Minimum Congestion Research Articles

Minimum Congestion and Wirelength of Embedding the nth Cartesian Product of K4 into Various Kinds of Grid Networks

Embedding an interconnection network into another network is one of the main problems in parallel processing and computing systems. Interconnection networks in multiprocessing systems facilitate effective communication among various system components. Obtaining the minimum cutwidth, congestion, and wirelength in graph embedding problems is of great significance for network design and architecture, where the minimum is taken over all embedding of guest graphs into host graphs, and addressing these issues can reduce time and cost in embedded design. This work aims to accurately calculate the minimum congestion and wirelength for the [Formula: see text] (the [Formula: see text]th Cartesian product of [Formula: see text]) layout into [Formula: see text]-dimensional, [Formula: see text]-dimensional, and [Formula: see text]-dimensional grids by the optimal solution of edge isoperimetric problem on [Formula: see text].

Congestion Relief Services by Vehicle-to-Grid Enabled Electric Vehicles Considering Battery Degradation

Battery electric vehicles (BEVs) offer substantial potential to enhance the electric grid through bi-directional charging technologies. In essence, BEVs, functioning as portable battery energy storage systems, play a pivotal role in enabling the seamless integration of renewable energy, grid optimization, and ancillary services. This article sets out to explore the value of BEVs equipped with Vehicle-to-Grid (V2G) for grid operators, particularly in the context of alleviating congestion. This valuable service, though not accompanied by direct monetary compensation for users, holds significant promise in minimizing congestion and renewable energy curtailment. This study utilizes the Day-Ahead Locational Marginal Price (LMP) data obtained from various locations within California Independent System Operator (CAISO) to ascertain the financial benefits to BEVs located on either side of congestion at different grid nodes, across various months. Similar analysis is performed on some of the largest solar energy plants in California. Mixed-integer linear programs are used to optimize the charging/discharging decisions for the BEV for maximizing revenue from LMP arbitrage and for minimizing the congestion component of LMP. Additionally, we take into account the impact of battery degradation, quantified as a cost per kilowatt-hour ($/kWh), and integrate this factor into our assessment to understand the evolving discharging behavior of BEVs. The article compares the benefits from the BEVs towards congestion minimization for the two different optimization scenarios, discusses seasonality, and addresses the importance of adequately compensating BEV users and incentivizing them to prioritize congestion relief during specific time intervals.

High traffic communication congestion control for wireless sensor networks based on harmony search optimization

In wireless sensor networks (WSNs), communication between the wireless nodes requires minimum response delay, minimum congestion and communication reliability. A wide variety of sensors produces a mixture of heterogeneous traffics with different reliability requirements. The article focuses on high traffic congestion which affects communication and produces latency. In the existing approaches, the congestion was controlled and the optimization was done during the time of node deployment. In the proposed method, high traffic congestion was controlled by a hop-by-hop approach which was applied in the statically deployed sensor nodes, the optimization was performed at the time of communication. To provide a uninterrupted communication to the WSNs the proposed approach analyses the occupancy ratio of the buffer and evaluates the downstream node congestion level. Here, the Harmony Search Algorithm is considered for design the optimal sensor network with Support Vector Machine (SVM). The experimental result shows the effectiveness and feasibility of the HSA-SVM environment. Also, it significantly enhances communication in diverse traffic conditions, specifically in heavy traffic areas with limited data.

Congestion Management Using an Optimized Deep Convolution Neural Network in Deregulated Environment

