Cryptographic wallets play a vital role in securing digital assets within blockchain networks by managing private keys that authorize secure transactions. However, side channel analysis (SCA) attacks have become a serious threat, enabling attackers to extract sensitive information by exploiting algorithmic weaknesses in microcontroller-based wallets, resulting in the loss of millions of dollars in digital assets. In hierarchically deterministic (HD) systems, the compromise of a single primary key can endanger all subsequent child keys, while the use of independent keys for each account introduces complexity and challenges in key management. This work presents HardVault, a field programmable gate array (FPGA)-based cryptocurrency wallet that supports both Bitcoin and Ethereum. HardVault introduces the first hardware wallet architecture that implements both non-deterministic (ND) and HD key generation modes directly in hardware, giving users the flexibility to choose either approach based on their security and usability needs. By leveraging constant-time operations and hardware-enforced private-key isolation, the design significantly improves resilience to SCA attacks. In addition, the architecture prioritizes resource efficiency to minimize area usage without compromising security, making it well-suited for compact, portable hardware wallet applications. Implementation on a ZCU104 FPGA shows that HardVault uses only 27% of available look-up tables (LUTs). Compared to the Trezor One cryptocurrency (crypto) wallet, the proposed implementation achieves$9\times $higher energy efficiency,$8\times $lower latency, and$7\times $higher throughput.

Ethereum leverages smart contracts (SCs) to power decentralized applications (dApps), with execution handled by the Ethereum virtual machine (EVM) within an Ethereum client. Other blockchain platforms, including Avalanche, Polkadot, Aurora, and Cardano, have also adopted the EVM. However, the performance of the EVM is often constrained by the limitations of general-purpose processors, a challenge that has been explored in the literature. This work aims to further address the limitation by proposing EVMx, a dedicated single-core SC execution engine implemented on a field programmable gate array (FPGA). EVMx follows a processor-like architecture inspired by the RISC philosophy. By exploiting the parallelism and high-speed processing capabilities of FPGA hardware, EVMx achieves a 61% to 99% reduction in execution time for commonly used operation codes compared to traditional central processing unit (CPU)-based environments. Furthermore, EVMx executes entire Ethereum blocks with a percentage reduction in execution time between 6% and 56% against comparable FPGA implementations and 98% to 99% compared to CPU-based EVMs in the literature. These results demonstrate the potential of EVMx to significantly accelerate SC execution and enhance the performance of EVM-compatible blockchains.

Ethereum blockchain uses smart contracts (SCs) to implement decentralized applications (dApps). SCs are executed by the Ethereum virtual machine (EVM) running within an Ethereum client. Moreover, the EVM has been widely adopted by other blockchain platforms, including Solana, Cardano, Avalanche, Polkadot, and more. However, the EVM performance is limited by the constraints of the general-purpose computer it operates on. This work proposes offloading SC execution onto a dedicated hardware-based EVM. Specifically, EVMx is an FPGA-based SC execution engine that benefits from the inherent parallelism and high-speed processing capabilities of a hardware architecture. Synthesis results demonstrate a reduction in execution time of 72% to 99% for commonly used operation codes compared to CPU-based SC execution environments. Moreover, the execution time of Ethereum blocks on EVMx is up to 6 ×faster compared to analogous works in the literature. These results highlight the potential of the proposed architecture to accelerate SC execution and enhance the performance of EVM-compatible blockchains.

EV charging infrastructures traditionally rely on untrusted centralized infrastructures that pose several privacy and security threats to EVs’ personal information. Targeted advertisements, privacy leaks and selling data to third parties are among the threats to privacy and security. By utilizing blockchain-based solutions, recent work address the security and privacy problems associated with EV charging protocols. Most of them are geared toward maintaining EV anonymity rather than preserving end-to-end privacy. As EV owners’ charging histories and payment information are associated with their wallet addresses on the blockchain, any threat of linkability of these blockchain addresses to physical identities can pose a serious risk to their privacy. In this paper, we propose a ring signature based privacy-preserving end-to-end charging station (CS) reservation and payment protocol, which provides EV owners with the ability to reserve and pay for a charging slot privately without sharing private information or exposing their identity or addresses at CS locations. Additionally, we provide EV owners with a decentralized charging slot information verification protocol with the help of secure multiparty computation (SMC), which allows them to verify available slots. A dispute resolution mechanism is also proposed that handles disputes between EVs and CSs and penalizes them accordingly by utilizing trusted execution environment (TEE). Results show that the proposed protocol ensures end-to-end EV owners’ privacy with low blockchain transaction and computation overhead.

