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The fast-changing world is evolving towards automation rapidly every day, and a good network system is inevitable anywhere to properly manage an automated system. Before implementing a network system, it is necessary to visualize and learn a protocol to understand in depth about the network system and identify the issues among the system. Packet Tracer is a cross-platform visual simulation tool designed by Cisco Systems that allows users to create network topologies and imitate modern computer networks. It helps network developers to design prototype models as well as provides a virtual network simulation platform that help to simulate projects and observe its efficiency in the real world. In this research, the authors have developed a prototype of network design of an airport using Cisco Packet Tracer. The process that depicts how network system will work in real life is explained and the simulation of data transmission is provided.

Wireless Mesh Networks (WMNs) have rapid real developments during the last decade due to their simple implementation at low cost, easy network maintenance, and reliable service coverage. Despite these properties, the nodes placement of such networks imposes an important research issue for network operators and influences strongly the WMNs performance. This challenging issue is known to be an NP-hard problem, and solving it using approximate optimization algorithms (i.e. heuristic and meta-heuristic) is essential. This motivates our attempts to present an application of the Coyote Optimization Algorithm (COA) to solve the mesh routers placement problem in WMNs in this work. Experiments are conducted on several scenarios under different settings, taking into account two important metrics such as network connectivity and user coverage. Simulation results demonstrate the effectiveness and merits of COA in finding optimal mesh routers locations when compared to other optimization algorithms such as Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Bat Algorithm (BA), African Vulture Optimization Algorithm (AVOA), Aquila Optimizer (AO), Bald Eagle Search optimization (BES), Coronavirus herd immunity optimizer (CHIO), and Salp Swarm Algorithm (SSA).

In network design problems,capacity constraints are modeled in three different ways depending on the application: directed, bidirected and undirected. In the literature, polyhedral investigations for strengthening mixed-integer formulations are done separately for each model. In this note, we examine the relationship between these models to provide a unifying approach and show that one can indeed translate valid inequalities from one to the others.

Productions of the industries around the world depend on using equipment and machines. Therefore, it is vital to support the supply of equipment and spare parts for maintenance operations, especially in strategic industries that separate optimization of inventory management, supplier selection, network design, and planning decisions may lead to sub-optimal solutions. The integration of forward and reverse spare part logistics network can help optimize total costs. In this paper, a mathematical model is presented for designing and planning an integrated forward-reverse repairable spare parts supply chain to make optimal decisions. The model considers the uncertainty in demand during the lead-time and the optimal assignment of repairable equipment to inspection, disassembly, and repair centers. A METRIC (Multi-Echelon Technique for recoverable Item Control) model is integrated into the forward-reverse supply chain to handle inventory management. A case study of National Iranian Oil Company (NIOC) is presented to validate the model. The non-linear constraints are linearized by using a linearization technique; then the model is solved by an iterative procedure in GAMS. A prominent outcome of the analyses shows that the same policies for repair and purchase of all the equipment and spare parts do not result in optimal solutions. Also, considering supply, repair, and inventory management decisions of spare parts simultaneously helps decision-makers enhance the supply chain's performance by applying a well-balanced repairing and purchasing policy.

The public transport system has an essential role in the sustainability of the urban environment. The accessibility is one of the critical functions for the increase of public transport efficiency. In this framework, the paper presents a methodology to enhance the accessibility of the public transport network. The methodology aims to design a network of dedicated bus lanes to operate in areas with low accessibility and improve the connections to metro and tram lines that provide rapid and high-capacity services.

Geometric features such as cylinders and planes are important objects of interest in terrestrial laser scanner surveys of complex scenes. The quality of the objects modelled from the laser scanner data is a function of many variables and geometric network design plays a key role in maximizing precision. The expected precision can be predicted at the planning stage from simulations of the environment to be scanned. However, this practice can incur a high computational load, even if performed in 2D rather than in 3D. In this paper, a closed-form solution to estimate geometric object precision is proposed as an efficient first order network design tool. It models the laser scanner measurement process with an observation distribution function that is introduced into the least-squares normal equations. Parameter precision is evaluated directly by solving a few (three to six) integrals and inverting the normal equations matrix. The method is presented for two cases of a circle lying in the horizontal plane and a 2D line scanned from a single location. Both a simplified circle model and a more general circle model are explored. The method is then extended using the summation of normals method to allow precision estimation from the combination of multiple scans from different locations. Results from many real datasets, 95 circles and 30 lines, show that the distributions of the range observations and derived Cartesian coordinates follow model predictions. Moreover, results demonstrate that the method can predict circle parameter standard deviations within 4%–6% of the experimental values. The agreement is at the 10% level for a very specific case due to inherent high parameter correlation. The agreement of line parameter standard deviations is much greater, approximately 0.1%. The results show the method can be a valuable tool to predict feature quality with minimal computational requirements. The method is beneficial to not only laser scanner network design but could also be to instantaneous 2D map construction performed for SLAM-based surveys.

