Vishrant Tripathi

Ananth Ram Rajagopalan, Jiahui Ni, Vishrant Tripathi

Dense indoor WLANs increasingly rely on multiple access points (APs) operating over partially overlapping spectrum to support latency-sensitive applications. In such deployments, simultaneous transmissions across APs create co-channel and adjacent-channel interference, making scheduling decisions interdependent and directly impacting information freshness. Motivated by emerging software-defined WLAN architectures that enable centralized coordination, we study the problem of minimizing network-wide Age of Information (AoI) in multi-AP WLANs. Unlike classical AoI scheduling that runs at a single AP, each scheduling decision is now coupled across APs due to interference. This leads to a new class of combinatorial AoI control problems with action-dependent time evolution. We first derive a lower bound on the achievable AoI under arbitrary scheduling policies. We then design stationary randomized policies that have constant-factor optimality guarantees relative to this bound. Building on these insights, we develop a Lyapunov drift-based online policy for systems with action-dependent frame lengths, and establish constant-factor guarantees using new ratio-based drift analysis. To enable scalable implementation, we further show that per-frame scheduling admits efficient polynomial-time local-search approximations under a submodularity assumption. Simulations using realistic WLAN layouts demonstrate about 50% AoI reduction over distributed single AP baselines.

Vishrant Tripathi, Luca Ballotta, Luca Carlone et al.

We investigate the problem of co-designing computation and communication in a multi-agent system (e.g. a sensor network or a multi-robot team). We consider the realistic setting where each agent acquires sensor data and is capable of local processing before sending updates to a base station, which is in charge of making decisions or monitoring phenomena of interest in real time. Longer processing at an agent leads to more informative updates but also larger delays, giving rise to a delay-accuracy-tradeoff in choosing the right amount of local processing at each agent. We assume that the available communication resources are limited due to interference, bandwidth, and power constraints. Thus, a scheduling policy needs to be designed to suitably share the communication channel among the agents. To that end, we develop a general formulation to jointly optimize the local processing at the agents and the scheduling of transmissions. Our novel formulation leverages the notion of Age of Information to quantify the freshness of data and capture the delays caused by computation and communication. We develop efficient resource allocation algorithms using the Whittle index approach and demonstrate our proposed algorithms in two practical applications: multi-agent occupancy grid mapping in time-varying environments, and ride sharing in autonomous vehicle networks. Our experiments show that the proposed co-design approach leads to a substantial performance improvement (18-82% in our tests).

Vishrant Tripathi, Eytan Modiano

We consider systems that require timely monitoring of sources over a communication network, where the cost of delayed information is unknown, time-varying and possibly adversarial. For the single source monitoring problem, we design algorithms that achieve sublinear regret compared to the best fixed policy in hindsight. For the multiple source scheduling problem, we design a new online learning algorithm called Follow-the-Perturbed-Whittle-Leader and show that it has low regret compared to the best fixed scheduling policy in hindsight, while remaining computationally feasible. The algorithm and its regret analysis are novel and of independent interest to the study of online restless multi-armed bandit problems. We further design algorithms that achieve sublinear regret compared to the best dynamic policy when the environment is slowly varying. Finally, we apply our algorithms to a mobility tracking problem. We consider non-stationary and adversarial mobility models and illustrate the performance benefit of using our online learning algorithms compared to an oblivious scheduling policy.