Hardware fingerprinting technologies are an integral part for security of interconnected devices, for which the Physical Unclonable Function (PUF) has attracted attention in industry and academia for over 20 years. PUFs exploit uncontrollable manufacturing variations to provide hardware-intrinsic fingerprints, which are hardware analogues to biometrics in humans. These fingerprints can be used as secrets that are highly sensitive to physical tampering. However, many questions remain on the applicability of PUFs given the prominent existence of modeling techniques that allow to predict or manipulate these secret fingerprints. In this survey, we analyze the trends and state-of-the-art in PUF modeling from 254 papers obtained from a systematic search and screening process. Our results provide an extensive list of PUF designs and protocols, which we classify based on three main perspectives: application, operational, and defensive. Similarly, we list and classify modeling techniques based on the defined PUF models and learning algorithms. Most of the surveyed papers consider modeling techniques purely as a vulnerability. However, we also include the perspective of modeling as an enabler for lightweight sharing of PUF secrets. Finally, we provide an exhaustive knowledge base and identify gaps and promising directions for future work in the field.

Ocean data collection, instrumental for addressing global issues such as climate change and biodiversity conservation, relies on IoT devices deployed on marine vessels. Despite their significance, traditional blockchain-based data collection systems have scalability and energy efficiency limitations. Moreover, ensuring the secure journey of data from its origin to a remote storage location is a critical concern often overlooked. This paper presents a system design fortified with a novel secure batch data-assisted IOTA scheme, enhancing the efficiency and security of data collection while reducing the computational load on small IoT devices. The proposed system is based on three core components: 1) lightweight cryptography, employing Hash-based Message Authentication Codes (HMAC) and Elliptic Curve Integrated Encryption Scheme (ECIES), chosen for their scalability and suitability for low-power IoT devices; 2) batch data aggregation, introduced in IOTA to augment throughput and reduce latency by processing large data volumes concurrently; and 3) secure and scalable data storage using the recent version of IOTA, namely Chrysalis. Rigorous testing on small IoT devices substantiates the acceptable performance of the system in terms of computation overhead compared to existing systems. By integrating IOTA Chrysalis, hybrid cryptography, and the batch data concept, this method offers an efficient, secure solution for large-scale ocean data collection.

Predicting survival outcomes for non-small cell lung cancer (NSCLC) patients is challenging due to the different individual prognostic features. This task can benefit from the integration of whole-slide images, bulk transcriptomics, and DNA methylation, which offer complementary views of the patient's condition at diagnosis. However, real-world clinical datasets are often incomplete, with entire modalities missing for a significant fraction of patients. State-of-the-art models rely on available data to create patient-level representations or use generative models to infer missing modalities, but they lack robustness in cases of severe missingness. We propose a Multimodal Contrastive Variational AutoEncoder (MCVAE) to address this issue: modality-specific variational encoders capture the uncertainty in each data source, and a fusion bottleneck with learned gating mechanisms is introduced to normalize the contributions from present modalities. We propose a multi-task objective that combines survival loss and reconstruction loss to regularize patient representations, along with a cross-modal contrastive loss that enforces cross-modal alignment in the latent space. During training, we apply stochastic modality masking to improve the robustness to arbitrary missingness patterns. Extensive evaluations on the TCGA-LUAD ($n=475$) and TCGA-LUSC ($n=446$) datasets demonstrate the efficacy of our approach in predicting disease-specific survival (DSS) and its robustness to severe missingness scenarios compared to two state-of-the-art models. Finally, we bring some clarifications on multimodal integration by testing our model on all subsets of modalities, finding that integration is not always beneficial to the task.

The rapid advances in quantum computing pressure the existing essential cryptographic algorithms. In this context, Quantum Key Distribution (QKD) has been proposed as a quantum safe solution for key distribution. However, current QKD systems require an authenticated classical channel which relies on pre-shared symmetric keys that are manually distributed and do not guarantee the identity of the hardware that hosts them. This paper proposes a novel scheme, the Physically Unclonable Quantum Key Distribution (PU-QKD) system, to intrinsically authenticate the communicating endpoints in QKD. The PU-QKD scheme leverages classical Physical Unclonable Functions (PUFs) to encode the data transmission in discrete variable QKD protocols (e.g., BB84). Our scheme maintains the information-theoretic security of QKD protocols by integrating the PUF as an additional layer. Authentication is bound to hardware, providing a robust fingerprint, while lower post-processing overhead increases key rates. Additionally, the proposed scheme is resilient against state-of-the-art PUF vulnerabilities, such as modeling attacks.

