A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Modern software cybernetics: New trends

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Software cybernetics research is to apply a variety of techniques from cybernetics research to software engineering research. For more than fifteen years since 2001, there has been a dramatic increase in work relating to software cybernetics. From cybernetics viewpoint, the work is mainly on the first-order level, namely, the software under observation and control. Beyond the first-order cybernetics, the software, developers/users, and running environments influence each other and thus create feedback to form more complicated systems. We classify software cybernetics as Software Cybernetics I based on the first-order cybernetics, and as Software Cybernetics II based on the higher order cybernetics. This paper provides a review of the literature on software cybernetics, particularly focusing on the transition from Software Cybernetics I to Software Cybernetics II. The results of the survey indicate that some new research areas such as Internet of Things, big data, cloud computing, cyber-physical systems, and even creative computing are related to Software Cybernetics II. The paper identifies the relationships between the techniques of Software Cybernetics II applied and the new research areas to which they have been applied, formulates research problems and challenges of software cybernetics with the application of principles of Phase II of software cybernetics; identifies and highlights new research trends of software cybernetic for further research

Improving intrusion detection systems using data mining techniques

Recent surveys and studies have shown that cyber-attacks have caused a lot of damage to organisations, governments, and individuals around the world. Although developments are constantly occurring in the computer security field, cyber-attacks still cause damage as they are developed and evolved by hackers. This research looked at some industrial challenges in the intrusion detection area. The research identified two main challenges; the first one is that signature-based intrusion detection systems such as SNORT lack the capability of detecting attacks with new signatures without human intervention. The other challenge is related to multi-stage attack detection, it has been found that signature-based is not efficient in this area. The novelty in this research is presented through developing methodologies tackling the mentioned challenges. The first challenge was handled by developing a multi-layer classification methodology. The first layer is based on decision tree, while the second layer is a hybrid module that uses two data mining techniques; neural network, and fuzzy logic. The second layer will try to detect new attacks in case the first one fails to detect. This system detects attacks with new signatures, and then updates the SNORT signature holder automatically, without any human intervention. The obtained results have shown that a high detection rate has been obtained with attacks having new signatures. However, it has been found that the false positive rate needs to be lowered. The second challenge was approached by evaluating IP information using fuzzy logic. This approach looks at the identity of participants in the traffic, rather than the sequence and contents of the traffic. The results have shown that this approach can help in predicting attacks at very early stages in some scenarios. However, it has been found that combining this approach with a different approach that looks at the sequence and contents of the traffic, such as event- correlation, will achieve a better performance than each approach individually

Secure Large Scale Penetration of Electric Vehicles in the Power Grid

As part of the approaches used to meet climate goals set by international environmental agreements, policies are being applied worldwide for promoting the uptake of Electric Vehicles (EV)s. The resulting increase in EV sales and the accompanying expansion in the EV charging infrastructure carry along many challenges, mostly infrastructure-related. A pressing need arises to strengthen the power grid to handle and better manage the electricity demand by this mobile and geo-distributed load. Because the levels of penetration of EVs in the power grid have recently started increasing with the increase in EV sales, the real-time management of en-route EVs, before they connect to the grid, is quite recent and not many research works can be found in the literature covering this topic comprehensively. In this dissertation, advances and novel ideas are developed and presented, seizing the opportunities lying in this mobile load and addressing various challenges that arise in the application of public charging for EVs. A Bilateral Decision Support System (BDSS) is developed here for the management of en-route EVs. The BDSS is a middleware-based MAS that achieves a win-win situation for the EVs and the power grid. In this framework, the two are complementary in a way that the desired benefit of one cannot be achieved without attaining that of the other. A Fuzzy Logic based on-board module is developed for supporting the decision of the EV as to which charging station to charge at. GPU computing is used in the higher-end agents to handle the big amount of data resulting in such a large scale system with mobile and geo-distributed nodes. Cyber security risks that threaten the BDSS are assessed and measures are applied to revoke possible attacks. Furthermore, the Collective Distribution of Mobile Loads (CDML), a service with ancillary potential to the power system, is developed. It comprises a system-level optimization. In this service, the EVs requesting a public charging session are collectively redistributed onto charging stations with the objective of achieving the optimal and secure operation of the power system by reducing active power losses in normal conditions and mitigating line congestions in contingency conditions. The CDML uses the BDSS as an industrially viable tool to achieve the outcomes of the optimization in real time. By participating in this service, the EV is considered as an interacting node in the system-wide communication platform, providing both enhanced self-convenience in terms of access to public chargers, and contribution to the collective effort of providing benefit to the power system under the large scale uptake of EVs. On the EV charger level, several advantages have been reported favoring wireless charging of EVs over wired charging. Given that, new techniques are presented that facilitate the optimization of the magnetic link of wireless EV chargers while considering international EMC standards. The original techniques and developments presented in this dissertation were experimentally verified at the Energy Systems Research Laboratory at FIU

