Faraneh Zarafshan

Notice of Retraction A novel weighted voting algorithm based on neural networks for fault-tolerant systems

Voting algorithms are used in a wide area of control systems from real-time and safety-critical control systems to pattern recognition, image processing and human organization systems in order to arbitrating among redundant results of processing in redundant hardware modules or software versions. From a point of view, voting algorithms can be categorized to agreement-based voters like plurality and majority or some voters which produce output regardless to agreement existence among the results of redundant variants. In some applications it is necessary to use second type voters including median and weighted average. Although both of median and weighted average voters are the choicest voters for highly available application, weighted average voting is often more trustable than median. Meanwhile median voter simply selects the mid-value of results; weighted average voter assigns weight to each input, based on their pre-determined priority or their differences, so that the share of more trustable inputs will increase rather than the inputs with low probable correctness. This paper introduces a novel weighted average voting algorithm based on neural networks that is capable of improving the rate of system reliability. Our experimental results showed that the neural weighted average voter has increases the reliability 116.63% in general and 309.82%, 130.27% and 9.37% respectively for large, medium and small errors in comparison with weighted average, and 73.87% in general and 160.44%, 83.59% and 7.52% respectively for large, medium and small errors in comparison with median voter.

A novel clustering algorithm for mobile ad hoc networks based on determination of virtual links' weight to increase network stability.

The stability of clusters is a serious issue in mobile ad hoc networks. Low stability of clusters may lead to rapid failure of clusters, high energy consumption for reclustering, and decrease in the overall network stability in mobile ad hoc network. In order to improve the stability of clusters, weight-based clustering algorithms are utilized. However, these algorithms only use limited features of the nodes. Thus, they decrease the weight accuracy in determining nodes competency and lead to incorrect selection of cluster heads. A new weight-based algorithm presented in this paper not only determines nodes weight using its own features, but also considers the direct effect of feature of adjacent nodes. It determines the weight of virtual links between nodes and the effect of the weights on determining nodes final weight. By using this strategy, the highest weight is assigned to the best choices for being the cluster heads and the accuracy of nodes selection increases. The performance of new algorithm is analyzed by using computer simulation. The results show that produced clusters have longer lifetime and higher stability. Mathematical simulation shows that this algorithm has high availability in case of failure.

An optimal parallel average voting for fault-tolerant control systems

Fault-tolerant systems are such systems that can continue their operation, even in presence of faults. Redundancy as one of the main techniques in implementation of fault-tolerant control systems uses voting algorithms to choose the most appropriate value among multiple redundant and probably faulty results. Average (mean) voter is one of the commonest voting methods which is suitable for decision making in highly-available and long-missions applications in which the availability and speed of the system is critical. In this paper, we introduce a new generation of average voter based on parallel algorithms. Since parallel algorithms normally have high processing speed and are especially appropriate for large scale systems, we have used them to achieve an optimal parallel average voting algorithm with a time complexity of O (log n) with equations processors on EREW shared-memory for applications where the size of input space is large.

An efficient Markov model for reliability analysis of predictive hybrid m-out-of-n systems

In this paper, predictive hybrid redundancy has been extended to large-scale control systems comprising n modules. In m-out-of-n systems, if m-out-of-n modules are in agreement, the system can report consensus; otherwise the system fails. While in our new extension, if there is no agreement, a history record of previous successful result(s) is used to predict the output. In order to analyze the reliability of this system, we present a Markov model based on which the reliability has been computed and compared with m-out-of-n redundancy. The results of simulation demonstrated that the new redundancy improves overall system reliability in all examined scenarios, especially when the number m is large.

A novel N-input voting algorithm for X-by-wire fault-tolerant systems.

