Jointly Optimizing Placement and Inference for Beacon-based Localization

The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot's location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy.Comment: Appeared at 2017 International Conference on Intelligent Robots and Systems (IROS

Wireless Sensor Networking in Challenging Environments

Recent years have witnessed growing interest in deploying wireless sensing applications in real-world environments. For example, home automation systems provide fine-grained metering and control of home appliances in residential settings. Similarly, assisted living applications employ wireless sensors to provide continuous health and wellness monitoring in homes. However, real deployments of Wireless Sensor Networks (WSNs) pose significant challenges due to their low-power radios and uncontrolled ambient environments. Our empirical study in over 15 real-world apartments shows that low-power WSNs based on the IEEE 802.15.4 standard are highly susceptible to external interference beyond user control, such as Wi-Fi access points, Bluetooth peripherals, cordless phones, and numerous other devices prevalent in residential environments that share the unlicensed 2.4 GHz ISM band with IEEE 802.15.4 radios. To address these real-world challenges, we developed two practical wireless network protocols including the Adaptive and Robust Channel Hopping (ARCH) protocol and the Adaptive Energy Detection Protocol (AEDP). ARCH enhances network reliability through opportunistically changing radio\u27s frequency to avoid interference and environmental noise and AEDP reduces false wakeups in noisy wireless environments by dynamically adjusting the wakeup threshold of low-power radios. Another major trend in WSNs is the convergence with smart phones. To deal with the dynamic wireless conditions and varying application requirements of mobile users, we developed the Self-Adapting MAC Layer (SAML) to support adaptive communication between smart phones and wireless sensors. SAML dynamically selects and switches Medium Access Control protocols to accommodate changes in ambient conditions and application requirements. Compared with the residential and personal wireless systems, industrial applications pose unique challenges due to their critical demands on reliability and real-time performance. We developed an experimental testbed by realizing key network mechanisms of industrial Wireless Sensor and Actuator Networks (WSANs) and conducted an empirical study that revealed the limitations and potential enhancements of those mechanisms. Our study shows that graph routing is more resilient to interference and its backup routes may be heavily used in noisy environments, which demonstrate the necessity of path diversity for reliable WSANs. Our study also suggests that combining channel diversity with retransmission may effectively reduce the burstiness of transmission failures and judicious allocation of multiple transmissions in a shared slot can effectively improve network capacity without significantly impacting reliability

Design and realization of precise indoor localization mechanism for Wi-Fi devices

Despite the abundant literature in the field, there is still the need to find a time-efficient, highly accurate, easy to deploy and robust localization algorithm for real use. The algorithm only involves minimal human intervention. We propose an enhanced Received Signal Strength Indicator (RSSI) based positioning algorithm for Wi-Fi capable devices, called the Dynamic Weighted Evolution for Location Tracking (DWELT). Due to the multiple phenomena affecting the propagation of radio signals, RSSI measurements show fluctuations that hinder the utilization of straightforward positioning mechanisms from widely known propagation loss models. Instead, DWELT uses data processing of raw RSSI values and applies a weighted posterior-probabilistic evolution for quick convergence of localization and tracking. In this paper, we present the first implementation of DWELT, intended for 1D location (applicable to tunnels or corridors), and the first step towards a more generic implementation. Simulations and experiments show an accuracy of 1m in more than 81% of the cases, and less than 2m in the 95%.Peer ReviewedPostprint (published version

Survey on wireless technology trade-offs for the industrial internet of things

Aside from vast deployment cost reduction, Industrial Wireless Sensor and Actuator Networks (IWSAN) introduce a new level of industrial connectivity. Wireless connection of sensors and actuators in industrial environments not only enables wireless monitoring and actuation, it also enables coordination of production stages, connecting mobile robots and autonomous transport vehicles, as well as localization and tracking of assets. All these opportunities already inspired the development of many wireless technologies in an effort to fully enable Industry 4.0. However, different technologies significantly differ in performance and capabilities, none being capable of supporting all industrial use cases. When designing a network solution, one must be aware of the capabilities and the trade-offs that prospective technologies have. This paper evaluates the technologies potentially suitable for IWSAN solutions covering an entire industrial site with limited infrastructure cost and discusses their trade-offs in an effort to provide information for choosing the most suitable technology for the use case of interest. The comparative discussion presented in this paper aims to enable engineers to choose the most suitable wireless technology for their specific IWSAN deployment

When the Industry Goes Wireless: Drivers, Requirements, Technology and Future Trends

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Preprint: Using RF-DNA Fingerprints To Classify OFDM Transmitters Under Rayleigh Fading Conditions

The Internet of Things (IoT) is a collection of Internet connected devices capable of interacting with the physical world and computer systems. It is estimated that the IoT will consist of approximately fifty billion devices by the year 2020. In addition to the sheer numbers, the need for IoT security is exacerbated by the fact that many of the edge devices employ weak to no encryption of the communication link. It has been estimated that almost 70% of IoT devices use no form of encryption. Previous research has suggested the use of Specific Emitter Identification (SEI), a physical layer technique, as a means of augmenting bit-level security mechanism such as encryption. The work presented here integrates a Nelder-Mead based approach for estimating the Rayleigh fading channel coefficients prior to the SEI approach known as RF-DNA fingerprinting. The performance of this estimator is assessed for degrading signal-to-noise ratio and compared with least square and minimum mean squared error channel estimators. Additionally, this work presents classification results using RF-DNA fingerprints that were extracted from received signals that have undergone Rayleigh fading channel correction using Minimum Mean Squared Error (MMSE) equalization. This work also performs radio discrimination using RF-DNA fingerprints generated from the normalized magnitude-squared and phase response of Gabor coefficients as well as two classifiers. Discrimination of four 802.11a Wi-Fi radios achieves an average percent correct classification of 90% or better for signal-to-noise ratios of 18 and 21 dB or greater using a Rayleigh fading channel comprised of two and five paths, respectively.Comment: 13 pages, 14 total figures/images, Currently under review by the IEEE Transactions on Information Forensics and Securit