Federated Learning (FL) has emerged as a promising solution to address data privacy concerns, but it also faces ob-stacles, including biased centralized aggregation. In this paper, we propose a novel decentralized FL architecture with round-based aggregator selection that enhances the accuracy of the global model while preserving data privacy for the users. Our proposed model consists of three distinct phases. Firstly, we address the issue of a central aggregator by introducing a model where we periodically select the aggregator as the best-performing client from participant nodes. Secondly, we train all clients using their respective data but excluding the client selected as an aggregator. In the third phase, we further refine the global model by training it on the raw data of the selected aggregator, leading to an enhanced global model. These three modules empower the proposed model to offer improved accuracy and decentralization. Our implementation yielded promising results, demonstrating that the proposed mode achieves an accuracy of 98.54 % along with enhanced decentralization. The proposed model has also validated different datasets and network conditions, such as the number of participant nodes. Furthermore, the performance of the proposed model is validated in the presence of a biased aggregator. The results demonstrate that the proposed model achieves 98.43 % accuracy and shows more robustness in the presence of a biased aggregator. Finally, the proposed model converges fast compared to other baseline models.

Climate change is a major issue that has disastrous impacts on the environment through different causes like the greenhouse gas (GHG) emission. Many energy utilities around the world intend to reduce GHG emissions by promoting different systems including carbon emission trading (CET), renewable energy certificates (RECs), and tradable white certificates (TWCs). However, these systems are centralized, highly regulated, and operationally expensive and do not meet transparency, trust and security requirements. Accordingly, GHG emission reduction schemes are gradually moving towards blockchain-based solutions due to their underpinning characteristics including decentralization, transparency, anonymity, and trust (independent from third parties). This paper performs a comprehensive investigation into the blockchain technology, deployed for GHG emission reduction plans. It explores existing blockchain solutions along with their associated challenges to effectively uncover their potentials. As a result, this study suggests possible lines of research for future enhancements of blockchain systems particularly their incorporation in GHG emission reduction.

Video conferencing has become an essential tool for working from home. However, poor audio/video quality resulting from unstable Internet connections undermines the productivity of important tasks. Additionally, the static monetization model for ISP networks, which employs third parties, cannot support on-demand and dynamic Quality-of-Service sessions that are necessary to maximize the Quality-of-Experience (QoE) of video conferencing. To address this, we introduce BlockQoS: Fair Monetization of On-Demand Quality-of-Service using Blockchains. BlockQoS allows clients to request and manage their Quality-of-Service requirements through a blockchain-based platform operating using a smart contract. It implements a decentralized monetization model to eliminate third parties, enforce transparency in service-level agreements (SLAs), and reduce blockchain operating costs by utilizing off-chain billing validated using zero-knowledge proofs (zk-SNARK). Additionally, we propose a Quality-of-Service delivery verification mechanism that enforces service level agreements on the hardware external to the blockchain, and a dynamic evaluation method based on the concept of Nash equilibrium in game theory that prevents malicious behavior by ISPs and users. We implemented BlockQoS over Ethereum with a Ryu controller, zk-SNARK, and SGX. Our experiments show that BlockQoS offers transaction cost reduction of up to 88% (gas cost) and latency reduction of up to 87% compared to the state-of-the-art on-chain solutions.

Current Internet of Things (IoT) infrastructures rely on cloud storage however, relying on a single cloud provider puts limitations on the IoT applications and Service Level Agreement (SLA) requirements. Recently, multiple decentralized storage solutions (e.g., based on blockchains) have entered the market with distinct architecture, Quality of Service (QoS) parameters and at lower price compared to the cloud storage. In this work, we introduce BAM: a neural network-based middleware designed for intelligent selection of storage technology for IoT applications. We first propose a blockchain-based data placement protocol and theoretically model a decision optimization problem, which jointly considers cloud, multi-cloud and decentralized storage technologies to select the appropriate medium to store large-scale IoT data, while ensuring data integrity, traceability, auditability and decision verifiability. We then propose a neural network-based maintenance reconfiguration, which aims to optimize the computational complexity of the middleware design along with the blockchain transaction and storage overhead by learning and predicting the applications parameters. We also propose the aggregation rate feedback functionality in our design and model it as a linear optimization problem to improve data quality and precision. Finally, we provide a reference implementation and perform extensive experiments, which demonstrate the effectiveness of the proposed design.

