With the increasing interest in connected vehicles along with electrification opportunities, there is an ongoing effort to automate the charging process of electric vehicles (EVs) through their capabilities to communicate with the infrastructure and each other. However, charging EVs takes time and thus in-advance scheduling is needed. As this process is done frequently due to limited mileage of EVs, it may expose the locations and charging pattern of the EV to the service providers, raising privacy concerns for their users. Nevertheless, the EV still needs to be authenticated to charging providers, which means some information will need to be provided anyway. While there have been many studies to address the problem of privacy-preserving authentication for vehicular networks, such solutions will be void if charging payments are made through traditional means. In this paper, we tackle this problem by utilizing distributed applications enabled by Blockchain and smart contracts. We adapt zero-knowledge proofs to Blockchain for enabling privacy-preserving authentication while removing the need for a central authority. We introduce two approaches, one using a token based mechanism and another utilizing the Pederson Commitment scheme to realize anonymous authentication. We also describe a protocol for the whole process which includes scheduling and charging operations. The evaluation of the proposed approaches indicates that the overhead of this process is affordable to enable real-time charging operations for connected EVs.

This paper presents a cloud-based and hybrid wireless mesh communication framework for bi-level, nested, distributed optimization of networked clusters of microgrids. The proposed optimization framework implements a diffusion-based, fully distributed algorithm on local wireless network and a quasi-distributed approach on wide-area internet-based cloud. The lower level of the bi-level optimization implements a distributed optimal economic dispatch solution for intra-microgrid among distributed energy resources, and the upper level implements a global optimal dispatch for inter-microgrid energy exchange. To demonstrate industrial applicability of the proposed framework, the IEC 61850 interoperability protocol is adopted to achieve a certain delay performance so that the distributed optimization convergence is guaranteed. First, hardware-based prototype intelligent electronic devices are developed using embedded systems. Then, the bi-level nested optimization algorithm is implemented for integration of networked microgrids. Finally, experimental results are demonstrated from a real-time smart grid testbed featuring realistic microgrids. The results demonstrate that the proposed framework meets communication requirements for distributed optimization of networked microgrids.

Direct Vehicle-to-vehicle (V2V) charge sharing system has the potential to provide more flexibility to electric vehicle (EV) charging without depending on the charging station infrastructure or building designated parking lots. It can also provide an opportunity to shift peak time utility load to off-peak times. However, the assignment between the EVs that demand energy and the EVs with surplus energy or existing charging stations is a challenging problem as it has to be performed in real-time considering their spatio-temporal distribution, availability and grid load. In this paper, we study this assignment problem specifically in a supplier non-intrusive scenario (without changing their mobility) and aim to understand the potential benefits of a direct V2V charge sharing system. To this end, we present two new algorithms to match the demander EVs to suppliers for charging. In the first one, the maximum system benefit is targeted under multiple system objectives with different priorities and considering the grid load. In the second one, individual EV priorities are taken into account and a stable matching process depending on predefined user preferences is provided. We conduct extensive simulations using real user commuting patterns and existing charging station locations in three different cities together with a newly developed probabilistic EV charging behavior model and EV mobility. Simulation results show that direct V2V charge sharing can reduce the energy consumption of EVs by 20-35% by providing a closer charging facility compared to charging station only case, offload grid charging power in peak times by 35-55%, and help sustain up to 2.2x EV charging requests without building new charging infrastructure, while causing a marginal increase (i.e., 3-9%) in the energy cycling of supplier EVs. The results also show that the proposed matching algorithms offer 80-90% more energy consumption reduction compared to the intrusive V2V charging at designated parking lots.

Considering post-disaster scenarios for intelligent traffic management and damage assessment where communication infrastructure may not be available, we advocate a swarm-of-drones mesh communication architecture that can sustain in-network connectivity among drones. The connectivity sustenance requirement stems from the fact that drones may move to various locations in response to service requests but they still need to cooperate for data collection and transmissions. To address this need, we propose a fully distributed connectivity maintenance heuristic which enables the swarm to quickly adapt its formation in response to the service requests. To select the moving drone(s) that would bring minimal overhead in terms of time and moving distance, the connected dominating set (CDS) concept from graph theory is utilized. Specifically, a variation of CDS, namely E-CDS, is introduced to address the needs of 3-D mobile swarm-of-drones. We then show that E-CDS is NP-Complete and propose a new distributed heuristic to solve it. Once the E-CDS is determined in advance, drones not part of this E-CDS set are picked for movement tasks. When the movement is to cause any disconnection with the rest of the swarm, other drones are also relocated to restore the connectivity. The proposed heuristics are implemented in ns-3 network simulator as part of the existing IEEE 802.11s mesh standard and the effectiveness is tested in terms of providing undisturbed services under different conditions. The results indicate that the proposed distributed heuristic almost matches the performance of a centralized solution and suits perfectly the needs of post-disaster traffic management.

With the increasing diversity of Distributed Denial-of-Service (DDoS) attacks, it is becoming extremely challenging to design a fully protected network. For instance, Stealthy Link Flooding Attack (SLFA) is a variant of DDoS attacks that strives to block access to a target area by flooding a small set of links, and it is shown that it can bypass traditional DDoS defense mechanisms. One potential solution to tackle such SLFAs is to apply Moving Target Defense (MTD) techniques in which network settings are dynamically changed to confuse/deceive attackers, thus making it highly expensive to launch a successful attack. However, since MTD comes with some overhead to the network, to find the best strategy (i.e., when and/or to what extent) of applying it has been a major challenge. The strategy is significantly influenced by the attacker's behavior that is often difficult to guess. In this work, we address the challenge of obtaining the optimal MTD strategy that effectively mitigates SLFAs while incurs a minimal overhead. We design the problem as a signaling game considering the network defender and the attacker as players. A belief function is established throughout the engagement of the attacker and the defender during this SLFA campaign, which is utilized to pick the best response/action for each player. We analyze the game model and derive a defense mechanism based on the equilibria of the game. We evaluate the technique on a Mininet-based network environment where an attacker is performing SLFAs and a defender applies MTD based on equilibria of the game. The results show that our signaling game-based dynamic defense mechanism can provide a similar level of protection against SLFAs like the extensive MTD solution, however, causing a significantly reduced overhead.