Single Routing Research Articles

Improving Quality of Service for Mobility Aware of Multi-path Routing Protocol for Load Balancing in Mobile Ad-Hoc Network

In Mobile Ad hoc Network (MANET), the single path QoS routing is disadvantageous as it may cause interference, fading, collision and link failures. For QoS routing, the load balancing is necessary, because it allows a router to take advantage of best of multiple shortest paths to a given destination. It also avoids congestion within the network using multipath. While using the MQRLB, the Load balancing may cause some of the major issues such as Link failure, Out of range, Limited Bandwidth, due to this the load balancing in multipath function is not efficient.To overcome these issues, in this paper, MMQRLBMultipath Mobility Aware QoS Routing Load Balancingwith AODV(Ad-hoc on demand distance vector)protocol is proposed to improve the QoS in Load balancing.

Limiting the Excess Broadcast in Multipath Routing Using Ant Colony Optimization

Single path routing in mobile adhoc network (MANET) suffers from frequent disconnections due to movement of nodes. So, multipath routing evolves as a solution to the single path routing. Many researchers are working on devising multipath routing protocols with new features and strategies. Ant colony optimization is also used by many investigators for developing new solutions for multipath routing in MANET. In this paper we have worked on controlling the broadcast being done by the intermediate nodes. We improved on our work done in Amanpreet Kaur et al. titled “ACO Agent Based Routing in AOMDV Environment” by making use of channel contention and backoff technique. We compared the newly designed algorithm with three broad categories unipath, multipath and ant based algorithm. The new algorithm performs better as compared with other algorithms on the basis of various QoS metrics like throughput, jitter, end to end delay, and number of packets send, lost in packets and delivery ratio.

DETERMINATION OF DOWNSTREAM FLOOD FLOW CONSIDERING INPUTS FROM DIFFERENT UPSTREAM RIVERS USING ANN

For estimating and forecasting of flood event, researchers and engineers mostly use the Muskingum flood routing method which is widely used throughout the world. The application of two parameter based Muskingum model is valid only for single inflow flood routing without any lateral inflow into the routing reach. However, normally a river is fed by a number of branch channels or rivulets at various upstream points. So, the single inflow-outflow Muskingum model cannot be applied in such situation. To overcome this problem, artificial Neural Network (ANN) has been applied in a river system considering inflow from various upstream rivers with a common outflow section. A simple static ANN model have been developed using concurrent discharge data. The model is applied in Mississippi River network starting from St. Louis, Montana to downstream section at Thebes, Illinois. In this reach, from St. Louis to Thebes, in the Mississippi river, a total of six lateral inflows confluence to the main river at different locations. Using ANN model, considering water discharge as input from all the upstream sections, water discharge at the most downstream section, Thebes is computed. Statistical performance analysis of the estimated data shows that ANN can be efficiently used for estimation of flood flow considering multiple inflows.

Defense Against Co-Operative Black-hole Attack and Gray-hole Attack in MANET

Mobile Ad-hoc networks (MANETs) can be termed as an autonomous system created by the collaboration of a set of motile nodes communicating with one another through available wireless media and each node behaves as an end system, as well as a router for packet forwarding. Each node is allowed to move freely, change locations and configure them to form a network. Finding an optimal and efficient path between the sender and receiver node is the main aim of routing protocols. Multipath routing protocols show a better result in comparison to single path routing protocols, for data transmission from source to destination extending lifetime of the network. Along with that security in routing for a protected communication between nodes has become a prime concern as any attack during routing may interrupt data transmission, paralyzing the whole network. During packet transmission through multiple paths a number of attacks make come into action by the unauthenticated and compromised nodes present within the network and black-hole attack is one of them. So our objective is to design a multipath routing protocol that detects and avoids the path containing black-hole. Our paper proposes a way to defense the black-hole and gray-hole attacks with the help of intelligence in MANET.

