Completed

1.     IoT-Powered Solar Irrigation System For Oil-Palm Prenursery, UIC210805, 2021-2023, Industrial Matching Grant YPPH-UMP, (Co-researcher), RM 66,000, Completed;

2.     IoT-Powered Solar Irrigation System For Oil-Palm Prenursery, RDU212801, 2021-2023, RDU Internal Grant, UMP (Co-researcher), RM 66,000, Completed;

3.     Elucidate The Mechanism Of Grasping Phenomenon Of Polyhedron Fingertip Irregular Geometry Object, RDU210317, 2021-2023, RDU Internal Grant, UMP (Co-researcher), RM 24,500, Completed;

4.     Dynamic of Rip Currents Under Extreme Storm Events and its Impact to the Public Safety, SRCJ2020, 2020-2022, External Grant, UPM-IIUM-UiTM Sustainable Research Grant, (Co-researcher), RM 20,000, Completed.

5.     Multi Microgrid Energy Management Using Fuzzy Type 2 Based Predictive Control, RDU210309, 2021-2023, RDU Internal Grant, UMP (Co-researcher), RM 23,000, Completed.

6.     Development of Monitoring and Control System Using Wireless IoT Communications for Energy Saving in The Workplace, PDU203229, 2020-2022, PDU Internal Grant, UMP (Project Leader), RM 30,500, Completed.

7.     Implementation Of an Enhanced Data Gathering Algorithm for Real-Time Internet of Thing Edge Device, Post Graduate Research Scheme PGRS200340, 2020-2023, UMP (Project Leader), RM 4,300, Completed.

8.     Development of Flood Monitoring and Warming System Based on Internet of Things using LoRaWAN Technology, Post Graduate Research Scheme PGRS200352, 2020-2022, UMP (Project Leader), RM 3, 300, Completed.

9.     Design And Implementation of Internet of Things Based Infant Healthcare System, PGRS210330, 2021-2023, UMP (Project Leader), RM 2, 200, Ongoing.

10.     Development and Fabrication of An IoT LoRa-based Parking Management System in the Holy Places of Mecca, MDP-IRI-10-2020, International Grant, King AbdulAziz University, Kingdom Saudi Arabia, (Principal Researcher), SR 100, 000, Completed.

11.     IoT based Intelligent Combinatorial Test Cases Generator System Based on Kidney Inspired Algorithm with Opposition Approach, PRGS/1/2019/ICT02/UMP/02/1 (RDU190803), 2019-2021 PRGS External Grant, Ministry of Education, Malaysia, (Co-researcher), RM 56,240, Completed.

12. Multipath Routing Protocol For Energy-Efficient And Reliable Data Transmission In The Internet Of Things Networks Using Multi-Criteria Based Mechanism, (Principal Researcher), FRGS

Duration: TWO years (2019-2021)  

Description: 

Internet of Things (IoT) enables global connectivity of a wide variety of objects with limited computing and battery capacity (e.g., sensors, RFID, smart-phones, appliances) to serve people in a collaborative manner automatically and intelligently for improving their life quality and growing the world’s economy. To successfully implement the IoT, routing protocols play crucial roles in providing effective and efficient communications and data transmission among IoT objects. Most of existing routing protocols are single path and mainly focus on ad hoc sensor networking scenarios; thus, are not responsive and robust enough for supporting IoT applications, especially with heavy traffic load. Multipath routing that aggregate multiple constraints into a comprehensive metric is important for achieving reliable data forwarding in the IoT. In order to tackle these challenges, this project aims to develop a new hybrid multipath routing algorithm based on multi-criteria metrics to provide a tradeoff between energy efficiency and Quality of Service (QoS) in IoT networks, thus, paving the way for efficient and green IoT communication. 

