Traditional Routing Techniques Research Articles

Ant Colony Optimization based algorithm to determine the optimum route for overhead power transmission lines

Electrical power distribution networks present one of the most essential infrastructure projects. Despite of the vitality of this sector and the huge budgets of their projects, but it is still depends on traditional manual routing techniques which are slow, expensive and inefficient comparing with the current AI revolutionary techniques. Hence, this research presented a novel AI model for optimizing “overhead power transmitting lines” (OHPTL) routing using Ant Colony Optimization (ACO) technique. The proposed technique depends on the core routine (which can generate random and valid routes and estimate their total cost) and searching algorithm (which uses ACO technique to find the optimum route among the generated routes). As a prototype, the proposed technique was codes in “Visual Basic for Applications” VBA, while MS-EXCEL was used to handle the graphical inputs and outputs. The functionality of the technique was validated using three virtual case studies and its optimization capability was tested using a real-life case study. The results showed that the total cost of the optimized route is about 75% of the existing route.

An Efficient Routing Protocol for Quantum Key Distribution Networks

Quantum key distribution (QKD) can provide point-to-point information-theoretic secure key services for two connected users. In fact, the development of QKD networks needs more focus from the scientific community in order to broaden the service scale of QKD technology to deliver end-to-end secure key services. Of course, some recent efforts have been made to develop secure communication protocols based on QKD. However, due to the limited key generation capability of QKD devices, high quantum secure key utilization is the major concern for QKD networks. Since traditional routing techniques do not account for the state of quantum secure keys on links, applying them in QKD networks directly will result in underutilization of quantum secure keys. Therefore, an efficient routing protocol for QKD networks, especially for large-scale QKD networks, is desperately needed. In this study, an efficient routing protocol based on optimized link-state routing, namely QOLSR, is proposed for QKD networks. QOLSR considerably improves quantum key utilization in QKD networks through link-state awareness and path optimization. Simulation results demonstrate the validity and efficiency of the proposed QOLSR routing protocol. Most importantly, with the growth of communication traffic, the benefit becomes even more apparent.

A Generic Secure Transmission Scheme Based on Random Linear Network Coding

Unlike general routing strategies, network coding (NC) can combine encoding functions with multi-path propagation over a network. This allows network capacity to be achieved to support complex security solutions. Moreover, NC has intrinsic security advantages against passive attacks over traditional routing techniques. However, due to the transmission of the global encoding kernels, the system is fragile to eavesdropping attacks with multiple probes. This paper proposes a generic unicast secure transmission scheme based on random linear network coding (RLNC). Specifically, the intended receiver generates a random matrix upon receiving the request from the source node, and then transmits each row vector of this matrix over a link reversely to the source node. Each intermediate node rearranges all received vectors to form a matrix by row, and then post-multiplies its local encoding kernel by this matrix to obtain a new matrix. Similarly, each row vector of the new matrix is reversely transmitted over a link to the source node. This procedure is performed until we have the source node, where the generalized inverse of the received matrix (or part of it) can be used as its local encoding kernel. Hence, the intended receiver can use the generated matrix (or the corresponding part) to decode the received data packets directly. We also analyze the security to demonstrate that the proposed scheme is at least as secure as other methods against wiretapping attacks. We also evaluate the performance of the proposed scheme to demonstrate its utility.

