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Journal articles on the topic 'Mobile opportunistic networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 September 2023

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1

Ciobanu, Radu Ioan, and Ciprian Dobre. "Opportunistic Networks." International Journal of Virtual Communities and Social Networking 5, no. 2 (2013): 11–26. http://dx.doi.org/10.4018/jvcsn.2013040102.

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When mobile devices are unable to establish direct communication, or when communication should be offloaded to cope with large throughputs, mobile collaboration can be used to facilitate communication through opportunistic networks. These types of networks, formed when mobile devices communicate only using short-range transmission protocols, usually when users are close, can help applications still exchange data. Routes are built dynamically, since each mobile device is acting according to the store-carry-and-forward paradigm. Thus, contacts are seen as opportunities to move data towards the destination. In such networks data dissemination is usually based on a publish/subscribe model. Opportunistic data dissemination also raises questions concerning user privacy and incentives. In this the authors present a motivation of using opportunistic networks in various real life use cases, and then analyze existing relevant work in the area of data dissemination. The authors present the categories of a proposed taxonomy that captures the capabilities of data dissemination techniques used in opportunistic networks. Moreover, the authors survey relevant techniques and analyze them using the proposed taxonomy.

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Vidhya Lakshmi, Vimitha R., and Gireesh Kumar T. "Opportunistic mobile social networks: architecture, privacy, security issues and future directions." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (2019): 1145. http://dx.doi.org/10.11591/ijece.v9i2.pp1145-1152.

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Mobile Social Networks and its related applications have made a very great impact in the society. Many new technologies related to mobile social networking are booming rapidly now-a-days and yet to boom. One such upcoming technology is Opportunistic Mobile Social Networking. This technology allows mobile users to communicate and exchange data with each other without the use of Internet. This paper is about Opportunistic Mobile Social Networks, its architecture, issues and some future research directions. The architecture and issues of Opportunistic Mobile Social Networks are compared with that of traditional Mobile Social Networks. The main contribution of this paper is regarding privacy and security issues in Opportunistic Mobile Social Networks. Finally, some future research directions in Opportunistic Mobile Social Networks have been elaborated regarding the data's privacy and security.

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3

Patra, Tapas Kumar, and Albert Sunny. "Forwarding in Heterogeneous Mobile Opportunistic Networks." IEEE Communications Letters 22, no. 3 (2018): 626–29. http://dx.doi.org/10.1109/lcomm.2018.2790393.

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4

Abouaroek, Musaeed, and Khaleel Ahmad. "Node Authentication Using NTRU Algorithm in Opportunistic Network." Scalable Computing: Practice and Experience 20, no. 1 (2019): 83–92. http://dx.doi.org/10.12694/scpe.v20i1.1481.

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The demand for using wireless paradigms for performing various information and communication operations has been exploded. The opportunistic networks is a special type of delay tolerant networks proposed to operate in an emergency manner to facilitate mobile connectivity between the nodes when there is no connectivity. These emergencies are caused either by human-made or natural disasters. Opportunistic Networks depend on mobile phones and other mobile devices that carry wireless technology. This paper is an attempt to expand the opportunistic network through the authentication nodes. We propose an NTRU algorithm for node authentication in opportunistic networks .NTRU algorithm is an asymmetric post-quantum cryptosystem. This algorithm is unbreakable and robust compared to RSA and ECC cryptosystem.

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Zhao, Yan, and Farzana Akter. "Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks." Security and Communication Networks 2022 (May 6, 2022): 1–11. http://dx.doi.org/10.1155/2022/3872214.

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The clustering algorithms play a crucial role for energy saving solutions in mobile opportunistic networks. If the selection of cluster head is made appropriately, then the energy can be consumed optimally. The existing clustering algorithms do not consider the optimal selection of the cluster head resulting in low survival rates and high energy consumption rates in nodes. The adaptive clustering is required in Industrial Internet of Things (IIoT) based sophisticated networks where seamless connectivity is imperative for rapid communication. In order to meet this research gap, an adaptive clustering algorithm for mobile opportunistic networks is proposed within military bases that uses a heuristic algorithm (GA) for adaptive clustering. An analysis of the opportunity network at the connected military base is carried out and the mobile opportunity model is constructed using the adaptive clustering for the similar traffic. In a mobile machine network, the next hop node is determined by the node clustering principle. A LEACH clustering protocol enables communication between cluster heads and base stations based on single-hop and multihop cluster nodes. In order to perform adaptive clustering of mobile network nodes based on network partitioning and scheduling of clusters, genetic algorithms are used. The proposed approach can be applied to the IIoT systems in places where adaptive clustering is required to optimize energy consumption, to reduce latency rates, and to enhance the throughput of mobile networks. The experimental findings suggest that the proposed adaptive strategy is capable of optimizing energy consumption rates, reducing network latency, and boosting efficiency by increasing throughput in mobile opportunistic networks.