Constructing routing structures for sensor data collection with dynamic traffic patterns

In continuous sensor data collection, the network traffic pattern usually exhibits the dynamically changing characteristics. Different traffic patterns in the network result in the different proportion of energy spent on receiving, transmitting and idle listening at each node and thus require different routing structures for load balancing. Most existing routing approaches do not model the idle listening in their work since they target at the full network traffic pattern in which each node generates one data packet to transport to the base station at each sampling interval. This paper attempts to construct a single routing infrastructure to maximize the network lifetime under dynamic traffic patterns. We investigate the DAG (Directed Acyclic Graph) routing structure which allows each node to have multiple parents and child nodes and thus can better distribute the traffic load across different routes. We formulate the problem of finding the lifetime-optimal DAG routing structure as a mixed integer programming problem. We present a greedy algorithm to obtain a near-lifetime-optimal DAG and propose two methods to make use of the obtained DAG structure for data collection. Simulation results demonstrate the efficiency of the greedy algorithm and the promising of the proposed DAG-based methods in extending the network lifetime under dynamic traffic patterns.

Robust Optimization for a Maritime Inventory Routing Problem

We consider a single product maritime inventory routing problem in which the production and consumption rates are constant over the planning horizon. The problem involves a heterogeneous fleet and multiple production and consumption ports with limited storage capacity. Maritime transportation is characterized by high levels of uncertainty, and sailing times can be severely influenced by varying and unpredictable weather conditions. To deal with the uncertainty, this paper investigates the use of adaptable robust optimization where the sailing times are assumed to belong to the well-known budget polytope uncertainty set. In the recourse model, the routing, the order of port visits, and the quantities to load and unload are fixed before the uncertainty is revealed, while the visit time to ports and the stock levels can be adjusted to the scenario. We propose a decomposition algorithm that iterates between a master problem that considers a subset of scenarios and an adversarial separation problem that searches for scenarios that make the solution from the master problem infeasible. Several improvement strategies are proposed aiming at reducing the running time of the master problem and reducing the number of iterations of the decomposition algorithm. An iterated local search heuristic is also introduced to improve the decomposition algorithm. A computational study is reported based on a set of real instances.

Robust gateway placement in wireless mesh networks

Wireless mesh networks (WMNs) are communication networks that provide wireless Internet access over areas with limited infrastructure. Each node in a WMN serves several clients in its coverage area and transfers their traffic over wireless media to a few gateway nodes that have wired connections to the Internet. In this paper, we consider the Internet gateway placement (IGP) problem along with operational problems such as routing and wireless transmission capacity allocation. To eliminate wireless interference, we adopt the spatial reuse time division multiple access (TDMA) method, in which wireless transmissions are scheduled to occur at different time slots. We also employ destination-based single path routing for its ease of implementation. We present two mixed integer linear programming (MILP) formulations, both of which jointly determine the minimum number of gateway nodes needed to support forecasted demand, the locations of these gateway nodes, the routing trees, and the time slot allocations to wireless links. These formulations differ in the flow constraints. We also present a set of valid inequalities for the formulation with the multi-commodity flow constraints. In most cases, the solution to the IGP problem is not unique. Therefore, we also introduce a local search algorithm to select the most robust solution against any demand forecast errors. On example networks, we compare the proposed formulations with and without the valid inequalities in terms of the exact solution performances and the linear programming (LP) relaxations. We also demonstrate our local search algorithm to improve robustness against forecast errors on these example networks.

Faster rollout search for the vehicle routing problem with stochastic demands and restocking

Rollout algorithms lead to effective heuristics for the single vehicle routing problem with stochastic demands (VRPSD), a prototypical model of logistics under uncertainty. However, they can be computationally intensive. To reduce their run time, we introduce a novel approach to approximate the expected cost of a route when executing any rollout algorithm for VRPSD with restocking. With a sufficiently large number of customers its theoretical speed-up factor is of big-o order 1/3. On a set of instances from the literature, our proposed technique applied to a known rollout algorithm and three variants thereof achieves speed-up factors that range from 0.26 to 0.34 when there are more than fifty customers, degrading only marginally the quality of the resulting routes. Our method also applies to the a priori case, in which case it is exact.

