Dynamic Routing

Innovations in mobile technology allow the use of Internet and smartphones for communicating disasters and coordinating evacuations. However, given the turbulent nature of disaster situations, the people and systems at crisis center are subjected to information overload, which can obstruct timely and accurate information sharing. A dynamic and automated evacuation plan that is able to predict future disaster outcome can be used to coordinate the affected people to safety in times of crisis. In this paper, we present a dynamic version of the shortest path algorithm of Dijkstra. The algorithm is able to compute the shortest path from the user's location (sent by the smartphone) to the safety area by taking into account possible affected areas in future. We aim at employing the computed routes on our mobile communication system for navigating affected people during emergency and disaster evacuations. Two simulation studies have validated the performance of the developed algorithm.

Innovations in mobile technology allow the use of Internet and smartphones for communicating disasters and coordinating evacuations. However, given the turbulent nature of disaster situations, the people and systems at crisis center are subjected to information overload, which can obstruct timely and accurate information sharing. A dynamic and automated evacuation plan that is able to predict future disaster outcome can be used to coordinate the affected people to safety in times of crisis. In this paper, we present a dynamic version of the shortest path algorithm of Dijkstra. The algorithm is able to compute the shortest path from the user's location (sent by the smartphone) to the safety area by taking into account possible affected areas in future. We aim at employing the computed routes on our mobile communication system for navigating affected people during emergency and disaster evacuations. Two simulation studies have validated the performance of the developed algorithm.

Abstract—At Delft University of Technology there is a project running on the development of a traffic information-system. The paper presents a using of XML for multimodal communication with an intelligent travel assistant. The personal Intelligent Travel Assistant (PITA) provides the user with a personalized, up-to-date advice on a handheld device, which takes into account actual delays and user preferences. The travelers themselves provide information about delays of trains, using GPS to estimate whether a user is experiencing a ...

The proliferation of small mobile devices and wireless networks has resulted in an increasing demand to support the applications found in wired environments on mobile devices. In real time replication systems, such as collaborative systems, this trend gives some new problems to address. The properties of wireless networks are low bandwidth and high latency, which change dynamically over time. The risk of the network getting congested is therefore high with the result that the user will not receive the important information in ...

In this paper we describe a disaster management system. It is assumed that each individual in the field is equipped with a PDA (Personal Digital Assistant) and that can communicate with other PDAs in the vicinity. Together the PDAs form an ad-hoc network. Users can enter their own observations to the PDA, like the position of victims, or a description of the current situation at particular location (e.g. smoke, emergency exits, traffic congestion). This information is entered in a special iconic language. Reversibly, the PDAs inform the users on the overall current situation of the crisis. In order to come to a shared view of the world, the knowledge that is present in the network has to be shared and fused. The proposed way to communicate is via a shared blackboard. This approach facilitates communication in a time and place independent way.

Network connectivity policies are crucial for assuring the security and availability of large-scale datacenter. Managing these policies is fraught with complexity and operator errors. The difficulties are exacerbated when deploying large scale offerings of public cloud services where multiple tenants are hosted within customized isolation boundaries. In these large-scale settings it is impractical to depend on human effort or trial and error to maintain the correctness and consistency of policies. We describe an approach for automatically validating network connectivity policies and its implementation in a tool called SecGuru. SecGuru can check selected properties of policies, e.g., is some traffic permitted or denied, and it can compare two policies yielding a semantic diff to summarize drifts. We use bit-vector logic to encode policies and semantic diffs; and the theorem prover Z3 as the underlying solver. A key contribution is a new algorithm for compactly enumerating symbolic diffs. We finally describe the experience of using SecGuru in Azure, a public cloud provider. Azure uses SecGuru for continuously monitoring policy configurations and alerting on errors, and also as a regression test suite to check policies before deployment. As a result of using SecGuru, today Azure proactively detects and avoids policy misconfigurations that lead to security and availability issues. 1 remark Isolating private addresses 2 deny ip 0.0.0.0/32 any 3 deny ip 10.0.0.0/8 any 4 deny ip 172.16.0.0/12 any 5 deny ip 192.0.2.0/24 any 6 ... 7 remark Anti spoofing ACLs 8 deny ip 128.30.0.0/15 any 9 deny ip 171.64.0.0/15 any 10 ...

