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Meirong Liu

dblp:35/4171 · DBLP profile ↗
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8ranked-venue papers
3as first author
0since 2021 · last 2013
0000-0003-4785-1753ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 3 · 2 first-authorArtificial intelligence and machine learning · 1Systems, architecture and hardware · 1Software engineering, systems software and programming languages · 1Human-computer interaction and ubiquitous computing · 1Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Internet of things and sensor networks · 87% Edge and fog computing · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Internet of things and sensor networks
mobile agent
0.212013
Interoperable mobile agents in heterogeneous wireless sensor networks · SenSys 2013
Internet of things and sensor networks
wireless sensor network
0.212013
Interoperable mobile agents in heterogeneous wireless sensor networks · SenSys 2013

Methods — techniques the papers use, named apart from their topics

REST architectural principles · 0.2
YearPublicationVenuePosition
2013 Distributed Resource Discovery in the Machine-to-Machine Applications
abstract
One challenging problem in Machine-to-Machine (M2M) applications is to efficiently discover the resources provided by a huge number of heterogeneous devices. This paper proposes distributed resource discovery architecture (DRD) for M2M applications. The DRD supports heterogeneous devices in resource description registration and discovery of resource value. It achieves interoperability among heterogeneous devices in disparate networks and enables resource access from the Internet. In the DRD, a resource registration component is designed for storing resource descriptions, a resource discovery component is designed to retrieve resource values on behalf of clients after getting address information by looking up resource descriptions. The DRD utilizes a peer-to-peer overlay to distribute workload and avoid single point of failure. A real-world prototype is implemented and verified with a simple demo. Preliminary evaluation on response time of resource discovery is provided.
Meirong Liu, Teemu Leppänen, Erkki Harjula, Zhonghong Ou, Mika Ylianttila, Timo Ojala
MASS1
2013 Interoperable mobile agents in heterogeneous wireless sensor networks
abstract
We demonstrate interoperable mobile agents for language- and platform-independent wireless sensor network programming, with low-power resource-constrained embedded devices as static nodes and Android-based smartphones as mobile nodes, over disparate networks: 6LoWPAN and Wi-Fi. Representational state transfer architectural principles are applied in agent composition, control and migration and exposing the system resources to the Web: the devices, agents, sensor data, tasks and data processing results. The adaptable agent composition includes the task code in any programming language, the agent migrates according to a resource list and the state, i.e. the intermediate task result, represents the agent as a system resource. Mobile agents are then utilized in two tasks: first to collect light sensor data cooperatively in location, saving the mobile node battery whenever possible, and secondly the magnetometer sensor data with the scanned Wi-Fi access points' signal strength is used to detect groups of mobile nodes moving in the same direction in real-time.
Teemu Leppänen, Jose Alvarez Lacasia, Archana Ramalingam, Meirong Liu, Erkki Harjula, Pauli Närhi, Jani Ylioja, Jukka Riekki, Kaoru Sezaki, Yoshito Tobe, Timo Ojala
SenSys4
2013 Mobile Agents for Integration of Internet of Things and Wireless Sensor Networks
abstract
We demonstrate interoperable mobile agents to integrate Internet of Things and wireless sensor networks with resource-constrained low-power embedded networked devices. We introduce adaptable composition for the mobile agent, complying with the Representational State Transfer principles, which are then used for agent migration, controlling the agent and exposing the data, system resources, tasks and services, to the Web. We gather requirements for the system and heterogeneous networked devices and present an application programming interface to enable mobile agents in these systems. The agents are demonstrated in a real-world prototype with smartphones and embedded networked devices, where we utilize a proxy component to expose system resources to the Web for human-machine interactions. Spanning over disparate networks and protocols, the proxy translates messages including the agent composition, between HTTP and Constrained Application Protocol. Lastly, we suggest an evaluation method for the agent communication and migration costs, considering the different types of system resources and utilization.
Teemu Leppänen, Meirong Liu, Erkki Harjula, Archana Ramalingam, Jani Ylioja, Pauli Närhi, Jukka Riekki, Timo Ojala
SMC2
2013 Distributed resource directory architecture in Machine-to-Machine communications
abstract
Machine-to-Machine (M2M) communications emerge to achieve ubiquitous communications among all the networked devices. One challenging problem in M2M applications is to discover the resources provided by the devices efficiently. This challenge arises from two perspectives: (1) a large number of heterogeneous devices co-exist and most of them are constrained devices (e.g., limited processing capability), (2) different communication protocols are utilized to get access to different types of resources provided by the devices (e.g., humidity information provided by a sensor). This paper proposes distributed resource directory architecture for M2M applications, referred to as DRD4M. The DRD4M supports heterogeneous devices using HTTP and CoAP protocols for resource registration and lookup. This enables the interoperability among heterogeneous devices and resource access to constrained devices from disparate network including the Internet. The DRD4M introduces two components: a resource registration component for registering resources and a resource lookup component with caching functionality for handling resource lookup with filtering. A peer-to-peer (P2P) overlay is introduced in DRD4M to connect resource peers to avoid single point of failure. A real-world prototype is implemented and is verified with a demo application. Preliminary performance evaluation in terms of response time of resource lookup is provided.
Meirong Liu, Teemu Leppänen, Erkki Harjula, Zhonghong Ou, Archana Ramalingam, Mika Ylianttila, Timo Ojala
WiMob1
2011 Wavelet Neural Network Approach for Testing of Switched-Current Circuits
Jierong Guo, Meirong Liu
J. Electron. Test.3
2011 Super-peer-based coordinated service provision
Meirong Liu, Timo Koskela 0001, Zhonghong Ou, Jiehan Zhou, Jukka Riekki, Mika Ylianttila
J. Netw. Comput. Appl.1
2010 User Experience in Added Value Location-Based Mobile Music Service
abstract
In this paper, the user experience in added value location-based mobile music service called the City Night Life (CNL) is evaluated and analyzed. The CNL service provides recommendations about entertainment premises in the proximity by matching their music styles with the user's personal music preferences. Entertainment premises are shown on a map interface and are marked with colors according to their match. The paper presents the components of the CNL service, the music profile, matching algorithm and the map-based user interface in a web browser. The user evaluation with 53 test users assessed the feasibility and business potential of the CNL service. Data was collected with both quantitative questionnaires and short interviews. The results indicate that the CNL service will be most useful in relatively large, unfamiliar cities. Furthermore, the matching algorithm was perceived to perform quite well.
Timo Koskela 0001, Sari Järvinen, Meirong Liu, Mika Ylianttila
ICWS3
2007 Probabilistic Neural Network Based Method for Fault Diagnosis of Analog Circuits
Yanghong Tan, Meirong Liu
ISNN (3)3