Congestion Aware MANET Routing Using Evolutionary Game Theory and Cross-Layer Design

Abstract

A wireless network without infrastructure is called MANET. In MANET, a node can move in any direction, at any speed, and at any time. There is no separate router on this network. Instead, the nodes act as a router. To transfer data in the network in an efficient manner, the network should be congestion-free or less congested. If the network has congestion, it would affect the performance of the network. In the routing process, if a node is aware of the neighbour node’s congestion and selects a congestion-free path, it leads to better performance of the network. In this paper, we have designed two approaches for improving the performance of the network. One is the cross-layer protocol and the other is the evolutionary game theory approach to find the least congestion path. Cross-layer involves transport layer and MAC layer of MANET to avoid congestion. The Linear Rank Selection method is one of the evolutionary game theory approaches used to find the Least Congested Node. We have enhanced this strategy in the GPSR protocol. The proposed EVO GPSR protocol performs 4.65% better throughput, 0.54% less network delay, 2.10% less routing overhead, and 8.20% better packet delivery ratio compared with GPSR protocol. Based on simulation, the performance of the proposed protocol is better than the GPSR protocol.

A Review of Power Management Approaches for Mobile Ad Hoc Networks

“…Next groups of research from the authors from Vij et al [20], Nobahary and Babaie [8], Venkatasubramanian [36], Hamza and Vigila [34], Kumar et al [28], Thanappan and Perumal [45], Reddy and Mungara [46], Phakathi et al [47], Ravi et al [48], Alghamdi [49], Satyanarayana et al [50], Vinayakan et al [51] and Saraswathi et al [52] were achieved two metric parameters by using the methods of GAME THEORY, CREDIT-BASED, EPO-FGA, PSO-GA, EEMST, CC, GAME THEORY, ML, QOS, FUZZY, HFO, FRAMEWORK, CONJUNCTION, AOMDV, HGFNN respectively.…”

“…Research on power management is still ongoing to accomplish all kinds of parameter metrics. TESAODV √ Rao and Singh [3] KF-MAC √ Musthafa et al [19] SNDA √ Jim and Gregory [22] AIS √ Abirami and Sumithra [21] NCV-AODV √ Ponnusamy [23] EE √ Rahmani et al [16] AUTOMATA √ Singh et al [17] OLSR √ Ramesh et al [24] MSD-SNDT √ Hasani and Babaie [25] FSN √ Nobahary and Babaie [8] SNMN √ Hadi et al [27] SNAODV √ Kumar et al [28] QOS √ Venkatesh and Chakravarthi [29] CHLD √ Raj Kumar and Bala [31] EECAO √ Sahu and Patil [43] ACO √ Al-Najjar [32] CLU √ Devika and Sudha [33] C-SEWO √ Braik et al [35] AGS-ROA √ Saravanan et al [39] E-CFSA √ Bisen et al [40] E-MAVMMF √ Arulprakash et al [41] EBDC √ Sivapriyaand Mohandas [38] E-CFSA √ Vij et al [20] MKMPE √ Nobahary and Babaie [8] GAME THEORY √ √ Venkatasubramanian [36] CREDIT-BASED √ √ Hamza and Vigila [34] EPO-FGA √ √ Kumar et al [28] EEMST √ √ Thanappan and Perumal [45] CC √ √ Reddy and Mungara [46] GAME THEORY √ √ Phakathi et al [47] QOS √ √ Ravi et al [48] FUZZY √ √ Alghamdi [49] HFO √ √ Satyanarayana et al [50] FRAMEWORK √ √ Vinayakan et al [51] CONJUNCTION √ √ Saraswathi et al [52] AOMDV √ √ Veeraiah and Krishna [9] HGFNN √ √ Namdev and Mishra [15] IDSM √ √ √ Sri et al [6] M-AODV √ √ √ Goyal et al …”