Modern network infrastructures face increasing demands for scalability, performance, and security, particularly in multi-tenant environments. Ensuring efficient traffic isolation and secure communication across multiple virtualized systems is a critical challenge, especially when dealing with high-throughput applications. In this paper, we address these challenges by leveraging SR-IOV-based virtualization to create isolated network slices and establishing secure communication channels, using Kyber and Dilithium (ML-KEM/ML-DSA) and IPsec tunnels. We connect 16 Virtual Machines (VMs) and another host system on a direct link. We reach a total of 71.79Gbit/s on the 100Gbit/s NVIDIA BlueField-2 Data Processing Unit (DPU), by configuring the OpenvSwitch (OvS) for steering the traffic on its ARM cores, and testing using multiple instances of iPerf3 on all the (v)hosts. This result represents a 95.36% utilization of the baseline network performance.

Resource-constrains are a significant challenge when designing secure IoT devices. To address this problem, the physical unclonable function (PUF) has been proposed as a lightweight security primitive capable of hardware fingerprinting. PUFs can provide device identification capabilities by exploiting random manufacturing variations, which can be used for authentication with a verifier that identifies a device through challenge-response interactions with its PUF. However, extensive research has shown that PUFs are inherently vulnerable to machine learning (ML) modeling attacks. Such attacks use challenge-response samples to train ML algorithms to learn the underlying parameters that define the physical PUF. In this article, we present a defensive technique to be used by the verifier called selective challenge-response pairs (CRPs). We propose generating challenges selectively, instead of randomly, to negatively affect the parameters of ML models trained by attackers. Specifically, we provide three methods: 1) binary-coded with padding (BP); 2) random shifted pattern (RSP); and 3) binary shifted pattern (BSP). We characterize them based on Hamming distance patterns, and evaluate their applicability based on their effect on the uniqueness, uniformity, and reliability of the underlying PUF implementation. Furthermore, we analyze and compare their resilience to ML modeling with the traditional random challenges on the well-studied XOR PUF, feed-forward PUF, and lightweight secure PUF, showing improved resilience of up to 2 times the number of CRPs. Finally, we suggest using our method on the interpose PUF to counter reliability-based attacks which can overcome selective CRPs and show that up to 4 times the number of CRPs can be exchanged securely.

The widespread adoption of Internet-of-Things (IoT) devices is elevating the security expectations of many application domains. Meanwhile, numerosity, hardware and software heterogeneity, and low cost of IoT devices makes meeting such expectations challenging. A key security function that IoT devices must possess is identity and the capability to authenticate themselves. However, traditional authentication mechanisms rely on hash-based cryptography, requiring complex hardware and computational resources. To mitigate this problem, the Physical Unclonable Function (PUF) has been proposed as a lightweight source of device-specific entropy that can be used for identifying IoT devices. However, a major challenge to this approach is protecting PUFs against Machine Learning (ML)-based modeling attacks, where an attacker can clone an authentic PUF after collecting enough training data from the communication protocol, e.g., as a passive eavesdropper. In this paper, we propose a Predictive Adversarial System (PAS) that aims to prevent ML modeling attacks by predicting the capabilities of an attacker in a PUF system. We analyze the best approaches to implement our system and evaluate their performance in terms of the modeling capacity that a passive attacker exhibits. Our experiments show that the proposed approach can increase the training data required for a successful modeling attack over one million samples, without increasing the security overhead of resource-constrained PUF-enabled IoT devices.

Data Processing Units (DPUs) are becoming increasingly popular, especially for use in conjunction with Warehouse-Scale Computers (WSCs) due to their ability to handle networking functions and data-centric workloads. Cost-performance, energy efficiency, network 1/0, and batch processing workloads are important design factors for WSCs. Recent developments in AI and the never-ending increase in demand for data processing, cloud computing, and HPC set the optimisation of all those design factors as a high priority. DPUs can be utilised to achieve significant improvements in all those areas. This includes in-line network processing and upcoming enhanced security paradigms such as post-quantum cryptography (PQC) for quantum re-silient communications or software-defined perimeters (SDP) for confidential computing implementations. Being P4-enabled and dRMT-based, DPUs allow for the reconfigurability of the network traffic without the need to change the hardware. However, the network performance on such devices is only sometimes deter-ministic since the actual traffic and the rules, both of which have to do with packet processing, are not known during compile time. In this paper, we envision how the network performance on DPUs can be accelerated. We describe the challenges that negatively impact the bandwidth and latency: the complex steering pipeline and the massive runtime needed to optimise. These challenges arise from the lack of information during compile time that is only known during runtime. Thus, we envision optimising during runtime by leveraging DPUs' reconfigurability on the network 110. For this, we discuss the significant factors that must be considered to accelerate the network performance on such devices and we propose a solution for them.