13. Design and Implementation of Flood Monitoring and Warning System Based on Internet of Things, (Principal Researcher)

Duration: TWO years (2019-2021)  

Description: 

Floods and excessive rainfall are unavoidable phenomena that can cause massive loss of people's lives and destruction of infrastructure. Flash floods rise rapidly in the flood prone area which result in properties damage, but the impact on human lives is rather preventable by the presence of monitoring systems. Although, there are many systems widely in practice by disaster management agencies in monitoring flood level. Most of these systems are very costly and sophisticated to be used and maintained. Furthermore, in most developing countries, the conventional flood gates in water canals are manually-operated, and suffer from the lack of real time monitoring of water levels which might lead to overflow in the canals and flash floods. Therefore, we have designed and fabricated an innovative, cost effective, and user-friendly flood monitoring and warning system (FMWS). The developed FMWS exploits an ultrasonic sensor with Arduino microcontroller to measure water level and identify the situation whether it is safe, cautious or dangerous based on predefined levels. The implemented prototype can act as a beacon for alerting individuals and/or authorities separately via SMS using GSM module or Bluetooth. However, our system effectiveness can be improved to support the reporting of data from multiple sensor monitoring unites simultaneously to a server or cloud storage via a central network gateway. Thus, this research project aims at enabling utilization of Internet of Things (IoT) technology for our existing FMWS using Long Range Wide Area Network (LoRaWAN). The selection of LoRa because it maintains wide range network connectivity, consumes low power and uses a low data transmission rate. As a result, the proposed IoT-Based flood monitoring network will collect information from multiple units spread in catchment areas and according to the risk with the water level, triggers early flood warnings to the authorities and population to take preventative action.  

14. IoT-Based Automation System for Smart Office (Principal Researcher), FLAGSHIP GRANT

Duration: ONE year (2019-2020)  

Description: 

Due to communications technology advancement, building automation system has grown in popularity.  Internet of Things applications are key players in IR 4.0 and there is a necessity to adopt and utilize such promising technology in Malaysia. Smart building is one of the IoT applications that allow the users to real-time monitor and control appliances over the Internet. Most existing systems are either locally or remotely controlled with the lack of a user-friendly interface. To overcome these limitations, this project proposes a hybrid (local and remote) IoT-based automation system which enables the transformation of any office into a smart connected asset which can be remotely monitored and controlled. A Long Range Wide Area Network (LoRaWAN) technology will be used as a gateway to the IoT Platform for connecting multiple LoRa nodes (smart sensors and actuators). Several sensors will be attached to under controlled appliances and placed throughout the office to track activities and events, and then send the sensed data wirelessly to a LoRa gateway. This allows the system support the independence of both mobile provider and user location. Raspberry pi and Arduino will act as microcontrollers for nodes and gateway, while LoRa and Wi-Fi as communication protocols. User can receive messages from sensors (temperature, humidity, motion, etc.) and send control commands (switching ON/OFF) to actuators over LoRa gateway regardless of location. The system then can be implemented to perform monitor and control of a selected office at FTeK to prove its effectiveness and gain awareness in the office conditions. The proposed system will be an enabler for easier, safer, and more comfortable life especially for very busy staffs. The expected outcomes of this project will contribute towards optimizing energy consumption and real time monitoring of offices in Smart Campus.

4. Energy and Mobility Awareness Multipath Routing Protocol for Large-Scale Smart City Deployments (Leader)

Duration: TWO years (2017-2019)  

Description: 

Ubiquitous smart mobile devices with embedded sensors are paving the way for Mobile Ad Hoc Networks (MANETs) that enable users to communicate directly, thereby playing a key role in Smart City and Internet of Things (IoT) applications. In such smart environments, people with smart devices (nodes) can freely self-organize and form self-configuring MANETs to forward data packets to a destination over multiple hops using routing protocol. However, mobility of nodes and limited energy resources are critical constraints for routing data between source-destination pairs. Therefore, routing protocol should be efficient in terms of the quality of service (QoS) and energy consumption to guarantee the data transmission over the wireless medium. This research project focuses on the implementation and evaluation of the performance of the proposed multipath battery and mobility aware routing scheme (MBMA-OLSR) in a Large-Scale Smart City deployment. The EXata network simulator is used to demonstrate and validate benefits of the innovative scheme under various simulation scenarios based on different parameters. This research will examine the effectiveness of the MBMA-OLSR routing scheme in Smart City scenarios, especially during the high mobility scenarios with heavy traffic load.

