My research asks how a low-power wireless system stays correct and on time when the resource it depends on is contested. That resource may be spectrum taken by a jammer, airtime taken by a neighboring operator, or a token budget that runs out before a control deadline. Most of this work targets low-power wide-area networks (LPWANs), which connect battery-powered sensors to a gateway across several kilometers. In particular it targets LoRa, the radio technology that dominates such deployments.

One design principle runs through all of it. The end device is the most constrained part of an Internet of Things (IoT) system, because a single battery has to carry it for years. Therefore the defense, the coordination, and the scheduling belong in the infrastructure, which is the gateway, the network server, or the ground segment. My dissertation, Dependable Low-Power Wide-Area Networking under Contention: Jamming, Coexistence, and Deadlines, develops that principle in four parts.

An attacker can silence a whole deployment by transmitting noise on the channel the sensors use. The sensors are too limited in energy to detect the attack, and far too limited to answer it. I therefore build defenses that run at the gateway and decode packets while the attack is still in progress.

I published the first anti-jamming work for LPWANs, which assigns channels and transmission power through a game between the network and the jammer, and it improves packet reception by up to 31.83× (EWSN ’23). Every design since then has handled a stronger attacker, and none of them changes anything at the node. Gateway-side decoding recovers LoRa packets under a single jammer and improves packet reception by 83.91× (IoTDI ’24). A defense against jammers that transmit LoRa chirps rather than noise improves it by 106.56× (MobiHoc ’25). A defense against jammers that collaborate improves it by 100× (EWSN ’26).

The state-of-the-art work before this line addressed jamming by positioning at least three gateways in a line, and it was effective only against a single jammer. My designs impose no such placement and no overhead at the LoRa nodes, so they apply to networks that are already deployed.

LPWAN deployments now share unlicensed spectrum with one another, and a node has no energy budget to spend on negotiating for it. I moved the learning off the nodes and onto the LoRa Network Server, which assigns transmission parameters across a dense deployment with deep reinforcement learning. Against the decentralized state of the art this improves packet reception by 70.97% and cuts energy consumption by 62.91% (INFOCOM ’25). A follow-on line of work coordinates several operators whose controllers all learn at the same time, without asking any of them to disclose their traffic (ICLR ’26 workshop).

A control loop is useful only if its messages arrive before their deadlines, and contention is what makes that hard. Burst-MAC handles the traffic bursts that follow an event in a LoRa network, and it improves packet reception by 4.5× during a burst (RTSS ’24). ReMix reclaims airtime that a mixed-criticality schedule reserves and never uses, which raises high-criticality deadline satisfaction in industrial wireless sensor-actuator networks from 32.9% to 85.9% (ICCCN ’26).

The same question now reaches large language model (LLM) agents. An agent that drives a physical actuator inherits the deadline of the control loop it sits in, and generating tokens takes time that the deadline may not allow. Treating such an agent as a best-effort service is therefore not sufficient. Deadline-aware token scheduling cuts the critical failure rate of an LLM serving system from 0.925 to 0.238 (RTSS ’26).

Long Range Frequency Hopping Spread Spectrum (LR-FHSS) carries an uplink for tens of kilometers, including an uplink to low Earth orbit, but a LoRa downlink fails beyond 1 to 2 km. The nodes in that gap receive no acknowledgment and no control. I repurpose the LR-FHSS physical layer as a waveform synthesizer that produces LoRa-compatible chirps, with receiver-side reconstruction that corrects hop discontinuity, offset drift, and phase distortion. A LoRa receiver decodes these transmissions with no change to its RF front end, and reliability at long range improves by up to 79% (SenSys ’26).

I am building a research program on dependable wireless and cyber-physical systems, and it runs in five directions. Direct-to-satellite IoT needs per-message delivery guarantees and a defense against attacks on the hop sequence, because a pass over a ground station lasts only a few minutes. AI agents that act on the physical world need real-time guarantees on the serving path, not only on the network. Connected vehicles need cooperative perception that a scheduler can admit against a deadline and a risk bound. City-scale coexistence needs coordination among operators who will not disclose their traffic to one another. Anti-jamming needs to move from one channel to all of them at once, which I plan to do by repurposing a concurrent-reception base station as a multi-channel special node.

I build and measure real systems. My experiments use software-defined radio (SDR) prototypes on USRP B200 with GNU Radio, outdoor deployments at kilometer scale, commercial off-the-shelf (COTS) LoRa nodes and gateways, a 15-node Tmote Sky testbed, and a three-car MentorPi testbed. At scale I use NS-3 and CARLA simulation, and PyTorch for the learning components.

The papers behind each part are listed on the publications page.