Routing Protocol For Networks Research Articles

Security Metrics and Authentication-based RouTing (SMART) Protocol for Vehicular IoT Networks

Security Metrics and Authentication-based RouTing (SMART) Protocol for Vehicular IoT Networks

Multi-objective may-badger optimizer based energy efficient routing protocol in dense wireless sensor network

Multi-objective may-badger optimizer based energy efficient routing protocol in dense wireless sensor network

A novel TT-SHO QoS aware and secured Vehicular Adhoc Network (VANET) routing protocol for smart intelligent transportation

A novel TT-SHO QoS aware and secured Vehicular Adhoc Network (VANET) routing protocol for smart intelligent transportation

Asynchronous entanglement routing for the quantum internet

With the emergence of the Quantum Internet, the need for advanced quantum networking techniques has significantly risen. Various models of quantum repeaters have been presented, each delineating a unique strategy to ensure quantum communication over long distances. We focus on repeaters that employ entanglement generation and swapping. This revolves around establishing remote end-to-end entanglement through repeaters, a concept we denote as the “quantum-native” repeaters (also called “first-generation” repeaters in some literature). The challenges in routing with quantum-native repeaters arise from probabilistic entanglement generation and restricted coherence time. Current approaches use synchronized time slots to search for entanglement-swapping paths, resulting in inefficiencies. Here, we propose a new set of asynchronous routing protocols for quantum networks by incorporating the idea of maintaining a dynamic topology in a distributed manner, which has been extensively studied in classical routing for lossy networks, such as using a destination-oriented directed acyclic graph or a spanning tree. The protocols update the entanglement-link topology asynchronously, identify optimal entanglement-swapping paths, and preserve unused direct-link entanglements. Our results indicate that asynchronous protocols achieve a larger upper bound with an appropriate setting and significantly higher entanglement rate than existing synchronous approaches, and the rate increases with coherence time, suggesting that it will have a much more profound impact on quantum networks as technology advances.

Towards an IoT based secured framework for smart cities

<p>Cities are evolving into smart urban environments, where various devices, technologies, services, connections, blocks, and storage systems are transforming to embrace smart solutions, departing from traditional counterparts. This intricate network of interconnected devices collectively constitutes the Internet of Things (IoT), promising users a life of enhanced convenience. However, this transformation also introduces challenges, particularly in managing loosely linked devices and transmitting information across networks. This study conducts a comprehensive review of the various components contributing to the formation of a smart city, scrutinizing the challenges and factors that influence them. The overarching objective is to identify specific areas within smart cities grappling with cybersecurity challenges and ascertain the relative significance of each area. Data for this research is gathered through questionnaires, expert opinions, and paired comparisons drawn from previous studies, and employs the Fuzzy Analytic Hierarchy Process approach to determine the weight of each factor and sub-factor, subsequently ranking them. Among the nine identified factors, the "Smart Security" factor is the most critical, with a weight of 0.198, signifying its paramount importance. To overcome the security challenges in smart cities, we introduce an advanced secure framework (ARPL) for detecting security threats, using the Routing Protocol for Low Power and Lossy Networks (RPL) as its foundation. The advanced framework is adept at identifying a variety of attacks. Performance assessments of the proposed framework encompass several key parameters, such as ADA, TPR, FPR, and end-to-end delay. The positive outcomes observed strongly endorse the effectiveness of the proposed framework, positioning it as an optimal solution for RPL-based smart environments.</p>

Performance enhancement of efficient clustering and routing protocol for wireless sensor networks using improved elephant herd optimization algorithm

Performance enhancement of efficient clustering and routing protocol for wireless sensor networks using improved elephant herd optimization algorithm

RT-Ranked: Towards Network Resiliency by Anticipating Demand in TSCH/RPL Communication Environments