Medical waste management (MWM) is an important and necessary problem in the COVID-19 situation for treatment staff. When the number of infectious patients grows up, the amount of MWMs increases day by day. We present medical waste chain network design (MWCND) that contains health center (HC), waste segregation (WS), waste purchase contractor (WPC), and landfill. We propose to locate WS to decrease waste and recover them and send them to the WPC. Recovering medical waste like metal and plastic can help the environment and return to the production cycle. Therefore, we proposed a novel viable MWCND by a novel two-stage robust stochastic programming that considers resiliency (flexibility and network complexity) and sustainable (energy and environment) requirements. Therefore, we try to consider risks by conditional value at risk (CVaR) and improve robustness and agility to demand fluctuation and network. We utilize and solve it by GAMS CPLEX solver. The results show that by increasing the conservative coefficient, the confidence level of CVaR and waste recovery coefficient increases cost function and population risk. Moreover, increasing demand and scale of the problem makes to increase the cost function. [ABSTRACT FROM AUTHOR]

In this paper, we present an analysis of important aspects that arise during the development of neural network applications. Our aim is to determine if the choice of library can impact the system's overall performance, either during training or design, and to extract a set of criteria that could be used to highlight the advantages and disadvantages of each library under consideration. To do so, we first extracted the previously mentioned aspects by comparing two of the most popular neural network libraries—PyTorch and TensorFlow—and then we performed an analysis on the obtained results, with the intent of determining if our initial hypothesis was correct. In the end, the results of the analysis are gathered, and an overall picture of what tasks are better suited for what library is presented. [ABSTRACT FROM AUTHOR]

Using a bicycle for commuting is still uncommon in US cities, although it brings many benefits to both the cyclists and to society as a whole. Cycling has the potential to reduce traffic congestion and emissions, increase mobility, and improve public health. To convince people to commute by bike, the infrastructure plays an important role because safety is one of the primary concerns of potential cyclists. This paper presents a method to find the best way to improve the safety of a bicycle network for a given budget and maximize the number of riders that could now choose bicycles for their commuting needs. This optimization problem is formalized as the bicycle network improvement problem (BNIP): it selects which roads to improve for a set of traveler origin–destination pairs, taking both safety and travel distance into account. The BNIP is modeled as a mixed-integer linear program (MIP) that minimizes a piecewise linear penalty function of route deviations of travelers. The MIP is solved using Benders decomposition to scale to large instances. The paper also presents an in-depth case study for the Midtown area in Atlanta, GA, using actual transportation data. The results show that Benders decomposition algorithm allows for solving realistic problem instances and that the network improvements may significantly increase the share of bicycles as the commuting mode. Multiple practical aspects are considered as well, including sequential road improvements, uneven improvement costs, and how to include additional data. [ABSTRACT FROM AUTHOR]

We study an integrated airline schedule design and fleet assignment model for constructing schedules by simultaneously selecting from a pool of optional flights and assigning fleet types to these scheduled flights. This is a crucial tactical decision that greatly influences airline profits. As passenger demand is often substitutable among available fare products (defined as a combination of an itinerary and a fare class) between the same origin–destination pair, we present an optimization approach that includes a passenger choice model for fare product selections. To tackle the formidable computational challenge of solving this large-scale network design problem, we propose a decomposition approach based on partitioning the flight network into smaller subnetworks by exploiting weak dependencies in network structure. The decomposition relies on a series of approximation analyses and a novel fare split problem to allocate optimally the fares of products that are shared by flights in different subnetworks. We present several reformulations that represent fleet assignment and schedule decisions and formally characterize their relative strengths. This gives rise to a new reformulation that is able to trade off strength and size flexibly. We conduct detailed computational experiments using two realistically sized airline instances to demonstrate the effectiveness of our approach. Under a simulated passenger booking environment with both perfect and imperfect forecasts, we show that the fleeting and scheduling decisions informed by our approach deliver significant and robust profit improvement over all benchmark implementations and previous models in the literature. [ABSTRACT FROM AUTHOR]