Network connectivity policies are crucial for assuring the security and availability of large-scale datacenter. Managing these policies is fraught with complexity and operator errors. The difficulties are exacerbated when deploying large scale offerings of public cloud services where multiple tenants are hosted within customized isolation boundaries. In these large-scale settings it is impractical to depend on human effort or trial and error to maintain the correctness and consistency of policies. We describe an approach for automatically validating network connectivity policies and its implementation in a tool called SecGuru. SecGuru can check selected properties of policies, e.g., is some traffic permitted or denied, and it can compare two policies yielding a semantic diff to summarize drifts. We use bit-vector logic to encode policies and semantic diffs; and the theorem prover Z3 as the underlying solver. A key contribution is a new algorithm for compactly enumerating symbolic diffs. We finally describe the experience of using SecGuru in Azure, a public cloud provider. Azure uses SecGuru for continuously monitoring policy configurations and alerting on errors, and also as a regression test suite to check policies before deployment. As a result of using SecGuru, today Azure proactively detects and avoids policy misconfigurations that lead to security and availability issues. 1 remark Isolating private addresses 2 deny ip 0.0.0.0/32 any 3 deny ip 10.0.0.0/8 any 4 deny ip 172.16.0.0/12 any 5 deny ip 192.0.2.0/24 any 6 ... 7 remark Anti spoofing ACLs 8 deny ip 128.30.0.0/15 any 9 deny ip 171.64.0.0/15 any 10 ...

This paper is intended to present optimal route finding system for road network application. It is used to operate in complicated mapping situations and new unknown environments. This technique can be implemented to find the optimal path from source to destination in region of map. This system use A* and Dijkstra algorithms to draw plan and develop the Geographic Information System(GIS) assisted optimal road network route finding and give shortest path. The optimal (or) shortest path based on distance or time or cost. And then evaluates the performance of these two algorithms.

The mass composition and all-particle energy spectrum of cosmic rays are determined by analysis of coincident events between the IceTop air shower array on the surface and the deep IceCube strings. IceTop mainly detects the electromagnetic component of high energy cosmic ray air showers, while the TeV muon bundle penetrates deep into the Antarctic ice and generates Cherenkov light, which is seen by IceCube. This analysis uses data taken from June 2010 to May 2011 when the detector was nearly complete with IceCube in its 79 string conguration and IceTop running 73 stations. Variables sensitive to composition and primary energy are based on the lateral signal distribution reconstructed at the surface by IceTop and the energy loss of the muon bundles reconstructed by IceCube. Using a neural network we determine the average mass A and all-particle flux in the energy range from a few PeV up to 1 EeV. We find that < log A > increases up to at least 100 PeV.

The combined information from cosmic ray air showers that trigger both the surface and underground parts of the IceCube Neutrino Observatory allows the reconstruction of both the energy and mass of the primary particle through the knee region of the energy spectrum and above. The properties of high-energy muon bundles, created early in the formation of extensive air showers and capable of penetrating deep into the ice, are related to the primary energy and composition. New methods for reconstructing the direction and composition-sensitive properties of muon bundles are shown. Based on a likelihood minimization procedure using IceCube signals, and accounting for photon propagation, ice properties, and the energy loss processes of muons in ice, the muon bundle energy loss is reconstructed. The results of the highenergy muon bundle reconstruction in the deep ice and the reconstruction of the lateral distribution of low energy particles in the surface detector can be combined to study primary composition and energy. The performance and composition sensitivity for both simulated and experimental data are discussed.

muon bundle, simple and more advanced reconstructions are used. The existing reconstructions I improved and new reconstructions I developed are explained in Chapter 4. High quality reconstruction are obtained by performing advanced hit cleaning algorithms on the detected signals before reconstructions and by removing low quality events. These methods are described in Chapter 5 and result in a high quality selection of data with reconstructed variables that are sensitive to primary mass and primary energy. The extraction of the primary mass and primary energy is done by a combination of a neural network approach, followed by a template fitting to extract the average mass for each reconstructed energy. The technical details of these analysis tools are explained in Chapter 6. Before the final energy spectrum and composition measurement is discussed in Chapter 8, I will first give an overview about the systematic uncertainties in this analysis in Chapter 7.

