Showing papers on "Routing table published in 2020"

An Adaptive Relay Selection Scheme for Enhancing Network Stability in VANETs

Abstract: Vehicular ad hoc networks (VANETs) are a special type of wireless ad hoc network that requires highly scalable routing strategies to establishing reliable end-to-end communication. Because of the high dynamic of VANETs, the mobility of vehicle nodes increases the control traffic overhead. Accordingly, establishing reliable end-to-end communication paths depends entirely on the routing mechanism and the type of nodes mobility information. In this paper, we propose a new improvement to the mechanism of the Optimized Link State Routing Protocol(OLSR) protocol, named Cluster-based Adept Cooperative Algorithm (CACA), where each vehicle estimate a reliable low-overhead path using the cluster-based QoS algorithm. The CACA algorithm is introduced to improve the ability of the MPR scheme for maintaining long-lived routes. Moreover, the network scalability is enhanced by adaptively selecting most sustainable paths based on a signal strength beacon and the mobility degree of a node, which reduces significantly minimizes the size of control messages overhead as well the routing tables recalculation process. Simulation experiments using the network simulator are presented to demonstrate the effectiveness of our solution. The results show that the proposed algorithm can improve network performance effectively relative to other algorithms.

An Efficient Load Balancing Scheme of Energy Gauge Nodes to Maximize the Lifespan of Constraint Oriented Networks

Abstract: Resource limited networks have various applications in our daily life. However, a challenging issue associated with these networks is a uniform load balancing strategy to prolong their lifespan. In literature, various schemes try to improve the scalability and reliability of the networks, but majority of these approaches assume homogeneous networks. Moreover, most of the technique uses distance, residual energy and hop count values to balance the energy consumption of participating nodes and prolong the network lifetime. Therefore, an energy efficient load balancing scheme for heterogeneous wireless sensor networks (WSNs) need to be developed. In this article, an energy gauge node (EGN) based communication infrastructure is presented to develop a uniform load balancing strategy for resource-limited networks. EGN measures the residual energy of the participating nodes i.e., C i ∈ Network. Moreover, EGN nodes advertise hop selection information in the network which is used by ordinary nodes to update their routing tables. Likewise, ordinary nodes use this information to uni-cast its collected data to the destination. EGN nodes work on built-in configuration to categorize their neighboring nodes such as powerful, normal and critical energy categories. EGN uses the strength of packet reply (SPR) and round trip time (RTT) values to measure the neighboring node's residual energy (E r ) and those node(s) which have a maximum E r values are advertised as reliable paths for communication. Furthermore, EGN transmits a route request (RREQ) in the network and receives route reply (RREP) from every node reside in its closed proximity which is used to compute the E r energy values of the neighboring node(s). If E r value of a neighboring node is less than the defined category threshold value then this node is advertised as non-available for communication as a relaying node. The simulation results show that our proposed scheme surpasses the existing schemes in terms of lifespan of individual nodes, throughput, packet loss ratio (PLR), latency, communication costs and computation costs, etc,. Moreover, our proposed scheme prolongs the lifespan of WSNs and as well as an individual node against exiting schemes in the operational environment.

Resilient Routing Mechanism for Wireless Sensor Networks With Deep Learning Link Reliability Prediction

Abstract: Wireless sensor networks play an important role in Internet of Things systems and services but are prone and vulnerable to poor communication channel quality and network attacks. In this paper we are motivated to propose resilient routing algorithms for wireless sensor networks. The main idea is to exploit the link reliability along with other traditional routing metrics for routing algorithm design. We proposed firstly a novel deep-learning based link prediction model, which jointly exploits Weisfeiler-Lehman kernel and Dual Convolutional Neural Network (WL-DCNN) for lightweight subgraph extraction and labelling. It is leveraged to enhance self-learning ability of mining topological features with strong generality. Experimental results demonstrate that WL-DCNN outperforms all the studied 9 baseline schemes over 6 open complex networks datasets. The performance of AUC (Area Under the receiver operating characteristic Curve) is improved by 16% on average. Furthermore, we apply the WL-DCNN model in the design of resilient routing for wireless sensor networks, which can adaptively capture topological features to determine the reliability of target links, especially under the situations of routing table suffering from attack with varying degrees of damage to local link community. It is observed that, compared with other classical routing baselines, the proposed routing algorithm with link reliability prediction module can effectively improve the resilience of sensor networks while reserving high-energy-efficiency.

