scholarly journals FTTCNF- Novel Approach for Fault Tolerant Topology Control in Manet

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Dr. D. Manohari, Et. al.
Keyword(s):  
Data Transmission ◽  
Topology Control ◽  
Fault Tolerant ◽  
Fuzzy Rule ◽  
Threshold Level ◽  
Stability Factor ◽  
Novel Approach ◽  
Control Message ◽  
Path Stability ◽  
Control Packets

In wireless networks it is observed that as nodes move unpredictably sudden link disconnections occur during transmission. This leads to frequent path changes and multiple path discoveries which subsequently increase transmission of control packets in network. The nodes in the network simply rebroadcast the received route request (RREQ) packet if they do not have the route to the required destination. In addition to this, frequent hello messages for neighbour set construction and maintenance also increase control message count in the network causing a flooding issue. In order to mitigate these problems, the proposed Fault Tolerant Topology Control Neuro Fuzzy method (FTTCNF), incorporates measures to improve the network stability and to reduce the control packets in the network. These measure 1.reduce control message transmissions among neighbours by  finding a stable path 2. neighbour node distance is computed based on the reception of a signal strength Indication (RSSI), 3. path stability  is  decided by the link expiry time (LET) which can be used to predict the neighbour mobility deviations. These factors, ( above mentioned distance, path stability factor  PSF, and LET) are subjected to the fuzzification process to identify the fuzzy rule values and are given as input to the neuron formation stage. Final neuron value is computed for all available paths and the maximum value path is chosen for data transmission. Energy level monitoring is also applied at each node to check the node’s current energy and should it go below the energy threshold level the node by itself, joining the routing process is avoided. Simulation results have proved that the proposed method significantly reduces the routing overhead and improves the stability of path during data transmission.

scholarly journals An Optimal Tail Selection in Risk Measurement

Risks ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 70
Author(s):  
Małgorzata Just ◽  
Krzysztof Echaust
Keyword(s):  
Mean Squared Error ◽  
Real Data ◽  
Random Variable ◽  
Threshold Level ◽  
Middle Part ◽  
Risk Measurement ◽  
Practical Applications ◽  
Tuning Parameters ◽  
Path Stability ◽  
Asymptotic Mean Squared Error

The appropriate choice of a threshold level, which separates the tails of the probability distribution of a random variable from its middle part, is considered to be a very complex and challenging task. This paper provides an empirical study on various methods of the optimal tail selection in risk measurement. The results indicate which method may be useful in practice for investors and financial and regulatory institutions. Some methods that perform well in simulation studies, based on theoretical distributions, may not perform well when real data are in use. We analyze twelve methods with different parameters for forty-eight world indices using returns from the period of 2000–Q1 2020 and four sub-periods. The research objective is to compare the methods and to identify those which can be recognized as useful in risk measurement. The results suggest that only four tail selection methods, i.e., the Path Stability algorithm, the minimization of the Asymptotic Mean Squared Error approach, the automated Eyeball method with carefully selected tuning parameters and the Hall single bootstrap procedure may be useful in practical applications.

UbiSrvInt

2009 ◽  
pp. 135-171
Author(s):  
Soe-Tsyr Yuan ◽  
Fang-Yu Chen
Keyword(s):  
Service Provision ◽  
Ad Hoc ◽  
Fault Tolerant ◽  
Service Needs ◽  
Novel Approach ◽  
Distributed Problem Solving ◽  
Wireless Service ◽  
Wireless Environments ◽  
P2p Applications ◽  
Performance Results

Peer-to-Peer applications harness sharing between free resources (storage, contents, services, human presence, etc.). Most existing wireless P2P applications concern merely the sharing of a variety of contents. For magnifying the sharing extent for wireless service provision in the vicinity (i.e., the wireless P2P environments), this chapter presents a novel approach (briefly named UbiSrvInt) that is an attempt to enable a pure P2P solution that is context aware and fault tolerant for ad-hoc wireless service provision. This approach empowers an autonomous peer to propel distributed problem solving (e.g., in the travel domain) through service sharing and execution in an intelligent P2P way. This approach of ad-hoc wireless service provision is not only highly robust to failure (based on a specific clustering analysis of failure correlation among peers) but also capable of inferring a user’s service needs (through a BDI reasoning mechanism utilizing the surrounding context) in ad-hoc wireless environments. The authors have implemented UbiSrvInt into a system platform with P-JXTA that shows good performance results on fault tolerance and context awareness.

A Multi-Layer Self-Healing Algorithm for WSNs

2019 ◽  
Vol 29 (05) ◽  
pp. 2050070 ◽  
Author(s):  
Sergio Diaz ◽  
Diego Mendez ◽  
Rolf Kraemer
Keyword(s):  
Network Lifetime ◽  
Topology Control ◽  
Clustering Algorithm ◽  
Disjoint Paths ◽  
Self Healing ◽  
Spatial Clusters ◽  
Area Of Interest ◽  
Control Message ◽  
Good Coverage ◽  
Message Overhead

The implementation of Wireless Sensor Networks (WSNs) is a challenging task due to their intrinsic characteristics, e.g., energy limitations and unreliable wireless links. Considering this, we have developed the Disjoint path And Clustering Algorithm (DACA) that combines topology control and self-healing mechanisms to increase the network lifetime with minimum loss of coverage. Initially, DACA constructs a tree that includes all nodes of the network by using the Collection Tree Protocol (CTP). This tree is an initial communication backbone through which DACA centralizes the information. Then, DACA builds a set of spatial clusters using Kmeans and selects the Cluster Heads (CHs) using Particle Swarm Optimization (PSO) and a multi-objective optimization (MOO) function. Subsequently, DACA reconstructs the tree using only the CHs. In this way, DACA reduces the number of active nodes in the network and saves energy. Finally, DACA finds disjoint paths on the reconstructed tree by executing the N-to-1 multipath discovery protocol. By doing so, the network can overcome communications failures with a low control message overhead. The simulations on Castalia show that DACA considerably extends the network lifetime by having a set of inactive nodes and disjoint paths that support the communication when active nodes die. Besides, DACA still maintains a good coverage of the area of interest despite the inactive nodes. Additionally, we evaluate the shape of the tree (i.e., the average number of hops) and the risk of connection loss of the network.

Improving Dependability of Robotics Systems, Experience from Application of Fault Tree Synthesis to Analysis of Transport Systems

2015 ◽  
Vol 3 (2) ◽  
pp. 38-62 ◽  
Author(s):  
Nidhal Mahmud
Keyword(s):  
Electric Vehicles ◽  
Fault Tolerant ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Transport Systems ◽  
Sequential Dependencies ◽  
Novel Approach ◽  
Assessment Techniques ◽  
Tree Synthesis

The use of robotics systems is increasingly widespread and spans a variety of application areas. From manufacturing, to surgeries, to chemical, these systems can be required to perform difficult, dangerous and critical tasks. The nature of such tasks places high demands on the dependability of robotics systems. Fault tree analysis is among the most often used dependability assessment techniques in various domains of robotics. However, there is still a lack of adjustment methods that can efficiently cope with the sequential dependencies among the components of such systems. In this paper, the authors first introduce some relevant techniques to analyze the dependability of robotics systems. Thereafter, an experience from research projects such as MAENAD (European automotive project investigating development of dependable Fully Electric Vehicles) is presented; emphasis is put on a novel approach to synthesizing fault trees from the components and that is suitable for modern high-technology robotics. Finally, the benefits of the approach are highlighted by using a fault-tolerant case study.

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