This paper introduces a novel incentivization scheme tailored for a leaderless Directed Acyclic Graph-based Distributed Ledger Technology. The distinctive feature of the proposed scheme lies in enabling a no-fee environment with a fixed supply token, all while incorporating a robust anti-spam mechanism and a well-defined incentivization scheme.

A significant portion of research on distributed ledgers has focused on circumventing the limitations of leader-based blockchains mainly in terms of scalability, decentralization and power consumption. Leaderless architectures based on directed acyclic graphs (DAGs) avoid many of these limitations altogether, but their increased flexibility and performance comes at the cost of increased design complexity, so their potential has remained largely unexplored. Management of write access to these ledgers presents a major challenge because ledger updates may be made in parallel, hence transactions cannot simply be serialised and prioritised according to token fees paid to validators. In this work, we propose an access control scheme for leaderless DAG-based ledgers which is based on consuming credits rather than paying fees in the base token. We outline a general model for this new approach and provide some simulation results showing promising performance boosts.

Usability of distributed ledgers is crucial to their mainstream adoption, especially for enterprise applications in which most users do not wish to operate full-node infrastructure. Some attempts have been made to solve the problem of user-node interaction for blockchains in which leaders assemble users’ transactions into blocks, but in the case of leaderless DAG-based ledgers such as IOTA, many of these solutions cannot be applied due to the absence of a shared mempool and the ability of nodes to issue blocks in parallel. In this work, we propose a user-node interaction mechanism for ledgers of this kind that is designed to balance user traffic across nodes and ensure the risk of a user experiencing a poor quality of service is low. Our mechanism involves users selecting nodes to issue their transactions to the ledger based on quality of service indicators advertised by the nodes. Simulation results are presented to illustrate the efficacy of the proposed policies.

Permissionless reputation-based distributed ledger technologies (DLTs) have been proposed to overcome blockchains’ shortcomings in terms of performance and scalability, and to enable feeless messages to power the machine-to-machine economy. These DLTs allow machines with widely heterogeneous capabilities to actively participate in message generation and consensus. However, the open nature of such DLTs can lead to the centralization of decision-making power, thus defeating the purpose of building a decentralized network. In this article, we introduce Healthor, a novel heterogeneity-aware flow-control mechanism for permissionless reputation-based DLTs. Healthor formalizes node heterogeneity by defining a health value as a function of its incoming message queue occupancy. We show that health signals can be used effectively by neighboring nodes to dynamically flow control messages while maintaining high decentralization. We perform extensive simulations, and show a 23% increase in throughput, a 76% decrease in latency and four times increased node participation in consensus compared to state-of-the-art. To the best of our knowledge, Healthor is the first system to systematically explore the ramifications of heterogeneity on DLTs and proposes a dynamic, heterogeneity-aware flow control. Healthor’s source code ( https://github.com/jonastheis/healthor ) and simulation result data set ( https://zenodo.org/record/4573698 ) are both publicly available.

In the Internet of Things (IoT) domain, devices need a platform to transact seamlessly without a trusted intermediary. Although distributed ledger technologies (DLTs) could provide such a platform, blockchains, such as Bitcoin, were not designed with IoT networks in mind, hence are often unsuitable for such applications: they offer poor transaction throughput and confirmation times, put stress on constrained computing and storage resources, and require high transaction fees. In this article, we consider a class of IoT-friendly DLTs based on directed acyclic graphs, rather than a blockchain, and with a reputation system in the place of Proof of Work (PoW). However, without PoW, the implementation of these DLTs requires an access control algorithm to manage the rate at which nodes can add new transactions to the ledger. We model the access control problem and present an algorithm that is fair, efficient, and secure. Our algorithm represents a new design paradigm for DLTs in which concepts from networking are applied to the DLT setting for the first time. For example, our algorithm uses distributed rate setting, which is similar in nature to transmission control used in the Internet. However, our solution features novel adaptations to cope with the adversarial environment of DLTs in which no individual agent can be trusted. Our algorithm guarantees utilization of resources, consistency, fairness, and resilience against attackers. All of these are achieved efficiently and with regard for the limitations of IoT devices. We perform extensive simulations to validate these claims.

Access control is a fundamental component of the design of distributed ledgers, influencing many aspects of their functionality, such as fairness, efficiency, traditional notions of network security, and adversarial attacks such as Denial-of-Service (DoS) attacks.1In this work, we consider the security of a recently proposed access control protocol for directed acyclic graph-based distributed ledgers. We present a number of attack scenarios and potential vulnerabilities of the protocol and introduce a number of additional features which enhance its resilience. Specifically, a blacklisting algorithm, which is based on a reputation-weighted threshold, is introduced to handle both spamming and multirate malicious attackers. A solidification request component is also introduced to ensure the fairness and consistency of the network in the presence of attacks. Finally, a timestamp component is also introduced to maintain the consistency of the network in the presence of multirate attackers. Simulations to illustrate the efficacy and robustness of the revised protocol are also presented.

