Safety and reliability have always been concerns for railway transportation.Knowing the exact location of a train enables the railway system to react toan unusual situation for the safety of human lives and properties. Generally,the accuracy of localisation systems is related with their deployment andmaintenance costs, which can be on the order of millions of dollars a year.Despite a lot of research efforts, existing localisation systems based on differenttechnologies are still limited because most of them either requireexpensive infrastructure (ultrasound and laser), have high database maintenance,computational costs or accumulate errors (vision), offer limitedcoverage (GPS-dark regions, Wi-Fi, RFID) or provide low accuracy (audiblesound). On the other hand, wireless sensor networks (WSNs) offer thepotential for a cheap, reliable and accurate solutions for the train localisationsystem. This thesis proposes a WSN-based train localisation system,in which train location is estimated based on the information gatheredthrough the communication between the anchor sensors deployed along thetrack and the gateway sensor installed on the train, such as anchor sensors;;geographic coordinates and the Received Signal Strength Indicator (RSSI).In the proposed system, timely anchor-gateway communication implies accuratelocalisation. How to guarantee effective communication between anchor sensors along the track and the gateway sensor on the train is a challenging problem for WSN-based train localisation. I propose a beacon driven sensors wake-up scheme (BWS) to address this problem. BWS allows each anchor sensor to run an asynchronous duty-cycling protocol to conserve energy and establishes an upper bound on the sleep time in one dutycycle to guarantee their timely wake-up once a train approaches. Simulationresults show that the BWS scheme can timely wake up the anchorsensors at a very low energy consumption cost.To design an accurate scheme for train localisation, I conducted on-siteexperiments in an open field, a railway station and a tunnel, and the results show that RSSI can be used as an estimator for train localisation andits applicability increases with the incorporation of another type of datasuch as location information of anchor sensors. By combining the advantagesof RSSI-based distance estimation and Particle Filtering techniques,I designed a Particle-Filter-based train localisation scheme and proposea novel Weighted RSSI Likelihood Function (WRLF) for particle update.The proposed localisation scheme is evaluated through extensive simulationsusing the data obtained from the on-site measurements. Simulationresults demonstrate that the proposed scheme can achieve significant accuracy,where average localisation error stays under 30 cm at the train speedof 40 m=s, 40% anchor sensors failure rate and sparse deployment. In addition,the proposed train localisation scheme is robust to changes in trainspeed, the deployment density and reliability of anchor sensors.Anchor sensors are prone to hardware and software deterioration such asbattery outage and dislocation. Therefore, in order to reduce the negativeimpacts of these problems, I designed a novel Consensus-based Anchor sensorManagement Scheme (CAMS), in which each anchor sensor performsa self-diagnostics and reports the detected faults in the neighbourhood.CAMS can assist the gateway sensor to exclude the input from the faultyanchor sensors. In CAMS, anchor sensors update each other about theiropinions on other neighbours and develops consensus to mark faulty sensors.In addition, CAMS also reports the system information such as signalpath loss ratio and allows anchor sensors to re-calibrate and verify theirgeographic coordinates. CAMS is evaluated through extensive simulationsbased on real data collected from field experiments. This evaluation alsoincorporated the simulated node failure model in simulations.Though there are no existing WSN-based train localisation systems availableto directly compare our results with, the proposed schemes are evaluatedwith real datasets, theoretical models and existing work wherever itwas possible. Overall, the WSN-based train localisation system enables theuse of RSSI, with combination of location coordinates of anchor sensors, aslocation estimator. Due to low cost of sensor devices, the cost of overallsystem remains low. Further, with duty-cycling operation, energy of thesensor nodes and system is conserved.
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