Routing Congestion Research Articles

Incremental Lagrangian Relaxation Based Discrete Gate Sizing and Threshold Voltage Assignment

Timing closure remains one of the most critical challenges of a physical synthesis flow, especially when the design operates under multiple operating conditions. Even if timing is almost closed at the end of the flow, last-mile placement and routing congestion optimizations may introduce new timing violations. Correcting such violations needs minimally disruptive techniques such as threshold voltage reassignment and gate sizing that affect only marginally the placement and routing of the almost finalized design. To this end, we transform a powerful Lagrangian-relaxation-based optimizer, used for global timing optimization early in the design flow, into a practical incremental timing optimizer that corrects small timing violations with fast runtime and without increasing the area/power of the design. The proposed approach was applied to already optimized designs of the ISPD 2013 benchmarks assuming that they experience new timing violations due to local wire rerouting. Experimental results show that in single corner designs, timing is improved by more than 36% on average, using 45% less runtime. Correspondingly, in a multicorner context, timing is improved by 39% when compared to the fully-fledged version of the timing optimizer.

Congestion Prediction in FPGA Using Regression Based Learning Methods

Design closure in general VLSI physical design flows and FPGA physical design flows is an important and time-consuming problem. Routing itself can consume as much as 70% of the total design time. Accurate congestion estimation during the early stages of the design flow can help alleviate last-minute routing-related surprises. This paper has described a methodology for a post-placement, machine learning-based routing congestion prediction model for FPGAs. Routing congestion is modeled as a regression problem. We have described the methods for generating training data, feature extractions, training, regression models, validation, and deployment approaches. We have tested our prediction model by using ISPD 2016 FPGA benchmarks. Our prediction method reports a very accurate localized congestion value in each channel around a configurable logic block (CLB). The localized congestion is predicted in both vertical and horizontal directions. We demonstrate the effectiveness of our model on completely unseen designs that are not initially part of the training data set. The generated results show significant improvement in terms of accuracy measured as mean absolute error and prediction time when compared against the latest state-of-the-art works.

Multiobjective fuzzy knowledge‐based bacterial foraging optimization for congestion control in clustered wireless sensor networks

SummaryWireless sensor networks (WSNs) include a number of wireless sensor nodes distributed in a geographical area. Due to the intrinsic and functional nature of WSNs, these networks face many challenges such as limited energy resources of sensor nodes and the routing congestions. Clustering is the most common routing approach to control congestion and achieve energy efficiency in WSNs, which ultimately prolongs the network lifetime. In the cluster‐based routing protocols, optimal selection of cluster heads (CHs) is an NP‐hard problem, and consequently, heuristic and metaheuristic algorithms can be employed to obtain a near‐optimal solution. In this paper, a fuzzy knowledge‐based metaheuristic model based on multiobjective fuzzy inference system (moFIS) and bacterial foraging optimization (BFO), named moFIS‐BFO, is proposed as an efficient routing protocol for clustered WSNs. In the moFIS‐BFO model, the moFIS is utilized to calculate the chance of each node for becoming a CH based on different criteria including degree difference, residual energy, total distance to neighbors, and distance to the base station. Taking into account the calculated chances of nodes, the BFO is employed to select proper CHs at every round. To control the queue in cluster headings, a priority ranking method is used to control congestions and avoid packet wastages. Simulation results demonstrate the superiority of the moFIS‐BFO protocol against the existing techniques to control congestion and prolong the network lifetime.

Telematics-Based Traffic Law Enforcement and Network Management System for Connected Vehicles

This article introduces a telematics-based traffic law enforcement and management system (SLEM), which leverages connected vehicle and telematics technologies. The system assigns each driver a real-time score that measures her/his driving performance. Using these driver scores, SLEM then adopts a personalized route guidance strategy that favors high-performing drivers by guiding them to less congested routes at the expense of low-performing drivers who are directed to alternative, slower routes. This routing strategy shifts the network traffic distribution pattern from the undesirable user equilibrium pattern to the system optimal pattern. Hence, SLEM not only incentivizes drivers to improve their driving performance but it also provides a mechanism to manage network congestion. A bilevel mathematical program and an efficient solution methodology were developed to derive SLEM's optimal routing strategy. A set of experiments that was conducted to evaluate the performance of SLEM under different operation scenarios showed that the adoption of SLEM's routing strategy reduced travel time during recurrent congestion situations by about 5%.

