Hongmei He

A Neural Network Model to Minimize the Connected Dominating Set for Self-Configuration of Wireless Sensor Networks

A wireless ad hoc sensor network consists of a number of sensors spreading across a geographical area. The performance of the network suffers as the number of nodes grows, and a large sensor network quickly becomes difficult to manage. Thus, it is essential that the network be able to self-organize. Clustering is an efficient approach to simplify the network structure and to alleviate the scalability problem. One method to create clusters is to use weakly connected dominating sets (WCDSs). Finding the minimum WCDS in an arbitrary graph is an NP-complete problem. We propose a neural network model to find the minimum WCDS in a wireless sensor network. We present a directed convergence algorithm. The new algorithm outperforms the normal convergence algorithm both in efficiency and in the quality of solutions. Moreover, it is shown that the neural network is robust. We investigate the scalability of the neural network model by testing it on a range of sized graphs and on a range of transmission radii. Compared with Guha and Khullers centralized algorithm, the proposed neural network with directed convergency achieves better results when the transmission radius is short, and equal performance when the transmission radius becomes larger. The parallel version of the neural network model takes time O(d) , where d is the maximal degree in the graph corresponding to the sensor network, while the centralized algorithm takes O(n 2). We also investigate the effect of the transmission radius on the size of WCDS. The results show that it is important to select a suitable transmission radius to make the network stable and to extend the lifespan of the network. The proposed model can be used on sink nodes in sensor networks, so that a sink node can inform the nodes to be a coordinator (clusterhead) in the WCDS obtained by the algorithm. Thus, the message overhead is O(M), where M is the size of the WCDS.

Multi-Capacity Combinatorial Ordering GA in Application to Cloud resources allocation and efficient virtual machines consolidation

Abstract This paper describes a novel approach making use of genetic algorithms to find optimal solutions for multi-dimensional vector bin packing problems with the goal to improve cloud resource allocation and Virtual Machines (VMs) consolidation. Two algorithms, namely Combinatorial Ordering First-Fit Genetic Algorithm (COFFGA) and Combinatorial Ordering Next Fit Genetic Algorithm (CONFGA) have been developed for that and combined. The proposed hybrid algorithm targets to minimise the total number of running servers and resources wastage per server. The solutions obtained by the new algorithms are compared with latest solutions from literature. The results show that the proposed algorithm COFFGA outperforms other previous multi-dimension vector bin packing heuristics such as Permutation Pack (PP), First Fit (FF) and First Fit Decreasing (FFD) by 4%, 34%, and 39%, respectively. It also achieved better performance than the existing genetic algorithm for multi-capacity resources virtual machine consolidation (RGGA) in terms of performance and robustness. A thorough explanation for the improved performance of the newly proposed algorithm is given.

MULTI-ATTRIBUTE DECISION MAKING BASED ON LABEL SEMANTICS

We propose label semantics as an integrated representation framework for probabilistic uncertainty and fuzziness in multiple-attribute decision making problems. Linguistic attribute hierarchies are then introduced as a means of modelling the complex and often imprecise functional relationships between low-level attributes or measurements and high-level decision or classification variables. Within this framework we introduce linguistic decision trees as a tool for information aggregation in multi-attribute decision problems and describe the process of information propagation through a hierarchy of linked decision trees. In addition, we consider the ranking of different alternatives or examples based on their linguistic descriptions of a high-level utility variable. Finally, we discuss how linguistic decision trees embedded in attribute hierarchies can be learnt from data.

The security challenges in the IoT enabled cyber-physical systems and opportunities for evolutionary computing & other computational intelligence

Internet of Things (IoT) has given rise to the fourth industrial revolution (Industrie 4.0), and it brings great benefits by connecting people, processes and data. However, cybersecurity has become a critical challenge in the IoT enabled cyber physical systems, from connected supply chain, Big Data produced by huge amount of IoT devices, to industry control systems. Evolutionary computation combining with other computational intelligence will play an important role for cybersecurity, such as artificial immune mechanism for IoT security architecture, data mining/fusion in IoT enabled cyber physical systems, and data driven cybersecurity. This paper provides an overview of security challenges in IoT enabled cyber-physical systems and what evolutionary computation and other computational intelligence technology could contribute for the challenges. The overview could provide clues and guidance for research in IoT security with computational intelligence.

