Enhanced Color-Theory-Based Dynamic Localization in Mobile Wireless Sensor Networks

Chang, Tzu-Chien; Wang, Kuochen; Hsieh, Yi‐Ling

doi:10.1109/wcnc.2007.567

Cited by 16 publications

(6 citation statements)

References 23 publications

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“…In contrast, Niculescu et al [34] proposed a positioning algorithm without ranging. During the node location process, the position of all nodes in the static node location is fixed [35]. Due to the instability of the statistical characteristics of the system, Mukherjee et al [36] proposed the ED-AE method to solve the power allocation of cooperative sensing and proved the importance of the problem of active node identification.…”

Section: Related Workmentioning

confidence: 99%

Back Propagation Neural Network Based Cluster Head Identification in MIMO Sensor Networks for Intelligent Transportation Systems

et al. 2020

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Wireless Sensor Network (WSN) is an essential technology for the Internet-of-Things (IoT) and intelligence-based applications. In the case of Intelligent Transportation Systems (ITS), the WSNs play an important role in safety and efficient traffic management. Therefore, there is enormous demand for energy efficient WSNs for dynamic resource allocation in vehicles and infrastructures. This work presents a Multi-Input Multi-Output (MIMO) technique model in WSNs, which addresses the Cluster Head (CH) recognition issue for MIMO sensor networks by using Back Propagation Neural Network (BPNN). The conventional CH identification suffers from a lack of location identification due to the dynamic and real-time environment. Thus, to obtain more precise positioning accuracy, the proposed work uses BPNN combined with a distributed gradient drop technique to calculate the position of the unknown CH. This reduces the distance estimation error, and the particle swarm optimization technique is further used to obtain the optimal weight and threshold of the network. The work is validated by using mathematical analysis, simulations, and comparison with existing techniques. The proposed model shows a better performance in terms of energy consumption, error rate, and computation time.

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Section: Related Workmentioning

confidence: 99%

Back Propagation Neural Network Based Cluster Head Identification in MIMO Sensor Networks for Intelligent Transportation Systems

et al. 2020

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“…Note that the above conditions can be satisfied when the pre-coder matrix and the compressor matrix are highly orthogonal [22] [23]. The transmission terminal randomly chooses an orthogonal precoder matrix l V in distributed linear interference alignment method, and then the receiver optimizes the initial compressor matrix k U from the orthogonal matrix.…”

Section: B Review Of Interference Alignment Mechanismmentioning

confidence: 99%

Network Anti-interference Technology Based on Hybrid Optimization Algorithm

Zhao

Wang

2014

JNW

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According to the cooperation reception strategy and the hybrid optimization, an interference alignment algorithm is proposed. The algorithm can minimize leakage interference and maximize ideal receiption signal power. Firstly, according to the receiver cooperation technology, the pre-coding vectors of transmitter terminal can be estimated by receiver terminal, where these vectors need not be known in advance. In addition, the sum of interference covariance matrix is enough to describe the statistical features of interference alignment problem, which can quickly devise cooperation reception structure. Therefore, based on the bargaining process of cooperation game theory, the optimal full-cooperation mechanism of all the receivers can be selected. The simulation results show that compared with traditionary distributed interference alignment mechanism, our proposed interference alignment mechanism based on cooperation reception strategy and hybrid optimization technology has excellent feasibility under various transmission-power conditions.

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“…As a result, many researchers have started to investigate the concept of mobility in WSNs. A number of algorithms that can estimate the location of mobile nodes within WSNs have been developed [20,[25][26][27][28][29][30][31][32]. Some algorithms, such as our own prior work on iCCA-MAP reported in [26,27] assume that the anchor nodes are static and one a few sensor nodes are mobile.…”

Section: Mobile Wsn Localization Algorithmsmentioning

confidence: 99%

Localization in Wireless Sensor Networks and Anchor Placement

Kunz

Tatham

2012

JSAN

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Applications of wireless sensor network (WSN) often expect knowledge of the precise location of the nodes. Many different localization protocols have been proposed that allow nodes to derive their location rather than equipping them with dedicated localization hardware such as GPS receivers, which increases node costs. We provide a brief survey of the major approaches to software-based node localization in WSN. One class of localization protocols with good localization performance patches together relative-coordinate, local maps into a global-coordinate map. These protocols require some nodes that know their absolute coordinates, called anchor nodes. While many factors influence the node position errors, in this class of protocols, using Procrustes Analysis, the placement of the anchor nodes can significantly impact the error. Through simulation, using the Curvilinear Component Analysis (CCA-MAP) protocol as a representative protocol in this category, we show the impact of anchor node placement and propose a set of guidelines to ensure the best possible outcome, while using the smallest number of anchor nodes possible

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Enhanced Color-Theory-Based Dynamic Localization in Mobile Wireless Sensor Networks

Cited by 16 publications

References 23 publications

Back Propagation Neural Network Based Cluster Head Identification in MIMO Sensor Networks for Intelligent Transportation Systems

Back Propagation Neural Network Based Cluster Head Identification in MIMO Sensor Networks for Intelligent Transportation Systems

Network Anti-interference Technology Based on Hybrid Optimization Algorithm

Localization in Wireless Sensor Networks and Anchor Placement

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