Modeling of Physical Unclonable Functions (PUFs) through Machine Learning (ML) algorithms has been widely applied to break their security. Currently, many different algorithms are capable of modeling a wide range of delay-based PUFs, preventing these primitives from being effectively applied to security applications such as authentication. To tackle this, other studies have developed PUF designs that prevent ML modeling attacks. However, these studies typically focus on defending against well-known Supervised Learning techniques, including Logistic Regression or Multi-Layer Perceptron. On the other hand, since ML is rapidly evolving, new techniques can be potentially applied to model the latest proposed PUFs. For this reason, in this paper, we study the applicability of a subfield of ML, namely Deep Reinforcement Learning (DRL), for PUF modeling and comparing it to the state-of-the-art. We find that DRL, specifically the Deep Q-Network (DQN) algorithm, can be as effective as state-of-the-art modeling attacks on XOR Arbiter PUF, making it a new threat to delay-based PUFs. Additionally, when considering PUFs with challenge obfuscation, such as the Interpose PUF, DQN outperforms the state-of-the-art, raising concerns about the long-term security of obfuscation techniques.

The integration of graphics processing units through virtualization offers significant potential to optimize resource utilization in distributed systems. Particularly in edge computing scenarios, the architecture of edge devices can be simplified by consolidating GPU usage at the edge gateway, which offers the improvement of the GPU utilization by sharing the GPU by multiple edge client devices. However, the effectiveness of this approach is heavily contingent upon network communication, which in edge environments often lacks the high-speed and low latency links as well as specialized protocols available in traditional data center settings for high performance computing, especially if wireless networks are deployed. Therefore, our study aims to comprehensively analyze and evaluate the performance implications of using the conventional transmission control protocol (TCP) versus the emerging QUIC protocol in such environments. We specifically focus on assessing QUIC's unique features, including concurrent streams, stream cancellation, and stream prioritization, which are not inherently present in TCP. Furthermore, we investigate the potential performance gains achievable through the adoption of asynchronous API calls, aiming to provide insights into optimizing GPU utilization in edge computing. We show the advantages of most of QUIC's features and asynchronous API calls by an experimental evaluation.

Centralized identity management systems face significant challenges, including vulnerabilities to single points of failure, scalability issues, and privacy and control concerns. While blockchain has been pivotal in addressing these challenges, it is not feasible for resource-constrained IoT devices due to high computation, energy demands and transaction fees. This study proposes a novel IOTA and Decentralized Identifiers (DIDs) Assisted Authentication (IDIA) system that leverages DIDs and a lightweight DLT, the IOTA Shimmer, to create a decentralized, scalable, and secure identity system tailored for the remote marine environment. The proposed system enhances security and reliability by utilizing the energy-efficient identity solutions of the IOTA Shimmer, significantly improving the security of maritime digital systems. We rigorously tested the system on a real testbed, conducting extensive experiments and comparative analysis to assess its performance in terms of computation overhead.

Cloud and edge computing play a crucial role in enabling the intelligence of Industry 4.0, while wireless tech-nologies like 5G and Wi-Fi 6 enhance its flexibility. However, selecting the appropriate technologies is nontrivial. In this paper, we present our measurements (over 360 hours) and analysis of response times in cloud and edge computing using different network access methods. We provide recommendations for tech-nology selection based on our findings. Our results highlight the unique advantages of 5G and Wi-Fi 6 at different percentiles, and the characteristics of cloud and edge computing in terms of workload processing and network propagation. The choice between these technologies should consider the Quality of Service (QoS) requirements and processing workloads of applications, as well as the computational resources of edge and cloud servers.

The hybrid cloud inherits the best aspects of both the public and private clouds. One such benefit is maintaining control of data processing in a private cloud whilst having nearly elastic resource availability in the public cloud. However, the public and private cloud combination introduces complexities such as incompatible security and control mechanisms, among others. The result is a reduced consistency of data processing and control policies in the different cloud deployment models. Cloud load-balancing is one control mechanism for routing applications to appropriate processing servers in compliance with the policies of the adopting organization. This article presents a process-mining influenced load-balancer for routing applications and data according to dynamically defined business rules. We use a high-level Colored Petri Net (CPN) to derive a model for the process mining-influenced load-balancer and validate the model employing live data from a selected hospital.

Underwater environments are emerging as a new frontier for data science thanks to an increase in deployments of underwater sensor technology. Challenges in operating computing underwater combined with a lack of high-speed communication technology covering most aquatic areas means that there is a significant delay between the collection and analysis of data. This in turn limits the scale and complexity of the applications that can operate based on these data. In this article, we develop underwater fog computing support using low-cost micro-clouds and demonstrate how they can be used to deliver cost-effective support for data-heavy underwater applications. We develop a proof-of-concept micro-cloud prototype and use it to perform extensive benchmarks that evaluate the suitability of underwater micro-clouds for diverse underwater data science scenarios. We conduct rigorous tests in both controlled and field deployments, using river and sea waters. We also address technical challenges in enabling underwater fogs, evaluating the performance of different communication interfaces and demonstrating how accelerometers can be used to detect the likelihood of communication failures and determine which communication interface to use. Our work offers a cost-effective way to increase the scale and complexity of underwater data science applications, and demonstrates how off-the-shelf devices can be adopted for this purpose.