Opportunistic Routing for Opto-Acoustic Internet of Underwater Things

Internet of Underwater Things (IoUT) is a technological revolution that could mark a new era for scientific, industrial, and military underwater applications. To mitigate the hostile underwater channel characteristics, this article considers a multimodal underwater network that hybridizes acoustic and optical wireless communications to achieve an ubiquitous control and high-speed low-latency networking performance, respectively. Since underwater optical wireless communications (UOWCs) suffer from limited range, it requires effective multihop routing solutions. In this regard, we propose a sector-based opportunistic routing (SectOR) protocol. Unlike the traditional unicast routing (TUR) techniques, which send packets to a unique relay, opportunistic routing (OR) targets a set of candidate relays by leveraging the broadcast nature of the UOWC channel. OR improves the packet delivery ratio as the likelihood of having at least one successful packet reception is much higher than that in TUR. Contingent upon the performance characterization of a single-hop link, we obtain a variety of local and global metrics to evaluate the fitness of a candidate set (CS) and develop candidate prioritization techniques for various OR metrics. Since rate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\leftrightarrow $ </tex-math></inline-formula> error and range <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\leftrightarrow $ </tex-math></inline-formula> beamwidth tradeoffs yield different CS diversities, we develop a candidate filtering and searching algorithm to find the optimal sector shaped coverage region by scanning the feasible search space. Moreover, a hybrid acoustic/optic coordination mechanism is considered to avoid duplicate transmission of the relays. Numerical results show that the SectOR protocol can perform even better than optimal unicast routing protocols in well-connected underwater networks.

Minimizing Total Logistics Cost for Long-Haul Multi-Stop Truck Transportation

Whenever there is sufficient demand, companies generally prefer the full truckload (TL) option for long-distance transport, resulting in large and less frequent shipment operations that can be costly if inventory carrying costs are high. Less than truckload (LTL) is another option for transport when carrying costs are high and/or there is insufficient demand. Shipment consolidation provides another option that combines many of the benefits of both TL and LTL. Shipment consolidation is a cost-effective transport solution that combines different size shipments into a single truckload. Combining many loads as a single load brings together economies of scale and potential savings. Traditional routing techniques that minimize distance are not suitable for shipments that have different origins and destinations because it can be beneficial to travel further to minimize overall transport and inventory cost, or what is termed total logistics cost (TLC). Effective consolidation of multi-stop routes to minimize TLC requires routing procedures that are more computationally intensive to find beneficial combinations of loads into consolidated shipments. In this study, we have developed a saving-based procedure to determine consolidated route sequences that minimize the TLC of shipments. Twenty-one data sets were produced using real city coordinates and population densities in North Carolina to demonstrate the effectiveness of the procedure. The solutions of the proposed method are compared with the solutions of the traditional Clarke and Wright (C-W) algorithm. Although the traditional C-W algorithm provides very fast solution times, the proposed method has produced much better solution values.

Routing in optical transport networks with deep reinforcement learning

Deep reinforcement learning (DRL) has recently revolutionized the resolution of decision-making and automated control problems. In the context of networking, there is a growing trend in the research community to apply DRL algorithms to optimization problems such as routing. However, existing proposals fail to achieve good results, often under-performing traditional routing techniques. We argue that the reason behind this poor performance is that they use straightforward representations of networks. In this paper, we propose a DRL-based solution for routing in optical transport networks (OTNs). Contrary to previous works, we propose a more elaborate representation of the network state that reduces the level of knowledge abstraction required for DRL agents and easily captures the singularities of network topologies. Our evaluation results show that using our novel representation, DRL agents achieve better performance and learn how to route traffic in OTNs significantly faster compared to state-of-the-art representations. Additionally, we reverse engineered the routing strategy learned by our DRL agent, and as a result, we found a routing algorithm that outperforms well-known traditional routing heuristics.

EMR-PL: Energy-efficient multipath routing based on link life prediction in ad hoc networks

In mobile ad-hoc networks, traditional multi-path routing techniques elect multiple good paths based on metrics like hop count, delay, or energy consumption, and are not suitable for multipath scenario, especially when the data transmission is supposed to take place simultaneously through all the selected paths. The present multi-path routing protocol EMR-PL estimates the life of routes depending upon the energy-oriented lifespan and velocity-oriented lifespan of links. Among all the discovered routes, the collection consuming minimum energy and having required lifespan is elected for communication. To the best of authors’ knowledge, the present article is the first one to focus on the single nodemulti-path problem and give solution to it. The data transfer is multi-track that is, all the routes belonging to the selected collection, simultaneously forward data packets. The protocol performance is evaluated in terms of data packet delivery ratio, message cost, average node lifetime, consumed energy and end-to-end delay.

