Permission-based fault tolerant mutual exclusion algorithm for mobile Ad Hoc networks

This study focuses on resolving the problem of mutual exclusion in mobile ad hoc networks. A Mobile Ad Hoc Network (MANET) is a wireless network without fixed infrastructure. Nodes are mobile and topology of MANET changes very frequently and unpredictably. Due to these limitations, conventional mutual exclusion algorithms presented for distributed systems (DS) are not applicable for MANETs unless they attach to a mechanism for dynamic changes in their topology. Algorithms for mutual exclusion in DS are categorized into two main classes including token-based and permission-based algorithms. Token-based algorithms depend on circulation of a specific message known as token. The owner of the token has priority for entering the critical section. Token may lose during communications, because of link failure or failure of token host. However, the processes for token-loss detection and token regeneration are very complicated and time-consuming. Token-based algorithms are generally non-fault-tolerant (although some mechanisms are utilized to increase their level of fault-tolerance) because of common problem of single token as a single point of failure. On the contrary, permission-based algorithms utilize the permission of multiple nodes to guarantee mutual exclusion. It yields to high traffic when number of nodes is high. Moreover, the number of message transmissions and energy consumption increase in MANET by increasing the number of mobile nodes accompanied in every decision making cycle. The purpose of this study is to introduce a method of managing the critical section,named as Ancestral, having higher fault-tolerance than token-based and fewer message transmissions and traffic rather that permission-based algorithms. This method makes a tradeoff between token-based and permission-based. It does not utilize any token, that is similar to permission-based, and the latest node having the critical section influences the entrance of the next node to the critical section, that is similar to token-based algorithms. The algorithm based on ancestral is named as DAD algorithms and increases the availability of fully connected network between 2.86 to 59.83% and decreases the number of message transmissions from 4j-2 to 3j messages (j as number of nodes in partition). This method is then utilized as the basis of dynamic ancestral mutual exclusion algorithm for MANET which is named as MDA. This algorithm is presented and evaluated for different scenarios of mobility of nodes, failure, load and number of nodes. The results of study show that MDA algorithm guarantees mutual exclusion,dead lock freedom and starvation freedom. It improves the availability of CS to minimum 154.94% and 113.36% for low load and high load of CS requests respectively compared to other permission-based lgorithm.Furthermore, it improves response time up to 90.69% for high load and 75.21% for low load of CS requests. It degrades the number of messages from n to 2 messages in the best case and from 3n/2 to n in the worst case. MDA algorithm is resilient to transient partitioning of network that is normally occurs due to failure of nodes or links

Availability Analysis of Predictive Hybrid M-Out-of-N Systems

In m-out-of-n system, if m-out-of-n modules agree, system can report consensus; otherwise, the system fails. On the other hand, in predictive hybrid system if there is no agreement, a history record of previous successful result(s) is used to predict the output. In order to analyze the availability of predictive hybrid redundancy system, Markov modeling is utilized. By using Markov model of the system in steady state, the availability is derived and compared with m-out-of-n system. The results of simulation demonstrated that the availability of predictive hybrid system is higher than m-out-of-n system especially for large m

Availability analysis of predictive hybrid M-out-of-N systems

In m-out-of-n system, if m-out-of-n modules agree, system can report consensus; otherwise, the system fails. On the other hand, in predictive hybrid system if there is no agreement, a history record of previous successful result(s) is used to predict the output. In order to analyze the availability of predictive hybrid redundancy system, Markov modeling is utilized. By using Markov model of the system in steady state, the availability is derived and compared with m-out-of-n system. The results of simulation demonstrated that the availability of predictive hybrid system is higher than m-out-of-n system especially for large m

Exact parallel plurality voting algorithm for totally ordered object space fault-tolerant systems

Plurality voter is one of the commonest voting methods for decision making in highly-reliable applications in which the reliability and safety of the system is critical. To resolve the problem associated with sequential plurality voter in dealing with large number of inputs, this paper introduces a new generation of plurality voter based on parallel algorithms. Since parallel algorithms normally have high processing speed and are especially appropriate for large scale systems, they are therefore used to achieve a new parallel plurality voting algorithm by using (n/log n) processors on EREW shared-memory PRAM. The asymptotic analysis of the new proposed algorithm has demonstrated that it has a time complexity of O (log n) which is less than time complexity of sequential plurality algorithm, i.e. Ω (n log n)

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

Cluster head selection using fuzzy logic and chaotic based genetic algorithm in wireless sensor network

Wireless sensor networks (WSNs) are composed of hundreds or thousands of sensor nodes in order to detect and transmit information from its surrounding environment. The sensor nodes have limited computation capability, limited power and small memory size. In these networks, sensor nodes are dependent on low power batteries to provide their energy. As energy is a challenging issue in these networks, clustering models are used to overcome this problem. In this paper, fuzzy logic and chaotic based genetic algorithms are combined to extend the lifetime of sensor nodes. In other words, fuzzy logic is proposed based on three variables- energy, density and centrality-to introduce the best nodes to base station as cluster head candidate. Then, the number and place of cluster heads are determined in base station by using genetic algorithm based on chaotic. Our simulation results in the NS-2 show the longer network lifetime of the proposed algorithm than the LEACH, DEEC, SEDEEC and GFS protocols

Blind digital speech watermarking based on Eigen-value quantization in DWT

This paper presents a new blind digital speech watermarking technique based on Eigen-value quantization in Discrete Wavelet Transform. Initially, each frame of the digital speech was transformed into the wavelet domain by applying Discrete Wavelet Transform. Then, the Eigen-value of Approximation Coefficients was computed by using Singular Value Decomposition. Finally, the watermark bits were embedded by quantization of the Eigen-value. The experimental results show that this watermarking technique is robust against different attacks such as filtering, additive noise, resampling, and cropping. Applying new robust transforms, adaptive quantization steps and synchronization techniques can be the future trends in this field

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 node’s competency and lead to incorrect selection of cluster heads. A new weight-based algorithm presented in this paper not only determines node’s 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 node’s 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 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