Traffic Congestion Prediction using Deep Reinforcement Learning in Vehicular Ad-Hoc Networks (VANETS)

In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.

Hybrid MEMS-CMOS ion traps for NISQ computing

Surging interest in engineering quantum computers has stimulated significant and focused research on technologies needed to make them manufacturable and scalable. In the ion trap realm this has led to a transition from bulk three-dimensional macro-scale traps to chip-based ion traps and included important demonstrations of passive and active electronics, waveguides, detectors, and other integrated components. At the same time as these technologies are being developed the system sizes are demanding more ions to run noisy intermediate scale quantum (NISQ) algorithms, growing from around ten ions today to potentially a hundred or more in the near future. To realize the size and features needed for this growth, the geometric and material design space of microfabricated ion traps must expand. In this paper we describe present limitations and the approaches needed to overcome them, including how geometric complexity drives the number of metal levels, why routing congestion affects the size and location of shunting capacitors, and how RF power dissipation can limit the size of the trap array. We also give recommendations for future research needed to accommodate the demands of NISQ scale ion traps that are integrated with additional technologies.

Multirow Complementary-FET (CFET) Standard Cell Synthesis Framework Using Satisfiability Modulo Theories (SMTs)

With the relentless scaling of technology nodes, the track number reduction of conventional (Conv.) cell is starting to reach its limitations due to limited routing resources, lateral p-n separations, and performance requirements. As a result, to exploit the benefits of 3-D architectures, complementary-FET (CFET) technology, which stacks P-FET on N-FET or vice versa, is proposed to release the restriction of p-n separation and reduce in-cell routing congestion by enabling p-n direct connections. However, CFET standard cell (SDC) synthesis demands a holistic reconsideration of multirow (MR) structure to maximize the cell and block-level area benefits due to limited in-cell routing tracks and routability that comes from the stacked structure and reduced cell height. In this article, we propose a satisfiability modulo theory (SMT)-based MR CFET SDC synthesis framework that simultaneously solves place-and-route to minimize the cell area by considering single-row and MR placement together. We enable explorations on upper/lower M0A/PC routing to leverage the shared-and-split structure across cell rows with the proposed MR dynamic complementary pin allocation scheme. We demonstrate that MR 2.5T CFET without and with upper/lower M0A/PC routing achieves 16.44% and 20.61% on the average reduced cell areas, respectively, compared to 3.5T CFET. Moreover, MR 2.5T CFET SDCs achieve 13.43% and 14.40% less block-level area and total wirelength on average compared to 3.5T CFET SDCs.

A Balanced Algorithm for In-City Parking Allocation: A Case Study of Al Madinah City

Parking in heavily populated areas has been considered one of the main challenges in the transportation systems for the past two decades given the limited parking resources, especially in city centres. Drivers often waste long periods of time hunting for an empty parking spot, which causes congestion and consumes energy during the process. Thus, finding an optimal parking spot depends on several factors such as street traffic congestion, trip distance/time, the availability of a parking spot, the waiting time on the lot gate, and the parking fees. Designing a parking spot allocation algorithm that takes those factors into account is crucial for an efficient and high-availability parking service. We propose a smart routing and parking algorithm to allocate an optimal parking space given the aforementioned limiting factors. This algorithm supports choosing the appropriate travel route and parking lot while considering the real-time street traffic and candidate parking lots. A multi-objective function is introduced, with varying weights of the five factors to produce the optimal parking spot with the least congested route while achieving a balanced utilization for candidate parking lots and a balanced traffic distribution. A queueing model is also developed to investigate the availability rate in candidate parking lots while considering the arrival rate, departure rate, and the lot capacity. To evaluate the performance of the proposed algorithm, simulation scenarios have been performed for different cases of high and low traffic intensity rates. We have tested the algorithm on in-city parking facility in the city of Al Madinah as a case study. The results show that the proposed algorithm is effective in achieving a balanced utilization of the parking lots, reducing traffic congestion rates on all routes to candidate parking lots, and minimizing the driving time to the assigned parking spot. Additionally, the proposed algorithm outperforms the MADM algorithm in terms of the selected three metrics for the five periods.

