The recent advancement of the Internet of Things (IoT) enables the possibility of data collection from diverse environments using IoT devices. However, despite the rapid advancement of low-power communication technologies, the deployment of IoT networks still faces many challenges. In this paper, we propose a hybrid, low-power, wide-area network (LPWAN) structure that can achieve wide-area communication coverage and low power consumption on IoT devices by utilizing both sub-GHz long-range radio and 2.4 GHz short-range radio. Specifically, we constructed a low-power mesh network with LoRa, a physical-layer standard that can provide long-range (kilometers) point-to-point communication using custom time-division multiple access (TDMA). Furthermore, we extended the capabilities of the mesh network by enabling ANT, an ultra low-power, short-range communication protocol to satisfy data collection in dense device deployments. Third, we demonstrate the performance of the hybrid network with two real-world deployments at the Purdue University campus and at the university-owned farm. The results suggest that both networks have superior advantages in terms of cost, coverage, and power consumption vis-à-vis other IoT solutions, like LoRaWAN.

Phishing attacks continue to pose a major headache for defenders of computing systems, with some insidious kinds of phishing messages appear to pass through filters by making seemingly simple structural and semantic changes to the messages. We tackle this problem in this paper, through the use of a machine learning classifier operating on a large corpus of phishing and legitimate messages. By understanding common phishing features, we design a system to extract features and elevate some to higher level features that are meant to defeat common phishing mail construction strategies. The algorithms are instantiated in a usable system called SAFE-PC. To evaluate SAFE-PC, we collect the largest known corpus of phishing messages (used in any publicly known study) from the central IT organization at a tier-1 research university. SAFE-PC is able to detect more than 70% of the emails that had eluded our production deployment of Sophos, a state-of-the-art email filtering tool today. It also performs better than SpamAssassin, a commonly used email filter. We also develop an online version of SAFE-PC, that can be incrementally retrained with new samples. Its detection performance is found to improve with time as new samples are fed in, while the time to retrain it stays constant.

Deep learning has shown incredible potential across a wide array of tasks, and accompanied by this growth has been an insatiable appetite for data. However, a large amount of data needed for enabling deep learning is stored on personal devices, and recent concerns on privacy have further highlighted challenges for accessing such data. As a result, federated learning (FL) has emerged as an important privacy-preserving technology that enables collaborative training of machine learning models without the need to send the raw, potentially sensitive, data to a central server. However, the fundamental premise that sending model updates to a server is privacy-preserving only holds if the updates cannot be “reverse engineered” to infer information about the private training data. It has been shown under a wide variety of settings that this privacy premise does not hold. In this article we provide a comprehensive literature review of the different privacy attacks and defense methods in FL. We identify the current limitations of these attacks and highlight the settings in which the privacy of an FL client can be broken. We further dissect some of the successful industry applications of FL and draw lessons for future successful adoption. We survey the emerging landscape of privacy regulation for FL and conclude with future directions for taking FL toward the cherished goal of generating accurate models while preserving the privacy of the data from its participants.

The field of IoT has blossomed and is positively influencing many application domains. In this paper, we bring out the unique challenges this field poses to research in computer systems and networking. The unique challenges arise from the unique characteristics of IoT systems such as the diversity of application domains where they are used and the increasingly demanding protocols they are being called upon to run (such as, video and LIDAR processing) on constrained resources (on-node and network). We show how these open challenges can benefit from foundations laid in other areas, such as, 5G cellular protocols, ML model reduction, and device-edge-cloud offloading. We then discuss the unique challenges for reliability, security, and privacy posed by IoT systems due to their salient characteristics which include heterogeneity of devices and protocols, dependence on the physical environment, and the close coupling with humans. We again show how the open research challenges benefit from reliability, security, and privacy advancements in other areas. We conclude by providing a vision for a desirable end state for IoT systems.

Smart manufacturing systems are considered the next generation of manufacturing applications. One important goal of the smart manufacturing system is to rapidly detect and anticipate failures to reduce maintenance cost and minimize machine downtime. This often boils down to detecting anomalies within the sensor data acquired from the system which has different characteristics with respect to the operating point of the environment or machines, such as, the RPM of the motor. In this paper, we analyze four datasets from sensors deployed in manufacturing testbeds. We detect the level of defect for each sensor data leveraging deep learning techniques. We also evaluate the performance of several traditional and ML-based forecasting models for predicting the time series of sensor data. We show that careful selection of training data by aggregating multiple predictive RPM values is beneficial. Then, considering the sparse data from one kind of sensor, we perform transfer learning from a high data rate sensor to perform defect type classification. We release our manufacturing database corpus (4 datasets) and codes for anomaly detection and defect type classification for the community to build on it. Taken together, we show that predictive failure classification can be achieved, paving the way for predictive maintenance.

Inefficient soil management is a hindrance to food productivity and a contributing factor to the degradation of soil quality. Despite decades of research confirming the benefits of using sensors in effective soil management and crop yield, they are still not widely used in farms for several reasons including high cost, difficulty in large area deployment, and requirement of labor-intensive wiring. In this paper, we report a low-cost battery-less chipless wireless sensor tag that can be batch-manufactured for large-scale agricultural surveillance. The sensor tag works based on backscattering techniques for the wireless measurements of the volumetric water content (VWC) of the soil. The sensor tag consists of shorted dipole resonators whose resonance frequencies are dependent on the effective permittivity of the VWC of its surrounding soil. The resonant frequency of the sensor tag is measured by an orthogonally polarized reader antenna that observes the peak in the backscattered spectrum. As a proof-of-concept, a sensor tag with two resonators of length 10 cm and 9 cm, is buried at a depth of 10 cm in the soil and is wirelessly read from a free-space distance of 50 cm, to demonstrate its capability to measure the changes in VWC. The resonators of length 10 cm and 9 cm have demonstrated a linear change of -3.92 MHz/VWC and -4.85 MHz/VWC, respectively, within the VWC range 4-27%. By changing the functional passivation layers on the demonstrated sensor tag, this concept can be applied to a wide range of low-cost subsoil sensors for precision agriculture applications.