Biblio

Named Data Network's (NDN) data-centric approach makes it a suitable solution in a networking scenario where there are connectivity issues as a result of the dynamism of the network. Coupling of this ability with the blockchain's well-documented immutable trustworthy-distributed ledger feature, the union of blockchain and NDN in an Internet-of-Battlefield-Things (IoBT) setting could prove to be the ideal alliance that would guarantee data exchanged in an IoBT environment is trusted and less susceptible to cyber-attacks and packet losses. Various blockchain technologies, however, require that each node has a ledger that stores information or transactions in a chain of blocks. This poses an issue as nodes in an IoBT setting have varying computing and storage resources. Moreover, most of the nodes in the IoT/IoBT network are plagued with limited resources. As such, there needs to be an approach that ensures that the limited resources of these nodes are efficiently utilized. In this paper, we investigate an approach that merges blockchain and NDN to efficiently utilize the resources of these resource-constrained nodes by only storing relevant information on each node's ledger. Furthermore, we propose a sharding technique called an Interest Group and introduce a novel consensus mechanism called Proof of Common Interest. Performance of the proposed approach is evaluated using numerical results.

Improved safety, high mobility and environmental concerns in transportation systems across the world and the corresponding developments in information and communication technologies continue to drive attention towards Intelligent Transportation Systems (ITS). This is evident in advanced driver-assistance systems such as lane departure warning, adaptive cruise control and collision avoidance. However, in connected and autonomous vehicles, the efficient functionality of these applications depends largely on the ability of a vehicle to accurately predict it operating parameters such as location and speed. The ability to predict the immediate future/next location (or speed) of a vehicle or its ability to predict neighbors help in guaranteeing integrity, availability and accountability, thus boosting safety and resiliency of the Vehicular Network for Mobile Cyber Physical Systems (VCPS). In this paper, we proposed a secure movement-prediction for connected vehicles by using Kalman filter. Specifically, Kalman filter predicts the locations and speeds of individual vehicles with reference to already observed and known information such posted legal speed limit, geographic/road location, direction etc. The aim is to achieve resilience through the predicted and exchanged information between connected moving vehicles in an adaptive manner. By being able to predict their future locations, the following vehicle is able to adjust its position more accurately to avoid collision and to ensure optimal information exchange among vehicles.

Cognitive radio network (CRN) is regarded as an emerging technology for better spectrum efficiency where unlicensed secondary users (SUs) sense RF spectrum to find idle channels and access them opportunistically without causing any harmful interference to licensed primary users (PUs). However, RF spectrum sensing and sharing along with reconfigurable capabilities of SUs bring severe security vulnerabilities in the network. In this paper, we analyze physical-layer security (secrecy rates) of SUs in CRN in the presence of eavesdroppers, jammers and PU emulators (PUEs) where SUs compete not only with jammers and eavesdroppers who are trying to reduce SU's secrecy rates but also against PUEs who are trying to compel the SUs from their current channel by imitating the behavior of PUs. In addition, a legitimate SU competes with other SUs with a sharing attitude for dynamic spectrum access to gain a high secrecy rate, however, the malicious users (i.e., attackers) attempt to abuse the channels egotistically. The main contribution of this work is the design of a game theoretic approach to maximize utilities (that is proportional to secrecy rates) of SUs in the presence of eavesdroppers, jammers and PUEs. Furthermore, SUs use signal energy and cyclostationary feature detection along with location verification technique to detect PUEs. As the proposed approach is generic and considers different attackers, it can be particularized to a situation with eavesdroppers only, jammers only or PUEs only while evaluating physical-layer security of SUs in CRN. We evaluate the performance of the proposed approach using results obtained from simulations. The results show that the proposed approach outperforms other existing methods.