QMRPRNS: Design of QoS multicast routing protocol using reliable node selection scheme for MANETs

Yadav, Ajay Kumar; Tripathi, Sachin

doi:10.1007/s12083-016-0441-8

Cited by 87 publications

(37 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The pattern of movement of the mobile nodes with their velocity, acceleration, and location that changes over time is described in this section. Since the performance of the routing protocol depends on the mobility patterns of the nodes, it is essential to design a proper mobility model . Let n a and n b be two nodes in the MANET that are positioned initially at

((),, p_{italicna}^{t}, q_{italicna}^{t})

and

((),, p_{italicnb}^{t}, q_{italicnb}^{t})

at a time t .…”

Section: Network Modelmentioning

confidence: 99%

E‐TDGO: An encrypted trust‐based dolphin glowworm optimization for secure routing in mobile ad hoc network

Mukhedkar¹,

Kolekar²

2020

Int J Communication

View full text Add to dashboard Cite

Mobile ad hoc networks (MANETs) are independent networks, where mobile nodes communicate with other nodes through wireless links by multihop transmission. Security is still an issue to be fixed in MANETs. Hence, a routing protocol named encrypted trust-based dolphin glowworm optimization (DGO) (E-TDGO) is designed using Advanced Encryption and trust-based optimization model for secure routing in MANET. The proposed E-TDGO protocol includes three phases, namely, k-path discovery, optimal path selection, and communication. At first, k paths are discovered based on the distance and the trust level of the nodes. From the k paths discovered, the optimal path is selected using a novel algorithm, DGO, which is developed by combining glowworm swarm optimization (GSO) algorithm and dolphin echolocation algorithm (DEA). Once the optimal path is selected, communication begins in the network such that E-TDGO protocol ensures security. The routing messages are encrypted using AES-128 with shared code and key to offer security. The experimental results show that the proposed E-TDGO could attain throughput of 0.11, delay of 0.01 second, packet drop of 0.44, and detection rate of 0.99, at the maximum number of rounds considered in the network of 75 nodes with attack consideration. K E Y W O R D SAES-128, mobility, optimization algorithm, route discovery, routing, security, trust | INTRODUCTIONPresently, ad hoc networks, being a popular and widely used network, are employed in various kinds of applications. These are the wireless networks that are self-organized having no centralized or fixed infrastructures. Ad hoc networks are composed of nodes, which can be both clients and routers. 1 Wireless ad hoc networks (WANETs) 2,3 are selfconfiguring and decentralized wireless network. One of the major features of ad hoc network is that it is independent of any preexisting infrastructure like routers in a wired network and every node in the network involves in routing. 4 The network functionality can be divided into three planes, namely, data plane, control plane, and management plane. The data plane handles the data packets, whereas the control plane implements the routing algorithms. Meanwhile, the network is configured and monitored by the management plane. In the data plane, ad hoc networks undergo several attacks due to certain characteristics, such as openness and dynamic topology. There are two types of attacks, such as (a) attacks interrupting normal delivery of packets in the data plane and (b) attacks diverting the network elements from using exact protocol and management policies in the control and the management planes. The attacks in the data plane are more complicated to detect and avoid, as they do not violate the protocol rules. 1 The two important aspects of these networks are routing and information broadcasting.Mobile ad hoc network (MANET) is an application of WANET, where the objective is to establish an efficient route between a pair of nodes such that the messages are delivered in a proper time interval. If rout...

show abstract

((),, p_{italicna}^{t}, q_{italicna}^{t})

and

((),, p_{italicnb}^{t}, q_{italicnb}^{t})

at a time t .…”

Section: Network Modelmentioning

confidence: 99%

E‐TDGO: An encrypted trust‐based dolphin glowworm optimization for secure routing in mobile ad hoc network

Mukhedkar¹,

Kolekar²

2020

Int J Communication

View full text Add to dashboard Cite

show abstract

“…In [11], the authors deploy a reliable neighbor node selection scheme by establishing stable links among the nodes on the basis of energy level of nodes involved in communication, mobility of the nodes, and distance between the nodes. For achieving stable links among the nodes in [11], the authors propose a quality of service (QoS) based multicast routing protocol using reliable neighbor node selection scheme (QMPRNS). However, the authors do not consider the effect of SNR on the stability of a link for a reliable neighbor node selection.…”

