Scale-Free Network Model with Evolving Local-World

Sun, Tao; Deng, Jingwei; Deng, Kejian

doi:10.1109/icnc.2008.309

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…They work well, especially with static metadata. an centralized prediction-based model was proposed by Tie-li in [6], and calculations were performed based on the probability of a matching query. The advantage of this method is that the search engine does not need to contact all of the sensors available in a network.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Work presented by Nitte et al [1] relied on the scale-free Barabasi–Albert (BA) network model. This model is mainly used for the generation of random scale-free networks and is based on preferential attachment (PA) [6]. This network is initialized by using N nodes and m 0 edges.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…As search engines receive a number of queries simultaneously, it is very difficult to manage and handle them efficiently by using currently available platforms. From this perspective, there are several algorithms proposed for real-time systems [6,7]. One of the common features of these approaches is to focus on centralized systems, and hence they cannot properly scale up to the huge number of devices or receive several queries.…”

Section: Introductionmentioning

confidence: 99%

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An Advanced Algorithm for Higher Network Navigation in Social Internet of Things Using Small-World Networks

Amin

Abbasi

Rehman

et al. 2019

Sensors

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The Internet of Things (IoT) is a recent evolutionary technology that has been the primary focus of researchers for the last two decades. In the IoT, an enormous number of objects are connected together using diverse communications protocols. As a result of this massive object connectivity, a search for the exact service from an object is difficult, and hence the issue of scalability arises. In order to resolve this issue, the idea of integrating the social networking concept into the IoT, generally referred as the Social Internet of Things (SIoT) was introduced. The SIoT is gaining popularity and attracting the attention of the research community due to its flexible and spacious nature. In the SIoT, objects have the ability to find a desired service in a distributed manner by using their neighbors. Although the SIoT technique has been proven to be efficient, heterogeneous devices are growing so exponentially that problems can exist in the search for the right object or service from a huge number of devices. In order to better analyze the performance of services in an SIoT domain, there is a need to impose a certain set of rules on these objects. Our novel contribution in this study is to address the link selection problem in the SIoT by proposing an algorithm that follows the key properties of navigability in small-world networks, such as clustering coefficients, path lengths, and giant components. Our algorithm empowers object navigability in the SIoT by restricting the number of connections for objects, eliminating old links or having fewer connections. We performed an extensive series of experiments by using real network data sets from social networking sites like Brightkite and Facebook. The expected results demonstrate that our algorithm is efficient, especially in terms of reducing path length and increasing the average clustering coefficient. Finally, it reflects overall results in terms of achieving easier network navigation. Our algorithm can easily be applied to a single node or even an entire network.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%