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Yan, Yeqing, Zhigang Chen, Jia Wu, and Leilei Wang. "An Effective Data Transmission Algorithm Based on Social Relationships in Opportunistic Mobile Social Networks." Algorithms 11, no. 8 (2018): 125. http://dx.doi.org/10.3390/a11080125.

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With the popularization of mobile communication equipment, human activities have an increasing impact on the structure of networks, and so the social characteristics of opportunistic networks become increasingly obvious. Opportunistic networks are increasingly used in social situations. However, existing routing algorithms are not suitable for opportunistic social networks, because traditional opportunistic network routing does not consider participation in human activities, which usually causes a high ratio of transmission delay and routing overhead. Therefore, this research proposes an effective data transmission algorithm based on social relationships (ESR), which considers the community characteristics of opportunistic mobile social networks. This work uses the idea of the faction to divide the nodes in the network into communities, reduces the number of inefficient nodes in the community, and performs another contraction of the structure. Simulation results show that the ESR algorithm, through community transmission, is not only faster and safer, but also has lower transmission delay and routing overhead compared with the spray and wait algorithm, SCR algorithm and the EMIST algorithm.

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Dash, Sanjit Kumar, Aiswaryalaxmi Pradhan, Sasmita Mishra, and Jibitesh Mishra. "Lightweight Opportunistic Mobile Data Offloading." International Journal of Mobile Devices, Wearable Technology, and Flexible Electronics 9, no. 1 (2018): 1–15. http://dx.doi.org/10.4018/ijmdwtfe.2018010101.

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Current cellular networks are overloaded due to the increasing number of smartphones and demands for bandwidth-eager multimedia content. Upgrading the existing infrastructure of the cellular system is the most straight forward solution to meet the growing demand. Apart from this, offloading mobile data through Wi-Fi can be a feasible solution. Mobile offloading via Wi-Fi is the latest emerging trend in research and industry. In this article, the authors have proposed a framework for mobile data offloading for both cellular and Wi-Fi networks. The authors have introduced a daemon process-based approach to make the entire process lightweight by using a suitable offloading decision algorithm. This article then formulates a mathematical model to evaluate its feasibility and accuracy for achieving optimum performance.

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8

Zhang, Junbao, Haojun Huang, Geyong Min, Wang Miao, and Dapeng Wu. "Social-Aware Routing in Mobile Opportunistic Networks." IEEE Wireless Communications 28, no. 2 (2021): 152–58. http://dx.doi.org/10.1109/mwc.001.2000189.

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9

Mtibaa, Abderrahmen. "Towards Node Cooperation in Mobile Opportunistic Networks." Qatar Foundation Annual Research Forum Proceedings, no. 2011 (November 2011): CSO12. http://dx.doi.org/10.5339/qfarf.2011.cso12.

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10

Li, He, Kaoru Ota, Mianxiong Dong, and Minyi Guo. "Mobile Crowdsensing in Software Defined Opportunistic Networks." IEEE Communications Magazine 55, no. 6 (2017): 140–45. http://dx.doi.org/10.1109/mcom.2017.1600719.

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11

Saha, Barun Kumar, and Sudip Misra. "Named Content Searching in Opportunistic Mobile Networks." IEEE Communications Letters 20, no. 10 (2016): 2067–70. http://dx.doi.org/10.1109/lcomm.2016.2587281.

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12

Radenkovic, Milena, Abderrahim Benslimane, and Derek McAuley. "Reputation Aware Obfuscation for Mobile Opportunistic Networks." IEEE Transactions on Parallel and Distributed Systems 26, no. 1 (2015): 230–40. http://dx.doi.org/10.1109/tpds.2013.265.

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13

Wang, Xia, Shengling Wang, Wenshuang Liang, Rongfang Bie, and Feng Zhao. "The dissemination distance of mobile opportunistic networks." Personal and Ubiquitous Computing 19, no. 7 (2015): 1011–19. http://dx.doi.org/10.1007/s00779-015-0884-6.

