Dr Feng Chen

Job: Senior Lecturer

Faculty: Technology

School/department: School of Computer Science and Informatics

Research group(s): Cyber Technology Institute (CTI) (Software Technology Research Laboratory STRL))

Address: De Montfort University, The Gateway, Leicester, LE1 9BH, United Kingdom

T: +44 (0)116 257 7860

E: fengchen@dmu.ac.uk

W: http://www.dmu.ac.uk/cti


Personal profile

I was awarded my BSc, Mphil and PhD in computer science at Nankai University, Dalian University of Technology and De Montfort University in 1991, 1994 and 2007. My PhD thesis focuses on modernising legacy systems through model construction and transformation. A unified approach is proposed in the context of Model Driven Architecture (MDA) to extract different level of models from legacy source code. 

I have been an active researcher in the area of software engineering and knowledge engineering, focusing on its emerging themes including Service Oriented Architecture, Model Driven Architecture, Cloud/Edge Computing, Mobile Computing, Internet of Things and Cyber Physical System, with the long term aim of building a coherent set of conceptual frameworks, methods and tool support for the development of Smart Environments and Ambient Assisted Living Systems. 

I have a strong background in a combination of academic research and industrial application. I have gained invaluable understanding of system software, distributed systems, real-time systems, critical systems and security systems through many projects. As research outputs, I have published 74 research papers in refereed conferences and Journals.

