To evaluate the amount of speed up and the amount of solution quality degradation we imple- mented our algorithm and used a state of the art solver Indigo to route the macro nodes. We executed it over a well recognized benchmark set. This benchmark set consists of three classes of problems: a class of clustered problems whose requests form geographical clusters; a class of random problems, whose requests are randomly scattered; a class that combines both clustered and random locations. All classes have two versions of the problems, one with large capacity and another with small capacity vehicles. Using the problems from the benchmark set, we combined several instances to create new instances of 10x size, and confirmed that the results obtained with 1000 request instances carry over to 10000 request instances.
Basically we use a Data Mining strategy, in which every new individual has its route analyzed to extract patterns (sequence of customers) within a given range [minP atternSize, maxP atternSize]. Each pattern found is stored in a structure called patternsList along with the frequency it has appeared in the solutions already ana- lyzed. In addition to these information, we also keep record of the average cost of the route in which the pattern was found so as to improve the robustness of the eval- uation criteria that decides how good a pattern is. Therefore we have two types of data to evaluate a pattern: frequence and average cost. Good patterns have high frequency and low average cost. Since cost value is usually much higher than the frequency value, this data must be normalized. Lets call nF requency the normal- ized frequency value, nAvgCost the normalized average cost. Therefore we define qualityIndex = (1 − nAvgCost) + nF requency, as the value used to evaluate the pat- terns, since it considers both measures. The closer to 2 the better. This is not the first time an approach combining a heuristic and a data mining algorithm is proposed for a vehicle routing problem. In , Santos et al proposed 4 approaches for a single vehicle routing problem, including one that combines a Genetic Algorithm with the data mining algorithm Apriori. Our approach is not based on their approach and is fairly different from the algorithm they developed.
Before presenting the MILP formulation of longest-path routingproblems, we describe how to use variables (or decision variables) to represent a routing result. For the problem shown in Fig. 2(a), since each pair of neighboring grid cells may associate with a line segment (or wire segment) in the final routing result, the routing region shown in Fig. 2(a) can be transformed into a graph illustrated in Fig. 2(b), where each vertex in (b) corresponds to a grid cell in (a). Note that directed edges are used in the graph instead of undirected edges because directed edges are required in our formulation in order to prevent subtours in final routing results. Prevention of subtours will be discussed in detail in Section IV.
Over the last decade and a half, Branch-and-price (BP) algorithms [Barnhart et al., 1998; Vanderbeck and Wolsey, 1996; Lübbecke and Desrosiers, 2005] have become an important tool to solve routingproblems [Salani, 2005]. In this chapter, we describe two Integer Programming Formulations for the Vehicle Routing Problem with Cross- Docking (VRPCD) and due to their exponential number of variables, we implemented two BP algorithms to solve them. In the following, we define the problem in the section 3.1, then we discuss the first formulation and a detailed description of the algorithm implementation in the section 3.2. The second approach is discussed in the section 3.3, concerning modeling and implementation issues, with focus on the solution of the pricing problems. An extensive review on the algorithms’ performance is given in the section 3.4 by means of computational results obtained by solving two sets of instances using the proposed algorithms. We conclude the chapter on section 3.5 where we also provide further research directions.
Clustering methods are used in many different applications, such as data mining and ad hoc networks. Several clustering schemes have been proposed for ad hoc networks. K-means clustering technique is one of the significant clustering algorithms that can solve many routingproblems in MANETs . Due to easy implementation and fast convergence, K-means clustering is an applicable clustering method specifically in mobile ad hoc networks. In contrast, there are some limitations like inadequate distribution of nodes in clusters, fixed cluster head and cluster members.
The goal of the MDVRPB is to determine the routes to be performed from the selected depots to the customers by a fleet of homogeneous vehicles in order to satisfy the demand of the customers (products to be collected or products to be delivered). The objective functions considered for the multiobjective version of the MDVRPB is to minimize the total traveled distance, the total time and the consumed energy. The first objective is the common function considered in the literature related to the vehicle routingproblems. The second objective is obtained by the allowed speed on each edge. In particular, we have considered a random speed between 30 km /hr to 90 km/hr for the complete graph on the benchmarking set of instances. Finally, the third objective is adopted from the idea of gas emission and consumption of energy introduced by Bektaş and Laporte (2011) and Demir et al. (2014).
The European FP7 project 4WARD is speciYing novel network architectures for replacing the current Intenet model. One of its greatest advances refers to the communication mechanism called Generic Path (GP). The success of GP architecture depends upon the speciication of routing and QoS control mechanisms, in order to correctly map physical resources (links, interfaces, etc.) for supporting the expected types of sessions. This paper presented the Routing and dynamic Resource Control (QoS-RRC), a suite of mechanisms addressing routingenabled QoS conrol approach to fulill the above requirements. QoS-RRC integrates QoS routing and resource over-provisioning to allow eiciently GP mapping into physical resources. QoS RRC was evaluated in NS-2, and demonstrated the accomplishment of its expected beneits in terms of signaling load and bandwidth allocation control.