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14

Wang, Liang, Zhiwen Yu, Dingqi Yang, Tao Ku, Bin Guo, and Huadong Ma. "Collaborative Mobile Crowdsensing in Opportunistic D2D Networks." ACM Transactions on Sensor Networks 15, no. 3 (2019): 1–30. http://dx.doi.org/10.1145/3317689.

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15

Kaushik, Bhanu, Honggang Zhang, Xinyu Yang, Xinwen Fu, Benyuan Liu, and Jie Wang. "Providing service assurance in mobile opportunistic networks." Computer Networks 74 (December 2014): 114–40. http://dx.doi.org/10.1016/j.comnet.2014.07.018.

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16

Ciobanu, Radu-Ioan, Daniel Gutierrez Reina, Ciprian Dobre, and Sergio L. Toral. "Context-adaptive forwarding in mobile opportunistic networks." Annals of Telecommunications 73, no. 9-10 (2018): 559–75. http://dx.doi.org/10.1007/s12243-018-0654-3.

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17

Martín-Pascual, Miguel Ángel, and Celia Andreu-Sánchez. "Practical Application of Mesh Opportunistic Networks." Applied System Innovation 6, no. 3 (2023): 60. http://dx.doi.org/10.3390/asi6030060.

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Opportunistic networks allow for communication between nearby mobile devices through a radio connection, avoiding the need for cellular data coverage or a Wi-Fi connection. The limited spatial range of this type of communication can be overcome by using nodes in a mesh network. The purpose of this research was to examine a commercial application of electronic mesh communication without a mobile data plan, Wi-Fi, or satellite. A mixed study, with qualitative and quantitative strategies, was designed. An experimental session, in which participants tested opportunistic networks developing different tasks for performance, was carried out to examine the system. Different complementary approaches were adopted: a survey, a focus group, and an analysis of participants’ performance. We found that the main advantage of this type of communication is the lack of a need to use data networks for one-to-one and group communications. Opportunistic networks can be integrated into professional communication workflows. They can be used in situations where traditional telephones and the Internet are compromised, such as at mass events, emergency situations, or in the presence of frequency inhibitors.

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18

Visca, Jorge, and Javier Baliosian. "rl4dtn: Q-Learning for Opportunistic Networks." Future Internet 14, no. 12 (2022): 348. http://dx.doi.org/10.3390/fi14120348.

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Opportunistic networks are highly stochastic networks supported by sporadic encounters between mobile devices. To route data efficiently, opportunistic-routing algorithms must capitalize on devices’ movement and data transmission patterns. This work proposes a routing method based on reinforcement learning, specifically Q-learning. As usual in routing algorithms, the objective is to select the best candidate devices to put forward once an encounter occurs. However, there is also the possibility of not forwarding if we know that a better candidate might be encountered in the future. This decision is not usually considered in learning schemes because there is no obvious way to represent the temporal evolution of the network. We propose a novel, distributed, and online method that allows learning both the network’s connectivity and its temporal evolution with the help of a temporal graph. This algorithm allows learning to skip forwarding opportunities to capitalize on future encounters. We show that explicitly representing the action for deferring forwarding increases the algorithm’s performance. The algorithm’s scalability is discussed and shown to perform well in a network of considerable size.

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19

Zhang, Sheng, Houzhong Liu, Caisen Chen, Zhaojun Shi, and William Wei Song. "Activity-based routing algorithm in opportunistic mobile social networks." International Journal of Distributed Sensor Networks 17, no. 9 (2021): 155014772110412. http://dx.doi.org/10.1177/15501477211041272.

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In opportunistic mobile social networks, nodes are clustered according to their interests or hobbies and take part in different activities regularly. We delve into the temporal and spatial mobility characteristics of network nodes and put forward an activity-based message opportunistic forwarding algorithm. The main idea of the algorithm is that we choose different message forwarding methods according to the situation of nodes participating in activities. If the source node and the destination node are both attend in the same activities, we select the best relay node which has the biggest delivery probability. While the source node and the destination node are not in the same activities at the same time, we need to find the optimal path which owns highest indirect delivery probability, and messages will be transmitted through the optimal path. The simulation results show that the proposed routing algorithm can not only improve the successful delivery ratio of messages but also reduce the network delay and the network overhead obviously, in comparison with the classical opportunistic routing algorithms, such as community-aware message opportunistic transmission algorithm, community-based message transmission scheme algorithm, PRoPHET, Epidemic algorithm, and interest characteristic probability prediction algorithm.

