Greedy Routing Research Articles

A Proactive Greedy Routing Protocol Precludes Sink-Hole Formation in Wireless Sensor Networks

A tree topology is a commonly employed topology for wireless sensor networks (WSN) to connect sensors to one or more remote gateways. In many-to-one traffic, routing imposes a heavy burden on downstream nodes, as the same routes are repeatedly used for packet forwarding from one or more sensor chains. The challenge is traffic paths that ensure balanced energy consumption at sink-hole to protect sensors from fast death. This paper proposes an energy consumption pattern-aware greedy routing protocol that proactively protects many-to-one topology from the sink-hole formation. The proposed protocol, Energy Balance-Based Energy Hole Alleviation in Tree Topology (EBEHA-T), precludes energy hole formation rather than retrospectively responding to a hole detection. Updated status of variations in energy consumption patterns at the sink-hole and construction feature of joint nodes in the tree topology aids in routing decision. Performance evaluation of EBEHA-T against benchmark method RaSMaLai shows increased energy-saving across the entire network and a marked improvement in energy consumption balance in energy hole zones. This precludes energy hole formation and the consequent network partitioning, leading to improved network lifetime beyond that of the RasMaLai.

Speed and Position Aware Dynamic Routing for Emergency Message Dissemination in VANETs

Vehicular Ad hoc Networks (VANETs) can help reduce traffic-related accidents by broadcasting Emergency Messages (EMs) in advance between vehicles. Due to the high-speed mobility of VANETs and attenuation of the wireless signal, reliable and fast transmission of EMs is a challenging task. Such as the chosen next-hop vehicle may have driven away from the neighborhood of the sender before receiving the EM, and rerouting may increase the delay when the EMs encounter transmission failure. To this end, we propose a Speed and Position aware Dynamic Routing (SPDR) for EM dissemination in VANETs. First, we introduce a speed metric dynamic greedy routing to provide a dynamic hop-by-hop rebroadcast of the EM. SPDR dynamically shrinks the Routing Decision Area (RDA) range based on the velocity variance of candidate neighbors and prioritizes the farthest vehicle in the shrunk RDA as the optimal next-hop, enhancing the reliable transmission of EMs. Then, we present a collaborative forwarding strategy to enable candidate neighbors to collaborate in communication. In case of transmission failure, SPDR elects the candidate vehicle close to the destination as the forwarder to reduce rerouting. Simulations in a practical motorway scenario using NS-2 and VanetMobiSim show that SPDR outperforms the existing protocols in terms of message delivery ratio, network throughput, and average dissemination delay.

Graph Theory Algorithms of Hamiltonian Cycle from Quasi‐Spanning Tree and Domination Based on Vizing Conjecture

In this study, from a tree with a quasi‐spanning face, the algorithm will route Hamiltonian cycles. Goodey pioneered the idea of holding facing 4 to 6 sides of a graph concurrently. Similarly, in the three connected cubic planar graphs with two‐colored faces, the vertex is incident to one blue and two red faces. As a result, all red‐colored faces must gain 4 to 6 sides, while all obscure‐colored faces must consume 3 to 5 sides. The proposed routing approach reduces the constriction of all vertex colors and the suitable quasi‐spanning tree of faces. The presented algorithm demonstrates that the spanning tree parity will determine the arbitrary face based on an even degree. As a result, when the Lemmas 1 and 2 theorems are compared, the greedy routing method of Hamiltonian cycle faces generates valuable output from a quasi‐spanning tree. In graph idea, a dominating set for a graph S = (V, E) is a subset D of V. The range of vertices in the smallest dominating set for S is the domination number (S). Vizing’s conjecture from 1968 proves that the Cartesian fabricated from graphs domination variety is at least as big as their domination numbers production. Proceeding this work, the Vizing’s conjecture states that for each pair of graphs S, L.

Greedy routing and the algorithmic small-world phenomenon

The algorithmic small-world phenomenon, empirically established by Milgram in the 60 s [1], was theoretically explained by Kleinberg in 2000 [2]. However, from today's perspective his model has several severe shortcomings that limit the applicability to real-world networks. In order to give a more convincing explanation, we study decentralized greedy routing in a random graph model (geometric inhomogeneous random graphs) which overcomes all previous shortcomings. We prove that greedy routing succeeds with constant probability, and in case of success almost surely finds an almost shortest path of length Θ(log⁡log⁡n), with stretch 1+o(1). Moreover, natural local patching methods ensure success probability 1, while maintaining the same stretch. These results also address the question whether there are efficient local routing protocols for the internet graph. Despite promising experimental studies the question remained unsolved theoretically. We give for the first time a rigorous, affirmative answer for a thoroughly validated model of the internet graph.

