## 1 Introduction  
Infrastructure monitoring has been an attractive subject for researchers in recent years. Advances in electronics and decreases in the cost of sensors and electrical components have made smart infrastructures a reality. Moreover, the proliferation of the internet has opened up new applications for the \"internet of things\" to serve as a backbone for infrastructure monitoring [1, 2, 3]. One of the main issues in infrastructure monitoring is power consumption and power availability. Power consumption of sensor nodes should be optimised while keeping their functionality to a suitable level. Another issue in the field of infrastructure monitoring, especially large scale infrastructure such as water pipe networks, is data handling. The sensor node in these systems should be capable of handling the produced data locally and then send the processed data to the control centre in order to minimise the required post processing and transmission packet size. Pipeline systems are responsible for transporting vital materials such as water, oil and gas. Any leakage in the pipe can cause major financial losses and possible environmental damages. Currently, buried pipelines are only monitored at key points, which can be spaced several kilometres apart. A system with a higher spatial resolution would provide operators with a better understanding of their network. In buried pipeline monitoring, sensor nodes are deployed in soil. The underground environment imposes major limitations on sensor nodes, such as poor RF transmission and lack of maintainability. Digging trenches in order to repair or replace nodes is extremely costly; therefore sensor nodes should have a long operational life without any maintenance. This means that sensor nodes are required to be robust and consume a small amount of energy in order to last their desired lifetime. Despite the limitations, Underground Wireless Sensor Networks (UWSN) have a wide range of applications [4, 5, 6]. Pipeline monitoring is one of the main areas in which UWSN can be used. A suitable UWSN for pipeline monitoring should be easy to deploy on existing and new pipes. Measurements of the pipes' condition should also be non-invasive to the pipe in order to maintain the structural integrity of the pipeline. This creates a need to design and develop new methods of measuring pipeline characteristics in order to monitor their structural integrity. Various methods are used in order to detect and locate leaks in pipes [7]. The main methods for pipe monitoring are acoustic measurements, pressure measurements, vision based systems, ground penetrating radar (GPR) based systems, fibre optic monitoring and multimodal systems. Vision based systems use a PIG (pipeline inspection gauge) with a form of image processing or laser scanning in order to find cracks and faults in pipelines [8]. These systems require access to the interior of the pipe to operate and only take measurements at long time intervals as it is costly to insert the PIGS into the pipe. Moreover they require high processing power or a skilled operator to analyse the results [9]. A vast amount of literature and research exists regarding the use of acoustic or vibration measurements for pipeline monitoring [5, 10, 11, 12, 13, 14, 15, 16]. The majority of these methods are based on the detection of the acoustic emissions from the pipe. The frequency and magnitude of these signals depends on pipe pressure, leak diameter and type of fluid inside the pipe [5]. These signals are then detected by hydrophones or accelerometers [10]. The location of the leak then can be calculated by different cross correlation methods. Although these systems look promising they have somedisadvantages, which make them currently not suitable to be deployed as a buried wireless sensor network system. Measuring the acoustic signal requires a high sampling rate which makes the nodes consume more power and last a shorter amount of time on the limited power supply available.