A P2P query algorithm based on Betweenness Centrality Forwarding in opportunistic networks

Author(s):  
Jianwei Niu ◽  
Yazhi Liu ◽  
Lei Shu ◽  
Bin Dai
2013 ◽  
Vol 9 (4) ◽  
pp. 331-345
Author(s):  
Jianwei Niu ◽  
Mingzhu Liu ◽  
Han-Chieh Chao

With the proliferation of high-end mobile devices that feature wireless interfaces, many promising applications are enabled in opportunistic networks. In contrary to traditional networks, opportunistic networks utilize the mobility of nodes to relay messages in a store-carry-forward paradigm. Thus, the relay process in opportunistic networks faces several practical challenges in terms of delay and delivery rate. In this paper, we propose a novel P2P Query algorithm, namely Betweenness Centrality Forwarding (PQBCF), for opportunistic networking. PQBCF adopts a forwarding metric called Betweenness Centrality (BC), which is borrowed from social network, to quantify the active degree of nodes in the networks. In PQBCF, nodes with a higher BC are preferable to serve as relays, leading to higher query success rate and lower query delay. A comparison with the state-of-the-art algorithms reveals that PQBCF can provide better performance on both the query success Ratio and query delay, and approaches the performance of Epidemic Routing (ER) with much less resource consumption.


2013 ◽  
Vol 24 (2) ◽  
pp. 230-242
Author(s):  
Liang-Yin CHEN ◽  
Zhen-Lei LIU ◽  
Xun ZOU ◽  
Zheng-Kun XU ◽  
Zhen-Qian GUO ◽  
...  

2014 ◽  
Vol 24 (3) ◽  
pp. 507-525 ◽  
Author(s):  
Lei WU ◽  
De-An WU ◽  
Ming LIU ◽  
Xiao-Min WANG ◽  
Hai-Gang GONG

2010 ◽  
Vol 30 (3) ◽  
pp. 723-728 ◽  
Author(s):  
Zhi REN ◽  
Yong HUANG ◽  
Qian-bin CHEN

2021 ◽  
Vol 104 ◽  
pp. 102208
Author(s):  
Samaneh Rashidibajgan ◽  
Thomas Hupperich ◽  
Robin Doss ◽  
Anna Förster

2021 ◽  
Vol 13 (12) ◽  
pp. 2333
Author(s):  
Lilu Zhu ◽  
Xiaolu Su ◽  
Yanfeng Hu ◽  
Xianqing Tai ◽  
Kun Fu

It is extremely important to extract valuable information and achieve efficient integration of remote sensing data. The multi-source and heterogeneous nature of remote sensing data leads to the increasing complexity of these relationships, and means that the processing mode based on data ontology cannot meet requirements any more. On the other hand, the multi-dimensional features of remote sensing data bring more difficulties in data query and analysis, especially for datasets with a lot of noise. Therefore, data quality has become the bottleneck of data value discovery, and a single batch query is not enough to support the optimal combination of global data resources. In this paper, we propose a spatio-temporal local association query algorithm for remote sensing data (STLAQ). Firstly, we design a spatio-temporal data model and a bottom-up spatio-temporal correlation network. Then, we use the method of partition-based clustering and the method of spectral clustering to measure the correlation between spatio-temporal correlation networks. Finally, we construct a spatio-temporal index to provide joint query capabilities. We carry out local association query efficiency experiments to verify the feasibility of STLAQ on multi-scale datasets. The results show that the STLAQ weakens the barriers between remote sensing data, and improves their application value effectively.


Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 33
Author(s):  
Enrique Hernández-Orallo ◽  
Antonio Armero-Martínez

One of the key factors for the spreading of human infections, such as the COVID-19, is human mobility. There is a huge background of human mobility models developed with the aim of evaluating the performance of mobile computer networks, such as cellular networks, opportunistic networks, etc. In this paper, we propose the use of these models for evaluating the temporal and spatial risk of transmission of the COVID-19 disease. First, we study both pure synthetic model and simulated models based on pedestrian simulators, generated for real urban scenarios such as a square and a subway station. In order to evaluate the risk, two different risks of exposure are defined. The results show that we can obtain not only the temporal risk but also a heat map with the exposure risk in the evaluated scenario. This is particularly interesting for public spaces, where health authorities could make effective risk management plans to reduce the risk of transmission.


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