Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey

Growing progress in sensor technology has constantly expanded the number and range of low-cost, small, and portable sensors on the market, increasing the number and type of physical phenomena that can be measured with wirelessly connected sensors. Large-scale deployments of wireless sensor networks (WSN) involving hundreds or thousands of devices and limited budgets often constrain the choice of sensing hardware, which generally has reduced accuracy, precision, and reliability. Therefore, it is challenging to achieve good data quality and maintain error-free measurements during the whole system lifetime. Self-calibration or recalibration in ad hoc sensor networks to preserve data quality is essential, yet challenging, for several reasons, such as the existence of random noise and the absence of suitable general models. Calibration performed in the field, without accurate and controlled instrumentation, is said to be in an uncontrolled environment. This paper provides current and fundamental self-calibration approaches and models for wireless sensor networks in uncontrolled environments

Distributed Object Tracking Using a Cluster-Based Kalman Filter in Wireless Camera Networks

Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges, such as the necessity to estimate, usually in real time, the constantly changing state of the target based on information acquired by the nodes at different time instants. To address these issues, we propose a distributed object tracking system which employs a cluster-based Kalman filter in a network of wireless cameras. When a target is detected, cameras that can observe the same target interact with one another to form a cluster and elect a cluster head. Local measurements of the target acquired by members of the cluster are sent to the cluster head, which then estimates the target position via Kalman filtering and periodically transmits this information to a base station. The underlying clustering protocol allows the current state and uncertainty of the target position to be easily handed off among clusters as the object is being tracked. This allows Kalman filter-based object tracking to be carried out in a distributed manner. An extended Kalman filter is necessary since measurements acquired by the cameras are related to the actual position of the target by nonlinear transformations. In addition, in order to take into consideration the time uncertainty in the measurements acquired by the different cameras, it is necessary to introduce nonlinearity in the system dynamics. Our object tracking protocol requires the transmission of significantly fewer messages than a centralized tracker that naively transmits all of the local measurements to the base station. It is also more accurate than a decentralized tracker that employs linear interpolation for local data aggregation. Besides, the protocol is able to perform real-time estimation because our implementation takes into consideration the sparsit- - y of the matrices involved in the problem. The experimental results show that our distributed object tracking protocol is able to achieve tracking accuracy comparable to the centralized tracking method, while requiring a significantly smaller number of message transmissions in the network

Towards formal models and languages for verifiable Multi-Robot Systems

Incorrect operations of a Multi-Robot System (MRS) may not only lead to unsatisfactory results, but can also cause economic losses and threats to safety. These threats may not always be apparent, since they may arise as unforeseen consequences of the interactions between elements of the system. This call for tools and techniques that can help in providing guarantees about MRSs behaviour. We think that, whenever possible, these guarantees should be backed up by formal proofs to complement traditional approaches based on testing and simulation. We believe that tailored linguistic support to specify MRSs is a major step towards this goal. In particular, reducing the gap between typical features of an MRS and the level of abstraction of the linguistic primitives would simplify both the specification of these systems and the verification of their properties. In this work, we review different agent-oriented languages and their features; we then consider a selection of case studies of interest and implement them useing the surveyed languages. We also evaluate and compare effectiveness of the proposed solution, considering, in particular, easiness of expressing non-trivial behaviour.Comment: Changed formattin

Understanding user interactions in stereoscopic head-mounted displays

2022 Spring.Includes bibliographical references.Interacting in stereoscopic head mounted displays can be difficult. There are not yet clear standards for how interactions in these environments should be performed. In virtual reality there are a number of well designed interaction techniques; however, augmented reality interaction techniques still need to be improved before they can be easily used. This dissertation covers work done towards understanding how users navigate and interact with virtual environments that are displayed in stereoscopic head-mounted displays. With this understanding, existing techniques from virtual reality devices can be transferred to augmented reality where appropriate, and where that is not the case, new interaction techniques can be developed. This work begins by observing how participants interact with virtual content using gesture alone, speech alone, and the combination of gesture+speech during a basic object manipulation task in augmented reality. Later, a complex 3-dimensional data-exploration environment is developed and refined. That environment is capable of being used in both augmented reality (AR) and virtual reality (VR), either asynchronously or simultaneously. The process of iteratively designing that system and the design choices made during its implementation are provided for future researchers working on complex systems. This dissertation concludes with a comparison of user interactions and navigation in that complex environment when using either an augmented or virtual reality display. That comparison contributes new knowledge on how people perform object manipulations between the two devices. When viewing 3D visualizations, users will need to feel able to navigate the environment. Without careful attention to proper interaction technique design, people may struggle to use the developed system. These struggles may range from a system that is uncomfortable and not fit for long-term use, or they could be as major as causing new users to not being able to interact in these environments at all. Getting the interactions right for AR and VR environments is a step towards facilitating their widespread acceptance. This dissertation provides the groundwork needed to start designing interaction techniques around how people utilize their personal space, virtual space, body, tools, and feedback systems

Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes

Probing cilia-driven flow in living embryos using femtosecond laser ablation and fast imaging

Embryonic development strictly depends on fluid dynamics. As a consequence, understanding biological fluid dynamic is essential since it is unclear how flow affects development. For example, the specification of the left-right axis in vertebrates depends on fluid flow where beating cilia generate a directional flow necessary for breaking the embryonic symmetry in the so-called left-right organizer. To investigate flow dynamics in vivo proper labeling methods necessitate approaches that are compatible with both normal biology and in vivo imaging. In this study, we describe a strategy for labeling and analyzing microscopic fluid flows in vivo that meets this challenge. We developed an all-optical approach based on three steps. First we used sub-cellular femtosecond laser ablation to generate fluorescent micro-debris to label the flow. The non-linear effect used in this technique allows a high spatial confinement and a low invasiveness, thus permitting the targeting of sub-cellular regions deep inside the embryo. Then, we used fast confocal imaging and 3Dparticle tracking were used to image and quantify the seeded flow. This approach was used to investigate the flow generated within zebrafish left-right organizer, a micrometer scale ciliated vesicle located deep inside the embryo and involved in breaking left-right embryonic symmetry. We mapped the velocity field within the vesicle and surrounding a single beating cilium, and showed that this method can address the dynamics of cilia-driven flows at multiple length scales. We could validate the flow features as predicted from previous simulations. Such detailed descriptions of fluid movements will be valuable in unraveling the relationships between cilia-driven flow and signal transduction. More generally, this all-optical approach opens new opportunities for investigating microscopic flow in living tissues