The current market's demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an alternative way to design this kind of system based on decentralized control using distributed, autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually do not consider true adaptation and re-configuration. Understanding how, in nature, complex things are performed in a simple and effective way allows us to mimic nature's insights and develop powerful adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufacturing systems. The paper provides an overview of some of the principles found in nature and biology and analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to solve complex engineering problems, especially in the manufacturing field. An industrial automation case study is used to illustrate a bio-inspired method based on potential fields to dynamically route pallets.

Mobile ad hoc network (MANET) is an emerging trend and it has to face many challenges and issues. An ad hoc network is a collection of mobile nodes that dynamically form a temporary network, without the use of existing infrastructure. When two nodes are not within the radio range of one another, they use intermediate nodes to route packets for them. Routing in MANET is a challenging problem which draws researcher&#39;s vision, due to nodes mobility, dynamic topology, frequent link breakage, limitation of nodes (memory, battery, bandwidth, and processing power), and lack of central point like base stations or servers. So by analysing and comparing different ad hoc routing protocols based on the metric throughput, packet delivery ratio, end to end delay which may give a solution to the challenges in the ad hoc routing in different situations. The mobility of nodes and instability of the wireless environment may result in link breaks between neighbouring nodes, even causes the route to...

This paper studies impact of varying Terrain Range on the performance of two reactive routing protocols AODV and DSR. For experimental purposes, initially we observed the performance of AODV with increasing Network Area from 1020m x 1020m to 2040m x 2040m. In another scenario we observed the performance of DSR with increasing Network Area from 1020m x 1020m to 2040m x 2040m. The performance of AODV and DSR are observed across Packet Delivery Ratio, Loss Packet Ratio and Routing overhead parameters. Our simulation results show that AODV and DSR perform equally for lower network size, while DSR is a better selection when network area is 2040m x 2040m.

This works studies the performance of dynamic routing in QoS networks. Basing on the simulation of realistic networks, the paper compares the behaviour of dynamic and static routing strategies when they are applied to transmit the packets of different traffic classes in a Diffserv environment. The study also shows the effects on dynamic routing of employing MPLS.

To deal with the "memory wall" problem, microprocessors include large secondary on-chip caches. But as these caches enlarge, they originate a new latency gap between them and fast L1 caches (inter-cache latency gap). Recently, Non-Uniform Cache Architectures (NUCAs) have been proposed to sustain the size growth trend of secondary caches that is threatened by wire-delay problems. NUCAs are size-oriented, and they were not conceived to close the inter-cache latency gap. To tackle this problem, we propose Light NUCAs (L-NUCAs) leveraging on-chip wire density to interconnect small tiles through specialized networks, which convey packets with distributed and dynamic routing. Our design reduces the tile delay (cache access plus one-hop routing) to a single processor cycle and places cache lines at a finer-granularity than conventional caches reducing cache latency. Our evaluations show that in general, L-NUCA improves simultaneously performance, energy, and area when integrated into both conventional or D-NUCA hierarchies.

The semi-annual alternation between polar day and polar night causes the South Pole atmosphere to undergo a large annual change. Seasonal changes of the atmosphere highly affect the properties of cosmic ray air showers. IceCube detects the high-energy muonic component of air showers with the 1 km 3 underground detector, whereas IceTop, the surface array, mainly measures the electromagnetic component. The influence due to the seasonal changes on the high-energy muon multiplicity, important for composition studies, is studied in simulation and data. To reduce this variation, a correction procedure based on the measured atmospheric temperature profile and the muon production longitudinal profile is proposed.

