Greedy Routing Research Articles

DCFH: A dynamic clustering approach based on fire hawk optimizer in flying ad hoc networks

In flying ad hoc networks (FANETs), unmanned aerial vehicles (UAVs) communicate with each other without any fixed infrastructure. Because of frequent topological changes, instability of wireless communication, three-dimensional movement of UAVs, and limited resources, especially energy, FANETs deal with many challenges, especially the instability of UAV swarms. One solution to address these problems is clustering because it maintains network performance and increases scalability. In this paper, a dynamic clustering scheme based on fire hawk optimizer (DCFH) is proposed for FANETs. In DCFH, each cluster head calculates the period of hello messages in its cluster based on its velocity. Then, a fire hawk optimizer (FHO)-based dynamic clustering operation is carried out to determine the role of each UAV (cluster head (CH) or cluster member (CM)) in the network. To calculate the fitness value of each fire hawk, a fitness function is suggested based on four elements, namely the balance of energy consumption, the number of isolated clusters, the distribution of CHs, and the neighbor degree. To improve cluster stability, each CH manages the movement of its CMs and adjusts it based on its movement in the network. In the last phase, DCFH defines a greedy routing process to determine the next-hop node based on a score, which consists of distance between CHs, energy, and buffer capacity. Finally, DCFH is simulated using the network simulator version 2 (NS2), and its performance is compared with three methods, including the mobility-based weighted cluster routing scheme (MWCRSF), the dynamic clustering mechanism (DCM), and the Grey wolf optimization (GWO)-based clustering protocol. The simulation results show that DCFH well manages the number of clusters in the network. It improves the cluster construction time (about 55.51%), cluster lifetime (approximately 11.13%), energy consumption (about 15.16%), network lifetime (about 2.6%), throughput (approximately 3.9%), packet delivery rate (about 0.61%), and delay (approximately 14.29%). However, its overhead is approximately 8.72% more than MWCRSF.

Hyperbolic-Embedding-Aided Geographic Routing in Intelligent Vehicular Networks

Intelligent vehicular networks can not only connect various smart terminals to manned or unmanned vehicles but also to roads and people’s hands. In order to support diverse vehicle-to-everything (V2X) applications in dynamic, intelligent vehicular networks, efficient and flexible routing is fundamental but challenging. Aimed to eliminate routing voids in traditional Euclidean geographic greedy routing strategies, we propose a hyperbolic-embedding-aided geographic routing strategy (HGR) in this paper. By embedding the network topology into a two-dimensional Poincaré hyperbolic disk, greedy forwarding is performed according to nodes’ hyperbolic coordinates. Simulation results demonstrated that the proposed HGR strategy can greatly enhance the routing success rate through a smaller stretch of the routing paths, with little sacrifice of routing computation time.

A Comprehensive Review of Geographic Routing Protocols in Wireless Sensor Network

To analyses the impact of high mobility, dynamic topologies, scalability and routing due to the more dynamic changes in network. To enhance mobile Ad-hoc network (MANET) self-organization capabilities by geographical routing algorithm during mobility. In this paper, a survey has been carried out on geographic routing protocols, such as hybrid routing, Greedy Routing, face-2 Algorithm, Perimeter Routing, quasi random deployment (QRD) techniques and time of arrival (TOA). An optimized multipath routing in wireless sensor network (WSN), energy utilization, detection of anonymous routing, node mobility prediction, data packet distribution strategies in WSN is analyzed. Geographic routing offers previous data packet information such as physical locations, packet elimination dependencies, storage capacity of topology, Associate costs and also identifies the dynamic behavior of nodes with respect to packets frequencies.

Greedy routing optimisation in hyperbolic networks

Finding the optimal embedding of networks into low-dimensional hyperbolic spaces is a challenge that received considerable interest in recent years, with several different approaches proposed in the literature. In general, these methods take advantage of the exponentially growing volume of the hyperbolic space as a function of the radius from the origin, allowing a (roughly) uniform spatial distribution of the nodes even for scale-free small-world networks, where the connection probability between pairs decays with hyperbolic distance. One of the motivations behind hyperbolic embedding is that optimal placement of the nodes in a hyperbolic space is widely thought to enable efficient navigation on top of the network. According to that, one of the measures that can be used to quantify the quality of different embeddings is given by the fraction of successful greedy paths following a simple navigation protocol based on the hyperbolic coordinates. In the present work, we develop an optimisation scheme for this score in the native disk representation of the hyperbolic space. This optimisation algorithm can be either used as an embedding method alone, or it can be applied to improve this score for embeddings obtained from other methods. According to our tests on synthetic and real networks, the proposed optimisation can considerably enhance the success rate of greedy paths in several cases, improving the given embedding from the point of view of navigability.

