Distance Vector Research Articles

Blackhole attacks in internet of things networks: a review

The internet of things (IoT) is one of data revolution area and is the following extraordinary mechanical jump after the internet. In terms of IoT, it is expected that electronic gadgets that are used on a regular basis would be connected to the current of the internet. IPv6 over low-power wireless personal area networks (6LoWPAN) is a one of IPv6 header pressure technology, and accordingly, it is vulnerable to attack. The IoT is a combination of devices with restricted resource assets like memory, battery power, and computational capability. To solve this, RPL or routing protocol for low power Lossy network is deploy by utilizing a distance vector scheme. One of denial of service (Dos) attack to RPL network is blackhole attack in which the assailant endeavors to become a parent by drawing in a critical volume of traffic to it and drop all packets. In this paper, we discuss research on numerous attacks and current protection methods, focusing on the blackhole attack. There is also discussion of challenge, open research issues and future perspectives in RPL security. Furthermore, research on blackhole attacks and specific detection technique proposed in the literature is also been presented.

An investigation on applicability of Ad-hoc routing protocols in Fog-assisted smart health care system

Cloud computing is widely adopted as it provides real-time services to users on a respectable scale in alignment with pay-per-use models. The heterogeneity and lively characteristics of the cloud network's components make failure inevitable. The failure also has an impact on the cloud service's dependability and accessibility. A computer paradigm called fog brings cloud computing services closer to edge hardware. Fog computing is being used more and more in applications that require faster real-time services, and the healthcare industry has embraced it heavily due to the urgent need to save lives. This work is aimed to investigate the suitable ad-hoc routing protocol for fog-assisted smart health monitoring system. By simulating with the network simulator NS-2 and using throughput, latency, and packet delivery ratio (PDR) as assessment criteria, the effectiveness of routing protocols is assessed. The protocols Ad-hoc on-demand Distance Vector (AODV), Destination Sequenced Distance Vector (DSDV), Dynamic Source Routing (DSR), and Optimized Link State Routing (OLSR) are being tested. The AODV exhibits encouraging results with other comparative protocols.

Secured mobile IOT ecosystem using Enhanced Multi-level Intelligent Trust Scheme

The Internet of Things (IoT) is a collection of heterogeneous, interconnected mobile ad-hoc networks (MANETs) devices. High-level security is always required to protect sensitive information travelling over MANET-IoT networks. IoT security ensures the privacy, confidentiality, and security of the users, infrastructure, and data. The Enhanced Multi-Level Intelligent Trust Scheme (EMITS) initially designs to authenticate and validate the data for the Mobile IoT (MIoT) ecosystem. The proposed model used the AODV (Adhoc On-Demand Distance Vector Routing) routing protocol with an intelligent trust scheme. This paper aims to present a MANET-IoT-based model combined with a hybrid cryptographic scheme that ensures secure communication between mobile IoT ecosystems against the black hole attack. The performance of the proposed EMITS-MIoT model is verified based on the packet delivery ratio (PDR), packet loss ratio (PLR), false positive ratio (FPR), false negative ratio (FNR), and attack detection rate (DR). The EMTS-IoT technique shows a 3–5% success in data transmission over existing techniques.

A Fast Instance Segmentation Technique for Log End Faces Based on Metric Learning