In the last few years, electric utility companies have increasingly invested into transactive energy systems. This trend was primarily caused by the integration of distributed energy resources (DERs) and internet-of-things (IoT) devices into their existing distribution networks. Influenced by the general interest in blockchain technologies, many industry specialists are considering new, more efficient peer-to-peer market structures for DERs. In this paper, we explore the trade-off between cost and traceability in the form of on-chain and off-chain solutions. We also propose ZipZap, a first step towards a blockchain-based local smart grid system. ZipZap is an ERC-1155 compliant solution with four different prototypes: Heavyweight, Featherweight, Lightweight and Weightless. Our evaluation uses realistic parameters and measures the impact of different types of metadata storage scopes, with some Ethereum prototypes showcasing gas cost reductions of more than 97% in comparison to our fully on-chain baseline.

Cloud gaming is a rising new trend for remote video gaming. Players send their commands using a thin-client device to a graphics rendering cloud server and receive a compressed video stream in response. However, video games with complex textures and motions, especially at high resolutions, require a substantial bitrate to deliver good visual quality. When the player’s Internet connection is constrained or fluctuates, the visual quality may be significantly reduced, which negatively impacts the playing experience. In this paper, we present an Action-awaRe COmbat moDEl (ARCODE) for massively multiplayer online role-playing games (MMORPGs) running on cloud gaming platforms to improve compression efficiency. ARCODE captures different action data for different object types in the battle scene and determines the importance of each object relative to the player in each game state, considering the actions at the time. Based on the significance of each object to the player, the model determines how frequently its position should be updated. Reducing the number of motion updates in the scene leads to fewer bits needed to encode the video frames. Our experimental results on various test cases show that, for similar visual quality as that of the traditional approach, ARCODE can reduce the video bitrate from 9% to over 40%.

The Intelligent Transportation System (ITS) has become essential for the economical and technological development of a country. The maturity of communication technologies (Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V)) and the amalgamation of smart grids, electric vehicles (EVs) and energy trading resulted in a storm of research opportunities for green ITS. In addition, the combination of vehicular communication technologies and ITS enable efficient selection of EV charging stations (CS) and scheduling EVs charging requirements in real-time. However, the untrusted centralized nature of energy markets and EV charging infrastructures result in several privacy and security threats to EV user's private information. These security and privacy threats include targeted advertisements, privacy leakage, selling data to third party, etc. In this work, we propose BlockEV, a blockchain-based efficient CS selection protocol for EVs to ensure the security and privacy of the EV users, availability of the reserved time slots at CSs, high Quality of Service (QoS) and enhanced EV user comfort. First, a blockchain-based framework is introduced to implement secure charging services and trusted reservation for EVs with the execution of smart contract. Second, we focus on the efficient CS selection and propose a mechanism for EVs to select the CS locally without sharing private information to CS, while fulfilling their service requirements. Evaluations show that the proposed BlockEV is scalable with significantly low blockchain transaction and storage overhead.

Distributed overlay-based publish/subscribe systems provide a selective and scalable communication paradigm for connecting components of a distributed application. Existing overlay-based systems only guarantee delivery of notifications to clients that are already known by all brokers in the overlay. Nonetheless, due to the propagation delay, it takes time for a client's interests to be received by all brokers comprising the overlay. The message propagation delay and unclear delivery guarantees during this time increase the complexity of developing distributed applications based on the pub/sub paradigm. In this paper, we propose a collection of message processing and delivery guarantees that allows clients to clearly define the set of publications they receive. Based on our evaluation, these delivery guarantees can reduce buffering requirements on clients by up to 10 times, prevent missing notifications due to the propagation delay, and provide clients with primitive building blocks that simplify application development. We evaluate our proposed routing algorithms and show that a pub/sub system can provide the proposed delivery guarantees without increasing its resource requirements or hindering its throughput.