The use of hardware accelerators, based on code and data offloading devoted to overcoming the CPU limitations in cores, is one of the main distinctive trends in high-end computing and related applications in the last decade. However, while code offloading is convenient for performance improvement, becoming a commonly used paradigm, memory access and management are a source of bottlenecks due to the need to interact with different address spaces. In this regard, NVidia introduced the CUDA Unified Memory model to avoid explicit memory copies between the machine hosting the accelerator device and the device itself and vice-versa. This paper shows a novel design and implementation of the support to the CUDA Unified Memory in open-source GPGPU virtualization services. The performance evaluation demonstrates that the overhead due to the virtualization and remoting is acceptable considering the possibility of sharing CUDA-enabled GPUs between various and heterogeneous machines hosted at the edge, in cloud infrastructures, or as accelerator nodes in an HPC scenario. A prototype implementation of the proposed solution is available as open-source.

Monitoring the impact of the pollutants on the sea is a crucial issue for coastal human activities, such as aquaculture. However, leveraging a continuous microbiological laboratory analysis is unfeasible for costs and practical reasons. Here we present a novel methodology finalized to predict water quality as categorized indexes leveraging an integrated approach between computational components and artificial intelligence techniques. As a paradigm demonstrator, we couple WaComM++ with AIQUAM. The use case presented is an application of AIQUAM in the Bay of Naples (Campania Region, Italy) for predicting bacteria contaminants in mussel farms. The results are encouraging as the model reached a correct prediction rate of 93%.

Summary Hybrid cloud, typically a combination of public and private cloud deployment models, is a rising paradigm due to the benefits it offers: full control of data and applications in the private cloud and elastic computing resource availability in the public cloud. This combination however brings an extra layer of complexity that can potentially erode the benefits and present serious challenges if not managed well. Among the challenges, ensuring business constraint compliance across the combination of cloud deployment models is a growing concern. Our article brings a sensitive, data‐ and process‐aware framework to bear on task scheduling in hybrid clouds with compliance to business constraints. Our proposed approach utilizes data from a real hybrid cloud‐based hospital billing system that is governed by complex and dynamic data processing rules. Our system successfully employs a process mining controlled algorithm to schedule tasks in the hybrid cloud to comply with the given set of business constraints.

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Summary The rise of the Internet of Things has generated high expectations about the improvement in people's lifestyles. In the last decade, we saw several examples of instrumented cities where different types of data were gathered, processed, and made available to inspire the next generation of scientists and engineers. In this framework, sensors and actuators became leading actors of technologically pervasive urban environments. However, in coastal areas, marine data crowdsourcing is difficult to apply due to the challenging operational conditions, extremely unstable network connectivity, and security issues in data movement. To fill this gap, we present a novel version of our DYNAMO transfer protocol (DTP), a platform‐independent data mover framework where data collected on board of vessels are stored locally and then moved from the edge to the cloud when the operating conditions are favorable. We evaluate the performance of DTP in a controlled environment with a private cloud by measuring the time it takes for the clouds ide to process and store a fixed amount of data while varying the number of microservice instances. We show that the time decreases exponentially when the number of microservice instances goes from 1 to 16 and it remains constant above that number.

Mobile Edge Computing (MEC) is a network architecture that takes advantage of resources available at the edge of the network to enhance the mobile user experience by decreasing the service latency. MEC solutions need to dynamically allocate the requests as close as possible to their users to avoid high latency. However, the request allocation does not depend only on the geographical location of the servers, but also on their requirements. The task of choosing and allocating appropriate servers in a MEC environment is challenging because it involves many parameters. This paper proposes a Stochastic Petri Net (SPN) model to represent a MEC scenario and analyze its performance. The model focuses on parameters that can directly impact the service Mean Response Time (MRT) and resource utilization level. We propose case studies with numerical analyzes using real-world values to validate the proposed model. The main objective is to provide a practical guide to assist infrastructure administrators to adapt their architectures, finding a trade-off between MRT and resource utilization level.