Systematic comparison of graph embedding methods in practical tasks.

Network embedding techniques aim to represent structural properties of graphs in geometric space. Those representations are considered useful in downstream tasks such as link prediction and clustering. However, the number of graph embedding methods available on the market is large, and practitioners face the nontrivial choice of selecting the proper approach for a given application. The present work attempts to close this gap of knowledge through a systematic comparison of 11 different methods for graph embedding. We consider methods for embedding networks in the hyperbolic and Euclidean metric spaces, as well as nonmetric community-based embedding methods. We apply these methods to embed more than 100 real-world and synthetic networks. Three common downstream tasks - mapping accuracy, greedy routing, and link prediction - are considered to evaluate the quality of the various embedding methods. Our results show that some Euclidean embedding methods excel in greedy routing. As for link prediction, community-based and hyperbolic embedding methods yield an overall performance that is superior to that of Euclidean-space-based approaches. We compare the running time for different methods and further analyze the impact of different network characteristics such as degree distribution, modularity, and clustering coefficients on the quality of the embedding results. We release our evaluation framework to provide a standardized benchmark for arbitrary embedding methods.

On Landmark Distances in Polygons

AbstractWe study the landmark distance function between two points in a simply connected planar polygon. We show that if the polygon vertices are used as landmarks, then the resulting landmark distance function to any given point in the polygon has a maximum principle and also does not contain local minima. The latter implies that a path between any two points in the polygon may be generated by steepest descent on this distance without getting “stuck” at a local minimum. Furthermore, if landmarks are increasingly added along polygon edges, the steepest descent path converges to the minimal geodesic path. Therefore, the landmark distance can be used, on the one hand in robotic navigation for routing autonomous agents along close‐to‐shortest paths and on the other for efficiently computing approximate geodesic distances between any two domain points, a property which may be useful in an extension of our work to surfaces in 3D. In the discrete setting, the steepest descent strategy becomes a greedy routing algorithm along the edges of a triangulation of the interior of the polygon, and our experiments indicate that this discrete landmark routing always delivers (i.e., does not get stuck) on “nice” triangulations.

A multi‐level routing method in vehicular ad hoc networks using unnamed aerial vehicle nodes

SummaryVehicular ad hoc networks (VANETs) are a subset of mobile ad hoc networks (MANETs) that can be used to communicate in intelligent transportation systems. In VANETs, each node in the network is a vehicle that exchanges data wirelessly with other vehicles. Many authors have considered using unnamed aerial vehicle (UAV) nodes to improve connectivity in VANETs. In recent years, UAVs have been used to enhance communications in VANETs. In this paper, a routing method for VANETs using UAV nodes is presented. The proposed method has three phases. The first phase deals with routing operations between vehicles. In this phase, whenever a vehicle has a packet to send, it selects the next hop to send the packet from the neighbor vehicles based on the parameters of distance, speed difference, and direction difference. The second phase is dedicated to routing operations between the vehicle and the UAV. In this phase, the roadside unit selects the best UAV based on distance and remaining energy, and the vehicle sends data to the UAV. The third phase is dedicated to UAV routing operations, and a greedy routing method between the UAVs based on distance and energy is presented. The proposed method is compared with U2RV and UVAR methods in terms of packet delivery rate, end‐to‐end delay, number of hops, and control overhead. The simulation results showed that the proposed method has improved in terms of packet delivery rate, the number of hops, and end‐to‐end delay.

