node mobility
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Author(s):  
Mr. M. Karthikeyan ◽  
Mrs P. Shanmuga Priya ◽  
Mrs S. Kiruthika

File searching turns out to be difficult since nodes in MANETs move around freely and can exchange information only when they are within the communication range. Broadcasting can quickly discover files, but it leads to the broadcast storm problem with high energy consumption. File sharing applications in mobile ad hoc networks (MANETs) have attracted more and more attention in recent years. The efficiency of file querying suffers from the distinctive properties of such networks including node mobility and limited communication range and resource. An intuitive method to alleviate this problem is to create file replicas in the network. However, despite the efforts on file replication, no research has focused on the global optimal replica creation with minimum average querying delay. Specifically, current file replication protocols in mobile ad hoc networks have two shortcomings.


2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Honghai Wu ◽  
Qianqian Sang ◽  
Yingda Wang ◽  
Huahong Ma ◽  
Ling Xing

The Flying Ad Hoc Network (FANET) is a kind of mobile network which is composed of flying UAVs (Unmanned Aerial Vehicles) as network nodes. With the high node mobility and dynamic topology, data transmission in FANETs is faced with severe challenges, such as unstable communication links and limited resources in the network. Due to the uncertainty of node encounters, data transmission is usually carried out by opportunistic transmission. However, the opportunistic delivery of data using multicopy routing not only consumes a large amount of network resources but also causes too much transmission overhead and low delivery quality. Thus, this paper designs a connectivity-based copy adaptive transmit routing algorithm (CCAT). Considering the network interruptions caused by high-speed node movement, CCAT makes packet forwarding decisions by estimating network connectivity and uses node transmit prediction value as forwarding utility value to control the number of copies of packets. Simulation results show that the delivery rate of CCAT can be improved by about 30% at the peak compared with traditional multicopy routing strategies and the transmission latency of CCAT is minimal.


2021 ◽  
Vol 576 ◽  
pp. 140-156
Author(s):  
Xiuwen Fu ◽  
Wenfeng Li ◽  
Yongsheng Yang

2021 ◽  
pp. 400-407
Author(s):  
P. Tamilselvi ◽  
T.N. Ravi

MANETs are self-organizing network architectures of mobile nodes. Due to node mobility, wireless network topologies dynamically various over time.   A novel link stability estimation technique called Hybridization of Brownboost Cluster and Random Forest Decision Tree with Optimized Route Selection (HBCRFDT-GORS) technique is introduced for increasing the reliable data delivery by eliminating the stale routes in MANET. Brown Boost technique is applied to find the route paths having the smaller number of hop counts to perform the data transmission. After that, the status of the mobile nodes in the selected route paths is determined based on the residual energy and signal strength. Then, a random forest decision tree is applied to correctly identify the stale routes by finding the link failure due to the selfish node and the corruptive node along the route path. Then the broken link is removed from the route path. After eliminating the stale route from the path, the HBCRFDT-GORS technique finds the alternative optimal route through the gradient free optimization.  The proposed HBCRFDT-GORS technique performs stale route elimination and improves reliable data delivery from source to destination. Simulation is conducted on different performance metrics such as routing overhead, packet delivery ratio, packet drop rate, and delay with respect to the number of data packets. The Network simulation results indicate that the HBCRFDT-GORS technique is improving the data delivery and and minimizing the delay as well as reducing the packet losses when compared to the baseline approaches.


Author(s):  
A. Sangeetha ◽  
T. Rajendran

As the advent of new technologies grows, the deployment of mobile ad hoc networks (MANET) becomes increasingly popular in many application areas. In addition, all the nodes in MANET are battery operated and the node mobility affects the path stability and creates excessive traffic leads to higher utilization of energy, data loss which degrades the performance of routing. So, in this paper we propose Levenberg–Marquardt logistic deep neural learning based energy efficient and load balanced routing (LLDNL-EELBR) which is a machine learning method to deeply analyze the mobile nodes to calculate residual load and energy and it also uses logistic activation function to select the mobile node having higher residual energy and residual load to route the data packet. Experimental evaluations of three methods (LLDNL-EELBR, multipath battery and mobility-aware routing scheme (MBMA-OLSR) and opportunistic routing with gradient forwarding for MANETs (ORGMA)) were done and the result reveals that LLDNL-EELBR method is able to increase the through put and minimizes the delay and energy consumption in MANET when compared to works under consideration.<br /><div> </div>


Circuit World ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Radha S. ◽  
G. Josemin Bala ◽  
Nagabushanam P.

Purpose Energy is the major concern in wireless sensor networks (WSNs) for most of the applications. There exist many factors for higher energy consumption in WSNs. The purpose of this work is to increase the coverage area maintaining the minimum possible nodes or sensors. Design/methodology/approach This paper has proposed multilayer (ML) nodes deployment with distributed MAC (DS-MAC) in which nodes listen time is controlled based on communication of neighbors. Game theory optimization helps in addressing path loss constraints while selecting path toward base stations (BS). Findings The simulation is carried out using NS-2.35, and it shows better performance in ML DS-MAC compared to random topology in DS-MAC with same number of BS. The proposed method improves performance of network in terms of energy consumption, network lifetime and better throughput. Research limitations/implications Energy consumption is the major problem in WSNs and for which there exist many reasons, and many approaches are being proposed by researchers based on application in which WSN is used. Node mobility, topology, multitier and ML deployment and path loss constraints are some of the concerns in WSNs. Practical implications Game theory is used in different situations like countries whose army race, business firms that are competing, animals generally fighting for prey, political parties competing for vote, penalty kicks for the players in football and so on. Social implications WSNs find applications in surveillance, monitoring, inspections for wild life, sea life, underground pipes and so on. Originality/value Game theory optimization helps in addressing path loss constraints while selecting path toward BS.


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