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With the rapid growth of wireless sensor networks (WSNs), they have become an integral and substantial part of people's life. As such WSN stands as an assuring outlook, but because of sensor's resource limitations and other prerequisites, optimal dual route discovery becomes an issue of concern. WSN along with central sink node is capable of handling wireless transmission, thus optimizing the network's lifetime by selecting the dual path. The major problem confronted in the application of security mechanisms in WSNs is resolving the issues amid reducing consumption of resources and increases security.

According to the proposed system, two metrics, namely, path length and packets delivery ratio are incorporated for identifying dual routes amid the source and destination. Thereafter by making use of the distance metric, the optimal dual route is chosen and data transmission is carried out amid the nodes. With the usage of the recommended routing protocol high packet delivery ratio is achieved with reduced routing overhead and low average end to end delay. It is clearly portrayed in the simulation output that the proposed on demand dual path routing protocol surpasses the prevailing routing protocol. Moreover, security is achieved make use of in accord the data compression reduces the size of the data. With the help of dual path, mathematical model of Finite Automata Theory is derived to transmit data from source to destination. Finite Automata Theory comprises Deterministic Finite Automata (DFA) that is being utilized for Dual Path Selection. In addition, data transition functions are defined for each input stage. In this proposed work, another mathematical model is 10; introduced to efficiently choose an alternate path between a receiver and transmitter for data transfer with qualified node as relay node using RR Algorithm. It also includes Dynamic Mathematical Model for Node Localization to improve the precision in location estimation using Node Localization Algorithm. As a result a simulator is built and various scenarios are elaborated for comparing the performance of the recommended dual path routing protocol with respect to the prevailing ones.

Reliability and fault-tolerance: The actual motive in utilizing the approach of multipath routing in sensor network was to offer path resilience in case of a node or link failures thus ascertaining reliable transmission of data. Usually in a fault tolerant domain, when the sensor node is unable to forward the data packets to the sink, alternative paths can be utilized for recovering its data packets during the failure of any link/node. Load balancing: Load balancing involves equalizing energy consumption of all the existing nodes, thereby degrading them together. Load balancing via clustering improves network scalability. The network's lifetime as well as reliability can be extended if varied energy level's nodes exist in sensor node. Quality of service (QoS): Improvement backing of quality of service with respect to the data delivery ratio, network throughput and end-to-end latency stands very significant in building multipath routing protocols for various network types. Reduced delay: There is a reduced delay in multipath routing since the backup routes are determined at the time of route discovery. Bandwidth aggregation: By dividing the data toward the same destination into multiple streams (by routing all to a separate path) can aggregate the effective bandwidth. The benefit being that, in case a node possesses many links with low bandwidth, it can acquire a bandwidth which is more compared to the individual link.

Few more new algorithms can be used to compare the QoS parameters.

Proposed mechanism with feedback ascertains improvised delivery ratio compared to the single path protocol since in case of link failure, the protocol has alternative route. In case there are 50 nodes in the network, the detection mechanism yields packet delivery of 95% and in case there are 100 nodes, the packet delivery is lowered to 89%. It is observed that the packet rate in the network is more for small node range. When the node count is 200, the packet ratio is low, which is lowered to 85%. With a node count of 400, the curve depicts the value of 87%. Hence, even with a decrease in value, it is superior than the existing protocols. The average end-to-end delay represents the transmission delay of the data packets that have been successfully delivered as depicted in Figure 6 and Table 3. The recommended system presents the queue as well as the propagation delay from the source to destination. The figure depicts that when compared to the single path protocol, the end-to-end delay can be reduced via route switching. End-to-end delay signifies the time acquired for the delay in the receival of the the retransmitted packet by each node. The comparison reveals that the delay was lower compared to the existing ones in the WSN. Proposed protocol aids in reducing consumption of energy in transmitter, receiver and various sensors. Comparative analysis of energy consumptions of the sensor in regard to the recommended system must exhibit reduced energy than the prevailing systems.

On demand dual path routing protocol. Hence it is verified that the on demand routing protocol comprises DFA algorithms determines dual path. Here mathematical model for routing between two nodes with relay node is derived using RR algorithm to determine alternate path and thus reduce energy consumption. Another dynamic mathematical model for node localization is derived using localization algorithm. For transmitting data with a secure and promising QoS in the WSNs, the routing optimization technique has been introduced. The simulation software environment follows the DFA. The simulation yields in improvised performance with respect to packet delivery ratio, throughput, average end-to-end delay and routing overhead. So, it is proved that the DFA possesses the capability of optimizing the routing algorithms which facilitates the multimedia applications over WSNs.

