Node importance evaluation method in wireless sensor network based on energy field model

Sun, Qindong; Qiao, Yimin; Wang, Jiamin; Shen, Si

doi:10.1186/s13638-016-0693-2

Cited by 10 publications

(11 citation statements)

References 14 publications

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“…It shows that removing the most important node always results in a maximum increase in the shortest distance from the source node to the sink node and has the largest transmission delay of network. Reference [17] proposes an approach based on an energy field model which evaluates the node importance by analyzing the status of data transmission among associated nodes. In reference [18], a weighted minimum path tree is used as the metric.…”

Section: Related Workmentioning

confidence: 99%

Node importance evaluation method based on multi-attribute decision-making model in wireless sensor networks

Yin

Cui

et al. 2019

J Wireless Com Network

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Identifying important nodes is very crucial to design efficient communication networks or contain the spreading of information such as diseases and rumors. The problem is formulated as follows: given a network, which nodes are the more important? Most current studies did not incorporate the structure change as well as application features of a network. Aiming at the node importance evaluation in wireless sensor networks, a new method which ranks nodes according to their structural importance and performance impact is proposed. Namely, this method considers two aspects of the network, network structural characteristics and application requirements. This method integrates four indicators which reflect the node importance, namely, node degree, number of spanning trees, delay, and network energy consumption. Firstly, the changes in the four indicators are analyzed using the node deletion method. Then, the TOPSIS multi-attribute decision-making method is applied to merge these four evaluation indicators. On this basis, a more comprehensive evaluation method (MADME) for node importance is obtained. Theory study reveals MADME method saves computational time. And the simulation results show the superiority of the MADME method over various algorithms such as the N-Burt method, betweenness method, DEL-Node method, and IE-Matrix method. The accuracy of the evaluation can be improved, and the key nodes determined by the MADME method have a more obvious effect on the network performance. Our method can provide guidance on influential node identification in the network.

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Section: Related Workmentioning

confidence: 99%

Node importance evaluation method based on multi-attribute decision-making model in wireless sensor networks

Yin

Cui

et al. 2019

J Wireless Com Network

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“…There have been some researches discussing temporal influence to recommendation accuracy, but most of them focus on discussing the effect to user preferences or popularity. 12,[39][40][41] Chen et al 12 and adapt factorization techniques to learn user-group affinity based on two different implicit engagement metrics. However, there are but few works considering the temporal influence to QoS performance of cloud services.…”

Section: Related Workmentioning

confidence: 99%

Temporal‐aware and sparsity‐tolerant hybrid collaborative recommendation method with privacy preservation

Meng

Zhang

et al. 2019

Concurrency and Computation

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Summary With the explosive growth of cloud services, how to design effective recommendation models has become more and more important. Temporal information has been proved to be an important factor affecting recommendation performance. Actually, both of user behaviors and QoS performance of services are time‐sensitive, especially in dynamic cloud environment. However, most existing collaborative recommendation methods seldom consider temporal influence to QoS performance. Furthermore, with the ever‐increasing number of security threats in clouds, privacy preservation becomes an important problem to be addressed in recommender systems. Based on these observations, in this paper, we propose a temporal‐aware and sparsity‐tolerant hybrid collaborative recommendation method with privacy preservation, where tensor factorization‐based CF model is integrated into neighborhood‐based CF model to achieve improved recommendation performance. Specifically, temporal influence is considered into recommendation model by distinguishing short‐term QoS metrics from long‐term QoS metrics. A privacy‐aware time aggregation mechanism is adopted to preserve the sensitive time information of users. Moreover, to deal with the sparsity problem, a sparsity‐oriented data QoS rating prediction mechanism based on tensor factorization technique is applied to predict the missing or unrated QoS rating data. Finally, predictions are made based on both stable and temporal nearest neighbors. Experiments are conducted to demonstrate the prediction performance of our proposal.

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“…In wireless communication networks, especially in heterogeneous sensor networks, the node energy is not static, and decreases gradually with time [18,19]. Therefore, if we randomly choose a fixed probability value to represent the roaming probability between nodes, it will undoubtedly deviate from the calculations of the network.…”

Section: Dynamic Node Probabilitymentioning

confidence: 99%

Energy-balance node-selection algorithm for heterogeneous wireless sensor networks

Khan

Singh

2018

ETRI Journal

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To solve the problem of unbalanced loads and the short network lifetime of heterogeneous wireless sensor networks, this paper proposes a node‐selection algorithm based on energy balance and dynamic adjustment. The spacing and energy of the nodes are calculated according to the proximity to the network nodes and the characteristics of the link structure. The direction factor and the energy‐adjustment factor are introduced to optimize the node‐selection probability in order to realize the dynamic selection of network nodes. On this basis, the target path is selected by the relevance of the nodes, and nodes with insufficient energy values are excluded in real time by the establishment of the node‐selection mechanism, which guarantees the normal operation of the network and a balanced energy consumption. Simulation results show that this algorithm can effectively extend the network lifetime, and it has better stability, higher accuracy, and an enhanced data‐receiving rate in sufficient time.

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Node importance evaluation method in wireless sensor network based on energy field model

Cited by 10 publications

References 14 publications

Node importance evaluation method based on multi-attribute decision-making model in wireless sensor networks

Node importance evaluation method based on multi-attribute decision-making model in wireless sensor networks

Temporal‐aware and sparsity‐tolerant hybrid collaborative recommendation method with privacy preservation

Energy-balance node-selection algorithm for heterogeneous wireless sensor networks

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