BRT: Bus-Based Routing Technique in Urban Vehicular Networks

Abstract: Routing data in Vehicular Ad hoc Networks is still a challenging topic. The unpredictable mobility of nodes renders routing of data packets over optimal paths not always possible. Therefore, there is a need to enhance the routing service. Bus Rapid Transit systems, consisting of buses characterized by a regular mobility pattern, can be a good candidate for building a backbone to tackle the problem of uncontrolled mobility of nodes and to select appropriate routing paths for data delivery. For this purpose, we propose a new routing scheme called Bus-based Routing Technique (BRT) which exploits the periodic and predictable movement of buses to learn the required time (the temporal distance) for each data transmission to Road-Side-Units (RSUs) through a dedicated bus-based backbone. Indeed, BRT comprises two phases: (i) Learning process which should be carried out, basically, one time to allow buses to build routing tables entries and expect the delay for routing data packets over buses, (ii) Data delivery process which exploits the pre-learned temporal distances to route data packets through the bus backbone towards an RSU (backbone mode). BRT uses other types of vehicles to boost the routing of data packets and also provides a maintenance procedure to deal with unexpected situations like a missing nexthop bus, which allows BRT to continue routing data packets. Simulation results show that BRT provides good performance results in terms of delivery ratio and end-to-end delay.

A Learning-Based Approach to Intra-Domain QoS Routing

Abstract: In traditional networks, routing table is essential for packet transmission due to the lack of the direction information about destination in the head of packet. However, it is feasible to make the address of device encode the routing information with the application of data technology. In this article, we propose new identities for networking routers -vectors, and a new routing principle based on these vectors is designed accordingly. These vectors encode the device distance information and serve as a pattern of the network topology. Then, routing decisions could be made by these vector calculations and only requirement of table query on the destination vector following the proposed routing principle. The proposed method is not limited in calculating the shortest path routing, but extend to solve the constrain routing problem. Besides, multi-paths routing is also available as long as multi-paths exist between the origin-destination pairs. The simulation results show that our proposed method works reliable and stable in routing tasks, and can achieve a remarkable performance when compared with the state-of-the-art work on the delay constrained least cost path (DCLC) problem.

Optimized clustering in vehicular ad hoc networks based on honey bee and genetic algorithm for internet of things

Abstract: In vehicular ad hoc network (VANET), the size of routing table can be reduced with the help of clustering architecture. The frequent changes in topology are the noteworthy characteristics of a VANET as its nature is dynamic. To manage the topology dynamics in VANET with less overhead, the concept of clustering can be used. Henceforth, an effective procedure that adjusts quickly to the topology changes should be designed. Firstly, the clustering problem (CP) in VANET is formulated into a dynamic optimization problem in this paper. Secondly, an optimization algorithm named Vehicular Genetic Bee Clustering (VGBC) based on honey bee algorithm and properties of genetic algorithm solves the CP in VANETs is suggested. In VGBC, individuals (bees) represent a realistic clustering structure and its fitness is measured on the basis of load balancing and stability. A technique that merges the properties of genetic algorithm and honey bee algorithm is proposed. It helps the population to handle the topology changes and harvest high quality solutions. The simulation results piloted for justification demonstrate that the VGBC form steady and balanced clusters. The simulation results are matched with state of the art clustering schemes in VANET. The VGBC outperform existing schemes in terms of cluster count, cluster duration, re-affiliation rate, computational overhead, load balancing, VANET lifetime and clustering overhead.

QROUTE: An Efficient Quality of Service (QoS) Routing Scheme for Software-Defined Overlay Networks