ment. CBCL is a questionnaire that assesses social compet- ence and behavioral problems in children and adolescents with age between 6 and 18 years old, and it is based on information provided by parents. The analyzed syndromes and profiles were the following ones: affective problems, anxiety problems, somatic problems, attention deficit/ hyperactivity problems, oppositional defiant disorder, con- duct problems, anxious depressed symptoms, withdrawn depressed behavior, somatic complaints, sluggish cognitive tempo, obsessive compulsive disorder, posttraumatic stress disorder, rule-breaking behavior, aggressive behavior, social problems, thought problems, attention problem, activities competence, social competence and school competence. Raw scores were elected, as suggested by the authors in clinical research 7 . Neuropsychological tests were WISC-III
The Ad-Hoc On-Demand Distance Vector routing protocol is a r eactive routing protocol. AODV protocol is a combination of Dynamic Source Routing (DSR) and DSDV protocol . It is a distance vector routing protocol and is capable of both unicast and multicast routing . It will maintain the routes only between the nodes which need to communicate. The routing information will be maintained as routing tables in each node. A routing table entry expires if it has not been used or reactivated for pre- specified expiration time. When a source node wants to send the packet to a destination node then the entries in the routing table will check whether there is a current route to the destination node or not, if there is a route then the packets will transmit to destination node in that path . If don’t have any valid route, then the route discovery process will be initiated. For route discovery AODV is using Routing Request (RREQ), Routing Reply (RREP) Packets . The RREQ packet containing the source node IP address, source node current sequence number, the destination node sequence number and broadcast ID . The advantage of AODV is that it creates no extra traffic for communication along the existing link but requires more time to establish a connection. It is simple and doesn’t require much memory or calculation.
In  authors formulate the problem of routing as a network optimization problem, and present a general linear programming (LP) formulation for modelling the problem. Kumar and al proposes the optimized algorithm for known traffic demand and then explain the performance ratio for this. The routing algorithms derived from these formulations usually claim analytical properties such as optimal resource utilization and throughput fairness. The simulation results demonstrate that their statistical problem formulation could effectively incorporate the traffic demand uncertainty in routing optimization, and its algorithm outperforms the algorithm which only considers the static traffic demand. To achieve this objective the problem for congestion has been designed.
The Open Shortest Path First (OSPF) protocol is an IGP that was created by the OSPF working group of the Internet Engineering Task Force [Moy, 1998]. In OSPF routing, the network administrator assigns integer weights to each link of the network. These weights are used as lengths to calculate the shortest paths between all pairs of routers, and the data is routed through the shortest paths. In the case of multiple shortest paths, the traffic is split evenly, among all outgoing links that belong to the shortest paths. This behavior is called Equal Cost Multi-Path (ECMP) rule. Figure 1.2 shows an example of OSPF routing with the ECMP rule. In the first case (a), all links have the same weight, and all the 10 megabits are routed through the shortest path (S → X → T ) of length 2. On the second case (b), there are two shortest paths of length 3: (S → X → T ) and (S → Z → Y → T ). Therefore, the flow is split evenly among links (S, X) and (S, Z).
This is the strategy followed in AntHocNet [2, 8, 22, 23], which is a reactive-proactive multipath algorithm for routing in MANETs. The structure of AntHocNet is quite similar to that of AntNet-FA with the addition of some components specific to MANETs those results in the presence of several types of ant-like agents. In particular, the design of AntHocNet features: reactive agents to setup paths toward a previously unknown destination, per-session proactive gathering of information, agents for the explicit management of link failure situations (because of mobility and limited radio range the established radio link between two nodes can easily break). Node managers are not really learning agents, but rather finite state machines responding more or less reactively to external events. This is partially due to the fact that in such highly dynamic environments it might be of questionable utility to rely on approaches strongly based on detecting and learning environment’s regularities. In the general case, some level of learning and proactivness is expected to be of some usefulness, but at the same time the core strategy should be a reactive one. This has been our design philosophy in this case.
According to great capabilities of WSNs, application of them is increasing in recent decade. But, they face to some challenges such as limitation of power, memory, CPU and etc. these issues of WSNs have a direct effects on algorithms that are designed to them because complex algorithms need much memory and CPU and they consume a great deal of energy. These extreme limitations of resource, separate WSNs from traditional networks . Based on the natural features of WSNs that distinguish them from other wireless networks such as ad hoc networks, routing in WSNs has very challenges. First, establishing comprehensive structure of address for deploying of the certain number of sensor nodes is impossible. So, traditional methods based on IP address (IP-based protocols) cannot be used to wireless sensor networks. Second, almost all applications of sensor networks need to sense the flow of data from multiple sources and transfer them to a special sink that it is as opposed to communication networks. Third if multiple sensors that are deployed in the adjacency of an event create same data, the data traffic is generated that it has an important redundancy in it. Such redundancy requires to be developed by the routing protocols to make energy and bandwidth utilization better. Finally, sensor node needs an accurate resource management because the resources of
A network which merges the usage of the public and the private networks and uses security software for the purpose of compressing, encrypting and masking the digital packets that are being transmitted in the network is called as Virtual Private Network (VPN). In VPN, the communication between the user ends is maintained such that it appears as if the source end is directly linked to the destination end over a concealed leased line. The private network, VPN uses the public network such as internet to link the remote locations with the users. In this study, we propose a new reliable protocol called as Topology Aware Reliable Routing Protocol (TARRP) for large scale VPN and compare its performance with the traditional protocol, Boarder Gateway Protocol (BGP). In this protocol, the communication between the end to end nodes takes place in two phases: Routing phase and authentication phase. In the routing phase, the upstream and the downstream routing paths are determined by the source node using the topology learning protocol. Based on the dynamic failure information of links, the sender selects the failure-free path towards the destination. In the authentication phase, the VPN gateway authenticates the packet before it is transmitted through the core. Thus, this technique efficiently allows the packet to be transmitted with ensured security. By simulation results, we show that our proposed protocol is better than the traditional routing protocol of VPN.
