A realistic mobility model for wireless networks of scale-free node connectivity

Lim, Sunho; Yu, Chansu; Das, Chita R.

doi:10.1504/ijmc.2010.032979

Cited by 16 publications

(5 citation statements)

References 35 publications

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Section: Related Workmentioning

confidence: 99%

“…In some research, mobile anchor nodes move among unknown nodes with mobility model without considering WSN conditions [24]- [27]. Lim et al [24] use the simplest mobility model in which mobile anchor nodes move randomly. Many other mobile models such as Random Waypoint (RWP), Gauss-Markov (GM) and Group mobility model have also been used in mobile anchor node assisted localization schemes [25]- [27].…”

Section: Related Workmentioning

confidence: 99%

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Robust Localization for Cognitive IoT via the Mobile Anchor Node Based on the Diameter-Varying Spiral Line

et al. 2019

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Research on IoT that merely aims at connecting and communicating is about to past. Thereafter, general objects should have the capability to learn, think, and understand both physical and social areas by themselves. Cognitive Internet of Things (CIoT) attempts to empower the current IoT with a ''brain'' for high-level intelligence, requiring networks to have the ability to bridge the physical and social worlds. This attempt means matching equipment and resources with people and their behavior. Therefore, accurate location information is crucial for equipment connecting to CIoT. This endeavor sets a higher requirement for the localization technology of wireless sensor networks in terms of accuracy, energy, and efficiency compared with that in the past. In this paper, we propose an efficient and accurate mobile anchor node assisted localization algorithm for WSNs based on diameter-varying spiral line (LDVSL), which broadcasts coordinates of the anchor node to assist localizing unknown sensor nodes. The proposed algorithm has two main innovations. First, we obtain the mobile anchor node position through a time and angle mechanism instead of GPS, given the unique characteristics of the diameter-varying spiral line. Second, the linear fitting method is adapted to select the key virtual node, which has the real maximum received signal strength indicator. Simulations indicate that the proposed LDVSL algorithm outperforms other similar algorithms in terms of average localization error and positionable node ratio. The simulations also show that the LDVSL is not affected by obstacles seriously and has good robustness. The LDVSL has a wide prospect of application in CIoT. INDEX TERMS CIoT, localization, mobile anchor node, diameter-varying spiral line, linear fitting. I. INTRODUCTION With the rapid development of wireless communication techniques in the past few years, IoT has been widely used as a cyber physical systems in the fields of modern intelligent services such as ecological protection, smart homes, food safety, environmental, logistics, transportation, and national information coverage [1], [2]. According to the latest surveys, approximately 600 billion devices will be connected to the IoT by 2020. With the increasing interconnectivity among general things or objects, many new services or The associate editor coordinating the review of this manuscript and approving it for publication was Min Jia. applications are emerging, generating massive data in an explosive manner [3]. However, many of the existing Internet of Things applications are still not intelligent enough to perceive data and perform decision making, and they are highly dependent on human beings for cognition processing [4]. Therefore, cognitive computing has gained the interest of IoT researchers [5]-[7]. Related researchers attempt to infuse intelligence into objects to learn from the physical world [8], [9]. The IoT with cognitive ability is called cognitive Internet of Things (CIoT), which enables objects and groups to learn data from connected dev...

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Robust Localization for Cognitive IoT via the Mobile Anchor Node Based on the Diameter-Varying Spiral Line

et al. 2019

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“…SC Instances These instances are generated on the basis of the well-known scale-free networks (see Barabasi and Albert (1999)). Scale-free networks frequently appear in the context of complex systems, including the World Wide Web, the internet backbone, infrastructure networks, airline connections, cellular networks, wireless networks, electric-power grids and many other contexts (see Strogatz (2001); Boccaletti et al (2006); Lim et al (2010)).…”

Section: Sc Their Topologies Resemble Different Geographic Local Strmentioning

confidence: 99%

The Recoverable Robust Two-Level Network Design Problem

Álvarez-Miranda

Ljubić

Raghavan

et al. 2015

INFORMS Journal on Computing

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We consider a network design application which is modeled as the two level network design problem under uncertainty. In this problem, one of the two available technologies can be installed on each edge and all customers of the network need to be served by at least the lower level (secondary) technology. The decision maker is confronted with uncertainty regarding the set of primary customers, i.e., the set of nodes that need to be served by the higher level (primary) technology. A set of discrete scenarios associated to the possible realizations of primary customers is available. The network is built in two stages. In the first-stage the network topology must be determined. One may decide to install the primary technology on some of the edges in the first stage, or one can wait to see which scenario will be realized, in which case, edges with the installed secondary technology may be upgraded, if necessary to primary technology, but at higher recovery cost. The overall goal then is to build a spanning tree in the first stage that serves all customers by at least the lower level technology, and that minimizes the first stage installation cost plus the worst-case cost needed to upgrade the edges of the selected tree, so that the primary customers of each scenario can be served using the primary technology. Using the recently introduced concept of recoverable robustness, we address this problem of importance in the design of telecommunication and distribution networks, and provide mixed integer programming models and a branch-and-cut algorithm to solve it.

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“…It has been shown that a good load balancing algorithm produces considerable performance improvement which is done by transferring tasks from MA to AH or over-loaded working agents (WA) to under-loaded working agents (WA) efficiently. Dynamic load balancing reallocates the computing tasks based on the current workload [2,3]. It is more suitable to implement in the real computing cluster environment [4].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy-Based Dynamic Load Decision Making Scheme in Cloud Computing

Chang

Wang

2013

AMR

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In this paper, a fuzzy-based dynamic resource allocation scheme for load balancing in cloud computing (FDLB) is presented to growth of the service type and user number in the mobile networks of the higher performance is required in service provision and throughput. Besides, the estimation of workload is difficult and time consuming, and the result causes an unstable system and unnecessary message passing overhead. The structure of the FDLB is composed of the design the load distributed agent (LDA). It is observed that the FDLB exhibits better adaptability, robustness, and fault-tolerant capability, thus yielding better performances compared to other existing algorithms.

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A realistic mobility model for wireless networks of scale-free node connectivity

Cited by 16 publications

References 35 publications

Robust Localization for Cognitive IoT via the Mobile Anchor Node Based on the Diameter-Varying Spiral Line

Robust Localization for Cognitive IoT via the Mobile Anchor Node Based on the Diameter-Varying Spiral Line

The Recoverable Robust Two-Level Network Design Problem

A Fuzzy-Based Dynamic Load Decision Making Scheme in Cloud Computing

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