Delay Analytical Models for Opportunistic Routing in Wireless Ad Hoc Networks

Opportunistic routing (OR) is a promising technology for improving the throughput of wireless networks. In contrast to traditional routing techniques that forward messages along predetermined paths, OR schemes allow selection of a set of candidate nodes as potential next-hop forwarders to utilize the broadcast nature of the wireless medium. An analysis of transmission latency and signaling overhead depend on the number of hops required to transmit a packet from source to destination. In this paper, we propose three models to analyze the number of transmissions for successfully delivering a packet with three OR schemes: single-forwarder OR with known topologies, multiple-forwarder OR with known topologies, and multiple forwarder OR with unknown topologies. These models are useful in the network planning. The simulation results verified the accuracy and usability of the three models.

F3TM: Flooding Factor based Trust Management Framework for secure data transmission in MANETs

Due to the absence of infrastructure support, secure data dissemination is a challenging task in scalable mobile ad hoc networks (MANETs) environment. In most of the traditional routing techniques for MANETs, either security has not been taken into account or only one aspect of security concern has been addressed without optimizing the routing performance. This paper proposes Flooding Factor based Framework for Trust Management (F3TM) in MANETs. True flooding approach is utilized to identify attacker nodes based on the calculation of trust value. Route Discovery Algorithm is developed to discover an efficient and secure path for data forwarding using Experimental Grey Wolf algorithm for validating network nodes. Enhanced Multi-Swarm Optimization is used to optimize the identified delivery path. Simulations are carried out in ns2 to assess and compare the performance of F3TM with the state-of-the-art frameworks: CORMAN and PRIME considering the metrics including delay, packet delivery ration, overhead and throughput. The performance assessment attests the reliable security of F3TM compared to the state-of-the-art frameworks.

Energy Conservation for Ad Hoc On-Demand Distance Vector Multipath Routing Protocol

Routing in ad hoc mobile networks is a problem which has not yet been satisfactorily solved. Traditional routing techniques are not well adapted to new networks. Indeed, their lack of reactivity with respect to the traffic and network changes means traditional routing techniques cannot easily be used except at the price of over-dimensioning of the network resources (network bandwidth, node memory utilization, node CPU load, etc.). In recent years the research community has been interested in the improvement of ad hoc routing, and among the solutions suggested multipath routing has been considered. Multiple paths are exploited in order to ensure reliability and a quick reaction to changes in topology with a low overhead generated by the control messages. In this article we present an extension of the well-known routing protocol AODVM (Ad hoc On-demand Distance Vector Multipath). We propose to improve the multipath routing strategy with a path classification to allow the paths with the best energy level to be chosen.

Cooperative relaying in Wi-Fi networks with network coding

Recently, network coding has emerged as a new cooperative technique for improving network throughputs over traditional routing techniques. Network coding allows intermediate nodes to combine packets using an algebraic function before forwarding. Selfless cooperation by the intermediate nodes is thus implicitly assumed in the network employing network coding. Such cooperation in principle should not happen for free but for mutual benefits shared by source-destination pairs and the cooperating intermediate nodes. Effective resource allocation techniques are thus required to efficiently utilize the limited network resources at the intermediate nodes. In this article, we propose one such effective buffer allocation algorithm, called buffer equalized opportunistic network coding (BE-ONC), to dynamically exploit buffer spaces at a relay node of a relay-based IEEE 802.11 network. The BE-ONC technique combines packets opportunistically based on dynamic buffer allocation at the relay. Through simulations, we illustrate that the proposed scheme improves over the classical packet scheduling schemes in terms of delay and successful packet delivery ratio in a cooperative relay-based Wi-Fi network. Our experimental results further confirm the potential benefits of BE-ONC in terms of packet delivery ratio, when compared with first-in first-out scheduling at the relay node. The guidelines for extending our proposed scheme to a more general scenario, where more than two users are involved, and the relay is allowed to transmit its own packet, are also provided.