Infrastructure Based Reactive Location Service for Packet Delivery in Vehicular Communication

VANETs are special kind of Mobile Ad Hoc Networks (MANETs) that are formed between moving vehicles on an as-needed basis. Routing in VANET uses a greedy routing protocol which requires geographical information like location of any vehicle. The primary objective of this paper is to reduce the transmission delay by using some Road Side Units (RSU) already deployed along the roads. RSU gather information about all the vehicles which are connected in its range. Here each and every vehicle need not be communicated but the vehicles that are nearest and which have the information about location of destination are only involved. We use enhanced RLS which is hybrid communication from source to destination. It results to reduce the flooding requests and make communication faster which leads to improve the packet delivery ratio and reduce the transmission delay or end to end delay. In addition to this the source vehicle sends a request with its velocity to RSU, it will communicate with other RSU’s for the destination vehicle. After finding the destination it will send the request with velocity of the destination. Based on the velocity of destination and intermediate nodes the source will increase its velocity to avoid the frequent link breakage in order to reduce the transmission delay. The proposed algorithm is simulated using NS2 and results shows that ERLS decreases the transmission delay and increases the packet delivery ratio.

Optimisation of the coalescent hyperbolic embedding of complex networks

Several observations indicate the existence of a latent hyperbolic space behind real networks that makes their structure very intuitive in the sense that the probability for a connection is decreasing with the hyperbolic distance between the nodes. A remarkable network model generating random graphs along this line is the popularity-similarity optimisation (PSO) model, offering a scale-free degree distribution, high clustering and the small-world property at the same time. These results provide a strong motivation for the development of hyperbolic embedding algorithms, that tackle the problem of finding the optimal hyperbolic coordinates of the nodes based on the network structure. A very promising recent approach for hyperbolic embedding is provided by the noncentered minimum curvilinear embedding (ncMCE) method, belonging to the family of coalescent embedding algorithms. This approach offers a high-quality embedding at a low running time. In the present work we propose a further optimisation of the angular coordinates in this framework that seems to reduce the logarithmic loss and increase the greedy routing score of the embedding compared to the original version, thereby adding an extra improvement to the quality of the inferred hyperbolic coordinates.

Comparison of GPSR and FPSR Algorithms Based on High-Speed Vehicle Information

With the progress of network information technologies, the application of wireless network in high-speed traffic vehicle system has been greatly expanded. However, due to the high-speed movement of nodes, the reliability of high-speed traffic network construction is still relatively low. Therefore, the network data processing of high-speed traffic information has become a hot issue of social concern. In this study, based on the network data system of vehicle information, the greedy perimeter stateless routing (GPSR) algorithm and data forwarding probability strategy based on relevance in vehicular network (FPSR) algorithm were introduced to collect vehicle information from the high-speed traffic examples. The data analysis results of the two algorithms were compared from aspects of the routing stability, delay rate, and success rate. The experimental results showed that the FPSR algorithm was superior in the routing stability and the success rate when the vehicle drove at high speed, while the GPSR algorithm was better in the delay rate and the success rate when the vehicle drove at a low speed. This study can help further understand the performance of the information network, provide technical support for vehicles on high-speed traffic, and provide a theoretical reference for modern intelligent transportation.

An intelligent fuzzy-based routing scheme for software-defined vehicular networks

Due to the lack of fully considering dynamic traffic information, in Vehicular Ad-hoc Networks (VANETs), existing routing schemes are easily stuck in local optimum, path redundancy, and congestion problems. By combining Software Defined Network (SDN) with VANET, the emerging Software Defined Vehicular Network (SDVN) can provide a global perspective of the traffic network to bridge the gap. In this paper, we introduce an intelligent fuzzy-based routing scheme for urban SDVN. First, a large urban area is divided into multiple sub-areas, in which each area is centered on an intersection. Second, the central controller maintains a routing table that records the priorities of packets be forwarded from an area to another, and all values in the routing table are initialized using Fuzzy Logic. Finally, we propose a hierarchical greedy routing with link stability (GLS) to calculate the routing path with the highest link stability according to the routing table. Meanwhile, considering the dynamic nature of vehicles in an area, Reinforcement Learning is employed to update the routing table during the routing process. Simulation results show that the proposed routing scheme achieved a significant improvement in performance over its counterparts.

LACCVoV: Linear Adaptive Congestion Control With Optimization of Data Dissemination Model in Vehicle-to-Vehicle Communication

Vehicle-to-vehicle communication assists road-side information exchange granting ease of access and sharing between users. The communication between the vehicles is short-lived due to interference and data congestion in the resource constraint medium. This manuscript introduces a linear adaptive congestion control (LACC) augmenting the benefits of greedy routing and data dissemination model (DDM). LACC focuses on selecting beneficiary vehicle by assessing its end-to-end service capacity and link stability preference. Different from the conventional greedy approach, routing is aided by a linear integer programming module for smart decisions on neighbor selection. The interrupts in data transmission and forwarding due to non-localized vehicles, congested routing paths and paused transmissions are addressed using LACC as a series of linear optimization. This helps to improve the performance of vehicular communication estimated using delay, message delivery, outage, and beacon messages.

