Greedy Routing Research Articles

Navigability of temporal networks in hyperbolic space

Information routing is one of the main tasks in many complex networks with a communication function. Maps produced by embedding the networks in hyperbolic space can assist this task enabling the implementation of efficient navigation strategies. However, only static maps have been considered so far, while navigation in more realistic situations, where the network structure may vary in time, remains largely unexplored. Here, we analyze the navigability of real networks by using greedy routing in hyperbolic space, where the nodes are subject to a stochastic activation-inactivation dynamics. We find that such dynamics enhances navigability with respect to the static case. Interestingly, there exists an optimal intermediate activation value, which ensures the best trade-off between the increase in the number of successful paths and a limited growth of their length. Contrary to expectations, the enhanced navigability is robust even when the most connected nodes inactivate with very high probability. Finally, our results indicate that some real networks are ultranavigable and remain highly navigable even if the network structure is extremely unsteady. These findings have important implications for the design and evaluation of efficient routing protocols that account for the temporal nature of real complex networks.

Taguchi Method to Measure the Impact of GPSR Routing Protocol Parameters in VANET Performances

Background/Objectives: Routing protocols play an important role in the performances of Vehicular Ad Hoc networks (VANET). Routing protocols procedures are controlled with a set of parameters from being dragged to undesired situations such as flooding the network with unnecessary overhead. These parameters have a direct impact on the efficiency of a routing protocol and the overall network performances. The impacts of these parameters on the network performances are less considered in the literature. Methods: This paper proposed the Taguchi delta analysis to measure the impact of Greedy Perimeter Stateless Routing (GPSR) protocols parameters on the performances of VANET. Then, the obtained results are investigated with iterative method to validate the Taguchi observations. Further, an offline optimization of the GPSR parameters is evaluated by performing Taguchi SNR method for fine-tuning the GPSR routing parameters. Application/ Improvements: GPSR with Taguchi tuned is compared with GPSR configuration in a realistic VANET scenario. The obtained results showed that, GPSR with Taguchi configuration outperformed the GPSR in terms of delay and PDR on the simulated scenarios. Keywords: GPSR, Optimization, Routing Parameters, Taguchi Method, VANET

A source location protection protocol based on dynamic routing in WSNs for the Social Internet of Things

With the development of the Internet of Things (IoT), a more humanity-related network called the Social Internet of Things (SIoT) is now evolving. WSNs are also part of the Social Internet of Things (SIoT), a new application of the Internet of Things (IoT). Considering the characteristics of sensor nodes, including limited resource, limited communication capability, and an uncontrollable environment, location privacy protection is a challenging problem for WSNs. In this paper, we propose a source location protection protocol based on dynamic routing to address the source location privacy problem. We introduce a dynamic routing scheme that aims at maximizing paths for data transmission. The proposed scheme first randomly chooses an initial node from the boundary of the network. Every package will travel a greedy route and a subsequent directed route before reaching the sink. Theoretical and experimental results show that our scheme can preserve source location privacy and defeat various privacy disclosure attacks (eavesdropping attack, hop-by-hop trace back attack, and direction-oriented attack) without affecting the network lifetime.

Stability of the Greedy Algorithm on the Circle

AbstractWe consider a single‐server system with service stations in each point of the circle. Customers arrive after exponential times at uniformly distributed locations. The server moves at finite speed and adopts a greedy routing mechanism. It was conjectured by Coffman and Gilbert in 1987 that the service rate exceeding the arrival rate is a sufficient condition for the system to be positive recurrent, for any value of the speed. In this paper we show that the conjecture holds true.© 2017 Wiley Periodicals, Inc.

All-optical tree-based greedy router using optical logic gates and optical flip-flops

Due to ever-increasing throughput demands, the lookup in conventional IP routers based on longest prefix matching is becoming a bottleneck. Additionally, the scalability of current routing protocols is limited by the size of the routing tables. Geometric greedy routing is an alternative to IP routing which replaces longest prefix matching with a simple calculation employing only local information for packet forwarding. For the first time, in this paper we propose a novel and truly all-optical geometric greedy router based on optical logic gates and optical flip-flops. The circuit of the router is constructed through the interconnection of SOAs and directional couplers. The successful functionality of the proposed router is verified through simulation. The circuit enables high data rate throughput.