GeoSVR: A map-based stateless VANET routing

Compared with traditional routing techniques, geographic routing has been proven to be more suitable for highly mobile environments like Vehicular Ad-Hoc Networks (VANETs) because of enhanced scalability and feasibility. These routings use greedy modes or forwarding paths to forward packets. However, the dynamic nature of vehicular network such as frequently changed topology, vehicles density and radio obstacles, could create local maximum, sparse connectivity and network partitions. We propose GeoSVR, a geographic stateless routing combined with node location and digital map. The proposed GeoSVR scheme enhances forwarding path to solve local maximum and sparse connectivity problem, and the proposed restricted forwarding algorithm overcomes unreliable wireless channel issues. In our study, simulations and real world experiments were conducted to evaluate the efficacy and efficiency of the proposed solution. Our results show GeoSVR can provide higher packet delivery ratio with comparable latency to other geographic routing schemes.

ARDEN: Anonymous networking in delay tolerant networks

Delay Tolerant Networks (DTNs) provide a communications infrastructure for environments lacking continuous connectivity. Such networks rely on the mobility of nodes and the resulting opportunistic connections to carry messages from source to destination. Unfortunately, exchanging packets with an arbitrary intermediary node makes privacy difficult to achieve in these systems as any adversary can easily act as an intermediary and determine the sender and receiver of a message. In this paper, we present ARDEN, an anonymous communication mechanism for DTNs based on a modified onion routing architecture. Instead of selecting specific nodes through which messages must pass as is traditionally done in onion routing, ARDEN uses Attribute-Based Encryption (ABE) to specify and manage groups that may decrypt and forward messages. Through simulation, we show that this approach not only increases throughput and reduces end-to-end latency over traditional onion routing techniques, but also adds minimal overhead when compared to DTN routing protocols that do not provide anonymity guarantees. Through this, we show that ARDEN is an effective solution for anonymous communication in intermittently connected networks such as DTNs.

Comparison of three artificial intelligence techniques for discharge routing

The inter-comparison of three artificial intelligence (AI) techniques are presented using the results of river flow/stage timeseries, that are otherwise handled by traditional discharge routing techniques. These models comprise Artificial Neural Network (ANN), Adaptive Nero-Fuzzy Inference System (ANFIS) and Genetic Programming (GP), which are for discharge routing of Kizilirmak River, Turkey. The daily mean river discharge data with a period between 1999 and 2003 were used for training and testing the models. The comparison includes both visual and parametric approaches using such statistic as Coefficient of Correlation (CC), Mean Absolute Error (MAE) and Mean Square Relative Error (MSRE), as well as a basic scoring system. Overall, the results indicate that ANN and ANFIS have mixed fortunes in discharge routing, and both have different abilities in capturing and reproducing some of the observed information. However, the performance of GP displays a better edge over the other two modelling approaches in most of the respects. Attention is given to the information contents of recorded timeseries in terms of their peak values and timings, where one performance measure may capture some of the information contents but be ineffective in others. Thus, this makes a case for compiling knowledge base for various modelling techniques.

Modelling vehicle routing in GIS

The field of vehicle routing has seen the development of a variety of mathematical techniques. While the practical application of these techniques has depended on the use of spatial information such as road networks, these techniques have developed independently of other users of spatial information. This paper argues that traditional routing techniques have neglected the importance of path constraints and that GIS approaches allow the modelling of an extended range of routing problems. The well-known taxonomy of routing problems by Bodin and Golden is extended to reflect the additional possibilities introduced by the use of GIS techniques. The paper concludes by suggesting that the synthesis of vehicle routing and GIS techniques in a spatial decision support system can greatly enhance the modelling of these problems.