The technical issue of congestion, which is predominantly found in deregulated power systems, is caused by the failure of transmission networks to satisfy load power demands. This failure is primarily caused due to an increase in loads or loss of transmission lines or generators in modern restructured power networks. This work introduces a CM approach using Deep Convolution Neural Network (DCNN) for minimizing congestion and supporting Independent System Operators (ISOs). The purpose of the work is to generate enhanced prediction outputs for congestion management with reduced error values. These objectives were achieved through the actual power rescheduling of generators. The proposed work adopts DCNN which is optimized using an Improved Lion Algorithm (LA) and aids in providing significant outcomes for congestion management with reduced error. By implementing customized IEEE 57-bus, IEEE 30-bus, and IEEE 118-bus test systems, the suggested approach has been successfully verified for its performance on test systems of varied sizes. This analysis incorporates restrictions such as line loads, bus voltage influence, generator, line limits, etc. The most important results for the test system indicating convergence profile, congestion cost, and change in real-power and voltage magnitude are obtained by the simulation in MATLAB, and on the basis of the obtained simulation outcomes, it is evident that the proposed Improved Lion Algorithm optimized Deep Convolution Neural Network displays phenomenal computation performance in minimizing congestion losses at minimum congestion costs. When compared to several contemporary optimization techniques, the suggested technique performs better in terms of congestion cost and losses by generating improved prediction outputs with reduced errors.

Warm-Starting Nested Rollout Policy Adaptation with Optimal Stopping

Nested Rollout Policy Adaptation (NRPA) is an approach using online learning policies in a nested structure. It has achieved a great result in a variety of difficult combinatorial optimization problems. In this paper, we propose Meta-NRPA, which combines optimal stopping theory with NRPA for warm-starting and significantly improves the performance of NRPA. We also present several exploratory techniques for NRPA which enable it to perform better exploration. We establish this for three notoriously difficult problems ranging from telecommunication, transportation and coding theory namely Minimum Congestion Shortest Path Routing, Traveling Salesman Problem with Time Windows and Snake-in-the-Box. We also improve the lower bounds of the Snake-in-the-Box problem for multiple dimensions.

Improved Congestion Control in Wireless Sensor Networks Using Clustering with Metaheuristic Approach

The wireless sensor network (WSN) assists an extensive range of sensor nodes and enables several real-time uses. Congestion on the WSN is based on high pocket traffic and low wireless communication capabilities under network topology. Highly loaded nodes will consume power quickly and increase the risk of the network going offline or breaking. Additionally, loss of packet and buffer overflows would result in an outcome of increased end-to-end delay, performance deterioration of heavily loaded nodes, and transport communication loss. In this paper, a novel congestion control system is proposed to diminish the congestion on network and to enhance the throughput of the network. Initially, cluster head (CH) selection is achieved by exhausting K-means clustering algorithm. After the selection of cluster head, an efficient approach for congestion management is designed to select adaptive path by using Adaptive packet rate reduction (APTR) algorithm. Finally, Ant colony optimization (ACO) is utilized for enhancement of wireless sensor network throughput. The objective function increases the wireless sensor network throughput by decreasing the congestion on network. The proposed system is simulated with (Network Simulator NS-2). The proposed K-means C-ACO-ICC-WSN attains higher throughput 99.56%, 95.62% and 93.33%, lower delay 4.16%, 2.12% and 3.11% and minimum congestion level 1.19%, 2.33% and 5.16% and the proposed method is likened with the existing systems as Fuzzy-enabled congestion control through cross layer protocol exploiting OABC on WSN (FC-OABC-CC-WSN), Optimized fuzzy clustering at wireless sensor networks with improved squirrel search algorithm (FLC-ISSA-CC-WSN) and novel energy-aware clustering process through lion pride optimizer (LPO) and fuzzy logic on wireless sensor networks (EAC-LPO-CC-WSN), respectively. Finally, the simulation consequences demonstrate that proposed system may be capable of minimizing that congestion level and improving the throughput of the network.

Heuristic DRP‐DG optimization strategy adopted for maximizing total social welfare in the real time Indian utility network