Primality generation is the cornerstone of several essential cryptographic systems. The problem has been a subject of deep investigations, but there is still a substantial room for improvements. Typically, the algorithms used have two parts – trial divisions aimed at eliminating numbers with small prime factors and primality tests based on an easy-to-compute statement that is valid for primes and invalid for composites. In this paper, we will showcase a technique that will eliminate the first phase of the primality testing algorithms. The computational simulations show a reduction of the primality generation time by about 30 percent in the case of 1024-bit RSA key pairs. This can be particularly beneficial in the case of decentralized environments for shared RSA keys as the initial trial division part of the key generation algorithms can be avoided at no cost. This also significantly reduces the communication complexity. Another essential contribution of the paper is the introduction of a new one-way function that is computationally simpler than the existing ones used in public-key cryptography. This function can be used to create new random number generators, and it also could be potentially used for designing entirely new public-key encryption systems.

Permission-less distributed ledgers provide a promising approach to deal with the Internet of Things (IoT) paradigm. Since IoT devices mostly generate data transactions and micro payments, distributed ledgers that use fees to regulate the network access are not an optimal choice. In this paper, we study a feeless architecture developed by IOTA and designed specifically for the IoT. Due to the lack of fees, malicious nodes can exploit this feature to generate an unbounded number of transactions and perform denial of service attacks. We propose to mitigate these attacks through verifiable delay functions. These functions, which are non-parallelizable, hard to compute and easy to verify, have been formulated only recently. In our work, we design a denial of service prevention mechanism which addresses network heterogeneity, limited node computational capabilities and hardware-specific implementation optimizations. Verifiable delay functions have mostly been studied from a theoretical point of view, but little has been done in tangible applications. Hence, this paper can be considered as a pioneer work in the field, since it builds a bridge between this theoretical mathematical framework and a real-world problem.

Densification through small cells and caching in base stations have been proposed to deal with the increasing demand for Internet content and the related overload on the cellular infrastructure. However, these solutions are expensive to install and maintain. Instead, using vehicles acting as mobile caches might represent an interesting alternative. In our work, we assume that users can query nearby vehicles for some time, and be redirected to the cellular infrastructure when the deadline expires. Beyond reducing costs, in such an architecture, through vehicle mobility, a user sees a much larger variety of locally accessible content within only few minutes. Unlike most of the related works on delay tolerant access, we consider the impact on the user experience by assigning different retrieval deadlines per content. In our paper, we provide the following contributions: (i) we model analytically such a scenario; (ii) we formulate an optimization problem to maximize the traffic offloaded while ensuring user experience guarantees; (iii) we propose two variable deadline policies; (iv) we perform realistic trace-based simulations, and we show that, even with low technology penetration rate, more than 60 percent of the total traffic can be offloaded which is around 20 percent larger compared to existing allocation policies.

This paper introduces a novel caching analysis that, contrary to prior work, makes no modeling assumptions for the file request sequence. We cast the caching problem in the framework of Online Linear optimization (OLO), and introduce a class of minimum regret caching policies, which minimize the losses with respect to the best static configuration in hindsight when the request model is unknown. These policies are very important since they are robust to popularity deviations in the sense that they learn to adjust their caching decisions when the popularity model changes. We first prove a novel lower bound for the regret of any caching policy, improving existing OLO bounds for our setting. Then we show that the Online Gradient Ascent (OGA) policy guarantees a regret that matches the lower bound, hence it is universally optimal. Finally, we shift our attention to a network of caches arranged to form a bipartite graph, and show that the Bipartite Subgradient Algorithm (BSA) has no regret.

Network utility maximization (NUM) is an iconic problem in network traffic management which is at the core of many current and emerging network design paradigms - and, in particular, software-defined networks (SDNs). Thus, given the exponential growth of modern-day networks (in both size and complexity), it is crucial to develop scalable algorithmic tools that are capable of providing efficient solutions in time which is dimension-free, i.e., independent-or nearly-independent-on the size of the system. To do so, we leverage a suite of modified gradient methods known as “mirror descent” and we derive a scalable and efficient algorithm for the NUM problem based on gradient exponentiation. We show that the convergence speed of the proposed algorithm only carries a logarithmic dependence on the size of the network, so it can be implemented reliably and efficiently in massively large networks where traditional gradient methods are prohibitively slow. These theoretical results are sub-sequently validated by extensive numerical simulations showing an improvement of several order of magnitudes over standard gradient methods in large-scale networks.