Reliability-Aware Service Chaining In Carrier-Grade Softwarized Networks

Network Function Virtualization (NFV) has revolutionized service provisioning in cloud datacenter networks. It enables the complete decoupling of Network Functions (NFs) from the physical hardware middle boxes that network operators deploy for implementing service-specific and strictly ordered NF chains. Precisely, NFV allows for dispatching NFs as instances of plain software called virtual network functions (VNFs) running on virtual machines hosted by one or more industry standard physical machines. Nevertheless, NF softwarization introduces processing vulnerability ( e.g. , failures caused by hardware or software, and so on). Since any failure of VNFs could break down an entire service chain, thus interrupting the service, the functionality of an NFV-enabled network will require a higher reliability compared with traditional networks. This paper encloses an in-depth investigation of a reliability-aware joint VNF chain placement and flow routing optimization. In order to guarantee the required reliability, an incremental approach is proposed to determine the number of required VNF backups. Through illustration, it is shown herein that the formulated single path routing model can be easily extended to support resource sharing between adjacent backup VNF instances. This paper advocates the absolute existence of a share-resource-based VNF assignment strategy that is capable of trading off all of the reliability, bandwidth, and computing resources consumption of a given service chain. A heuristic is proposed to work around the complexity of the presently formulated integer linear programming (ILP). Thorough numerical analysis and simulations are conducted in order to verify and assert the validity, correctness, and effectiveness of this proposed heuristic reflecting its ability to achieve very close results to those obtained through the resolution of the complex ILP within a negligible amount of time. Above and beyond, the proposed resource-sharing-based VNF placement scheme outperforms existing resource-sharing agnostic schemes by 15.6% and 14.7% in terms of bandwidth and CPU utilization respectively.

On the Cross-Layer Orchestration to Address IP Router Outages With Cost-Efficient Multilayer Restoration in IP-Over-EONs

Due to the flexibility and adaptivity of elastic optical networks (EONs), IP-over-EON would be a promising infrastructure for next-generation backbone networks. As a backbone network usually carries massive traffic, one always needs to properly address its network survivability issue. The network survivability of an IP-over-EON can be affected not only by the fiber cuts in the EON layer but also by the router outages in the IP layer. In this work, we study how to realize the cross-layer orchestration to address IP router outages with cost-efficient multilayer restoration (MLR) in IP-over-EONs. Specifically, we consider the situation in which a single router outage happens in an IP-over-EON and propose MLR algorithms to minimize the additional operational expense (OPEX) due to MLR. We first design three MLR strategies to fully explore the flexibility and adaptivity of IP-over-EONs. Then, with the strategies, we formulate an integer linear programming (ILP) model to find the MLR scheme in which the additional OPEX due to incremental usages of sliceable bandwidth-variable transponders (SBV-Ts) and frequency slots (FSs) and lightpath reconfigurations is minimized. We also propose an auxiliary graph (AG) based heuristic algorithm to reduce the time complexity. The proposed algorithms are evaluated with extensive simulations, and the results indicate that, compared with an existing benchmark, they can effectively reduce the additional OPEX of MLR.

Competitive Algorithms from Competitive Equilibria

We introduce and study a general scheduling problem that we term the Polytope Scheduling problem (PSP). In this problem, jobs can have different arrival times and sizes, and the rates assigned by the scheduler to the jobs are subject to arbitrary packing constraints. The PSP framework captures a variety of scheduling problems, including the classical problems of unrelated machines scheduling, broadcast scheduling, and scheduling jobs of different parallelizability. It also captures scheduling constraints arising in diverse modern environments ranging from individual computer architectures to data centers. More concretely, PSP models multidimensional resource requirements and parallelizability, as well as network bandwidth requirements found in data center scheduling. We show a surprising result—there is a single algorithm that is O (1) competitive for all PSP instances when the objective is total completion time, and O (1) competitive for a large sub-class of PSP instances when the objective is total flow time. This algorithm simply uses the well-known Proportional Fairness (PF) algorithm to perform allocations each time instant. Though P F has been extensively studied in the context of maximizing fairness in resource allocation, we present the first analysis in adversarial and general settings for optimizing job latency. Further, P F is non-clairvoyant, meaning that the algorithm doesn’t need to know jobs sizes until their completion. We establish our positive results by making novel connections with Economics, in particular, the notions of market clearing, Gross Substitutes, and Eisenberg-Gale markets. We complement these positive results with a negative result: We show that for the total flow time objective, any non-clairvoyant algorithm for general PSP has a strong lower bound on the competitive ratio unless given a poly-logarithmic speed augmentation. This motivates the need to consider sub-classes of PSP when studying flow time. The sub-class for which we obtain positive results not only captures several well-studied models, such as scheduling with speedup curves and related machine scheduling, but also captures as special cases hitherto unstudied scheduling problems, such as single source flow routing, routing multicast (video-on-demand) trees, and resource allocation with substitute resources.

