Abstract
While smart objects begin to permeate our environment, enabling interaction between smart objects in the Internet of Things (IoT) becomes a challenge. Opportunistic networks (OPPNET) are a form of mobile ad-hoc networks. In OPPNET, the communications between smart objects intermittently occur when an object contacts another. OPPNET plays an important role as an enabler for communication in IoT. In OPPNET, in order for nodes to forward message to destination, they not only need to transfer messages but also store and carry messages as relay nodes. The forwarding algorithms in opportunistic networks need to exploit human and social characteristics such as mobility pattern and social relationship. In this paper, we propose long-term location patterns based forwarding scheme (LTLP) which utilizes long-term movement pattern for predicting future location probability. In the proposed scheme, each nodes records its own location pattern and analyze the pattern of its movement. After the analysis of movement pattern is finished, we create location tree of nodes to forward the message to destination. We analyze the proposed scheme on the NS-2 network simulator with the home-cell community-based mobility model (HCMM). Experimental results show that the proposed scheme outperforms most known forwarding schemes in balancing network traffic and transmission delay.
| Original language | English |
|---|---|
| Title of host publication | IEEE World Forum on Internet of Things, WF-IoT 2018 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 401-406 |
| Number of pages | 6 |
| Volume | 2018-January |
| ISBN (Electronic) | 9781467399449 |
| DOIs | |
| Publication status | Published - 2018 May 4 |
| Event | 4th IEEE World Forum on Internet of Things, WF-IoT 2018 - Singapore, Singapore Duration: 2018 Feb 5 → 2018 Feb 8 |
Other
| Other | 4th IEEE World Forum on Internet of Things, WF-IoT 2018 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 18/2/5 → 18/2/8 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
- Hardware and Architecture
- Information Systems and Management
- Safety, Risk, Reliability and Quality
Cite this
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Long-term location pattern based forwarding scheme in opportunistic networks. / Jang, Kiyoung; Park, Jiho; Yang, Sung-Bong.
IEEE World Forum on Internet of Things, WF-IoT 2018 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 401-406.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Long-term location pattern based forwarding scheme in opportunistic networks
AU - Jang, Kiyoung
AU - Park, Jiho
AU - Yang, Sung-Bong
PY - 2018/5/4
Y1 - 2018/5/4
N2 - While smart objects begin to permeate our environment, enabling interaction between smart objects in the Internet of Things (IoT) becomes a challenge. Opportunistic networks (OPPNET) are a form of mobile ad-hoc networks. In OPPNET, the communications between smart objects intermittently occur when an object contacts another. OPPNET plays an important role as an enabler for communication in IoT. In OPPNET, in order for nodes to forward message to destination, they not only need to transfer messages but also store and carry messages as relay nodes. The forwarding algorithms in opportunistic networks need to exploit human and social characteristics such as mobility pattern and social relationship. In this paper, we propose long-term location patterns based forwarding scheme (LTLP) which utilizes long-term movement pattern for predicting future location probability. In the proposed scheme, each nodes records its own location pattern and analyze the pattern of its movement. After the analysis of movement pattern is finished, we create location tree of nodes to forward the message to destination. We analyze the proposed scheme on the NS-2 network simulator with the home-cell community-based mobility model (HCMM). Experimental results show that the proposed scheme outperforms most known forwarding schemes in balancing network traffic and transmission delay.
AB - While smart objects begin to permeate our environment, enabling interaction between smart objects in the Internet of Things (IoT) becomes a challenge. Opportunistic networks (OPPNET) are a form of mobile ad-hoc networks. In OPPNET, the communications between smart objects intermittently occur when an object contacts another. OPPNET plays an important role as an enabler for communication in IoT. In OPPNET, in order for nodes to forward message to destination, they not only need to transfer messages but also store and carry messages as relay nodes. The forwarding algorithms in opportunistic networks need to exploit human and social characteristics such as mobility pattern and social relationship. In this paper, we propose long-term location patterns based forwarding scheme (LTLP) which utilizes long-term movement pattern for predicting future location probability. In the proposed scheme, each nodes records its own location pattern and analyze the pattern of its movement. After the analysis of movement pattern is finished, we create location tree of nodes to forward the message to destination. We analyze the proposed scheme on the NS-2 network simulator with the home-cell community-based mobility model (HCMM). Experimental results show that the proposed scheme outperforms most known forwarding schemes in balancing network traffic and transmission delay.
UR - http://www.scopus.com/inward/record.url?scp=85050380091&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050380091&partnerID=8YFLogxK
U2 - 10.1109/WF-IoT.2018.8355164
DO - 10.1109/WF-IoT.2018.8355164
M3 - Conference contribution
AN - SCOPUS:85050380091
VL - 2018-January
SP - 401
EP - 406
BT - IEEE World Forum on Internet of Things, WF-IoT 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -