An Efficient Routing Algorithm Based on Interest Similarity and Trust Relationship Between Users in Opportunistic Networks

2018 ◽  
pp. 273-284
Author(s):  
Xueyang Qin ◽  
Xiaoming Wang ◽  
Yaguang Lin ◽  
Liang Wang ◽  
Lichen Zhang
Keyword(s):  
Routing Algorithm ◽  
Opportunistic Networks ◽  
Trust Relationship ◽  
Interest Similarity

scholarly journals A Routing Algorithm Based on Network Connectivity Assessment for Maritime Opportunistic Networks

2021 ◽  
Vol 187 ◽  
pp. 200-205
Author(s):  
Meiqi Ji ◽  
Xuerong Cui ◽  
Juan Li ◽  
Tong Xu ◽  
Shibao Li ◽  
...  
Keyword(s):  
Routing Algorithm ◽  
Network Connectivity ◽  
Opportunistic Networks

scholarly journals An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1915
Author(s):  
Shupei Chen ◽  
Zhigang Chen ◽  
Jia Wu ◽  
Kanghuai Liu
Keyword(s):  
Social Networks ◽  
Data Transmission ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Routing Algorithm ◽  
Opportunistic Networks ◽  
Membership Functions ◽  
Inference System ◽  
Fuzzy Input ◽  
Delay Tolerant

In opportunistic networks, the requirement of QoS (quality of service) poses several major challenges to wireless mobile devices with limited cache and energy. This implies that energy and cache space are two significant cornerstones for the structure of a routing algorithm. However, most routing algorithms tackle the issue of limited network resources from the perspective of a deterministic approach, which lacks an adaptive data transmission mechanism. Meanwhile, these methods show a relatively low scalability because they are probably built up based on some special scenarios rather than general ones. To alleviate the problems, this paper proposes an adaptive delay-tolerant routing algorithm (DTCM) utilizing curve-trapezoid Mamdani fuzzy inference system (CMFI) for opportunistic social networks. DTCM evaluates both the remaining energy level and the remaining cache level of relay nodes (two-factor) in opportunistic networks and makes reasonable decisions on data transmission through CMFI. Different from the traditional fuzzy inference system, CMFI determines three levels of membership functions through the trichotomy law and evaluates the fuzzy mapping from two-factor fuzzy input to data transmission by curve-trapezoid membership functions. Our experimental results show that within the error interval of 0.05~0.1, DTCM improves delivery ratio by about 20% and decreases end-to-end delay by approximate 25% as compared with Epidemic, and the network overhead from DTCM is in the middle horizon.

scholarly journals Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 49 ◽  
Author(s):  
Peijun Zou ◽  
Ming Zhao ◽  
Jia Wu ◽  
Leilei Wang
Keyword(s):  
Network Topology ◽  
Dynamic Change ◽  
Routing Algorithm ◽  
Mobility Model ◽  
Opportunistic Networks ◽  
Link Quality ◽  
Delivery Ratio ◽  
Node Mobility ◽  
Trajectory Prediction ◽  
Routing Performance

Due to the dynamic change of the opportunistic network topology and the lack of stable information transmission paths between nodes, the traditional topology-based routing algorithm cannot achieve the desired routing performance. To address of this problem, this paper proposes a routing algorithm based on trajectory prediction (RATP). The routing protocol based on trajectory prediction can efficiently and quickly adapt to the network link quality instability and the dynamic changes of network topology. RATP algorithm constructs a node mobility model by analyzing the historical mobility characteristics of the nodes. According to the node prediction information, the metric value of the candidate node is calculated, and the node with the smaller metric value is selected as the data forwarding node, which can effectively reduce the packet loss rate and avoids excessive consumption. Simulation results show that compared with other algorithms, the proposed algorithm has higher data delivery ratio, and end-to-end data delay and routing overhead are significantly reduced.

scholarly journals Exploiting user interest similarity and social links for micro-blog forwarding in mobile opportunistic networks

2014 ◽  
Vol 11 ◽  
pp. 106-131 ◽  
Author(s):  
S.M. Allen ◽  
M.J. Chorley ◽  
G.B. Colombo ◽  
E. Jaho ◽  
M. Karaliopoulos ◽  
...  
Keyword(s):  
Opportunistic Networks ◽  
User Interest ◽  
Mobile Opportunistic Networks ◽  
Interest Similarity

scholarly journals A Message Matching Routing Algorithm Based on Node Sociality in Opportunistic Networks

2018 ◽  
Vol 129 ◽  
pp. 175-177
Author(s):  
Nan Zhao ◽  
Feng Zeng ◽  
Wenjia Li
Keyword(s):  
Routing Algorithm ◽  
Opportunistic Networks

An Efficient Routing Algorithm for Opportunistic Networks Based on Network Coding

2013 ◽  
Vol 756-759 ◽  
pp. 2214-2219 ◽  
Author(s):  
Yu Kun Yao ◽  
Zhi Hu Liu ◽  
Zhi Ren ◽  
Ya Di Wen
Keyword(s):  
Network Coding ◽  
Data Transmission ◽  
Routing Algorithm ◽  
Opportunistic Networks ◽  
The Novel ◽  
Data Packets ◽  
Redundant Data ◽  
Memory Overhead ◽  
The One ◽  
Network Overhead

To address the problems in Opportunistic Networks that nodes employing routing algorithms based on epidemic mechanism cant sense their neighboring nodes timely and many redundant data packets permeate through the network, an efficient routing algorithm based on XOR network coding was proposed (Xor Network Coding based Epidemic Routing, XNCER). When a node gets a Hello message, it broadcasts, instead of unicasting, a SV(Summary Vector) packet, in return, to all its neighboring nodes. Therefore, the data transmission can be initiated immediately, and the XOR network coding can also be leveraged so as to enhance the efficiency of data transmission. Moreover, the packets destined to the one hop neighboring nodes obtain the priority in transmission, and are determined whether to be deleted based on the ACK mechanism running in the MAC layer in order to cut down the memory overhead and restrain redundant packets from dissemination. Theoretical analysis and extensive simulation results show that the new algorithm obtains an overall improvement in terms of network overhead, average end-to-end delay as well as delivery rate. The novel algorithm can effectively solve the problems of sensing neighboring nodes and data redundancy.

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