Search for: All records

The deployment of deep learning models for real-time image classification on resource-constrained sensor devices presents significant challenges. These devices face strict limitations in computational power, energy capacity, and memory resources, making it difficult to achieve both high accuracy and low latency. Current approaches either compromise model performance through compression or incur substantial overhead by offloading computation to remote servers. We introduce a novel distributed progressive inference platform that addresses these limitations by dynamically balancing local and remote computation. Our system employs reinforcement learning to make intelligent decisions about when and where to perform inference. Experimental results across multiple standard datasets demonstrate that our approach achieves up to 3% higher accuracy while reducing network traffic and preserving battery life compared to existing methods. 

IoT deployments in smart spaces can enable the development of useful services for their inhabitants. However, the diversity of smart spaces and their sensor infrastructures makes it challenging to develop space-agnostic applications. Moreover, existing schemas addressing interoperability challenges often lack the vocabulary needed to represent the integration of smart space systems and their inhabitants. We present a schema to annotate inhabited smart spaces in support of inhabitant-oriented applica- tions. Our schema integrates well-known ontologies to represent inhabitants, events/activities, and the space itself, along with their interconnections. It also supports the representation of uncertain information from IoT and mobile sensors (e.g., a person’s location or occupancy/attendance at an event). Additionally, we introduce an annotation tool that uses an easy-to-use GUI to describe a smart space based on our schema. We demonstrate the potential of our approach through a series of SPARQL queries and a system deployed at the UCI campus that annotates sensor data to support a space-agnostic occupancy monitoring application. 

Data regulations like GDPR require systems to support data erasure but leave the definition of erasure open to interpretation. This ambiguity makes compliance challenging, especially in databases where data dependencies can lead to erased data being inferred from remaining data. We formally define a precise notion of data erasure that ensures any inference about deleted data, through dependencies, remains bounded to what could have been inferred before its insertion. We design erasure mechanisms that enforce this guarantee at minimal cost. Additionally, we explore strategies to balance cost and throughput, batch multiple erasures, and proactively compute data retention times when possible. We demonstrate the practicality and scalability of our algorithms using both real and synthetic datasets. 

Smart city transportation infrastructure will soon demand the development of reliable underground IoT (IoUT) communication. In this paper, we develop a novel analytical model, MAME (Material Aware Measurement Enhanced), to capture signal propagation properties in wireless IoUT networks to achieve reliable data transport. A driving motivation is monitoring underground infrastructure systems (e.g., pipelines and storm drains) for early detection of anomalies and failures to guide human investigation and intervention. We analyze the feasibility of successfully receiving wireless data packets from underground (UG) sensor nodes through multiple material layers and under diverse environmental conditions. Our proposed approach integrates physics-based modeling and empirical studies with small-scale testbeds (in our lab and outdoors) with multiple channel setups and physical layer attributes. We derive a novel MAME approach to model signal propagation in both 802.11-based WiFi and LoRaWAN networks. The resulting MAME model is shown to capture communication behavior in WiFi and LoRaWAN networks accurately. The MAME model is used to augment the popular NS3 simulator to explore scaled-up underground networks and varying channel conditions (e.g., soil moisture level). Such a combined analytical-empirical approach will enable the communication control plane and application layer to better predict channel conditions for improved IoUT network design. 

Efficient water use, particularly in the realm of irrigation, has emerged as a critical concern in regions suffering from persistent drought, such as California and Florida. With the advent of smart irrigation controllers encouraged by environmental policies, a new paradigm of water management is gaining traction. Among these, the Rachio smart controller has garnered significant attention. However, without direct feedback or actual water usage data, optimizing these irrigation systems for enhanced efficiency remains challenging. This paper introduces Water-COLOR, a novel recommendation system integrated within the Rachio smart controller's framework to address this challenge. The system leverages similar landscape profiles to suggest irrigation schedules that are both water-efficient and user-preferable. By analyzing manual user interactions with the controller, Water-COLOR infers user satisfaction, which, along with estimated water usage, informs the adaptation of irrigation plans. The system eschews the need for additional sensors, thereby reducing infrastructure requirements. Our evaluation demonstrates consistent performance across diverse climatic regions and indicates that the system's recommendations could significantly contribute to water conservation efforts. The results not only showcase the potential of Water-COLOR to enhance the efficiency of existing smart irrigation systems but also open avenues for deploying real-time, data-driven environmental solutions. 

In Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys '23). Association for Computing Machinery, New York, NY, USA, 

Process Mining is a technique for extracting process models from event logs. Event logs contain abundant explicit information related to events, such as the timestamp and the actions that trigger the event. Much of the existing process mining research has focused on discovering the process models behind these event logs. However, Process Mining relies on the assumption that these event logs contain accurate representations of an ideal set of processes. These ideal sets of processes imply that the information contained within the log represents what is really happening in a given environment. However, many of these event logs might contain noisy, infrequent, missing, or false process information that is generally classified as outliers. Extending beyond process discovery, there are many research efforts towards cleaning the event logs to deal with these outliers. In this paper, we present an approach that uses hidden Markov models to filter out outliers from event logs prior to applying any process discovery algorithms. Our proposed filtering approach can detect outlier behavior, and consequently, help process discovery algorithms return models that better reflect the real processes within an organization. Furthermore, we show that this filtering method outperforms two commonly used filtering approaches, namely the Matrix Filter approach and the Anomaly Free Automation approach for both artificial event logs and real-life event logs.