Single image crowd counting is a challenging computer vision problem with wide applications in public safety, city planning, traffic management, etc. With the recent development of deep learning techniques, crowd counting has aroused much attention and achieved great success in recent years. This survey is to provide a comprehensive summary of recent advances on deep learning-based crowd counting techniques via density map estimation by systematically reviewing and summarizing more than 200 works in the area since 2015. Our goals are to provide an up-to-date review of recent approaches, and educate new researchers in this field the design principles and trade-offs. After presenting publicly available datasets and evaluation metrics, we review the recent advances with detailed comparisons on three major design modules for crowd counting: deep neural network designs, loss functions, and supervisory signals. We study and compare the approaches using the public datasets and evaluation metrics. We conclude the survey with some future directions. [ABSTRACT FROM AUTHOR]

We consider a new problem of designing a network with small s-t effective resistance. In this problem, we are given an undirected graph G = (V, E), two designated vertices s, t ∈ V, and a budget k. The goal is to choose a subgraph of G with at most k edges to minimize the s-t effective resistance. This problem is an interpolation between the shortest path problem and the minimum cost flow problem and has applications in electrical network design. We present several algorithmic and hardness results for this problem and its variants. On the hardness side, we show that the problem is NP-hard, and the weighted version is hard to approximate within a factor smaller than two assuming the small-set expansion conjecture. On the algorithmic side, we analyze a convex programming relaxation of the problem and design a constant factor approximation algorithm. The key of the rounding algorithm is a randomized path-rounding procedure based on the optimality conditions and a flow decomposition of the fractional solution. We also use dynamic programming to obtain a fully polynomial time approximation scheme when the input graph is a series-parallel graph, with better approximation ratio than the integrality gap of the convex program for these graphs. [ABSTRACT FROM AUTHOR]

In the literature, hub-networks have often been modeled such as only one mode is considered for all transportation. Also, the consolidated demand flows are assumed to be transferred directly between each origin-destination hub pairs. The previous assumptions impose restrictions on the practical applications of such hub-networks. In fact, various transport modes are usually retained for freight transport, and intermodal terminals (e.g., rail terminals) may not realistically be fully connected. Thus, to assist decision makers, we investigate if the appropriate use of more eco-friendly transportation modes in incomplete networks may contribute to the accomplishment of the significant global reduction goals in carbon emissions. In this paper, we study the intermodal green p-hub median problem with incomplete hub-network. For each p located hub nodes, the hub-network is connected by at most q hub-links. The objective is to minimize the total transportation-based CO 2 emission costs incurred through the road- and rail-transportation of each o-d demand flows. We present a MILP formulation for the studied problem and propose a novel genetic algorithm to solve it. A penalty cost is considered on solutions where train capacity is exceeded. Additionally, we present a best-path construction heuristic to generate the initial population. Furthermore, we develop a demand flows routing heuristic to efficiently determine the partition of demand flows in the incomplete road-rail network. And we implement novel crossover and mutation operators to produce new off-springs. Extensive computational experiments show that the proposed solution approach outperforms the exact solver CPLEX. Also, we perform a comparison between the unimodal and intermodal cases, and offer a discussion on the tuning of freight trains. [ABSTRACT FROM AUTHOR]

A continuous increase in the world population and reduction in quantity and quality of resources is putting enormous pressure on nations to ensure a balance between food demand and supply. The same is highlighted at number two in the United Nations' sustainable development goals as the Zero Hunger goal. However, a significant portion of produced food worldwide is damaged or wasted in the food supply chain. This study focuses on minimizing waste in the food supply chain by highlighting the importance of strategy, information systems, and network design in the food supply chain. The authors collected data from 211 respondents from 48 firms and analyzed it through structural equation modeling. It has been found that information system has great potential to minimize waste in the food supply chain. Moreover, strategy and network design significantly improve organizational capabilities to minimize waste in the food supply chain. The finding provides constructive insights to food companies to incorporate and operationalize recommended measures to minimize waste in the food supply chain so that the world can counter the food shortage and waste/damage problem and the Zero Hunger goal can successfully be achieved. [ABSTRACT FROM AUTHOR]

