Sangpetch, Akkarit

Preferred name

Sangpetch, Akkarit

Main Affiliation

School of Engineering

akkarit.sa@kmitl.ac.th

Now showing 1 - 7 of 7

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Structural vibration sensing to evaluate animal activity on a pig farm
(2018-11-04)
Bonde, Amelie
;
Sangpetch, Akkarit
;
Pan, Shijia
;
Woramontri, Woranun
;
Zhang, Pei
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RDI: Real digital identity based on decentralized pki
(2018-07-02)
Boontaetae, Pongpayak
;
Sangpetch, Akkarit
;
Sangpetch, Orathai
Establishing a digital identity plays a vital part in the digital era. It is crucial to authenticate and identify the users in order to perform online transactions securely. For example, internet banking applications normally require a user to present a digital identity, e.g., username and password, to allow users to perform online transactions. However, the username-password approach has several downsides, e.g., susceptible to the brute-force attack. Public key binding using Certificate Authority (CA) is another common alternative to provide digital identity. Yet, the public key approach has a serious drawback: all CAs in the browser/OS' CA list are treated equally, and consequently, all trusts on the certificates could be invalidated by compromising only a single root CA's private key.We propose a Real Digital Identity based approach, or RDI, on decentralized PKI scheme. The core idea relies on a combination of well-known parties (e.g., a bank, a government agency) to certify the identity, instead of relying on a single CA. These parties, collectively known as Trusted Source Certificate Authorities (TSCA), formed a network of CAs. The generated certificates are stored in the blockchain controlled by smart contract. RDI creates a digital identity that can be trusted based on the TSCAs' challenge/response and it is also robust against a single point of trust attack on traditional CAs.
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Thoth: Automatic resource management with machine learning for container-based cloud platform
(2017-01-01)
Sangpetch, Akkarit
;
Sangpetch, Orathai
;
Juangmarisakul, Nut
;
Warodom, Supakorn
Platform-As-A-Service (PaaS) providers often encounter fluctuation in computing resource usage due to workload changes, resulting in performance degradation. To maintain acceptable service quality, providers may need to manually adjust resource allocation according to workload dynamics. Unfortunately, this approach will not scale well as the number of applications grows. We thus propose Thoth, a dynamic resource management system for PaaS using Docker container technology. Thoth automatically monitors resource usage and dynamically adjusts appropriate amount of resources for each application. To implement the automatic-scaling algorithm, we select three algorithms, namely Neural Network, Q-Learning and our rule-based algorithm, to study and evaluate. The experimental results suggest that Q-Learning can the best adapt to the load changes, followed by a rule-based algorithm and NN. With Q-Learning, Thoth can save computing resources by 28.95% and 21.92%, compared to Neural Network and the rule-based algorithm respectively, without compromising service quality.
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Automated attribute inference for IOT data visualization service
(2019-01-01)
Sangpetch, Orathai
;
Sangpetch, Akkarit
;
Nartnorakij, Jittinat
;
Vejprasitthikul, Narawan
As data becomes vital to urban development of modern cities, Thailand has initiated a smart city project on pilot cities around the country. We have implemented an interoperable data platform for smart city to enable Internet of Things (IoT) data exchanges among organizations through APIs. One of the key success is that people can access and visual the data. However, data can have various attributes since standard has not completely established and adopted. Therefore, it is difficult to automate the process to achieve comprehensive visualization. Traditionally, we require developers to manually examine data streams to determine which data attribute should be presented. This process can be very time consuming. The visualization system must be manually updated whenever a source stream modifies its data attributes. This problem becomes an impediment to implement a scalable cloud-based visualization service. To mitigate this challenge, we propose an automated attribute inference approach to automatically select key visualizable attribute from heterogeneous streams of data sources. We have experimented with different data attribute selection algorithms, namely an empirical rule-based system and the chosen machine learning algorithms. We implement and evaluate the proposed selection algorithms through our 3D visualization program in order to get the feedback from users.
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Security context framework for distributed healthcare IoT platform
(2016-01-01)
Sangpetch, Orathai
;
Sangpetch, Akkarit
As Internet of Things (IoT) is entering mainstream, data privacy and security in information exchange becomes a major concern and a barrier for potential adopters, especially in healthcare regime. Information from health IoT devices and services is sensitive and confidential. While many existing works have proposed enhancements and security prospects for individual devices and components in IoT ecosystems, they still do not address the underlying challenge which is the lack of sufficient security within systems. Effective security has to be built-in, not patched upon. To efficaciously tackle the challenge in distributed IoT systems, we present a security context framework which applies adaptive security contexts to properly track data of interest. The proposed solution can achieve accountability and track information propagation, involving devices, services and parties who have responsibility and potential legal liability. This could help leverage not just technical but also policy and legal aspects to enable health IoT adoption.
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VDEP: VM Dependency Discovery in Multi-tier Cloud Applications
(2015-08-19)
Sangpetch, Akkarit
;
Kim, Hyong S.
The automatic discovery of dependencies in distributed Cloud applications is very useful for large scale deployments. Dependencies can be used to identify the anomalies due to errors, failures or the performance bottleneck in applications. Although existing dependency models can be useful, we believe more comprehensive dependency model would improve anomaly detection in large scale distributed applications. We propose a VM dependency discovery system and introduce dependency primitives that incorporate complex application behavior/interaction patterns. We also formulate response time characteristics for each dependency primitive. Using the component dependencies and traffic monitoring, we develop a stochastic model to estimate the response time probability distribution for components and overall application. We evaluate and validate our system with various production applications. Experiments show that we can accurately discover application dependencies and also predict not only the average response time but the 95th percentile response time within 8% of the actual response time.
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Dependable Sensing System for Pig Farming
(2019-11-01)
Ariyadech, Sripong
;
Bonde, Amelie
;
Sangpetch, Orathai
;
Woramontri, Woranun
;
Siripaktanakon, Wachirawich
We have deployed our smart sensing system in a real commercial farm complex for at least 17 months. One of critical factors to success of the sensing system deployment is the resilience and fault tolerance of the system in a harsh environment with unreliable infrastructure and limited access. Power interruptions and intermittent connectivity is not uncommon. Sensors must work even submerging in animal excretion. Correct and continuous streams of sensor data is essential to our smart farming analytics. To make our sensing system sustain such challenges, we have designed and implemented the system with a capability of self-rejuvenation to ensure system liveness. We also equip it with our anomaly detection system to examine small sensor connectivity logs in order to identify potential faulty or deteriorating sensors or external event abnormality with minimal manual intervention.

Sangpetch, Akkarit

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