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n many countries, sharing has become a significant approach to problems of spectrum allocation and assignment. As this approach moves from concept to reality, it is reasonable to expect an increase in interference or usage conflict events between sharing parties. Scholars such as Coase, Demsetz, Stigler, and others have argued that appropriate enforcement is critical to successful contracts (such as spectrum sharing agreements) and Polinsky, Shavell, and others have analyzed enforcement mechanisms in general. While many ex-ante measures may be used, reducing the social costs of ex-ante enforcement means shifting the balance more toward ex-post measures. Ex post enforcement requires detection, data collection, and adjudication methods. At present, these methods are ad hoc (operating in a decentralized way between parties) or fairly costly (e.g., relying on the FCC Enforcement Bureau). The research presented in this paper is the culmination of an NSF-funded inquiry into how and what enforcement functions can be automated. 

In many countries, sharing has become a significant approach to problems of spectrum allocation and assignment. As this approach moves from concept to reality, it is reasonable to expect an increase in interference or usage conflict events between sharing parties. Scholars such as Coase, Demsetz, Stigler, and others have argued that appropriate enforcement is critical to successful contracts (such as spectrum sharing agreements) and Polinsky, Shavell, and others have analyzed enforcement mechanisms in general. While many ex-ante measures may be used, reducing the social costs of ex-ante enforcement means shifting the balance more toward ex-post measures. Ex post enforcement requires detection, data collection, and adjudication methods. At present, these methods are ad hoc (operating in a decentralized way between parties) or fairly costly (e.g., relying on the FCC Enforcement Bureau). The research presented in this paper is the culmination of an NSF-funded inquiry into how and what enforcement functions can be automated. 

The transition of autonomous vehicles into fleets requires an advanced control system design that relies on continuous feedback from the tires. Smart tires enable continuous monitoring of dynamic parameters by combining strain sensing with traditional tire functions. Here, we provide breakthrough in this direction by demonstrating tire-integrated system that combines direct mask-less 3D printed strain gauges, flexible piezoelectric energy harvester for powering the sensors and secure wireless data transfer electronics, and machine learning for predictive data analysis. Ink of graphene based material was designed to directly print strain sensor for measuring tire-road interactions under varying driving speeds, normal load, and tire pressure. A secure wireless data transfer hardware powered by a piezoelectric patch is implemented to demonstrate self-powered sensing and wireless communication capability. Combined, this study significantly advances the design and fabrication of cost-effective smart tires by demonstrating practical self-powered wireless strain sensing capability.