Wireless Sensor Networks for Urban Information Systems: Preliminary Results of Integration of an Electric Vehicle as a Mobile Node

2017 ◽  
pp. 190-199 ◽  
Author(s):  
J. J. Fernández-Lozano ◽  
J. A. Gomez-Ruiz ◽  
Miguel Martín-Guzmán ◽  
Juan Martín-Ávila ◽  
Socarras Bertiz Carlos ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Information Systems ◽  
Sensor Networks ◽  
Electric Vehicle ◽  
Mobile Node ◽  
Wireless Sensor ◽  
Preliminary Results

SYLPH

2011 ◽  
Vol 3 (2) ◽  
pp. 1-15 ◽  
Author(s):  
Ricardo S. Alonso ◽  
Dante I. Tapia ◽  
Juan M. Corchado
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ambient Intelligence ◽  
Wireless Sensor ◽  
Context Aware ◽  
Preliminary Results ◽  
Ubiquitous Systems ◽  
Heterogeneous Wireless Sensor Networks ◽  
Innovative Solutions ◽  
Flexible Applications

The significance that Ambient Intelligence (AmI) has acquired in recent years requires the development of innovative solutions. In this sense, the development of AmI-based systems requires the creation of increasingly complex and flexible applications. The use of context-aware technologies is an essential aspect in these developments in order to perceive stimuli from the context and react upon it autonomously. This paper presents SYLPH, a novel platform that defines a method for integrating dynamic and self-adaptable heterogeneous Wireless Sensor Networks (WSN). This approach facilitates the inclusion of context-aware capabilities when developing intelligent ubiquitous systems, where functionalities can communicate in a distributed way. A WSN infrastructure has been deployed for testing and evaluating this platform. Preliminary results and conclusions are presented in this paper.

An Enhanced Monte Carlo Localization Algorithm for Mobile Node in Wireless Sensor Networks

2013 ◽  
Vol 401-403 ◽  
pp. 1800-1804 ◽  
Author(s):  
Shi Ping Fan ◽  
Yong Jiang Wen ◽  
Lin Zhou
Keyword(s):  
Wireless Sensor Networks ◽  
Monte Carlo ◽  
Sensor Networks ◽  
Mobile Node ◽  
Wireless Sensor ◽  
Distribution Area ◽  
Localization Algorithm ◽  
Monte Carlo Localization ◽  
Sample Points ◽  
Unknown Node

There are some common problems, such as low sampling efficiency and large amount of calculation, in mobile localization algorithm based on Monte Carlo localization (MCL) in wireless sensor networks. To improve these issues, an enhanced MCL algorithm is proposed. The algorithm uses the continuity of the nodes movement to predict the area where the unknown node may reach, constructs high posteriori density distribution area, adds the corresponding weights to the sample points which fall in different areas, and filters the sample points again by using the position relations between the unknown node and its one-hop neighbors which include anchor nodes and ordinary nodes. Simulation results show that the localization accuracy of the algorithm is superior to the traditional localization algorithm. Especially when the anchor node density is lower or the unknown nodes speed is higher, the algorithm has higher location accuracy.

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