Linguistic Decision Making for Robot Route Learning

Machine learning enables the creation of a nonlinear mapping that describes robot-environment interaction, whereas computing linguistics make the interaction transparent. In this paper, we develop a novel application of a linguistic decision tree for a robot route learning problem by dynamically deciding the robots behavior, which is decomposed into atomic actions in the context of a specified task. We examine the real-time performance of training and control of a linguistic decision tree, and explore the possibility of training a machine learning model in an adaptive system without dual CPUs for parallelization of training and control. A quantified evaluation approach is proposed, and a score is defined for the evaluation of a models robustness regarding the quality of training data. Compared with the nonlinear system identification nonlinear auto-regressive moving average with eXogeneous inputs model structure with offline parameter estimation, the linguistic decision tree model with online linguistic ID3 learning achieves much better performance, robustness, and reliability.

Robot control code generation by task demonstration in a dynamic environment

Within mobile robotics, one of the most dominant relationships to consider when implementing robot control code is the one between the robots sensors and its motors. When implementing such a relationship, efficiency and reliability are of crucial importance. The latter aspects often prove challenging due to the complex interaction between a robot and the environment in which it exists, frequently resulting in a time consuming iterative process where control code is redeveloped and tested many times before obtaining an optimal controller. In this paper, we address this challenge by implementing an alternative approach to control code generation, which first identifies the desired robot behaviour and represents the sensor-motor task algorithmically through system identification using the NARMAX modelling methodology. The control code is generated by task demonstration, where the sensory perception and velocities are logged and the relationship that exists between them is then modelled using system identification. This approach produces transparent control code through non-linear polynomial equations that can be mathematically analysed to obtain formal statements regarding specific inputs/outputs. We demonstrate this approach to control code generation and analyse its performance in dynamic environments.

A k-hyperplane-based neural network for non-linear regression

For the time series prediction problem, the relationship between the abstracted independent variables and the response variable is usually strong non-linear. We propose a neural network fusion model based on k-hyperplanes for non-linear regression. A k-hyperplane clustering algorithm is developed to split the data to several clusters. The experiments are done on an artificial time series, and the convergence of k-hyperplane clustering algorithm and neural network gradient training algorithm is examined. The dimension of inputs affect the clustering performance very much. Neural network fusion can get some compensation in performance. It is shown that the prediction performance of the model for the time series is very good. The model can be further exploited for many real applications.

One-and two-page crossing numbers for some types of graphs

The simplest graph drawing method is that of putting the vertices of a graph on a line (spine) and drawing the edges as half-circles on k half planes (pages). Such drawings are called k-page book drawings and the minimal number of edge crossings in such a drawing is called the k-page crossing number. In a one-page book drawing, all edges are placed on one side of the spine, and in a two-page book drawing all edges are placed either above or below the spine. The one- and two-page crossing numbers of a graph provide upper bounds for the standard planar crossing. In this paper, we derive the exact one-page crossing numbers for four-row meshes, present a new proof for the one-page crossing numbers of Halin graphs, and derive the exact two-page crossing numbers for circulant graphs . We also give explicit constructions of the optimal drawings for each kind of graph.

Linguistic Attribute Hierarchies for Multiple-Attribute Decision Making

We propose label semantics as an integrated representation framework for probabilistic uncertainty and fuzziness in multiple-attribute decision making problems. Linguistic attribute hierarchies are then introduced as a means of modelling the complex and often imprecise functional relationships between low-level attributes or measurements and high-level decision or classification variables.

Review of cybersecurity issues in industrial critical infrastructure: manufacturing in perspective

ABSTRACT Nowadays, the industrial sector is being challenged by several cybersecurity concerns. Direct attacks by malicious persons and (or) software form part of the severe jeopardies of industrial control systems (ICSs). These affect products/production qualities, brand reputations, sales revenues, and aggravate the risks to health and safety of human lives. These have been enabled due to progressive adoption of technology trends like Industry 4.0, BYOD, mobile computing, and Internet-of-Things (IoT), in the quest for improved relevance and value of production decisions, minimised operational overheads, optimum resource utilisation, markets globalisation, etc. However, several security vulnerabilities and risks have also emerged, and are increasingly being exploited in the industrial sector especially manufacturing. To manage this phenomenon, refined and holistic (combining people, process, and technology perspectives) security strategies and solutions are required to enhance security in ICS. This paper offers an insightful review of possible solution path beginning with the understanding of ICS security trends relative to cyber threats, vulnerabilities, attacks and patterns, agents, risks, and the impacts of all these on the industrial environment and entities that depend on it. Such episteme can improve security awareness, proficiency for respective stakeholders, and advance the development of appropriate security mechanisms, and adoption of recommendations.