Wireless Sensor Networks

Implementing Wireless Sensor Networks for Environmental Monitoring Early Detection Communication Systems

A staged implementation with focused testing and validation at each stage is essential to a robust and reliable sensor network for monitoring natural conditions for disaster alert. It can also ease the task of expanding and modifying the system.


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The Internet of Things (IoT) and Big Data applies to M2M communication that stretches beyond manufacturing processes and infotainment apps on smartphones and tablets. Predicting and preparing for natural disasters is not an easy task. Geologists, meteorologists, local and national government preparedness agencies and many more depend on the constant set of data being collected from a diverse array of distributed wireless sensors in their ongoing efforts. They have the tough job of interpreting the data collected. The more challenging job, however, is implementing that wireless sensor network and ensuring that the information the sensors gather is communicated reliably and in time. 

Challenges comparable to these are faced by engineers implementing wireless networks spanning all types of applications. Not every solution has to contend with intense rainfall velocity like the example use case described here, but the implementation of wireless networks for various uses across the globe is challenging. As you will see, the method of modeling and selection and testing with the right hardware and software tools is the key to success.

The speed, efficiency and rapid analysis of real time data is essential for critical disaster mitigation communication systems. The reliability of data gathered and communicated can be a pivotal point in terms of reducing loss of human life and property. Global Positioning System (GPS), Global Information System (GIS) and infrared spectrum core technology platform architectures are among the advanced technology applications that have been created to serve the data analysis needs of disaster mitigation communication systems. However, they depend on the sensors to collect and communicate data quickly and effectively. Thus, they need a seamless, optimized and fully integrated system solution. 

The requirements of such a solution include, at minimum:

  • Hardware and Software for predictive modeling
  • Seamless IP connectivity in a mobile IP environment
  • Implementation of a solution that allows flexibility for software-defined radio, 3G and Wi-Fi data radios with variable data rate capabilities
  • Remote configuration and management of diverse sensors and accessories such as dam debris flow, ground vibration detectors, water level meters, infrared CCTV cameras for live feed, etc.


Understanding Requirements

To really come up with the needed solution also takes understanding the conditions under which the solution will have to reliably run 24 hours a day, 7 days a week and 365 days a year—ideally for years on end. In the particular use case we are looking at called “Hill Top”—a state-of-the-art advanced detection and storm warning plus debris flow monitoring system—the coverage area was widespread and some of the places in the coverage area were not easy to access. Yet if conditions in these difficult to reach areas change, nearby populated areas could be severely affected. 

Using sophisticated modeling software, in this case the Artemis software suite that is available in Lilee Systems TransAir intelligent connectivity solutions, engineers were able to create the basic system architecture for the Hill Top project. This architecture, as depicted in Figure 1, features sensors and base stations that enable high quality, long distance, all weather/terrain and optimized bandwidth communication transmission of key early warning sensor data and information. The solution includes multiple remote sensor data gathering posts that rapidly communicate information to a base station aggregator facility that is designed to prioritize and begin data processing. Data and information is then transmitted to a central data processing center for final analysis and determination of critical natural disaster management decisions.

Figure 1
The sensors used in the application are the end points that communicate real-time data to the strategically positioned base stations in the coverage region, ensuring that the critical data reaches the control centers to support proactive emergency readiness in the anticipated event of a natural disaster.

Care in Implementation

Prior to installing the solution components, it is highly advisable to test the model with properly calibrated transmitters and receivers. For the Hill Top project, modeling and testing was broken up into four phases to ensure that the full solution would work with minimized unknown variables. Phase 1 was for the long distance radio transmission of video surveillance data. Phase 2 included the medium distance radio transmission for the sensors and video. Phase 3 covered the flood detection and advance warning broadcast system. Phase 4 brought all the gathered data back to the larger central emergency response system. At each phase, testing was conducted and findings were validated by means of a software tool, in this case the Apollo software suite that also is integrated in the Lilee Systems TransAir intelligent connectivity solution. For the Hill Top Project, Apollo served to collect, analyze and measure the health of the data/video radio system waveforms and the quality of the radio signals from sensors to base to central control. 

Through Apollo, it was possible to review single-click views of waveforms and locations, verify signal to noise ratios and validate antenna tower heights. It was also possible to validate the actual performance of heat map simulations while debugging and continuously improving the data/video network system.

By modeling and testing in this order, the overall solution ensured that flaws could be captured and corrected early and before adding the next phase. For Hill Top, it was possible to measure the signal to noise ratio for signal packets and thus fine tune the system preamplifiers and attenuators to optimize transmitted data signal “packets.” This contributes to the reliable decoding of each data packet transmitted and received.

As each phase of the project was completed, it was found that a larger mix of sensors could be integrated to enhance the warning system. This is highlighted in Figures 2 and 3. Figure 2 depicts the initial level of sensor data gathered and communicated. Figure 3 extends this to cover a more detailed degree of emergency preparedness. 

Figure 2
Basic Detection and Advanced Warning Network. Unique sensors in the overall solution gather information and channel it to the monitoring station. Basic data processing starts at the monitoring station and is expanded on at the control station. A designated portion of information can also be communicated out to additional mobile network points.

Figure 3
Voice, Data, Video Convergence in a Community Mesh Network Hilltop Project. Through a phased process, multiple sensor networks can be brought online to enhance the view of the monitored environment showing each selection of data independently and how it correlates with the other types of data collected via the other sensor networks. The complete actionable data collected allows decision makers to make educated real-time choices.

If we consider this use case as compared to an agricultural use or industrial automation flow, we see that by expanding sensor networks in phases it is possible to increase the degree of M2M communication achievable, and in turn the information gathered allows for process improvements and optimization.  Additionally, the complement of information gathered can be used to create a very user-friendly interface for not only the decision makers, but also for staff members and even the public. In the Hill Top project, it was possible to bring together all the data from the wireless networks in a public web-based portal such that the community related to the monitored area could stay up to date on the latest environmental conditions. Figure 4 is a snapshot of the portal where the public can get real-time updates with details including rain gauge, geophone, radar water level, soil moisture, water flow, weather station and tilt.

Through this use case, it is apparent that multiple wireless networks can be implemented to create very comprehensive views of data. However, trying to bring all the networks online in one fell swoop is not advisable. To ensure that all possible interference for signal integrity and communication is accounted for, modeling and validation should be done using effective tools. This approach not only allows the natural and/or man-made obstacles to be identified, it also gives communications engineers the necessary opportunities to fine tune the system from edge to control center. This level of early detail also means that future remote configuration and maintenance is optimized. This is the technology equivalent to the construction philosophy of “measure twice, cut once.” If we get things right from the start, the future effort is well managed and the overall solution can be brought online on time and on budget. 

When it comes to developing solutions that are intended to keep people safe, there is little room for error. A well-modeled and validated wireless network communications system that gets the right information to the right personnel at the right time is the key to success whether they are preparing for natural disasters or making educated business decisions. 

Lilee Systems
Santa Clara, CA.
(408) 988-8672