The mass composition and all-particle energy spectrum of co smic rays are determined by analysis of coincident events between the IceTop air shower array on t he surface and the deep IceCube strings. IceTop mainly detects the electromagnetic component of high energ y cosmic ray air showers, while the TeV muon bundle penetrates deep into the Antarctic ice and generates Ch renkov light, which is seen by IceCube. This analysis uses data taken from June 2010 to May 2011 when t he detector was nearly complete with IceCube in its 79 string conguration and IceTop running 73 stations. Variables sensitive to composition and primary energy are based on the lateral signal distribution reconstructed at the surface by IceTop and the energy loss of the muon bundles reconstructed by IceCube. Using a neural network we determine the average mass A and all-particle flux in the energy range from a few PeV up to 1 EeV. We find that &lt; logA &gt; increases up to at least 100 PeV. Corresponding authors:...

Wavelength routed optical networks have emerged as a technology that can effectively utilize the enormous bandwidth of the optical ®ber. Wavelength converters play an important role in enhancing the ®ber utilization and reducing the overall call blocking probability of the network. Placement of wavelength converters is an NP-complete problem (K.C. Lee, V.O.K. Li, IEEE Journal of Lightwave Technology 11(5/6) (1993) 962±970) in an arbitrary mesh network. In this paper, new Integer Linear Program (ILP) formulations have been proposed for the static and dynamic routing and wavelength assignment problem to reduce the number of conversions. We use Genetic Algorithms (GAs) for placing limited range wavelength converters in arbitrary mesh wavelength routed optical networks. The objective is to achieve near optimal placement of full and limited range wavelength converters resulting in reduced blocking probabilities and low distortion of the optical signal. Certain heuristics are used to obtain starting solutions for the GAs to enable it to converge faster. The results obtained using GAs are compared with the heuristic method of placement. We study the range of loads for which converters are useful. We observe that limited range converters placed near optimally at a few nodes can provide almost the same blocking probability as full range wavelength converters placed at all the nodes and that increasing the number of converters yields only a marginal improvement in blocking probability. We also observe that uniform placement of converters can be adopted in ring networks at low offered loads. We study the effect of uniform placement at higher loads and suggest that when used with reservation in the routing algorithm, converters ensure fairness to all nodes. Simulations have been carried out on a 12-node ring network and 14-node NSFNET.

A large number of studies on routing for shared protection focus on minimizing the network transport capacity in a static routing framework. A smaller number of studies have been conducted on dynamic routing. Most of them do not meet the scalability requirements of multi-domain networks. This paper reviews the recent works in dynamic routing for shared protection in multi-domain networks, and proposes a quantitative comparison amongst the most efficient approaches. Some of the remaining challenges are discussed at the end of the paper.

The MonALISA (Monitoring Agents in a Large Integrated Services Architecture) framework provides a set of distributed services for monitoring, control, management and global optimization for large scale distributed systems. It is based on an ensemble of autonomous, multi-threaded, agent-based subsystems which are registered as dynamic services. They can be automatically discovered and used by other services or clients. The distributed agents can collaborate and cooperate in performing a wide range of management, control and global optimization tasks using real time monitoring information.

Mobile ad hoc network (MANET) is a collection of mobile nodes that are connected each other. Considering the potential relay nodes based on power constraint is a difficult problem to obtain efficient throughput. This problem can be solved by selecting the minimum distance nodes as relay nodes from destination. In the proposed method, the delay is analyzed by selecting the relay nodes in multi-hop. Relay nodes are selected when the destination node is not in the communication range. There exists more than one relay node between the source and destination in multi-hop. Thus, the packets are transferred from source to destination through these relay nodes. If the destination node is not in the transmission range, then consider one-hop neighbors of source and calculate the distance from destination to these one-hop neighbors. The network topology is designed using the NS2 tool which gives routing tables, to provide the information about neighbors and also about the selected next hop node. The node with minimum distance is taken as selected next hop node. This node is considered as a relay node. In this manner, relay nodes are selected between source and destination. We can reduce the packet loss and delay.

