ZigBee Based Location Estimation in Home Networking Environments

Cho, Hyunggi; Kang, Myungseok; Kim, Jong-Hoon; Kim, Hagbae

doi:10.1093/ietisy/e90-d.10.1706

Cited by 9 publications

(9 citation statements)

References 2 publications

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“…In general, the MLE finds the parameters which maximizes the likelihood function, or equivalently, minimizes the negative of the log-likelihood function. Thus, the ‫̂ݖ‬ parameter obtained with this method is the estimated location for a sensor [5]. If the data is known to be described well by a particular statistical model (e.g.…”

Section: Maximum Likelihood Estimation Methodsmentioning

confidence: 99%

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An Improvement in Maximum Likelihood Location Estimation Algorithm in Sensor Networks

Abbasi¹,

Farahi²,

Javadi³

2011

IJCSES

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Section: Maximum Likelihood Estimation Methodsmentioning

confidence: 99%

“…Where, Z j is the reference nodes coordinate 5) Finally, the estimated positions send to the central node to performing the MLE localization algorithm.…”

Section: Improvement Methodsmentioning

confidence: 99%

An Improvement in Maximum Likelihood Location Estimation Algorithm in Sensor Networks

Abbasi¹,

Farahi²,

Javadi³

2011

IJCSES

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“…A comparison of LAN, active RFID, and ZigBee is shown in Table 2 [13,14,15]. It is still too early to say which technology will dominate the market.…”

Section: B Context-aware Computing and Ubiquitous Commercementioning

confidence: 99%

Personalized Service Provision in a Context-Aware Shopping Environment

Chang

Hsu

2012

2012 Fourth International Conference on Computational Intelligence, Communication Systems and Networks

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With the advance and pervasion of mobile devices and fast growth of high speed Internet connections, our life is inevitable to be changed. These devices are combined with sensors such as Radio Frequency Identification (RFID) operating on wireless infrastructure to form a wireless sensor networks (WSN) which could facilitate our life in many ways. We would expect a shopping experience in which customers use their mobile device to query and select products, locate items in store, and then check out. In addition, each customer would prefer to receive the customized services according to the surrounding condition. In this paper, we adopt RFID technology, personalized recommendation strategy, and Web services to develop a context-aware shopping environment in which personalized services are provided. The experiment results show the system is useful and could enhance customers' shopping experience.

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“…Most of the previous references are based on mesh WSNs. Localization with Zigbee and RSSI in a cluster-tree WSN is studied in a few studies works such as [17]. For range-based localization algorithms, the success of the location discovery depends on the network connectivity [12].…”

Section: Introductionmentioning

confidence: 99%

LACFA: an algorithm for localization aware cluster formation in wireless sensor networks

Moragrega

Closas

Ibars

2011

J Wireless Com Network

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The ability of a sensor node to determine its position is a fundamental requirement for many applications in wireless sensor networks (WSNs). In this article, we address a scenario where a subset of sensors, called anchor nodes, knows its own position and helps other nodes determine theirs through range-based positioning techniques. Such techniques benefit from a high degree of connectivity, since range measurements from at least four anchor nodes are necessary (three-dimensional scenario). On the other hand, WSN topologies, most notably the cluster-tree topology, tend to limit connectivity between nodes to save energy. This results in very poor performance of the network in terms of localization. In this article, we propose LACFA, a network formation algorithm that increases the probability of localization of sensors in a cluster-tree topology. It does so by properly allocating anchor nodes to different clusters during the network formation phase. Our algorithm achieves very high localization probability when compared with existing cluster formation algorithms, at no additional cost. Moreover, a distributed cluster formation algorithm, with no need for any centralized information exchange mechanisms, is defined.

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ZigBee Based Location Estimation in Home Networking Environments

Cited by 9 publications

References 2 publications

An Improvement in Maximum Likelihood Location Estimation Algorithm in Sensor Networks

An Improvement in Maximum Likelihood Location Estimation Algorithm in Sensor Networks

Personalized Service Provision in a Context-Aware Shopping Environment

LACFA: an algorithm for localization aware cluster formation in wireless sensor networks

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