The increasing complexity of smartphone applications and services necessitate high battery consumption, but the growth of smartphones' battery capacity is not keeping pace with these increasing power demands. To overcome this problem, researchers gave birth to the Mobile Cloud Computing (MCC) research area. In this paper, we advance on previous ideas, proposing and implementing a Near Field Communication (NFC)-based computation offloading framework. This research is motivated by the advantages of NFC's short distance communication, its better security, and its low battery consumption characteristics. We design a new NFC communication protocol that overcomes the limitations of the default NFC protocol; removing the need for constant user interaction, the one-way communication restraint, and the limit on low data size transfer. Via the implemented framework, parts of mobile applications can be offloaded to other mobile devices or cloudlets equipped with an NFC reader. We present experimental results of the energy consumption and the time duration of computationally and data intensive representative applications: (i) RSA key generation and encryption, (ii) gaming/puzzles, (iii) face detection, (iv) media download from the Internet, and (v) data transferring between the mobile and the cloudlet. We show that when the helper device is more powerful than the device offloading the computations, the execution time of the tasks is reduced. Finally, we show that devices that offload application parts considerably reduce their energy consumption due to the low-power NFC interface and the benefits of offloading.

The commencement of EU's General Data Protection Regulation (GDPR) has led to massive compliance and consent activities on websites. But did the new regulation result in fewer third party server appearances? Based on an eight months longitudinal study from February to September 2018 of 1250 popular websites in Europe and US, we present a mapping of the subtle shifts in the third party topology before and after May 25, 2018. The 1250 websites cover 39 European countries from EU, EEA, and outside EU, belonging to categories that cover both public-oriented citizen services, as well as commercially-oriented sites. The developments in the numbers and types of third party vary for categories of websites and countries. Analyzing the number of third parties over time, even though we notice a decline in the number of third parties in websites belonging to certain categories, we are cautious about attributing these effects to the general assumption that GDPR would lead to less third party activity. We believe that it is quite difficult to draw conclusions on cause-effect relationships in such a complex environment with many impacting factors.

Task scheduling in hybrid clouds has been widely used to achieve scalability and security goals for cloud computing. If not well-managed, the challenges of scheduling tasks in a hybrid cloud have the potential to diminish desired benefits due to an extra layer of complexity introduced by two or more cloud deployment models. Apart from efficiency and cost challenge in scheduling tasks across separate clouds, the level of compliance to a set of business rules is a growing concern in cloud computing. This paper proposes a cost-effective model for scheduling tasks over a hybrid cloud with compliance to a given set of business rules. The proposed solution employs the applicative scenario of a hospital billing system with a rule for processing a class of bills only in the private datacenter. Our system successfully employs the Hopcroft Karp algorithm in assigning tasks to appropriate virtual machines and Event Calculus formalisations to monitor compliance. The results of the simulation show the cost-effective use of process mining monitoring to schedule tasks in compliance with business rules in a hybrid cloud.

Data crowdsourcing is a increasingly pervasive and lifestyle-changing technology, due to the flywheel effect that results from the interaction between the internet of things and cloud computing. In smart cities, for example, many initiatives harvest valuable data from citizen sensors. However, this paradigm has not seen significant use in coastal and marine monitoring and management due to the challenges of the marine environment. In this work, we describe how this situation can be overcome via the adoption of an open technology ecosystem that provides leisure vessels with a platform for on-board data acquisition, storage, and processing, leveraging off-the-shelf mobile technologies. We introduce DYNAMO, a infrastructure designed to collect marine environmental data from a distributed sensor network carried by leisure vessels. The resulting crowdsourced data can be used to improve operational weather and marine predictions via the use of data assimilation methods. We show our preliminary results about the DYNAMO Daemon, a SignalK server we embedded in the native level of the Android operating system enabling the data gathering and transfer from vessels to the cloud.

This work demonstrates a real-time 3D hand tracking application that runs via computation offloading. The proposed framework enables the application to run on low-end mobile devices such as laptops and tablets, despite the fact that they lack the sufficient hardware to perform the required computations locally. The network connection takes the place of a GPGPU accelerator and sharing resources with a larger workstation becomes the acceleration mechanism. The unique properties of a generative optimizer are examined and constitute a challenging use-case, since the requirement for real-time performance makes it very latency-sensitive.

The continuous proliferation of mobile and wearable smart devices, together with their increasing computational power and multitude of sensors, has given birth to innovative applications that enhance the world with virtual layers of processed information. In this paper, we present Talk2Me, an augmented reality social network framework that enables users to disseminate information in a distributed way and view others' information instantly. Talk2Me advertises users' messages, together with their face-signature, to every nearby device in a Device-to-Device fashion. When a user looks at nearby persons through her camera-enabled wearable devices (e.g., Google Glass), the framework automatically extracts the face-signature of the person of interest, compares it with the previously captured signatures, and presents the information shared by this person to the user. We design and implement Talk2Me to be lightweight, given that it runs on mobile devices with limited power. We analyze different content dissemination strategies to find the best protocol that yields reliable and fast information-spreading, while reducing the number of packets and containing the energy consumption on the devices. We design a novel face recognition algorithm for this specific scenario with a small number of face features and limited computing capability. Evaluation results of the prototype with real users and extensive simulations validate the performance and usability of our design, showing the potentials of the augmented reality social network framework in real-world scenarios.