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20

SMITH, ANTHONY, and RICHARD HILL. "MEASURING EFFICIENCY IN OPPORTUNISTIC AD HOC NETWORKS." Journal of Interconnection Networks 12, no. 03 (2011): 189–203. http://dx.doi.org/10.1142/s0219265911002940.

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Opportunistic Networks are examples of wireless, ad hoc networks where there is an absence of a continuous end-to-end path. The proliferation of mobile device usage creates opportunities for nodes to forward packets in a dynamic way, utilizing nodes as they present themselves. In more conventional, static network infrastructures, it is typical to measure efficiency of message passing between nodes. We review approaches to the measurement of efficiency in networks, and propose a qualitative and quantitative metrics framework and simulation model that would be suitable for the evaluation of performance in opportunistic networks.

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21

Chen, Bangyuan, and Lingna Chen. "NPLP: An Improved Routing-Forwarding Strategy Utilizing Node Profile and Location Prediction for Opportunistic Networks." Information 10, no. 10 (2019): 306. http://dx.doi.org/10.3390/info10100306.

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Opportunistic networks are considered as the promising network structures to implement traditional and typical infrastructure-based communication by enabling smart mobile devices in the networks to contact with each other within a fixed communication area. Because of the intermittent and unstable connections between sources and destinations, message routing and forwarding in opportunistic networks have become challenging and troublesome problems recently. In this paper, to improve the data dissemination environment, we propose an improved routing-forwarding strategy utilizing node profile and location prediction for opportunistic networks, which mainly includes three continuous phases: the collecting and updating of routing state information, community detection and optimization and node location prediction. Each mobile node in the networks is able to establish a network routing matrix after the entire process of information collecting and updating. Due to the concentrated population in urban areas and relatively few people in remote areas, the distribution of location prediction roughly presents a type of symmetry in opportunistic networks. Afterwards, the community optimization and location prediction mechanisms could be regarded as an significant foundation for data dissemination in the networks. Ultimately, experimental results demonstrate that the proposed algorithm could slightly enhance the delivery ratio and substantially degrade the network overhead and end-to-end delay as compared with the other four routing strategies.

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22

Xiao, Mingjun, Jie Wu, and Liusheng Huang. "Community-Aware Opportunistic Routing in Mobile Social Networks." IEEE Transactions on Computers 63, no. 7 (2014): 1682–95. http://dx.doi.org/10.1109/tc.2013.55.

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23

Do, Tien Van, Nam H. Do, Ádám Horváth, and Jinting Wang. "Modelling opportunistic spectrum renting in mobile cellular networks." Journal of Network and Computer Applications 52 (June 2015): 129–38. http://dx.doi.org/10.1016/j.jnca.2015.02.007.

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24

Pal, Sujata, and Sudip Misra. "DISIDE: Distributed strategy identification in opportunistic mobile networks." Computer Communications 71 (November 2015): 119–28. http://dx.doi.org/10.1016/j.comcom.2015.08.020.

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25

Thomas, Bryce, Raja Jurdak, and Ian Atkinson. "Opportunistic content diffusion in mobile ad hoc networks." Ad Hoc Networks 45 (July 2016): 34–46. http://dx.doi.org/10.1016/j.adhoc.2016.02.022.

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26

Wang, Shengling, Min Liu, Xiuzhen Cheng, Zhongcheng Li, Jianhui Huang, and Biao Chen. "Opportunistic Routing in Intermittently Connected Mobile P2P Networks." IEEE Journal on Selected Areas in Communications 31, no. 9 (2013): 369–78. http://dx.doi.org/10.1109/jsac.2013.sup.0513033.

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27

Fan, Xiaoguang, Victor O. K. Li, and Kuang Xu. "Fairness Analysis of Routing in Opportunistic Mobile Networks." IEEE Transactions on Vehicular Technology 63, no. 3 (2014): 1282–95. http://dx.doi.org/10.1109/tvt.2013.2282341.

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28

Lai, Yongxuan, Zhengkun Chen, Weicong Wu, and Tianli Ma. "Multiple-resolution content sharing in mobile opportunistic networks." Wireless Communications and Mobile Computing 15, no. 16 (2014): 1991–2003. http://dx.doi.org/10.1002/wcm.2472.

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29

Yang, Yibo, Honglin Zhao, Jinlong Ma, and Xiaowei Han. "Social-aware data dissemination in opportunistic mobile social networks." International Journal of Modern Physics C 28, no. 09 (2017): 1750115. http://dx.doi.org/10.1142/s0129183117501157.

