Routing In Large Networks Research Articles

Towards energy balancing optimization in wireless sensor networks: A novel quantum inspired genetic algorithm based sinks deployment approach

A Wireless Sensor Network (WSN) with multiple sinks alleviates the bottleneck problem of imbalanced energy consumption and leads to improved scalability and convenience in data collection for diverse range of large-scale applications. However, introducing more than one sink also brings a new set of challenges to overcome. To minimize the uneven energy consumption among sensors for longevity of the WSN, it is required to find optimal locations of the sinks that will permit most of the sensors to forward their data using minimum hops and to determine the optimal route for each sink with consistent route length. Routing in large networks is an NP-Hard problem. Previous schemes have solved this problem using multiple sinks deployment either in planned ways or random manner without simultaneously considering the possibilities of network topological changes, edge cases of sensors, and problem of uneven path length. In this context, this paper proposes a quantum inspired genetic algorithm based multiple sinks deployment approach (Q-GEMS) for energy consumption balancing among sensors to extend the network lifetime. Specially, the exiting approaches have focused on either minimizing energy consumption or reducing the path length. In addition to addressing these constraints, the proposed Q-GEMS is designed to be resilient which adapts to any changes in the planned topologies of deployed sinks in anticipation of sensor failures and external influences. Also to achieve greater energy consumption balance among sensors by equalizing the path length and bring down the cost of WSN by minimizing the number of sinks, novel heuristics have been proposed for positioning the sinks, binding the sensors to sinks, evaluating the quality of solutions and updating the Q-bit population. The experimental results show that the proposed Q-GEMS performs comparatively better than state of the art approaches and achieving network lifetime 5.1%, 21%, and 24% longer than that of EEMS, EESS, and DPSO+PSO respectively.

A Secure Protocol against Selfish and Pollution Attacker Misbehavior in Clustered WSNs

Wireless sensor networks (WSNs) have been widely used for applications in numerous fields. One of the main challenges is the limited energy resources when designing secure routing in such networks. Hierarchical organization of nodes in the network can make efficient use of their resources. In this case, a subset of nodes, the cluster heads (CHs), is entrusted with transmitting messages from cluster nodes to the base station (BS). However, the existence of selfish or pollution attacker nodes in the network causes data transmission failure and damages the network availability and integrity. Mainly, when critical nodes like CH nodes misbehave by refusing to forward data to the BS, by modifying data in transit or by injecting polluted data, the whole network becomes defective. This paper presents a secure protocol against selfish and pollution attacker misbehavior in clustered WSNs, known as (SSP). It aims to thwart both selfish and pollution attacker misbehaviors, the former being a form of a Denial of Service (DoS) attack. In addition, it maintains a level of confidentiality against eavesdroppers. Based on a random linear network coding (NC) technique, the protocol uses pre-loaded matrices within sensor nodes to conceive a larger number of new packets from a set of initial data packets, thus creating data redundancy. Then, it transmits them through separate paths to the BS. Furthermore, it detects misbehaving nodes among CHs and executes a punishment mechanism using a control counter. The security analysis and simulation results demonstrate that the proposed solution is not only capable of preventing and detecting DoS attacks as well as pollution attacks, but can also maintain scalable and stable routing for large networks. The protocol means 100% of messages are successfully recovered and received at the BS when the percentage of lost packets is around 20%. Moreover, when the number of misbehaving nodes executing pollution attacks reaches a certain threshold, SSP scores a reception rate of correctly reconstructed messages equal to 100%. If the SSP protocol is not applied, the rate of reception of correctly reconstructed messages is reduced by 90% at the same case.

Quantum-Inspired Ant-Based Energy Balanced Routing in Wireless Sensor Networks

Background: Limited energy capacity of battery operated Wireless Sensor Networks (WSNs) is the prime impediment in the ubiquity of WSNs as the network lifetime depends on the available energy at the nodes. Prolonging the network lifetime is the principal issue in WSNs and the challenge lies in devising a strategy for judicious use of available energy resources. Routing has been one of the most commonly used strategies for minimizing and balancing the energy consumption of nodes in a WSN. Methods: Routing in large networks has been proved to be NP-Hard and therefore meta heuristic techniques have been applied for handling this problem. Quantum-inspired algorithms are relatively new meta heuristic techniques which have been shown performing better than their traditional counter- parts. Therefore, Quantum inspired ant Based Energy balanced Routing (QBER) algorithm has been proposed in this paper for addressing the problem of energy balanced routing in WSNs. Results: Simulation results confirm that the proposed QBER algorithm performs comparatively better than other quantum inspired routing algorithms for WSNs. Conclusion:

Efficient Geometric Routing in Large-Scale Complex Networks with Low-Cost Node Design

The growth of the size of the routing tables limits the scalability of the conventional IP routing. As scalable routing schemes for large-scale networks are highly demanded, this paper proposes and evaluates an efficient geometric routing scheme and related low-cost node design applicable to large-scale networks. The approach guarantees that greedy forwarding on derived coordinates will result in successful packet delivery to every destination in the network by relying on coordinates deduced from a spanning tree of the network. The efficiency of the proposed scheme is measured in terms of routing quality (stretch) and size of the coordinates. The cost of the proposed router is quantified in terms of area complexity of the hardware design and all the evaluations involve comparison with a state-of-the-art approach with virtual coordinates in the hyperbolic plane. Extensive simulations assess the proposal in large topologies consisting of up to 100K nodes. Experiments show that the scheme has stretch properties comparable to geometric routing in the hyperbolic plane, while enabling a more efficient hardware design, and scaling considerably better in terms of storage requirements for coordinate representation. These attractive properties make the scheme promising for routing in large networks.

