Heuristic Relay-Node Selection in Opportunistic Network Using RNN-LSTM Based Mobility Prediction
- PDF / 2,927,780 Bytes
- 26 Pages / 439.37 x 666.142 pts Page_size
- 5 Downloads / 236 Views
Heuristic Relay‑Node Selection in Opportunistic Network Using RNN‑LSTM Based Mobility Prediction C. P. Koushik1 · P. Vetrivelan1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract A Mobile Ad hoc Network (MANET) is a network composed of numerous autonomous mobile nodes. In recent times, the opportunistic network, a type of MANET is gaining a lot of significance among the researchers, as it is capable of communicating with the sink node through an efficient selection of relay nodes. In the opportunistic networks, the node does not seek any knowledge about the network topology as it selects the efficacious relay node for transmission of packets. However, MANET requires nodal information about network topology. In the opportunistic network, the data stockpiled in the packets are transmitted from a source node to a sink node by utilizing relay node opportunistically for every hop. However, this type of communication leads to delayed data delivery with increased hops as a consequence of the unsystematic selection of relay nodes. To overcome these constraints, this article focuses on the selection of optimal relay nodes for attaining faster data delivery, by unveiling the location and by predicting the mobility pattern of the neighbor nodes. Hence, this research paper proposes Particle Swarm Optimization algorithm for the selection of optimal relay nodes by locating the neighbor nodes within an established InterCommunication Range employing Cartesian based localization technique and by analyzing their mobility pattern using recurrent neural network-long short-term memory prediction model. The results of the proposed methodology are compared with four other existing methods, namely, MaxProp, Spray and Wait, and Epidemic. The comparative results infer that the proposed method is efficient in terms of performance, reduced hops, reduced delay with enhanced packet delivery ratio, and improved overhead ratio. Keywords Inter-communication range · Localization · Opportunistic network · Particle swarm optimization · Recurrent neural network-long short-term memory
* P. Vetrivelan [email protected] C. P. Koushik [email protected] 1
School of Electronics Engineering, VIT Chennai Campus, Chennai 600127, India
13
Vol.:(0123456789)
C. P. Koushik, P. Vetrivelan
1 Introduction A MANET is a collection of wireless mobile nodes that temporarily forms a network without using the infrastructure, centralized access points, or the administration. MANETs can be utilized in an extensive range of applications as they have the capability of establishing networks anywhere and anytime without the help of an established infrastructure [1]. An opportunistic network is one kind of a MANET, where contacts of the network are intermittent or where the performance of the link is extremely variable. In such type of a network, a complete path from the source node to the sink node does not exist [2]. Thus, for the purpose of making communication possible in an opportunistic network, the nodes present interm
Data Loading...