Hyperbolic Embedding for Efficient Computation of Path Centralities and Adaptive Routing in Large-Scale Complex Commodity Networks

Computing the most central nodes in large-scale commodity networks is rather important for improving routing and associated applications. In this paper, we introduce a novel framework for the analysis and efficient computation of routing path-based centrality measures, focusing on betweenness and traffic load centrality. The proposed framework enables efficient approximation and in special cases accurate computation of the aforementioned measures in large-scale complex networks, as well as improving/adapting commodity (traffic) routing by identifying and alleviating key congestion points. It capitalizes on network embedding in hyperbolic space and exploits properties of greedy routing over hyperbolic coordinates. We show the computational benefits and approximation precision of our approach by comparing it with state-of-the-art path centrality computation techniques. We demonstrate its applicability on real topologies, characteristic of actual large-scale commodity networks, e.g., data, utility networks. Focusing on two graph embedding types, Rigel and greedy, we compare their impact on the performance of our framework. Then, we exemplify and statistically analyze the dynamic routing adaptation, via the variation of the minimum-depth spanning tree employed for greedy embedding in hyperbolic space. Notably, this allows for efficient routing adaptation according to a simple, distributed computation that can be applied during network operation to alleviate arising bottlenecks.

Scalable Back-Bone-Assisted Hop Greedy Routing for VANETS

Scalable Back-Bone-Assisted Hop Greedy Routing for VANETS

Collective navigation of complex networks: Participatory greedy routing

Many networks are used to transfer information or goods, in other words, they are navigated. The larger the network, the more difficult it is to navigate efficiently. Indeed, information routing in the Internet faces serious scalability problems due to its rapid growth, recently accelerated by the rise of the Internet of Things. Large networks like the Internet can be navigated efficiently if nodes, or agents, actively forward information based on hidden maps underlying these systems. However, in reality most agents will deny to forward messages, which has a cost, and navigation is impossible. Can we design appropriate incentives that lead to participation and global navigability? Here, we present an evolutionary game where agents share the value generated by successful delivery of information or goods. We show that global navigability can emerge, but its complete breakdown is possible as well. Furthermore, we show that the system tends to self-organize into local clusters of agents who participate in the navigation. This organizational principle can be exploited to favor the emergence of global navigability in the system.

Fast Distributed Consensus Through Path Averaging on Random Walks

Distributed computation of average consensus is an important function in numerous wireless sensor networks and ad-hoc networks applications. In light of the severe resource constraints characterizing these embedded networked systems, it is of paramount importance the design of effective algorithms with low computational, communication, and energy requirements. Randomized gossip are a category of network algorithms that entail communication and information exchange among nodes selected with probabilistic mechanisms. They are considered attractive solutions for solving consensus problems because of their decentralized nature, conceptual simplicity, and capability of adapting to structural network modifications. However, the number of iterations needed by randomized gossip to converge towards consensus is an issue to be addressed to reduce latency and to limit the impact of the algorithm on the lifetime of battery operated devices. Solutions to the problem have been proposed that make use of localization and greedy geographic routing to build overlay graphs on top of a randomized gossip protocol, thus enabling information averaging among non-neighbor nodes and accelerating convergence. The main drawback of this type of approach is that each node is required to know its position, which is not always affordable in terms of costs or not even possible because of the lack of GPS signal. In this article we propose a novel approach to the consensus averaging problem based on a random walk mechanism to identify nodes involved in a computation round, and on a path averaging technique to update more than two nodes into the same round. The resulting algorithm doesn’t rely on any positioning mechanism. Experimental results provide evidence of the improved performance of the proposed method with respect to standard randomized gossip, with considerable gain in terms of convergence speed.

Routing in a polygonal terrain with the shortest beacon watchtower

In a paper by Biro et al. [3], a novel twist on guarding in art galleries, motivated by geographical greedy routing in sensor networks, is introduced. A beacon is a fixed point that when activated induces a force of attraction that can move points within the environment. The effect of a beacon is similar to standard visibility with some additional properties. The effects of a beacon are asymmetric leading to separate algorithms to compute the “beacon kernel” and “inverse beacon kernel”. In Biro [2]O(n2) time algorithms are given to compute the beacon kernel and the inverse beacon kernel in simple polygons. In this paper we revisit the problem of computing the shortest watchtower to guard a 2D terrain, using the properties of beacons, and we present an O(nlog⁡n) time algorithm that computes the shortest beacon watchtower. In doing this we introduce O(nlog⁡n) time algorithms to compute the beacon kernel and the inverse beacon kernel in a monotone polygon. We also prove that Ω(nlog⁡n) time is a lower bound for computing the beacon kernel of a monotone polygon.

