Decentralized trust is a fundamental requirement for blockchain networks where peers lack mutual trust, yet the majority are assumed to be honest for correct network operation. Traditional mechanisms to address this rely on analyzing the blockchain data structure, a process that is resource-intensive for constrained devices. This paper explores Aurora, a novel solution which adds another layer of trust to blockchain networks: It leverages network exploration to achieve trustless and decentralized transaction verification for resource-constrained blockchain clients. Aurora employs a probabilistic algorithm based on hypergeometric distribution to identify sets of peers likely to contain an honest majority. By exploring the network structure, Aurora constructs a set of network peers containing a majority of honest, i.e. trusted, peers with strong correctness guarantees, without relying on a central authority. The probabilistic approach is highly-efficient compared to the deterministic alternative, as demonstrated by analytical analysis and extensive simulations on a realistic Bitcoin-like network. Aurora allows IoT devices to act as ultra-light clients interacting efficiently with the constructed set of peers: An Aurora client checks whether a transaction exists in a valid ledger block by collecting a majority vote from the set without downloading the entire ledger or header chain. Unlike many existing light client algorithms, Aurora seamlessly integrates with existing consensus protocols and offers a novel mechanism for decentralized trust in blockchain networks.

Hierarchical federated learning (HFL) designs introduce intermediate aggregator nodes between clients and the global federated learning server in order to reduce communication costs and distribute server load. One side effect is that machine learning model replication at scale comes “for free” as part of the HFL process: model replicas are hosted at the client end, intermediate nodes, and the global server level and are readily available for serving inference requests. This creates opportunities for efficient model serving but simultaneously couples the training and serving processes and calls for their joint orchestration. This is particularly important for continual learning, where serving a model while (re)training it periodically, upon specific triggers, or continuously, takes place over shared infrastructure spanning the computing continuum. Consequently, training and inference workloads can interfere with detrimental effects on performance. To address this issue, we propose an inference load-aware HFL orchestration scheme, which makes informed decisions on HFL configuration, considering knowledge about inference workloads and the respective processing capacity. Applying our scheme to a continual learning use case in the transportation domain, we demonstrate that by optimizing aggregator node placement and device-aggregator association, significant inference latency savings can be achieved while communication costs are drastically reduced compared to flat centralized federated learning.

In addition to blockchain, a new paradigm is gaining momentum in the filed of distributed ledger technology—directed acyclic graphs. This paper compares the two paradigms focusing on features relevant to distributed systems using the following representative implementations: Bitcoin, Ethereum and Nano. We examine the applied data structures for maintaining the ledger, consensus mechanisms, transaction confirmation confidence, ledger size, and scalability.

Summary Cloud of Things (CoT) is a novel concept driven by the synergy of the Internet of Things (IoT) and cloud computing paradigm. The CoT concept has expedited the development of smart services resulting in the proliferation of their real world deployments. However, new research challenges arise because of the transition of research‐driven and proof‐of‐concept solutions to commercial offerings, which need to provide secure, energy‐efficient, and reliable services. An open research issue in the CoT is to provide a satisfactory level of security between various IoT devices and the cloud. Existing solutions for secure CoT communication typically use devices with pre‐loaded and pre‐configured parameters, which define a static setup for secure communication. In contrast to existing pre‐configured solutions, we present an adaptable model for secure communication in CoT environments. The model defines six secure communication operations to enable CoT entities to autonomously and dynamically agree on the security protocol and cryptographic keys used for communication. Further on, we focus on device agreement and present an original solution, which uses the Agile Cryptographic Agreement Protocol in the context of CoT. We verify our solution by a prototype implementation of CoT device agreement based on required security level, which takes into account the capabilities of communicating devices. Our experimental evaluation compares the average processing times of the proposed secure communication operations demonstrating the viability of the proposed solution in real‐world deployments. Copyright © 2016 John Wiley & Sons, Ltd.