SRP-HEE: A Modified Stateless Routing Protocol based on Homomorphic Energy based Encryption for Wireless Sensor Network

Due to the wireless nature, the sensors node data are prone to location privacy of source and classification of the packet by unauthorized parties. Data encryption is one of the most effective ways to thwart unauthorized access to the data and trace information. Traditional wireless network security solutions are not viable for WSNs In this paper, a novel distributed forward aware factor based heuristics towards generating greedy routing using stateless routing is SRP-HEE for wireless sensor network. The model employs the homomorphic Energy based encryption technique. Energy based Encryption model is devoted as homomorphic mechanism due to their less computational complexity. Additionally, privacy constraint becoming a critical issue in the wireless sensor networks (WSNs) because sensor nodes are generally prone to attacks which deplete energy quickly as it is exposed to mobile sink frequently for data transmission. Through inclusion of the Forward aware factor on the Greedy routing strategies, it is possible to eliminate the attacking node which is depleting the energy of the source node. Heuristic conditions are used for optimizing the sampling rate and battery level for tackling the battery capacity constraints of the wireless sensor nodes. The Node characteristics of the propagating node have been analysed utilizing kalman filter and linear regression. The cooperative caching of the network information will enable to handle the fault condition by changing the privacy level of the network. The Simulation results demonstrate that SRP-HEE model outperforms existing technique on basis of Latency, Packet Delivery Ratio, Network Overhead, and Energy Utilization of nodes.

On the design of trust and mobility based evaluation for intelligent collaborative UAVs assisted VANETs

In recent days, vehicles usage and speed are highly increased that leads to an increase in energy consumption, delay, and overhead in the network. In this paper, a novel trajectory is introduced to achieve maximum reliability namely trust and mobility-based evaluation for intelligent collaborative (TMIC)-UAVs assisted VANETs. Reactive multipath greedy routing protocol (RMGR) is the hybrid routing protocol and it is the combination of ad hoc on-demand multipath distance vector (AOMDV) with greedy geographic forwarding (GGF) which is used for routing in frequently changeable network topology. To protect the network from malfunctions, effective trust evaluation (ETE) is performed by calculating the direct trust and indirect trust. Finally, to achieve effective communication among the UAVs, hybrid optimization is performed which is the combination of the genetic algorithm (GA) and the crow swarm optimization (CSO) algorithm. For validation network simulator (NS3) is used and the results show that this approach achieves high energy efficiency, delivery ratio, and reduction in delay when compared with the earlier research.

TEPG: a traffic engineering based power-aware greedy routing algorithm in backbone networks with bundled links

TEPG: a traffic engineering based power-aware greedy routing algorithm in backbone networks with bundled links

Analysis of Greedy Strategies in Geographic Routing of VANETs

The study and creation of networks that are independent of any pre-existing infrastructure has become highly popular in recent years. These networks fall under the category of Vehicular Ad-hoc Networks (VANETs), in which each car takes part in routing by sending data to other vehicles. However, geographic routing attracts VANETs networks because they lack route discovery and routing databases and the accessibility of low-cost GPS systems as well as the advent of the 128-bit address structure of IPv6 have enhanced the advantages of geographical routing over classical routing techniques. Additionally, the proposed algorithms based on greedy routing are the main method for the next hop selection in multi-hop communication for optimal and optimized solution in geographic routing. In this article, the study takes place with a number of hops per route and greedy algorithm selection success ratio in VANETs with source-to-destination pairs. A comparative study is taken out in which LLP affects node density, hop count, and transmission range. Monte Carlo simulations have been done with two important network parameters which are transmission range and node density. This study will be a guideline for the choice of greedy decisions to meet the objectives of network applications.

Position-Independent and Section-Based Source Location Privacy Protection in WSN

Privacy of critical locations (or events) is essential when monitored by Wireless Sensor Networks. To mitigate such issues, in this paper a new privacy protection technique named Position-independent and Section-based Source Location Privacy (PSSLP) is developed. A biased random walk and greedy walk using a three- or four-phase routing strategy is employed here, where the number of phases depends on the network segment in which the source is situated. The biased random walk is intended to send packets away from the source of information and make routing paths appear dynamic to the eavesdropper, whereas, the greedy routing ensures that the packets converge at the base station. The objective of the solution is to achieve a uniform amount of privacy irrespective of the position of the asset in the network without compromising the network lifetime. Performance evaluation is done using developed analytical models and simulation results reveal that PSSLP achieves 8247.06- and 33.0- folds improvement in terms safety period and NLT respectively compared to no source location privacy (SLP) protection technique (i.e., shortest path routing (SPR) technique).

