Distributed quantum computing (DQC) is being actively investigated as a means of scaling the number of qubits across multiple connected quantum devices. This includes quantum circuit compilation and execution management on multiple quantum devices in the network. The latter aspect is very challenging because, while reducing the makespan of job batches remains a relevant objective, novel quantum-specific constraints must be considered, including QPU utilization, non-local gate rate, and the latency associated with queued DQC jobs. In this work, a range of scheduling strategies is proposed, simulated, and evaluated, including heuristics that prioritize resource maximization for QPU utilization, node selection based on heterogeneous network connectivity, asynchronous node release upon job completion, and a scheduling strategy based on reinforcement learning with proximal policy optimization. These approaches are benchmarked against traditional FIFO and LIST schedulers under varying DQC job types and network conditions for the allocation of DQC jobs to devices within a network.

Academic and industrial sectors have been engaged in a fierce competition to develop quantum technologies, fueled by the explosive advancements in quantum hardware. While universal quantum computers have been shown to support up to hundreds of qubits, the scale of quantum annealers has reached three orders of magnitude (i.e., thousands of qubits). Therefore, quantum algorithms are becoming increasingly popular in a variety of fields, with optimization being one of the most prominent. This work aims to explore the topic of quantum optimization by comprehensively evaluating the technologies provided by D-Wave Systems. To do so, a model for the energy optimization of data centers is proposed as a benchmark. D-Wave quantum and hybrid solvers are compared, in order to identify the most suitable one for the considered application. To highlight its advantageous performance capabilities and associated solving potential, the selected D-Wave hybrid solver is then contrasted with CPLEX, a highly efficient classical solver.

In Smart Farming, geospatial machine learning has the potential to enable data-driven crop management across large areas for optimization of resource consumption. In this work, we describe an edge-to-cloud system designed for soil-plant moisture detection and classification in a realistic scenario, where ground truth data are scarce and inaccurate and yet district-level irrigation water provisioning must rely on these data. Global data sources are scouted and integrated with purposely-designed edge and fog components to support soil and crop data collection, data filtering and validation, and moisture assessment. Exploiting suitable combinations of large scale data (retrieved in public satellite data hubs) and specific but inaccurate manual data, sufficiently accurate moisture assessments are obtained by machine learning algorithms, thereby avoiding expensive manual labeling of large data collections as well as human inspection of individual plots. The resulting accuracy (in the order of 80%) is comparable with that obtained by human-laboured approaches.

Nowadays, quantum computing has reached the engineering phase, with fully-functional quantum processors integrating hundreds of noisy qubits. Yet – to fully unveil the potential of quantum computing out of the labs into the business reality – the challenge ahead is to substantially scale the qubit number, reaching orders of magnitude exceeding thousands of fault-tolerant qubits. To this aim, the distributed quantum computing paradigm is recognized as the key solution for scaling the number of qubits. Indeed, accordingly to such a paradigm, multiple small-to-moderate-scale quantum processors communicate and cooperate for executing computational tasks exceeding the computational power of single processing devices. The aim of this survey is to provide the reader with an overview about the main challenges and open problems arising with distributed quantum computing from a computer and communications engineering perspective. Furthermore, this survey provides an easy access and guide towards the relevant literature and the prominent results in the field.

A new frontier in Smart Agriculture is merging nanobiotechnology with edge computing, for on-field raw data collection and processing. Smart plant sensors communicate plant chemical signals to on-field agricultural and phenotyping equipment. Particularly promising are the Organic Electro-chemical Transistors (OECTs), i.e., devices that can measure the ionic content of liquid samples and biological systems. In this work, we present and evaluate several algorithms for solving a mathematical model that describes the behavior of OECT devices, in order to translate raw values like electrical currents, to meaningful information about the monitored plant stem, e.g., the concentration of ions and water saturation. Our Rust-based algorithm implementations are energy-efficient and suitable for real-time execution on constrained edge devices, as we demonstrate providing several experimental results that concern the quality of model solution, memory footprint, execution time, and the energy cost. The experiments were carried out using two different Arm Cortex-M processors, an ultra low power one and a high performance one.

