ConservationBots: Autonomous Aerial Robot for Fast Robust Wildlife Tracking in Complex Terrains

Today, the most widespread, widely applicable technology for gathering data relies on experienced scientists armed with handheld radio telemetry equipment to locate low-power radio transmitters attached to wildlife from the ground. Although aerial robots can transform labor-intensive conservation tasks, the realization of autonomous systems for tackling task complexities under real-world conditions remains a challenge. We developed ConservationBots-small aerial robots for tracking multiple, dynamic, radio-tagged wildlife. The aerial robot achieves robust localization performance and fast task completion times -- significant for energy-limited aerial systems while avoiding close encounters with potential, counter-productive disturbances to wildlife. Our approach overcomes the technical and practical problems posed by combining a lightweight sensor with new concepts: i) planning to determine both trajectory and measurement actions guided by an information-theoretic objective, which allows the robot to strategically select near-instantaneous range-only measurements to achieve faster localization, and time-consuming sensor rotation actions to acquire bearing measurements and achieve robust tracking performance; ii) a bearing detector more robust to noise and iii) a tracking algorithm formulation robust to missed and false detections experienced in real-world conditions. We conducted extensive studies: simulations built upon complex signal propagation over high-resolution elevation data on diverse geographical terrains; field testing; studies with wombats (Lasiorhinus latifrons; nocturnal, vulnerable species dwelling in underground warrens) and tracking comparisons with a highly experienced biologist to validate the effectiveness of our aerial robot and demonstrate the significant advantages over the manual method.Comment: 33 pages, 21 figure

Sensing task handover for indoor clustered wireless sensor network

This paper presents the proposal of implementing sensing task handover initiation control for wireless sensor network deployed in indoor environment. The structure of the wireless sensor network is based on indoor clustering so that sensor nodes are grouped in different clusters according to room partitions. For such a network, a simple and efficient multi-node handover initiation control method is proposed for the decision of handover initiation by comparing the combined received signal strengths and the number of effective nodes between two neighbouring clusters. Experiments were conducted to test the possibility and evaluate the performance of the proposed method. The results show that sensing task handover is possible to happen at accurate time while crossing boundary between two clusters by applying the proposed method

ROLAX: LOCATION DETERMINATION TECHNIQUES IN 4G NETWORKS

In this dissertation, ROLAX location determination system in 4G networks is presented. ROLAX provides two primary solutions for the location determination in the 4G networks. First, it provides techniques to detect the error-prone wireless conditions in geometric approaches of Time of Arrival (ToA) and Time Difference of Arrival (TDoA). ROLAX provides techniques for a Mobile Station (MS) to determine the Dominant Line-of-Sight Path (DLP) condition given the measurements of the downlink signals from the Base Station (BS). Second, robust RF fingerprinting techniques for the 4G networks are designed. The causes for the signal measurement variation are identified, and the system is designed taking those into account, leading to a significant improvement in accuracy. ROLAX is organized in two phases: offline and online phases. During the offline phase, the radiomap is constructed by wardriving. In order to provide the portability of the techniques, standard radio measurements such as Received Signal Strength Indication (RSSI) and Carrier to Interference Noise Ratio(CINR) are used in constructing the radiomap. During the online phase, a MS performs the DLP condition test for each BS it can observe. If the number of the BSs under DLP is small, the MS attempts to determine its location by using the RF fingerprinting. In ROLAX, the DLP condition is determined from the RSSI, CINR, and RTD (Round Trip Delay) measurements. Features generated from the RSSI difference between two antennas of the MS were also used. The features, including the variance, the level crossing rate, the correlation between the RSSI and RTD, and Kullback-Leibler Divergence, were successfully used in detecting the DLP condition. We note that, compared to using a single feature, appropriately combined multiple features lead to a very accurate DLP condition detection. A number of pattern matching techniques are evaluated for the purpose of the DLP condition detection. Artificial neural networks, instance-based learning, and Rotation Forest are particularly used in the DLP detection. When the Rotation Forest is used, a detection accuracy of 94.8\% was achieved in the live 4G networks. It has been noted that features designed in the DLP detection can be useful in the RF fingerprinting. In ROLAX, in addition to the DLP detection features, mean of RSSI and mean of CINR are used to create unique RF fingerprints. ROLAX RF fingerprinting techniques include: (1) a number of gridding techniques, including overlapped gridding; (2) an automatic radiomap generation technique by the Delaunay triangulation-based interpolation; (3) the filtering of measurements based upon the power-capture relationship between BSs; and (4) algorithms dealing with the missing data. In this work, software was developed using the interfaces provided by Beceem/Broadcom chip-set based software. Signals were collected from both the home network (MAXWell 4G network) and the foreign network (Clear 4G network). By combining the techniques in ROLAX, a distance error in the order of 4 meters was achieved in the live 4G networks

