Winston K.G. Seah

Victoria University of Wellington

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Bryan Ng

Victoria University of Wellington

Alvin Valera

Victoria University of Wellington

Hwee-Pink Tan

Singapore Management University

Ramesh Rayudu

Victoria University of Wellington

Adrián Pekár

Budapest University of Technology and Economics

Zhi Ang Eu

Agency for Science, Technology and Research

Pius W.Q. Lee

Agency for Science, Technology and Research

Marcelo H. Ang

National University of Singapore

Hwee‐Xian Tan

Sengkang General Hospital

Hang Yu

Beijing University of Posts and Telecommunications

Cooperative Institutions

National University of Singapore

Institute for Infocomm Research

Victoria University of Wellington

Agency for Science, Technology and Research

Inner Mongolia University

Ateneo de Manila University

Chinese Academy of Sciences

Nanyang Technological University

Beijing University of Posts and Telecommunications

University of Science and Technology of China

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Seamless Multi-hop Handover in IPv6 Based Hybrid Wireless Networks

Lecture notes in computer science (2005)

Tonghong Li Qunying Xie Jing Wang Winston K.G. Seah

Soft handover

Packet loss

10.1007/978-3-540-30582-8_92

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Citations (2)

A Bayesian Approach To Distributed Anomaly Detection In Edge AI Networks

IEEE Transactions on Parallel and Distributed Systems (2022)

Murugaraj Odiathevar Winston K.G. Seah Marcus Frean

The challenge of anomaly detection is to obtain an accurate understanding of expected behaviour which is intensified when the data are distributed heterogeneously. Transmitting raw data to a central site incurs high communication overhead and raises privacy issues. The concept of Edge AI allows computation to be performed at the edge site allowing for quick decision making in mission critical scenarios such as self-driving cars. A model is learnt locally and its parameters are transmitted and aggregated. However, existing methods of aggregation do not account for variance and heterogeneous distribution of data. They also do not consider edge constraints such as limited computational, memory and communication capabilities of edge devices. In this work, a fully Bayesian approach is employed by means of a Bayesian Random Vector Functional Link AutoEncoder being incorporated with Expectation Propagation for distributed training. Our anomaly detection system operates without any transmission of raw data, is robust under inhomogeneous network densities and under uneven and biased data distributions. It allows for asynchronous updates to converge in a few iterations and is a relatively simple neural network addressing edge constraints without compromising on performance as compared to existing more complex models.

Autoencoder

Edge device

10.1109/tpds.2022.3151853

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Citations (11)

Hybrid Mobile Wireless Sensor Network Cooperative Localization

Chee Kiat Seow Winston K.G. Seah Z. Liu

Although there has been much effort to improve localization in both wireless sensor networks and robotics, few have combined the localization techniques of both worlds so that they can cooperatively and mutually benefit each other. Therefore, we seek to design a localization algorithm based on statistical framework that can combine the localization methodology of both autonomous robotics and sensor networks, such that a two-way cooperative localization can be accomplished instead of the usual one-way only cooperation. To achieve this, we need to integrate three important research areas: wireless sensor networks, robotics and statistical filtering. In this paper, we will present the key elements of our approach and simulation results to validate the scheme. We prove that mobile robots and static sensor nodes can cooperatively help each other in their localization by first localizing the mobile robots with the static nodes, after which the mobile robots will provide feedback to the static nodes so that the static nodes can then refine their own location estimates.

10.1109/isic.2007.4450856

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Citations (17)

TARANTULAS: Mobility-enhanced Wireless Sensor-Actuator Networks

Winston K.G. Seah K.Z. Liu M.H. Jr.Jae-Deok Lim S.V. Rao

Amidst the numerous active research efforts in wireless sensor networks which aim to push beyond the limits, we aim to significantly enhance the effectiveness of wireless sensors by integrating controlled mobility to realize a networked system of mobile sensors that functions collectively as an integrated unit. The All-teRrain Advanced Network of Ubiquitous mobile Asynchronous Systems (TARANTULAS) project builds a system of networked wireless sensors and actuators carried on mobile robots that are able to operate in all kinds of terrain. To achieve this objective involves the integration of three key research areas: robotics, ad hoc networking and, localization and positioning. We not only use the robots to improve the connectivity and localization of the sensor networks, they concurrently search for targets. In this paper, we present key elements of the algorithms and technologies developed by the project. In particular, we show that the mobile robots and static nodes mutually support each other: robots move to fill the communication gaps to enhance connectivity and localization while static nodes serve as landmark nodes to help robots search for targets.