The diameter of the logs on a vehicle is a critical part of the logistics and transportation of logs. However, the manual size-checking method is inefficient and affects the efficiency of log transportation. The example segmentation methods can generate masks for each log end face, which helps automate the check gauge of logs and improve efficiency. The example segmentation model uses rectangle detection to identify each end face and then traverses the rectangular boxes for mask extraction. The traversal of rectangular boxes increases the time consumption of the model and lacks separate handling of the overlapping areas between rectangular boxes, which causes a decline in mask extraction accuracy. To address the above problems, we propose a fast instance segmentation method for further improving the efficiency and accuracy of log-checking diameter. The method uses a convolutional neural network to extract the mask image, rectangular frame prediction image, and embed the vector image from the input image. The mask image is used to extract the log end face region, and the rectangular frame prediction image generates an enveloping rectangular frame for each log, which in turn divides the log end face region into instances. For the overlapping regions of rectangular boxes, a metric learning paradigm is used to increase the embedding vector distance between pixels located in different logs and decrease the embedding vector distance between pixels of the same log, and finally the mask pixels of the overlapping regions of rectangular boxes are instantiated according to the pixel embedding vectors. This method avoids repeated calls to the contour extraction algorithm for each rectangular box and enables fine delineation of pixels in the overlapping rectangular box region. To verify the efficiency of the proposed algorithm, the log working pile is photographed in different scenes using a smartphone to obtain the end face recognition database and divide the training set, validation set, and test set according to 8:1:1. Secondly, the proposed model is used to obtain log end face masks, and the log end face ruler diameter is determined by an edge-fitting algorithm combined with a ruler. Finally, the practicality of the algorithm is evaluated by calculating the check-rule diameter error, running speed, and the error of wood volume calculation under different national standards. The proposed method has 91.2% and 50.2 FPS of mask extraction accuracy and running speed, respectively, which are faster and more accurate than the mainstream instance segmentation model. The relative error of the proposed method is −4.62% for the check-rule diameter and −4.25%, −5.02%, −6.32%, and −5.73% for the wood volume measurement under the Chinese, Russian, American, and Japanese raw wood volume calculation standards, respectively. Among them, the error of the calculated timber volume according to our standard is the smallest, which indicates that the model in this paper is more suitable for application in domestic log production operations.

Invulnerability Improvement of Multipath Protocols in Different Scenarios

Developments in computer communications and networks enabled the deployment of exciting new areas. Meanwhile, Efficient network and communication technologies also promote the development of routing protocols. AODV(Ad hoc On-Demand Distance Vector Routing, AODV) routing protocol has invulnerability to different mobile attributes of agent nodes and network damage scenarios. However, this invulnerability performance can not meet the needs of mobile multi-agent cooperative communication. Therefore, this paper proposes an improved responsive multipath routing protocol MD_AODV(Multipath Destroy-resistance AODV, MD_AODV). This routing protocol is based on AODV routing protocol. It is mainly improved in three aspects: multichannel concurrency mechanism, node load balancing mechanism and shortest path maintenance mechanism. Firstly, in this research, an improved multipath routing protocol is modeled and implemented, which is based on OPNET Network simulation platform. Then, network models with different degrees of impairment are built. Combines the mobile attributes of mobile multi-agent task coordination to carry out invulnerability simulation experiments . These experiments study the invulnerability of the improved multipath routing protocol. Finally, this paper evaluates and compares the routing invulnerability of MD_AODV, AODV and AOMDV(Ad-hoc On-Demand Multipath Distance Vector, AOMDV). The evaluation results show that MD_AODV routing has better invulnerability when the network is severely damaged

Simulation of Elliptical Curve Cryptography in IPSec on Ad-Hoc Networks

Ad-hoc networks have gained significant attention in the realm of communication due to the proliferation of mobile and IoT devices and wireless networks. Ad hoc Networks offer a decentralized approach, where each node can function as a router and a terminal. Ensuring data safety and integrity in Ad hoc Networks remains a challenge, necessitating the use of robust security mechanisms. This research focuses on the simulation of Elliptical Curve Cryptography (ECC) in the IPsec protocol on ad-hoc networks. ECC, known for its strong security and smaller key sizes, provides an effective means of protecting data packets from potential attacks. The Ad hoc On Demand Multipath Distance Vector (AODV) routing protocol is employed for secure data transmission in a Ad hoc Networks. The main objective is to maintain packet security in the face of hostile environments and active adversaries. Furthermore, the results obtained from the NS-2 simulator are compared with Advanced Encryption Standard (AES), and Rivest-Shamir-Adleman (RSA). Evaluation metrics such as Quality of Service (QoS), average processing time, and average end-to-end delay are utilized. This study addresses the challenges faced by ad-hoc networks in an increasingly digital world. By exploring the implementation of ECC-based cryptography, it contributes to the development of secure communication protocols in ad-hoc networks. The findings offer insights into the efficacy of ECC in protecting data transmission and its comparative performance with other cryptographic techniques. Ultimately, this research aims to advance secure communication protocols, ensuring reliable data exchange in diverse applications and scenarios.

