## 5 A Case Study on Reliable Pipeline Monitoring Using Cyber Physical Sensor Networks
### Data Transmission and Delivery  
After the parameters from sensors are successfully measured and recorded, a mechanism to transmit the data to the base station is required. Reliable and secure transmission of data is significantly important. For this purpose, various network architecture and topologies have been proposed [57, 58].  
Factors such as real time sensing node design, pipeline and network infrastructure, connectivity of nodes to base station and battery life or duty cycle directly affect data transmission [59].  
Several wireless networking standards such as Bluetooth, ZigBee, and DASH7 are frequently used in integration for sensor network implementation. For pipelines situated in remote areas, it is desirable to make use of long range networks like GSM and GPRS to transmit data collected on the backhaul network. Wireless HART (WiHART) [36] technology has been used as the first open wireless standard for industrial process control in sensor networks. For infrastructure monitoring of linear and hierarchical designs, different wireless standards of transmission may be employed at different hierarchy levels.  
The choice of the transmission standard will depend on the cost and the desired range. Wireless signals get severely attenuated in the underground and underwater transmission resulting in unreliability. The effect of extreme path loss, reflection/refraction, multi-path fading, reduced propagation velocity, and noise on the propagation of electromagnetic waves in underground networks has been described in [39]. Several alternatives to underground problems have been provided in [60, 61] that make use of electro-magnetic induction for transmitting signals. Underground communication may also be improved by activating the soil property sensors only when a leak is suspected and by wiring the processing hubs to the coils that prevent transmission losses. Acoustics can also be used for communication wherein the details on the effect of path loss, noise, multi-path fading, doppler spread, and long and variable propagation delay on the acoustic signals used for pipelines underwater can be found in [62].  
Before sending the sensor readings over to base station, the gateway node needs to compress the information in a meaningful form to transfer maximum information on limited bandwidth [63]. This includes extracting several features from the bulk of data such as mean and variance. The techniques used for data aggregation discussed in [18, 64] include Fuzzy ART, Maximum Likelihood Estimator (MLE) and Moving Average Filter (MAF). Fuzzy ART implements a neural network module that classifies the available information into groups and assigns probabilities to each group in order to make decisions for the incoming data based on those probabilities [64]. In case of MLE, the data observed from all sensors is used to create a likelihood function. The function is then maximized over all possible values of mean and standard deviation. MAF method computes average of the inputs to provide an output value that is useful in noisy channels where input values differ a lot from the mean of averaged out scores [65].  
After the fused and compressed meaningful data reaches the base station, an algorithm is run on it to make an inference about the condition of the pipeline and the carried fluid or gas. The algorithms differ based on the type of sensing technique applied and the parameter that is under consideration. Haar Wavelet transform can be used to detect pressure pulses that localize leaks in a pipeline with reduced complexity [66]. The acoustic/vibration signals are cross correlated to detect leakages. The challenge in this technique is to synchronize the signals in time to make an adequate deduction. The delay of receiving the HF noise caused by the leaks at different nodes can be calculated by their physical distance and speed of the acoustic signals traveling across the pipe.