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Opportunistic Mobile Social Networks (OMSNs), formed by mobile users with social relationships and characteristics, enhance spontaneous communication among users that opportunistically encounter each other. Such networks can be exploited to improve the performance of data forwarding. Discovering optimal relay nodes is one of the important issues for efficient data propagation in OMSNs. Although traditional centrality definitions to identify the nodes features in network, they cannot identify effectively the influential nodes for data dissemination in OMSNs. Existing protocols take advantage of spatial contact frequency and social characteristics to enhance transmission performance. However, existing protocols have not fully exploited the benefits of the relations and the effects between geographical information, social features and user interests. In this paper, we first evaluate these three characteristics of users and design a routing protocol called Geo-Social-Interest (GSI) protocol to select optimal relay nodes. We compare the performance of GSI using real INFOCOM06 data sets. The experiment results demonstrate that GSI overperforms the other protocols with highest data delivery ratio and low communication overhead.

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30

Kurdi, Shivan Fazil. "Mobility-Based Routing in Opportunistic Networks." International Journal of Technology Diffusion 3, no. 2 (2012): 28–35. http://dx.doi.org/10.4018/jtd.2012040103.

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In Opportunistic Networks (OppNets) nodes are only intermittently connected. A complete path from the sender node to the receiver does not exist. Mobile objects exploit direct contact for message transmission without relying on an existing end to end infrastructure. In such networks, routing is a challenging issue. Nevertheless, routing protocols in the mobility-based class of OppNets exploit some context information such as node mobility information and patterns to make forwarding decision, since the effectiveness of routing depends on node mobility. The aim of this research is to identify, evaluate, and compare the mobility-based routing algorithms of OppNets based on the simulation results obtained from published literature. The research findings indicate that mobility-based algorithms are suitable for conditions where network bandwidth and devices are considered significant constraints. They provide average delivery ratio with less resource consumption. In brief, they are ideal when network traffic and resource consumption are taken into consideration.

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Liu, Kanghuai, Zhigang Chen, Jia Wu, and Leilei Wang. "FCNS: A Fuzzy Routing-Forwarding Algorithm Exploiting Comprehensive Node Similarity in Opportunistic Social Networks." Symmetry 10, no. 8 (2018): 338. http://dx.doi.org/10.3390/sym10080338.

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At the dawn of big data and 5G networks, end-to-end communication with large amounts of data between mobile devices is difficult to be implemented through the traditional face-to-face transmission mechanism in social networks. Consequently, opportunistic social networks proposed that message applications should choose proper relay nodes to perform effective data transmission processes. At present, several routing algorithms, based on node similarity, attempt to use the contextual information related to nodes and the special relationships between them to select a suitable relay node among neighbors. However, when evaluating the similarity degree between a pair of nodes, most existing algorithms in opportunistic social networks pay attention to only a few similar factors, and even ignore the importance of mobile similarity in the data transmission process. To improve the transmission environment, this study establishes a fuzzy routing-forwarding algorithm (FCNS) exploiting comprehensive node similarity (the mobile and social similarities) in opportunistic social networks. In our proposed scheme, the transmission preference of the node is determined through the fuzzy evaluation of mobile and social similarities. The suitable message delivery decision is made by collecting and comparing the transmission preference of nodes, and the sustainable and stable data transmission process is performed through the feedback mechanism. Through simulations and the comparison of social network algorithms, the delivery ratio in the proposed algorithm is 0.85 on average, and the routing delay and network overhead of this algorithm are always the lowest.

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32

Ge, Lige, and Shengming Jiang. "An Efficient Opportunistic Routing Based on Prediction for Nautical Wireless Ad Hoc Networks." Journal of Marine Science and Engineering 10, no. 6 (2022): 789. http://dx.doi.org/10.3390/jmse10060789.

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Nautical wireless ad hoc networks are becoming increasingly popular in oceans due to their easy deployment and self-curing capability. They may alternate frequently between connected mobile ad hoc networks and partitioned opportunistic networks due to mobility in large spaces. Traditional mobile ad hoc network routing is used to find the shortest route for connected networks. However, for opportunistic networks, routing schemes with a broadcast nature mainly exploit the reduction in message duplication and the local relaying technologies described in the literature, which may lead to unnecessary resource waste and low packet delivery ratios. To solve the problem, we propose an efficient opportunistic routing scheme based on prediction for nautical wireless ad hoc networks. The scheme first develops an effective candidate intermediate region to recognize the unavailability of some apparently qualified intermediate nodes, and then takes into account the packet reception ratio between nodes and relay advancement prediction, to improve packet delivery. The proposed scheme achieves performance improvements regarding packet loss ratio and throughput with a tolerable latency increase, compared to other schemes.