The energy sector is subjected to an increase in power demand year after year. This increase in energy demand will continue due to the need for energy for modern societies. The service providers should face this situation smartly using renewable energy sources to avoid the intermittent nature of these sources and the fluctuations in the demand. Demand response programs (DRP) can play a vital role in increasing the correlation between the loads and the available generation from renewable energy sources (RES). Modern power systems recommend the use of distributed generation (DG) sources to reduce the transmission losses, contingency, and cost of energy and to increase the system reliability, efficiency, and power system loadability. Optimal DR use in the DG can further improve its performance which is the main focus of this paper. Here, a novel DRP-DG strategy is introduced to size and allocate the DG units for minimum congestion in IEEE 33-bus benchmark power system and real Indian power system having 17 buses. The active involvement of the consumers in the DRP facilitates the ultimate objective of maximized total social welfare (TSW) with minimized generation costs. To justify the above multi-objective optimization, two stages of heuristic optimization are introduced in which the rules are fired in the primary stage for determining the optimal size of DG. Implementation of DRP for achieving comprehensive objective function is vindicated in the secondary stage. A real-time Indian utility network is considered and a trade-off output is realized using heuristic fuzzy logic (FL) and genetic algorithm (GA). The results obtained from using the proposed strategy proved its superiority in achieving substantial cost reduction and maximum TSW compared to other soft computing techniques.

Congestion-Aware Rectilinear Steiner Tree Construction Using PB-SAT

In this paper, Pseudo-Boolean Satisfiability (PB-SAT)-based congestion reduction and overflow minimization techniques are introduced for constructing congestion-aware rectilinear Steiner trees for global routing in IC design. Since congestion in different routing channels is a big issue in routing nets, various congestion minimization approaches have been proposed by many researchers. However, the increasing complexity of integrated circuits and the reduction of their size make the congestion problem more complicated. The proposed method can produce a congestion-free routing solution by generating a suitable rectilinear Steiner tree for circuit nets. To reduce the complexity of the problem, the nets are solved individually using a Minimum Bounding Box (MBB)-based heuristic technique. The algorithm is validated by utilizing the well-known ISPD 98 global routing benchmarks. Experimental results prove that the methodology is capable of routing most of the nets within the given routing capacity satisfying all the routing parameters. The results also show that the algorithm can produce congestion-aware Steiner trees efficiently within a nominal time-bound.

Hybrid Deep Neural Network-Based Generation Rescheduling for Congestion Mitigation in Spot Power Market

In the open-access power market environment, the continuously varying loading and accommodation of various bilateral and multilateral transactions, sometimes leads to congestion, which is not desirable. In a day ahead or spot power market, generation rescheduling (GR) is one of the most prominent techniques to be adopted by the system operator (SO) to release congestion. In this paper, a novel hybrid Deep Neural Network (NN) is developed for projecting rescheduled generation dispatches at all the generators. The proposed hybrid Deep Neural Network is a cascaded combination of modified back-propagation (BP) algorithm based ANN as screening module and Deep NN as GR module. The screening module segregates the congested and non-congested loading scenarios resulting due to bilateral/multilateral transactions, efficiently and accurately. However, the GR module projects the re-scheduled active power dispatches at all the generating units at minimum congestion cost for all unseen congested loading scenarios instantly. The present approach provides a ready/instantaneous solution to manage congestion in a spot power market. During the training, the Root Mean Square Error (RMSE) is evaluated and minimized. The effectiveness of the proposed method has been demonstrated on the IEEE 30-bus system. The maximum error incurred during the testing phase is found 1.191% which is within the acceptable accuracy limits.

Traffic Congestion Minimization Study for Hingna Area of Nagpur City, MS. India

Traffic Congestion Minimization Study for Hingna Area of Nagpur City, MS. India

Congestion Control and Optimal Size of a Photovoltaic Device Using Multiverse Optimization Technique

Congestion management plays an important role in the operation, control, and safety of the grid. This paper proposes the multiverse optimization (MVO) algorithm for the congestion management of the IEEE 30 bus system, aiming to identify line congestion, and eliminate it at the minimum congestion price (i.e., the minimum loss). The continuation power flow (CPF) mechanism is adopted to analyze the voltage stability and maximum load capacity of the grid. The MVO algorithm helps to boost the voltage with a photovoltaic (PV) device, whenever the grid became unstable. The optimal position of the device is found through six iterations, and the fitness function is found capable of maximizing loading parameters, while minimizing power loss. The new approach is evaluated under different operating conditions, namely, in the presence of an MVO-tuned PV grid, and in the absence of a PV grid. The results show that the MVO-tuned PV grid performed much better than the grid without a PV.