Greedy Intersection-Mode Routing Strategy Protocol for Vehicular Networks

The advantages of vehicular ad hoc networks (VANETs) have been acknowledged, particularly during the last decade. Furthermore, VANET-related issues have been addressed by different researchers. Forwarding information professionally in a VANET is considered a challenging task precisely at the intersections where forwarding the information turns out to be extremely problematic. To elucidate this problem, many researchers have established routing protocols. The improved greedy perimeter stateless routing protocol (IGPSR) has been suggested, in the direction of employing greedy-mode proceeding traditional transportation's streets as well as to employ intersection-method at the joints. In view of greedy mode, the selection of the following stage is as in GPSR. By contrast, in the mode at an intersection, we would expect the vehicle guidelines to govern the following stage. The recreated consequences expose the algorithm, which is anticipated to undeniably demonstrate its competency.

Spatio-Temporal Routing in Episodically Connected Vehicular Networks

The spatio-temporal routing in episodically connected vehicular networks es a communication pattern where the destination nodes is a set of vehicles that transit the destination region during the specified destination region. The spatio-temporal routing is well suited to support Smart City and Intelligent Transportation Systems applications (i.e. road safety, crowdsensing, entertainment). In this paper, we present Oportunistic Greedy Routing over Street-layout Graph (OGRoSG), a novel spatio-temporal routing protocol for episodically connected vehicular networks. Unlike previous works in the literature, OGRoSG takes advantage of information available in modern navigation systems, such as the streets map and the geographic position. The protocol performs greedy geographic routing, the notion of being closer to the destinationare a is based on the progress towards the destination region on the streets-map graph and the direction of the vehicles. In order to achieve this, the vehicles process the destination region and the street graph to generate a shortest-paths tree with an extended destination regionas root. This extended destination region guarantees that it covers the entire destination region and the paths that interconnect it. We evaluate the performance of the OGRoSG protocol using simulations in NS-3,using mobility traces generated in a map based in the city of Murcia, Spain. Our results show that OGRoSG outperforms a set of Spatio-Temporal variants of the Epidemic, Spray & Wait and Binary Spray & Wait routing protocols in terms of delivery ratio and overhead induced in the network.

Hyperbolic mapping of human proximity networks

Human proximity networks are temporal networks representing the close-range proximity among humans in a physical space. They have been extensively studied in the past 15 years as they are critical for understanding the spreading of diseases and information among humans. Here we address the problem of mapping human proximity networks into hyperbolic spaces. Each snapshot of these networks is often very sparse, consisting of a small number of interacting (i.e., non-zero degree) nodes. Yet, we show that the time-aggregated representation of such systems over sufficiently large periods can be meaningfully embedded into the hyperbolic space, using methods developed for traditional (non-mobile) complex networks. We justify this compatibility theoretically and validate it experimentally. We produce hyperbolic maps of six different real systems, and show that the maps can be used to identify communities, facilitate efficient greedy routing on the temporal network, and predict future links with significant precision. Further, we show that epidemic arrival times are positively correlated with the hyperbolic distance from the infection sources in the maps. Thus, hyperbolic embedding could also provide a new perspective for understanding and predicting the behavior of epidemic spreading in human proximity systems.

LEER: Layer-Based Energy-Efficient Routing Protocol for Underwater Sensor Networks

Acoustic signals are used for communication in underwater sensor networks (UWSNs) because radio signals attenuate heavily when propagating in water, while optical signals have large scattering in water. Data transmission in UWSNs faces great challenges due to the characteristics of underwater acoustic channels. Moreover, high energy consumption and long latency bring about increased challenges for the designs of routing protocols in UWSNs. In this paper, we propose a routing protocol called Layer-based Energy-Efficient Routing (LEER) protocol to address the route failure problem with greedy routing, as well as the long end-to-end delay and high energy consumption problems. In LEER, each node extracts the layer field information from hello packets received and updates its own layer to avoid the problem of routing a packet to a void area. All nodes with the LEER protocol forward packets to a sink node without the need for any location information. Simulation results show that the LEER protocol outperforms the depth-based routing (DBR) protocol in terms of the delivery rate and end-to-end delay.