Research group affiliations

Cyber Technology Institute

Publications and outputs 

  • An Experimental Study of Learning Behaviour in an ELearning Environment
    An Experimental Study of Learning Behaviour in an ELearning Environment Alhasan, K.; Chen, Liming; Chen, Feng To reach an adaptive eLearning course, it is crucial to control and monitor the student behaviour dynamically to implicitly diagnose the student learning style. Eye tracing can serve that purpose by investigate the gaze data behaviour to the learning content. In this study, we conduct an eye tracking experiment to analyse the student pattern of behaviour to output his learning style as an aspect of personalisation in an eLearning course. We use the electroencephalography EEG Epoc that reflects users emotions to improve our result with more accurate data. Our objective is to test the hypothesis whether the verbal and visual learning Styles reflect actual preferences according to Felder and Silverman Learning Style Model in an eLearning environment. Another objective is to use the outcome presented in this experiment as the starting point for further exhaustive experiments. In this paper, we present the actual state of our experiment, conclusions, and plans for future development.
  • A semantics-based approach to sensor data segmentation in real-time Activity Recognition
    A semantics-based approach to sensor data segmentation in real-time Activity Recognition Triboan, Darpan; Chen, Liming; Chen, Feng; Wang, Zumin Activity Recognition (AR) is key in context-aware assistive living systems. One challenge in AR is the segmentation of observed sensor events when interleaved or concurrent activities of daily living (ADLs) are performed. Several studies have proposed methods of separating and organising sensor observations and recognise generic ADLs performed in a simple or composite manner. However, little has been explored in semantically distinguishing individual sensor events directly and passing it to the relevant ongoing/new atomic activities. This paper proposes Semiotic theory inspired ontological model, capturing generic knowledge and inhabitant-specific preferences for conducting ADLs to support the segmentation process. A multithreaded decision algorithm and system prototype were developed and evaluated against 30 use case scenarios where each event was simulated at 10sec interval on a machine with i7 2.60GHz CPU, 2 cores and 8GB RAM. The result suggests that all sensor events were adequately segmented with 100% accuracy for single ADL scenarios and minor improvement of 97.8% accuracy for composite ADL scenario. However, the performance has suffered to segment each event with the average classification time of 3971ms and 62183ms for single and composite ADL scenarios, respectively. Department of Information Engineering, Dalian University, China 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.
  • Users’ Privacy Concerns in IoT based Applications
    Users’ Privacy Concerns in IoT based Applications Psychoula, Ismini; Singhy, D.; Chen, Liming; Chen, Feng; Holzingerz, A.; Ning, H. In recent years user privacy has become an important aspect in the development of the Internet of Things (IoT) services due to their privacy invasive nature. However, there has been comparatively little research so far that aims to understanding users’ notion of privacy in connection with IoT. In this work, we aim to understand how and if contextual factors affect users’ privacy perceptions of IoT environments. To ascertain privacy perceptions, we deployed a public online survey (N=236) and contacted interviews (N=41) to explore factors that could have an influence. Although a lot of the participants identified privacy risks in IoT and rated the collected information items with high privacy ratings, we find that quite a large number of participants would still decide to have the offered IoT service if they find it useful and practical for their daily lives despite the infringement on their privacy. We conclude by highlighting and analyzing the qualitative comments of the participants and suggest possible solutions for the identified issues.
  • Mining Learning Styles for Personalised eLearning
    Mining Learning Styles for Personalised eLearning Alhasan, K.; Chen, Liming; Chen, Feng In drive of inspecting user behaviour in an adaptive eLearning system, we investigate the eye gaze movement combined with the emotional state of learners with our experiment using eye tracker and the electroencephalography (EEG) device. Not only the gaze behaviour indicates type of learning style, but also shows the set of cognitive activities and emotions that can contribute in learning process. This paper discusses the first set of results of our experiment associated with monitoring the eye gaze behaviour. Six postgraduate students participated in this study. Two key findings were identified by combining many methods including boxplots and ANOVA. First, there is no effect on the difficulty level on the visual/ verbal learner behaviour. Second, there is no difference in verbal/visual learner behavior towards their preferences in different learning materials. Also, the paper describes the implemented experiment approach in our smart lab. And finally, this paper exposes our analysis and investigation of learning styles relation with the different courses, and how the eye behaviour is affected accordingly. The results of EEG data will be analysed and correlated to our findings in the next piece of work.
  • Rising Star Evaluation in Heterogeneous Social Network
    Rising Star Evaluation in Heterogeneous Social Network Ding, F.; Liu, Y.; Chen, X.; Chen, Feng Rising stars are junior individuals in the social network who will have high impacts with time accumulation. Rising star evaluation has become a research hotspot in network analysis area recently, which is helpful for decision support, resource allocation, and other practical problems. As a traditional social network, academic social network is stressed because of its heterogeneity and regular data structure. In this paper, we assume there are inside factors influencing individuals behaviors. We process the network parameters and mine inner factors via factor analysis, and train a decision tree to evaluate furture impact. Experiment is processed on America Physics Society(APS) dataset, and the result shows our method has better performance than state-of-the-arts. School of Software, Dalian University of Technology, Dalian, China. School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou, China. Open Access