Dataflow-Aware Macro Placement Based on Simulated Evolution Algorithm for Mixed-Size Designs

This article proposes a novel approach to handle macro placement. Previous works usually apply the simulated annealing (SA) algorithm to handle this problem. However, the SA-based approaches usually have difficulty in handling preplaced macros and require longer runtime. To resolve these problems, we propose a macro placement procedure based on the corner stitching data structure and then apply an efficient and effective simulated evolution algorithm to further refine placement results. In order to relieve local routing congestion, we propose to expand areas of movable macros according to the design hierarchy before applying the macro placement algorithm. Finally, we extend our macro placement methodology to consider dataflow constraint so that dataflow-related macros can be placed at close locations. The experimental results show that our approach obtains a better solution than a previous macro placement algorithm and a tool. Besides, placement quality can be further improved when the dataflow constraint is considered.

Hybrid Congestion Sharing and Route Repairing Protocol for Bluetooth Networks

Bluetooth is a widespread wireless technology standard for limited wireless networks that permits Bluetooth devices to create a one-hop (piconet) or multi-hop (scatternet) network. During data transmission, a large number of links passing through a single master or bridge device may create congestion problems in a Bluetooth network. Therefore, routing in a multi-hop dynamic Bluetooth network, where a number of routing masters and routing bridges exist, sometimes create technical problems in a scatternet. Mobility and failure of routing devices disconnects the routing links and link reconstruction process consumes more resources that eventually decrease the performance. In this paper, Hybrid Congestion Sharing and Route Repairing protocol for Bluetooth networks” (HCSRR) is proposed. The objective of this paper is to provide an efficient technique for scatternet congestion avoidance and route maintenance. The proposed protocol is implemented and compared with most relevant protocols. From simulation results, it is observed that the HCSRR outperforms the existing protocols.

Predicting Networks-on-Chip traffic congestion with Spiking Neural Networks

Network congestion is one of the critical reasons for degradation of data throughput performance in Networks-on-Chip (NoCs), with delays caused by data-buffer queuing in routers. Local buffer or router congestion impacts on network performance as it gradually spreads to neighbouring routers and beyond. In this paper, we propose a novel approach to NoC traffic prediction using Spiking Neural Networks (SNNs) and focus on predicting local router congestion so as to minimize its impact on the overall NoCs throughput. The key novelty is utilizing SNNs to recognize temporal patterns from NoC router buffers and predicting traffic hotspots. We investigate two neural models, Leaky Integrate and Fire (LIF) and Spike Response Model (SRM) to check performance in terms of prediction coverage. Results on prediction accuracy and precision are reported using a synthetic and real-time multimedia applications with simulation results of the LIF based predictor providing an average accuracy of 88.28%–96.25% and precision of 82.09%–96.73% as compared to 85.25%–95.69% accuracy and 73% and 98.48% precision performance of SRM based model when looking at congestion formations 30 clock cycles in advance of the actual hotspot occurrence.

Multipath Reliable Routing Using Cluster Based Communication

In this research we proposed the protection in ANT Optimized primarily based multipath congestion routing performance. Here the situation of DDoS is simulated and examines their impact in dynamic network. The multipath protocol like AOMDV is balance the all load by way of offering alternative path however not knowledgeable at each and every condition. The DDoS attacker is blocking off the whole viable direction in community by way of flooding large quantity of redundant packets in dynamic network. The attacker is that the intermediate node and this attacker contamination is continuously dispersing infection and the entire network overall performance is dumped. The proposed security scheme is identified attacker and their loss effect. Attacker is utterly disabled by way of proposed protection mechanism and their loss is also evaluated. The proposed approach is now not only detecting but also stop community from DDoS attack. The overall performance of protection scheme and assault is measured in three distinctive scenarios of a range of node densities. The proposed scheme is offers attacker free routing and get better network performance after making use of it. The performance of ANT OPTIMIZED is almost equal. The packets receiving, throughput, and PDR are bettering but the loss of packets and unnecessary flooding is decreased in dynamic network.

Complementary-FET (CFET) Standard Cell Synthesis Framework for Design and System Technology Co-Optimization Using SMT

