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In this paper, we propose GreenCoin – an energy-efficient cryptocurrency system with mining protocols designed to favor locations with relatively higher availability of renewable energy. Traditionally, crypto coin mining involves solving complex mathematical problems by high-end computing devices consuming an enormous amount of electricity, thus adversely affecting net carbon emissions. To reduce cost and emissions, GreenCoin uses a modified proof of stake (PoS) consensus algorithm, which itself is more energy efficient compared to other state-of-the-art methods. Our modified PoS algorithm, called Green PoS (GPoS), allows GreenCoin to favor nodes (with reward and privilege) located in regions with higher availability of renewable energy. We present a detailed system architecture of GreenCoin and explain the operating method of GPoS. We also provide results from empirical studies demonstrating the renewable energy-aware approach of GreenCoin. 

Data replication facilitates availability and recovery in a distributed environment. However, concurrent updates to multiple replicas result in divergence of data. Conflict-Free Replicated Data Types (CRDTs) are abstract data types that provide a principled approach to asynchronously reconcile this divergence. We propose a different perspective on the divergence of data, whereby we treat data divergences as versions of the data. That is, instead of treating it only as a problem that needs to be solved, we consider it also to be a feature that provides a way to track versioning and evolution of data. Versioning information is helpful in multiple scenarios, such as provenance tracking and system debugging. Doing so allows us to leverage concepts such as the version tree found in the literature for persistent (versioned) data structures. We show that many techniques used in CRDTs to order elements can be derived from version trees, which predates CRDTs by more than 20 years. Using version trees for maintaining order and append-only logs for storage, we propose a method to ensure convergence of arbitrary data types, while maintaining information related to the evolution of data. 

In this paper, we investigate how to automatically persist versioned data structures in distributed settings (e.g. cloud + edge) using append-only storage. By doing so, we facilitate resiliency by enabling program state to survive program activations and termination, and program-level data structures and their version information to be accessed programmatically by multiple clients (for replay, provenance tracking, debugging, and coordination avoidance, and more). These features are useful in distributed, failure-prone contexts such as those for heterogeneous and pervasive Internet of Things (IoT) deployments. We prototype our approach within an open-source, distributed operating system for IoT. Our results show that it is possible to achieve algorithmic complexities similar to those of in-memory versioning but in a distributed setting. 

Due to the proliferation of IoT and the popularity of smart contracts mediated by blockchain, smart home systems have become capable of providing privacy and security to their occupants. In blockchain-based home automation systems, business logic is handled by smart contracts securely. However, a blockchain-based solution is inherently resource-intensive, making it unsuitable for resource-constrained IoT devices. Moreover, time-sensitive actions are complex to perform in a blockchainbased solution due to the time required to mine a block. In this work, we propose a blockchain-independent smart contract infrastructure suitable for resource-constrained IoT devices. Our proposed method is also capable of executing time-sensitive business logic. As an example of an end-to-end application, we describe a smart camera system using our proposed method, compare this system with an existing blockchain-based solution, and present an empirical evaluation of their performance.