As detailed in the MINES presentation, the research is structured according to the following themes:

• T1 - Adaptive communication architecture for resilient sensing and actuation.
• T2 - Stochastic models for analysis and runtime decision.
• T3 - Cross-layer in-network collaboration protocols to enhance the overall QoS.
• T4 - Middleware solution that embeds the models and implements the adaptive protocols.
• T5 - Evaluation using focused emergency scenario use cases.

We outline below the key results so far for which detail is provided in the papers available in the publications page.

FireDeX: A prioritized IoT data exchange middleware for emergency response - T1 & T4 (Middleware'18)

Real-time event detection and targeted decision making for emerging mission-critical applications, e.g., smart fire fighting, requires systems that extract and process relevant data from connected IoT devices in the environment. We propose FireDeX, a cross-layer middleware that facilitates timely and effective exchange of data for coordinating emergency response activities. FireDeX adopts a publish-subscribe data exchange paradigm with brokers at the network edge to manage prioritized delivery of mission-critical data from IoT sources to relevant subscribers. It incorporates parameters at the application, network, and middleware layers into a data exchange service that accurately estimates end-to-end performance metrics (e.g. delays, success rates). We introduce an extensible queueing theoretic model that abstracts these cross-layer interactions as a network of queues, thereby making it amenable for rapid analysis. We propose novel algorithms that utilize results of this analysis to tune data exchange configurations (event priorities and dropping policies) while meeting situational awareness requirements and resource constraints. FireDeX leverages Software-Defined Networking (SDN) methodologies to enforce these configurations in the IoT network infrastructure. Performance evaluation through simulated experiments in a smart building fire response scenario demonstrate significant improvement to mission-critical data delivery under a variety of conditions. Our application-aware prioritization algorithm improves the value of exchanged information by 36% when compared with no prioritization; the addition of our network-aware drop rate policies improves this performance by 42% over priorities only and by 94% over no prioritization.

Probabilistic event dropping for intermittently connected subscribers over pub/sub systems - T2 (ICC'19)

Internet of Things (IoT) aim to leverage data from multiple sensors, actuators and devices for improving peoples' daily life and safety. Multiple data sources must be integrated, analyzed from the corresponding application and notify interested stakeholders. To support the data exchange between data sources and stakeholders, the publish/subscribe (pub/sub) middleware is often employed. Pub/sub provides additional mechanisms such as reliable messaging, event dropping, prioritization, etc. The event dropping mechanism is often used to satisfy Quality of Service (QoS) requirements and ensure system stability. To enable event dropping, basic approaches apply finite buffers or data validity periods and more sophisticated ones are information-aware. We introduce a pub/sub mechanism for probabilistic event dropping by considering the stakeholders' intermittent connectivity and QoS requirements. We model the pub/sub middleware as a network of queues which includes a novel ON/OFF queueing model that enables the definition of join probabilities. We validate our analytical model via simulation and compare our mechanism with existing ones. Experimental results can be used as insights for developing hybrid dropping mechanisms.

Multi-sensor calibration planning in IoT-enabled smart spaces - T3 (ICDCS'19)

Emerging applications in smart cities and communities require massive IoT deployments using sensors/actuators (things) that can enhance citizens' quality of life and public safety. However, budget constraints often lead to limited instrumentation and/or the use of low-cost sensors that are subject to drift and bias. This raises concerns of robustness and accuracy of the decisions made on uncertain data. To enable effective decision-making while fully exploiting the potential of low-cost sensors, we propose to send mobile units (e.g., trained personnel) equipped with high-quality (more expensive) and freshly-calibrated reference sensors so as to carry out calibration in the field. We design and implement an efficient cooperative approach to solve the calibration planning problem, which aims at minimizing the cost of the recurring calibration of multiple sensor types in the long-term operation. We propose a two-phase solution that consists of a sensor selection phase that minimizes the average cost of recurring calibration, and a path planning phase that minimizes the travel cost of multiple calibrators which have load constraints. We provide fast and effective heuristics for both phases. We further build a prototype that facilitates the mapping of the deployment field and provides navigation guidance to mobile calibrators. Extensive use-case-driven simulations show our proposed approach significantly reduces the average cost compared to naive approaches: up to 30% in a moderate-sized indoor case, and higher in outdoor cases depending on the scale.

User-centric context inference for mobile crowdsensing - T3 (IoTDI'19)

Mobile crowdsensing is a powerful mechanism to aggregate hyper-local knowledge about the environment. Indeed, users may contribute valuable observations across time and space using the sensors embedded in their smartphones. However, the relevance of the provided measurements depends on the adequacy of the sensing context with respect to the phenomena that are analyzed. This work concentrates more specifically on assessing the sensing context when gathering observations about the physical environment beyond its geographical position in the Euclidean space, i.e., whether the phone is in-/out-pocket, in-/out-door and on-/under-ground. We introduce an online learning approach to the local inference of the sensing context so as to overcome the disparity of the classification performance due to the heterogeneity of the sensing devices as well as the diversity of user behavior and novel usage scenarios. Our approach features a hierarchical algorithm for inference that requires few opportunistic feedbacks from the user, while increasing the accuracy of the context inference per user.