The rapid proliferation of Internet of Things (IoT) deployments across critical domains demands dependable systems that maintain reliability despite dynamic environmental conditions and resource constraints. Traditional approaches to ensuring data integrity, communication reliability, and security often assume independent and identically distributed (i.i.d.) data (concept drift), making them illsuited for the non-stationary nature of IoT environments. In practice, sensor faults, network partitions, and evolving attack behaviors frequently disrupt system performance, emphasizing the need for adaptive and resilient frameworks that can sustain trustworthy operation amid heterogeneous application demands, device capabilities, and network traffic dynamics. This research presents a cohesive framework of adaptive learning systems that collectively address these challenges across the IoT stack. Our contributions include energy-aware collaborative sensing with trust management, robust anomaly detection for noisy edge environments, fault-tolerant communication middleware, generative adaptation for network intrusion detection under concept drift, and continual learning for digital twins in non-stationary settings. Through extensive evaluation across multiple IoT domains, we demonstrate that these frameworks significantly enhance the dependability of data-driven IoT systems by enabling autonomous adaptation to changing conditions while operating under limited resource budgets.
Distributed machine learning faces fundamental challenges arising from heterogeneous data, shifting client populations, and volatile inference workloads. This dissertation advances the thesis that middleware-not the models themselves-must serve as an active, adaptive decision layer that unifies training-time and inference-time robustness. On the training side, FLIPS performs privacy-preserving participant selection via label-distribution clustering within TEEs, improving fairness and convergence under non-IID data, while ShiftEx detects covariate and label shifts in streaming environments and dynamically creates or reuses experts to accelerate adaptation with minimal retraining. On the inference side, OptiSeq improves the reliability of LLM-based API sequencing by selecting example orderings using inference-time log-probability reasoning, and VOILA formulates multimodal fidelity selection as a value-of-information optimization, achieving large cost reductions with calibrated accuracy guarantees. Together, these systems establish adaptive middleware as a unifying approach for improving efficiency, robustness, and accuracy across modern distributed ML deployments.
We demonstrate OptiSeq [1], an edge-cloud middleware system that converts natural language queries into executable API sequences with high reliability in distributed environments. A central challenge in such systems is that large language models (LLMs) can generate incorrect API sequences, causing costly retries that consume edge bandwidth and add cloud latency. Our demonstration shows how OptiSeq, a lightweight inference-time example-ordering optimization engine, reduces these failures by intelligently arranging in-context examples before prompting the LLM. In our distributed architecture, (1) natural language queries originate at the edge, (2) relevant examples are retrieved from an edge-hosted vector store, (3) OptiSeq runs within the middleware pipeline to optimize prompt construction, (4) a cloud-hosted LLM generates API sequences, and (5) a distributed API execution layer carries out the calls. Through live demonstrations on real-world API sequencing tasks, we show that OptiSeq-guided prompting reduces retry attempts by 35-45 % while maintaining high accuracy. This work presents the first demonstration of ordering-aware middleware for natural-language-to-API sequence generation deployed across edge-cloud systems.
Highly available applications rely on replicated data, but complex event interleavings between application logic and replicated data libraries (RDLs) often cause subtle integration bugs. Detecting such bugs is challenging due to the inherent nondeterminism of distributed execution, as certain bugs can only manifest under specific interleavings. Correctness testing, therefore, requires replaying all possible interleavings-a challenging task due to the combinatorial explosion of the interleaving space. My doctoral dissertation addresses this challenge with ER-pi, a middleware framework that exercises all possible interleavings between the application code and RDL; it also eliminates redundant and impossible interleavings via novel pruning techniques. Initial results show that ER-pi successfully reproduces 12 real-world bugs across multiple open-source RDLs while significantly reducing the interleaving search space. Our ongoing work extends this foundation with interleaving prioritization, ranking interleavings execution by their likelihood of exposing faults-particularly those introduced by recent code changes, thus accelerating bug discovery. This research supports developers responsible for ensuring the correctness and reliability of replicated data systems.