Intelligent control of mobile robot with redundant manipulator & stereovision: quantum / soft computing toolkit

The task of an intelligent control system design applying soft and quantum computational intelligence technologies discussed. An example of a control object as a mobile robot with redundant robotic manipulator and stereovision introduced. Design of robust knowledge bases is performed using a developed computational intelligence – quantum / soft computing toolkit (QC/SCOptKBTM). The knowledge base self-organization process of fuzzy homogeneous regulators through the application of end-to-end IT of quantum computing described. The coordination control between the mobile robot and redundant manipulator with stereovision based on soft computing described. The general design methodology of a generalizing control unit based on the physical laws of quantum computing (quantum information-thermodynamic trade-off of control quality distribution and knowledge base self-organization goal) is considered. The modernization of the pattern recognition system based on stereo vision technology presented. The effectiveness of the proposed methodology is demonstrated in comparison with the structures of control systems based on soft computing for unforeseen control situations with sensor system

Research on key techniques of flexible workflow based approach to supporting dynamic engineering design process

Error on title page - correct year of award is 2015 not 2013.Engineering design process (EDP) is a highly dynamic and creative process, and the capability in managing an EDP is considered as a major differentiating factor between competing enterprises. The most important prerequisite to establish an engineering design process excellence is a proper management of all the design process activities and the associated information. The most important impact in recent years on the EDP and on the activities of designers has come from computer-based data processing. Workflow, the automation of a business processes in whole or part, is a useful tool for modelling and managing a business process which can be reprensented by a workflow model (computerized process definition). By considering the dynamic characteristics of EDP, an EDP management system must be flexible enough to support the creative and dynamic EDP. After the introduction of engineering design process and its new trend, as well as flexible workflow technology, reviews of both engineering design process and its supporting flexible workflow technology shows that there is a need for a holistic framework to automate and coordinate design activities in the creative and dynamic EDP, and the flexible workflow technology should also be improved comprehensively in flexibility and intelligence in order to support better engineering design management. By introducing the relations between the EDP and flexible workflow, a virtual workflow and an autonomic flexible workflow built upon autonomic computing is investigated, and an innovative engineering design process management framework based on multi-autonomic objects flexible workflow is proposed. For the flexible workflow modelling in the framework, a dynamic instance-based flexible workflow modelling method is proposed for multi-autonomic objects flexible workflow. In order to improve the intelligence of flexible workflow, after examining the principle of flexible workflow intelligence in flexible workflow, a new flexible workflow autonomic object intelligence algorithm based on both extended Mamdani fuzzy reasoning and neural network is proposed, weighted fuzzy reasoning algorithm, as well as precise and fuzzy hybrid knowledge reasoning algorithm is designed; a bionic flexible workflow adaptation algorithm is proposed to improve the intelligence of autonomic object flexible workflow further. According to the characteristic of EDP, such as cross-enterprises and geographical distribution, and in order to realize the flexible execution of distributed flexible workflow engine, a distributed flexible workflow engine architecture based on web service is proposed and a flexible workflow model description method based on extended WSDL (Web Service Description Language) and BPEL4WS (Business Process Execution Language for Web Services) is proposed. A flexible workflow prototype system supporting engineering design process is implemented according to the proposed EDP management framework in Microsoft VS.Net 2005 environment. The framework is demonstrated by the application in an EDP of a MTO company, and it shows that the proposed framework can support the creative and dynamic process in an efficient way. Finally, the strengths and weakness of the framework as well as the prototype system is discussed based on the results of the evaluation, and the proposed areas of future work are given.Engineering design process (EDP) is a highly dynamic and creative process, and the capability in managing an EDP is considered as a major