A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks.

Quick and accurate detection of inside packet drop attackers is of critical importance to reduce the damage they can have on the network. Trust mechanisms have been widely used in wireless sensor networks for this purpose. However, existing trust models are not effective because they cannot distinguish between packet drops caused by an attack and those caused by normal network failure. We observe that insider packet drop attacks will cause more consecutive packet drops than a network abnormality. Therefore, we propose the use of consecutive packet drops to speed up the detection of inside packet drop attackers. In this article, we describe a new trust model based on consecutive drops and develop a hybrid trust mechanism to seamlessly integrate the new trust model with existing trust models. We perform extensive OPNET (Optimized Network Engineering Tool) simulations using a geographic greedy routing protocol to validate the effectiveness of our new model. The simulation results show that our hybrid trust model outperforms existing trust models for all types of inside packet drop attacks, not only in terms of detection speed and accuracy as it is designed for, but also in terms of other important network performance metrics, such as packet delivery rate, routing reliability, and energy efficiency.

Duo: A High-Throughput Reconfigurable Datacenter Network Using Local Routing and Control

The performance of many cloud-based applications critically depends on the capacity of the underlying datacenter network. A particularly innovative approach to improve the throughput in datacenters is enabled by emerging optical technologies, which allow to dynamically adjust the physical network topology, both in an oblivious or demand-aware manner. However, such topology engineering, i.e., the operation and control of dynamic datacenter networks, is considered complex and currently comes with restrictions and overheads. We present Duo, a novel demand-aware reconfigurable rack-to-rack datacenter network design realized with a simple and efficient control plane. Duo is based on the well-known de Bruijn topology (implemented using a small number of optical circuit switches) and the key observation that this topology can be enhanced using dynamic (''opportunistic'') links between its nodes. In contrast to previous systems, Duo has several desired features: i) It makes effective use of the network capacity by supporting integrated and multi-hop routing (paths that combine both static and dynamic links). ii) It uses a work-conserving queue scheduling which enables out-of-the-box TCP support. iii) Duo employs greedy routing that is implemented using standard IP longest prefix match with small forwarding tables. And iv) during topological reconfigurations, routing tables require only local updates, making this approach ideal for dynamic networks. We evaluate Duo in end-to-end packet-level simulations, comparing it to the state-of-the-art static and dynamic networks designs. We show that Duo provides higher throughput, shorter paths, lower flow completion times for high priority flows, and minimal packet reordering, all using existing network and transport layer protocols. We also report on a proof-of-concept implementation of Duo's control and data plane.

Online Routing Over Parallel Networks: Deterministic Limits and Data-driven Enhancements

Over the past decade, GPS-enabled traffic applications such as Google Maps and Waze have become ubiquitous and have had a significant influence on billions of daily commuters’ travel patterns. A consequence of the online route suggestions of such applications, for example, via greedy routing, has often been an increase in traffic congestion since the induced travel patterns may be far from the system optimum. Spurred by the widespread impact of traffic applications on travel patterns, this work studies online traffic routing in the context of capacity-constrained parallel road networks and analyzes this problem from two perspectives. First, we perform a worst-case analysis to identify the limits of deterministic online routing. Although we find that deterministic online algorithms achieve finite, problem/instance-dependent competitive ratios in special cases, we show that for a general setting the competitive ratio is unbounded. This result motivates us to move beyond worst-case analysis. Here, we consider algorithms that exploit knowledge of past problem instances and show how to design data-driven algorithms whose performance can be quantified and formally generalized to unseen future instances. We then present numerical experiments based on an application case for the San Francisco Bay Area to evaluate the performance of the proposed data-driven algorithms compared with the greedy algorithm and two look-ahead heuristics with access to additional information on the values of time and arrival time parameters of users. Our results show that the developed data-driven algorithms outperform commonly used greedy online-routing algorithms. Furthermore, our work sheds light on the interplay between data availability and achievable solution quality. History: Accepted by Andrea Lodi, Area Editor for Design and Analysis of Algorithms–Discrete. Funding: This work was supported by National Science Foundation (NSF) Award 1830554 and by the German Research Foundation (DFG) under [Grant 449261765]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2023.1275 .