Time-slotted Channel Hopping (TSCH) Media Access Control (MAC) was specified to target the Industrial Internet of Things needs. This MAC balances energy, bandwidth, and latency for deterministic communications in unreliable wireless environments. Building a distributed or autonomous TSCH schedule is arduous because the node negotiates cells with its neighbours based on queue occupancy, latency, and consumption metrics. The Minimal TSCH Configuration defined by RFC 8180 was specified for bootstrapping a 6TiSCH network and detailed configurations necessary to be supported. In particular, it adopts Routing Protocol for Low Power and Lossy networks (RPL) Non-Storing mode, which reduces the node’s network awareness. Dealing with unpredicted traffic far from the forwarding node is difficult due to limited network information. Anticipating this unexpected flow from multiple network regions is essential because it can turn the forwarding node into a network bottleneck leading to high latency, packet discard or disconnection rates, forcing RPL to change the topology. To cope with that, this work proposes a new mechanism that implements an RPL control message option for passing forward the node’s cell demand, allowing the node to anticipate the proper cell allocation for supporting the traffic originating by nodes far from the forwarding point embedded in Destination-Oriented Directed Acyclic Graph (DODAG) Information Object (DIO) and Destination Advertisement Object (DAO) RPL control messages. Implementing this mechanism in a distributed TSCH Scheduling developed in Contiki-NG yielded promising results in supporting unforeseen traffic bursts and has the potential to significantly improve the performance and reliability of TSCH schedules in challenging network environments.

Parallel Deployment and Performance Analysis of a Multi-Hop Routing Protocol for 5G Backhaul Networks Using Cloud and HPC Platforms

Parallel Deployment and Performance Analysis of a Multi-Hop Routing Protocol for 5G Backhaul Networks Using Cloud and HPC Platforms

Performance analysis of routing protocols for vehicular networks

Performance analysis of routing protocols for vehicular networks

Energy‐Efficient Routing Algorithm for Optimizing Network Performance in Underwater Data Transmission Using Gray Wolf Optimization Algorithm

Due to the aquatic nature of communication in the underwater world, the underwater acoustic sensor network (UASN) is commonly used. However, it has inherent limitations, such as limited bandwidth, high transmission energy, long propagation delays, void regions, and expensive battery replacement. Improving network lifetime (NL) is the primary objective since replacing batteries in UWSN is very expensive and challenging. NL is improved by having a high packet delivery ratio (PDR), reduced dead nodes, and reduced energy consumption (EC). If two more node batteries are depleted, they become dead nodes, causing partitions on the network and resulting in a void region problem. Void regions occur when a node has no forwarder node to forward data packets toward the sink node. Void nodes affect the routing techniques’ overall performance regarding end‐to‐end delay (EED), data loss, and EC. So, the primary objective of this work is to avoid void regions. For the same, this paper proposes a void hole detection algorithm. The algorithm selects the best next hop node based on the fitness function calculated by the gray wolf optimization (GWO) algorithm, considering only the vertical directions despite horizontal directions, further reducing the EED. The proposed approach is simulated using MATLAB, and the evaluation is based on data broadcast copies, PDR, EC, dead node number (DNN), average operational time (AOT), NL, and EED. The paper has presented a comparison with weighting depth and forwarding area division depth‐based routing (WDFAD‐DBR) routing protocol for underwater acoustic sensor network (UASN) and energy and depth variance‐based opportunistic void avoidance scheme (EDOVS) for UASN. WDFAD‐DBR avoids void holes by selecting forwarding nodes and taking the weighting sum of depth differences of two hops; in comparison, EDOVS considers not only the depth parameters but also the normalized residual energy. The proposed paper contributes to developing an energy‐efficient routing algorithm that removes void nodes by selecting the appropriate forwarder node in void regions based on the GWO algorithm. The proposed work increases the network lifetime by avoiding void regions and balancing the EC. The simulation results show that the proposed algorithm gains more than 20% PDR, less EC, and 60% less broadcasted copies of data packets and has more NL than the WDFAD‐DBR, and 10% improved PDR and lesser broadcasted copies were sent than EDOVS along with the enhanced NL, by varying the transmission range the proposed algorithm showing the better performance in terms of EC, PDR, and DNN along with the values of 61.6, 0.97, and 35 (scenario node 60 and transmission range 600 m) and 64.1, 0.89, and 25, respectively, by variable network size.