Abstract: Many computer network applications impose constraints for multiple quality of service (QoS) metrics, e.g., delay, packet loss, bandwidth, and jitter. These QoS constraints cannot be guaranteed by the Internet due to its best-effort service model. Overlay networks have been an effective technique at the application layer to support multiple QoS constraints of networking applications. In software-defined overlay networks, software-defined networking (SDN) paradigm is introduced in the overlay networks to enable centralized and efficient routing of traffic in the overlay networks, thus, enabling better QoS. One of the main challenges in software-defined overlay networks is the fast-changing overlay link QoS characteristics. However, the existing routing algorithms for satisfying multiple QoS constraints in software-defined overlay networks involve high route computation time and thus these routing algorithms cannot adapt to the fast-changing overlay link QoS characteristics. Moreover, as we scale the size of overlay networks, the size of forwarding tables increases exponentially. This is because the existing routing schemes for ensuring multiple QoS constraints use both the source and the destination address for data-plane forwarding. This leads to pushing a huge amount of forwarding table entries by the controller through the network and thus limiting the size of the overlay network. We propose an efficient routing scheme, QROUTE, for satisfying multiple QoS constraints in software-defined overlay networks. QROUTE consists of a control plane routing algorithm which has significantly low route computation time because of employing a novel directed-acyclic-graph (DAG) based approach. QROUTE also reduces the forwarding entries in the data plane by using a QoS-metric-based forwarding scheme. We extensively evaluate QROUTE using traces from a global overlay service provider. We also examine QROUTE on a testbed of P4-BMv2 switches controlled by the ONOS controller using P4Runtime protocol. We find that QROUTE outperforms other state-of-the-art QoS routing schemes in route computation time, size of the forwarding tables and meeting the QoS requirements of various applications.

Multi-hop Deflection Routing Algorithm Based on Reinforcement Learning for Energy-Harvesting Nanonetworks

Abstract: Nanonetworks are composed of interacting nano-nodes, whose size ranges from several hundred cubic nanometers to several cubic micrometers. The extremely constrained computational resources of nano-nodes, the fluctuations in their energy caused by energy harvesting processes, and their very limited transmission range at Terahertz (THz)-band frequencies (0.1-10 THz), make the design of routing protocols in nanonetworks very challenging. A multi-hop deflection routing algorithm based on reinforcement learning (MDR-RL) is proposed in this paper to dynamically and efficiently explore the routing paths during packet transmissions. Firstly, new routing and deflection tables are implemented in nano-nodes, so that nano-nodes can deflect packets to other neighbors when route entries in the routing table are invalid. Secondly, one forward updating scheme and two feedback updating schemes based on reinforcement learning are designed to update the tables, namely, on-policy and off-policy updating schemes. Finally, extensive simulations in networks simulator-3 are conducted to analyze the performance of MDR-RL using different updating policies, as well as to compare the performance with other machine learning routing algorithms based on Neural Networks and Decision Tree. The results show that the MDR-RL can increase the packet delivery ratio and number of delivered packets, and can decrease the packet average hop count.

A bio-inspired clustering in mobile adhoc networks for internet of things based on honey bee and genetic algorithm

Abstract: In mobile adhoc networks for internet of things, the size of routing table can be reduced with the help of clustering structure. The dynamic nature of MANETs and its complexity make it a type of network with high topology changes. To reduce the topology maintenance overhead, the cluster based structure may be used. Hence, it is highly desirable to design an algorithm that adopts quickly to topology dynamics and form balanced and stable clusters. In this article, the formulation of clustering problem is carried out initially. Later, an algorithm on the basis of honey bee algorithm, genetic algorithm and tabu search (GBTC) for internet of things is proposed. In this algorithm, the individual (bee) represents a possbile clustering structure and its fitness is evaluated on the basis of its stability and load balancing. A method is presented by merging the properties of honey bee and genetic algorithms to help the population to cope with the topology dynamics and produce top quality solutions that are closely related to each other. The simulation results conducted for validation show that the proposed work forms balance and stable clusters. The simulation results are compared with algorithms that do not consider the dynamic optimization requirements. The GTBC outperform existing algorithms in terms of network lifetime and clustering overhead etc.

Effective cache replacement strategy (ECRS) for real-time fog computing environment