Distributed blockchain ledgers are on the verge of becoming a disruptive technology, profoundly impacting a wide range of industries and established applications, such as cryptocurrency, and allowing for novel use cases in both the public sector (e.g., eGovernment, eHealth, etc.) and the private sector (e.g., finance, supply chain management, etc.). Blockchains promise the ability to maintain critical information in a trustworthy repository without any centralized management. The reliability of blockchain-enabled applications is based on the innate immutability of stored data, maintained through cryptographic means, which enables blockchains to provide transparency, efficiency, auditability, trust, and security. As the technology is still in its infancy, a number of pain points must be addressed in order to make distributed ledgers more dependable, scalable, and pervasive. In this paper, we present the research landscape in distributed ledger technology (DLT). To do so, we describe a taxonomy of blockchain applications called blockchain generations. We also present the DCS properties (Decentralization, Consistency, and Scalability) as an analogy to the CAP theorem. Furthermore, we provide a general structure of the blockchain platform which decomposes the distributed ledger into six layers: Application, Modeling, Contract, System, Data, and Network. Finally, we classify research angles across three dimensions: DCS properties impacted, targeted applications, and related layers.

Transactional operation processing among clients is increasingly required of publish/subscribe (pub/sub) systems in enterprise settings. For instance, in workflow management, dispatching or consolidating process instances require publications and (un-) subscriptions by different clients to be executed according to ACID semantics. As pub/sub systems are usually optimized for performance and scalability, such properties are often neglected, which results in unexpected system behavior. In this paper, we provide a model for supporting multiclient transactions in pub/sub. We formalize ACID properties for pub/sub, and define a consistency model and isolation level required in the aforementioned scenarios. We present three approaches for two transaction types: S-TX, where a coordinator has full static knowledge about all operations in a transaction, and D-TX/D-TXNI, where operations by other clients are dynamic and unknown to the coordinator. We describe algorithms realizing these approaches and experimentally evaluate them by comparing to a baseline mechanism, which simulates these guarantees partially with manual waits between operations. Our results show that the uncertainty introduced by the dynamic behavior renders D-TX/D-TXNI costly, and suitable only for small configurations or rare occasions. S-TX, in contrast, offers enriched semantics for many applications in a scalable manner without disrupting regular event routing.

Large-scale applications require a scalable data dissemination service with advanced filtering capabilities. We propose the use of a content-based publish/subscribe system with support for top-k filtering in the context of such applications. We focus on the problem of top-k subscription filtering, where a publication is delivered only to the k highest scoring subscribers. The naive approach to perform filtering early at the publisher edge works only if complete knowledge of the subscriptions is available, which is not compatible with the well-established covering optimization in scalable content-based publish/subscribe systems. We propose an efficient rank-cover technique to reconcile top-k subscription filtering with covering. We extend the covering model to support top-k and describe a novel algorithm for forwarding subscriptions to publishers while maintaining correctness. Finally, we compare our solutions to a baseline covering system. In a typical setting, our optimized solution is scalable and provides over 81% of the covering benefit.

Traditional pub/sub systems cannot adequately handle workloads of applications with dynamic, short-lived subscriptions such as location-based social networks, predictive stock trading, and online games. Subscribers must continuously interact with the pub/sub system to remove and insert subscriptions, thereby inefficiently consuming network and computing resources, and sacrificing consistency. In the aforementioned applications, we recognize that the changes in the subscriptions can follow a predictable pattern over some variable (e.g., time). In this paper, we present a new type of subscription, called evolving subscription, which encapsulates these patterns and allow the pub/sub system to autonomously adapt to the dynamic interests of the subscribers without incurring an expensive re-subscription overhead. We propose a general model for expressing evolving subscriptions and a framework for supporting them in a pub/sub system. To this end, we propose three different designs to support evolving subscriptions, which are evaluated and compared to the traditional resubscription approach in the context of two use cases: online games and high-frequency trading. Our evaluation shows that our solutions can reduce subscription traffic by 96.8% and improve delivery accuracy when compared to the baseline resubscription mechanism.