Quantum compilation is the problem of translating an input quantum circuit into the most efficient equivalent of itself, taking into account the characteristics of the device that will execute the computation. Compilation strategies are composed of sequential passes that perform placement, routing and optimization tasks. Noise-adaptive compilers do take the noise statistics of the device into account, for some or all passes. The noise statistics can be obtained from calibration data, and updated after each device calibration.

Private set intersection is an important problem with implications in many areas, ranging from remote diagnostics to private contact discovery. In this work, we consider the case of two-party PSI in the honest-but-curious setting. We propose a protocol that solves the server-aided PSI problem using delegated blind quantum computing. More specifically, the proposed protocol allows Alice and Bob (who do not have any quantum computational resources or quantum memory) to interact with Steve (who has a quantum computer) in order for Alice and Bob to obtain set intersection such that privacy is preserved. In particular, Steve learns nothing about the clients' input, output, or desired computation. The proposed protocol is correct, secure and blind against a malicious server, and characterized by a quantum communication complexity that is linear in the input size.

In this paper, we propose a scheme for representing semantic metadata of IoT devices in compact identifiers and DNS names to enable simple discovery and search with standard DNS servers. Our scheme defines a binary identifier as a sequence of bits: a Context to use and several bits of fields corresponding to semantic properties specific to the Context. The bit string is then encoded as base32 characters and registered in DNS. Furthermore, we use the compact semantic DNS names to offer support for search and discovery. We propose to take advantage of the DNS system as the basic functionality for querying and discovery of semantic properties related to IoT devices.We have defined three specific Contexts for hierarchical semantic properties as well as logical and geographical locations. For this last part, we have developed two prototypes for managing geo-identifiers in LoRa networks, one based on Node and the Redis in-memory database, the other one based on the CoreDNS server.

Current smart farming solutions are either local or cloud-based. The first approach limits the types and scale of available data processing services. In the second approach, the physical and virtual distance between sensors/actuators and the cloud has a significant impact on latency, which leads to quality of service degradation. Another major issue of the all-in-cloud approach is related to data privacy, which is not yet completely regulated. In this paper, we define an N-tier fog architecture and service placement scenarios that concern realistic smart farming applications with data privacy constraints. Then, we introduce a service placement policy that leverages the N-tier fog architecture to cope with availability, deadline satisfaction and data privacy objectives. Finally, we demonstrate the viability of the proposed approach by means of a simulation analysis.

In the emerging industrial Internet of Things (IIoT) scenario, machine-to-machine communication is a key technology to set up environments, wherein sensors, actuators, and controllers can exchange information autonomously. However, many current communication frameworks do not provide enough dynamic interoperability and security. Hence, in this article, we propose a novel communication framework based on Message Queuing Telemetry Transport (MQTT) broker bridging, which, in an IIoT scenario, can foster dynamic interoperability across different production lines or industrial sites, guaranteeing, at the same time, a higher degree of isolation and control over the information flows, thereby increasing the overall security of the whole scenario. The solution we propose also supports dynamic authentication and authorization and has been practically implemented and evaluated in a proper small-scale IIoT testbed, encompassing PLCs, IIoT gateways, and MQTT brokers with novel and extended capabilities. The evaluation results demonstrate a linear time complexity for all the considered implementations and bridging modes of the extended brokers. Moreover, all considered access token encapsulation techniques demonstrate a minimum overhead in comparison with standard MQTT brokers.

The level of adoption of Precision Agriculture (PA) technologies is still very different from one country to another and from one region to another in the same country. A major challenge is to develop PA from a best practice pursued by a minority of enlightened farmers to a widespread practice with sizeable impact on the use of environmental resources. One of the obstacles hindering PA is the lack of quantitative data readily accessible to farmers to guide their daily operations. In this paper, we present the information system developed within project POSITIVE to support and enhance precision irrigation across the whole Emilia-Romagna region. To this purpose, the POSITIVE information system establishes a service transforming satellite and sensor data into biophysical parameters and vegetation indices with full regional coverage. These data are automatically fed into a public irrigation advisory service (Irrinet+) thereby enabling precision irrigation and fertigation on a regional scale. Irrigation maps can be sent as advice to farmers or directly commanded to registered irrigation machines. The architecture of the distributed information system and the open protocols developed to achieve scalability and enable interaction of multiple heterogeneous components are reported in the paper.

