Design & Implementation of Real-Time Broadcast System of Urban Public Transport Crowding Index Based on the Internet of Things

2014 ◽  
Vol 1049-1050 ◽  
pp. 1753-1758 ◽  
Author(s):  
Si Jun Zhang ◽  
Shu Yan Chen

Real-time broadcast system of public transport crowding index belongs to the broadcast system of urban public transport environment in ITS. The remaining seats and vehicle weight of the bus can effectively reflect the current crowding of public transport. This paper proposes a real-time broadcast system about the crowding index in public transport based on the Internet of things. The system can be divided into four submodules: wireless sensor network node, Sink node, terminal analysis and evaluation system and site display platform.Operating datas in public transport collected by wireless sensor networking node are sent to terminal analysis and evaluation system by GPRS wireless communication mode. This system can help people choose the best travel route. It may be helpful for managers to make a better traffic assignment.

Author(s):  
S. Sundar ◽  
Piyush Arora ◽  
Sarthak Agrawal ◽  
R. Kumar ◽  
Harish M. Kittur

<p>In the last few years,there has been big interest in adhoc wireless network as they have tremendous military and commercial potential[1].Traditionally to test various parameters in the MANET , the most popular approach is to use mobile phone and Laptops and use the popular WIFI based protocol . But in the recent years there is a huge attraction towards the Internet Of things and specifically wireless sensor network. In this paper we are going to test the MANET protocol using zigbee based XBee modules specifally to determine the Range and Throughput of the Xbee netowork using XCTU Software . The sensor network will be deployed in the car parking application to see the parameters in the real time and dynamically see the sustainability of the network .The network is being designed keeping in mind that the nodes are mobile and at the same time the network does not require a standard infrastructure.</p>


Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2417
Author(s):  
Andrzej Michalski ◽  
Zbigniew Watral

This article presents the problems of powering wireless sensor networks operating in the structures of the Internet of Things (IoT). This issue was discussed on the example of a universal end node in IoT technology containing RFID (Radio Frequency Identification) tags. The basic methods of signal transmission in these types of networks are discussed and their impact on the basic requirements such as range, transmission speed, low energy consumption, and the maximum number of devices that can simultaneously operate in the network. The issue of low power consumption of devices used in IoT solutions is one of the main research objects. The analysis of possible communication protocols has shown that there is a possibility of effective optimization in this area. The wide range of power sources available on the market, used in nodes of wireless sensor networks, was compared. The alternative possibilities of powering the network nodes from Energy Harvesting (EH) generators are presented.


2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Jun Huang ◽  
Liqian Xu ◽  
Cong-cong Xing ◽  
Qiang Duan

The design of wireless sensor networks (WSNs) in the Internet of Things (IoT) faces many new challenges that must be addressed through an optimization of multiple design objectives. Therefore, multiobjective optimization is an important research topic in this field. In this paper, we develop a new efficient multiobjective optimization algorithm based on the chaotic ant swarm (CAS). Unlike the ant colony optimization (ACO) algorithm, CAS takes advantage of both the chaotic behavior of a single ant and the self-organization behavior of the ant colony. We first describe the CAS and its nonlinear dynamic model and then extend it to a multiobjective optimizer. Specifically, we first adopt the concepts of “nondominated sorting” and “crowding distance” to allow the algorithm to obtain the true or near optimum. Next, we redefine the rule of “neighbor” selection for each individual (ant) to enable the algorithm to converge and to distribute the solutions evenly. Also, we collect the current best individuals within each generation and employ the “archive-based” approach to expedite the convergence of the algorithm. The numerical experiments show that the proposed algorithm outperforms two leading algorithms on most well-known test instances in terms of Generational Distance, Error Ratio, and Spacing.


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