Qingtao Wu

A Novel Physarum-Inspired Routing Protocol for Wireless Sensor Networks

There is a tradeoff between routing efficiency and energy equilibrium for sensor nodes in wireless sensor networks (WSNs). Inspired by the large and single-celled amoeboid organism, slime mold Physarum polycephalum, this paper presents a novel Physarum-inspired routing protocol (P-iRP) for WSNs to address the above issue. In P-iRP, a sensor node can choose the proper next hop by using a proposed Physarum-inspired selecting next hop model (P-iSNH), which comprehensively considers the distance, energy residue, and location of the next hop. As a result, the P-iRP can get a rather low algorithm complexity of O ( n ) , which greatly reduces the processing delay and saves the energy of sensors. Moreover, by theoretical analysis, the P-iSNH always has an equilibrium solution for multiple next hop candidates, which is vital factor to the stability of routing protocol. Finally, simulation results show that P-iRP can perform better in many scenarios and achieve the effective tradeoff between routing efficiency and energy equilibrium compared to other famous algorithms.

A QoS-Satisfied Prediction Model for Cloud-Service Composition Based on a Hidden Markov Model

Various significant issues in cloud computing, such as service provision, service matching, and service assessment, have attracted researchers’ attention recently. Quality of service (QoS) plays an increasingly important role in the provision of cloud-based services, by aiming for the seamless and dynamic integration of cloud-service components. In this paper, we focus on QoS-satisfied predictions about the composition of cloud-service components and present a QoS-satisfied prediction model based on a hidden Markov model. In providing a cloud-based service for a user, if the user’s QoS cannot be satisfied by a single cloud-service component, component composition should be considered, where its QoS-satisfied capability needs to be proactively predicted to be able to guarantee the user’s QoS. We discuss the proposed model in detail and prove some aspects of the model. Simulation results show that our model can achieve high prediction accuracies.

Analysis and application of Bio-Inspired Multi-Net Security Model

With the rapid development of network technologies and deteriorating of network environment, traditional single-net security system cannot satisfy the security requirement. The excellent security performance of biological systems impels the bio-inspired network security theory to be a hot research area currently. Based on Bio-inspired Multidimensional Network Security Model we have put forward, we have advanced Bio-inspired Multi-Net Security system by implementing the functional distribution of different subnets of different transient states in Multi-Net Paralleling structure. Firstly, parameter estimation and modified algorithms of Hidden Markov Model are introduced to construct the mathematical mode of B-MNS; secondly, the integrated performance of our modified B-MNS has been tested and its simulation has been carried out. So the feasibility, validity and high efficiency of our model have been demonstrated theoretically, and practically.

Reputation Revision Method for Selecting Cloud Services Based on Prior Knowledge and a Market Mechanism

The trust levels of cloud services should be evaluated to ensure their reliability. The effectiveness of these evaluations has major effects on user satisfaction, which is increasingly important. However, it is difficult to provide objective evaluations in open and dynamic environments because of the possibilities of malicious evaluations, individual preferences, and intentional praise. In this study, we propose a novel unfair rating filtering method for a reputation revision system. This method uses prior knowledge as the basis of similarity when calculating the average rating, which facilitates the recognition and filtering of unfair ratings. In addition, the overall performance is increased by a market mechanism that allows users and service providers to adjust their choice of services and service configuration in a timely manner. The experimental results showed that this method filtered unfair ratings in an effective manner, which greatly improved the precision of the reputation revision system.

Research on Grade Optimization Self-tuning Method for System Dependability Based on Autonomic Computing

In order to enhance the service performance, the preservation and increase of system dependability are researched and a system dependability self-tuning method is proposed based on autonomic computing. The self-tuning method attempts to achieve the sustained growth of system dependability by on-line evaluation, dynamic prediction and tuning of self-tuning scheme. There are four tuning methods random interval, settled interval, dependability threshold value and event trigger, which are discussed in the simulation experimentation. The tuning effects that affect system dependability increment are analyzed, and the optimal tuning opportunities for each tuning method are given. The result of simulation experiment shows that the tuning methods will ensure the positive increase of system dependability increment except random interval.

A Novel Bio-inspired Trusted Routing Protocol for Mobile Wireless Sensor Networks

Routing in mobile wireless sensor networks (MWSNs) is an extremely challenging issue due to the features of MWSNs. In this paper, we present a novel bio-inspired trusted routing protocol (B-iTRP) based on artificial immune system (AIS), ant colony optimization (ACO) and Physarum optimization (PO). For trust mechanism, B-iTRP monitors neighbors’ behavior in real time and then assesses neighbors’ trusts based on AIS. For routing strategy, each node proactively finds routes to the Sink based on ACO. When a backward ant is on the way to return source, it senses the energy residual and trust value of each node on the discovered route, and calculates the link trust and link energy of the route. Moreover, B-iTRP also assesses the availability of route based on PO to maintain the route table. Simulation results show how B-iTRP can achieve the effective performance compared to existing state-of-the-art algorithms.

A Cloud Service Resource Classification Strategy Based on Feature Similarity

There are now a vast array of heterogeneous cloud service resources, which makes it difficult to identify suitable services for the various types of cloud users. A classification of cloud service resources would help users find suitable cloud services more easily. We propose such a classification strategy, which has two parts. First, we improve the original naive Bayesian classification algorithm, designing a Bayesian classification algorithm based on feature similarity. Second, to improve the efficiency of the classification algorithm, we design a parallel programming model using the Hadoop platform. Simulation results show that the proposed classification strategy is feasible and effective, improving not only the resource classification accuracy but also greatly enhancing the processing efficiency for large-scale cloud service resources

P-iRP: Physarum-Inspired Routing Protocol for Wireless Sensor Networks

There is a trade-off between routing efficiency and energy equilibrium for sensor nodes in wireless sensor networks (WSNs). Inspired by the large and single-celled amoeboid organism-slime mold physarum polycephalum, this paper presents a novel physarum-inspired routing protocol (P-iRP) for WSNs to address the above issue. In P-iRP, a sensor node selects its proper next hop by using a proposed physarum-inspired selecting next hop model (PSN), which considers comprehensively the distance, energy residue and location of the next hop. We introduce the PSNs routing selecting strategy and detail PiRPs algorithms. Simulation results show how P-iRP can achieve the effective trade-off between routing efficiency and energy equilibrium compared to existing classical algorithms.

A Novel Multi-x Cooperative Decision-making Mechanism for Cognitive Internet of Things

Cognitive Internet of Things (CIoT) mainly consists of a group of autonomous nodes (ANs) which are commonly combined into domains and should have the intelligence to perceive, analyze, decide and act. Cooperation has shown to be a good technique for collective behavior of ANs that locally interact with each other in distributed environments. In this paper, we study the cooperative decision-making mechanism of multi-ANs and multi-domains and present the corresponding cooperative decision-making process in CIoT. Multi-ANs cooperation deals with the cases that one AN cannot meet the QoS and network performance object (NPO) and multi-domains cooperation addresses the cases that the ANs of only one domain cannot meet the QoS and NPO. Based on the cooperative decision-making mechanism, the simulative experiments are done and show that the NPO can be satisfied perfectly.

VPRS-Based Knowledge Discovery Approach in Incomplete Information System

Through changing the equivalence relation in the incomplete information system, a new variable precision rough set model and an approach for knowledge reduction are proposed. To overcome no monotonic property of the lower approximation, a cumulative variable precision rough set model is explored, and the basic properties of cumulative lower and upper approximation operators are investigated. The example proves that the cumulative variable precision rough set model has wide range of applications and better result than variable precision rough set model.