Abstract: Fog Computing (FC) utilizes the resources close to the edge of the network. It supports real time applications such as healthcare, industrial systems, and intelligent traffic signs. FC needs data to be cached in various intermediate nodes to be easily found by the network. Therefore, an efficient caching scheme is essential. Data caching can improve the data availability in FC by reducing access latency and bandwidth. As nodes in FC may have similar tasks and share common interests, cooperative caching can be used to reduce the bandwidth, latency, and power consumption. The originality of this paper is concentrated on introducing an Effective Cache Replacement Strategy (ECRS) and routing algorithm for real-time FC environment with a novel cache replacement and prefetching policies. ECRS is composed of two main modules which are: (i) Path Finding Procedure: to ensure that there is a route between each pair of FNs. And (ii) Data Searching Procedure: to find the required data for the given task. ECRS divides the network into fog regions and each region has a master node that manages the communication in the fog region. Unlike other caching techniques that employ reactive routing protocols, ECRS employs a novel built-in table driven routing strategy with no additional penalties. Such behavior significantly minimizes the query delay. The secret lies in collecting the routing information during the message request forwarding, then fill the routing tables accordingly. ECRS has been compared against recent cooperative caching strategies. Experimental results have shown that ECRS achieved the maximum Cache Hit Ratio, while minimizing Average Query Delay (AQD), Average Hop Count, and the Power Consumption of the network. These results have been achieved due to the high accuracy of using fuzzy and the efficiency of using graph based routing algorithm as the graph can be easily used for finding the shortest paths in fast time. Unlike previous algorithms, ECRS achieves the least AQD. Accordingly, ECRS is a suitable algorithm in the case of real-time systems in FC which leads to load balancing.

AFSndn: A novel adaptive forwarding strategy in named data networking based on Q-learning

Abstract: Named Data Networking (NDN) is a new network architecture, which employs a new content-centric communication model to replace the traditional host-centric communication model. In TCP/IP network, data packets are forwarded by routers according to routing table established previously. While in NDN, routing nodes can dynamically make forwarding decisions based on network status. By considering this forwarding feature, we proposed a novel adaptive forwarding strategy in Named Data Networking (AFSndn) based on Q-learning to minimize the delivery time. AFSndn is divided into two phases—Exploration phase and Exploitation phase. The Exploration phase aims to collect information, while the Exploitation phase aims to dynamically forward interest packets. Simulation experiment results show that AFSndn has better performance compared to others famous algorithms.

Trust Routing Protocol Based on Cloud-Based Fuzzy Petri Net and Trust Entropy for Mobile Ad hoc Network

Abstract: The trust-based routing mechanisms are proposed to enhance the security of the mobile ad hoc network (MANET), which use the performance metrics of a node to evaluate the trust value of the node. However, some performance metrics are fuzzy, which are easier to be described qualitatively than to be expressed quantitatively. Therefore, the inability to quantitatively express these performance metrics leads to the inaccuracy in the calculation of the trust values of nodes. Meanwhile, some routing mechanisms add the path with the highest credibility to routing table without considering the hop counts of the route in route selection, which reduces quality of service (QoS) of the routing. Aiming at the above problems, firstly, we use cloud model to deal with the fuzziness of performance metrics. Specifically, a trust reasoning model based on cloud model and fuzzy Petri net (FPN) is presented to evaluate the credibility of nodes. Then we propose a routing algorithm based on trust entropy. Routes with the minimum trust entropy are selected to add to routing table. This routing algorithm can reflect the comprehensive effect of route hops and the trust values of nodes on routing selection, thus improving QoS in MANET. Finally, the TUE-OLSR protocol is established based on the trust entropy routing algorithm and the optimized link state routing (OLSR) protocol. What’s more, the effectiveness of TUE-OLSR protocol is verified by simulation experiments, which illustrate that TUE-OLSR protocol performs better than existing trust-based OLSR protocols in terms of packet delivery ratio and average latency.

Optimized Distance Range Free Localization Algorithm for WSN

Abstract: DV-Hop and its various improvements overexpose hop size and hop count for localization. Whereas hop size is always erroneous and hops path is not a straight line, which leads to a faulty location estimation. The proposed model optimized distance range free (ODR) localization algorithm limits the use of hop size and hop count to approximate nearly a straight line distance between a known and an unknown node without additional hardware and without increasing extra communication. The refrain use of hop size and hop count improves localization accuracy of ODR and makes it robust against those network variables which affect the hop size accuracy adversely. In fact, ODR modifies the last two steps of DV-Hop. DV-Hop finds hop size in its second step. Here ODR rectifies this hop size and then a centroid is obtained from the minimum distant anchor nodes for an unknown node. Now a minimum possible distance known as base distance is estimated with a routing table assistance. In the last step, DV-Hop uses least square regression to localize, while ODR exploits linear optimization to comprehend the base distance for localization. The paper establishes ODR analytically and rugged with ranging error of the omnidirectional antenna coverage pattern experimentally.