Maximizing the Spread of Effective Information in Social Networks

Influence maximization through social networks has aroused tremendous interests nowadays. However, people’s various expressions or feelings about a same idea often cause ambiguity via word of mouth. Consequently, the problem of how to maximize the spread of “effective information” still remains largely open. In this paper, we consider a practical setting where ideas can deviate from their original version to invalid forms during message passing, and make the first attempt to seek a union of users that maximizes the spread of effective influence, which is formulated as an Influence Maximization with Information Variation (IMIV) problem. To this end, we model the information as a vector, and quantify the difference of two arbitrary vectors as a distance by a matching function. We further establish a process where such distance increases with the propagation and ensure the recipient whose vector distance is less than a threshold can be effectively influenced. Due to the NP-hardness of IMIV, we greedily select users that can approximately maximize the estimation of effective propagation. Especially, for networks of small scales, we derive a condition under which all the users can be effectively influenced. Our models and theoretical findings are further consolidated through extensive experiments on real-world datasets.

Efficient Hashing Method Using 2D-2D PCA for Image Copy Detection

Image copy detection is an important technology of copyright protection. This paper proposes an efficient hashing method for image copy detection using 2D-2D (two-directional two-dimensional) PCA (Principal Component Analysis). The key is the discovery of the translation invariance of 2D-2D PCA. With the property of translation invariance, a novel model of extracting rotation-invariant low-dimensional features is designed by combining PCT (Polar Coordinate Transformation) and 2D-2D PCA. The PCT can convert an input rotated image to a translation matrix. Since the 2D-2D PCA is invariant to translation, the low-dimensional features learned from the translation matrix are rotation-invariant. Moreover, vector distances of low-dimensional features are stable to common digital operations and thus hash construction with the vector distances is of robustness and compactness. Three open image datasets are exploited to conduct various experiments for validating efficiencies of the proposed method. The results demonstrate that the proposed method is much better than some representative hashing methods in the performances of classification and copy detection.

Performance evaluation of dynamic source routing protocol with variation in transmission power and speed

<span lang="EN-US">Mobile ad-hoc network (MANET) is a set of mobile wireless nodes (devices) which is not rely on a fixed infrastructure. In MANETs, each device is responsible for routing its data according to a specific routing protocol. The three most common MANET routing protocols are: dynamic source routing protocol (DSR), optimized link state routing protocol (OLSR), and ad-hoc on-demand distance vector (AODV). This paper proposes an efficient evaluation of DSR protocol by testing the MANETs routing protocol with variation in transmission power at different speeds. The performance analysis has been given using optimized network engineering tools (OPNET) modeler simulations and evaluated using metrics of average end to end delay and throughput. The results show that the throughput increases as the transmission power increases up to a certain value after which the throughput decreases, also the network work optimally at a certain transmission power which varied at different speed.</span>

Dynamic source routing protocol with transmission control and user datagram protocols

Dynamic source routing protocol (DSR) is a common routing protocol in wireless network without infrastructure, called ad-hoc network, DSR used just above internet protocol (IP) at the network layer. The upper transport layer provides reliability by transmission control protocol (TCP) and user datagram protocol (UDP). The choice between DSR/TCP and DSR/UDP is an actual issue for network designers and engineers. The question arises: which one provides better quality of service (QoS) parameters, less delay and jitter, greater throughput, and data rates. This paper focuses on the study and analysis of DSR and comparison of DSR/TCP and DSR/UDP by simulation in network simulator (NS2) environment. Another comparison of DSR and ad hoc on-demand distance vector (AODV) is provided. Design and simulation of the protocols in ad hoc network accurately describe the behavior in real system and QoS parameters are obtained.

Secure and Light Weight Aodv (Slw-Aodv) Routing Protocol for Resilience Against Blackhole Attack in Manets

This paper aimed at the detection of blackhole attacks and proposed a new method called as Secure and Light Weight Adhoc On demand Distance Vector Routing (SLW-AODV). SLW-AODV is an extended version of the traditional AODV routing protocol. The proposed SLW-AODV ensures resilience for both blackhole and cooperative blackhole attacks. It employs a simple Challenge, Response and Confirm (CRC) strategy with chaotic maps for the identification of both blackhole and cooperative blackhole attacks. SLW-AODV identifies the attacked nodes at both route discovery and data forwarding process. For experimental validation, we have conducted extensive simulations and the performance is validated through Packet Delivery Ratio, Throughput and Average end-to-end delay. The obtained performance metrics shows an outstanding performance than the state-of-the art methods.