In this paper, we consider the problem of entanglement verification across the quantum memories of any two nodes of a quantum network. Its solution can be a means for detecting (albeit not preventing) the presence of intruders that have taken full control of a node, either to make a denial-of-service attack or to reprogram the node. Looking for strategies that only require local operations and classical communication (LOCC), we propose two entanglement verification protocols characterized by increasing robustness and efficiency.

In this paper, we propose IoTChain, a combination of the OSCAR architecture [1] and the ACE authorization framework [2] to provide an E2E solution for the secure authorized access to IoT resources. IoTChain consists of two components, an authorization blockchain based on the ACE framework and the OSCAR object security model, extended with a group key scheme. The blockchain provides a flexible and trustless way to handle authorization while OSCAR uses the public ledger to set up multicast groups for authorized clients. To evaluate the feasibility of our architecture, we have implemented the authorization blockchain on top of a private Ethereum network. We report on several experiments that assess the performance of different architecture components.

Summary Mobile applications are increasingly taking advantage of sophisticated Location‐Based Services (LBSs). Currently, most available LBSs rely upon centralized infrastructures, thus raising serious concerns as regards user privacy. Because of that, researchers proposed a number of interesting decentralized, peer‐to‐peer protocols for LBSs, although their effectiveness and efficiency was mostly evaluated only by means of simulations. Even when prototypes have been realized, performance results cannot be replicated or extended due to nonpublicly available or closed software. This paper aims at improving the state of the art by presenting both simulations and field tests results for our Adaptive Distributed Geographic Table (ADGT) overlay scheme. The field tests have been carried out in our University Campus by sets of students using mobile applications based on Adgt.js, a novel and truly cross‐platform, WebRTC‐based framework for the development of ADGT‐based peer‐to‐peer LBSs. The tested LBS example, ie, a messaging mobile app based on Adgt.js, illustrates how straightforward and capable such a framework is.

The Internet of Things (IoT) requires a compact naming scheme, which can also bring significant advantages to service registration and discovery. We propose a novel approach, denoted as distributed naming service, which provides a new naming scheme as well as an efficient service discovery protocol for wireless sensor networks. It is based on three pillars: 1) Bloom filters, to create compact names from node descriptions; 2) message propagation strategies, to publish and discover information-not only names-within the network; and 3) distributed caches, to store names within the network. In this paper, we assume ContikiMAC at layer 2, IPv6 and Routing Protocol for Low-Power and Lossy Networks (RPL) at layer 3, and we present two particular UDP-based message propagation strategies that take advantage of the RPL protocol at layer 3. We evaluate the performance of the proposed solutions through Contiki/Cooja simulations and on a real testbed, using the open and large scale FIT IoT-LAB.

Mobile Cloud Computing (MCC) is an emerging paradigm aiming to elastically extend the range of resource-intensive tasks supported by mobile devices, leveraging upon broadband connectivity and cloud-based resources. In literature, almost all MCC models focus on mobile devices, considering the Cloud as a system endowed with unlimited resources. In this paper, we illustrate a novel MCC model characterized by the presence of adaptive loops, i.e., feedback interactions between the mobile device and the Cloud, with the purpose to enforce adaptive behavior on both sides. Indeed, the Cloud adapts its resource allocation (number of activated virtual machines) to the workload provided by mobile devices. On the other hand, feedback from the Cloud allows mobile devices to improve offloading decisions. The performance of the whole system is heavily affected by the auto-scaling strategy adopted by the Cloud. By means of simulations, we have evaluated the impact of two very different auto-scaling strategies. Quantitative results are reported and discussed.