Greedy Cluster Based Routing for Wireless Sensor Networks

In recent years, applications of wireless sensor networks have evolved in many areas such as target tracking, environmental monitoring, military and medical applications. Wireless sensor network continuously collect and send data through sensor nodes from a specific region to a base station. But, data redundancy due to neighbouring sensors consumes energy, compromising the network lifetime. In order to improve the network lifetime, a novel cluster based local route search method, called, Greedy Cluster-based Routing (GCR) technique in wireless sensor network. The proposed GCR method uses arbitrary timer in order to participate cluster head selection process with maximum neighbour nodes and minimum distance between the source and base station. GCR constructs dynamic routing improving the rate of network lifetime through Mass Proportion value. Also, GCR uses a greedy route finding strategy for balancing energy consumption. Experimental results show that GCR achieves significant energy savings and prolong network lifetime.

GRACO: a geographic greedy routing with an ACO based void handling technique

Geographic routing has gained much attention as a basic routing primitive in wireless sensor networks (WSNs) due to its memory-less, scalability, efficiency and low overhead features. However, it suffers from communication holes, where no next hop candidate is closer to the destination than the node currently holding the packet. This paper proposes greedy with ACO-based recovery routing protocol (GRACO), an efficient geographic routing protocol with a novel void recovery strategy based on ant colony optimisation (ACO). GRACO is able to adaptively adjust the forwarding mechanism to avoid the blocking situation and effectively deliver data packets. Compared to greedy-face-greedy (GFG), one of the best performing geographic routing protocols, simulation results demonstrate that GRACO can successfully find shorter routing paths with higher delivery rate, less control packet overhead and shorter end-to-end delay.

Two-hop distance estimation in wireless sensor networks

In wireless sensor networks, the accurate estimation of distances between sensor nodes is essential. In addition to the distance information available for immediate neighbors within a sensing range, the distance estimation of two-hop neighbors can be exploited in various wireless sensor network applications such as sensor localization, robust data transfer against hidden terminals, and geographic greedy routing. In this article, we propose a two-hop distance estimation method, which first obtains the region in which the two-hop neighbor nodes possibly exist and then takes the average of the distances to the points in that region. The improvement in the estimation accuracy achieved by the proposed method is analyzed in comparison with a simple summation method that adds two single-hop distances as an estimate of a two-hop distance. Numerical simulation results show that in comparison with other existing distance estimation methods, the proposed method significantly reduces the distance estimation error over a wide range of node densities.

Void-Handling Techniques for Routing Protocols in Underwater Sensor Networks: Survey and Challenges

From the view of routing protocols in underwater sensor networks (UWSNs), the presence of communication void, where the packet cannot be forwarded further using the greedy mode, is perhaps the most challenging issue. In this paper, we review the state of the art of void-handling techniques proposed by underwater geographic greedy routing protocols. To this, we first review the void problem and its negative impact on the category of the geographic greedy routing protocols, which does not entail any void recovery technique. It is followed by a discussion about the constraints, challenges, and features associated with the design of void-handling techniques in UWSNs. Afterward, currently available void-handling techniques in UWSNs are classified and investigated. They can be classified into two main categories: 1) location-based and 2) depth-based techniques. The advantages and disadvantages of each technique along with the recent advances are then presented. Finally, we present a qualitative comparison of these techniques and also discuss some possible future directions.

Improving Resiliency in SDN using Routing Tree Algorithms

The ability to recover the control logic after a failure is detected in specific time window is called resiliency. The Software Defined Network (SDN) is an emerged and powerful architecture which allow to separate the control plane from forwarding. This decoupling architecture brings new difficulties to the network resiliency because link failure between switch and controller could defunct the forwarding plane. It has been identified that the resiliency of the network can be improved by choosing the correct place for the controller and by choosing proper routing tree once the controller location is known. In this work, we have analysed the performance of various Routing Tree algorithms on different network topology generated by Bernoulli Random Graph model and found that Greedy Routing Tree (GRT) provides the maximum resiliency. The Closeness Centrality Theorem has proposed to find the best controller position and later analysed the performance of various single controller placement algorithms on GRT for finding the overall improvement of the resiliency of the network.

