A selective multipath routing protocol for ubiquitous networks

Ladas, Alexandros; Deepak, G C; Pavlatos, Nikolaos; Politis, Christos

doi:10.1016/j.adhoc.2018.04.013

Cited by 20 publications

(9 citation statements)

References 33 publications

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“…The Hybrid Random Late Detection (HRLD) routing protocol uses the Past Interaction History (PIH) for updating the packet delivery on node interaction [23] [50] [29]. Past interaction history (PIH) has (Table 5) quality factors such as source node location, receiver node location, time taken to transmit packets, minimum route path, and trust values.…”

Section: Past Interaction History With Transmission Datamentioning

confidence: 99%

Intelligent route discovery towards rushing attacks in ad hoc wireless networks

Allimuthu

Mahalakshmi

2021

J Ambient Intell Human Comput

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MANET (Mobile Ad-hoc Networks) are distributed or delegated away from a central server, authoritative location of wireless networks that communicate without pre-existing structure. Ad-hoc networks are compromising the many types of attacks and routing. In MANET, the routing plays a vital role in terms of packets interaction and data transmission. Due to decentralized control, the MANET data transmission becomes insecure because of dispersed routing on the mobile ad-hoc nodes. Since the efficient route on MANET only controls the packets and does not simplify the route between the source to the destination, the maintenance of route interaction becomes a crucial process. Maintain effective data transactions over the MANET network, and it is essential to improve the route and locate the attacker. Nevertheless, MANET allows for route interaction against security threads. In this research article, four processing schemes are suggested to preserve the security measures against routing protocols. Especially in node communication, the rushing attacker has a significant impact on packet-based data transmission in MANET. Also, for this research, an Attacker detection automation of the Bees Colony Optimization (ADABCP) method is used, as a result of which the desired result is brought about in the effective attacker detection on the routing process. Moreover, the proposed Hybrid Random Late Detection (HRLD) routing protocol manages the MANET routing and overcomes the MANET congestion communication. The Swift Implicit Response Round Trip Time (SIRT) mechanism is generated by the Route Finding Manipulation (RFM) to enhance the performance. This RFM scheme helps to find the optimal routing in a secured manner. The proposed (SIRT-ADABCP-HRLD) approach was compared to the existing ESCT, ZRDM-LFPM, and ENM-LAC approaches, found to have improved by routing and data transmission. Compared to the conventional method, the method mentioned above achieves a better ratio for the end-to-end delay, communication overhead, packet delivery ratio, network lifetime, and energy consumption.

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Section: Past Interaction History With Transmission Datamentioning

confidence: 99%

Intelligent route discovery towards rushing attacks in ad hoc wireless networks

Allimuthu

Mahalakshmi

2021

J Ambient Intell Human Comput

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“…• Period 2: The data of (level (3,4,5)) < the number of PCH and SCH of the level (1) * max storage unit Thus, level (5) sends the data to level (4), level (4) aggregates the data and sends it to the pre-level and so on until it reaches the sink node.…”

Section: Inter-cluster Routingmentioning

confidence: 99%

“…During the last years, WSN has gained significant attention in both academic research and IT industry field due to its flexibility and its communication capability; especially when traditional networks such as Long-Term Evolution (LTE) or Terrestrial Trunked Radio(TETRA) are not operational [4]. However, WSN faces several profound issues and challenges may adversely affect its potential performance such as frequently changing network topology, longer end-to-end delay, route coupling, and high packet loss.…”

Section: Introductionmentioning

confidence: 99%

A Congestion-Aware Clustering and Routing (CCR) Protocol for Mitigating Congestion in WSN

et al. 2019

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Wireless sensor networks (WSN) have been investigated as a powerful distributed sensing application to enhance the efficiency of embedded systems and wireless networking capabilities. Although WSN has offered unique opportunities to set the foundation for using ubiquitous and pervasive computing, it suffered from several issues and challenges such as frequently changing network topology and congestion issue which affect not only network bandwidth usage but also performance. The main objective of this study is to introduce a congestion-aware clustering and routing (CCR) protocol to alleviate the congestion issue over the network. The CCR protocol is proposed to decrease end-to-end delay time and prolong the network lifetime through choosing the suitable primary cluster head (PCH) and the secondary cluster head (SCH). The experimental results demonstrate that the effectiveness of the CCR protocol to satisfy the quality of service (QoS) requirements in increasing the network lifetime and raising the number of packets sent alike. Moreover, the CCR outperforms other state-of-the-art techniques in decreasing the overflow of data, and thus the network bandwidth usage is reduced.INDEX TERMS Congestion control, clustering protocols, pervasive computing, quality of service (QoS), routing protocols, ubiquitous computing, wireless sensor network (WSN).

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“…For link instabilities, energy resource constraints and node mobility of frequently topology changing network, [49] introduced Multipath-ChaMeLeon (M-CML) routing protocols to stabilize and enhance the QoS and performance of the network. The introduced routing protocol helps to minimize routing overhead and energy consumption of mobile nodes by cutting down the amount of generated of duplicate packets in the network.…”

Section: Related Workmentioning

confidence: 99%

Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm

et al. 2019

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To facilitate connectivity to the internet, the easiest way to establish communication infrastructure in areas affected by natural disaster and in remote locations with intermittent cellular services and/or lack of Wi-Fi coverage is to deploy an end-to-end connection over Mobile Ad-hoc Networks (MANETs). However, the potentials of MANETs are yet to be fully realized as existing MANETs routing protocols still suffer some major technical drawback in the areas of mobility, link quality, and battery constraint of mobile nodes between the overlay connections. To address these problems, a routing scheme named Mobility, Residual energy and Link quality Aware Multipath (MRLAM) is proposed for routing in MANETs. The proposed scheme makes routing decisions by determining the optimal route with energy efficient nodes to maintain the stability, reliability, and lifetime of the network over a sustained period of time. The MRLAM scheme uses a Q-Learning algorithm for the selection of optimal intermediate nodes based on the available status of energy level, mobility, and link quality parameters, and then provides positive and negative reward values accordingly. The proposed routing scheme reduces energy cost by 33% and 23%, end to end delay by 15% and 10%, packet loss ratio by 30.76% and 24.59%, and convergence time by 16.49% and 11.34% approximately, compared with other well-known routing schemes such as Multipath Optimized Link State Routing protocol (MP-OLSR) and MP-OLSRv2, respectively. Overall, the acquired results indicate that the proposed MRLAM routing scheme significantly improves the overall performance of the network.

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A selective multipath routing protocol for ubiquitous networks

Cited by 20 publications

References 33 publications

Intelligent route discovery towards rushing attacks in ad hoc wireless networks

Intelligent route discovery towards rushing attacks in ad hoc wireless networks

A Congestion-Aware Clustering and Routing (CCR) Protocol for Mitigating Congestion in WSN

Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm

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