With the relentless scaling of technology nodes, design technology co-optimization (DTCO) for the conventional (Conv.) cell structure is starting to reach its limitations due to limited routing resources, lateral p-n separations, and performance requirements. As a result, system technology co-optimization (STCO) has been proposed to exploit the benefits of 3-D architectures. Complementary-FET (CFET) technology, which stacks p-FET on n-FET or vice versa, can release the restriction of p-n separation and reduce in-cell routing congestion by enabling p-n direct connections. However, CFET standard cell (SDC) synthesis demands holistic considerations to maximize the area benefit of scaling at the block level due to the extremely limited routability that comes from the stacked structure and reduced cell height. In this article, we propose a satisfiability modulo theory (SMT)-based CFET SDC synthesis framework that simultaneously solves place-and-route to generate optimized layouts. We first demonstrate that the CFET structure achieves 10.94% and 21.27% reduction on average cell area and metal length, respectively, and 15.10% smaller block-level area compared to Conv. structure as scaling down to 3.5T architecture. For routability, the proposed constraint-based minimum pin length/minimum pin opening and objective-based edge-based pin-separation/M2 track use reduce up to 48% #DRVs at the block level compared to the previous work. Then, through extensive DTCO explorations on ground design rules and #BEOLs, 3.5T CFET SDCs achieve up to 6.50% smaller block-level areas than 4.5T CFET SDCs. Finally, with the assistance of STCO and DTCO, 3.5T CFET SDCs achieve 21.0% on average reduced block-level areas compared to 4.5T Conv. SDCs.

The Yellow Active Queue Management Algorithm in ICN Routers Based on the Monitoring of Bandwidth Competition

Deploying the active queue management (AQM) algorithm on a router is an effective way to avoid packet loss caused by congestion. In an information-centric network (ICN), routers not only play a role of packets forwarding but are also content service providers. Congestion in ICN routers can be further summarized as the competition between the external forwarding traffic and the internal cache response traffic for limited bandwidth resources. This indicates that the traditional AQM needs to be redesigned to adapt to ICN. In this paper, we first demonstrated mathematically that allocating more bandwidth for the upstream forwarding flow could improve the quality of service (QoS) of the whole network. Secondly, we propose a novel AQM algorithm, YELLOW, which predicts the bandwidth competition event and adjusts the input rate of request and the marking probability adaptively. Afterwards, we model YELLOW through the totally asymmetric simple exclusion process (TASEP) and deduce the approximate solution of the existence condition for each stationary phase. Finally, we evaluated the performance of YELLOW by NS-3 simulator, and verified the accuracy of modeling results by Monte Carlo. The simulation results showed that the queue of YELLOW could converge to the expected value, and the significant gains of the router with low packet loss rate, robustness and high throughput.

Cross‐layer multipath congestion control, routing and scheduling design in ad hoc wireless networks

Demands for stable and efficient transmission are increasing due to the ongoing expansion of global wireless data traffic. One of the most promising ways to moderate burden of traffic load and decrease the queuing delay is to design cross-layer protocols. This paper considers a utility maximization problem for multipath ad hoc wireless networks via joint cross-layer congestion control, routing and scheduling design. In the formulated utility maximization problem, rate, scheduling and queuing delay constraints are involved under the condition of fixed channels and time varying channels. To solve this utility maximization problem, two scheduling methods are proposed, that is, perfect scheduling and distributed scheduling. In perfect scheduling, all nodes in the network contribute to the scheduling process, while distributed method only takes into account adjacent nodes. For both methods, the global convergence is proved. A comprehensive simulation evaluation is performed which shows that this proposed algorithms outperform existing cross layer protocols in terms of increasing source rate and reducing congestion price.

Basic Design Architecture of Congestion Notification System: A Real Time Road Traffic Information Enabler

Road traffic congestion is a prominent challenge of today’s urban center. As a push factor of urban centers, it impact negatively on socio-economic well-being of cities. However, contemporary innovative transport technology of Intelligent Transport System (ITS) is bridging the traveler information gaps, through installation and deployment of smart transport infrastructure such as Congestion Notification System at critical traffic intersections and points that aggravate road traffic congestion. This paper therefore provides a detailed explanation on the configuration and basic architecture of a primary Congestion Notification System (CNS) stating its working principles in providing real time road traffic congestion level information to motorist, prior entering the congestion zones or section of the road. Thus, engendering informed decision by motorists on alternative routes rather than the congested route.

Placement and Routing Methods Considering Shape Constraints of JTL for RSFQ Circuits

Rapid single-flux-quantum (RSFQ) circuits are developing rapidly, but there are still many layouts that need to be completed manually, greatly reducing the design efficiency. We report the shape constraints of Josephson transmission line (JTL), and then propose automatic placement and routing methods for RSFQ circuits. First, for the pipeline structure of concurrent-flow clocking, a detailed placement algorithm based on simulated annealing is proposed, which use D Flip-Flop (DFF) insertion, column placement, and intra-column perturbation strategies to ensure the synchronization of clock phase while completing the placement of logic cells. More importantly, for the shape constraints of JTL, a new two-step JTL routing method is proposed. The first step is dummy wire routing, and the shape violations and routing congestion caused by shape constraints are solved to search legal paths for JTL routing; in the second step, timing optimization is performed when replacing dummy wire with JTL cells. Experimental results show that the proposed automatic methods achieve a 5% area-reduction and a 20× speed-up compared to manual design.

Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges.

The increasing population across the globe makes it essential to link smart and sustainable city planning with the logistics of transporting people and goods, which will significantly contribute to how societies will face mobility in the coming years. The concept of smart mobility emerged with the popularity of smart cities and is aligned with the sustainable development goals defined by the United Nations. A reduction in traffic congestion and new route optimizations with reduced ecological footprint are some of the essential factors of smart mobility; however, other aspects must also be taken into account, such as the promotion of active mobility and inclusive mobility, encouraging the use of other types of environmentally friendly fuels and engagement with citizens. The Internet of Things (IoT), Artificial Intelligence (AI), Blockchain and Big Data technology will serve as the main entry points and fundamental pillars to promote the rise of new innovative solutions that will change the current paradigm for cities and their citizens. Mobility-as-a-service, traffic flow optimization, the optimization of logistics and autonomous vehicles are some of the services and applications that will encompass several changes in the coming years with the transition of existing cities into smart cities. This paper provides an extensive review of the current trends and solutions presented in the scope of smart mobility and enabling technologies that support it. An overview of how smart mobility fits into smart cities is provided by characterizing its main attributes and the key benefits of using smart mobility in a smart city ecosystem. Further, this paper highlights other various opportunities and challenges related to smart mobility. Lastly, the major services and applications that are expected to arise in the coming years within smart mobility are explored with the prospective future trends and scope.

An Effective Traffic Management System Using Connected Dominating Set Forwarding (CDSF) Framework for Reducing Traffic Congestion in High Density VANETs

In recent times, congestion in traffic is bit cost effective and several traffic management methods are utilized to balance the traffic as per the capacity of roads. However, many methods fails in pre-analyzing the network events to regulate the traffic flow. Also, the dynamic movement of vehicles fails in adapting the vehicle units to follow the changing topology. Hence, it is necessary for packet forwarding between source and destination nodes in a precise manner, where the packet loss and communication overhead should be in control. In this paper, we utilize the concept of Connected Dominating Set (CDS) approach in traffic management for reducing the flow of traffic streaming in main routes. The CDS approach dynamically adapts itself with the changing topology and acts as a virtual backbone to reduce traffic overhead. It restricts the increased flow of vehicles to the dominators, since traffic congestion in such areas increases. The CDS forwarding (CDSF) finds the dominating or high congestion routes and diverts the traffic to other areas based on the level of traffic in sub-routes. The CDSF method works on an assumption that edges on road segments has multiple vehicles, where it increases the network efficiency. However, we make careful consideration of choosing the other road segments to traverse the required packets from source to destination with reduced packet loss. The construction of a virtual backbone network with series of neighboring backbone nodes traverses efficiently the packets. Further, choosing vehicles with low velocity improves the probability of connectivity in a specific area and increases the network throughput. This improves the re-routing flexibility and makes the packets to reach the destination without traffic congestion in VANETs. The CDSF method and control strategies are tested in NS2 and SUMO simulator under different traffic conditions. The results of simulation show that CDSF traffic management system reduces significantly the traffic congestion and overall traffic cost that includes CDF, average latency, average end-to-end delay, Hop count, packet error rate, percentage delivery ratio, data delivery rate, throughput. The result shows that the proposed method 0.345 times improved packet delivery ratio, 0.35 times improved throughput and 0.7 times reduced delay than the existing methods. Further, the CDSF method controls the traffic density by reducing the maximum queue length and traffic controlling time.

A logistic distribution model of new short sea shipping line along a mutimodal corridor in Italy

The purpose of the application focuses on an intermodal model (RPL) to simulate the transport choice for freight sending on the most relevant corridor Naples–Milan. In this, operate a rail- road system with the introduction a new short sea shipping (SSS) intermodal line (Naples Sea Genova road Milan). The paper considers a collaboration with a multimodal transport operator, with many logistic platforms in Italy to analyze the degree of competition inside corridor. An application along this very congested route Milan (Segrate interport) - Nola (Naples interport) was used. The econometric models applied to operator choices are a random parameter logit model vs multinomial logit model with frequency, type of load and cost as main parameters.