Energy Efficient Location Error Resilient Connectivity in Wireless Sensor Networks

Geographic routing for realistic conditions is often permeated with localization errors resulting in poor performance and high energy consumption. We propose a simple yet novel geographic routing method to accomplish routing in the presence of location and channel errors. In this algorithm, it is proposed that next hop node is selected based on the combined probability of distance, estimated location error and estimated channel access probability associated with neighbor nodes. The metrics observed for performance were the successful packet delivery rate and the energy consumed. Our algorithms performance is better compared to greedy forwarding techniques of algorithms such as Greedy routing scheme (GRS) and geographic random forwarding (GeRaF). Simulations show the throughput for our algorithm is better compared to others along with reducing the energy wasted on lost packets.

ENRICHING WALKING ROUTES WITH TOURISM ATTRACTIONS RETRIEVED FROM CROWDSOURCED USER GENERATED DATA

Abstract. It is always a tourism challenge – and aspiration – to discover scenery routes and tourism attractions in unfamiliar areas. Tourism information is getting more extensive, comprehensive and complex, so first-time tourists have to manage and mine large volumes of data to better plan their trip. Nowadays, geotagged photos are uploaded by users to social media photo-sharing online websites, which become more popular and commonly used by travelers to share their tourism experiences. Handling, mining and interpreting these user-generated ‘digital footprints’ can be used to reconstruct travel trajectories of users to recover their activity and knowledge. In this research, we showcase Flickr geotagged crowdsource photo database as a source for mining users’ trajectories to effectively compute walking tourism routes. Our methodology mines tourism context by conceptualizing a set of adaptive spatiotemporal descriptors to identify photographers that show tourism activity of first-time visitors. By implementing spatial clustering, we find popular locations that are traversed by these tourism-oriented photographers’ trajectories. To analyze our approach, we develop a greedy route computation algorithm that seeks the most popular traversed locations between origin and destination points defined by the user. Results for two cities are presented, proving the robust mining and retrieving of valuable tourism context and information from social media photos. We evaluate and validate our results by comparing the computed walking routes to recognized tourism information. The computed walking routes are scenery and pass through the main popular tourism sights and landmarks in the city, including additional attractive places that are frequently visited by tourism-photographers.

An improved distance‐based ant colony optimization routing for vehicular ad hoc networks

SummaryVehicular ad hoc network (VANET) has earned tremendous attraction in the recent period due to its usage in a wireless intelligent transportation system. VANET is a unique form of mobile ad hoc network (MANET). Routing issues such as high mobility of nodes, frequent path breaks, the blind broadcasting of messages, and bandwidth constraints in VANET increase communication cost, frequent path failure, and overhead and decrease efficiency in routing, and shortest path in routing provides solutions to overcome all these problems. Finding the shortest path between source and destination in the VANET road scenario is a challenging task. Long path increases network overhead, communication cost, and frequent path failure and decreases routing efficiency. To increase efficiency in routing a novel, improved distance‐based ant colony optimization routing (IDBACOR) is proposed. The proposed IDBACOR determines intervehicular distance, and it is triggered by modified ant colony optimization (modified ACO). The modified ACO method is a metaheuristic approach, motivated by the natural behavior of ants. The simulation result indicates that the overall performance of our proposed scheme is better than ant colony optimization (ACO), opposition‐based ant colony optimization (OACO), and greedy routing with ant colony optimization (GRACO) in terms of throughput, average communication cost, average propagation delay, average routing overhead, and average packet delivery ratio.

Resilient greedy routing on GPS-free surface sensor networks

We design a greedy routing scheme specifically for GPS-free large-scale wireless sensor networks deployed on surfaces of complex-connected three-dimensional settings. Compared with other greedy embedding–based surface network routing scheme, the proposed one is cut free such that no pair of nodes suffers a long detour to reach each other. The routing scheme is designed to be resilient to node or link failures especially under random node or link failure model where each node in a network has an equal and independent probability of failure during some time interval. The proposed algorithm is fully distributed and scalable to both the size and the topological complexity of a network. Each sensor node requires only limited and constant storage. Simulation results show the proposed routing scheme with a higher successful delivery ratio, a lower average stretch factor, and a lower normalized communication cost compared with other resilient routing methods.