Neighbour Node Ratio AODV (NNR-AODV) Routing Protocol for Wormhole Attack Detection in Manets

This paper aimed at the detection of wormhole attack and proposed a new method called as Neighbour Node Ratio Adhoc On demand Distance Vector Routing (NNR-AODV). NNR-AODV is an extended version of the traditional AODV routing protocol. The proposed NNR-AODV calculates the neighbour node count for every node and based on that it will decide whether the wormhole is present or not. Furthermore, NNR-AODV is able to detect both external and internal wormhole attacked nodes. Also, NNR-AODV derived a Neighbor node Threshold value which is based on the cumulative distances between nodes present in the wormhole attack. For experimental validation, we have accomplished an extensive simulation and the performance is measured through Number of bogus links, Detection rate, False positive Rate, Packet delivery ratio and Packet loss ratio. The obtained results have shown superior performance in the detection of wormhole attacks than the existing methods.

Improved Hybrid Routing Protocol (IHRP) in MANETs Based on Situation Based Adaptive Routing

Without the need of a fixed foundation or base station, the Mobile Ad hoc Network creates its own wireless network. One of the most troublesome aspects of Mobile Ad hoc Network (MANET) is the occurrence of unexpected loss of network connectivity. As a result of this problem, packets continue to drop, and we must restore the connection by sending Route Request (RREQ) and Route Reply (RREP). As a result, network performance will suffer yet another setback. We used the scenario routing technique to combine the Dream Multipath Routing (DMR), Ad hoc on-demand multipath distance vector (AOMDV), Optimized link-state routing (OLSR), and Ad-hoc on Demand Vector (AODV) routing protocols to build the IHRP routing protocol in this work. According to previous studies, (AODV) is more suited when node motion is high. The purpose of DREAM Multipath Routing (DMR) is to maintain node mobility and location information coordinated. Route packet flooding is prevented by computing the expected chance of node relocation. The number of mobile nodes in the wireless network fluctuates, and the DMR operates on each one individually. In the network, each node maintains a list of nearby nodes and their current locations. Using the AOMDV routing protocol is effective for load balancing and preventing congestion on the network. OLSR is a good fit for networks that priorities link reliability above other considerations when routing traffic. When using the aforementioned (DMR, AODV, AOMDV, and OLSR) protocols to create the IHRP routing protocol, we are capable of better regulation of network behavior and perform. In the case of 100 nodes, data is sent for analysis for The Improved Hybrid Routing Protocol (IHRP), Zone Routing Protocol (ZRP), AOMDV, AODV, and OLSR routing protocols. For data send, the performance of IHRP, ZRP, AOMDV, AODV, and OLSR is 11513, 10240, 10225, 10558, and 9184, respectively, and for 50 nodes, the performance of IHRP, ZRP, AOMDV, AODV, and OLSR is 11151, 9807, 9636, 9586, and 7470, respectively. Thus, with 100 and 50 nodes, the Improved Hybrid Routing Protocol (IHRP) outperforms the AOMDV, ZRP, AODV, and OLSR routing protocols. In the case of 100 nodes and 50 nodes, the data receive analysis for IHRP, ZRP, AOMDV, AODV, and OLSR routing protocols is 11513, 10240, 10225, 10558, and 9184, respectively, and 9367, 8714, 8370, 6730, and 7298, respectively, So IHRP also receives data faster than ZRP, AOMDV, AODV, and OLSR routing protocols. The IHRP outperforms AOMDV, AODV, ZRP, and OLSR routing protocols in terms of data transmit, receive, data drop, PDR, throughput, E-E latency, and NRL.