Summary The increasing demand of computational and storage resources is shifting users toward the adoption of cloud technologies. Cloud computing is based on the vision ofcomputing as utility, where users no more need to buy machines but simply access remote resources made available on‐demand by cloud providers. The relationship between users and providers is defined by a service‐level agreement, where the non‐fulfillment of its terms is regulated by the associated penalty fees. Therefore, it is important that the providers adopt proper monitoring and managing strategies. Despite their reduced application, intelligent agents constitute a feasible technology to add autonomic features to cloud operations. Furthermore, the volunteer computing paradigm—one of the Information and Communications Technology (ICT) trends of the last decade—can be pulled alongside traditional cloud approaches, with the purpose to ‘green’ them. Indeed, the combination of data center and volunteer resources, managed by agents, allows one to obtain a more robust and scalable cloud computing platform. The increased challenges in designing such a complex system can benefit from a simulation‐based approach, to test autonomic management solutions before their deployment in the production environment. However, currently available simulators of cloud platforms are not suitable to model and analyze such heterogeneous, large‐scale, and highly dynamic systems. We propose theAVOCLOUDYsimulator to fill this gap. This paper presents the internal architecture of the simulator, provides implementation details, summarizes several notable applications, and provides experimental results that measure the simulator performance and its accuracy. The latter experiments are based on real‐world worldwide distributed computations on top of the PlanetLab platform. Copyright © 2015 John Wiley & Sons, Ltd.

Summary The widespread and ubiquitous availability of Internet access enables the collective sharing of huge amount of data generated by heterogeneous sources. For example, the information, which will be exchanged among entities (sensors, people, and services) of future smart cities to enhance the security and lifestyle of their citizens, poses the challenging question of how this information can be efficiently and effectively maintained across the city. In this article, we propose a decentralized approach, based on the distributed geographic table (DGT) overlay scheme, which exploits geo‐referenced information about nodes to achieve efficient data management. After recalling DGT main concepts, we illustrate the possible node types and how information can be published and retrieved within the network. To cope with the unavoidable node failures and disconnections, our approach leverages upon randomized network coding to increase the robustness of publish/retrieval operations. Evaluation is carried out through an extensive simulation analysis for a realistic urban scenario using the metrics of efficiency in data publication/search, resource availability, and storage occupancy requirements. Results show the approach effectiveness for large‐scale sharing of geo‐referenced information and tradeoffs between redundancy overhead and resource availability. A few results obtained with a preliminary DGT implementation are also presented in the paper. Copyright © 2014 John Wiley & Sons, Ltd.

One envisioned distinctive feature of smart cities is the interconnection among mobile users and vehicles, to support the fulfillment of location-based services. This can be obtained with centralized architectures, and with all the problems of scalability and robustness that such a solution involves. On the other hand, a more complex but more reliable, completely distributed approach can overcome this kind of problems. In this paper, we present the Adaptive Distributed Geographic Table (ADGT), a peer-to-peer overlay scheme suitable for the development of location-based services. In particular, the ADGT allows to efficiently retrieve peers or resources, to broadcast messages within any geographical region, and to be automatically notified about any type of information around any geographical location, following the publish/subscribe model. What mainly differentiates the ADGT from the other solutions in literature is the adaptivity of the overlay's topology to peers' mobility. Actually, the ADGT has the capability to adapt the neighborhood of each mobile peer depending on speed and direction. We have evaluated the ADGT by simulating different scenarios, and the results show that it acts well, ensuring high quality of messages dissemination and low cost in terms of data usage.

Mobile cloud computing (MCC) is an emerging paradigm to transparently provide support for demanding tasks on resource-constrained mobile devices by relying on the integration with remote cloud services. Research in this field is tackling the multiple conceptual and technical challenges (e.g., how and when to offload) that are hindering the full realization of MCC. The NAM framework is a general tool to describe networks of hardware and software autonomic entities, providing or consuming services or resources, that can be applied to MCC scenarios. In this paper, we focus on NAM's features related to the key aspects of MCC, in particular those concerning code mobility capabilities and autonomic offloading strategies. Our first contribution is the definition of a restricted set of mobility actions supporting MCC. The second contribution is a formal semantics for those actions, which allows us to better understand the behavior of MCC systems and paves the way for the application of formal reasoning techniques. As an outcome, we also derive a more precise formalization of the core NAM features, which may contribute to further development of that framework and the related middleware.