Caching content at the edge of mobile networks is considered as a promising way to deal with the data tsunami. In addition to caching at fixed base stations or user devices, it has been recently proposed that an architecture with public or private transportation acting as mobile relays and caches might be a promising middle ground. While such mobile caches have mostly been considered in the context of delay tolerant networks, in this paper we argue that they could be used for low cost video streaming without the need to impose any delay on the user. Users can prefetch video chunks into their playout buffer from encountered vehicle caches (at low cost) or stream from the cellular infrastructure (at higher cost) when their playout buffer empties while watching the content. Our main contributions are: (i) to model the playout buffer in the user device and analyze its idle periods which correspond to bytes downloaded from the infrastructure; (ii) to optimize the content allocation to mobile caches; and to minimize the expected number of non-offloaded bytes. We perform trace-based simulations to support our findings showing that up to 60 percent of the original traffic could be offloaded from the main infrastructure.

Pushing popular content to small cells with local storage (“helper” nodes) has been proposed to cope with the ever-growing data demand. Nevertheless, the collective storage of a few nearby helper nodes may not suffice to achieve a high hit rate in practice. In this paper, we introduce the concept of “soft cache hits” (SCHs). An SCH occurs if a user's requested content is not in the local cache, but the user can be (partially) satisfied by a related content that is. In case of a cache miss, an application proxy (e.g., YouTube) running close to the helper node (e.g., at a multi-access edge computing server) can recommend the most related files that are locally cached. This system could be activated during periods of predicted congestion, or for selected users (e.g., low-cost plans), to improve cache hit ratio with limited (and tunable) user quality of experience performance impact. Beyond introducing a model for soft cache hits, our next contribution is to show that the optimal caching policy should be revisited when SCHs are allowed. In fact, we show that optimal caching with SCH is NP-hard even for a single cache. To this end, we formulate the optimal femto-caching problem with SCH in a sufficiently generic setup and propose efficient algorithms with provable performance. Finally, we use a large range of real datasets to corroborate our proposal.

Pushing popular content to cheap ``helper'' nodes (e.g., small cells with local storage) during off-peak hours has recently been proposed to cope with the increase in mobile data traffic. If the requested content is available locally at a helper node, both user and operator performance could benefit. Nevertheless, the collective storage of a few nearby helper nodes does not usually suffice to achieve a high hit rate in practice. In this paper, we investigate the concept of ``soft cache hits'' where, if the original content is not available, some locally cached related contents can be recommended. Given that Internet content consumption is entertainment-oriented, we argue that there exist scenarios where a user might accept an alternative content (e.g., better download rate for alternative content, low rate plans), thus avoiding to access expensive/congested links. We formulate the problem of optimal edge caching with soft cache hits in a sufficiently generic setup, propose an efficient algorithm, and analyze the expected gains. We then show using synthetic and real datasets of related video contents that promising caching gains could be achieved in practice.

Densification through small cells and caching in base stations have been proposed to deal with the increasing demand for Internet content and the related overload on the cellular infrastructure. However, these solutions are expensive to install and maintain. Instead, using vehicles acting as mobile caches might represent an interesting alternative. In our work, we assume that users can query nearby vehicles for some time, and be redirected to the cellular infrastructure when the deadline expires. Beyond reducing costs, in such an architecture, through vehicle mobility, a user sees a much larger variety of locally accessible content within only few minutes. Unlike most of the related works on delay tolerant access, we consider the impact on the user experience by assigning different retrieval deadlines per content. In our paper, we provide the following contributions: (i) we model analytically such a scenario; (ii) we formulate an optimization problem to maximize the traffic offloaded while ensuring user experience guarantees; (iii) we propose a variable deadline policy; (iv) we perform realistic trace-based simulations, and we show that, even with low technology penetration rate, more than 60% of the total traffic can be offloaded which is around 20% larger compared to existing allocation policies.

The increasing demand for mobile data is overloading the cellular infrastructure. Small cells and edge caching is being explored as an alternative, but installation and maintenance costs for sufficient coverage are significant. In this work, we perform a preliminary study of an alternative architecture based on two main ideas: (i) using vehicles as mobile caches that can be accessed by user devices; compared to small cells, vehicles are more widespread and require lower costs; (ii) combining the mobility of vehicles with delayed content access to increase the number of cache hits (and reduce the load on the infrastructure). Contrary to standard DTN-type approaches, in our system max delays are guaranteed to be kept to a few minutes (beyond this deadline, the content is fetched from the infrastructure). We first propose an analytical framework to compute the optimal number of content replicas that one should cache, in order to minimize the infrastructure load. We then investigate how to optimally refresh these caches to introduce new contents, as well as to react to the temporal variability in content popularity. Simulations suggest that our vehicular cloud considerably reduces the infrastructure load in urban settings, assuming modest penetration rates and tolerable content access delays.