A novel energy­-efficient routing protocol for delay-tolerant networks

A novel energy­-efficient routing protocol for delay-tolerant networks

An Energy-efficient Multi-hop Routing Protocol for 3D Bridge Wireless Sensor Network Based on Secretary Bird Optimization Algorithm

An Energy-efficient Multi-hop Routing Protocol for 3D Bridge Wireless Sensor Network Based on Secretary Bird Optimization Algorithm

An efficient secure and energy resilient trust-based system for detection and mitigation of sybil attack detection (SAN).

In the modern digital market flooded by nearly endless cyber-security hazards, sophisticated IDS (intrusion detection systems) can become invaluable in defending against intricate security threats. Sybil-Free Metric-based routing protocol for low power and lossy network (RPL) Trustworthiness Scheme (SF-MRTS) captures the nature of the biggest threat to the routing protocol for low-power and lossy networks under the RPL module, known as the Sybil attack. Sybil attacks build a significant security challenge for RPL networks where an attacker can distort at least two hop paths and disrupt network processes. Using such a new way of calculating node reliability, we introduce a cutting-edge approach, evaluating parameters beyond routing metrics like energy conservation and actuality. SF-MRTS works precisely towards achieving a trusted network by introducing such trust metrics on secure paths. Therefore, this may be considered more likely to withstand the attacks because of these security improvements. The simulation function of SF-MRTS clearly shows its concordance with the security risk management features, which are also necessary for the network's performance and stability maintenance. These mechanisms are based on the principles of game theory, and they allocate attractions to the nodes that cooperate while imposing penalties on the nodes that do not. This will be the way to avoid damage to the network, and it will lead to collaboration between the nodes. SF-MRTS is a security technology for emerging industrial Internet of Things (IoT) network attacks. It effectively guaranteed reliability and improved the networks' resilience in different scenarios.

Machine learning for QoS and security enhancement of RPL in IoT-Enabled wireless sensors

Internet of Things (IoT) networks rely on wireless sensors for data collection and transmission, making them vulnerable to security threats that undermine their Quality of Service (QoS). The Routing Protocol for Low-Power and Lossy Networks (RPL) is crucial for efficient data transmission in IoT networks, but its performance can be significantly degraded by attacks such as Rank, Sinkhole and Wormhole attacks. These threats disrupt network integrity by manipulating routing information, attracting traffic through malicious nodes and tunneling data to malicious endpoints. This paper presents a novel machine learning-based framework to enhance RPL's security and QoS. Our approach integrates a random forest model for precise traffic classification, a reinforcement learning module for dynamic and adaptive routing, and a modified RPL objective function that incorporates classification outcomes into routing decisions. Simulations demonstrate that our framework significantly improves network throughput, reduces latency, and enhances packet delivery ratios while maintaining low jitter. Furthermore, it achieves a high detection rate, minimal false positives, and swift response to security incidents, thereby robustly securing the RPL protocol and enhancing QoS in IoT-enabled wireless sensor networks. The findings of this research offer substantial contributions to the field, providing a comprehensive solution to strengthen RPL against prevalent security threats.

Source-Based Disjoint Multi-Path Routing Protocol for Underwater Acoustic Networks: Protocol Design and Sea Trials

Source-Based Disjoint Multi-Path Routing Protocol for Underwater Acoustic Networks: Protocol Design and Sea Trials

Advanced Recursive Best-First Search (RBFS) based Routing Protocol for Multi-hop and Multi-Channel Cognitive Wireless Mesh Networks

Cognitive Wireless Mesh Network (CWMN) is an opportunistic network in which radio channels can be assigned according to their availability to establish connections among nodes. After establishing a radio connection among nodes, it is necessary to find an optimal route from the source node to the destination node in the network. If there remain more channels among nodes, the minimum weighted channel should be taken into account to establish expected routes. The graph theoretic approach fails to model the multi-channel cognitive radio networks due to abrupt failure in finding new successful routes as it can’t figure multi-channel networks. In this paper, a multi-edged graph model is being proposed to overcome the problems of cognitive radio networks, such as flooding problems, channel accessing problems etc. A new channel accessing algorithm has been introduced, and optimal routes have been selected using a heuristic algorithm named RBFS. Simulation results are compared with DJKSTRA based routing protocols.