Scalable $\epsilon$-Optimal Decision-Making and Stochastic Routing in Large Networks via Distributed Supervision of Probabilistic Automata

A scalable distributed stochastic routing algorithm (GODDeS) is developed to effectively exploit high quality paths in lossy ad-hoc wireless environments, typically with a large number of nodes. The routing problem is modeled as an optimal control problem for a decentralized Markov decision process via a probabilistic local broadcast model, with links characterized by locally known or estimated drop probabilities that either remain constant on average or change slowly. Equivalence of this optimization problem to that of performance maximization of probabilistic automata allows us to effectively apply the theory of quantitative measures of probabilistic regular languages, and design a distributed, highly efficient, scalable, and stationary routing policy that very nearly minimizes source-to-sink drop probabilities across the network. Theoretical results provide rigorous guarantees on global performance, showing that the algorithm achieves near-global optimality in polynomial time, and worst case asymptotic...

Improving routing in large networks inside autonomous system

The internet is seen as the interconnection between different large networks together. Administrators use interior gateway protocol for routing inside these large networks. Managing routing issues in the network is a challenging task for administrators although they have a choice of selecting from a primitive routing protocol like routing information protocol (version 1) to the advanced routing protocol like open shortest path first protocol or intermediate system-to-intermediate system (IS–IS) protocol. Irrespective of the above mentioned protocols issue of scalability is troubling administrators. They let their network grow up to the limit where it becomes unmanageable. Our analytic study with the help of simulations shows that the these large networks become manageable and improves overall performance of routing and forwarding if route reflectors are used to segment large networks inside autonomous systems.

Virtual Area Routing: a Scalable Intra-Domain Routing Scheme

As a distributed link state protocol, OSPF shows poor scalability when dealing with intra-domain routing in large networks. In this paper we present a new routing scheme called Virtual Area Routing (VAR) aimed to overcome the scalability problems of OSPF. The control plane is separated from data plane and is undertaken by dedicated elements in VAR. We show that VAR can simplify network configuration and management as well as avoid unnecessary route computation. Finally we conduct experiments in real topology and the result shows VAR can reduce control overhead and improve network performance.

Partitioning techniques for reducing computational effort of routing in large networks

A new scheme is presented for partitioning a network having a specific number of nodes and degree of connectivity such that the number of operations required to find a constrained path between a source node and destination node, averaged over all source–destination pairs, is minimised. The scheme can speed up the routing function, possibly by orders of magnitude under favourable conditions, at the cost of a sub-optimal solution.

A distributed cache architecture with snooping for QoS routing in large networks

To meet the diverse quality-of-service (QoS) requirements of emerging multimedia applications, communication networks should provide end-to-end QoS guarantees. QoS routing is the first step towards this goal. The route computing overhead caused by on-demand calculation of QoS routes, especially in large networks with heavy traffic, is a concern and can cause scalability problems. This paper addresses this problem by introducing a novel distributed cache architecture. The distributed nature of the proposed cache architecture facilitates its deployment in large networks. To maximize the performance of the distributed cache architecture, cache snooping has been proposed to alleviate the side effects of network states fluctuations on the cached route so that the overall routing performance is significantly improved. Assuming a bandwidth-based QoS model, in performance evaluation of the proposed distributed cache architecture, we use a broad range of realistic network topologies, network traffic conditions, routing protocols, and aggregation techniques to evaluate different aspects of the proposed cache architecture under different conditions. The results confirm that the route caching is quite effective in reduction of route computing overhead. In addition, our results suggest that the cache snooping can significantly increase the overall routing performance, especially in the presence of highly inaccurate network state information.

Topology distribution cost vs. efficient routing in large networks

Routing a message in a network is efficient (in terms of weight of the path used to carry the message) when nodes know the full topology of the network. This may not be the case in large networks since a network may be composed of smaller autonomous pieces by design or by requirements on performance, with each piece having less than complete information about other pieces. We present a trade-off between the amount of topology information exchanged among these pieces and the efficiency of routing in the network. The large network that we study is a collection of networks connected by boundary nodes. Each boundary node knows the topology of its network and the connectivity of networks to each other. The question addressed here is how much topology information about each network should be distributed to other networks in order to achieve reasonably efficient routing.