differentiating factor between competing enterprises. The most important prerequisite to establish an engineering design process excellence is a proper management of all the design process activities and the associated information. The most important impact in recent years on the EDP and on the activities of designers has come from computer-based data processing. Workflow, the automation of a business processes in whole or part, is a useful tool for modelling and managing a business process which can be reprensented by a workflow model (computerized process definition). By considering the dynamic characteristics of EDP, an EDP management system must be flexible enough to support the creative and dynamic EDP. After the introduction of engineering design process and its new trend, as well as flexible workflow technology, reviews of both engineering design process and its supporting flexible workflow technology shows that there is a need for a holistic framework to automate and coordinate design activities in the creative and dynamic EDP, and the flexible workflow technology should also be improved comprehensively in flexibility and intelligence in order to support better engineering design management. By introducing the relations between the EDP and flexible workflow, a virtual workflow and an autonomic flexible workflow built upon autonomic computing is investigated, and an innovative engineering design process management framework based on multi-autonomic objects flexible workflow is proposed. For the flexible workflow modelling in the framework, a dynamic instance-based flexible workflow modelling method is proposed for multi-autonomic objects flexible workflow. In order to improve the intelligence of flexible workflow, after examining the principle of flexible workflow intelligence in flexible workflow, a new flexible workflow autonomic object intelligence algorithm based on both extended Mamdani fuzzy reasoning and neural network is proposed, weighted fuzzy reasoning algorithm, as well as precise and fuzzy hybrid knowledge reasoning algorithm is designed; a bionic flexible workflow adaptation algorithm is proposed to improve the intelligence of autonomic object flexible workflow further. According to the characteristic of EDP, such as cross-enterprises and geographical distribution, and in order to realize the flexible execution of distributed flexible workflow engine, a distributed flexible workflow engine architecture based on web service is proposed and a flexible workflow model description method based on extended WSDL (Web Service Description Language) and BPEL4WS (Business Process Execution Language for Web Services) is proposed. A flexible workflow prototype system supporting engineering design process is implemented according to the proposed EDP management framework in Microsoft VS.Net 2005 environment. The framework is demonstrated by the application in an EDP of a MTO company, and it shows that the proposed framework can support the creative and dynamic process in an efficient way. Finally, the strengths and weakness of the framework as well as the prototype system is discussed based on the results of the evaluation, and the proposed areas of future work are given

A Blended Artificial Intelligence Approach for Spectral Classification of Stars in Massive Astronomical Surveys

[Abstract] This paper analyzes and compares the sensitivity and suitability of several artificial intelligence techniques applied to the Morgan–Keenan (MK) system for the classification of stars. The MK system is based on a sequence of spectral prototypes that allows classifying stars according to their effective temperature and luminosity through the study of their optical stellar spectra. Here, we include the method description and the results achieved by the different intelligent models developed thus far in our ongoing stellar classification project: fuzzy knowledge-based systems, backpropagation, radial basis function (RBF) and Kohonen artificial neural networks. Since one of today’s major challenges in this area of astrophysics is the exploitation of large terrestrial and space databases, we propose a final hybrid system that integrates the best intelligent techniques, automatically collects the most important spectral features, and determines the spectral type and luminosity level of the stars according to the MK standard system. This hybrid approach truly emulates the behavior of human experts in this area, resulting in higher success rates than any of the individual implemented techniques. In the final classification system, the most suitable methods are selected for each individual spectrum, which implies a remarkable contribution to the automatic classification process.This work was supported by Ministry of Science, Innovation and Universities (FEDER RTI2018-095076-B-C22) and Xunta de Galicia (ED431B 2018/42)Xunta de Galicia; ED431B 2018/4

An adaptable fuzzy-based model for predicting link quality in robot networks.

