Andrew Markham

Evolution and sustainability of a wildlife monitoring sensor network

As sensor network technologies become more mature, they are increasingly being applied to a wide variety of applications, ranging from agricultural sensing to cattle, oceanic and volcanic monitoring. Significant efforts have been made in deploying and testing sensor networks resulting in unprecedented sensing capabilities. A key challenge has become how to make these emerging wireless sensor networks more sustainable and easier to maintain over increasingly prolonged deployments. In this paper, we report the findings from a one year deployment of an automated wildlife monitoring system for analyzing the social co-location patterns of European badgers (Meles meles) residing in a dense woodland environment. We describe the stages of its evolution cycle, from implementation, deployment and testing, to various iterations of software optimization, followed by hardware enhancements, which in turn triggered the need for further software optimization. We report preliminary descriptive analyses of a subset of the data collected, demonstrating the significant potential our system has to generate new insights into badger behavior. The main lessons learned were: the need to factor in the maintenance costs while designing the system; to look carefully at software and hardware interactions; the importance of a rapid initial prototype deployment (this was key to our success); and the need for continuous interaction with domain scientists which allows for unexpected optimizations.

Lightweight map matching for indoor localisation using conditional random fields

Indoor tracking and navigation is a fundamental need for pervasive and context-aware smartphone applications. Although indoor maps are becoming increasingly available, there is no practical and reliable indoor map matching solution available at present. We present MapCraft, a novel, robust and responsive technique that is extremely computationally efficient (running in under 10 ms on an Android smartphone), does not require training in different sites, and tracks well even when presented with very noisy sensor data. Key to our approach is expressing the tracking problem as a conditional random field (CRF), a technique which has had great success in areas such as natural language processing, but has yet to be considered for indoor tracking. Unlike directed graphical models like Hidden Markov Models, CRFs capture arbitrary constraints that express how well observations support state transitions, given map constraints. Extensive experiments in multiple sites show how MapCraft outperforms state-of-the art approaches, demonstrating excellent tracking error and accurate reconstruction of tortuous trajectories with zero training effort. As proof of its robustness, we also demonstrate how it is able to accurately track the position of a user from accelerometer and magnetometer measurements only (i.e. gyro- and WiFi-free). We believe that such an energy-efficient approach will enable always-on background localisation, enabling a new era of location-aware applications to be developed.

WILDSENSING: Design and deployment of a sustainable sensor network for wildlife monitoring

The increasing adoption of wireless sensor network technology in a variety of applications, from agricultural to volcanic monitoring, has demonstrated their ability to gather data with unprecedented sensing capabilities and deliver it to a remote user. However, a key issue remains how to maintain these sensor network deployments over increasingly prolonged deployments. In this article, we present the challenges that were faced in maintaining continual operation of an automated wildlife monitoring system over a one-year period. This system analyzed the social colocation patterns of European badgers (Meles meles) residing in a dense woodland environment using a hybrid RFID-WSN approach. We describe the stages of the evolutionary development, from implementation, deployment, and testing, to various iterations of software optimization, followed by hardware enhancements, which in turn triggered the need for further software optimization. We highlight the main lessons learned: the need to factor in the maintenance costs while designing the system; to consider carefully software and hardware interactions; the importance of rapid prototyping for initial deployment (this was key to our success); and the need for continuous interaction with domain scientists which allows for unexpected optimizations.

Non-Line-of-Sight Identification and Mitigation Using Received Signal Strength

Indoor wireless systems often operate under non-line-of-sight (NLOS) conditions that can cause ranging errors for location-based applications. As such, these applications could benefit greatly from NLOS identification and mitigation techniques. These techniques have been primarily investigated for ultra-wide band (UWB) systems, but little attention has been paid to WiFi systems, which are far more prevalent in practice. In this study, we address the NLOS identification and mitigation problems using multiple received signal strength (RSS) measurements from WiFi signals. Key to our approach is exploiting several statistical features of the RSS time series, which are shown to be particularly effective. We develop and compare two algorithms based on machine learning and a third based on hypothesis testing to separate LOS/NLOS measurements. Extensive experiments in various indoor environments show that our techniques can distinguish between LOS/NLOS conditions with an accuracy of around 95%. Furthermore, the presented techniques improve distance estimation accuracy by 60% as compared to state-of-the-art NLOS mitigation techniques. Finally, improvements in distance estimation accuracy of 50% are achieved even without environment-specific training data, demonstrating the practicality of our approach to real world implementations.

Revealing the hidden lives of underground animals using magneto-inductive tracking

Currently, there is no existing method for automatically tracking the location of burrowing animals when they are underground, consequently zoologists only have a partial view of their subterranean behaviour and habits. Conventional RF based methods of localization are unsuitable because electromagnetic waves are severely attenuated by soil and moisture. Here, we use an as yet unexploited method of localization, namely magneto-inductive (MI) localization. Magnetic fields are not affected by soil or water, and thus have virtually unattenuated ground penetration. In this paper, we present a method that allows the position of an animal to be determined through soil. Not only does this enable the study of behaviour, it also allows the structure of the tunnel to be automatically mapped as the animal moves through it. We describe the application for tracking wild European Badgers (Meles meles) within their burrows, providing experimental data from a two month deployment.