Energy Efficient Maximal Throughput Resource Scheduling Strategy in Satellite Networks

In this letter, a greedy routing strategy is proposed for resource scheduling in Earth observation mission to achieve the maximum throughput and improve the energy efficiency. Resource time-expanded graph (RTEG) is introduced to depict the time-evolving topology and resources of satellite networks. Based on RTEG, an energy efficient resource allocation algorithm (EERA) is designed to reduce the total energy consumption and maximize the throughput in data transmission by properly scheduling the transceiver resource, storage resource and power resource of satellites. Simulation results show that EERA achieves preferred energy efficiency when reaching the maximum throughput.

DCGCR: Dynamic Clustering Green Communication Routing for Intelligent Transportation Systems

For the effective green communications amongst the vehicles, the energy-efficient routing protocol for intelligent transportation system (ITS) is essential. Due to the high speed and recurring topological variations of Vehicular sensor Networks, identifying a connected route with a sufficient latency is a difficult task with many constraints and obstacles. Therefore, to overcome this, we developed the statistical approach to theoretically determine the load congestion and consumption of energy during the lifetime of the sensor network for ITS. Hence, dynamic clustering green communication routing (DCGCR) protocol is proposed for vehicular communication. To manage energy consumption and enhance the lifetime of the network deployed on the roadside units (RSU), we analyze the evolution of energy holes and apply our analytical conclusions for ITS with WSN routing. The proposed routing protocol considers various metrics: i) energy consumption of vehicular sensor nodes,ii) network stability iii) reliability and iv) amount of data exchange among vehicles. The efficiency of the proposed computational model in calculating the lifetime of the vehicular network and energy hole evolution process is demonstrated through extensive computation results. DCGCR approach is compared with the various energy-aware routing algorithms namely, Dynamic Energy Balanced Routing (DEBR), Geographic Greedy Routing (GGR), double cost function-based routing (DCFR) and found that proposed approach achieves more accuracy with 7% less failure rate.

SPEED-UP, and energy-efficient GPSR protocol for WSNs using IOT

The development of new applications for intelligent cities, medicine, agriculture and energy harvesting would be a promising concept using wireless sensor networks based on the Internet of Things (IoT-WSNs). At the moment, the research was aimed at reducing the limited energy supply of IoT devices, which has a serious impact on their architecture. To ensure rapid and stable connection, new and efficient methods of energy systems must be designed. To handle data transfer in WSNs and to handle the problem of detector energy supply, the authors presented an improved Speed Up-Greedy Perimeter Stateless Routing Protocol for Wireless Sensor Networks (SU-GPSR) and Energy Harvest Greedy Perimeters Stateless Routing Protocol (EH-GPSR). SU-GPSR, unlike other variations of GPSR algorithms, provides complete energy consumption while assessing the next jump for local nodes and mobile nodes in an integrated framework. Additionally, established measurements are recorded using the SU-GPSR skip selection method. To calculate the amount of energy collected, EH-GPSR uses an EH rate that is generated by Mobilized Minimum Path Recovery Time (MMRT) method, as it improves the method of greedy routing. To incrementally choose the next hop, it particularly uses a weighted method that merges the node's data cost with the EH rate. In both packed and systems, the simulation study infers that SU-GPSR performs better than EH-GPSR and FA-GPSR, whereas EH-GPSR increases the node life with a speed by 20% when contrasted to the other GPSR method.

A Hierarchical Network with Fault Tolerance by a Multi-Factor Method for Neighborhood Area Network in Smart Grid.

The neighborhood area network of a smart grid usually has hierarchical wireless communication. Due to forwarding and processing more data, the upper-layer nodes are more likely to suffer congestion and energy exhaustion. This phenomenon leads to the failure of uploading data to the control center. To solve this problem, this paper proposes a scheme for constructing a multi-factor fault-tolerant hierarchical network. This scheme firstly defines a criterion for the generation of redundant links by multi-factor method in a hierarchical wireless network with the characteristics of the neighborhood area network. Then the redundant links are used to reconstruct the existing topology of the neighborhood area network for improving fault tolerance. Finally, a greedy routing algorithm is put forward to select a proper data transmission path by bypassing low energy nodes, further reducing the failure of uploading data to the control center. The simulation results show that the proposed scheme can effectively improve the fault tolerance of the network topology of the wireless neighborhood area network and balance the network energy consumption. Compared with the original scheme, the proposed scheme improves the fault tolerance by 35% and the relative transmission rate by 21%.