  • Privacy Modelling and Management for Assisted Living within Smart Homes
    Privacy Modelling and Management for Assisted Living within Smart Homes Psychoula, Ismini; Chen, Liming; Chen, Feng Ambient Assisted Living (AAL) technologies create intelligent systems to assist the aging population for a healthier and safer life in their living environment. Such systems usually offer context-aware, personalized and adaptive services. However, these kinds of systems make extensive and intensive use of personal data, which makes privacy protection a critical issue. In this paper, we propose a framework for privacy modeling computation and management for AAL within Smart Homes. We analyze the privacy features in the smart home that affect the privacy of the users. Based on these features a metric is developed to compute the sensitivity of the collected information and consequently the potential privacy risk. A simple implementation of the proposed framework is then applied to a real world smart home living environment at Great Northern Haven, in which data were collected and the framework was evaluated. This study offers an effective and practical approach to evaluate the privacy risk of users and proposes a metric that can be used for access control and recommendation of privacy settings to the users of the AAL environments. 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
  • Wearable accelerometer based extended sleep position recognition
    Wearable accelerometer based extended sleep position recognition Fallmann, S.; van Veen, R.; Chen, Liming; Walker, D.; Chen, Feng; Pan, C. Sleep positions have an impact on sleep quality and therefore need to be further analyzed. Current research on position tracking includes only the four basic positions. In the context of wearable devices, energy efficiency is still an open issue. This research presents a way to detect eight positions with higher granularity under energy efficient constraints. Generalized Matrix Learning Vector Quantization is used, as it is a fast and appropriate method for environments with limited computation resources, and has not been seen for this kind of application before. The overall model trained on individuals performs with an averaged accuracy of 99.8%, in contrast to an averaged accuracy of 83.62% for grouped datasets. Real world application gives an accuracy of around 98%. The results show that energy efficiency will be feasible, as performance stays similar for lower sampling rate. This is a step towards a mobile solution which gives more insight in person's sleep behaviour. 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
  • Semantic modelling for learning styles and learning material in an e-learning environment
    Semantic modelling for learning styles and learning material in an e-learning environment Alhasan, K.; Chen, Liming; Chen, Feng Various learners with various requirements have led to the raise of a crucial concern in the area of e-learning. A new technology for propagating learning to learners worldwide, has led to an evolution in the e-learning industry that takes into account all the requirements of the learning process. In spite of the wide growing, the e-learning technology is still lacking the ability to achieve the best personalised learning path for each learner resulting in performance dissatisfaction. Recent research indicates that each learner has a unique way of learning that leads to different preferences in the selection of the learning resources. Thus, the learning material must be tailored for the individual learners in order to meet their own personal needs. In this paper, we present a novel approach for designing a model for an adaptive e-learning course and learning styles based on ontology and semantic web technologies. In this approach, we build an adaptive student profile through analysing the pattern of the learner s behaviour while using the e-learning course in accordance to the Felder- Silverman learning style model (FSLSM). 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 URI link.
  • Reality and Perception: Activity monitoring and data collection within a real-world smart home
    Reality and Perception: Activity monitoring and data collection within a real-world smart home Fallmann, S.; Psychoula, Ismini; Chen, Liming; Chen, Feng; Doyley, J.; Triboan, Darpan Smart home technologies have been developing rapidly in the last few years. However, there is still a lack of annotated rich datasets that can be used for different analysis purposes by researchers. The motivation for this study is driven by the need of self-management for chronic disease patients and the often neglected privacy aspects. The study describes the extension of an existing smart home environment at Great Northern Haven (GHN) with ambient and wearable devices. The discussed principles include the design of the experiment, data collection strategies and encountered challenges in regards to the sensors, connection problems and occupation with multiple inhabitants. The file attached to this record is the author's final peer reviewed version.
  • A Game-Theoretic Based QoS-Aware Capacity Management for Real-Time EdgeIoT Applications
    A Game-Theoretic Based QoS-Aware Capacity Management for Real-Time EdgeIoT Applications Aliyu, Suleiman Onimisi; Chen, Feng; He, Ying; Yang, Hongji More and more real-time IoT applications such as smart cities or autonomous vehicles require big data analytics with reduced latencies. However, data streams produced from distributed sensing devices may not suffice to be processed traditionally in the remote cloud due to: (i) longer Wide Area Network (WAN) latencies and (ii) limited resources held by a single Cloud. To solve this problem, a novel Software-Defined Network (SDN) based InterCloud architecture is presented for mobile edge computing environments, known as EdgeIoT. An adaptive resource capacity management approach is proposed to employ a policy-based QoS control framework using principles in coalition games with externalities. To optimise resource capacity policy, the proposed QoS management technique solves, adaptively, a lexicographic ordering bi-criteria Coalition Structure Generation (CSG) problem. It is an onerous task to guarantee in a deterministic way that a real-time EdgeIoT application satisfies low latency requirement specified in Service Level Agreements (SLA). CloudSim 4.0 toolkit is used to simulate an SDN-based InterCloud scenario, and the empirical results suggest that the proposed approach can adapt, from an operational perspective, to ensure low latency QoS for real-time EdgeIoT application instances.

