Hanoi city is currently dealing with rapidly increasing air pollution that result from variety of sources. The main cause of pollution is exhaust gas from traffic system with a very large number of private vehicles. In order to help the city's environment authorities monitor the level of air pollution, a wireless sensor network is currently under development to collect traffic pollution data measured by a number of gas sensors. This paper focuses on how to process pollution data and visualize level of pollution relying on available datasets collected from sensor network. The volume of data collected from each area of the city can be very large and dynamic due to the number of mobile sensors deployed in the same area at the same time and their measurement frequency. First, we present a method for processing raw data using calibration and data clustering techniques. Second, we describe how measurement datasets are visually represented on the city's online map on the basis of mathematical interpolation method that corresponding to characteristics of environmental data. And then we also use computer graphic technique to improve the visualization quality. Finally, this paper show the result of those methods with sample data collected from an urban district of Hanoi City on a website by which we do not only provide to viewer the actual level of pollution by position but also by time.

No abstract

No abstract

The Internet Engineering Task Force (IETF) standardized the Constrained Application Protocol (CoAP) for Internet of Things (IoT) devices to meet the demands of IoT applications. Due to the constrained IoT environment, CoAP was designed based on UDP as a lightweight protocol with simple congestion control, which leverages the basic binary exponential backoff. However, the basic congestion control of CoAP is unable to effectively perform reliable bursty data transfer in IoT networks. Recent studies have indicated that CoAP and its modifications still suffer from critical performance problems regarding congestion control, throughput, and delay. The current congestion control of the CoAP does not support bursty data transfer. In contrast to the current schemes that focus on a loss-based mechanism and a retransmission time-out (RTO) calculation, we propose a new rate control scheme, RCOAP, for reliable bursty data transfer in IoT networks. RCOAP uses the concept of regulating the transmission rate of CoAP sources. The key features of RCOAP are 1) estimating the initial sending rate by probing the bottleneck bandwidth, 2) adjusting the sending rate according to the dynamic network condition, and 3) distinguishing between losses due to congestion and losses due to wireless errors for the purpose of maintaining high throughput. Simulation results indicate that RCOAP is suitable for bursty data transfer. RCOAP shows a throughput increase of approximately 135% compared to the basic CoAP, CoCoA, and CoCoA+ under the same conditions while maintaining a low delay, loss rate, and a low number of retransmission attempts.

This paper presents a smart data forwarding method based on adaptive levels in order to collect data in a wide area with a limited number of sensors in wireless sensor networks (WSNs). WSN nodes move on predefined trajectories. In comparison to other works, each WSN node is assigned an adaptive level, which is frequently updated based on levels and weights of other neighbor nodes. Measured data will be forwarded from nodes with higher levels on the outermost trajectories to nodes with lower levels on inner trajectories, until they reach the center. The proposed method has been tested with eight sensor nodes and one base station to cover an area of 14.6 km 2 of an urban district of Hanoi City.