15. IoT based Intelligent Combinatorial Test Cases Generator System Based on Kidney Inspired Algorithm with Opposition Approach  (CO- Researcher), PRGS.

Duration: TWO years (2019-2021)  

Description: 

The Internet of Things (IoT) continues to grow as uniquely identifiable objects are added to the internet. However, the functionality and security of these devices has come into question. A common problem in IoT systems is the large number of the combinations of hardware, operational, and software configurations that required to be tested to ensure the IoT systems are free of bugs. Due to the limitations of time and cost, there is a need for testing efforts minimization but with sufficient testing efforts. Generating covering all software features a test suite is an NP-hard problem. However, there are already many existing tools either commercially or as research prototypes, but there is a leak to find one prototype that can address the support of many features while generating test list as well as none of them can be available online. The objective of this project is to develop IoT based intelligent combinatorial test cases generator systems utilizing kidney inspired algorithm and opposition approach. 

16. Formulation of Deep Learning Models Using FPGA and Non-Zero Approximation Algorithm for IoT Devices  (CO- Researcher)

Duration: ONE years (2019-2021)  

Description: 

Internet-of-Things has developed very fast recently, which can connect different devices to each other. These devices are usually embedded with software, sensors, electronics and some connective functions. Most of them are energy restrained, which means they have limited performance and requires low cost and less energy consuming. IoT devices are used to collect and transfer data to build the information network. In order to make sense of these raw data and derive some meaningful information from it, deep learning algorithms based on Artificial Neural Network (ANN) can be used. However, the current deep learning algorithms are not suitable for the IoT devices because of the high power consumption of deep learning due to the high performance and large memories are needed. So, it is necessary to optimize the deep learning algorithms to accommodate the resource-constrained IoT devices. Therefore, the problems we need to solve are how to recognize different patterns in a more energy-efficient way and how to improve the current deep learning methods to make it more suitable for IoT devices?. The aim of this study is to optimize the current deep learning algorithms to make it more suitable for the IoT devices, which means can be applied to low power consumption devices. The methodology of the study will be: 1) reduce the complex computation by analyzing the sparsity of two deep learning models; Deep Belief Networks (DBN) and Convolutional neural networks (CNN) and applying the near-zero approximation algorithm. 3) implement the ANN accelerator on FPGA. This is to help IoT devices to build a highly computational and data-intensive machine learning systems with low power consumption. This will help the low-power client devices to increase the computing efficiency and speed up the process of feature extraction and pattern recognition which will benefit the development of the IoT.

17. A Novel Hybrid Harmony Search Algorithm with Nomadic People Optimizer Algorithm for Global Optimization and Feature Selection

 (CO- Researcher)

Duration: ONE years (2019-2021)  

Description: 

Many metaheuristic inspired algorithms have been extensively employed to a variety of practical applications. Nomadic People Optimizer (NPO) is a recently proposed metaheuristic algorithm which mimics the nomadic people in searching for better place that has enough food and water. In this project, a hybrid Harmony Srearch Algorithm with NPO, namely Harmony Nomadic People Optimizer (HNPO) is proposed which combines the strengths of both the algorithms effectively with the aim to generate promising candidate solutions in order to achieve global optima efficiently. In order to validate the competence of the proposed hybrid HNPO, a widely utilized set of 23 benchmark test functions having a wide range of dimensions and varied complexities, will be used in this study. Furthermore, in order to demonstrate the applicability of the proposed algorithm at solving complex real-world problems,HNPO will also employed to solve the feature selection problem as well. The HNPO as feature selection approach is tested on 21 widely employed datasets acquired from the University of California at Irvine (UCI) repository. 