Application of SDN Network Traffic Prediction Based on Speech Recognition in Educational Information Optimization Platform.

This paper constructs a SDN network traffic prediction model based on speech recognition and applies it to the educational information optimization platform. By analyzing the influencing factors of SDN network equipment, communication links, and network traffic, this paper constructs the initial index set of SDN network traffic situation. In the data plane of SDN, the queue management algorithm is used to control the flow. On this basis, an IRS mechanism is proposed based on the advantages of SDN centralized control and the difference of transmission performance requirements between large and small streams. For the transmission of large traffic, IRS adopts greedy routing and multipath routing based on the remaining bandwidth to make the traffic evenly distributed in the network, and IRS adds the scheduling strategy based on IP addressing to avoid packet disorder. Simulation results show that the effectiveness of this algorithm can reach 95.67% at the highest, and the MSE convergence is 0.0021 at the lowest. At the same time, this method completes the quantitative evaluation of SDN network traffic situation, effectively solves the problem that SDN traffic situation labels cannot be determined, and opens a new vision of global state observation for SDN network management. This research can provide some technical support for the educational information optimization platform.

Electric van-based robot deliveries with en-route charging

We present a two-echelon electric van-based robot delivery system with en-route charging for last-mile delivery in logistics operations. Each of the vans is equipped with a single robot, and the robots can visit areas with van access restrictions, such as pedestrianized areas or university campuses. The time during which electric vans are carrying robots can be used to recharge the robots, thereby increasing the efficiency of the distribution system.To model the proposed system, we present a mixed integer program. We note that the energy transfer from a van to its robot needs time and will cause the available travel distance of a van to decrease and that of a robot to increase. Focusing on the new time-distance-energy trade-off problem, which increases the difficulty checking the feasibility of any given route, we further propose a greedy route evaluation approach and a linear programming-based route evaluation method. An adaptive large neighborhood search algorithm is presented for solving larger instances. A sensitivity analysis for vehicle charging modes, charging rates, and maximum battery capacities shows that using en-route charging, while appropriately increasing battery level and charging rate can have useful effects on cost.

A real time health care cyber attack detection using ensemble classifier

Healthcare has been a significant issue for many individuals in recent years. The Internet of things (IoT) and wireless communications may support many medical applications, including early diagnosis and real-time monitoring. Healthcare expenditures may be reduced by using secure and practical strategies to detect life-threatening emergencies in real-time quickly. The aim of the proposed tool is the detection of attacks targeting the Healthcare cyber-physical system. In this method, the wise greedy routing technique is initially used for sensor node placement and creation. The transmitted data is normalized and grouped using an agglomerative mean shift maximization clustering method. The abnormal health features are extracted using a multi-heuristic cyber ant optimization-based feature extraction process. Then the attack is detected by using the Ensemble crossover XG boost classifier. An optimized gradient tree boosting system creates decision trees in a sequential form. From the results, the concluded proposed system effectively monitors patient health conditions, detects data breaches, and improves cloud security. Simulated results show that our proposed strategy improves detection accuracy, increases the proportion of true positives, and reduces false positives by a significant margin.

An Intelligent SDN-Based Clustering Approach for Optimizing IoT Power Consumption in Smart Homes

As a novel technology, the Internet of Things (IoT) has many applications in diverse fields, especially in smart homes. IoT includes a variety of communication networks and technologies which facilitate communication between heterogeneous devices. One of the primary challenges of IoT is energy consumption. This paper introduces a new Software Defined Network-based (SDN-based) clustering approach using intelligent algorithms for energy conservation in IoT. The proposed method uses an evolutionary algorithm to identify the required number of clusters and ensures their distribution in the environment. A virtual network is also employed to ensure network coverage and the formation of balanced clusters. Clustering, steady, and routing are the main steps of the proposed method that the clustering step is done in SDN. By expanding the steady phase and leveraging energy-based greedy routing, the network’s lifetime increases. After simulation in MATLAB, the proposed method is tested then the results are compared with other well-known algorithms. The evaluation results indicate that the proposed method has improved in terms of metrics such as energy consumption and network lifetime. The proposed approach improves energy consumption by 31%, 28%, 8% and 21% than FPA, MCFL, BEEG and NodeRanked respectively. The lifetime has been improved by 34% and 71% than BEEG and NodeRanked, respectively, and more than 100% for MCFL and FPA.