Click here to view a full listing of Feng Chen's publications and outputs.

Key research outputs


Research interests/expertise

Software Engineering

Software Re-engineering

Distributed Computing

Software Evolution

Software Architecture

Program/Model Transformation

Domain Specific Modelling

Knowledge Engineering

Areas of teaching

Software Engineering 


2015 to 2016, PGCertHE, De Montfort University, UK

Apr. 2003 to Jul. 2007, Doctor of Philosophy in Software Engineering, Software Technology Research Laboratory (STRL), De Montfort University, UK

Sep. 1991 to Jul. 1994, Master Degree in Computer Science, Computer Science and Engineering Department, Dalian University of Technology, China

Sep. 1987 to Jul. 1991, Bachelor Degree in Computer Science, Computer Science Department, NanKai University, China

Courses taught

Postgraduate Course:

Advanced Requirements Engineering and Software Architecture/ Requirement Analysis and Cloud-based Systems

Software Project Management and Testing/ Software Quality Assurance and Testing

Undergraduate Course:

Server and Storage Solutions,

Lab of Java Programming/ OO Software Design 

Membership of professional associations and societies

IEEE Membership


SOCRADES EU Project, EU Research project, 03/2009-03/2010. The developed engineering tool was used by Ford UK Ltd.

SPADzeroTM Sign Communication Control System for Dailys UK Ltd., 06/2007-12/2007. SPADzeroä is a stand-alone executable software product for data distribution to remote display.

FermaT Integrated Platform (FIPfor Software Maintenance Ltd. (SML), UK, 07/2005–06/2007. The software product has been used for program analysis, demonstration and training by SML.

SIFT Project for Fuji Film Company, 04/2004-10/2004. The developed image processing software package was tested and delivered to Fuji Film Company.

PLC Simulation System for 2000t/d Cement Product Line, 06/2002-12/2002. The system was used by Beijing Building Materials Industry School for teaching and training.

MIS for Environmental Protection Administration, 08/2001-12/2002. The system was installed in Dalian and Dandong Bureau of Environmental Protection Administration. The software won the Second Class of Dalian Information System Application Prize, China.

Multi-interface Virtual Operating System Based on Windows (VRTOS), 02/2001-02/2002. VRTOS was used for developing W-CDMA and 3G protocol stack in Dalian HuaChang Electronics & Communication Technology Co. Ltd., China.

IC Card Tax Declaration System, 06/1998-12/1999. The system was filed as a practical new style patent of China, titled “General Smart Card Declaration Terminal” (ZL 96 2 39055.0).

Counselling System for Entry-Exit Inspection and Quarantine, 08/1995-02/1996. The Software was awarded Golden Snake Prize in 1996. The software was used by Dalian Entry-Exit Inspection and Quarantine for more than 10 years.

Phosphate Elimination Control System for Prominent GMBH, Germany, Research project, Part of BMFT Grant (02WA9029/1). 01/1995-07/1995. The neuro-fuzzy learning algorithm based controller was installed in Heldelberg sewage plant, Germany to improve the effort of biological phosphate elimination and reduces 30% usage of the chemical dosage.

Conference attendance

Local Chair, IEEE Smart World Congress (SWC2019)

General Chair2nd Workshop on advanced Technologies for Smarter Assisted Living solutions: Towards an open Smart Home infrastructure (SmarterAAL'19)

Program Co-Chair, IEEE International Conference on Ubiquitous Intelligence and Computing (UIC 2018), IEEE Outstanding Leadership Award

Program Chair, IEEE International Workshop on Software Cybernetics 2017 (IWSC2017)

Program Chair, IEEE International Workshop on Software Cybernetics 2016 (IWSC2016)

Technical program committee member, more than 10 international conferences

Key research outputs

(IF=4.639) Triboan, D. Chen, L., Chen, F., Wang, Z. (2019) A semantics-based approach to sensor data segmentation in real-time Activity Recognition. Future Generation Computer Systems. 93, 224-236

(IF=3.557) Ding F., Liu Y., Chen X., Chen F. (2018). Rising Star Evaluation in Heterogeneous Social Network. IEEE Access. 6, 29436-29443

(IF=3.241) Zhang, Y., Guo, H., Chen, F., Yang, H. (2017) Weighted kernel mapping model with spring simulation based watershed transformation for level set image segmentation. Neurocomputing. 249, 1-18

(IF=2.278) Yang, H. Chen, F., Aliyu, Y. (2017) Modern software cybernetics: New trends. Journal of Systems and Software, 124, 169-186

(IF=1.924) Triboan, D. Chen, L., Chen, F., Wang, Z. (2017) Semantic segmentation of real-time sensor data stream for complex activity recognition. Personal and Ubiquitous Computing. 21(3), 411-425

Cope, J., Siewe, F., Chen, F., Maglaras, L., Janicke, H. (2017) On data leakage from non-production systems, Information & Computer Security, 25(4), 454-474, Emerald

Cope, J., Maglaras, L., Siewe, F., Chen, F., Janicke (2017) A Framework for Minimising Data Leakage from Non-Production Systems. Book chapter, In book: Bigdata Analytics: Tools and Technology for Effective Planning. Chapman and Hall/CRC.