Tasks related to advanced management of telecommunications networks, such as optimal dynamic routing or dynamic allocation of bandwidth for virtual paths in an ATM network, call for a method to estimate the current call arrival intensities. Such an estimate must be based on the observed history of the call arrival instants. In this paper, we study window type estimators with the purpose of giving a clear idea of their accuracy. In particular, the simple straight case and exponential windows are considered and applied to an example where the correlation function of the intensity process is of the exponential type -a system for which the exponential window, in fact, is the optimal window. It turns out that the accuracy is controlled by a single dimensionless traffic parameter. The achievable accuracy is little affected by the shape of the window function. It also turns out that a Bayesian approach, applied in an earlier study, gives only a slight improvement over the window based estimator. More important instead is the proper choice of the size of the window. In order to find the optimal size, one has to know some basic characteristics of the intensity process. We briefly address the problem how these can be inferred from the long term statistics of the counting process.

Tasks related to advanced management of telecommunications networks, such as optimal dynamic routing or dynamic allocation of bandwidth for virtual paths in an ATM network, call for a method to estimate the current call arrival intensities. Such an estimate must be based on the observed history of the call arrival instants. In this paper, we study window type estimators with the purpose of giving a clear idea of their accuracy. In particular, the simple straight case and exponential windows are considered and applied to an example where the correlation function of the intensity process is of the exponential type -a system for which the exponential window, in fact, is the optimal window. It turns out that the accuracy is controlled by a single dimensionless traffic parameter. The achievable accuracy is little affected by the shape of the window function. It also turns out that a Bayesian approach, applied in an earlier study, gives only a slight improvement over the window based estimator. More important instead is the proper choice of the size of the window. In order to find the optimal size, one has to know some basic characteristics of the intensity process. We briefly address the problem how these can be inferred from the long term statistics of the counting process.

With standard assumptions the routing and wavelength assignment problem (RWA) can be viewed as a Markov Decision Process (MDP). The problem, however, defies an exact solution because of the huge size of the state space. Only heuristic algorithms have been presented up till now. In this paper we propose an approach where, starting from a given heuristic algorithm, one obtains a better algorithm by the first policy iteration. In order to estimate the relative costs of states, we make a simulation on the fly studying, at each decision epoch, the consequences of all the alternatives actions. Being computationally intensive, this method can be used in real time only for systems with slow dynamics. Off-line it can be used to assess how close the heuristic algorithms come to the optimal policy. Numerical examples are given about the policy improvement.

lower priority packets at the point of congestion, We consider a distributed algorithm for dynambecause of the time delay involved in making the ically adjusting the input rate of each session of entry points aware of the congestion build-up a voice or data network so as to exercise flow

In this paper a modification on Levenberg-Marquardt algorithm for MLP neural network learning is proposed. The proposed algorithm has good convergence. This method reduces the amount of oscillation in learning procedure. An example is given to show usefulness of this method. Finally a simulation verifies the results of proposed method.

This study aims to analyze the impact of severe weather on supply chains in Jazan region, Saudi Arabia, in order to develop a solution, by examining the weather patterns, road maintenance reports, vehicle traffic data, and customer survey results. Key findings reveal that the customers satisfaction rate was 67.7%, 38.7% of customers experienced delivery delays and 11% received damaged goods, with infrastructure issues, road closures and poor visibility beingprimary causes. Based on the findings, a conceptual framework is proposed for a real-time supply chain system aimed at mitigating these challenges. The system integrates dynamic routing algorithms, participatory hazard reporting, and predictive analytics to address logistical inefficiencies. Real-time weather and traffic data are processed to optimize delivery routes, while hazard reports submitted by drivers enhance situational awareness. Predictive analytics utilizes historical data to anticipate disruptions, enabling proactive decision-making. The system is designed to adapt to Jazan's unique conditions but can be extended to other regions and user groups to improve driving efficiency during severe weather. While the system remains untested, its conceptual design addresses key logistical challenges highlighted by the data analysis. The study contributes to the field of supply chain resilience by presenting a scalable and adaptive framework that leverages real-time data, participatory inputs, and predictive tools to enhance operational efficiency. Future work will focus on prototype development and testing to evaluate its practical effectiveness and scalability.