Mobile Cloud Computing (MCC) is a technique for increasing the performance of mobile apps and reducing their energy consumption through code and data offloading. Building an MCC infrastructure is a difficult task due to its inherent complexity and the involvement of different components. This paper proposes an approach for estimating applications' performance and data traffic volume generated by tasks offloading. This work proposes a Stochastic Petri Net (SPN)-based formal framework to represent the partitioning of applications in a method-call level. Our framework considers the available network bandwidth to send and receive tasks to the cloud. The modeling strategy represents the use and sharing of the actual available bandwidth for offloading operations. The approach enables designers to plan and tune MCC architectures based on Mean Time to Execute (MTTE) and Throughput estimation. Using our strategy it is possible to estimate the impact of the bandwidth variation on the application's MTTE and Throughput. In addition, the strategies proposed in this work may be adapted to support MCC applications in real time providing on-the-fly probabilistic performance predictions. One case study was performed to evaluate the approach. Our proposed approach has proven to be feasible and it highlights the most appropriate strategies for offloading.

Models, algorithms, and tools for highly heterogeneous computing environments

Summary Fast technology development has influenced the widespread use of low‐power devices in different scientific, environmental, and everyday life areas, giving birth to the Internet of Things. In this paper, we focus on the context of marine studies, addressing the problem of marine bathymetry data processing and analysis via pervasive and Internet‐connected sensors and low‐power distributed devices. Pervasive and Internet‐connected low‐power devices (as the components involved in the sensing and processing actions) made diverse and different “things” as a worldwide‐distributed system. Given the high complexity of the algorithms involved in these studies, which usually involve general‐purpose graphic processing unit (GPGPU) computation, it is impossible for the limited devices to perform the required calculations. To overcome these limitations, in this paper, we propose and implement a vertical application of GVirtuS, the open‐source GPGPU virtualization and remoting service, for achieving high performance geographical data interpolation in a high performance cloud computing scenario. We present an innovative implementation by comparing, in terms of performance and accuracy, the inverse distance weighting and kriging interpolation methods in their parallel implementations leveraging on CUDA‐enabled GPGPUs. We present a real‐world use case related to high‐resolution bathymetry interpolation in a crowdsource data context in the Bay of Pozzuoli, Italy.

During the last years, researche'rs have proposed solutions to help smartphones improve execution time and reduce energy consumption by offloading heavy tasks to remote entities. Lately, inspired by the promising results of message forwarding in opportunistic networks, many researchers have proposed strategies for task offloading towards nearby mobile devices, giving birth to the Device-to-Device offloading paradigm. None of these strategies, though, offers any mechanism that considers selfish users and, most importantly, that motivates and defrays the participating devices who spend their resources. In this paper, we address these problems and propose the design of a framework that integrates an incentive scheme and a reputation mechanism. Our proposal follows the principles of the Hidden Market Design approach, which allows users to specify the amount of resources they are willing to sacrifice when participating in the offloading system. The underlying algorithm, that users are not aware of, is based on a truthful auction strategy and a peer-to-peer reputation exchange scheme. Extensive simulations on real traces depict how our designed mechanism achieves higher offloading rate and produces less traffic compared to three benchmark algorithms. Finally, we show how collaborating devices get rewarded for their contribution, while selfish ones get sidelined by others.

Mobile Cloud Computing (MCC) helps increasing performance of intensive mobile applications by offloading heavy tasks to cloud computing infrastructures. The first step in this procedure is partitioning the application into small tasks and identifying those that are better suited for offloading. The method call partitioning strategy splits the code into a set of method calls that are offloaded to remote servers. Quite often, many applications need to make use of multiple servers for parallel processing of intensive computational operations. Predicting the behavior of such parallelizable applications is not an easy task. Deciding the number of remote servers determines the performance of the applications and the costs of the cloud usage. On one hand, users are interested in improving the performance of their applications, so they would like to use as many servers as possible, but on the other hand, they would also like to reduce their costs by using fewer cloud resources. In this paper, we propose a Stochastic Petri Net (SPN) modeling strategy to represent method call executions of mobile cloud systems. This approach enables a designer to plan and optimize MCC environments in which SPNs represent the system behavior and estimate the execution time of parallelizable applications.