Routing Clustering Protocol for 3D Wireless Sensor Networks Based on Fragile Collection Ant Colony Algorithm

Abstract: Compared with two-dimensional wireless sensor networks (2DWSNs), three-dimensional wireless sensor networks (3DWSNs) have higher node energy consumption and weaker load balancing. To alleviate these problems, we propose a new multi-hop routing clustering protocol for a wide range of 3DWSNs. In terms of clustering, nodes will run for cluster head (CH) by timing broadcast based on their remaining energy and their average distance from surrounding nodes. Two new identity nodes are added between the CH and the member nodes, the load transfer node responsible for transferring the energy consumption of the CH, and the secondary cluster head node (SCH) acting as the CH for the next round. In order to reduce energy consumption in key node areas, we also enable key nodes to communicate directly with the sink node. In the aspect of routing construction, SN generates the routing table corresponding to each CH through our proposed fragile ant colony algorithm. Because CH and SCH exist at the same time and have a certain replacement order, the SN only needs to obtain the location of each CH when the network is first clustered, and can continuously generate a new routing table corresponding to the CH, which reduces the energy consumption of the network in the routing path construction. We simulate this protocol, LEACH protocol, AZ-SEP protocol, and UCNPD protocol in a three-dimensional environment. The results show that this protocol is better than the other three protocols in terms of network lifetime and network load balance, respectively.

A new position based routing algorithm for vehicular ad hoc networks

Abstract: The main requirement to make safer journey in VANET environment is minimum delay with high packet delivery rate. This ensures that all data packets are received with minimal delay to prevent any accident. This paper presents a new algorithm for VANET class routing protocol that covers sparse and coarse region of vehicles. It takes the advantage of road layout to improve the performance of routing in VANETs. The proposed algorithm uses real-time GPS tracking system to obtain traffic information for creating road based paths from source node to destination node. The optimize forwarding is used to figure out the forwarding node along the road pattern that form the path to deliver the data packets. The results shows that proposed algorithm obtain better results considering the various simulation parameters.

SmartEntry: Mitigating Routing Update Overhead with Reinforcement Learning for Traffic Engineering

Abstract: Traffic Engineering (TE) has been used by Internet service providers to improve their network performance and provide better service quality to users. While flow-based TE is an alternative, destination-based TE is a more readily deployed solution. This is because destination-based forwarding is ubiquitously supported by today's routers. A challenge faced by state-of-the-art destination-based TE solutions is considerable time taken by a centralized controller to update traffic split ratios for each entry of the forwarding table of each router. This could impose a fundamental limitation on how responsively the network can react to dynamic changes of traffic demands. In this paper, we propose SmartEntry, a destination-based routing solution coupled with Reinforcement Learning (RL) to reduce the number of the forwarding entries that need to be updated to respond to dynamic change of traffic demands. SmartEntry forwards majority traffic on Equal-Cost Multi-Path (ECMP) and redistributes a small portion of traffic using our proposed RL algorithm. SmartEntry adopts Linear Programming (LP) to produce reward signals. This RL + LP combined approach turns out to be surprisingly effective. We evaluate SmartEntry by conducting extensive experiments on different network topologies with both real and synthesized traffic. The simulation results show that SmartEntry achieves near-optimal performance with a saving of 90% forwarding entry updates, and generalizes well to unseen traffic matrices.

Routing Strategy of Integrated Satellite-Terrestrial Network Based on Hyperbolic Geometry

Abstract: The integrated satellite-terrestrial network has the characteristics of large scale, complex, high dynamic and heterogeneousness. Adopting traditional routing methods in the integrated satellite-terrestrial network will cause problems of poor scalability and large routing overhead. Greedy forwarding strategy based on network mapping with hyperbolic geometry works well in large scale network. However, there is no study on applying the network mapping with hyperbolic geometry to complex networks beyond two dimensions, including the integrated satellite-terrestrial network. Based on the method of spherical polar projection, this paper proposes a hyperbolic coordinates mapping algorithm in three-dimensional geographic space suitable for the integrated satellite-terrestrial network. This algorithm gives nodes of heterogeneous layers in the integrated satellite-terrestrial network a unified expression based on four-dimensional hyperbolic coordinates, which helps to quickly identify and locate nodes without global information distribution and scheduling when routing. The routing strategy using greedy forwarding strategy based on this algorithm only costs low storage overhead, as it does not need routing tables. Simulations demonstrate that the performance of the algorithm is hardly affected by the exponential expansion of the network size, which means the property of scalability is excellent. Also, it is stable under heterogeneous network structure, and maintains a stable routing success rate for optimal path selection around 93% with a time complexity of O(n).