The Internet of Things requires a naming service that can also be beneficial for searching for services and applications. To avoid the traditional DNS approach in which a DNS server provides mapping of names to IP addresses in its domain, we propose a novel network service called DINAS (DIstributed NAming Service). It is based on three pillars: 1) Bloom filters for creating compact names from node descriptions, 2) distributed caches for storing names within the network, and 3) overlay routing strategies to publish and discover information - not only names - within the network. In this work, we assume ContikiMAC at Layer 2, 6LoWPAN at Layer 2.5, IPv6 and RPL (routing protocol) at Layer 3, and we present a particular UDP-based overlay routing strategy. We evaluate the proposal by Cooja simulations and compare its performance with a centralized naming service at the sink. The results show that DINAS outperforms the centralized solution.

The main reason of wasting time in search of free parking spaces is the lack of information, in particular for open/roadside parking availability. Various ICT-based solutions have been proposed to solve this issue, but still suffering from limited integration among each other and with external online services, such as touristic information services. In this paper we illustrate a modular, service-oriented smart parking system, which includes web applications for parking operators and end users, as well as mobile applications for end users and parking controllers. The proposed system allows (1) operators to draw parking areas and define their details, (2) end users to be guided to the most suitable parking area, with also the indication of points of interest, and (3) controllers to monitor all vehicles that have been parked in their area. Another important feature is the possibility for end users to share their knowledge about parking occupancy, which is very useful when a parking area is not provided with precise availability counters. The smart parking system has been successfully evaluated in our Campus.

Mobile Cloud Computing (MCC) is a model for transparent elastic augmentation of mobile device capabilities via ubiquitous wireless access to cloud storage and computing resources. The main purpose of MCC is to exploit the context-aware dynamic offload of demanding mobile applications to the Cloud, in order to improve their performance while saving energy and extending battery lifetime of devices. In this paper we extend a pre-existing MCC taxonomy, and we illustrate how the autonomic approach enabled by the open source NAM4J middleware with code migration support can effectively address MCC requirements. We recall the architecture of NAM4J and show its capabilities in the context of an Ambient Intelligence (AmI) MCC application for the Android platform.

Virtualization and distributed computing are two key pillars that guarantee scalability of applications deployed in the Cloud. In Autonomous Cooperative Cloud-based Platforms, autonomous computing nodes cooperate to offer a PaaS Cloud for the deployment of user applications. Each node must allocate the necessary resources for applications to be executed with certain QoS guarantees. If the QoS of an application cannot be guaranteed a node has mainly two options: to allocate more resources (if it is possible) or to rely on the collaboration of other nodes. Making a decision is not trivial since it involves many factors (e.g. the cost of setting up virtual machines, migrating applications, discovering collaborators). In this paper we present a model of such scenarios and experimental results validating the convenience of cooperative strategies over selfish ones, where nodes do not help each other. We describe the architecture of the platform of autonomous clouds and the main features of the model, which has been implemented and evaluated in the DEUS discrete-event simulator. From the experimental evaluation, based on workload data from the Google Cloud Backend, we can conclude that (modulo our assumptions and simplifications) the performance of a volunteer cloud can be compared to that of a Google Cluster.

Location-Based Services (LBSs) are information or entertainment services where the request, the response and served contents depend on the physical position of the requesting device. LBS are frequently used to implement Traffic Information Systems (TIS), which are increasingly based on user-contributed information. In this paper we present the first prototype of our solution for a decentralized, smartphone-based TIS, called D4V, that allows each participant vehicle to efficiently discover data or services located near any chosen geographic position. The experimental evaluation has shown that D4V could be effectively used on the road to reduce the number of drivers involved in traffic jams, as well as to disseminate alert messages about potentially dangerous road stretches, thus allowing drivers to reduce risks and nuisances along their paths.

To cope with millions users becoming increasingly connected to Internet on the move, location-based services may be better supported by decentralized infrastructures enabling improved scalability, access rate and resiliency. In this context, our previous work introduced the Distributed Geographical Table (DGT), an overlay scheme that builds and maintains virtual neighborhood relationships between peers with heterogeneous connections. In this paper we illustrate a smartphone-based vehicular network that uses the DGT, and we show its robustness against disconnections caused by the unavailability of connectivity/coverage (mostly occurring in rural areas), as well as overlay reconnections due to vertical handovers (mostly occurring in highly serviced urban areas). The simulative analysis of sample scenarios based on experimental measurements of coverage and connection throughput, carried out across/around Parma urban area, gives us valuable insights for defining an integrated model that will combine the DGT, user/vehicle mobility and connectivity/coverage types.