18.  New Pre-Processing and Feature Reduction Steps to Enhance the Deep Learning Algorithms Performance for Early-Stage Alzheimer’s Disease Detection (CO- Researcher)

Duration: TWO years (04/2018-04/2020)  

Description: 

Recently, deep learning becomes big trends in machine learning and had many recent successes in many fields. Deep learning is suffering from two limitations; the slow learning and high computation complexity. These limitations affect the deep learning performance.  The aim of this research project is to enhance the potential performance of the deep learning by overcome these limitations. This aim can be achieved by proposing to add a preprocessing and data reduction steps in the deep learning algorithm. The first enhancement is proposing a preprocessing step before entering the first layer (convolution 1). This preprocessing will be a combination of two methods. Feature Standardization where the input data will be standardization to have zero mean and unit variance. Then, the image will transfer to a bit-plane format where bit-plane requires smaller storage space than grayscale. Bit-plane 7 of the enhanced image is used as the input to the next step in deep learning.  The second enhancement step is applying a two-dimensional Principle Component (2DPCA) algorithm for the output of the preprocessing step. This is to reduce the dimensionality of the input data, where 2DPCA will decompose the original data and select the important information.  These two proposed steps are needed to improve the performance of the deep learning algorithm by significantly reducing the computation and speeding the process of the deep learning.  A deep learning with potential classification performance is the expected outcome after achieving our objectives.   This enhanced deep learning will be used to design an accurate early detection system for Alzheimer’s disease which officially listed as one of top ten leading causes of death in the world, especially in Malaysia. Since, the treatment process is financially costly, with an unclearly understood cause and no curative treatment, the early diagnosis of Alzheimer’s and its prodromal stage is very important. 

19. Modified Greedy Algorithm Strategy for Combinatorial Testing Problem with Constraints Supports (Member)

Duration: TWO years (08/2016-08/2018)  

Description: 

A software should be tested before released to the market to be sure that a software has been achieved the quality assurance measurement objectives. Therefore. One of the testing types is the combinatorial interaction testing (CIT) which is intended to detect the faults that may be occurred between the system feature interactions. The test case generation is the most active area of CIT research. The generation process of the efficient test suite with minimum size from the huge number of test cases can be considered as one of the optimization problem. Thus, several researchers have been addressing the combinatorial interaction testing issues by developing the various strategies based on a search-based approach or a pure-computational approach, although, these are useful, but most of them have a lack to support the constraints combinations during generation the test list. The aim of this research will introduced two new CIT strategies by adapting a greedy algorithm called Greedy Test generation Strategy(GTS). In addition, to enhance the GTS, some modifications to the greedy algorithm will be introduced. Then the new version of GTS strategy will be called a Modified Greedy Test generation Strategy (MGTS). An evaluation will be done to compare between the two new strategies’ results to evaluate their performances, and decide which is the best in generating a minimum final test suite size among them?

20. Ensemble Mobile Platform and Security Tools for Innovative Digital Economy Environment (Sub-Project: Enhancing protocol for peer to peer and peer to multipoint communications ), 2011-2015

Part of the project under LRGS (Long-Term Research Grant Scheme) Top-Down, sponsored by Malaysia's Ministry of Higher Education. 

Description: 

This project involves with researching and developing a new platform based on a new cutting edge technology that includes: a light-weight operating system that will provide the virtual environment; communication interface and protocols; middlewares and application layer interface. This project also studies new security and digital forensic algorithms and techniques required for performing vulnerability analysis, forensic analysis for audit and trust purposes, and security assessment in the proposed environment. 

In the proposed advanced computing environment, various computing devices using single or multiple interfaces and technology/standard need to communicate and cooperate efficiently with a certain level of security. These computing devices may be supported by different type of operating system with different features and level of security support. In order to ensure that all operations within the environment can be carried out seamlessly in ad hoc manner, there is a need for a common platform to be developed. 

The purpose of this project is to investigate and implement a new functional layered model of the common mobile platform with secured and trusted ensemble computing environment for Innovative Digital Economy Environment. This mobile platform includes a light-weight operating system for the environment that will provide the virtual environment, communication interface and protocols, and middleware for providing basic functionalities as well as to provide security, privacy and trusted environment. An Application Layer Interface will also be provided for the application developers. A resource and network management will be responsible for coordinating and managing the resources of the computing device efficiently and resiliently. Furthermore, new security, trustworthy and forensic algorithms and techniques will be developed with the aim for optimum protection. This can be achieved by developing a security tools which has capability to perform protection and prevention, vulnerability analysis, forensic analysis and security assessment in targeted applications.

21.  Framework for Radio Resource Management in OFDMA-Based Mobile Wireless Networks (Mobile WiMAX) (Member).


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