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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. 

Event logs contain abundant information, such as activity names, time stamps, activity executors, etc. However, much of existing trace clustering research has been focused on applying activity names to assist process scenarios discovery. In addition, many existing trace clustering algorithms commonly used in the literature, such as k-means clustering approach, require prior knowledge about the number of process scenarios existed in the log, which sometimes are not known aprior. This paper presents a two-phase approach that obtains timing information from event logs and uses the information to assist process scenario discoveries without requiring any prior knowledge about process scenarios. We use five real-life event logs to compare the performance of the proposed two-phase approach for process scenario discoveries with the commonly used k-means clustering approach in terms of model’s harmonic mean of the weighted average fitness and precision, i.e., the F1 score. The experiment data shows that (1) the process scenario models obtained with the additional timing information have both higher fitness and precision scores than the models obtained without the timing information; (2) the two-phase approach not only removes the need for prior information related to k, but also results in a comparable F1 score compared to the optimal k-means approach with the optimal k obtained through exhaustive search. 

In this paper, we discuss how data-driven approaches using emerging IoT and machine learning based analytics can revolutionize the resilience and ef=iciency of urban water systems. Key challenges in creating a next generation water infrastructure includes issues of how and where to place instruments to gather a wide variety of information useful for improving operational ef=iciencies and for damage detection after major disasters. We discuss how an understanding of deployed infrastructure in diverse geographies and the dynamics of interconnected systems can help design more effective placement of technology solutions. We showcase recent work illustrating how knowledge of network structures and their behavior can help to more effectively instrument and gather operational data and how AI-based approaches utilizing geospatial data more effectively can help to maintain real-time awareness of system states which allows decision makers to more effectively monitor and control their systems. 

An increasing number of community spaces are being instrumented with heterogeneous IoT sensors and actuators that enable continuous monitoring of the surrounding environments. Data streams generated from the devices are analyzed using a range of analytics operators and transformed into meaningful information for community monitoring applications. To ensure high quality results, timely monitoring, and application reliability, we argue that these operators must be hosted at edge servers located in close proximity to the community space. In this paper, we present a Resource Efficient Adaptive Monitoring (REAM) framework at the edge that adaptively selects workflows of devices and operators to maintain adequate quality of information for the application at hand while judiciously consuming the limited resources available on edge servers. IoT deployments in community spaces are in a state of continuous flux that are dictated by the nature of activities and events within the space. Since these spaces are complex and change dynamically, and events can take place under different environmental contexts, developing a one-size-fits-all model that works for all types of spaces is infeasible. The REAM framework utilizes deep reinforcement learning agents that learn by interacting with each individual community spaces and take decisions based on the state of the environment in each space and other contextual information. We evaluate our framework on two real-world testbeds in Orange County, USA and NTHU, Taiwan. The evaluation results show that community spaces using REAM can achieve > 90% monitoring accuracy while incurring ~ 50% less resource consumption costs compared to existing static monitoring and Machine Learning driven approaches. 

Process mining is a technique for extracting process models from event logs. Event logs contain abundant information related to an event such as the timestamp of the event, the actions that triggers the event, etc. Much of existing process mining research has been focused on discoveries of process models behind event logs. How to uncover the timing constraints from event logs that are associated with the discovered process models is not well-studied. In this paper, we present an approach that extends existing process mining techniques to not only mine but also integrate timing constraints with process models discovered and constructed by existing process mining algorithms. The approach contains three major steps, i.e., first, for a given process model constructed by an existing process mining algorithm and represented as a workflow net, extract a time dependent set for each transition in the workflow net model. Second, based on the time dependent sets, develop an algorithm to extract timing constraints from event logs for each transition in the model. Third, extend the original workflow net into a time Petri net where the discovered timing constraints are associated with their corresponding transitions. A real-life road traffic fine management process scenario is used as a case study to show how timing constraints in the fine management process can be discovered from event logs with our approach.