Cooperative Vehicle Tracking in Large Environments

Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can be easily achieved by providing vehicles with a constant communication link to a control centre and having the vehicles broadcast their position. The problem changes dramatically when vehicles operate within a large environment of potentially hundreds of square kilometres and in difficult terrain. This thesis presents algorithms for cooperative tracking of vehicles based on a vehicle motion model that incorporates the properties of the working area, and information collected by infrastructure collection points and other mobile agents. The probabilistic motion prediction approach provides long-term estimates of vehicle positions using motion profiles built for the particular environment and considering the vehicle stopping probability. A limited number of data collection points distributed around the field are used to update the position estimates, with negative information also used to improve the estimation. The thesis introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates and inter-vehicle measurements to be relayed among vehicles and finally conveyed to the collection points for an improved position estimate. It uses a store-and-synchronise concept to deal with intermittent communication and aims to disseminate data in an opportunistic manner. A nonparametric filtering algorithm for cooperative tracking is proposed to incorporate the information harvested, including the negative, relative, and time delayed observations. An important contribution of this thesis is to enable the optimisation of fleet scheduling when full coverage networks are not available or feasible. The proposed approaches were validated with comprehensive experimental results using data collected from a large-scale mining operation

Cooperative Vehicle Tracking in Large Environments

Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can be easily achieved by providing vehicles with a constant communication link to a control centre and having the vehicles broadcast their position. The problem changes dramatically when vehicles operate within a large environment of potentially hundreds of square kilometres and in difficult terrain. This thesis presents algorithms for cooperative tracking of vehicles based on a vehicle motion model that incorporates the properties of the working area, and information collected by infrastructure collection points and other mobile agents. The probabilistic motion prediction approach provides long-term estimates of vehicle positions using motion profiles built for the particular environment and considering the vehicle stopping probability. A limited number of data collection points distributed around the field are used to update the position estimates, with negative information also used to improve the estimation. The thesis introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates and inter-vehicle measurements to be relayed among vehicles and finally conveyed to the collection points for an improved position estimate. It uses a store-and-synchronise concept to deal with intermittent communication and aims to disseminate data in an opportunistic manner. A nonparametric filtering algorithm for cooperative tracking is proposed to incorporate the information harvested, including the negative, relative, and time delayed observations. An important contribution of this thesis is to enable the optimisation of fleet scheduling when full coverage networks are not available or feasible. The proposed approaches were validated with comprehensive experimental results using data collected from a large-scale mining operation

Analysis of dynamic path loss based on the RSSI model for rupture location analysis in underground wireless sensor networks and its implications for Earthquake Early Warning System (EEWS)

Sensors deployed in underground tunnels found that radio frequency signals suffer significant signal strength attenuation which can result in considerable variation of link quality on the receiving end. This study analyzes the received signal strength index (RSSI) based on  the development of a theoretical wireless sensor model for  data  collection by  enabling  sensors  to  determine  the  location  from  which  each  data packet is obtained. To improve positioning accuracy, the complex radio wave propagation environment requires the use of a voronoi cell to minimize signal attenuation. A relatively simple calculation is used to predict the intensity and perception range of the received wireless signals to measure the extent of signal reduction in the attenuating rock medium. Simulation results show that RSSI-based localization and wireless network lifetime and throughput measurements are more accurate when the node concept is applied to the self-locating rupture zones than the maximum likelihood estimation method. The proposed minimum energy relay routing technique based on beacon node chain deployment is found to help correct localization errors resulting from interference caused by the underground tunnel environment. The extent of localization and power of the sensor nodes are determined based on the beacon node chain deployment of tunnel wireless sensor networks. The algorithm accounts for the distance and the corresponding RSSI between adjacent beacon nodes to calculate the actual path loss parameter in the tunnel. The proposed model can serve as the theoretical basis for locating ruptures in underground wireless sensor network nodes, thus maximizing the monitoring range of large scale tectonic environments while minimizing equipment cost. We recommend that this model can be field tested through a series of experiments by researchers and engineers working in seismology, telecommunication, and information technology.<br /

Wearable Bluetooth Sensors for Capturing Relational Variables and Temporal Variability in Relationships: A Construct Validation Study

The advent of wearable sensor technologies has the potential to transform organizational research by offering the unprecedented opportunity to collect continuous, objective, highly granular data over extended time periods. Recent evidence has demonstrated the potential utility of Bluetooth-enabled sensors, specifically, in identifying emergent networks via colocation signals in highly controlled contexts with known distances and groups. Although there is proof of concept that wearable Bluetooth sensors may be able to contribute to organizational research in highly controlled contexts, to date there has been no explicit psychometric construct validation effort dedicated to these sensors in field settings. Thus, the two studies described here represent the first attempt to formally evaluate longitudinalBluetooth data streams generated in field settings, testing their ability to (a) show convergent validity with respect to traditional self-reports of relational data; (b) display discriminant validitywith respect to qualitative differences in the nature of alternative relationships (i.e., advice vs. friendship); (c) document predictive validity with respect to performance; (d) decompose variance in network-related measures into meaningful within- and between-unit variability over time; and (e) complement retrospective self-reports of time spent with different groups where there is a “ground truth” criterion. Our results provide insights into the validity of Bluetooth signals with respect to capturing variables traditionally studied in organizational science and highlight how the continuous data collection capabilities made possible by wearable sensors can advance research far beyond that of the static perspectives imposed by traditional data collection strategies

Reducing energy consumption in mobile ad-hoc sensor networks

PhD ThesisRecent rapid development of wireless communication technologies and portable mobile devices such as tablets, smartphones and wireless sensors bring the best out of mobile computing, particularly Mobile Ad-hoc Sensor Networks (MASNETs). MASNETs are types of Mobile Ad-hoc Networks (MANETs) that are designed to consider energy in mind because they have severe resource constraints due to their lack of processing power, limited memory, and bandwidth as in Wireless Sensor Networks (WSNs). Hence, they have the characteristics, requirements, and limitations of both MANETs and WSNs. There are many potential applications of MASNETs such as a real-time target tracking and an ocean temperature monitoring. In these applications, mobility is the fundamental characteristic of the sensor nodes, and it poses many challenges to the routing algorithm. One of the greatest challenge is to provide a routing algorithm that is capable of dynamically changing its topology in the mobile environment with minimal consumption of energy. In MASNETs, the main reason of the topology change is because of the movement of mobile sensor nodes and not the node failure due to energy depletion. Since these sensor nodes are limited in power supply and have low radio frequency coverage, they easily lose their connection with neighbours, and face diffi culties in updating their routing tables. The switching process from one coverage area to another consumes more energy. This network must be able to adaptively alter the routing paths to minimize the effects of variable wireless link quality, topological changes, and transmission power levels on energy consumption of the network. Hence, nodes prefer to use as little transmission power as necessary and transmit control packets as infrequently as possible in energy constrained MASNETs. Therefore, in this thesis we propose a new dynamic energy-aware routing algorithm based on the trans- mission power control (TPC). This method effectively decreases the average percentage of packet loss and reduces the average total energy consumption which indirectly pro- long the network lifetime of MASNETs. To validate the proposed protocol, we ran the simulation on the Avrora simulator and varied speed, density, and route update interval of mobile nodes. Finally, the performance of the proposed routing algorithm was measured and compared against the basic Ad-hoc On-demand Distance Vector (AODV) routing algorithm in MASNETs.The Ministry of Education of Malaysia: The Universiti Malaysia Sarawak