Sensor technology and sensor networks have evolved so rapidly that they are now considered a core driver of the Internet of Things (IoT), however data analytics on IoT streams is still in its infancy. This paper introduces an approach to sensor data analytics by using the OpenIoT1middleware; real time event processing and clustering algorithms have been used for this purpose. The OpenIoT platform has been extended to support stream processing and thus we demonstrate its flexibility in enabling real time on-demand application domain analytics. We use mobile crowd-sensed data, provided in real time from wearable sensors, to analyse and infer air quality conditions. This experimental evaluation has been implemented using the design principles and methods for IoT data interoperability specified by the OpenIoT project. We describe an event and clustering analytics server that acts as an interface for novel analytical IoT services. The approach presented in this paper also demonstrates how sensor data acquired from mobile devices can be integrated within IoT platforms to enable analytics on data streams. It can be regarded as a valuable tool to understand complex phenomena, e.g., air pollution dynamics and its impact on human health.

A sliding window top-k ( top-k/w ) query monitors incoming data stream objects within a sliding window of size w to identify the k highest-ranked objects with respect to a given scoring function over time. Processing of such queries is challenging because, even when an object is not a top-k/w object at the time when it enters the processing system, it might become one in the future. Thus a set of potential top-k/w objects has to be stored in memory while its size should be minimized to efficiently cope with high data streaming rates. Existing approaches typically store top-k/w and candidate sliding window objects in a k-skyband over a two-dimensional score-time space. However, due to continuous changes of the k-skyband, its maintenance is quite costly. Probabilistic k-skyband is a novel data structure storing data stream objects from a sliding window with significant probability to become top-k/w objects in future. Continuous probabilistic k-skyband maintenance offers considerably improved runtime performance compared to k-skyband maintenance, especially for large values of k , at the expense of a small and controllable error rate. We propose two possible probabilistic k-skyband usages: ( i ) When it is used to process all sliding window objects, the resulting top-k/w algorithm is approximate and adequate for processing random-order data streams. ( ii ) When probabilistic k-skyband is used to process only a subset of most recent sliding window objects, it can improve the runtime performance of continuous k-skyband maintenance, resulting in a novel exact top-k/w algorithm. Our experimental evaluation systematically compares different top-k/w processing algorithms and shows that while competing algorithms offer either time efficiency at the expanse of space efficiency or vice-versa, our algorithms based on the probabilistic k-skyband are both time and space efficient.

A novel class of mobile Internet of Things applications falls under the category of mobile crowdsensing, whereby large amounts of sensed data are collected and shared by mobile sensing and computing devices for the purposes of observing phenomena of common interest. Challenges arise with respect to

In the context of communication services, presence is defined as the willingness and ability of a user to communicate across a set of devices with other users, and thus an up-to-date user presence status represents an essential prerequisite for real-time communications. Smartphones are a rich source of presence-related contex information, however; this information is currently not applied by the prevailing over-the-top communication systems to implicitly change user presence status in accordance with his/her context and typical daily behavior. Smartphone battery limitations and the abundance of context data generated from built-in sensors and mobile applications are the major factors limiting the adoption of rich presence solutions in state-of-the-art communication solutions. This paper presents an approach to learning and inferring user presence status on smartphones using the available context data with a goal to enable non intrusive and energy-efficient maintenance of presence status without user intervention. We apply the Mobile Data Challenge (MDC) data set collected during the Lausanne Data Collection Campaign from October 2009 until March 2011 in our evaluations.

Presence management, i.e., the ability to automatically identify the status and availability of communication partners, is becoming an invaluable tool for collaboration in enterprise contexts. In this paper, we argue for efficient presence management by means of a holistic view of both physical cont

In this paper we present the AlvisP2P IR engine, which enables efficient retrieval with multi-keyword queries from a global document collection available in a P2P network. In such a network, each peer publishes its local index and invests a part of its local computing resources (storage, CPU, bandwidth) to maintain a fraction of a global P2P index. This investment is rewarded by the network-wide accessibility of the local documents via the global search facility. The AlvisP2P engine uses an optimized overlay network and relies on novel indexing/retrieval mechanisms that ensure low bandwidth consumption, thus enabling unlimited network growth. Our demonstration shows how an easy-to-install AlvisP2P client can be used to join an existing P2P network, index local (text or even multimedia) documents with collection-specific indexing mechanisms, and control access rights to them.