Real-time tracking of massive numbers of mobile devices, either carried by humans or embedded into vehicles, is a challenging problem whose solution may pave the way for a large set of valuable applications, ranging from social networking to ambient intelligence. A centralized approach, i.e. a server collects position data and provides it to interested consumers, is highly questionable, as performance can hardly scale up to the needs several million concurrent users. On other hand, a decentralized peer-to-peer approach, for which positioning data would flow directly among mobile devices may be very appealing, provided that messages to be routed are not too frequent and too expensive in terms of bandwidth usage. In this context we propose a peer-to-peer overlay scheme called Distributed Geographic Table (DGT), where each participant can efficiently retrieve node or resource information (data or services) located near any chosen geographic position. In particular, we describe a DGT-based localization protocol, that allows each peer for proactively discovering and tracking all the peers that are geographically near to itself. We provide a performance analysis of our protocol, referring to a simulated (although realistic) scenario where several hundred vehicles move on a real map. Our results show that the solution is efficient, scalable and highly adaptable to different application scenarios.

Mobile computing environments may be very critical for traditional QoS management techniques often relying on centralized resource coordinating services, due to the unavoidable high unstability of network and service layers. On the other hand, a peer-to-peer approach for continuous service provisioning to mobile users may maintain locally an updated list of service providers, and allow clients to switch sources depending on the experienced service quality. In this paper we present a QoS-aware reputation management system for service-oriented P2P networks which aims at improving the quality of service provider selection within purely decentralized mobile environments. The selection process is based on the evaluations provided by other service consumers in terms of a set of application specific QoS parameters.

Abstract Next generation e‐learning platforms will support cooperative use of geographically distributed educational resources as an aggregated environment, thus enabling a more effective knowledge exchange. Sharing this goal, our research activity addresses the definition and implementation of a service‐based infrastructure for content distribution and multimedia streaming. The proposed architecture provides enough flexibility, extensibility, and scalability to cope with the ever‐increasing heterogeneity and mobility of edge devices. In this paper, we initially illustrate the general objectives and requirements in the development of an e‐learning oriented application for multimedia content discovery, access, and distribution in a virtual community. Architectural considerations have motivated the use of Grid technologies as they provide access to resources and services of different administrative domains in a transparent, seamless, and secure way. The Grid‐based architecture has been endowed with a number of services including discovery and streaming of multimedia objects, multimedia content update, quality of service (QoS) management, and support for user authentication and authorization. A multimedia application based on the architecture is described and evaluated on a multi‐domain network. Copyright © 2005 John Wiley & Sons, Ltd.

Distributed telerobotic applications exploiting Internet-related technologies, such as virtual laboratories and on-line robots, require effective techniques for timely delivery of sensory data to remote clients. In these systems, there is a need to distribute increasing quantities of sensory data to a potentially large number of clients during system operation. In this paper, we describe and evaluate three implementations of a sensory data distribution subsystem in the context of a CORBA-based framework for telerobotic applications. Experimental results show that solutions exploiting CORBA services and based on the event channel paradigm represent a viable alternative to ad-hoc solutions. The overhead associated to CORBA services becomes less significant with larger message size. Moreover, these services ensure portability, extensibility, reduction in programming complexity, and improved scalability when the number of clients increases.

The technological developments in distributed systems have led to new telerobotic applications, such as virtual laboratories and remote maintenance of complex equipment. These applications must satisfy both the general requirements of distributed computing, e.g. location transparency and interoperability, and the domain-specific requirements of reconfigurability, guaranteed performance, real-time operation, and cooperation among robots and sensory systems. In this paper, we describe a software framework for distributed telerobotic systems exploiting advanced CORBA features, including Asynchronous Method Invocation and real-time priorities. The framework allows development of portable multithreaded client-server applications supporting concurrent and preemptable actions in the target robot system, and has been evaluated in a laboratory setup including a robot manipulator and two cameras accessible by multiple clients.