Effective Power Congestion Management Technique Using Hybrid Nelder–Mead – Grey Wolf Optimizer (HNMGWO) in Deregulated Power System

The aim of this article is to apply Hybrid Nelder–Mead – Grey Wolf Optimizer (HNMGWO) in congestion management problem with optimal congestion cost by rescheduling generators in the power system. When the transmission power lines are overloaded in the power system, the following strategies are followed: generator rescheduling and installation of FACTS devices on the lines and load curtailment. But, load curtailment is not performed as the deregulated system encourages customer satisfaction. Thus, generator rescheduling is selected for problem solution as it does not involve the creation of any new infrastructure. Grey Wolf Optimizer (GWO) is one of the recent optimization techniques which work based on the leadership hierarchy and the hunting technique of grey wolves. The effective local search is performed by the Nelder–Mead (NM) method and the output is used to initialize the population for GWO, which searches global best value. In this paper, the HNMGWO is attempted based on the positive outcome of both NM and GWO. This proposed algorithm has been applied and results have been discussed related to the tasks of Independent System Operator. The proposed HNMGWO is applied in a standard IEEE 30 bus and IEEE 118 bus systems and thereby results are compared with standard GWO, Particle Swarm Optimization (PSO), fuzzy adaptive PSO, genetic algorithm, and bacterial foraging algorithms. The result shows that the proposed work is more effective by consuming minimum congestion cost in reduced rescheduled power and power loss.

Megaregional Passenger Transportation Hub Location Problem Considering Congestion Effects

The need to make effective plans for locating transportation hubs is of increasing importance in the megaregional area, as recent research suggests that the growing intercity travel demand affects the efficiency of a megaregional transportation system. This paper investigates a hierarchical facility location problem in a megaregional passenger transportation network. The aim of the study is to determine the locations of hub facilities at different hierarchical levels and distribute the demands to these facilities with minimum total cost, including investment, transportation, and congestion costs. The problem is formulated as a mixed-integer nonlinear programming model considering the service availability structure and hub congestion effects. A case study is designed to demonstrate the effectiveness of the proposed model in the Wuhan metropolitan area. The results show that the congestion effects can be addressed by reallocating the demand to balance the hub utilisation or constructing new hubs to increase the network capacity. The methods of appropriately locating hubs and distributing traffic flows are proposed to optimise the megaregional passenger transportation networks, which has important implications for decision makers.

Application of Assignment Problem and Traffic Intensity in Minimization of Traffic Congestion

In our daily life, we are facing several problems; Traffic congestion is one of them, which is becoming serious day by day. High number of vehicles on road, insufficient infrastructure, unreasonable developments are some reasons for increasing traffic congestion. The aim of this paper is to introduce a mathematical model which can be applied to traffic control system having fixed time signal with preset time to minimize traffic congestion at an intersection. In this paper, we use the concept of traffic intensity and Assignment model for traffic congestion minimization.

QED driven QAOA for network-flow optimization

We present a general framework for modifying quantum approximate optimization algorithms (QAOA) to solve constrained network flow problems. By exploiting an analogy between flow-constraints and Gauss' law for electromagnetism, we design lattice quantum electrodynamics (QED)- inspired mixing Hamiltonians that preserve flow constraints throughout the QAOA process. This results in an exponential reduction in the size of the configuration space that needs to be explored, which we show through numerical simulations, yields higher quality approximate solutions compared to the original QAOA routine. We outline a specific implementation for edge-disjoint path (EDP) problems related to traffic congestion minimization, numerically analyze the effect of initial state choice, and explore trade-offs between circuit complexity and qubit resources via a particle-vortex duality mapping. Comparing the effect of initial states reveals that starting with an ergodic (unbiased) superposition of solutions yields better performance than beginning with the mixer ground-state, suggesting a departure from the ``short-cut to adiabaticity" mechanism often used to motivate QAOA.