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Munjal, Aarti, Tracy Camp, and Nils Aschenbruck. "Changing Trends in Modeling Mobility." Journal of Electrical and Computer Engineering 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/372572.

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A phenomenal increase in the number of wireless devices has led to the evolution of several interesting and challenging research problems in opportunistic networks. For example, the random waypoint mobility model, an early, popular effort to model mobility, involves generatingrandommovement patterns. Previous research efforts, however, validate that movement patterns are not random; instead, human mobility is predictable to some extent. Since the performance of a routing protocol in an opportunistic network is greatly improved if the movement patterns of mobile users can be somewhat predicted in advance, several research attempts have been made to understand human mobility. The solutions developed use our understanding of movement patterns to predict the future contact probability for mobile nodes. In this work, we summarize the changing trends in modeling human mobility asrandommovements to the current research efforts that model human walks in a more predictable manner. Mobility patterns significantly affect the performance of a routing protocol. Thus, the changing trend in modeling mobility has led to several changes in developing routing protocols for opportunistic networks. For example, the simplest opportunistic routing protocol forwards a received packet to a randomly selected neighbor. With predictable mobility, however, routing protocols can use the expected contact information between a pair of mobile nodes in making forwarding decisions. In this work, we also describe the previous and current research efforts in developing routing protocols for opportunistic networks.

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Zhang, Zhen Yu, Shao Jie Wen, and Wen Zhong Yang. "Based on Energy-Efficient Opportunistic Multicast Routing for Mobile Wireless Sensor Networks." Applied Mechanics and Materials 599-601 (August 2014): 851–55. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.851.

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Energy-efficient is one of the main problems among all the problems in mobile wireless sensor networks, for improving the energy efficiency of the nodes in the multicast environment and complete the communication works with the minimum cost, we present a based on energy-efficient opportunistic multicast routing for mobile wireless sensor networks. In order to increase the reliability of links, we use opportunistic routing and collaboration mechanism to realize the routing method, and then use the path aggregation to reduce the energy consumption of the whole network. The results of simulation show that, the method presented in this paper is effective in reducing the consumption of links cost.

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Yang, Guisong, Zhiwei Peng, and Xingyu He. "Data Collection Based on Opportunistic Node Connections in Wireless Sensor Networks." Sensors 18, no. 11 (2018): 3697. http://dx.doi.org/10.3390/s18113697.

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The working–sleeping cycle strategy used for sensor nodes with limited power supply in wireless sensor networks can effectively save their energy, but also causes opportunistic node connections due to the intermittent communication mode, which can affect the reliability of data transmission. To address this problem, a data collection scheme based on opportunistic node connections is proposed to achieve efficient data collection in a network with a mobile sink. In this scheme, the mobile sink first broadcasts a tag message to start a data collection period, and all nodes that receive this message will use the probe message to forward their own source information to the mobile sink. On receiving these probe messages, the mobile sink then constructs an opportunistic connection random graph by analyzing the source information included in them, and calculates the optimal path from itself to each node in this random graph, therefore a spanning tree could be generated with the mobile sink play as the root node, finally, it broadcasts this spanning tree so that each node could obtain an optimal path from itself to the mobile sink to forward the sensing data. In addition, a routing protocol that adapts to different nodes operating statuses is proposed to improve the reliability of data transmission. Simulation results show that the proposed scheme works better concerning the packet delivery rate, energy consumption and network lifetime.

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Socievole, Annalisa, Antonio Caputo, Floriano De Rango, and Peppino Fazio. "Routing in Mobile Opportunistic Social Networks with Selfish Nodes." Wireless Communications and Mobile Computing 2019 (February 3, 2019): 1–15. http://dx.doi.org/10.1155/2019/6359806.

