New Advances on Environment Monitoring with Wireless Sensor Network

Nowadays environment is one of the main concerns of worldwide governments as witness to the international COP21 conference in Paris due to climate change which may induce a dramatic human disaster such as mass migration and starvation. It is accepted by almost all researchers in different disciplines that human activities (e.g., deforestation) are increasing CO₂ in the atmosphere which in turn creates a greenhouse effect. Accordingly, the temperature of the atmosphere increases. Therefore, it is a big challenge for the scientific community to find new techniques to support sustainable development on the one hand to preserve environment and planet resource (e.g., fresh water and clean air) and on the other hand to produce enough good quality food to feed 9 or 10 billion people in world population in 2050. WSN is an emergent and multidisciplinary science, a very active and competitive research area, and can be used to collect large scale environmental data. WSN has unprecedented levels of integration and cross-layering of hardware, software, and sensor (NEMS/MEMS). Until now academic research institutions are the main contributors driving and developing the core technologies dedicated to IoT: TinyOS (UC Berkeley), Contiki (SICS), 6LoWPAN, RPL, and CoAP (IETF) which enable P2P connection of WSN nodes through Internet. Since last year, IoT ecosystem has reached a new stage, thanks to the big main ICT actors like IBM, INTEL, SAP, TI, ARM, HUAWEI, and so forth. On the one hand, back-end cloud IoT-based servers are available such as Bluemix (IBM), HANA (SAP), and Azure (Microsoft) to support sensory data with light weighted open standard OASIS/MQTT protocol. On the other hand, a tremendous advance in the Low Power Wide Area “LPWA” wireless network such as Sigfox, LoRA (ISM free frequency bands), and Narrow Band IoT “NB-IoT” (mobile network) has been made. All these available WSN technologies will enable implementation of real world large scale continuous environment data acquisition by combining aerial (satellite and UAS) and terrestrial remote sensing network (WSN). Thus, for precision agriculture, large scale continuous environment data acquisition will allow the scientists of different fields to understand more deeply the interaction between the plants and its environment (e.g., weather and soil moisture). Accordingly, it becomes possible to develop effective and viable strategies to limit or eradicate the use of pesticides and chemical fertilizer to ultimately enhance biodiversity and the supply of ecosystem services.

In developed countries, to treat air pollution issue, all the big cities (e.g., in EU) deploy toxic gas detection stations to sense air quality and an alarm message is broadcasted when the air pollution is over EU defined standard threshold. The cost and the form factor of toxic gas detection reference stations limit their large scale deployment. Thanks to the advanced low cost and low energy consumption gas sensors such as MOX and electrochemical sensors, the network of toxic gas sensor may be scaled to cover a large area to detect the presence of toxic gases such as O₃, NO₂, and NO and microparticles. In fact, when the presence of O₃ is higher than 100 or 120 ppb, it will impact not only on the population health but also on the crop yield (5 to 20%).

Moreover, WSN has brought new scientific problems in hardware and software design, communication, data management, and data analysis. In an effort to demonstrate the current advances and scientific problems in this specialized field of sensor networks and to stimulate discussion on the future research directions on intelligent environmental monitoring, a special issue has been dedicated to the new advances on environment monitoring with wireless sensor network.

This special issue presents a total of 7 papers focusing on different aspects of WSN. Six papers link WSN with specific applications: J. Zhang et al. with forest monitoring, Y. Zhang et al. with agriculture, S. Cai et al. with traffic security, Q. Wu et al. with atmosphere pollution monitoring, M. Jiang et al. with real-time environmental monitoring, and B. Dou et al. with validation of coarse scale remote sensing products. In contrast, the research work of S. Siddiqui et al. investigates the protocol of wireless communication, which is a basic technical topic and concerning all applications. This study introduces the mechanism of altering the polling interval distribution for the MAC protocol, based on the application's optimization objective (delay or energy). Optimal operating parameters have been studied in order to combine delay and energy consumption as optimization criteria. Results show that the energy consumption improves for the exponentially distributed polling intervals, while deterministic polling intervals provide better delay performance.

The work of Y. Zhang et al., although aimed at serving the agricultural applications, also addresses a common technical issue in communication network, that is, the efficient data transmission. In order to avoid congestion when transferring a large amount of monitoring data, this work proposed a new method of constrained route based on the metric of delay and capacity (CR-MDC) to find route path in satellite sensor network. This new scheme exhibits lower blocking probability and maximum link utilization but higher average network delay than the Dijkstra-based routing protocol which is proposed in existing literature.

Three papers introduce their works focusing on hardware and data analysis. First, B. Dou et al. measured a variety of land surface parameters concerning the radiation, soil, and vegetation. And the radiation parameters measured by WSN are adopted to validate satellite remote sensing product. In this study, the accuracy and spatial representativeness of WSN measurements are discussed. Then, Q. Wu et al. detected four types of toxic gases, specifically, Cl₂, CO, NO₂, and SO₂, using gas detection technologies and wireless communication technologies. The feasibility of the system and validity of measurement are verified in industrial experiments. Finally, S. Cai et al. have measured pavement structural temperature (PST) with their network architecture. The proposed smart sensor network has been used in the experimental research of PST distribution of asphalt and cement pavement. It is also worthwhile to mention that a website is designed to distribute, analyze, and visualize the PST measurement data.

In the paper by M. Jiang et al., managing the data collected by the sensor network is a major topic besides the hardware. In this research, a generic distributed system model based on the concept of data streams was proposed for real-time environmental monitoring; and a prototype system, the Galilee middleware system, was built to support various applications in a reliable and easily manageable way. Dataset of water quality of Lake Soyang was presented as demonstration of this system.

Another important issue concerning the sensor network is the scheme of nodes deployment. J. Zhang et al. proposed an improved deterministic deployment algorithm based on particle swarm optimization to increase perceived probability for forest monitoring. By analyzing the coverage probability of the monitoring area with different deployment models, the monitoring areas of their directional sensor network have increased sixteen percent, compared with random deployment. The monitoring efficiency is boosted in the optimized nodes deployment. It is worthwhile to mention that the paper of B. Dou et al. also addressed the issue of optimal layout of the sensor nodes.