The suitability of peer-to-peer (P2P) approaches for full-text Web retrieval has recently been questioned because of the claimed unacceptable bandwidth consumption induced by retrieval from very large document collections. In this contribution we formalize a novel indexing/retrieval model that achieves high performance, cost-efficient retrieval by indexing with highly discriminative keys (HDKs) stored in a distributed global index maintained in a structured P2P network. HDKs correspond to carefully selected terms and term sets appearing in a small number of collection documents. We provide a theoretical analysis of the scalability of our retrieval model and report experimental results obtained with our HDK-based P2P retrieval engine. These results show that, despite increased indexing costs, the total traffic generated with the HDK approach is significantly smaller than the one obtained with distributed single-term indexing strategies. Furthermore, our experiments show that the retrieval performance obtained with a random set of real queries is comparable to the one of centralized, single-term solution using the best state-of-the-art BM25 relevance computation scheme. Finally, our scalability analysis demonstrates that the HDK approach can scale to large networks of peers indexing Web-size document collections, thus opening the way towards viable, truly-decentralized Web retrieval.

In this paper, we present a query-driven indexing/retrieval strategy for efficient full text retrieval from large document collections distributed within a structured P2P network. Our indexing strategy is based on two important properties: (1) the generated distributed index stores posting lists for carefully chosen indexing term combinations, and (2) the posting lists containing too many document references are truncated to a bounded number of their top-ranked elements. These two properties guarantee acceptable storage and bandwidth requirements, essentially because the number of indexing term combinations remains scalable and the transmitted posting lists never exceed a constant size. However, as the number of generated term combinations can still become quite large, we also use term statistics extracted from available query logs to index only such combinations that are frequently present in user queries. Thus, by avoiding the generation of superfluous indexing term combinations, we achieve an additional substantial reduction in bandwidth and storage consumption. As a result, the generated distributed index corresponds to a constantly evolving query-driven indexing structure that efficiently follows current information needs of the users. More precisely, our theoretical analysis and experimental results indicate that, at the price of a marginal loss in retrieval quality for rare queries, the generated index size and network traffic remain manageable even for web-size document collections. Furthermore, our experiments show that at the same time the achieved retrieval quality is fully comparable to the one obtained with a state-of-the-art centralized query engine.

We describe a query-driven indexing framework for scalable text retrieval over structured P2P networks. To cope with the bandwidth consumption problem that has been identified as the major obstacle for full-text retrieval in P2P networks, we truncate posting lists associated with indexing features to a constant size storing only top-k ranked document references. To compensate for the loss of information caused by the truncation, we extend the set of indexing features with carefully chosen term sets. Indexing term sets are selected based on the query statistics extracted from query logs, thus we index only such combinations that are a) frequently present in user queries and b) non-redundant w.r.t the rest of the index. The distributed index is compact and efficient as it constantly evolves adapting to the current query popularity distribution. Moreover, it is possible to control the tradeoff between the storage/bandwidth requirements and the quality of query answering by tuning the indexing parameters. Our theoretical analysis and experimental results indicate that we can indeed achieve scalable P2P text retrieval for very large document collections and deliver good retrieval performance.

Internet search has a strong business model that permits a free service to users, so it is difficult to see why, if at all, there should be open source offerings as well. This paper first discusses open source search and a rationale for the computer science community at large to get involved. Because there is no shortage of core open source components for at least some of the tasks involved, the Alvis Consortium is building infrastructure for open source search engines using peer-to-peer and subject specific technology as its core, based on this rationale. We view open source search as a rich future playground in which information extraction and retrieval components can be used and intelligent agents can operate.

Two heuristic search techniques, genetic algorithm and simulated annealing, were applied in searching for optimal solution of wavelength assignment problem in all-optical transmission network with wavelength division multiplex. The assignment results were compared for a case study-a core part of the European Optical Network.