Cache Improvement Based on Routing Protocol for AD-HOC Networks: Systematic Literature Review

Cache Improvement Based on Routing Protocol for AD-HOC Networks: Systematic Literature Review

Evaluation of 6LoWPAN Generic Header Compression in the Context of a RPL Network.

The Internet of Things (IoT) facilitates the integration of diverse devices, leading to the formation of networks such as Low-power Wireless Personal Area Networks (LoWPANs). These networks have inherent constraints that make header and payload compression an attractive solution to optimise communication. In this work, we evaluate the performance of Generic Header Compression (6LoWPAN-GHC), defined in RFC 7400, for IEEE 802.15.4-based networks running the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). Through simulation and real-device experiments, we study the impact of 6LoWPAN-GHC on energy consumption and delays and investigate for which scenarios 6LoWPAN-GHC is beneficial. We show that all RPL control packets are compressible by 6LoWPAN-GHC, which reduces their transmission delay and as such their vulnerability to interference. However, for the devices under study transmitting at 250 kbit/s, the energy gain obtained from sending a compressed packet is outweighed by the energy needed to compress it. The use of 6LoWPAN-GHC causes an energy increase of between 2% and 26%, depending on the RPL packet type. When the range is more important than the bandwidth and a sub-GHz band is used at 10 kbit/s, an energy gain of 11% to 29% can be obtained, depending on the type of RPL control packet.

A New Lightweight Routing Protocol for Internet of Mobile Things Based on Low Power and Lossy Network Using a Fuzzy-Logic Method

The IoT devices with embedded mobile devices create the Internet of Mobile Things (IoMT) paradigm. Mobility is not supported by the routing protocol for low-power and lossy networks (RPL) created for static networks. IoMT has raised routing challenges such as link failure, instability, energy depletion, packet loss, and handover delay in the network. In this context, IoMT Fuzzy-based RPL (IoMT-FRPL) is proposed in this paper to enhance routing performance. Receiving Signal Strength Indicator (RSSI), Euclidean distance, Hop Count, and Expected Transmission Count (ETX) metrics are built into the fuzzy interface system for the mobile nodes in the network to conserve energy. The IoMT-FRPL consists of the following three key steps: The first steps are data transmission and motion investigation, the second is fuzzy-based prediction of a new static parent for the mobile node, and the third is verifying the unique attachment point. When conventional RPL, mRPL, and EMA-RPL were compared to IoMT-performance FRPL's in Cooja/Contiki 2.7, the simulation results revealed improvements in energy consumption, handover delay, packet delivery rate (PDR), and signaling cost.

Prediction Of Compromised Iot Infrastructure Using Machine Learning

The rapid growth of the Internet of Things (IoT) has led to big advancements in Fog Computing, Smart Cities, and Industry 4.0. These areas handle complex data and need strong protection against cyberattacks. To make IoT devices use power more efficiently, the IETF created the RPL protocol (Routing Protocol for Low-Power and Lossy Networks). However, due to its sophisticated design it is vulnerable to attacks. One of the most successful attacks against this protocol is flooding attacks, which leads to resource exhaustion in nodes. Consequently, there is a pressing need for new security methods. Nonetheless, there is a scarcity of readily accessible, comprehensive, and organized datasets specifically tailored for IoT, as well as benchmark datasets, to train and assess machine learning models. Therefore, the primary focus of this research is on creating new labeled IoT-specific datasets with the COOJA simulator and processing these packets with machine learning algorithms. Decision Tree, Random Forest, K-Nearest Neighbor and Artificial Neural Network algorithms were compared for identifying the Flooding Attacks. The average accuracy obtained for each of the above algorithm is 89.24%, 91.128%, 89.25% and 89.73% respectively.