In previous works the features and a complete for- mulation for circuit-switched networks of a multiple objective dynamic routing method (MODR) of periodic state dependent routing type were presented. The aim of the model is to resolve a very complex network bi-objective dynamic routing prob- lem, by recurring to a heuristic for synchronous path selec- tion enabling to obtain a good compromise solution in terms of two network performance measures. In this paper we present a study on the performance of variants of the MODR heuristic of synchronous path selection by using relaxations of the val- ues previously calculated for the two network objective func- tions. This study permitted the development of an improved version of the initial heuristic. Also a comparison of the ana- lytical values of the network objective functions obtained with selected variants of the initial heuristic with the corresponding results from a known reference method, the real time network routing (RTNR) metho...

In this paper a state‐space formulation of the Muskingum‐Cunge routing scheme is proposed. The state‐space formulation utilizes real‐time discharge measurements, accounts for modeling and observation errors, and allows real‐time updating through a Kalman filter estimator. The new model is tested in two different geotechnical conditions to forecast six‐hour discharge values in hypothetical channels. For realism, the geomorphologic characteristics of these case studies are determined on the basis of the regime theory. DWOPER, a field‐tested numerical dynamic routing model, was used to provide ground truth data for the validation of the proposed model.

The dynamic joint routing and admission control problem in multiple class multiple source-destination virtual circuit networks is considered. A nonlinear dynamic queueing model for virtual circuit networks that considers the dynamic interaction among the virtual circuit and packet processes is introduced. Then a multi-objective cost function of rejecting & maintaining virtual circuits, as well as of delaying & servicing packets is defined. The combined problem is formulated as an optimal control problem. Necessary optimality conditions are provided by Pontryagin's maximum principle. Sufficient optimality conditions based on the convexity of the Hamiltonian function are also given. For the finite horizon, the optimal controls can be found after numerically solving a Two-Point Boundary-Value Problem. For the long-run stationary equilibrium, the state-dependent routing and admission controls are derived.

We propose a novel genetic algorithm for solving the dynamic routing and wavelength assignment (DRWA) problem in wavelengthrouted optical networks. The algorithm not only obtains low call blocking probability, but it also employs a very short computation time. Moreover, it is capable of providing fairness among connections, that is, to offer approximately the same quality of service (in terms of blocking probability) for all source-destination node pairs. Since requirements on optical network availability are highly severe, we also propose an extension of the algorithm to provide fault-tolerance capability at the optical layer. It is achieved by means of protection, where each optical connection request is provided with a pair of lightpaths (a primary and a backup lightpath). Again, the genetic algorithm proves to be highly ef®cient, in this case, at performing routing and wavelength assignment of pairs of lightpaths.

We present a new algorithm based on ant colony optimization to solve the dynamic routing and wavelength assignment problem in a distributed manner. The algorithm uses the ant colony optimization metaheuristic to obtain updated information about the network state, which is then used to find the routes and wavelengths to establish new connections through an adaptive process able to deal with dynamic changes in network state. By means of simulation we show that the algorithm leads to lower blocking probability and lower mean setup time than other distributed algorithms.

Internet routers use router tables to classify incoming packets based on the information carried in the packet headers. Packet classification is one of the network bottlenecks, especially when a high update rate becomes necessary. Much of the research in the router-table area has focused on static prefix tables, where updates usually require the rebuilding of the whole router table. Some router-table designs rely on the relatively short IPv4 addresses to achieve desired efficiency. However, these designs have bad scalability in terms of the prefix length. We propose several schemes to represent one-dimensional dynamic range tables, that is, tables into/from which rules are inserted/deleted concurrent with packet classification, and filters are specified as ranges. Our schemes allow real-time update and at the same time provide efficient lookup. The lookup and update complexities of our schemes are logarithmic functions of the number of the filters. The first scheme P ST , which is based on priority search trees, uses the most specific rule tie breaker. The second scheme is called BOB (Binary search tree On Binary search tree). This scheme uses the highest priority tie breaker. In order to utilize the wide cache line size and reduce the tree height, a third scheme is developed in which the top level viii tree is a B-Tree. This scheme also uses the highest priority tie breaker. All three schemes are suitable for prefix filters as well as for range filters in which no two filters have intersecting ranges. In addition, the P ST also can handle a conflict-free range set. ix CHAPTER 1 1 We may assume either that all priorities are distinct or that selection among rules that have the same priority may be done in an arbitrary fashion