Inferring router ownership based on the classification of intra- and inter-domain links

Research on router ownership inference is central to many Internet studies, such as network failure diagnosis, network boundary identification, network resilience assessment, and inter-domain congestion detection. The existing router ownership inference method bdrmapIT has relatively few constraints on routers at the end of traceroute paths, resulting in some inference errors. In this paper, a router ownership inference method based on the classification of intra- and inter-domain links is proposed. In this method, the differentiating Internet Protocol (IP) address vector distance feature, the autonomous system relationship feature of the IP link, and the fan-in and fan-out features are designed to support the discrimination of IP link types. The use of additional information derived from the link type enriches the basis for router ownership inference and improves the accuracy of the inference result. Experimental results show that the accuracy reaches 96.4% and 94.6% on the two verification sets, respectively, which is 3.2–11.2% better than the existing typical methods.

RPLAD3: anomaly detection of blackhole, grayhole, and selective forwarding attacks in wireless sensor network-based Internet of Things.

Routing protocols transmit vast amounts of sensor data between the Wireless Sensor Network (WSN) and the Internet of Things (IoT) gateway. One of these routing protocols is Routing Protocol for Low Power and Lossy Networks (RPL). The Internet Engineering Task Force (IETF) defined RPL in March 2012 as a de facto distance-vector routing protocol for wireless communications with lower energy. Although RPL messages use a cryptographic algorithm for security protection, it does not help prevent internal attacks. These attacks drop some or all packets, such as blackhole or selective forwarding attacks, or change data packets, like grayhole attacks. The RPL protocol needs to be strengthened to address such an issue, as only a limited number of studies have been conducted on detecting internal attacks. Moreover, earlier research should have considered the mobility framework, a vital feature of the IoT. This article presents a novel lightweight system for anomaly detection of grayhole, blackhole, and selective forwarding attacks. The study aims to use a trust model in the RPL protocol, considering attack detection under mobility frameworks. The proposed system, anomaly detection of three RPL attacks (RPLAD3), is designed in four layers and starts operating immediately after the initial state of the network. The experiments demonstrated that RPLAD3 outperforms the RPL protocol when defeating attacks with high accuracy and a true positive ratio while lowering power and energy consumption. In addition, it significantly improves the packet delivery ratio and decreases the false positive ratio to zero.

Performance Analysis by Improving Energy Efficiency in IoT Based Wireless Sensor Network for Routing Algorithm

Objectives: To improve the energy efficiency in IoT-based wireless sensor networks. To increase the sensor node lifetime in any wireless sensor network (WSN) based on the Energy-efficient cluster and routing in WSN. The advancement in the electronic industry has to develop the low powerconsuming and low-cost devices for multiple purposes. Methods: A new algorithm is proposed to improve the energy efficiency in IoT-based WSN for routing algorithms. The low power consuming devices are wisely chosen for the respected application and their power management. This study is intended to provide efficient routing algorithms with less energy consumption in wireless sensor networks. Findings: A wireless sensor network deployed with a number of non-static nodes in a communication network are beneficial for numerous fields such as the military, medical sector, structural health monitoring, transportation industries, etc. In this paper, the performance parameters like average throughput, average end-to-end delay, packet delivery ratio, average node energy, and normalized routing load are analyzed. Novelty: We designed and simulated the Distributed IoT-based wireless network system to analyze the information of nodes roaming in the vicinity of the zone area. In proposed technique energy efficiency is increased compared to the conventional algorithm. In node centric routing protocol, energy efficiency is increased from 55% to 68%. In data centric routing protocol, energy efficiency is increased from 65% to 75%. In source-initiated routing protocol, energy efficiency is increased from 68% to 80%. In destination-initiated routing protocol, energy efficiency is increased from 72% to 85%. Keywords: Ad Hoc OnDemand stance Vector (AODV); DestinationSequenced DistanceVector Routing (DSDV); Dynamic source routing (DSR); MQ Telemetry Transport (MQTT); Quality of Services (QoS); Wireless Sensor Network (WSN)

Adaptive Weighted K-Nearest Neighbor Trilateration Algorithm for Visible Light Positioning