We are witnessing a technological revolution with a broad impact ranging from daily life (e.g., personalized medicine and education) to industry (e.g., data-driven healthcare, commerce, agriculture, and mining). At the core of this transformation lies "data". This transformation is facilitated by embedded devices, collectively known as Internet of Things (IoT), which produce real-time feeds of sensor data which are collected and processed to produce a dynamic physical model used for optimized real-time decision making. At the infrastructure level, there is a need to develop a scalable architecture for processing massive volumes of present and historical data at an unprecedented velocity to support the IoT paradigm. To cope with such extreme scale, we argue for the need to revisit the hardware and software co-design landscape in light of two key technological advancements. First is the virtualization of computation and storage over highly distributed data centers spanning across continents. Second is the emergence of a variety of specialized hardware accelerators that complement traditional general-purpose processors. Further efforts are required to unify these two trends in order to harness the power of big data. In this paper, we present a formulation and characterization of the hardware acceleration landscape geared towards real-time analytics in the cloud. Our goal is to assist both researchers and practitioners navigating the newly revived field of software and hardware co-design for building next generation distributed systems. We further present a case study to explore software and hardware interplay for designing distributed real-time stream processing.

Distributed overlay-based publish/subscribe systems provide a selective, scalable, and decentralized approach to data dissemination. Due to the dynamic communication flows between data producers and consumers, the overlay topology of such systems can become inefficient over time and therefore requires adaptation to the existing load. Existing studies propose algorithms to design overlay topologies which are optimized for specific workloads. However, the problem of generating a plan to incrementally transform the current topology to an optimized one has been largely ignored. In this paper, we present IPITT, an approach based on integer programming for the incremental topology transformation (ITT) problem. Given the current topology and a target topology, IPITT generates a transformation plan with a minimal number of steps in order to lessen service disruption. Furthermore, we introduce a plan execution mechanism and evaluate our approach on an existing publish/subscribe system. Based on our evaluation, IPITT can reduce plan computation time by a factor of 10 and generates plans with an execution time up to 55% shorter than those of existing approaches.

We investigate the use of content-based publish/subscribe for data dissemination in large-scale applications with expressive filtering requirements. In particular, we focus on top-k subscription filtering, where a publication is delivered only to the k best ranked subscribers, as ordered using expressive semantics such as relevance, fairness, and diversity. The naive approach to perform filtering early at the publisher edge works only if complete knowledge of the subscriptions is available, which is not compatible with the well-established covering optimization in scalable content-based publish/subscribe systems. We propose an efficient rank-cover technique to reconcile top-k subscription filtering with covering. We extend the covering model to support top-k and describe a novel algorithm for forwarding subscriptions to publishers while maintaining correctness. We also establish a framework for supporting different types of ranking semantics and propose an implementation to support fairness. Finally, we compare our solutions to a baseline covering system and perform sensitivity analysis to demonstrate that our optimized rank-cover algorithm retains both covering and fairness while achieving properties advantageous to our targeted workloads. In a typical setting, our optimized solution is scalable, selects fairly, and provides over 81% of the covering benefit.

There has been a recent push in the database community towards supporting real-time analytical queries (OLAP) while sustaining a large volume of fine-grained updates (OLTP). Supporting these types of workloads require both an efficient data storage layer as well as a distributed architecture. In this demo, we address the latter point with our Distributed Lineage-based Data Store (DL-Store), which is a distributed data processing engine. DL-Store is built on top of L-Store, which is a lineage-based storage architecture designed to handle mixed OLTP and OLAP workloads, and provides scalability and elasticity by supporting multiple L-Store nodes. To maintain the desired consistency semantics, DL-Store employs a distributed transaction handler component which can horizontally scaled by provisioning additional transaction manager nodes. We leverage partitioning in the record space of the transactions to minimize communication across transaction managers while ensuring consistent execution. The demo shows our implementation of DL-Store over Apache Spark using a variety of use cases.