On localisation with robust power control for safety critical wireless sensor networks

A hybrid methodology is proposed for use in low power, safety critical wireless sensor network applications, where quality-of-service orientated transceiver output power control is required to operate in parallel with radio frequency-based localization. The practical implementation is framed in an experimental procedure designed to track a moving agent in a realistic indoor environment. An adaptive time synchronized approach is employed to ensure the positioning technique can operate effectively in the presence of dataloss and where the transmitter output power of the mobile agent is varying due to power control. A deterministic multilateration-based positioning approach is adopted and accuracy is improved by filtering signal strength measurements overtime to account for multipath fading. The location estimate is arrived at by employing least-squares estimation. Power control is implemented at two separate levels in the network topology. First, power control is applied to the uplink between the tracking reference nodes and the centralized access point. A number of algorithms are implemented highlighting the advantage associated with using additional feedback bandwidth, where available, and also the need for effective time delay compensation. The second layer of power control is implemented on the uplink between the mobile agent and the access point and here quantifiable improvements in quality of service and energy efficiency are observed. The hybrid paradigm is extensively tested experimentally on a fully compliant 802.15.4 testbed, where mobility is considered in the problem formulation using a team of fully autonomous robots.A hybrid methodology is proposed for use in low power, safety critical wireless sensor network applications, where quality-of-service orientated transceiver output power control is required to operate in parallel with radio frequency-based localization. The practical implementation is framed in an experimental procedure designed to track a moving agent in a realistic indoor environment. An adaptive time synchronized approach is employed to ensure the positioning technique can operate effectively in the presence of dataloss and where the transmitter output power of the mobile agent is varying due to power control. A deterministic multilateration-based positioning approach is adopted and accuracy is improved by filtering signal strength measurements overtime to account for multipath fading. The location estimate is arrived at by employing least-squares estimation. Power control is implemented at two separate levels in the network topology. First, power control is applied to the uplink between the tracking reference nodes and the centralized access point. A number of algorithms are implemented highlighting the advantage associated with using additional feedback bandwidth, where available, and also the need for effective time delay compensation. The second layer of power control is implemented on the uplink between the mobile agent and the access point and here quantifiable improvements in quality of service and energy efficiency are observed. The hybrid paradigm is extensively tested experimentally on a fully compliant 802.15.4 testbed, where mobility is considered in the problem formulation using a team of fully autonomous robots

Minimal Infrastructure Radio Frequency Home Localisation Systems

The ability to track the location of a subject in their home allows the provision of a number of location based services, such as remote activity monitoring, context sensitive prompts and detection of safety critical situations such as falls. Such pervasive monitoring functionality offers the potential for elders to live at home for longer periods of their lives with minimal human supervision. The focus of this thesis is on the investigation and development of a home roomlevel localisation technique which can be readily deployed in a realistic home environment with minimal hardware requirements. A conveniently deployed Bluetooth ® localisation platform is designed and experimentally validated throughout the thesis. The platform adopts the convenience of a mobile phone and the processing power of a remote location calculation computer. The use of Bluetooth ® also ensures the extensibility of the platform to other home health supervision scenarios such as wireless body sensor monitoring. Central contributions of this work include the comparison of probabilistic and nonprobabilistic classifiers for location prediction accuracy and the extension of probabilistic classifiers to a Hidden Markov Model Bayesian filtering framework. New location prediction performance metrics are developed and signicant performance improvements are demonstrated with the novel extension of Hidden Markov Models to higher-order Markov movement models. With the simple probabilistic classifiers, location is correctly predicted 80% of the time. This increases to 86% with the application of the Hidden Markov Models and 88% when high-order Hidden Markov Models are employed. Further novelty is exhibited in the derivation of a real-time Hidden Markov Model Viterbi decoding algorithm which presents all the advantages of the original algorithm, while producing location estimates in real-time. Significant contributions are also made to the field of human gait-recognition by applying Bayesian filtering to the task of motion detection from accelerometers which are already present in many mobile phones. Bayesian filtering is demonstrated to enable a 35% improvement in motion recognition rate and even enables a floor recognition rate of 68% using only accelerometers. The unique application of time-varying Hidden Markov Models demonstrates the effect of integrating these freely available motion predictions on long-term location predictions