GRACO: a geographic GReedy routing with an ACO based void handling technique

Geographic routing has gained much attention as a basic routing primitive in wireless sensor networks (WSNs) due to its memory-less, scalability, efficiency and low overhead features. However, it suffers from communication holes, where no next hop candidate is closer to the destination than the node currently holding the packet. This paper proposes greedy with ACO-based recovery routing protocol (GRACO), an efficient geographic routing protocol with a novel void recovery strategy based on ant colony optimisation (ACO). GRACO is able to adaptively adjust the forwarding mechanism to avoid the blocking situation and effectively deliver data packets. Compared to greedy-face-greedy (GFG), one of the best performing geographic routing protocols, simulation results demonstrate that GRACO can successfully find shorter routing paths with higher delivery rate, less control packet overhead and shorter end-to-end delay.

Constrained energy-efficient routing in time-aware road networks

Routing problems find applications in many location-based services. Existing works which answer route queries mainly focus on static road networks rather than time-aware road networks. Observe the fact that (i) the same road segment may be driven with different speeds during different time intervals, and (ii) users usually prefer driving on a route that consumes minimum energy within a travel time budget. Motivated by this, this paper proposes the Constrained Energy-Efficient Time-Aware Routing problem, denoted as CEETAR. We take time factor into consideration and utilize a time-aware speed model and a time-aware polynomial energy cost model. To solve CEETAR, we propose a dynamic programming solution, and then propose an approximation algorithm which uses the branch and bound, and scaling strategy with provable approximation bounds to answer the query of CEETAR in real-world dense road networks. In addition, we also propose a greedy route planning algorithm. Experimental results demonstrate that our approximation algorithms can efficiently answer CEETAR queries with high accuracy.

Space Shuffle: A Scalable, Flexible, and High-Performance Data Center Network

Data center applications require the network to be scalable and bandwidth-rich. Current data center network architectures often use rigid topologies to increase network bandwidth. A major limitation is that they can hardly support incremental network growth. Recent work proposes to use random interconnects to provide growth flexibility. However routing on a random topology suffers from control and data plane scalability problems, because routing decisions require global information and forwarding state cannot be aggregated. In this paper we design a novel flexible data center network architecture, Space Shuffle (S2), which applies greedy routing on multiple ring spaces to achieve high-throughput, scalability, and flexibility. The proposed greedy routing protocol of S2 effectively exploits the path diversity of densely connected topologies and enables key-based routing. Extensive experimental studies show that S2 provides high bisectional bandwidth and throughput, near-optimal routing path lengths, extremely small forwarding state, fairness among concurrent data flows, and resiliency to network failures.

Link Quality and Energy Aware Geographical Routing in MANETs using Fuzzy Logics

In literature, varieties of topology and geographical routing protocols have been proposed for routing in the MANETs. It is widely accepted that the geographical routings are a superior decision than topological routings. Majority of geographical routing protocols assume an ideal network model and choose the route that contains minimum number of hops. However, in reality, nodes have limited battery power and wireless links are additionally unreliable, so they may highly affect the routing procedure. Thus, for reliable data transmission, condition of the network such as link quality and residual energy must be considered. This paper aims to propose a novel multi-metric geographical routing protocol that considers both links-quality and energy metric along with progress metric to choose the next optimal node. The progress is determined by utilizing greedy as well as compass routing rather than pure greedy routing schemes. To combine these metrics, fuzzy logics are used to get the optimal result. Further, the protocol deals with “hole” problem and proposes a technique to overcome it. Simulations show that the proposed scheme performs better in terms of the packet delivery ratio, throughput and residual energy than other existing protocols.

Greedy Routing by Network Distance Embedding

Greedy routing has been applied to both wireline and wireless networks due to its scalability of routing state and resiliency to network dynamics. In this work, we solve a fundamental problem in applying greedy routing to networks with arbitrary topologies, i.e., how to construct node coordinates such that greedy routing can find near-optimal routing paths for various routing metrics. We propose Greedy Distance Vector (GDV), the first greedy routing protocol designed to optimize end-to-end path costs using any additive routing metric, such as: hop count, latency, ETX, ETT, etc. GDV requires no physical location information. Instead, it relies on a novel virtual positioning protocol, VPoD, which provides network distance embedding. Using VPoD, each node assigns itself a position in a virtual space such that the Euclidean distance between any two nodes in the virtual space is a good estimate of the routing cost between them. Experimental results using both real and synthetic network topologies show that the routing performance of GDV is better than prior geographic routing protocols when hop count is used as metric and much better when ETX is used as metric. As a greedy routing protocol, the routing state of GDV per node remains small as network size increases. We also show that GDV and VPoD are highly resilient to dynamic topology changes.