Joint routing, link capacity dimensioning, and switch port optimization for dynamic traffic in optical networks

This paper considers a challenging problem: to simultaneously optimize the cost and the quality of service in opaque wavelength division multiplexing (WDM) networks. An optimization problem is proposed that takes the information including network topology, traffic between end nodes, and the target level of congestion at each link/node in WDM networks. The outputs of this problem include routing, link channel capacities, and the optimum number of switch ports locally added/dropped at all switch nodes. The total network cost is reduced to maintain a minimum congestion level on all links, which provides an efficient trade-off solution for the network design problem. The optimal information is utilized for dynamic traffic in WDM networks, which is shown to achieve the desired performance with the guaranteed quality of service in different networks. It was found that for an average link blocking probability equal to 0.015, the proposed model achieves a net channel gain in terms of wavelength channels ( ) equal to 35.72 , 39.09 , and 36.93 compared to shortest path first routing and equal to 29.41 , 37.35 , and 27.47 compared to alternate routing in three different networks.

A Survey on Scheduling Based Mac Protocol for Congestion Minimization in Internet of Things (IoT)

Recently, in Internet era the most common technology ubiquitous to develop smart environment is Internet of things (IoT) and Wireless Sensor Networks (WSNs).These technologies deployed enormously to formulate wide applications in area of Smart homes, Industrial automation, and security destined applications and information trailing. The huge development in wireless technology is due to great exploration in MEMS concept and Embedded Systems. Huge evolution in this technique leads to access different Medium Access Control (MAC) protocol and this protocol used to access multiple nodes peculiarly in wireless channel. The projected MAC protocol designed to enhance network lifetime. Essentially, the network leads to lot of congestion due to non-availability of IoT equipment and less available resources for various environmental applications. The simulated performance ensures that the conventional algorithm limits their dynamic service quality for IoT based applications. The above setbacks motivate the researches to develop the survey in existing scheduling based MAC protocol by highlighting their parameters.

On the approximability of robust network design

Given the dynamic nature of traffic, we investigate the variant of robust network design where we have to determine the capacity to reserve on each link so that each demand vector belonging to a polyhedral set can be routed. The objective is either to minimize congestion or a linear cost. Routing is assumed to be fractional and dynamic (i.e., dependent on the current traffic vector). We first prove that the robust network design problem with minimum congestion cannot be approximated within any constant factor. Then, using the ETH conjecture, we get a Ω(log⁡nlog⁡log⁡n) lower bound for the approximability of this problem. This implies that the well-known O(log⁡n) approximation ratio established by Räcke in 2008 is tight. Using Lagrange relaxation, we obtain a new proof of the O(log⁡n) approximation. An important consequence of the Lagrange-based reduction and our inapproximability results is that the robust network design problem with linear reservation cost cannot be approximated within any constant ratio. This answers a long-standing open question of Chekuri (2007). We also give another proof of the result of Goyal et al. (2009) stating that the optimal linear cost under static routing can be Ω(log⁡n) more expensive than the cost obtained under dynamic routing. Finally, we show that even if only two given paths are allowed for each commodity, the robust network design problem with minimum congestion or linear cost is hard to approximate within some constant.

Optimization of spanning tree stretch and congestion for graphs

In spanning tree optimization of graphs, there are two important criteria, namely, dilation (stretch) and congestion. This gives rise to two combinatorial optimization problems as follows. The minimum stretch spanning tree problem is to find a spanning tree of a graph such that the maximum distance of all embedded edges in the tree is minimized. The minimum congestion spanning tree problem is to find a spanning tree of a graph such that the maximum number of overlapped edges for all embedded edges in the tree is minimized. These problems bring us two graph-theoretic parameters, tree stretch and tree congestion. In this paper we mainly study the structural properties of tree stretch and tree congestion, including the duality of fundamental cycle and fundamental cocycle, extremal property, lower and upper bounds, optimality characterization, and optimal value computation.

TraffSim - A Traffic Simulator for Investigations of Congestion Minimization through Dynamic Vehicle Rerouting

TraffSim - A Traffic Simulator for Investigations of Congestion Minimization through Dynamic Vehicle Rerouting