The increasing complexity of smartphone applications and services necessitate high battery consumption but the growth of smartphones' battery capacity is not keeping pace with these increasing power demands. To overcome this problem, researchers gave birth to the Mobile Cloud Computing (MCC) research area. In this paper we advance on previous ideas, by proposing and implementing the first known Near Field Communication (NFC)-based computation offloading framework. This research is motivated by the advantages of NFC's short distance communication, with its better security, and its low battery consumption. We design a new NFC communication protocol that overcomes the limitations of the default protocol; removing the need for constant user interaction, the one-way communication restraint, and the limit on low data size transfer. We present experimental results of the energy consumption and the time duration of two computationally intensive representative applications: (i) RSA key generation and encryption, and (ii) gaming/puzzles. We show that when the helper device is more powerful than the device offloading the computations, the execution time of the tasks is reduced. Finally, we show that devices that offload application parts considerably reduce their energy consumption due to the low-power NFC interface and the benefits of offloading.

Summary Low‐power devices are usually highly constrained in terms of CPU computing power, memory, and GPGPU resources for real‐time applications to run. In this paper, we describe RAPID, a complete framework suite for computation offloading to help low‐powered devices overcome these limitations. RAPID supports CPU and GPGPU computation offloading on Linux and Android devices. Moreover, the framework implements lightweight secure data transmission of the offloading operations. We present the architecture of the framework, showing the integration of the CPU and GPGPU offloading modules. We show by extensive experiments that the overhead introduced by the security layer is negligible. We present the first benchmark results showing that Java/Android GPGPU code offloading is possible. Finally, we show the adoption of the GPGPU offloading into BioSurveillance, a commercial real‐time face recognition application. The results show that, thanks to RAPID, BioSurveillance is being successfully adapted to run on low‐power devices. The proposed framework is highly modular and exposes a rich application programming interface to developers, making it highly versatile while hiding the complexity of the underlying networking layer.

The proliferation of mobile devices combined with advances in the area of low-power wireless communication, such as Wi-Fi Direct and Bluetooth 4.0, gave rise to a new computation paradigm known as Device-to-Device (D2D) offloading. In this scenario, devices collaborate with each other using short wireless links to create ad-hoc P2P networks for distributed task execution. Experiments on human movement, a non-negligible factor in the D2D context, have shown that people move in group or meet frequently, which suggests that D2D is possible. In this work, we examine the case of parallel compression of smartphone recorded videos with the help of nearby devices. First, we present a mathematical formulation of the problem that optimizes the compression time on the number of nearby helping devices, and show that the problem can be mapped as a water-filling problem. Then, we present real results of the compression time and energy when the compression is performed on one device and when it is parallelized among collaborating devices. To obtain these results, we implemented an Android application that is able to detect nearby devices, connect with them using Wi-Fi Direct, send video chunks for compression, receive and merge compressed chunks into one full compressed video.

During the last years, researchers have proposed solutions to help smartphones offload heavy tasks to remote entities in order to improve execution time and reduce energy consumption. Lately, inspired by the promising results of message forwarding in opportunistic networks, many researchers have proposed strategies for task offloading towards nearby mobile devices. None of these strategies, though, proposes any mechanism that considers selfish users and, most importantly, that motivates and defrays the participating devices who spend their resources. In this paper, we address these problems and propose the design of a framework that integrates an incentive scheme and a reputation mechanism. Our proposal follows the principles of the Hidden Market Design approach, which allows users to specify the amount of resources they are willing to “sacrifice” when participating in the offloading system. The underlying algorithm, that users are not aware of, is based on a truthful auction strategy and a peer-to-peer reputation exchange scheme. Extensive simulations on real traces depict how our designed mechanism achieves higher offloading rate and produces less traffic compared to three benchmark algorithms. Finally, we show how collaborating devices get rewarded for their contribution, while selfish ones get sidelined by others.

Mobile Cloud Computing (MCC) combines mobile computing and cloud computing aiming to aid performance of mobile devices. The idea is simple: thin devices offload heavy methods to resource-rich servers in the clouds. We believe that in the near future MCC will adopt more advanced offloading techniques. In particular, in this paper we envision a scenario where offloading frameworks will have to deal with GPU code offloading. Amazon already offers instances with Graphics Processing Units (GPU), which can be used for this purpose. We propose and implement MCC-Adviser, a simulation tool that can predict the performance of GPUs with different number of cores using Stochastic Petri Nets. We tested MCC-Adviser in a case study with one of the expensive Amazon GPU instances. The simulations showed that it is possible to minimize costs, while satisfying user's quality of service requirements, by utilizing less powerful instances.

Mobile-cloud offloading mechanisms delegate heavy mobile computation to the cloud. In real life use, the energy tradeoff of computing the task locally or sending the input data and the code of the task to the cloud is often negative, especially with popular communication intensive jobs like social-networking, gaming, and emailing. We design and build a working implementation of CDroid, a system that tightly couples the device OS to its cloud counterpart. The cloud-side handles data traffic through the device efficiently and, at the same time, caches code and data optimally for possible future offloading. In our system, when offloading decision takes place, input and code are likely to be already on the cloud. CDroid makes mobile cloud offloading more practical enabling offloading of lightweight jobs and communication intensive apps. Our experiments with real users in everyday life show excellent results in terms of energy savings and user experience.