Psychoula, I., Chen, L., Chen, F. (2017) “Privacy modelling and management for assisted living within smart homes”, e-Health Networking, Applications and Services (Healthcom), IEEE. (Best Paper)

(IF=1.063) Triboan, D. Chen, L., Chen, F., Wang, Z. (2016) Towards a Service-oriented Architecture for a Mobile Assistive System with Real-time Environmental Sensing. Tsinghua Science & Technology. 21(6), 581-597

Chen, F., Al-Bayatti, A., Siewe, F. (2016) Context-aware Cloud Computing for Personal Learning Environment. Book chapter, In book: Cloud-Based STEM Education for Improved Learning Outcomes, IGI Global.

(IF=0.613) Chen, F. et al. (2014) A Precondition-based Approach to Workflow Oriented Software Re-engineering. Computer Science and Information Systems, 11(1), 1-27

AlHakami, H., Chen, F., Janicke, H. (2014) An Extended Stable Marriage Problem Algorithm for Clone Detection. International Journal of Software Engineering & Applications (IJSEA). 5(4), 103-122

AlHakami, H., Chen, F., Janicke, H. (2014) SMP-Based Approach for Intelligent Service Interaction, International Journal of Advanced Computer Science and Applications (IJACSA). Special Issue on Extended Papers from Science and Information Conference 2014, 124-131

Current research students

Alhasan, Khawla  Second Supervisor

Aliyu, Suleiman  First Supervisor

Da Silva Machado, Eduardo  Second Supervisor

Fallmann, Sarah  Second Supervisor

Ma, Fengbao  First Supervisor

Psychoula, Ismini  Second Supervisor 

Singh, Lakhvir  Second Supervisor 

Triboan, Darpan  Second Supervisor

Yagnik, Tarjana  First Supervisor

Zheng, Yumei  Second Supervisor

Externally funded research grants information

"Acrossing - Advanced TeChnologies and PlatfoRm fOr Smarter ASsisted LivING" (2016-2019, € 3.8M, DMU € 810K). Co-I, Coordinator of EU Horizon 2020 project.

"Affordable Virtual Engineering Tools" (2014-2015, £60K). PI, TSB KTP project.

Internally funded research project information

“A Feasibility Study of the Use of Smart Textiles to Support Assistive Living” (2017-2018, £2K). PI, internal RIF project, De Montfort University, UK

Ÿ“Marketing of Professional Training Courses within Cyber Technologies” (3 months, 2016). Supervisor, Internship project, De Montfort University, UK

Published patents

"General Smart Card Declaration Terminal" (1998). A Practical New Style Patent of China (ZL 96 2 39055.0, First Order in Four Inventers).

Professional esteem indicators


IEEE Transactions on Reliability; Journal of Systems and Software (Elsevier);

Personal and Ubiquitous Computing Journal (Springer);

Journal of Software: Evolution and Process (Wiley);

Arabian Journal for Science and Engineering;

Journal of Ambient Intelligence and Humanized Computing (Springer);

Journal of Computational Methods in Sciences and Engineering (IOS). 


PhD external examiner: Masoud Khamallag, University of Bradford, Towards an Improved Framework of E-Government Implementation in Chaotic Environment; Proposed Social Collaboration Model. 2018

PhD external examiner: Richard Greenwell, Edinburgh Napier University, An Approach to the Semantic Intelligence Cloud. 2017

PhD internal examiner at De Montfort University: more than 10 students.


External academic panel member for Review of Course Provision: Postgraduate Computer Science and Technology, University of Bedfordshire, July 2016.

External academic panel member for a validation of BSc (Hons) Software Engineering and Network Engineering programmes at University Campus Suffolk in Ipswich, April 2015.


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