Network design, a cornerstone of mathematical optimization, is about defining the main characteristics of a network satisfying requirements on connectivity, capacity, and level-of-service. It finds applications in logistics and transportation, telecommunications, data sharing, energy distribution, and distributed computing. In multicommodity network design, one is required to design a network minimizing the installation cost of its arcs and the operational cost to serve a set of point-to-point connections. The definition of this prototypical problem was recently enriched by additional constraints imposing that each origin-destination of a connection is served by a single path satisfying one or more level-of-service requirements, thus defining the Network Design with Service Requirements. These constraints are crucial, for example, in telecommunications and computer networks to ensure reliable and low-latency communication. In this paper we provide a new formulation for the problem, where variables are associated with paths satisfying the end-to-end service requirements. We present a fast algorithm for enumerating all the exponentially many feasible paths, and when this is not viable, we provide a column generation scheme that is embedded into a branch-and-cut-and-price algorithm. Extensive computational experiments on a large set of instances show that our approach can move a step further in the solution of the network design with service requirements compared with the current state-of-the-art. [ABSTRACT FROM AUTHOR]

In this paper, we bound the integrality gap and the approximation ratio for maximum plane multiflow problems and deduce bounds on the flow-multicut-gap. We consider instances where the union of the supply and demand graphs is planar and prove that there exists a multiflow of value at least half the capacity of a minimum multicut. We then show how to convert any multiflow into a half-integer flow of value at least half the original multiflow. Finally, we round any half-integer multiflow into an integer multiflow, losing at most half the value thus providing a 1/4-approximation algorithm and integrality gap for maximum integer multiflows in the plane. [ABSTRACT FROM AUTHOR]

We present a framework to address a class of sequential decision-making problems. Our framework features learning the optimal control policy with robustness to noisy data, determining the unknown state and action parameters, and performing sensitivity analysis with respect to problem parameters. We consider two broad categories of sequential decision-making problems modeled as infinite horizon Markov decision processes (MDPs) with (and without) an absorbing state. The central idea underlying our framework is to quantify exploration in terms of the Shannon entropy of the trajectories under the MDP and determine the stochastic policy that maximizes it while guaranteeing a low value of the expected cost along a trajectory. This resulting policy enhances the quality of exploration early on in the learning process, and consequently allows faster convergence rates and robust solutions even in the presence of noisy data as demonstrated in our comparisons to popular algorithms, such as $Q$ -learning, Double $Q$ -learning, and entropy regularized Soft $Q$ -learning. The framework extends to the class of parameterized MDP and RL problems, where states and actions are parameter dependent, and the objective is to determine the optimal parameters along with the corresponding optimal policy. Here, the associated cost function can possibly be nonconvex with multiple poor local minima. Simulation results applied to a 5G small cell network problem demonstrate the successful determination of communication routes and the small cell locations. We also obtain sensitivity measures to problem parameters and robustness to noisy environment data. [ABSTRACT FROM AUTHOR]

We study the relation between the linear programming relaxation of two classes of models for the Steiner tree problem with hop constraints. One class is characterized by having hop‐indexed arc variables. Although such models have proved to have a very strong linear programming bound, they are not easy to use because of the huge number of variables. This has motivated some studies with models involving fewer variables that use, instead of the hop‐indexed arc variables, hop‐indexed node variables. In this article, we contextualize the linear programming relaxation of these node‐based models in terms of the linear programming relaxation of known arc‐based models. We show that the linear programming relaxation of a general node‐based model is implied by the linear programming relaxation of a straightforward arc‐based model. [ABSTRACT FROM AUTHOR]

