Long-lived Path Routing With Received Signal Strength for Ad Hoc Networks

2006 ◽  
Author(s):  
Ruay-Shiung Chang ◽  
Shing-Jiuan Leu
Keyword(s):  
Ad Hoc Networks ◽  
Ad Hoc ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Hoc Networks

scholarly journals An Efficient Implementation of a Method to Detect Sybil Attacks in Vehicular Ad hoc Networks using Received Signal Strength Indicator

2019 ◽  
Vol 9 (1) ◽  
pp. 2796-2800
Keyword(s):  
Ad Hoc Networks ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Sybil Attack ◽  
Received Signal Strength Indicator ◽  
Smart Transportation ◽  
Vehicle Communication ◽  
Hoc Networks

Vehicular Ad Hoc Networks (VANET) are useful in implementing a smart transportation system by enabling ad hoc vehicle to vehicle communication. Sybil attack is considered to be one of the most dangerous threats to VANET. Sybil aggressor can produce different phony personalities with false messages to extremely hinder the ordinary elements of wellbeing related applications. In this paper, we are presenting an implementation of a method to detect Sybil attack using received signal strength indicator.

Node Localization in Ad-hoc Networks

2011 ◽  
pp. 115-135
Author(s):  
Zhonghai Wang ◽  
Seyed (Reza) Zekavat
Keyword(s):  
Ad Hoc Networks ◽  
Wireless Channels ◽  
Ad Hoc ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Node Localization ◽  
Mitigation Techniques ◽  
Hoc Networks

This chapter introduces node localization techniques in ad-hoc networks including received signal strength (RSS), time-of-arrival (TOA) and direction-of-arrival (DOA). Wireless channels in ad-hoc networks can be categorized as LOS and NLOS. In LOS channels, the majority of localization techniques perform properly. However, in NLOS channels, the performance of these techniques reduces. Therefore, non-line-of-sight (NLOS) identification and mitigation techniques, and localization techniques for NLOS scenarios are briefly reviewed.

Radio Propagation Models: A Performance Analysis based Impact of Sparse and Dense Deployment of Nodes under Different Propagation Models in Mobile Ad-hoc Networks (MANETs)

2021 ◽  
Author(s):  
Altaf Hussain ◽  
Muhammad Rafiq Khan
Keyword(s):  
Ad Hoc Networks ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Signal Strength ◽  
Propagation Model ◽  
Strong Impact ◽  
Propagation Models ◽  
Mobile Ad Hoc ◽  
Hoc Networks ◽  
The Impact

Abstract Mobile Ad-hoc Network (MANET) is the most emerging and fast expanding technology since the last two decades. One of the major issue and challenging area in MANET is the process of routing due to dynamic topologies and high mobility of mobile nodes. The exchange of information from source to a destination is known as the process of routing. Spectacular amount of attention has been paid by researchers to reliable routing in ad-hoc networks. Efficiency and accuracy of a protocol depends on many parameters in these networks. In addition to other parameters node velocity and propagation models are among them. Calculating signal strength at receiver is the responsibility of a propagation model while mobility of nodes is responsible for topology of the network. A huge amount of loss in performance is occurred due to variation of signal strength at receiver and obstacles between transmissions. Simulation tools are developed to analyze the weakness and strength of protocols along with different parameters that may impact the performance. The choice of a propagation models have an abundant effect on performance on routing protocols in MANET. In this research, it has been analyzed to check the impact of different propagation models on the performance of Optimized Link State Routing (OLSR) in Sparse and Dense scenarios in MANET. The simulation has been carried out in NS-2 by using performance metrics as average Throughput, average packet drop and average latency. The results predicted that propagation models and mobility has a strong impact on the performance of OLSR in considered scenarios.

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