Publish/Subscribe is known as a scalable and efficient data dissemination mechanism. In a mobile environment, there is an added challenge for the pub/sub system to economizemobile bandwidth, which is especially precious in areas not wellcovered by mobile providers. While well-known compressionmethods such as GZip or Deflate are generally useful in suchsituations, we propose using Shared Dictionary Compression(SDC) to achieve a greater level of bandwidth efficiency. SDCrequires a dictionary, generated upfront, to be shared betweentwo communicating peers before it can be used. We proposea design where brokers forming the pub/sub overlay can be incharge of generating and propagating the shared dictionary. Oursolution employs an adaptive algorithm, executed at the brokers, which creates and maintains the dictionaries over time. Withthis approach, it is possible to reduce the required bandwidth byup to 88% including the introduced dictionary overhead. Ourdemo shows this approach applied to a smartphone applicationcommunicating with a publish/subscribe broker using the MQTTprotocol.

Modern applications for distributed publish/subscribe systems often require stream aggregation capabilities along with rich data filtering. When compared to other distributed systems, aggregation in pub/sub differentiates itself as a complex problem which involves dynamic dissemination paths that are difficult to predict and optimize for a priori, temporal fluctuations in publication rates, and the mixed presence of aggregated and non-aggregated workloads. In this paper, we propose a formalization for the problem of minimizing communication traffic in the context of aggregation in pub/sub. We present a solution to this minimization problem by using a reduction to the well-known problem of minimum vertex cover in a bipartite graph. This solution is optimal under the strong assumption of complete knowledge of future publications. We call the resulting algorithm "Aggregation Decision, Optimal with Complete Knowledge" (ADOCK). We also show that under a dynamic setting without full knowledge, ADOCK can still be applied to produce a low, yet not necessarily optimal, communication cost. We also devise a computationally cheaper dynamic approach called "Aggregation Decision with Weighted Publication" (WAD). We compare our solutions experimentally using two real datasets and explore the trade-offs with respect to communication and computation costs.

Massively multiplayer online games (MMOGs), which are typically supported by large distributed systems, require a scalable, low latency messaging middleware that supports the location-based semantics and the loosely coupled interaction of multiplayer games components. In this paper, we present three different pub/sub-driven designs for a MMOG networking engine that account for the highly interactive and massive nature of these games. Each design uses not only different pub/sub approaches (from topic-based to content-based) but also serves varying degrees of responsibilities. In particular, some of them integrate game functionality, such as interest management, into the network engine. We implement, evaluate, and compare our proposed designs in the MMOG prototype Mammoth. Our real-world results show the viability of pub/sub while at the same time highlighting clear trade-offs between the different designs used, especially in the number and frequency of the various message types, such as subscriptions.

The amount of content served on social networks can overwhelm users, who must sift through the data for relevant information. To facilitate users, we develop and implement dissemination of ranked data in social networks. Although top-k computation can be performed centrally at the user, the size of the event stream can constitute a significant bottleneck. Our approach distributes the top-k computation on an overlay network to reduce the number of events flowing through. Experiments performed using real Twitter and Facebook datasets with 5K and 30K query subscriptions demonstrate that social workloads exhibit properties that are advantageous for our solution.

Total ordering is a messaging guarantee increasingly required of content-based pub/sub systems, which are traditionally focused on performance. The main challenge is the uniform ordering of streams of publications from multiple publishers within an overlay broker network to be delivered to multiple subscribers. Our solution integrates total ordering into the pub/sub logic instead of offloading it as an external service. We show that our solution is fully distributed and relies only on local broker knowledge and overlay links. We can identify and isolate specific publications and subscribers where synchronization is required: the overhead is therefore contained to the affected subscribers. Our solution remains safe under the presence of failure, where we show total order to be impossible to maintain. Our experiments demonstrate that our solution scales with the number of subscriptions and has limited overhead for the non-conflicting cases. A holistic comparison with group communication systems is offered to evaluate their relative scalability.

Massively Multiplayer Online Games are considered large distributed systems where the game state is partially replicated across the server and thousands of clients. Given the scale, game engines typically offer only relaxed consistency without well-defined guarantees. In this paper, we leverage the concept of transactions to define consistency models that are suitable for gaming environments. We define game specific levels of consistency that differ in the degree of isolation and atomicity they provide, and demonstrate the costs associated with their execution. Each action type within a game can then be assigned the appropriate consistency level, choosing the right trade-off between consistency and performance. The issue of durability and fault-tolerance of game actions is also discussed.