This study proposes a bi-level optimization program to represent the electric vehicle (EV) charging infrastructure design and utilization management problem with user-equilibrium (UE) decisions. The upper level aims to minimize total facility deployment costs and maximize the revenue generated from EV charging collections, while the lower level aims to minimize the EV users’ travel times and charging expenses. An iterative technique is implemented to solve the bi-level mixed-integer non-linear program that generates theoretical lower and upper bounds to the bi-level model and solves it to global optimality. A set of conditions are evaluated to show the convergence of the algorithm in a finite number of iterations. The numerical results, based on three demand levels, indicate that the proposed bi-level model can effectively determine the optimal charging facility location, physical capacity, and demand-responsive pricing scheme. The average charging price in medium demand level is increased by 38.21% compared to the lower level demand due to the surge in charging needs and highly utilized charging stations. [ABSTRACT FROM AUTHOR]

Due to the relatively high investment costs of district heating networks, their optimal design is crucial for a high ROI. A software tool supporting decision makers and network planners to find the cost optimal network topology is introduced. Beside topology the tool also estimates diameters of the district heating pipes. The optimization is based on user defined cost data, geo-referenced input of supply and consumer nodes, all possible laying routes for the pipes and technical boundary conditions. Further constraints can be considered, e.g., the usage of an already existing network or possible obstacles, e.g., costly crossings of rivers or railroad tracks. The optimization usually leads to an unmeshed network. Meshes which are needed to guarantee uninterrupted service can only be enforced by the manually definition of existing pipes. To ensure an easy-to-use interface for geo-referenced data the tool is provided as a plugin for the geographic information system QGIS.The optimization process consists of a combination of classical non-linear methods, minimal spanning tree calculations and evolutionary algorithms. It allows solution of the mentioned problem for one specific user defined design case. In addition to the geo-referenced topology of the network, the diameter of each pipe and the total investment costs, the result of the computation provides further technical specifications, e.g., mean flow velocities and pressure losses, in the considered case. So far, different scenarios can only be calculated separately.With a short training period the tool is largely intuitive to use. It is currently used in university courses in order to understand the challenges involved in the planning and design process of district heating networks. It is also used in research projects and is continuing to be developed.

Oscillatory behaviors, which are ubiquitous in transcriptional regulatory networks, are often subject to inevitable biological noise. Thus, a natural question is how transcriptional regulatory networks can robustly achieve accurate oscillation in the presence of biological noise. Here, we search all two- and three-node transcriptional regulatory network topologies for those robustly capable of accurate oscillation against the parameter variability (extrinsic noise) or stochasticity of chemical reactions (intrinsic noise). We find that, no matter what source of the noise is applied, the topologies containing the repressilator with positive autoregulation show higher robustness of accurate oscillation than those containing the activator-inhibitor oscillator, and additional positive autoregulation enhances the robustness against noise. Nevertheless, the attenuation of different sources of noise is governed by distinct mechanisms: the parameter variability is buffered by the long period, while the stochasticity of chemical reactions is filtered by the high amplitude. Furthermore, we analyze the noise of a synthetic human nuclear factor κB (NF-κB) signaling network by varying three different topologies and verify that the addition of a repressilator to the activator-inhibitor oscillator, which leads to the emergence of high-robustness motif—the repressilator with positive autoregulation—improves the oscillation accuracy in comparison to the topology with only an activator-inhibitor oscillator. These design principles may be applicable to other oscillatory circuits.

A decentralized power distribution network consisting of smart microgrids introduces opportunities to trade with energy called transactive energy. However, research studies in the existing literature suggest that several standardized information models for TE do not meet the network architecture’s reliability, flexibility, and security requirements. This limitation is mainly due to the static nature of traditional IP infrastructure. To achieve these requirements in the network architecture, this study investigates the optimized application of software-defined network architecture for transactive energy in smart microgrid systems. Through literature research, unique design approaches in an SDN architecture are identified that improve the reliability, flexibility, and security of the SDN architecture. These design approaches include a decentralized controller network layout, redundant link configuration, a mesh network topology, and data encryption. The proposed solution uniquely combines these design approaches into a single optimized SDN solution for TESMS. To validate the improvements of the findings from the literature research, each design approach is simulated in this study using Mininet SDN emulator and AnyLogic system simulation software. The proposed solution is then applied to a use-case scenario that shows the improvements required for TESMS. The use-case scenario shows significant improvement in the data path uptime. An improvement of 0.27% is achieved, which equates to a 2 h per month increase in the data path uptime. The results of the simulation show that the proposed SDN architecture improves the reliability and flexibility of a traditional SDN network. Furthermore, enabling encryption between the nodes improves the security of the SDN architecture.