Distribution of mobile agents in vulnerable networks

AbstractAdvances in the Internet and the computer industry have created many new application areas for network routing such as Grid computing and also brings new challenges to traditional routing techniques. In this paper we propose a mobile agent‐based routing model in vulnerable networks for these applications. To characterize the behaviors of mobile agents and their effects on the network performance, we analyze the population distribution of mobile agents as a measurement of the computational resource consumption. Our analysis reveals theoretical insights into the statistical behaviors of mobile agents and provides useful tools for effectively managing mobile agents in large networks. Copyright © 2006 John Wiley &amp; Sons, Ltd.

Integrated location management and location-aided routing system for mobile ad hoc networks

We present an integrated location management and location-aided routing system for mobile ad hoc network (MANET) which organizes the MANET into a two-level routing hierarchy with the help of Voronoi diagrams. The location information of mobile nodes in a Voronoi zone is summarized using bloom filters and distributed among the location servers in the zones. In the integrated system, the destination's location is learned during routing in the overlay network of the hierarchy. Theoretical analysis and simulation results show that the proposed system reduces the routing time by about 25% over traditional location-aided routing techniques for transaction-type traffic while incurring only small management overhead and low storage requirement.

Traffic engineering based on local states in Internet protocol-based radio access networks

The purpose of this research is to develop and evaluate a traffic engineering architecture that uses local state information. This architecture is applied to an Internet protocol radio access network (RAN) that uses multi-protocol label switching (MPLS) and differentiated services to support mobile hosts. We assume mobility support is provided by a protocol such as the hierarchical mobile Internet protocol. The traffic engineering architecture is router based — meaning that routers on the edges of the network make the decisions onto which paths to place admitted traffic. We propose an algorithm that supports the architecture and uses local network state in order to function. The goal of the architecture is to provide an inexpensive and fast method to reduce network congestion while increasing the quality of service (QoS) level when compared to traditional routing and traffic engineering techniques. We use a number of different mobility scenarios and a mix of different types of traffic to evaluate our architecture and algorithm. We use the network simulator ns-2 as the core of our simulation environment. Around this core we built a system of pre-simulation, during simulation, and post-processing software that enabled us to simulate our traffic engineering architecture with only very minimal changes to the core ns-2 software. Our simulation environment supports a number of different mobility scenarios and a mix of different types of traffic to evaluate our architecture and algorithm.

Exact routing with search space reduction

The layout problem in VLSI-design can be broken up into the subtasks partitioning, floorplanning, placement, and routing. In the routing phase, a large number of connections between the blocks and cells have to be established, while intersections lead to short circuits and, therefore, have to be avoided. We present an approach for exact routing of multiterminal nets that complements traditional routing techniques. It is particularly well suited for an application to dense problem instances and the completion of routing in subregions, which turn out to be difficult for routing tools based on heuristic methods. The exact router proposed uses symbolic methods, i.e., MDDs (multivalued decision diagrams) for representation of the routing space. For the necessary computations of routing solutions, we profit considerably from the efficient basic operations on MDDs. All possible solutions to the routing problem are represented by one single MDD and, once this MDD is given, routability can be decided within constant time. To reduce the search space of possible routing solutions, so-called forced cells are computed. Experimental results are given to show the feasibility and the practicability of the approach.

ROUTING IN RANDOM MULTISTAGE INTERCONNECTIONS NETWORKS: COMPARING EXHAUSTIVE SEARCH, GREEDY AND NEURAL NETWORK APPROACHES

The problem of establishing point-to-point communication routes in a random multistage interconnection network (RMIN) is addressed. A neural network routing scheme is presented. This routing scheme is compared to two more traditional routing techniques—namely, exhaustive search routing and greedy routing. The main criterion that is examined is the ability of each routing methodology to solve routing problems. Results are obtained through simulation of the routing methodologies for three different RMINs. The sample RMINs are relatively small since the neural network router in its present form will only be competitive for small RMINs. The simulations show that the three routing schemes perform similarly for the three sample RMINs. Other criteria that will be touched upon are the speed and the resource utilization of each routing methodology and the pros and cons of each approach will be discussed. The results suggest that neural network routers may be appropriate for some communication applications involving RMINs.