Let us remember some basic and repeated aspects in civilizations such as Mesopotamia 10 , Egypt, China, India and pre-Columbian America. The word Mathematics or mathematician did not exist (both of Greek origin), therefore, the "mathematical" requirements demanded by the problems of this stage are directly linked to what today we would call accounting, keeping record books, amounts used, earnings, etc ... It is true that there were own rules and methods, even quite developed (as examples enough, the calculation of the square root, the falsi regulation method, the Mayan calendar, among others). A second part of this stage begins with the emergence of Greek Mathematics, characterized by these central questions: deductive organization, geometric orientation, the ideal of disinterested science (hence we have the division between Pure and Applied Mathematics), the relationship with Philosophy (more than mathematical schools are schools of natural philosophy) and with two contributions that have made Mathematics as we know it today: the notion of angle and the idea of the classical proof.
1) Sleep deprivation attacks: This kind of attack is actually more specific to the mobile ad hoc networks. The aim is to drain off limited resources in the mobile ad hoc nodes (e.g. the battery powers), by constantly makes them busy processing unnecessary packets. In a routing protocol, sleep deprivation attacks might be launched by flooding the targeted node with unnecessary routing packets. For instance, attackers could flood any node in the networks by sending a huge number of route request (RREQ), route replies (RREP) or route error (RERR) packets to the targeted node. As a result, that particular node is unable to participate in the routing mechanisms and rendered unreachable by the other nodes in the networks.
Abstract- Routing tables of all the routers needs frequent updates due topology changes resulting because of link failures or link metric modifications. Each of those updates may cause transient routing loops. These loops pose significant stability problems in Wireless Networks. Distributed routing algorithms capable of avoiding such transient loops in network path are deemed efficient. Some earlier approaches like Shortest path routing (Dijkstra) etc. have problems maintaining the balance between node delays and link delays. Besides an earlier algorithm, Distributed Path Computation with Intermediate Variables (DIV) guarantees steady-state, with no transient loops. It’s ability to operate with existing distributed routing algorithms to guarantee that the directed graph induced by the routing decisions stays acyclic by implementing an update mechanism using simple message exchanges between neighboring nodes that guarantees loop freedom at all times. It outperforms existing loop prevention algorithms in several key metrics such as frequency of synchronous updates and the ability to maintain paths during transitions. But still frequency of updates is still an open issue and we address that problem specifically by implementing and using proactive source routing (PSR) protocol. Compared to existing routing protocols,It requires no timestamp for routing updates. In PSR the update messages are easily integrated into the tree structure, so that the computation overhead can be significantly reduced.
Abstract. Vehicular networks represent a special type of wireless network that has gained the attention of researchers over the past few years. Routing protocols for this type of network must face several challenges, such as high mobility, high speeds and frequent network disconnections. This paper proposes a vehicular routing algorithm called RouteSpray that in addition to using vehicular routes to help make routing decisions, uses controlled spraying to forward multiple copies of messages, thus ensuring better delivery rates without overloading the network. The results of experiments performed in this study indicate that the RouteSpray algorithm delivered 13.46% more messages than other algorithms reported in the literature. In addition, the RouteSpray algorithm kept the buffer occupation 73.38% lower.
A wireless ad-hoc network consists of a collection of "peer" mobile nodes that are capable of communicating with each other without help from a fixed infrastructure. The interconnections between nodes are capable of changing on a continual and arbitrary basis. Nodes within each other's radio range communicate directly via wireless links, while those that are far apart use other nodes as relays. Nodes usually share the same physical media; they transmit and acquire signals at the same frequency band. However, due to their inherent characteristics of dynamic topology and lack of centralized management security, MANET is vulnerable to various kinds of attacks. Blackhole attack is one of many possible attacks in MANET. One type of black hole attack can occur when the malicious node on the path directly attacks the data traffic by intentionally dropping, delaying or altering the data traffic passing through it. In other type, a malicious node sends a forged Route REPly (RREP) packet to a source node that initiates the route discovery in order to pretend to be a destination node. By comparing the destination sequence number contained in RREP packets when a source node received multiple RREP, it judges the greatest one as the most recent routing information and selects the route contained in that RREP packet. In case the sequence numbers are equal it selects the route with the smallest hop count. If the attacker