Information Centric Strategies for Scalable Data Transport in Cyber Physical Systems (CPSs)

Cyber-Physical Systems (CPSs) represent the next generation of computing that is ubiquitous, wireless and intelligent. These networked sens- ing systems are at the intersection of sensing, communication, control, and computing [16]. Such systems will have applications in numerous elds such as vehicular systems and transportation, medical and health care systems, smart homes and buildings, etc. The proliferation of such sensing systems will trigger an exponential increase in the computational devices that exchange data over existing network infrastructure. Transporting data at scale in such systems is a challenge [21] mainly due to the underlying network infrastructure which is still resource con- strained and bandwidth-limited. Eorts have been made to improve the network infrastructure [5] [2] [15]. The focus of this thesis is to put forward information-centric strategies that optimize the data transport over existing network infrastructure. This thesis proposes four dierent information-centric strategies: (1) Strategy to minimize network congestion in a generic sensing system by estimating data with adaptive updates, (2) An adaptive information exchange strategy based on rate of change of state for static and mobile networks, (3) Spatio-temporal strategy that maintains spatial resolution by reducing redundant transmissions, (4) Proximity-dependent data transfer strategy to ensure most updated information in high-density regions. Each of these strategies is experimentally veried to optimize the data transport in their respective setting