10.1109/sutc.2006.1636223

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Citations (7)

Autonomic link management in wireless backhaul networks with OpenFlow and traffic prediction

2022 IEEE/CIC International Conference on Communications in China (ICCC) (2017)

Maria Libunao Bryan Ng Winston K.G. Seah

Microwave radio is the predominant technology for backhaul or backbone networks, and its usage is expected to increase further. A way to increase capacity in this type of network is to aggregate multiple physical links into a single logical link. However, power is being wasted when the aggregated capacity is not being fully utilised. There are cost savings to be gained by only having the required number of physical links on. This paper presents a system that utilises extensions of the OpenFlow protocol to dynamically switch on/off physical links to meet capacity requirements while minimising power consumption while the Autoregressive Integrated Moving Average (ARIMA) model is used to predict the traffic load to be carried on aggregated (microwave) links. The proposed system is implemented on commercial-off-the-shelf microwave routers and experimentally validated on a testbed.

OpenFlow

Backhaul (telecommunications)

Link (geometry)

10.1109/iccchina.2017.8330357

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Citations (4)

Security threats and solutions in MANETs: A case study using AODV and SAODV

Journal of Network and Computer Applications (2012)

Jan von Mulert Ian Welch Winston K.G. Seah

Vulnerability

Distance-vector routing protocol

10.1016/j.jnca.2012.01.019

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Citations (105)

EABC: Energy-aware Centrality-based Caching for Named Data Networking in the IoT

Xingyun He Liu Hu Wuyungerile Li Alvin Valera Winston K.G. Seah

10.1109/wowmom60985.2024.00049

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Citations (0)

Routing over Best Links is not necessarily Better in Wireless Multi-hop Networks

Shutao Lu Wuyungerile Li Yintu Bao Alvin Valera Winston K.G. Seah

Geographic routing

Radio networks

Multipath routing

10.1109/iwqos61813.2024.10682958

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Admission Control with Latency Considerations for 5G Mobile Edge Computing

Zhang Ye Wuyungerile Li Winston K.G. Seah

The fifth generation (5G) mobile network is a new generation of broadband mobile communication technology with the potential to address the increasing demands of new user services and applications that have stringent low latency and high bandwidth requirements. Besides the enhanced mobile broadband (eMBB) which is an evolution of broadband services from previous generations, the ultra reliable low latency communication (URLLC) service comes with stringent delay requirements that are needed to support new applications like autonomous vehicles, augmented/virtual reality, etc. Mobile or multiple access edge computing (MEC) emerged to provide services and computing resources for users at the network edge to provide faster access speeds and lower end-to-end delays. To better meet user needs and maximize resource utilization, network resources need to be allocated and managed efficiently. Admission control for user requests is one of the methods used that can effectively prevent network congestion, thereby improving the overall performance of the system. In this paper, we propose a RED-based Admission Control with Latency Considerations (REDAL) algorithm for user admission control that aims to increase throughput, meet users' delay requirements and reduce packet discard rate. By explicitly accounting for user traffic delay constraints and bandwidth requirements, we are able to meet the strict delay constraints of URLLC traffic while meeting the bandwidth requirements of eMBB traffic. We validate our approach in an MEC scenario to demonstrate high resource utilization and also keeping the request discard rate below 20%.

Admission control

Mobile broadband

Mobile Edge Computing

10.1109/wowmom57956.2023.00031

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Citations (1)

Incorporating Like-Minded Peers to Overcome Friend Data Sparsity in Session-Based Social Recommendations

arXiv (Cornell University) (2024)

Chunyan An Yating Li Qiang Yang Winston K.G. Seah Zhixu Li

Session-based Social Recommendation (SSR) leverages social relationships within online networks to enhance the performance of Session-based Recommendation (SR). However, existing SSR algorithms often encounter the challenge of "friend data sparsity". Moreover, significant discrepancies can exist between the purchase preferences of social network friends and those of the target user, reducing the influence of friends relative to the target user's own preferences. To address these challenges, this paper introduces the concept of "Like-minded Peers" (LMP), representing users whose preferences align with the target user's current session based on their historical sessions. This is the first work, to our knowledge, that uses LMP to enhance the modeling of social influence in SSR. This approach not only alleviates the problem of friend data sparsity but also effectively incorporates users with similar preferences to the target user. We propose a novel model named Transformer Encoder with Graph Attention Aggregator Recommendation (TEGAARec), which includes the TEGAA module and the GAT-based social aggregation module. The TEGAA module captures and merges both long-term and short-term interests for target users and LMP users. Concurrently, the GAT-based social aggregation module is designed to aggregate the target users' dynamic interests and social influence in a weighted manner. Extensive experiments on four real-world datasets demonstrate the efficacy and superiority of our proposed model and ablation studies are done to illustrate the contributions of each component in TEGAARec.

10.48550/arxiv.2409.02702

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