Voting is an important operation in multichannel computation paradigm and realization of ultrareliable and real-time control systems that arbitrates among the results of N redundant variants. These systems include N-modular redundant (NMR) hardware systems and diversely designed software systems based on N-version programming (NVP). Depending on the characteristics of the application and the type of selected voter, the voting algorithms can be implemented for either hardware or software systems. In this paper, a novel voting algorithm is introduced for real-time fault-tolerant control systems, appropriate for applications in which N is large. Then, its behavior has been software implemented in different scenarios of error-injection on the system inputs. The results of analyzed evaluations through plots and statistical computations have demonstrated that this novel algorithm does not have the limitations of some popular voting algorithms such as median and weighted; moreover, it is able to significantly increase the reliability and availability of the system in the best case to 2489.7% and 626.74%, respectively, and in the worst case to 3.84% and 1.55%, respectively.

A Novel Fuzzy Diffusion Approach for Improving Energy Efficiency in Wireless Sensor Networks

Directed Diffusion is a Data Centric routing protocol. In data centric protocols data is named based on the attribute-value pairs. Directed diffusion protocol includes flooding the interested data, establishing direction paths by using gradients set-up and selecting one or more direction paths to reinforce the data stream. The main difficulty of Directed Diffusion is flooding in which there is some communication overhead with some nodes which are naturally unable to coordinate in monitoring the interested event. We use a fuzzy logic controller to reduce the communication overheads during flooding and routing the data stream from the source(s) to a sink node. The fuzzy logic controller evaluates the potentiality of intermediate nodes to coordinate in mission, based on each nodes traffic load, energy residual and size of data which can be maintained. The simulation results show that new Fuzzy diffusion approach produces at most 56.5% and in average 28.81% successful results and has improved the total energy residual at least 19.38% and in average 1888.87% in comparison with directed diffusion protocol.

PAV: Parallel Average Voting Algorithm for Fault-Tolerant Systems

Fault-tolerant systems are such systems that can continue their operation, even in presence of faults. Redundancy as one of the main techniques in implementation of fault-tolerant control systems uses voting algorithms to choose the most appropriate value among multiple redundant and probably faulty results. Average (mean) voter is one of the commonest voting methods which is suitable for decision making in highly-available and long-missions applications in which the availability and speed of the system is critical. In this paper we introduce a new generation of average voter based on parallel algorithms which is called as parallel average voter. The analysis shows that this algorithm has a better time complexity (log n) in comparison with its sequential algorithm and is especially appropriate for applications where the size of input space is large.

Notice of Retraction Accurate and efficient reliability Markov model analysis of predictive hybrid m-out-of-n systems

In recent years we perceive many researches on new techniques for improving the performance of fault-tolerant control systems. Prediction of correct system output is one of these techniques which can calculate and predict the probable correct system output when the ordinary techniques are incapacitated to make decision. In this paper, a performance model of predictive m-out-of-n hybrid redundancy is introduced and analyzed. The results of equations and mathematical relations based on Markov model demonstrated that this approach can improve the system reliability in comparison with traditional m-out-of-n system.

Ancestral Dynamic Voting Algorithm for Mutual Exclusion in Partitioned Distributed Systems

Data replication is a known redundancy used in fault-tolerant distributed system. However, it has the problem of mutual exclusion of replicated data. Mutual exclusion becomes difficult when a distributed system is partitioned into two or more isolated groups of sites. In this study, a new dynamic algorithm is presented as a solution for mutual exclusion in partitioned distributed systems. The correctness of the algorithm is proven, and simulation is utilized for availability analysis. Simulations show that the new algorithm, ancestral dynamic voting algorithm, improves the availability and lifetime of service in faulty environments, regardless of the number of sites and topology of the system. This algorithm also prolongs the lifetime of service to mutual exclusion for full and partial topologies especially for the situations where there is no majority. Furthermore, it needs less number of messages transmitted. Finally, it is simple and easy to implement.

Markov Process Modeling for Wireless Sensor Network Availability with QOS Constraints

The purpose of this paper is calculating the availability of wireless sensor network for a virtual grid by using Markov model. Since, wireless sensor networks are constraints by energy, their energy consumption should be limited within a way to guarantee their quality of service. In this paper, availability and quality of service in a wireless sensor network with particular coverage are investigated by using an energy optimized algorithm and Markov process.