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When the connection to Internet is not available during networking activities, an opportunistic approach exploits the encounters between mobile human-carried devices for exchanging information. When users encounter each other, their handheld devices can communicate in a cooperative way, using the encounter opportunities for forwarding their messages, in a wireless manner. But, analyzing real behaviors, most of the nodes exhibit selfish behaviors, mostly to preserve the limited resources (data buffers and residual energy). That is the reason why node selfishness should be taken into account when describing networking activities: in this paper, we first evaluate the effects of node selfishness in opportunistic networks. Then, we propose a routing mechanism for managing node selfishness in opportunistic communications, namely, SORSI (Social-based Opportunistic Routing with Selfishness detection and Incentive mechanisms). SORSI exploits the social-based nature of node mobility and other social features of nodes to optimize message dissemination together with a selfishness detection mechanism, aiming at discouraging selfish behaviors and boosting data forwarding. Simulating several percentages of selfish nodes, our results on real-world mobility traces show that SORSI is able to outperform the social-based schemes Bubble Rap and SPRINT-SELF, employing also selfishness management in terms of message delivery ratio, overhead cost, and end-to-end average latency. Moreover, SORSI achieves delivery ratios and average latencies comparable to Epidemic Routing while having a significant lower overhead cost.

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37

Guo, Mei, and Min Xiao. "MSSN: An Attribute-Aware Transmission Algorithm Exploiting Node Similarity for Opportunistic Social Networks." Information 10, no. 10 (2019): 299. http://dx.doi.org/10.3390/info10100299.

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Recently, with the development of big data and 5G networks, the number of intelligent mobile devices has increased dramatically, therefore the data that needs to be transmitted and processed in the networks has grown exponentially. It is difficult for the end-to-end communication mechanism proposed by traditional routing algorithms to implement the massive data transmission between mobile devices. Consequently, opportunistic social networks propose that the effective data transmission process could be implemented by selecting appropriate relay nodes. At present, most existing routing algorithms find suitable next-hop nodes by comparing the similarity degree between nodes. However, when evaluating the similarity between two mobile nodes, these routing algorithms either consider the mobility similarity between nodes, or only consider the social similarity between nodes. To improve the data dissemination environment, this paper proposes an effective data transmission strategy (MSSN) utilizing mobile and social similarities in opportunistic social networks. In our proposed strategy, we first calculate the mobile similarity between neighbor nodes and destination, set a mobile similarity threshold, and compute the social similarity between the nodes whose mobile similarity is greater than the threshold. The nodes with high mobile similarity degree to the destination node are the reliable relay nodes. After simulation experiments and comparison with other existing opportunistic social networks algorithms, the results show that the delivery ratio in the proposed algorithm is 0.80 on average, the average end-to-end delay is 23.1% lower than the FCNS algorithm (A fuzzy routing-forwarding algorithm exploiting comprehensive node similarity in opportunistic social networks), and the overhead on average is 14.9% lower than the Effective Information Transmission Based on Socialization Nodes (EIMST) algorithm.

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38

Wang, Zhifei, Gang Xu, Na Zhang, Zhihan Qi, Fengqi Wei, and Liqiang He. "Ferry Node Identification Model for the Security of Mobile Ad Hoc Network." Security and Communication Networks 2021 (January 5, 2021): 1–13. http://dx.doi.org/10.1155/2021/6682311.

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Abstract:

An opportunistic network is a special type of wireless mobile ad hoc network that does not require any infrastructure, does not have stable links between nodes, and relies on node encounters to complete data forwarding. The unbalanced energy consumption of ferry nodes in an opportunistic network leads to a sharp decline in network performance. Therefore, identifying the ferry node group plays an important role in improving the performance of the opportunistic network and extending its life. Existing research studies have been unable to accurately identify ferry node clusters in opportunistic networks. In order to solve this problem, the concepts of k-core and structural holes have been combined, and a new evaluation indicator, namely, ferry importance rank, has been proposed in this study for analyzing the dynamic importance of nodes in a network. Based on this, a ferry cluster identification model has been designed for accurately identifying the ferry node clusters. The results of the simulations conducted for verifying the performance of the proposed model show that the accuracy of the model to identify the ferry node clusters is 100%.

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39

Yang, Ming Xia, Shuang Xia Han, Cai Yun Yang, Lu Zhang, and Dong Fen Ye. "Survey on Node Mobility Model for Opportunistic Network." Applied Mechanics and Materials 52-54 (March 2011): 1253–57. http://dx.doi.org/10.4028/www.scientific.net/amm.52-54.1253.