Magneto-inductive networked rescue system (MINERS): taking sensor networks underground

Wireless underground networks are an emerging technology which have application in a number of scenarios. For example, in a mining disaster, flooding or a collapse can isolate portions of underground tunnels, severing wired communication links and preventing radio communication. In this pa-per, we explore the use of low frequency magnetic fields for communication, and present a new hardware platform that features triaxial transmitter/receiverantenna loops. We point out that the fundamental problem of the magnetic channel is the limited bitrate at long ranges, due to the extreme path loss of 60 dB/decade. To this end, we present two complementary techniques to address this limitation. Firstly, we demonstrate magnetic vector modulation, a technique which modulates the three dimensional orientation of the magnetic vector. This increases the gross bitrate by a factor of over 2.5, without an increase in transmission power or bandwidth. Secondly, we show how in a multi-hop network latencies can be dramatically reduced by receiving multiple parallel streams of frequency multiplexed data in a many-to-one configuration. These techniques are demonstrated on a working hardware platform, which for flexible operation, features a software defined magnetic transceiver. Typical communication range is approximately 30 m through rock.

Underground Localization in 3-D Using Magneto-Inductive Tracking

Localization of mobile devices underground is extremely challenging, with radio propagation (such as used in GPS and VHF) severely attenuated by soil and moisture. However, low frequency magnetic fields are able to penetrate the ground with minimal loss. Mobile underground tracking devices record magnetic field strengths generated by an array of transmitting coils placed above the area of interest. This information is stored in flash memory, for opportunistic upload over a conventional radio link when the device is above ground. As a particular application of this technology, the underground movements of wild European badgers (Meles meles) were tracked in 3-D within their burrow systems, by equipping them with lightweight tracking collars. Typical localization accuracy is 0.45 m RMS over a 15 m × 15 m area and collar lifetime is of the order of 9 months from a 1.4 Ah lithium cell.

Distortion Rejecting Magneto-Inductive Three-Dimensional Localization (MagLoc)

Localization is a research area that, due to its overarching importance as an enabler for higher level services, has attracted a vast amount of research and commercial interest. For the most part, it can be claimed that GPS provides an unparalleled solution for outdoor tracking and navigation. However, the same cannot yet be said about positioning in GPS-denied or challenged environments, such as indoor environments, where obstructions such as floors and walls heavily attenuate or reflect high-frequency radio signals. This has led to a plethora of competing solutions targeted toward a particular application scenario, yielding a fragmented solution landscape. In this paper, we present a fresh approach to 3-D positioning based on the use of very low frequency (kHz) magneto-inductive (MI) fields. The most important property of MI positioning is that obstacles such as walls, floors, and people that heavily impact the performance of competing approaches are largely “transparent” to the quasi-static magnetic fields. MI has a number of challenges to robust operation that distort positions, including the presence of ferrous materials and sensitivity to user rotation. Through signal processing and sensor fusion across multiple system layers, we show how we can overcome these challenges. We showcase its highly accurate 3-D positioning in a number of environments, with positioning accuracy below 0.8 m even in heavily distorted areas.

Climate and the Individual: Inter-Annual Variation in the Autumnal Activity of the European Badger (Meles meles)

We establish intra-individual and inter-annual variability in European badger (Meles meles) autumnal nightly activity in relation to fine-scale climatic variables, using tri-axial accelerometry. This contributes further to understanding of causality in the established interaction between weather conditions and population dynamics in this species. Modelling found that measures of daylight, rain/humidity, and soil temperature were the most supported predictors of ACTIVITY, in both years studied. In 2010, the drier year, the most supported model included the SOLAR*RH interaction, RAIN, and30cmTEMP (w = 0.557), while in 2012, a wetter year, the most supported model included the SOLAR*RH interaction, and the RAIN*10cmTEMP (w = 0.999). ACTIVITY also differed significantly between individuals. In the 2012 autumn study period, badgers with the longest per noctem activity subsequently exhibited higher Body Condition Indices (BCI) when recaptured. In contrast, under drier 2010 conditions, badgers in good BCI engaged in less per noctem activity, while badgers with poor BCI were the most active. When compared on the same calendar dates, to control for night length, duration of mean badger nightly activity was longer (9.5 hrs ±3.3 SE) in 2010 than in 2012 (8.3 hrs ±1.9 SE). In the wetter year, increasing nightly activity was associated with net-positive energetic gains (from BCI), likely due to better foraging conditions. In a drier year, with greater potential for net-negative energy returns, individual nutritional state proved crucial in modifying activity regimes; thus we emphasise how a ‘one size fits all’ approach should not be applied to ecological responses.

Human Interactive Secure ID Management in Body Sensor Networks

Security and privacy protection for body sensor networks are nontrivial: inadequate protection could lead to the leakage of sensitive personal information and other attacks. Unfortunately, current solutions mainly rely on static and machine-oriented pre-distributed IDs, which have several weaknesses. In order to solve this problem, we propose dynamic binding protocol and eXtensible Resource Identifier (XRI) based ID scheme. Thanks to them, IDs are dynamically allocated and easily managed. In addition, we show how a human interactive channel can be used for wireless sensor network security protection.