Techniques For Secure Blockchain Routing

Abstract: Described herein are systems and methods for providing secure blockchain routing utilizing an extended blockchain protocol. In some embodiments, a blockchain routing node may join an overlay network including a plurality of blockchain routing nodes. The blockchain routing node may receive a plurality of forwarding tables from the plurality of blockchain routing nodes in accordance with an extended blockchain protocol. The blockchain routing node may determine a routing table for the overlay network based at least on part on the plurality of forwarding tables. In some embodiments, the blockchain routing node may route a payload message to a destination blockchain routing node in the overlay network in accordance with the determined routing table.

Seamless clustering multi-hop routing protocol based on improved artificial bee colony algorithm

Abstract: An important issue in the mobile sink wireless sensor networks (MSWSNs) is sensor energy optimization. In order to alleviate the problem of unbalanced network load and high energy consumption in MSWSNs, we proposed a new data collection protocol in this paper: seamless clustering multi-hop routing protocol based on improved artificial bee colony algorithm (IABCP). Because of limited by the communication sensing range and intelligence of ordinary nodes, routing paths can only be constructed by crude methods. And the movement of the sink node will generate a large amount of energy consumption for locating the sink node. In order to solve this problem, we assign the task of routing table generation to the sink node, which will generate the routing table through the improved artificial bee colony algorithm. In addition, we adopt a new method to select cluster head (CH) nodes; node uses the average energy of the surrounding nodes and its own residual energy to calculate the claimed cluster head time. Moreover, we added a sub cluster head CH-β node. When the CH node reaches the number of replacement rounds, the CH-β node becomes CH directly. The simulation results show that our routing protocol is more robust compared with three other protocols.

Dual-Channel-Based Mobile Ad Hoc Network Routing Technique for Indoor Disaster Environment

Abstract: The existing network infrastructure may not work well in a disaster environment caused by a fire or an earthquake. Instead of relying on the existing infrastructure, communicating through a mobile ad hoc network (MANET) is recommended because MANET can configure a network without an infrastructured communication system. In addition, firefighters conducting emergency activities in harsh environments surrounded by flames and smoke need a communication system to assist their rapid firefighting operations. Existing work is not suitable for indoor firefighter communications because they did not consider the indoor disaster environment well. In this proposed scheme, dual channels (i.e., 2.4 GHz and sub-GHz bands) are used for an efficient routing table configuration. Data frame and HELLO message are exchanged through the 2.4 GHz band, while the neighbor list of each node is exchanged through the sub-GHz band. Each node can configure the routing table based on the exchanged neighbor list. A performance evaluation is conducted to compare the proposed technique with enhanced versions of optimized link state routing (OLSR) and destination-sequenced distance vector routing (DSDV). The results show that the proposed scheme outperforms the other two MANET routing algorithms (i.e., OLSR-mod and DSDV-mod) in terms of the packet delivery ratio (PDR), end-to-end delay, and initial routing table configuration time approximately 27.8%, 4.7%, and 166.7%, respectively.

A Time Interval based Blockchain Model for Detection of Malicious Nodes in MANET Using Network Block Monitoring Node

Abstract: Mobile Ad Hoc Networks (MANETs) are infrastructure-less networks that are mainly used for establishing communication during the situation where wired network fails. Security related information collection is a fundamental part of the identification of attacks in Mobile Ad Hoc Networks (MANETs). A node should find accessible routes to remaining nodes for information assortment and gather security related information during route discovery for choosing secured routes. During data communication, malicious nodes enter the network and cause disturbances during data transmission and reduce the performance of the system. In this manuscript, a Time Interval Based Blockchain Model (TIBBM) for security related information assortment that identifies malicious nodes in the MANET is proposed. The proposed model builds the Blockchain information structure which is utilized to distinguish malicious nodes at specified time intervals. To perform a malicious node identification process, a Network Block Monitoring Node (NBMN) is selected after route selection and this node will monitor the blocks created by the nodes in the routing table. At long last, NBMN node understands the location of malicious nodes by utilizing the Blocks created. The proposed model is compared with the traditional malicious node identification model and the results show that the proposed model exhibits better performance in malicious node detection.