Section: Related Workmentioning

confidence: 99%

“…It is important to mention here that a CH transmits periodic Hello messages to its neighbors after every Δ interval to indicate its presence in the neighborhood [32]. This small Δ interval is mapped to the interarrival time between the consecutive Hello messages, given by (11). Hence, the transition rates can be estimated on the basis of link state samples at a particular time step of the Markov process.…”

Section: Estimating the Transition Rates For Link Connectivitymentioning

confidence: 99%

“…. Now, by taking into account , as given by (11), the next Hello message(s) arrival instance at a neighboring node, i.e., ( + ) for the process [5], at next time step of the Markov process, satisfies…”

Section: Continuous Time Analysis Of Dynamic Link Connectivitymentioning

confidence: 99%

“…A volume of literature is available, which addresses link stability among the randomly deployed network nodes for effective communication in MANETs [4][5][6][7][8][9][10][11][12][13][14][15][16]. However, there are a number of shortcomings in the existing literature, in terms of calculating the optimum transmission rate of Hello message(s), transition rate estimation between connection and disconnection states, probabilities of connectivity and disconnectivity, and stability of neighboring nodes in terms of received signal strength (RSS) and signal-to-noise ratio (SNR) at different time steps of the Markov process.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Dynamic Link Connectivity Strategy Using Hello Messaging for Maintaining Link Stability in MANETs

Naushad

Abbas

et al. 2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Maintaining link stability among randomly deployed network nodes is one of the key challenges for effective communication in mobile ad hoc networks (MANETs). Under uniform speed and random trajectory of mobile nodes, there must be a unified model to determine an adequate strategy that addresses the issue of link stability in MANETs. We present a novel dynamic link connectivity (DLC) strategy that maintains link stability through efficient link connectivity among the neighboring nodes using Hello messaging. We also perform stochastic analysis of the proposed strategy, which predicts the future link status among the neighboring nodes at different time steps of a Markov process. We find that the link stability is affected by the received signal strength, signal-to-noise ratio, transition rates between the connection and disconnection states, and probabilities of link connectivity and disconnectivity at steady state. Analytical and simulation results indicate efficacy of the proposed strategy in terms of reduced communication overhead, lower propagation delay, and better energy efficiency of the network. The results also demonstrate that the proposed strategy minimizes the average response time, increases the throughput, and reduces the packet loss ratio, thereby, maintaining efficient link stability among the neighboring nodes.

show abstract

IoT enabled lung cancer detection and routing algorithm using CBSOA‐based ShCNN

Selvan¹,

Jingle

Maram

et al. 2022

Adaptive Control & Signal

View full text Add to dashboard Cite

The Internet of Things (IoT) has tremendously spread worldwide, and it influenced the world through easy connectivity, interoperability, and interconnectivity using IoT devices. Numerous techniques have been developed using IoT-enabled health care systems for cancer detection, but some limitations exist in transmitting the health data to the cloud. The limitations can be accomplished using the proposed chronological-based social optimization algorithm (CBSOA) that effectively transmits the patient's health data using IoT network, thereby detecting lung cancer in an effective way. Initially, nodes in the IoT network are simulated such that patient's health data are collected, and for transmission of such data, routing is performed in order to transmit the health data from source to destination through a gateway based on cloud service using CBSOA.The fitness is newly modeled by assuming the factors like energy, distance, trust, delay, and link quality. Finally, lung cancer detection is carried out at the destination point. At the destination point, the acquired input data is fed to preprocessing phase to make the data acceptable for further mechanism using data normalization. Once the feature selection is done using Canberra distance, then the lung cancer detection is performed using shepard convolutional neural network (ShCNN). The process of routing as well as training of ShCNN is performed using the CBSOA algorithm, which is devised by the inclusion of the chronological concept into the social optimization algorithm. The proposed approach has achieved a maximum accuracy of 0.940, maximum sensitivity of 0.941, maximum specificity of 0.928, and minimum energy of 0.452.

show abstract

QMRPRNS: Design of QoS multicast routing protocol using reliable node selection scheme for MANETs

Cited by 87 publications

References 38 publications

E‐TDGO: An encrypted trust‐based dolphin glowworm optimization for secure routing in mobile ad hoc network

E‐TDGO: An encrypted trust‐based dolphin glowworm optimization for secure routing in mobile ad hoc network

A Novel Dynamic Link Connectivity Strategy Using Hello Messaging for Maintaining Link Stability in MANETs

IoT enabled lung cancer detection and routing algorithm using CBSOA‐based ShCNN

Contact Info

Product

Resources

About