Wireless Mesh Networks (WMNs) are increasingly being used in a variety of applications. To fully utilize the network resources of WMNs, it is critical to design a topology that provides the best client coverage and network connectivity. This issue is solved by determining the best solution for the mesh router placement problem in WMN (MRP-WMN). Because the MRP-WMN is known to be NP-hard, it is typically solved using approximation algorithms. This is also why we are conducting this work. We present an efficient method for solving the MRP-WMN using the Multi-Verse Optimizer algorithm (MVO). A new objective function for the MRP-WMN is also proposed, which takes into account two important performance metrics, connected client ratio and connected router ratio. Experiment results show that when the MVO algorithm is applied to the MRP-WMN problem, the connected client ratio increases by 15.1%, 11.5%, and 5.9% on average, and the path loss reduces by 1.3, 0.9, and 0.6 dB when compared to the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA), respectively. [ABSTRACT FROM AUTHOR]

Traffic signal control is one effective way to alleviate traffic congestion. Anticipatory traffic signal control determines signal settings from a network planning perspective, which takes into account the influence of travelers' route choice response and triggers better equilibrium flow patterns for better network performance. For the route choice response, it is usually predicted by a response function known as traffic assignment model. However, the response behavior can never be precisely modeled, leading to a mismatch between the modeled and real traffic flow patterns. This model-reality mismatch generally contributes to suboptimal control performance and hence brings unexpected congestion in real-life traffic operations. This study aims to address the model-reality mismatch and proposes an effective anticipatory traffic control for real operations. A metamodel is introduced that serves as a surrogate of the unknown structural model bias. Then an iterative optimizing control scheme is applied to correct the model bias by learning from observations. By integrating the model-based control design with data-driven learning techniques, the metamodeling framework is able to enhance the control performance. Moreover, the analytical model bias formulation allows theoretical investigation of the model approximation error. To further improve the control performance, a joint traffic model parameter estimation is developed, hence achieving a better model calibration jointly with the model bias correction. The proposed control method is examined on a test network. Numerical examples confirm the effectiveness of the proposed method in improving control performance despite the model-reality mismatch. Comparison results show that the proposed method outperforms the traditional model-based control method and an improvement of 14.8% in total travel time is achieved in the example network. [ABSTRACT FROM AUTHOR]

One of the most critical issues for any production organization to compete in the current competitive business environment is supply chain network design (SCND). Besides, by increasing greenhouse gas emissions, various organizations have imposed many restrictions to prevent the overproduction of these gases in their production planning. This study develops a mathematical model for the location-allocation-inventory problem based on a real case study to design a three-echelon coffee supply chain network. The primary purpose of the problem is to minimize supply chain costs. Minimizing the CO2 emission was also considered to address the increasing eco-friendly challenges. The problem includes various strategic and tactical decisions, such as the number and capacity of manufacturing centers (MCs) and distribution centers (DCs) to be established, how to allocate factories to distribution centers and DCs to customers, material flow throughout the system, and the number of products stored in warehouses. It should be noted the problem's parameters are considered fuzzy to get closer to reality. Finally, by performing several sensitivity analyses, valuable managerial insights were obtained. Applying the developed model to the case study of a coffee company assisted managers in making optimal strategic decisions to establish new MCs and DCs, and reduce CO2 emissions. [ABSTRACT FROM AUTHOR]

Emerging communication technologies allow to reconfigure the physical network topology at runtime, enabling demand-aware networks (DANs): networks whose topology is optimized toward the workload they serve. However, today, only little is known about the fundamental algorithmic problems underlying the design of such demand-aware networks. This paper presents the first bounded-degree, demand-aware network, $\textit {cl-DAN} $ , which minimizes both congestion and route lengths. The degree bound $\Delta $ is given as part of the input. The designed network is provably (asymptotically) optimal in each dimension individually: we show that there do not exist any bounded-degree networks providing shorter routes (independently of the load), nor do there exist networks providing lower loads (independently of the route lengths). The main building block of the designed $\textit {cl-DAN} $ networks are $\textit {ego-trees}$ : communication sources arrange their communication partners in an optimal tree, individually. While the union of these ego-trees forms the basic structure of $\textit {cl-DANs}$ , further techniques are presented to ensure bounded degrees (for scalability). [ABSTRACT FROM AUTHOR]