Mobile ad hoc networks (MANET) are self-created and self organized by a collection of mobile nodes, interconnected by multi-hop wireless paths in a strictly peer to peer fashion. Ad-Hoc routing protocols such as Dynamic source routing (DSR), Ad-hoc on demand distance vector (AODV) and Destination sequence Vector (DSDV) has been proposed to solve the routing problem in Ad-hoc networks. Extended research has been done in comparing the different proposed ad-hoc routing protocol under various network conditions. Packet delivery ratio (PDR), routing overhead, path optimality, end to end delay and throughput are some parameters commonly used in the comparisons. This survey paper provides the analyzed performance of the routing protocol under the various conditions. KeywordsMANET , AODV, DSR, Throughput.

This review paper examines recent advancements in vehicle routing optimization under time uncertainty, focusing on the vehicle routing problem (VRP). It systematically analyzes research papers to identify strategies for optimizing routes despite temporal uncertainties, covering key areas such as optimization algorithms, uncertainty modeling techniques, and simulation methods. The study investigates dynamic dispatching models, reliability considerations, and multiobjective optimization approaches. By synthesizing existing literature, this paper presents the current state of research in vehicle routing under time uncertainty and suggests potential future research directions. Our findings indicate that integrating robust optimization techniques with advanced simulation methods could significantly enhance decision-making processes in uncertain environments. Additionally, the paper highlights the role of machine learning and artificial intelligence in developing adaptive algorithms that respond to dynamic changes in real-time. As the need for efficient logistics solutions grows, this comprehensive review underscores the importance of addressing uncertainties in vehicle routing to improve operational efficiency, reduce costs, and enhance customer satisfaction.

The use of dynamic routing methods has an important impact on the performance of multi-service networks namely in overload and failure conditions. The paper describes and compares the performance of variants of reference dynamic routing methods (DAR and RTNR). Two variants of DAR resulting from the extension to multi-service networks of the original formulation of the method are presented. Also a simplification of the trunk reservation mechanism per traffic class of RTNR is considered. The major results of extensive experimentation with these variants of the routing methods using a discrete event simulation platform are discussed. The simulation model includes Poisson and Engset type multi-class traffic flows. Conclusions concerning the relative performance of the methods at network level and per traffic class are put forward.

A new improved version of a dynamic routing algorithm of locally restorable QoS guaranteed paths, will be described. The algorithm calculates simultaneously the active and local protection paths, with protection bandwidth sharing. A method for minimal allocation of ...

Recent interest in using tomography for network monitoring has raised the fundamental issue of whether it is possible to use only a small number of probing nodes (beacons) for monitoring all edges of a network in the presence of dynamic routing. Past work has shown that minimizing the number of beacons is NP-hard, and has provided approximate solutions that may be fairly suboptimal. In this paper, we use a two-pronged approach to compute an efficient beacon set: (i) we formulate the need for, and design algorithms for, computing the set of edges that can be monitored by a beacon under all possible routing states; and (ii) we minimize the number of beacons used to monitor all network edges. We show that the latter problem is NPcomplete and use an approximate placement algorithm that yields beacon sets of sizes within 1 + ln(|E|) of the optimal solution, where E is the set of edges to be monitored. Beacon set computations for several Rocketfuel ISP topologies indicate that our algorithm may reduce the number of beacons yielded by past solutions by more than 50%.

