NYU Summer Undergraduate Research Internship Program at LARX
The following students successfully completed a 10-week research intensive summer internship program (Jun. 4 - Aug. 6, 2018) at NYU LARX. The main focus of their research was to design novel mechanisms for IoT enabled smart city applications. A brief summary of their projects is provided below:
1. Rundon Chen - Automated Pickup Decisions in Autonomous Taxis: A Revenue-Maximizing Mechanism Design
Rundong is a rising senior in Electrical Engineering at NYU Tandon School of Engineering. His research abstract is as follows:
- Autonomous cars are the future of transportation. Companies, e.g., Uber and Waymo, are investing heavily on replacing human drivers through their self-driving car programs. But Autonomous taxis will come with many challenges and hence engineering opportunities. Many decisions would need to be automated. In traditional taxi companies, the taxi drivers are allocated to customers by a centralized system. In situations where there is no centralized ownership of taxis or the lack of a dispatch system, it may be inevitable to make distributed and independent decisions. A naive approach can be to make decisions on a first come first serve basis. However it may not be the best in terms of revenue and profitability. With the aid of data on past taxi trips, autonomous taxis can be more strategic in making pickup decisions to maximize their revenue. This work is aimed at developing an informed decision-making framework for selective pickups by autonomous taxis as compared greedy and myopic approaches.
2. Jin Shang - Online Transmission Mechanism Design for Wireless IoT Sensors with Energy Harvesting under Power Saving Mode
Jin is a rising senior in Mathematics and Computer Science at NYU Abu Dhabi. His research abstract is as follows:
- The Internet of things (IoT) comprises of wireless sensors and actuators connected via access points to the Internet. Often, the sensing devices are remotely deployed with limited battery power and equipped with energy harvesting equipment such as solar panels. Theses devices transmit realtime data to the base stations which is used in detection of other applications. Under sufficient power availability, wireless transmissions from sensors can be scheduled at regular time intervals to maintain realtime detection and information retrieval by the base station. However, once the battery is significantly depleted, the devices enters into power saving mode and is required to be more selective in transmitting information to the base station (BS). Transmitting a particular piece of sensed data will result in power consumption while discarding it might result in loss of utility at the BS. The goal is to design an optimal dynamic policy which enables the device to decide whether to transmit or to discard a piece of sensing data particularly under the power saving mode. This will enable the sensor to prolong its operation while causing minimum loss of utility of the application. We develop a mathematical model to capture the utility of the IoT sensor transmissions and use tools from dynamic programming to derive an optimal realtime transmission policy that is based on the statistics of information arrival, the likelihood of harvested energy, and the availability of the wireless channel.