In purpose of this paper is to propose a novel scheme to process XPath-based keyword search over Extensible Markup Language (XML) streams, where one can specify query keywords and XPath-based filtering conditions at the same time. Experimental results prove that our proposed scheme can efficiently and practically process XPath-based keyword search over XML streams.

To allow XPath-based keyword search over XML streams, it was attempted to integrate YFilter (Diao et al., 2003) with CKStream (Hummel et al., 2011). More precisely, the nondeterministic finite automation (NFA) of YFilter is extended so that keyword matching at text nodes is supported. Next, the stack data structure is modified by integrating set of NFA states in YFilter with bitmaps generated from set of keyword queries in CKStream.

Extensive experiments were conducted using both synthetic and real data set to show the effectiveness of the proposed method. The experimental results showed that the accuracy of the proposed method was better than the baseline method (CKStream), while it consumed less memory. Moreover, the proposed scheme showed good scalability with respect to the number of queries.

Due to the rapid diffusion of XML streams, the demand for querying such information is also growing. In such a situation, the ability to query by combining XPath and keyword search is important, because it is easy to use, but powerful means to query XML streams. However, none of existing works has addressed this issue. This work is to cope with this problem by combining an existing XPath-based YFilter and a keyword-search-based CKStream for XML streams to enable XPath-based keyword search.

The purpose of this paper is to propose a novel ant colony optimization (ACO) approach to optimal control. The standard ACO algorithms have proven to be very powerful optimization metaheuristic for combinatorial optimization problems. They have been demonstrated to work well when applied to various nondeterministic polynomial‐complete problems, such as the travelling salesman problem. In this paper, ACO is reformulated as a model‐free learning algorithm and its properties are discussed.

First, it is described how quantizing the state space of a dynamic system introduces stochasticity in the state transitions and transforms the optimal control problem into a stochastic combinatorial optimization problem, motivating the ACO approach. The algorithm is presented and is applied to the time‐optimal swing‐up and stabilization of an underactuated pendulum. In particular, the effect of different numbers of ants on the performance of the algorithm is studied.

The simulations show that the algorithm finds good control policies reasonably fast. An increasing number of ants results in increasingly better policies. The simulations also show that although the policy converges, the ants keep on exploring the state space thereby capable of adapting to variations in the system dynamics.

This paper introduces a novel ACO approach to optimal control and as such marks the starting point for more research of its properties. In particular, quantization issues must be studied in relation to the performance of the algorithm.

The paper presented is original as it presents the first application of ACO to optimal control problems.

Natural collective phenomena, for example, the movement of crowds of pedestrians and the impressive nest formations of social insects, provide us with an existence proof that sophisticated constructions may be built by swarms of relatively simple artificial agents. The constructions often appear to have required impressive control and coordination – yet each agent in the collective does not appear to be provided with an internal world model or blue‐print for the complete construction. These macroscopic structures emerge as the consequence of interaction of agents, carrying out simple rules based upon the local state of the world, which includes the interaction between agents and the growing structure. In an attempt to understand the underpinning principles of structure formation in collectives of minimal mobile agents the paper focuses on an investigation of automata‐like agents in a two‐dimensional lattice. All agents start their evolution at the same site on the lattice. Every agent moves at random until it finds a neighbourhood it likes more than other neighbourhoods. The agents form a stationary structure of their immobile bodies. The paper focuses upon the parameterisation of the rule space and the mapping between parameter space and the resulting global structure formed by the agents.

The study aims to reveal regularities of collective decision in groups of varying cooperativity and to investigate the influence of individualism–collectivism cultural dimension on the emergence of collective behavioral entities.

An agent-based model of group behavior is used to simulate the logic of collective binary decisions defined by reaching a consensus threshold. The key parameter of the model is the cooperativity of interaction between individuals which locates the group in individualism–collectivism cultural dimension. The probabilities of collective decisions are measured as a function of individual preferences and the strength of cooperation between agents.

Strong, weak and middle point cooperativities of inter-individual interaction define three distinct regimes of collective decision logic, namely, individualism, conformism and criticality. The critical organization allows the group to generate coherent non-predetermined collective behavior in statistical agreement with individual preferences.

Emergent collective behavior with coherent and nondeterministic decision-making can be modeled in an agent-based approach with local interaction between individuals tuned to a critical point. Variation of a single cooperativity parameter accounts for a continuous transition between organizations of the group inherent to inert matter and living systems.

A new organization of social systems distinguished by internally relaxed relations and large-scale freedom is found beyond the traditional individualism–collectivism contraposition. This state is reached by tuning behavioral logic of individuals to a critical balance of individual and collective values.