Non-Elementary Formulations for Single Vehicle Routing Problems with Pickups and Deliveries

We study the class of one-to-many-to-one single vehicle routing problems with pickups and deliveries, in which a single capacitated vehicle is used to serve a set of customers requiring a delivery, a pickup, or both. These problems have many real-world applications, including beverage distribution, courier service transportation, and reverse logistics. We first concentrate on a well-studied problem in this class, known as the single vehicle routing problem with deliveries and selective pickups (SVRPDSP), in which deliveries are mandatory but pickups are optional and generate a revenue if performed, and customers requiring both a delivery and a pickup (combined demand) can be visited either once or twice. Most exact algorithms in the literature solve SVRPDSP by looking for elementary tours on an extended network that is obtained by transforming each combined demand customer into two different customers, one requiring only the delivery and the other one only the pickup. Because this can result in a significant loss in performance, in this work we focus instead on the original problem network and present formulations that can yield non-elementary tours. Through the use of Benders decomposition, valid inequalities, and tailored optimization techniques based on branch-and-cut frameworks, we develop exact algorithms that outmatch previous results in the literature and obtain proven optimal solutions for all benchmark instances. We then generalize the algorithms to solve several other vehicle routing problems with pickups and deliveries, including the cases of split deliveries, intermediate drop-offs, mandatory pickups, and multiple vehicles. The online appendix is available at https://doi.org/10.1287/opre.2017.1639 .

Single allocation p-hub median location and routing problem with simultaneous pick-up and delivery

We introduce the single allocation p-hub median location and routing problem with simultaneous pick-up and delivery based on observations from real life hub networks. The aim of our problem is to minimize the cost of transferring the flow between hubs and routing the flow in the network. We propose several mixed integer programming formulations and two heuristic approaches based on multi start simulated annealing and ant colony system to solve these problems. Extensive results demonstrate that using our methods good solutions can be found despite the computationally challenging nature of the problem.

ABORt: Acknowledgement-Based Opportunistic Routing Protocol for High Data Rate Multichannel WSNs

The ease of deployment and the auto-configuration capabilities of Wireless Sensor Networks (WSNs) make them very attractive in different domains like environmental, home automation or heath care applications. The use of multichannel communications in WSNs helps to improve the overall performance of the network. However, in heavy traffic scenarios, routing protocols should be adapted to allow load balancing and to avoid losing data packets due to congestion and queue overflow. In this paper, we present an Acknowledgement-Based Opportunistic Routing (ABORt) protocol designed for high data rate multichannel WSNs. It is a low overhead protocol that does not rely on synchronization for control traffic exchange during the operational phase of the network. ABORt is an opportunistic protocol that relies on link layer acknowledgements to disseminate routing metrics, which helps to reduce overhead. The performance of ABORt is evaluated using the Cooja simulator and the obtained results show that ABORt has a high packet delivery ratio with reduced packet end-to-end delay compared to two single channel routing protocols and two multichannel routing protocols that use number of hops and expected transmission count as routing metrics.

Stochastic single vehicle routing problem with ordered customers and partial fulfilment of demands

ABSTRACTWe consider the problem of finding the optimal routing of a single vehicle that starts its route from a depot and delivers a product to N customers that are served according to a particular order. The vehicle during its route can return to the depot for replenishment. It is assumed a stochastic demand for each customer. The actual demand of each customer becomes known upon the vehicle's arrival at the customer's site. It is permissible to satisfy fully or to satisfy partially or not to satisfy the demand of a customer. The cost structure includes travel costs between consecutive customers, travel costs between the customers and the depot and penalty costs if a customer's demand is not satisfied or if it is satisfied partially. A dynamic programming algorithm is developed for the determination of the optimal routing policy. It is shown that the optimal routing policy has a specific threshold-type structure. Furthermore, if we consider the same problem without the assumption that the customers are ordered, numerical experiments indicate that the optimal routing strategy can be computed for N smaller or equal to nine.

An Enhanced Source Location Privacy Protection Technique for Wireless Sensor Networks using Randomized Routes

Asset monitoring and tracking is an important application of wireless sensor networks (WSNs). Tiny sensors collect information about the assets and convey this message to the base station using multi-hop routing techniques. For instance, in habitat monitoring application, the nodes collect details of the endangered species and report to the central controller, i.e., the base station. Preserving the privacy of these assets from the attackers is imperative. An attacker may backtrack the message flow and eventually capture the asset. In this work, we aim to improve the source location privacy, which is measured by the safety period, by designing a new routing technique where randomized routes in the whole network are generated distributively between the node of origin and the base station. The diversity of the routing paths will lengthen the backtracking period of the attacker and thus increase the safety period. The key feature of the solution is that it achieves improved source location privacy without hampering the network lifetime. Unlike the existing solutions, the proposed technique does not employ any fake sources that decreases the network lifetime due to generation of large number of dummy packets. The solution performs quite well even when the asset is near the base station. The proposed method is analysed and compared with forward random walk and phantom single path routing schemes. Simulation results demonstrate that the proposed method achieves improved privacy level with more uncertainty in the routing paths than the current techniques.

