation scenarios with different real-time traffics were extracted from online maps. The results show that the TFSSs of Changsha varied from 67.92 to 74.05 due to the insufficient firefighting resources and the dynamic traffic conditions, and the fire service level of Changsha can be defined as fair. By employing spatial visualization, we observed that the fire service demand points with moderate and poor fire service levels were mainly located in rural areas, and those with good and fair fire service levels were located in urban areas.

Reduced Worst-Case Communication Latency Using Single-Cycle Multihop Traversal Network-on-Chip

The communication latency in traditional network-on-chip (NoC) with hop-by-hop traversal is inherently restricted by the distance between source-destination communicating pairs. SMART, as one of the dynamically reconfigurable NoC architectures, enables the new feature of single-cycle long-distance communication by building a direct bypass path between distant cores dynamically at runtime. With the increasing of the number of integrated cores in multi/many-core systems, SMART has been deemed a promising communication backbone in such systems. However, SMART is generally optimized for average-case performance for best-effort traffics, not offering real-time guaranteed services for real-time traffics, and thus SMART often shows extremely poor real-time performance (e.g., schedulability). To make SMART latency-predictable for real-time traffics, by combining with the single-cycle bypass forwarding technique, in this article, we first propose a priority-preemptive scheduling to allow contending packets to be arbitrated according to predefined priorities. Based on the priority-based scheduling, for the real-time packet flows with given flow mapping and predefined priorities, we then propose a real-time communication analysis model, by considering shared virtual channels (or priority levels) and arbitrary-deadline real-time packet flows, to predict the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">worst-case communication latency</i> and validate the schedulability. Through theoretical and experimental comparison, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">worst-case communication latency</i> of the analyzed packet flows is reduced significantly compared with that of the traditional priority-preemptive NoCs with hop-by-hop traversal and the original distance-based SMART, thus improving the schedulability.

A Design of Packet Scheduling Algorithm to Enhance QoS in High-Speed Downlink Packet Access (HSDPA) Core Network

Voice over Internet Protocol (VOIP) in an efficient manner is a basic requirement of modern era. The real time and non-real traffics demand customized communication provisioning to get guarantee of service. For this we proposed a user fulfillment design for facilitating packets switching in 3G cellular network to insure provisioning of QoS (quality of service) in DiffServ (Differentiated Services) Network. To enhance QoS for real time traffic by reducing delay, packet loss and jitter, we proposed Low latency queuing (LLQ) algorithm. In this paper, we focused on packet scheduling, Diffserv and QoS classes mapping into Universal Mobile telecommunication System (UMTS) classes and buffering. To associate different types of real time multimedia traffic, the QoS provisioning mechanism used different code points of Diffserv. The new idea in LLQ is to map the video and voice traffics against two separate queues and used priority queuing in Low latency queuing for voice traffic. The results got from reproductions shows that proposed calculation meets the QoS prerequisites.

Travel Route Prediction Using Travel Habits and Real-Time Traffic Condition

The traffic problems of cities at home and abroad, especially major cities, have become focus issues. To solve this problem, traffic guidance is one of the most important solutions. For traffic guidance, precisely prediction of travel route of the users becomes one more and more urgent issue. To find out which factor affects the prediction mostly or to propose one efficient route prediction algorithm is attracting more researchers’ attentions. In this paper, we proposed one route prediction model to deal with the problem by considering both travel habits and real-time traffic condition. By embedding LSTM model, the proposed approach has the ability of predict sequence data to predict the travel route. Also the logistical model is introduced to embedding the real-time traffic condition data into the proposed model. Experiment results have evaluated the performance of the proposed approach.

Performance Evaluation of Priority Load-Aware Scheduling (PLAS) Algorithm for IEEE802.16 Networks

In recent years, there has been an increase in the transmission of multimedia services due to the emergence of wireless broadband (WiBB) technologies such as WiMAX. WiMAX supports multiple QoS classes for transmission of different multimedia applications. However, satisfying the requirements of these applications with the often-limited resources has been a major challenge, which requires an efficient scheduling scheme such as PLAS. PLAS is a variant of LAWRR designed to reduce queuing delay of real-time traffics in WiBB networks. Several simulation experiments were conducted to evaluate the performance of PLAS against the LAWRR algorithm. The results revealed that PLAS outperforms the other scheme in terms of average delay for low input traffic. However, the metric (delay) used and the traffics generated for the simulation are not adequate to realistically evaluate the performance of a scheduling algorithm in a typical metropolitan area network. In this study, we further evaluated the performance of PLAS and LAWRR under varying higher input traffics, and an additional performance metric, using discrete event simulation. The results demonstrated that the PLAS achieved better results in terms of queuing delay and throughput compared to LAWRR. The improved performance will lead to better user experience, which will increase the number of subscribers and consequently increase revenue for service providers.

Smart Real-Time Traffic Optimization System

Today’s traffic situations in busy cities require a smarter traffic signal optimization methodology to regulate the traffic in an orderly manner. Traffic signals are set to a fixed time limit in most of the road junctions. This leads to a dead lock in many junctions because they do not recognize the density of traffic in each road. People want to reach their destinations as quickly as possible. Our proposed Smart Real-Time Traffic Optimization System (SRTOS) provides optimization of traffic signals based on real time Google traffic data. In Google maps, traffic data is indicated through different colors (dark red, red, yellow, orange and green). These colors are processed to find out the traffic around a particular traffic junction and preference is given to the road that has a maximum traffic in the traffic junction, thereby easing out the congestion in that junction. Following similar patterns of activities for all other traffic junctions, wherever possible, will be helpful to drastically ease the flow of traffic patterns of a particular city. This is to fulfill the objective of smoother traffic flow without making people to wait and waste their valuable time