It is often essential for robots to maintain wireless connectivity with other systems so that commands, sensor data, and other situational information can be exchanged. Unfortunately, maintaining sufficient connection quality between these systems can be problematic. Robot mobility, combined with the attenuation and rapid dynamics associated with radio wave propagation, can cause frequent link quality (LQ) issues such as degraded throughput, temporary disconnects, or even link failure. In order to proactively mitigate such problems, robots must possess the capability, at the application layer, to gauge the quality of their wireless connections. However, many of the existing approaches lack adaptability or the framework necessary to rapidly build and sustain an accurate LQ prediction model. The primary contribution of this dissertation is the introduction of a novel way of blending machine learning with fuzzy logic so that an adaptable, yet intuitive LQ prediction model can be formed. Another significant contribution includes the evaluation of a unique active and incremental learning framework for quickly constructing and maintaining prediction models in robot networks with minimal sampling overhead

Towards a novel biologically-inspired cloud elasticity framework

With the widespread use of the Internet, the popularity of web applications has significantly increased. Such applications are subject to unpredictable workload conditions that vary from time to time. For example, an e-commerce website may face higher workloads than normal during festivals or promotional schemes. Such applications are critical and performance related issues, or service disruption can result in financial losses. Cloud computing with its attractive feature of dynamic resource provisioning (elasticity) is a perfect match to host such applications. The rapid growth in the usage of cloud computing model, as well as the rise in complexity of the web applications poses new challenges regarding the effective monitoring and management of the underlying cloud computational resources. This thesis investigates the state-of-the-art elastic methods including the models and techniques for the dynamic management and provisioning of cloud resources from a service provider perspective. An elastic controller is responsible to determine the optimal number of cloud resources, required at a particular time to achieve the desired performance demands. Researchers and practitioners have proposed many elastic controllers using versatile techniques ranging from simple if-then-else based rules to sophisticated optimisation, control theory and machine learning based methods. However, despite an extensive range of existing elasticity research, the aim of implementing an efficient scaling technique that satisfies the actual demands is still a challenge to achieve. There exist many issues that have not received much attention from a holistic point of view. Some of these issues include: 1) the lack of adaptability and static scaling behaviour whilst considering completely fixed approaches; 2) the burden of additional computational overhead, the inability to cope with the sudden changes in the workload behaviour and the preference of adaptability over reliability at runtime whilst considering the fully dynamic approaches; and 3) the lack of considering uncertainty aspects while designing auto-scaling solutions. This thesis seeks solutions to address these issues altogether using an integrated approach. Moreover, this thesis aims at the provision of qualitative elasticity rules. This thesis proposes a novel biologically-inspired switched feedback control methodology to address the horizontal elasticity problem. The switched methodology utilises multiple controllers simultaneously, whereas the selection of a suitable controller is realised using an intelligent switching mechanism. Each controller itself depicts a different elasticity policy that can be designed using the principles of fixed gain feedback controller approach. The switching mechanism is implemented using a fuzzy system that determines a suitable controller/- policy at runtime based on the current behaviour of the system. Furthermore, to improve the possibility of bumpless transitions and to avoid the oscillatory behaviour, which is a problem commonly associated with switching based control methodologies, this thesis proposes an alternative soft switching approach. This soft switching approach incorporates a biologically-inspired Basal Ganglia based computational model of action selection. In addition, this thesis formulates the problem of designing the membership functions of the switching mechanism as a multi-objective optimisation problem. The key purpose behind this formulation is to obtain the near optimal (or to fine tune) parameter settings for the membership functions of the fuzzy control system in the absence of domain experts’ knowledge. This problem is addressed by using two different techniques including the commonly used Genetic Algorithm and an alternative less known economic approach called the Taguchi method. Lastly, we identify seven different kinds of real workload patterns, each of which reflects a different set of applications. Six real and one synthetic HTTP traces, one for each pattern, are further identified and utilised to evaluate the performance of the proposed methods against the state-of-the-art approaches