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Opportunistic networks is one of the newest hot research spots in wireless networks after mobile ad hoc net-works(MANET) and wireless sensor networks(WSN). Mobility model describes mobility manners of nodes. It has been widely used in research on wireless network. This paper firstly introduced, classifies, and compares the current familiar mobility models. Secondly, it classifies, and compares the current familiar mobility models. Next, it was discussed that current research focus on new mobility models, analysis of nodes mobility features, trace strategy, and evaluation of mobility model. Finally, this paper involved what calls for further study.

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40

Wu, Yahui, Su Deng, and Hongbin Huang. "Information Spreading in Mobile Opportunistic Networks with Limited Infectability." Journal of the Physical Society of Japan 82, no. 11 (2013): 114001. http://dx.doi.org/10.7566/jpsj.82.114001.

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41

Guidec, Frédéric, Nicolas Le Sommer, and Yves Mahéo. "Opportunistic Software Deployment in Disconnected Mobile Ad Hoc Networks." International Journal of Handheld Computing Research 1, no. 1 (2010): 24–42. http://dx.doi.org/10.4018/jhcr.2010090902.

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This paper presents a middleware platform allowing the dissemination of software components on handheld devices forming a disconnected MANET. It is based on a model that exploits peer-to-peer and opportunistic interactions between neighboring devices to deploy component-based applications, without relying on any kind of infrastructure network. Each device runs a deployment manager, which strive to fill a local component repository with software components so as to be able to satisfy the deployment requests expressed by the user. To do so the deployment manager interacts with peer managers located on neighboring devices, providing its neighbors with copies of software components it owns locally, while obtaining itself from these neighbors copies of components it lacks. The platform also provides communication facilities adapted to disconnected MANETs that notably allow efficient multi-hop exchanges.

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42

Goudar, Gourish, and Suvadip Batabyal. "Point of Congestion in Large Buffer Mobile Opportunistic Networks." IEEE Communications Letters 24, no. 7 (2020): 1586–90. http://dx.doi.org/10.1109/lcomm.2020.2986230.

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43

You, Lei, Jianbo Li, Changjiang Wei, Jixing Xu, and Chenqu Dai. "A Data Item Selection Mechanism for Mobile Opportunistic Networks." International Journal of Distributed Sensor Networks 10, no. 5 (2014): 541065. http://dx.doi.org/10.1155/2014/541065.

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44

Wang, Eric Ke, Yueping Li, Yunming Ye, S. M. Yiu, and Lucas C. K. Hui. "A Dynamic Trust Framework for Opportunistic Mobile Social Networks." IEEE Transactions on Network and Service Management 15, no. 1 (2018): 319–29. http://dx.doi.org/10.1109/tnsm.2017.2776350.

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45

Wang, Fang, Zhaocheng Wang, Zhixing Yang, and Sheng Chen. "Contact duration aware cache refreshing for mobile opportunistic networks." IET Networks 5, no. 4 (2016): 93–103. http://dx.doi.org/10.1049/iet-net.2015.0086.

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Misra, Sudip, Sujata Pal, and Barun Kumar Saha. "Distributed Information-Based Cooperative Strategy Adaptationin Opportunistic Mobile Networks." IEEE Transactions on Parallel and Distributed Systems 26, no. 3 (2015): 724–37. http://dx.doi.org/10.1109/tpds.2014.2314687.

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Zakhary, Sameh, and Abderrahim Benslimane. "On location-privacy in opportunistic mobile networks, a survey." Journal of Network and Computer Applications 103 (February 2018): 157–70. http://dx.doi.org/10.1016/j.jnca.2017.10.022.

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Saha, Barun Kumar, Sudip Misra, and Sujata Pal. "SeeR: Simulated Annealing-Based Routing in Opportunistic Mobile Networks." IEEE Transactions on Mobile Computing 16, no. 10 (2017): 2876–88. http://dx.doi.org/10.1109/tmc.2017.2673842.

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49

Zakhary, Sameh, Milena Radenkovic, and Abderrahim Benslimane. "Efficient Location Privacy-Aware Forwarding in Opportunistic Mobile Networks." IEEE Transactions on Vehicular Technology 63, no. 2 (2014): 893–906. http://dx.doi.org/10.1109/tvt.2013.2279671.

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Zhou, Huan, Hongyang Zhao, Jiming Chen, Chi Harold Liu, and Jialu Fan. "Adaptive Working Schedule for Duty-Cycle Opportunistic Mobile Networks." IEEE Transactions on Vehicular Technology 63, no. 9 (2014): 4694–703. http://dx.doi.org/10.1109/tvt.2014.2312934.

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