Learning, Incentives, and Optimization
for Networked Systems

The emergence of the Internet-of-Things allows an ever-larger array of devices, ranging from cloud servers to smartphones to low-power sensors, to support new services like smart cities, generative AI (artificial intelligence), and mixed reality. These applications and services increasingly require seamless cooperation between data, network and computing resources contributed from multiple devices in order to function effectively and scale to billions of users.

Our research uses learning, incentives, and optimization to bridge two fundamental constraints on device cooperation in networked systems: (i) devices' willingness to work together, e.g., if devices have different owners; and (ii) their ability to work together, due to limited data, network, or computing resources. We design, build, and analyze markets for network and compute resources, distributed learning algorithms designed to run on networked devices, and prediction algorithms and models for data with networked structure.

Network and Computing Marketplaces

The proliferation of IoT and machine learning applications has substantially increased utilization of network and computing resources, leading to increased operating costs that may not be economically sustainable. Our work, which began with research on smart data pricing (SDP) in 2012, develops economic theories of the markets for data, communication, and computing resources and the mechanisms that govern applications' and services' increasing competition for these resources. Such markets aim to efficiently allocate resources to devices according to their heterogeneous, and potentially dynamic, needs. A common challenge is the dynamics and uncertainty of user demands over time, which often leads to dynamic pricing mechanisms in which users can pay for dynamic amounts of resources that match their demands at different times.

Planning for uncertainty

Provider aggregation

Dynamic pricing mechanisms

Networked Learning

Many emerging applications that utilize edge and fog computing are based on machine learning and artificial intelligence, which have achieved remarkable success in a range of areas like vision, gaming, and autonomous vehicles. Running such machine learning algorithms in practice, however, requires considering how heterogeneous devices may contribute to such algorithms, accounting for the data they collect, the local computations they run, and the updates that they make to common models or inferences. Our work designs and analyzes new machine learning algorithms that both incentivize devices to make more useful contributions and optimally leverage these contributions in training or making inferences from learning models.

Learning with limited resources

Collaborative machine learning

Networked Data

The pervasiveness of Internet-connected devices in the Internet-of-Things allows these devices and their human owners to cooperate with each other by forming spatial and social relationships respectively in physical (e.g., smart cities) and virtual (e.g., online social networks) communities. Our work models these relationships to predict future user and device interactions from historical data and design mechanisms for devices to run these applications. Predicting and incentivizing user interactions in such settings is particularly difficult since there are large numbers of users present with dynamic and heterogeneous spatial and social relationships. For example, vehicles can move towards many different locations in a city, and each direction of movement will have a different impact on road congestion and other vehicles' chosen movements. Our ongoing work uses machine learning approaches to handle these complexities.

Incentivizing spatial mobility

Characterizing virtual communities