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.

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.

Sparse  Wireless  Sensor  Networks  using several  mobile  nodes  and  a  small  number  of  static sensor  nodes  have  been  widely  used  for  many applications,  especially  for  traffic-generated  pollution monitoring.  This  paper  proposes  a  method  for  data collection and forwarding using Mobile Elements (MEs), which are moving on predefined trajectories in contrast to previous works that use a mixture of MEsand static nodes. In our method, MEscan be used as data collector as well as dynamic bridges for data transfer. We design the  trajectories  in  such  a  way,  that  they  completely cover  the  deployed  area  and  data  will  be  gradually forwarded  from  outermost  trajectories  to  the  center whenever  a  pair  of MEs contacts  each  other  on  an overlapping road distance of respective trajectories. The method  is based  on  direction-oriented  level  and  weight assignment.  We  analyze  the  contact  opportunity  for data  exchange  while MEs move.  The  method  has  been successfully tested for traffic pollution monitoring in an urban area.

In Internet of Things (IoT) networks, congestion is growing with the increasing number of devices, and a large amount of collected data must be transferred. Congestion control is one of the most significant challenges for such networks. The Constrained Application Protocol (CoAP) has been adopted for the IoT to satisfy the demand for smart applications. However, CoAP uses a basic congestion control algorithm that operates only when congestion occurs. Thus, the basic CoAP and most similar loss-based congestion control schemes have remaining issues for burst data transfer in dynamic network environments. This paper proposes a novel rate-based congestion control scheme using fuzzy control for CoAP, called FuzzyCoAP. We use the round-trip time gradient and bottleneck bandwidth gradient as inputs for FuzzyCoAP to infer the degree of congestion. FuzzyCoAP uses this indicator to predict early congestion and adjusts the sending rate to avoid congestion. FuzzyCoAP uses the congestion degree to update the variable RTO for retransmissions. On the other hand, FuzzyCoAP dynamically checks for the available bandwidth to gain high performance for burst data transfer. Various simulation experiments have demonstrated the feasibility of the FuzzyCoAP in different traffic scenarios. We compared the proposed scheme with representative loss-based CoAP schemes, that is, the basic CoAP. The simulation results proved that FuzzyCoAP provides high performance in terms of delay, throughput, loss rate, and retransmissions compared with the basic CoAP.

Congestion is an important issue in Internet of Things (IoT) networks with constrained devices and a growing number of applications. This paper investigated the problem of congestion control for burst traffic in such networks. We highlight the shortcomings of the current constrained application protocol (CoAP) in its inability to support burst traffic and rate control. Subsequently, we propose an analytical model for CoAP burst traffic and a new rate-control algorithm for CoAP to avoid congestion. A CoAP sender increases or decreases the transmission rate depending on the congestion detection. Using simulations, we compared the performance of the proposed algorithm with the current CoAP in various traffic scenarios. Experimental results show that the proposed algorithm is efficient for burst traffic and provides better performance in terms of delay, throughput, retransmission, packet duplication, and packet loss compared to CoAP.