## 2 Related Work
They concluded that networks with heterogeneous latency profiles must monitor fairness amongst nodes and, based on that, must preclude some nodes from being miners. [PERSON] et al. [39] discussed the trust issue between grid service providers and regular consumers, proposing a secure blockchain-based mechanism called grid monitoring. This mechanism offers verifiable provider-centric and consumer-centric information on regular energy consumption and applicable tariff rates to create trust between consumers and providers. The authors also presented comprehensive layer-based architecture with semantics for the systems, which guaranteed transparency, provenance, and immutability. [PERSON] et al. [40] presented another study in which they considered the security issues of energy monitoring in SG networks. Similarly, [PERSON] et al. [41] fused blockchain security with attribute-based access control (ABAC) to prevent unauthorized data access over the SG with favorable results. Several other researchers have focused on the dynamic load management of EV charging and other related concerns, such as security and privacy. [PERSON] et al. [42] showed that uncoordinated EV charging, for even 10% of the total EVs, can give rise to unacceptable levels of instability in the grid. [PERSON] et al. [43] modeled the statistical patterns of EV owner charging and load optimization for the grid by utilizing V2G capability, where an EV adjusts its charging rate or returns stored energy to the system. The authors found that it was possible to accommodate a large number of EVs using their proposed algorithm without causing instability in the SG and that the algorithm performance further improved with V2G capability in the system. [PERSON] et al. [44] presented an attacker's perspective and proposed a strategy for dynamic load-altering by manipulating EV charging to destabilize the grid using the feedback control theory. In a similar survey, [PERSON] et al. [45] also described bidirectional charging as a means to tackle the issue of massive EV deployment. [PERSON] et al. [46] addressed privacy concerns and dynamic charging tariffs through their blockchain-based smart contract publishing. Their study served as an honest and verifiable record of current electricity tariffs on individual charging stations and customers' bidding processes without revealing their location data for privacy. Quite recently, [PERSON] et al. [47], [PERSON] et al. [48], and [PERSON] et al. [49] also considered the problems of EV charging from the electric grid as they become ubiquitous. The proposed algorithms optimized the EV charging behaviors, reduced the costs associated with charging, and effectively balanced the regional load profiles of the grids. On the other hand, [PERSON] et al. [50] and [PERSON] et al. [51] considered the communications plane of autonomous, connected vehicles for vehicle-to-vehicle (V2V), vehicle-to-renewable (V2R), and renewable-to-vehicle (R2V) communication scenarios. The authors argued that EVs will use similar communication technologies to communicate with centralized and roadside equipment, e.g., 802.11 bd (for DSRC) and 5G-NR V2X (for C-V2X), to transmit their current battery levels and the intended destination of travel, so that the system can calculate the best optimum charging schedule for their commute. [PERSON] et al. [52] discussed reliable wireless technologies for smart metering applications in blockchain-based SGs. [PERSON] et al. [53], [PERSON] and [PERSON] [54], [PERSON] et al. [55], and [PERSON] et al. [56] discussed the integration of supervisory control and data acquisition (SCADA) technology with SG. Moreover, [PERSON] et al. [56] highlighted various factors for its successful integration, like real-time performance reporting by sensors for safeguarding against cyber-attacks, for which resilient network infrastructure is required. They proposed a hybrid system using legacy message forwarding and software-defined networking (SDN) switches. The detailed literature review presented in this section is also summarized in Table 2.