Effective Routing Technique: Augmenting Data Center Switch Fabric Performance

Abstract: Today, data center networks (DCNs) are built using multi-tier architecture. These large-scale networks face many challenges, such as security, delay, low throughput, loops, link oversubscription, TCP Incast and Outcast, etc. In this paper, a TCAM (Ternary Content Addressable Memory) based routing technique is proposed, augmenting the routing capabilities of multi-tier architectures in large scale networks. The routing complexities in these architectures are rectified and improved by implementing an additional TCAM based routing table in Leaf/ToR switches for a specific number of compute nodes in particular Pods, and it is scalable to whole datacenter nodes. To test the model, we implemented two prototype models: one depicting our proposed TCAM based switch and the other is a typical Top-of-the-Rack (ToR) switch and compared the performance of the proposed model and if any overhead introduced in it. The preliminary results show that our TCAM based routing table technique is fast and it forwards the network packets at line-rate, does not introduce considerable latency, on-chip resources power consumptions is less than 3%, and helps to solve or mitigate the above critical problems that are present in the current large DC's three-tier architecture, especially in Top of the Rack and aggregation layers switches.

On Ad hoc On-Demand Distance Vector Routing in Low Earth Orbit Nanosatellite Constellations

Abstract: The increasing demand for applications of the Internet of Things (IoT) has produced the emergence of use cases with challenging scenarios for cellular access. In such use cases, Low Earth Orbit (LEO) small satellite constellations provide global coverage for regions isolated from terrestrial infrastructure, but pose difficulties for routing given their dynamic nature. In this paper, we study ad hoc distance vector routing for LEO small satellite constellations. We provide a qualitative evaluation based on performance metrics such as scalability, robustness and energy consumption. Then, we propose Ad hoc On-Demand Distance Vector (AODV) as an applicable protocol and compare it with flooding with an implementation of a network simulator in C# for various constellation sizes and routing options. Our results show that routing across the constellation seam and the active route timeout play a critical role in the Packet Delivery Ratio (PDR) and packet throughput. Although reasonably functional, we conclude that AODV should be complemented with proactive routing in tightly controlled polar constellations, since pre-computed routing tables will perform better under specific conditions.

A hybrid link protection scheme for ensuring network service availability in link-state routing networks

Abstract: The internet is playing an increasingly crucial role in both personal and business activities. In addition, with the emergence of real-time, delay sensitive and mission-critical applications, stringent network availability requirement is put forward for internet service providers (ISPs). However, commonly deployed intradomain link-state routing protocols react to link failures by globally exchanging link state advertisements and recalculating routing table, inevitably causing significant forwarding discontinuity after a failure. Therefore, the loop-free criterion (LFC) approach has been widely deployed by many ISPs for coping with the single network component failure scenario in large internet backbones. The success of LFC lies in its inherent simplicity, but this comes at the expense of letting certain failure scenarios go unprotected. To achieve full failure coverage with LFC without incurring significant extra overhead, we propose a novel link protection scheme, hybrid link protection (HLP), to achieve failure resilient routing. Compared to previous schemes, HLP ensures high network availability in a more efficient way. HLP is implemented in two stages. Stage one provides an efficient LFC based method (MNP-e). The complexity of the algorithm is less than that of Dijkstra's algorithm and can provide the similar network availability with LFC. Stage two provides backup path protection (BPP) based on MNP-e, where only a minimum number of links need to be protected, using special paths and packet headers, to meet the network availability requirement. We evaluate these algorithms in a wide spread of relevant topologies, both real and synthetic, and the results reveal that HLP can achieve high network availability without introducing conspicuous overhead. HLP not only needs around 10% time of that of full protection, but also provides full protection capabilities that full protection provide.

Optimizing Routing Path Selection Method Particle Swarm Optimization

Abstract: In view of the two shortcomings of the AODV routing protocol, they do not consider the bandwidth, delay and cost in the actual network, and the routing table has only one path from the basic node t...