We introduce SIGAR, a Sensor Integration Gateway using Augmented Reality, which combines RFID-based passive sensing with AR for real-time visualization. Using batteryless, wireless RFID sensors, SIGAR eliminates the need for power sources, enabling sustainable and cost-effective monitoring. A mobile app automatically detects sensors within the camera's field of view and overlays realtime sensory data onto the physical environment. Demonstrated through applications like force, soil moisture and light sensing, SIGAR provides intuitive, context-aware insights for environmental monitoring, inventory management, and more. This fusion of AR and passive sensing bridges digital and physical worlds, offering scalable, low-power IoT solutions.

Sensing allows us to interact with and quantify the natural world. Despite the advancements in sensor versatility, sensing systems still suffer from limited adoption due to their dependence on batteries, complex interfaces, energy-harvesting modules, and readout latency. To address these challenges, we present ZenseTag --- a miniaturized, sticker-like platform that can interface commercial sensors directly with COTS RFID tags. ZenseTag exploits the impedance response of COTS sensors to the measured stimulus at Radio Frequencies, tuned to the UHF RFID band. It combines reliable hardware realization of differential analog sensing with robust software for accurate, low-latency sensor readouts, even in the presence of multipath effects.

Sensors enable us to digitally capture stimuli like moisture, light, and force. Despite their low cost, reliability, and scalability, the lack of widespread adoption of IoT has hindered the realization of true ubiquitous sensing. A likely reason is that the current sensor platforms are bulky due to the batteries and complex electronics needed to interface sensors communication systems. In this work, we present a fully-passive, miniaturized, flexible form factor sensor interface titled ZenseTag that uses minimal electronics to read and communicate analog sensor data, directly at radio frequencies (RF). We exploit the fundamental principle of resonance, where a sensor's terminal impedance becomes most sensitive to the measured stimulus at its resonant frequency. This enables ZenseTag to read out the sensor variation using only energy harvested from wireless signals. We demonstrate its implementation with a 15x10mm flexible PCB that connects sensors to a printed antenna and passive RFID ICs, enabling near real-time readout through a performant GUI-enabled software.

We address long-standing open questions raised by Williamson, Goemans, Vazirani and Mihail pertaining to the design of approximation algorithms for problems in network design via the primal-dual method (Combinatorica 15(3):435-454, 1995). Williamson et al. prove an approximation ratio of two for connectivity augmentation problems where the connectivity requirements can be specified by uncrossable functions. They state: "Extending our algorithm to handle non-uncrossable functions remains a challenging open problem. The key feature of uncrossable functions is that there exists an optimal dual solution which is laminar... A larger open issue is to explore further the power of the primal-dual approach for obtaining approximation algorithms for other combinatorial optimization problems." Our main result proves a 16-approximation ratio via the primal-dual method for a class of functions that generalizes the notion of an uncrossable function. There exist instances that can be handled by our methods where none of the optimal dual solutions have a laminar support. We present applications of our main result to three network-design problems. 1) A 16-approximation algorithm for augmenting the family of small cuts of a graph G. The previous best approximation ratio was O(log |V(G)|). 2) A 16⋅⌈k/u_min⌉-approximation algorithm for the Cap-k-ECSS problem which is as follows: Given an undirected graph G = (V,E) with edge costs c ∈ ℚ_{≥0}^E and edge capacities u ∈ ℤ_{≥0}^E, find a minimum cost subset of the edges F ⊆ E such that the capacity across any cut in (V,F) is at least k; u_min (respectively, u_max) denote the minimum (respectively, maximum) capacity of an edge in E, and w.l.o.g. u_max ≤ k. The previous best approximation ratio was min(O(log|V|), k, 2u_max). 3) A 20-approximation algorithm for the model of (p,2)-Flexible Graph Connectivity. The previous best approximation ratio was O(log|V(G)|), where G denotes the input graph.