An energy-saving routing algorithm for opportunity networks based on asynchronous sleeping mode

Zhou, Chunyue; Tian, Hui; Yang, Dong; Zhong, Baitong

doi:10.1016/j.compeleceng.2021.107088

Cited by 11 publications

(9 citation statements)

References 5 publications

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“…There are typically only one or two source nodes per community. The basis of community reconstruction is the correlation degree between source nodes and neighboring nodes [28,29]. That is because the inner edge density of each community structure is significantly higher than the edge density between communities [30][31][32].…”

Section: Data Delivery Model Designmentioning

confidence: 99%

Modified Data Delivery Strategy Based on Stochastic Block Model and Community Detection in Opportunistic Social Networks

Gou

2022

Wireless Communications and Mobile Computing

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The nodes in the opportunistic network make up communities according to the relevance between them. Some of the structural characteristics of an opportunistic network can be reflected by the structure of the communities that exist in the network. Therefore, finding community from the network is of great significance for people to better study, use, and transform the network. The overlap of communities is considered to be an important feature of communities. Almost all community discovery algorithms were based on nonoverlapping communities in the past. A node in a nonoverlapping community belongs to only one community. However, there are overlapping and interrelated characteristics between communities, so it is not in line with the actual environment of the network. As a result, the previous algorithms have many shortcomings in the face of practical application scenarios, coupled with the limitation of the computing capacity of mobile devices; data transmission for low delay and the low energy consumption is difficult to meet the requirements. In the study, we formulate the problem of dividing nodes into several communities in the opportunistic social network as how to build communities dynamically according to weight distribution. Then, we propose a modified data delivery strategy based on stochastic block model and community detection (DDBSC). The simulation results show that, compared with other algorithms in the experiments, the strategy proposed in this paper exhibits good performance in terms of overhead, energy consumption, and delivery rate.

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Section: Data Delivery Model Designmentioning

confidence: 99%

Modified Data Delivery Strategy Based on Stochastic Block Model and Community Detection in Opportunistic Social Networks

Gou

2022

Wireless Communications and Mobile Computing

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“…11 Based on the Epidemic algorithm, Chancay-Garcı´a et al 12 considered that the amount of transmitted information is limited by the transmission speed and contact time and proposed an epidemic extension protocol called Xpread. Similarly, on the basis of the Epidemic algorithm, Chunyue et al 13 proposed an asynchronous sleep mechanism that allows nodes to determine whether to enter the sleep state based on their own energy status, real-time status, and other information. It dynamically controls the sleep time and wake-up time to ensure that nodes will not miss possible communication opportunities.…”

Section: Routing Algorithm Based On Opportunistic Forwardingmentioning

confidence: 99%

“…If l T and l P are the same link, noted l E , then the indirect delivery probability can be calculated by formula (13), where…”

Section: Message Forwarding Strategy Without Same Activitiesmentioning

confidence: 99%

Activity-based routing algorithm in opportunistic mobile social networks

Zhang

Liu

Chen³

et al. 2021

International Journal of Distributed Sensor Networks

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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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“…Borah et al [6] suggest an energy-aware routing protocol for OppNets; the next best hop selection of a message relies on the node residual energy and its location based on delivery probability. Chunyue et al [7] propose the EASE routing algorithm, combined with an asynchronous sleeping mechanism, making each node judge the sleeping condition and the wake-up time according to the historical encounter information and the node movement status. De Vit et al [8] proposed a method of classifying trajectories that, together with the fixed schedules of bus tables, seeks to determine the times of meetings between mobile nodes.…”

Section: Related Workmentioning

confidence: 99%

A Trajectory Inference-based Technique for Energy Efficient Store-and-Forward Technology

Vit

Marcon

Silveira

et al. 2021

2021 Wireless Days (WD)

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The wireless network infrastructure is critical for many applications, often operating over energy-sensitive networks with large communication delays. The store-andforward technology used in Delay/Disruption Tolerant Networking (DTN) can minimize this problem. However, DTN requires efficient energy consumption technology for increasing the mobile node lifetime, especially when the connection opportunities among nodes change over time. This paper proposes an energy-saving technique on store-and-forward technology that saves energy by employing a node trajectory inference model based on machine learning for communication control. Experimental results indicate more than 47% of energy-saving on data communication applying the proposed trajectory inference model.

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An energy-saving routing algorithm for opportunity networks based on asynchronous sleeping mode

Cited by 11 publications

References 5 publications

Modified Data Delivery Strategy Based on Stochastic Block Model and Community Detection in Opportunistic Social Networks

Modified Data Delivery Strategy Based on Stochastic Block Model and Community Detection in Opportunistic Social Networks

Activity-based routing algorithm in opportunistic mobile social networks

A Trajectory Inference-based Technique for Energy Efficient Store-and-Forward Technology

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