Individualist, conformist and critical regimes of collective organization distinct in terms of integrity, complexity and determinism are recognized as universal organizational classes of nature spanning both inert and living systems. In particular, individualist and conformist regimes produce simple deterministic behavior typical for gases and solid bodies, whereas the critical organization generates complex nondeterministic behavior inherent to living organisms.

Attempts to characterise some aspects of the new wave of reaction‐diffusion and ant based computation, and to discuss their place in the class of fully distributed load‐balancing algorithms that solve the dynamic load‐balancing problem of communication networks. The main question of the paper states: what are the advantages of the intellectualisation of the control agents and what are the costs of smartness? We start our investigation with random walk techniques and the electricity paradigm, carry on with the reaction‐diffusion approach, and finish the construction of the computational hierarchy with the ant paradigm and smart agents.

End‐of‐life (EOL) products have become a major environmental issue among countries and manufacturers. This is due to the growing number of EOL products and their hazardous contents. Many collection strategies and pilot projects have been conducted to manage EOL products, especially Waste Electrical and Electronics equipments (WEEEs). However, as characteristics of a population are different to one another, a customized collection strategy is required. The purpose of this paper is to find an effective collection strategy which considers cost and environmental impact simultaneously.

This paper presents an integrated collection strategy which combines a Fuzzy Colored Petri Net forecasting method and collection network model to collect EOL products. Colored Petri Net is used in modeling the integrated collection strategy. To test the collection strategy, a case study on mobile phone collection in Australia is presented.

The integrated collection strategy developed in this paper finds that by providing demographic data and historical sales of a relevant product in a certain location, the best strategy to collect EOL products in that location can be determined. This paper finds that the best strategy that suits one location might be different to other locations.

This paper presents a model which provides a customized collection strategy that follows the characteristics of a population. This strategy allows government organizations or manufacturers to simulate the strategies to collect EOL products in different locations.

The aim of the present paper is to investigate the behavior of collective motion of living biological organisms in the two-dimensional (2D) plane by adopting a new approach based on the use of Langevin dynamics. Langevin dynamics is a powerful tool to study these systems because they present a stochastic process due to collisions between their constituents.

In this paper, the dynamical properties and scaling behavior of self-propelled particles were studied numerically by using Langevin dynamics. These dynamics have been affected by the use of only the alignment zone of radius R.

The results indicated that the system’s velocity increases with time and reaches to finite value at the equilibrium phase.

This result is more consistent with that of Vicsek’s model. However, the system’s velocity decreases exponentially with the applied noise without taking the zero value for the highest noise value.

As well as, the crossover time of the growth kinetic system decreases exponentially with noise.

Scaling behavior has been checked for this system and the corresponding results prove that behavior scales with the same law of the one in Vicsek’s model but with different scaling exponents.

The phase transition observed in Vicsek’s model cannot be reproduced by the Langevin dynamics model, which describes more about the dynamical properties of self-propelled systems.

In this study, a hybrid rough and grey set-based rule model is designed for diagnosis of one type of blood cancer called multiple myeloma (MM). The grey clustering method is used to combine the same condition attributes and to improve the validity of the final model.

Some tools of the rough set theory (RST) and grey incidence analysis (GIA) are used in this research to analyze the serum protein electrophoresis (SPE) test results. An RST-based rule model is extracted based on the laboratory SPE test results of patients. Also, one decision attribute and 15 condition attributes are used to extract the rules. About four rule models are constructed due to the different algorithms of data complement, discretization, reduction and rule generation. In the following phases, the condition attributes are clustered into seven clusters by using a grey clustering method, the value set of the decision attribute is decreased by using manual discretizing and the number of observations is increased in order to improve the accuracy of the model. Cross-validation is used for evaluation of the model results and finally, the best model is chosen with 5,216 rules and 98% accuracy.

In this paper, a new rule model with high accuracy is extracted based on the combination of the grey clustering method and RST modeling for diagnosis of the MM disease. Also, four primary rule models and four improved rule models have been extracted from different decision tables in order to define the result of SPE test of patients. The maximum average accuracy of improved models is equal to 95% and related to the gamma globulins percentage attribute/object-related reducts (GA/ORR) model.

The total number of observations for rule extraction is 115 and the results can be improved by further samples. To make the designed expert system handy in the laboratory, new computer software is under construction to import data automatically from the electrophoresis machine into the resultant rule model system.

The main originality of this paper is to use the RST and GST together to design and create a hybrid rule model to diagnose MM. Although many studies have been carried out on designing expert systems in medicine and cancer diagnosis, no studies have been found in designing systems to diagnose MM. On the other hand, using the grey clustering method for combining the condition attributes is a novel solution for improving the accuracy of the rule model.