An adaptive weighted K-nearest neighbor (AWKNN) trilateration positioning algorithm fused with the channel state information (CSI) is proposed to optimize the accuracy of the visible light positioning. The core concept behind this algorithm is to combine the WKNN algorithm with ranging based on the CSI. The direct path distance estimated by the CSI is utilized to construct a position set consisting of multiple positions and a corresponding distance database containing multiple distance vectors. The error parameters of the weighted combinations of different distance vectors are calculated iteratively to evaluate the impact of different K-values and weights on the positioning accuracy. The proposed algorithm can achieve high-precision trilateration positioning by adaptively selecting the K-value and weight. A typical 4 m × 4 m × 3 m indoor multipath scene with four LEDs is established to simulate the positioning performance. The simulation results reveal that the mean error of the CSI-based AWKNN algorithm achieves 1.84 cm, with a root mean square error (RMSE) of 2.13 cm. Compared with the CSI-based least squares (LS) method, the CSI-based nonlinear LS method, and the CSI-based WKNN method, the average error of this method is decreased by 29%, 16%, and 17%, whereas the RMSE is reduced by 35%, 14%, and 19%.

Quality optimization of OSPFV3 and EIGRP for IPV4 in hierarchical network environment using GNS3 tool

In network world, due to shortage of IP addresses in version 4, a new version of IP address, namely IPv6, has come into play, but due to non-exchange ability between IPv4 and IPv6, different routing protocol is come to play for IPv6 that is RIPng, OSPFv3 and EIGRP for IPv6 in the place of RIP, OSPFv2 and EIGRP in IPv4 environment. Transmission of data is based on the routing protocol which selects the best routes in network world. Two types of interior routing protocol which selects the best routes in network world that is distance vector and link state. In this research we compare two types of routing protocol are chosen as the simulation base that is OSPFv3 (Link state) and EIGRP IPv6 (Distance Vector) in terms of convergence and router CPU usage. These routing protocols are mostly used in network as well as internet. In this project, we are going to use GNS3 to simulate and compare OSPF and EIGRPv6 with authentication and without authentication in large hierarchical network using ipv4 and ipv6 environment. On top of everything, as a second step GNS3 simulator installed. Familiarity with GNS3 is essential to achieve this research study. As a second step, a certain hierarchical routing scheme scenarios will be created. Secondly, OSPFv3 and EIGRP IPv6 without authentication implemented and configured to get the required results in terms of CPU processing power and convergence time. Later, OSPFv3 and EIGRP IPv6 with authentication implemented and configured to get results for a comparison in terms of resources usage and convergence. As a final stage overall results of the research study compared to analyze the research parameters which are with and without authentication on both routing protocols. At the end of this research study, the result is drawn and presented in the thesis and a final comparative analysis presented in tabular format. In this Research study compare two parameters, CPU utilization and Processing power with and without authentication. We found that OSPFv3 uses less power as compared to EIGRP for IPv6 without authentication in hierarchal environment, but it takes more time to converge and with authentication it is also found that it ospfv3 uses less power as compared to EIGRP for IPv6 while EIGPR for IPv6 is faster than OSPFv3.

CR-IOT based selfish attack detection via RSSI-LSTM

Internet of Things-based technologies rely on cooperating between nodes to increase network capacity. A selfish or malicious node is a node that does not cooperate with other nodes in the network. Selfish nodes raise their interests by using facilities provided by other nodes. Selfish cognitive radio attacks significantly degrade the performance of Cognitive Radio networks, which pose a serious security threat and malicious nodes often abuse the network and damage its facilities. To overcome this issue, a novel Cognitive radio on-demand distance vector (CRODV) is proposed. The proposed CRODV technique utilize (Received Signal Strength Indicator) RSSI-based Long Short-Term Memory (LSTM) to detect attacks. This is accomplished by counting the number of active channels to and from the test node in a specific area. If the values don't match, the test node in question is self-centered or introverted. If both values are the same, the test node is normal. Simulations were carried out in MATLAB to test the proposed CRODV. A comparison is made between the suggested framework and existing methods in terms of packet delivery ratio, route dis-connectivity ratio, throughput, detection time, and end-to-end delay. The experimental results demonstrates that the CRODV technique reduce the end-to-end delay by 27.4%, 33.24%, and 40.14% when compared with UKF, HMM, and ALAD techniques.

Editorial Issue 34.2

Editorial Issue 34.2