Date of Award
Doctor of Philosophy
Electrical and Computer Engineering
Internet of Things (IoT) has great potential in enabling many beneficial applications (i.e., connected vehicle applications). Named Data Networking (NDN) recently emerges as a promising networking paradigm in supporting IoT due to its data-centric architecture. In this dissertation, we present our research on design a scalable, efficient and secure ndn-based data retrieval framework for Internet of Things. Our work includes three parts:First, we envision an NDN-based Connected Vehicles (CV) application framework with a distributed data service model, as CV is a typical scenario of IoT. The scalability of the framework is greatly challenged by the fast mobility and vast moving area of vehicles. To handle such an issue, we develop a novel hyperbolic hierarchical NDN backbone architecture (H2NDN) by exploiting the location dependency of CV applications. H2NDN designs the backbone routers topology and the data/interest namespace by following the hierarchical architecture of geographic locations. The efficient data searching only requires static forwarding information base (FIB) configuration over NDN routers. To avoid overloading high-level routers, H2NDN integrates hyperbolic routing through carefully designed hyperbolic planes.Second, a distributed adaptive caching strategy is proposed to improve the efficiency of data caches on NDN routers. NDN provides native support to cache data at routers for future Interest packets. As we model the caching problem, the goal of cache allocation is to maximize the savings of Interest/Data forwarding hops under the limited cache space on each router. We discuss the impracticality of global optimization and provide the local caching method. Extensive ndnSIM based simulation with real traffic data proves the efficiency and scalability of the proposed H2NDN architecture.Finally, although NDN provides some security advantages such as secures data directly and uses name semantics to enable applications to reason about security, employing NDN to support IoT applications nevertheless presents some new challenges about security. In this dissertation, we focus on two resultant attacks that are not effectively handled in current studies, namely the targeted blackhole attack and the targeted content poisoning attack. We propose a lightweight and efficient approach named SmartDetour to tackle the two attacks. To ensure high scalability and collusion-resilience, SmartDetour lets each router respond to attacks (i.e., packet drops or corrupted data) independently in order to isolate attackers. The core solution contains a reputation-based probabilistic forwarding strategy and a proactive attacker detection algorithm. Extensive ndnSIM based simulation demonstrates the efficiency and accuracy of the proposed SmartDetour.
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