River level forecasting has been undertaken by the National Weather Service (NWS) in the Suwannee River basin since the late 1970's. Among others, forecast sites were established in the lower reach of the Santa Fe River, a major tributary to the Suwannee River. Since that time, extensive riverbank and floodplain development has occurred upstream of those forecast sites in the Santa Fe River. During the 1980's and 1990's, some riverine stretches experienced significant flooding events, which at times were not preceded by adequate warning from the existing flood warning network. In response to a request from affected residents, the NWS and the Suwannee River Water Management District (SRWMD) cooperated in establishing the first new forecast site in the basin in over 20 years. Data review and analysis, field surveys, and public meetings were conducted to identify the optimal gage location and establish the appropriate action, cautionary, and flood stages. Data analysis showed the existence of two distinct types of flood events in the basin, one inherent to rainfall over the basin and the second due to backwater effects. The Southeast River Forecast Center (SERFC) has incorporated the new forecast site in the National Weather Service River Forecast System (NWSRFS) and it is in the process of redefining the existing setting for the Santa Fe River. The enhancements in the new setting will include the use of dynamic routing to account for the backwater effects in the Santa Fe River due to flooding in the Suwannee River basin.

This paper deals with the dynamic routing problem in ATM cell-switching networks. We present a mathematical programming model based on cell loss and a Tabu Search algorithm with short-term memory that is reinforced with a long-term memory procedure. The estimation of the quality of the solutions is fast, due to the specific encoding of the feasible solutions. The Tabu Search algorithm reaches good quality solutions, outperforming other approaches such as Genetic Algorithms and the Minimum Switching Path heuristic, regarding both cell loss and the CPU time consumption. The best results were found for the more complex networks with a high number of switches and links.

We propose both robust and data-driven approaches to a fluid model of call centers that incorporates random arrival rates with abandonment to determine staff levels and dynamic routing policies. Resulting models are tested with real data obtained from the call center of a US bank. Computational results show that the robust fluid model is significantly more tractable as compared to the data-driven one and producing better solutions to call centers in most experiments.

Since optical WDM networks are becoming one of the alternatives for building up backbones, dynamic routing, and wavelength assignment with delay constraints (DRWA-DC) in WDM networks with sparse wavelength conversions is important for a communication model to route requests subject to delay bounds. Since the NP-hard minimum Steiner tree problem can be reduced to the DRWA-DC problem, it is very unlikely to derive optimal solutions in a reasonable time for the DRWA-DC problem. In this paper, we circumvent to apply a meta-heuristic based upon the ant colony optimization (ACO) approach to produce approximate solutions in a timely manner. In the literature, the ACO approach has been successfully applied to several well-known combinatorial optimization problems whose solutions might be in the form of paths on the associated graphs. The ACO algorithm proposed in this paper incorporates several new features so as to select wavelength links for which the communication cost and the transmission delay of routing the request can be minimized as much as possible subject to the specified delay bound. Computational experiments are designed and conducted to study the performance of the proposed algorithm. Comparing with the optimal solutions found by an ILP formulation, numerical results evince that the ACO algorithm is effective and robust in providing quality approximate solutions to the DRWA-DC problem.

Recent observations have caused a drastic shift in the conception of the hippocampus as a homogeneous structure that subserves cognitive functions, either spatial maps or short term episodic memory, to a structure that is associated with both cognitive and emotional functions. In fact, the assignment of cognitive functions to the hippocampus is restricted to its dorsal sector. In contrast, the ventral hippocampus (VH) appears to be associated with control of behavioral inhibition, stress and emotional memory, but not with strictly cognitive functions. Curiously, the VH but not the dorsal hippocampus (DH) is associated with the development of affective disorders. In line with these collective observations, we and others have found that the ability to evoke a sustained long term potentiation (LTP), a cellular correlate of learning and memory, is much lower in the VH compared to the DH. Strikingly, acute stress as well as direct exposure to corticosterone affect DH and VH in an opposite manner; causing facilitation of LTP in the VH and its suppression in the DH. This double dissociative action results from activation of different steroid receptor species in the DH and VH. Since the DH and VH differ in efferent connectivity, and since the strength of LTP can be considered as an indicator of strength of synaptic connectivity, these results suggest that stress regulates the routes by which the hippocampus is functionally linked to the rest of the brain such that under stress, the ventral route to the amygdala is enabled while the dorsal route to the neocortex is suppressed. This selective routing may underlie the complex outcome of stress on hippocampal and amygdala physiology and behavior. V