RPRTD: Routing protocol based on remaining time to encounter nodes with destination node in delay tolerant network using artificial neural network

The OSPF cost is proportionally indicated the transmitting packet overhead through a certain interface and inversely proportional to the interface bandwidth. Thus, this cost may minimized by direct packet transmitting to the other side via various probable paths simultaneously. Logically, the minimum weight path is the optimum path. This paper propose a novel Fuzzy Artificial Neural Network to create Smart Routing Protocol Algorithm. Consequently, the Fuzzy Artificial Neural Network Overlap has been reduced from (0.883 ms) to (0.602 ms) at fuzzy membership 1.5 to 4.5 respectively. This indicated the transmission time is two-fold faster than the standard overlapping time (1.3 ms).

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Development and Simulation of Artificial Neural Network Based Decision on Parametric Values for Performance Optimization of Reactive Routing Protocol for MANET Using Qualnet

2010 International Conference on Computational Intelligence and Communication Networks ◽

10.1109/cicn.2010.43 ◽

2010 ◽

Cited By ~ 1

Author(s):

Satish K. Shah ◽

Dharmistha D. Vishwakarma

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Routing Protocol ◽

Performance Optimization ◽

Reactive Routing ◽

Artificial Neural

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AN ARTIFICIAL NEURAL NETWORK ALGORITHM FOR DYNAMIC PROGRAMMING

International Journal of Neural Systems ◽

10.1142/s0129065790000114 ◽

1990 ◽

Vol 01 (03) ◽

pp. 211-220 ◽

Cited By ~ 9

Author(s):

Chinchuan Chiu ◽

Chia-Yiu Maa ◽

Michael A. Shanblatt

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Dynamic Programming ◽

Optimal Path ◽

Dynamic Programming Algorithm ◽

Performance Measure ◽

Programming Algorithm ◽

Network Dynamic ◽

Destination Node ◽

Artificial Neural

An artificial neural network (ANN) formulation for solving the dynamic programming problem (DPP) is presented. The DPP entails finding an optimal path from a source node to a destination node which minimizes (or maximizes) a performance measure of the problem. The optimization procedure is implemented and demonstrated using a modified Hopfield–Tank ANN. Simulations show that the ANN can provide a near-optimal solution during an elapsed time of only a few characteristic time constants of the circuit for DPPs with sizes as large as 64 stages with 64 states in each stage. An application of the proposed algorithm to an optimal control problem is presented. The proposed artificial neural network dynamic programming algorithm is attractive due to its radically improved speed over conventional techniques especially where real-time near-optimal solutions are required.

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An Improved PRoPHET Routing Protocol in Delay Tolerant Network

The Scientific World JOURNAL ◽

10.1155/2015/623090 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 12

Author(s):

Seung Deok Han ◽

Yun Won Chung

Keyword(s):

Routing Protocol ◽

Delay Tolerant Network ◽

Source Node ◽

Destination Node ◽

Average Delay ◽

Intermediate Node ◽

Delivery Probability ◽

History Of ◽

Delay Tolerant ◽

Overhead Ratio

In delay tolerant network (DTN), an end-to-end path is not guaranteed and packets are delivered from a source node to a destination node via store-carry-forward based routing. In DTN, a source node or an intermediate node stores packets in buffer and carries them while it moves around. These packets are forwarded to other nodes based on predefined criteria and finally are delivered to a destination node via multiple hops. In this paper, we improve the dissemination speed of PRoPHET (probability routing protocol using history of encounters and transitivity) protocol by employing epidemic protocol for disseminating messagem, if forwarding counter and hop counter values are smaller than or equal to the threshold values. The performance of the proposed protocol was analyzed from the aspect of delivery probability, average delay, and overhead ratio. Numerical results show that the proposed protocol can improve the delivery probability, average delay, and overhead ratio of PRoPHET protocol by appropriately selecting the threshold forwarding counter and threshold hop counter values.

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A New QoS-Aware Routing Protocol for MANET Using Artificial Neural Network

Journal of Computing and Information Technology ◽

10.20532/cit.2016.1002643 ◽

2016 ◽

Vol 24 (3) ◽

pp. 221-235 ◽

Cited By ~ 1

Author(s):

Prakash Srivastava ◽

Rakesh Kumar

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Routing Protocol ◽

Artificial Neural

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Analisis Unjuk Kerja Protokol Spray and Focus di Jaringan Opportunistic

AITI ◽

10.24246/aiti.v15i1.67-74 ◽

2018 ◽

Vol 15 (1) ◽

pp. 67-74

Author(s):

Maria Hilary ◽

Bambang Soelistijanto

Keyword(s):

Routing Protocol ◽

Relay Node ◽

Delay Tolerant Network ◽

Destination Node ◽

Delivery Probability ◽

Performance Matrix ◽

Result Show ◽

Working Day ◽

One Simulator ◽

Delay Tolerant

Delay Tolerant Network (DTN) is a wireless connection which does not need infrastructure in its formation. In this research the writer test the performance of a spray and focus routing protocol in opportunistic network using ONE SIMULATOR. Performance matrix used are delivery probability, overhead, delay, and drop. Scenario used in every test are increasing the number of nodes, increasing the number of copy pesan, additional TTL (time-to-live), and addition of buffer capacity. The test result show that spray and focus routing protocol is better in working day movement when TTL is increasing because spray and focus uses transitivity to choise the best relay node so the message can reach its destination in a shorter time even though nodes are often forming a community. The delivery probabilty reached 99.4% which is mean in every 100 messages, 99 messages are sucessfully delivered to destination node.

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