SSAAR: Enhanced Spatial Reusability for Optimal Path Selection in Wireless Sensor Networks

The advancements made in the wireless sensor networks have enabled the research communities to delve into the study of spatial reusability concepts. Due to the restricted capacity in wireless links, it is a significant to select the optimal route for packet transmission. The achievement of optimal route selection is verified by the high end-to-end throughput rate. Routing in WSNs is very challenging due to the inherent characteristics that distinguish these networks from other wireless networks like mobile ad hoc networks or cellular networks. In this paper, we have proposed an improved spatial reusability process using Single Path and Anypath Routing Protocols (SSAAR). The task of single path routing is to minimize the cost of the nodes in wireless links, whereas the task of any path routing is to assists the intermediate node who overhears the packet to participate in packet forwarding. Experimental results have shown the efficacy of our proposed work in terms of cost minimization functionalities. Keywords: Cost Minimization, Packet Forwarding, Single Path and Anypath Routing Protocols, Wireless Sensor Networks, Wireless Links

Two Phased Routing Protocol Incorporating Distributed Genetic Algorithm and Gradient Based Heuristic in Clustered WSN

In wireless cluster networks with a single non mobile sink, finding the optimal cluster assignment is a non-trivial problem. The inherently non centralized nature of wireless sensor networks poses a problem as majority of the learning algorithms are centralized. It is also desirable that single routing algorithm be applicable regardless of whether the sensor network is a dense single-hop network or a sparse multi-hop network. In this paper we present the two phased routing incorporating distributed genetic algorithm and gradient based heuristic (TRIGGER) as an attempt to solve these problems. In the first phase of TRIGGER a distributed (island model) genetic algorithm based clustering is employed to find a spatially optimal cluster assignment. In the second phase a gradient based routing forwards the already aggregated data to the sink. We discuss the rationale behind the two phased nature of TRIGGER. We demonstrate the effectiveness of TRIGGER with extensive simulations and discuss the results.

Algorithms for a special class of state-dependent shortest path problems with an application to the train routing problem

We study the state-dependent shortest path problem and focus on its application to the Single Train Routing Problem consisting of a rail network with only double-track segments, where the objective is to route one train through an empty network as fast as possible. We show that the Single Train Routing Problem is NP-hard. We investigate the solution properties and present sufficient conditions for optimality. Different conditions on the parameters are given to guarantee that certain local route selection is optimal. Then, a dynamic programming heuristic is introduced and conditions when the proposed heuristic can obtain the optimal solution in polynomial time are also discussed. Experimental results show the efficiency of the proposed heuristics for general problem settings.

Energy balancing RPL protocol with multipath for wireless sensor networks

With the rising trend of the incidence and prevalence of chronic diseases all over the world, proactive healthcare systems with wireless sensor network (WSN) technology have attracted people’s extensive attention. One of the researches, such as routing protocol for low power and lossy networks (RPL) only takes into consideration a single metric, energy, hops or routing quality, and so on. To extend the survival time of the network effectively and maximize the utilization of energy, we need to consider both node metric and link metric that affect the network, and design IRPL (Improved RPL) protocol. We propose the life cycle index (LCI) as path selection objective function. The index takes node metric (node energy and node hops) and link metric (throughput, packet loss, link quality) into consideration. In order to detect and distribute congestion information, we add congestion detection factor to the index. According to node energy, hop and congestion detection factor, we optimize the calculation method of rank. Our method redesigns parent node selection strategy. Not only does the strategy select the best parent node by using the improved index, but also saves other parent nodes that meet the conditions. Meanwhile, we propose a multipath scheme by using the DODAG structure, and use the scheme to solve the congestion problem. The simulation results show that the scheme shows better performance in terms of network load, end-to-end delay, packet delivery ratio, percentage of the optimal parent node change, energy consumption and network lifetime.