This paper presents small world in motion (SWIM), a new mobility model for ad hoc networking. SWIM is relatively simple, is easily tuned by setting just a few parameters, and generates traces that look real-synthetic traces have the same statistical properties of real traces in terms of intercontact times, contact duration, and frequency among node couples. Furthermore, it generates social behavior among nodes and models networks with complex social communities as the ones observed in the real traces. SWIM shows experimentally and theoretically the presence of the power-law and exponential decay dichotomy of intercontact times, and, most importantly, our experiments show that predicts very accurately the performance of forwarding protocols for PSNs like Epidemic, Delegation, Spray&Wait, and more complex, social-based ones like BUBBLE. Moreover, we propose a methodology to assess protocols on model with a large number of nodes. To the best of our knowledge, this is the first such study. Scaling of mobility models is a fundamental issue, yet never considered in the literature. Thanks to SWIM, here we present the first analysis of the scaling capabilities of Epidemic Forwarding, Delegation Forwarding, Spray&Wait, and BUBBLE.

The cloud seems to be an excellent companion of mobile systems, to alleviate battery consumption on smartphones and to backup user's data on-the-fly. Indeed, many recent works focus on frameworks that enable mobile computation offloading to software clones of smartphones on the cloud and on designing cloud-based backup systems for the data stored in our devices. Both mobile computation offloading and data backup involve communication between the real devices and the cloud. This communication does certainly not come for free. It costs in terms of bandwidth (the traffic overhead to communicate with the cloud) and in terms of energy (computation and use of network interfaces on the device). In this work we study the fmobile software/data backupseasibility of both mobile computation offloading and mobile software/data backups in real-life scenarios. In our study we assume an architecture where each real device is associated to a software clone on the cloud. We consider two types of clones: The off-clone, whose purpose is to support computation offloading, and the back-clone, which comes to use when a restore of user's data and apps is needed. We give a precise evaluation of the feasibility and costs of both off-clones and back-clones in terms of bandwidth and energy consumption on the real device. We achieve this through measurements done on a real testbed of 11 Android smartphones and an equal number of software clones running on the Amazon EC2 public cloud. The smartphones have been used as the primary mobile by the participants for the whole experiment duration.

Thanks to their power, the many sensors they embed, and their inherent connectivity to Internet, smartphones are certainly becoming the primary source of multimedia content and the main tool for content sharing. In this demo, we analyze the complexity of real-time video streaming among smartphone users. Firstly, we show that the traditional solution-a unique server receiving and dispatching all devices' content-suffers from scalability issues. Then, we present StreamSmart, a distributed system for real-time video streaming of smartphones, that leverages a virtual P2P network of smartphone software clones on the cloud. In StreamSmart, the captured content is forwarded from the sharing device to its own cloud clone, that in turn forwards it to the clones of other users. These latter transmit the content to the respective devices and, at the same time, contribute to further spread it to other possible clones in the network. We show that the StreamSmart system is highly scalable, responsive, and fault tolerant.

The Internet of the future will be of things: Large scale IoT systems integrating various technologies (tracking, wired and wireless sensor and actuator networks, enhanced communication protocols, distributed intelligence for smart objects) will change the way we live and interact with the environment. Unfortunately, a standardization for IoT systems that allows for integration of sensors, data, services and applications in a smooth way and for interoperability of different technologies was still missing.

Smartphones have exploded in popularity in recent years, becoming ever more sophisticated and capable. As a result, developers worldwide are building increasingly complex applications that require ever increasing amounts of computational power and energy. In this paper we propose ThinkAir, a framework that makes it simple for developers to migrate their smartphone applications to the cloud. ThinkAir exploits the concept of smartphone virtualization in the cloud and provides method-level computation offloading. Advancing on previous work, it focuses on the elasticity and scalability of the cloud and enhances the power of mobile cloud computing by parallelizing method execution using multiple virtual machine (VM) images. We implement ThinkAir and evaluate it with a range of benchmarks starting from simple micro-benchmarks to more complex applications. First, we show that the execution time and energy consumption decrease two orders of magnitude for a N-queens puzzle application and one order of magnitude for a face detection and a virus scan application. We then show that a parallelizable application can invoke multiple VMs to execute in the cloud in a seamless and on-demand manner such as to achieve greater reduction on execution time and energy consumption. We finally use a memory-hungry image combiner tool to demonstrate that applications can dynamically request VMs with more computational power in order to meet their computational requirements.

The battery limits of today smartphones require a solution. In the scientific community it is believed that a promising way of prolonging battery life is to offload mobile computation to the cloud. State of the art offloading architectures consists of virtual copies of real smartphones (the clones) that run on the cloud, are synchronized with the corresponding devices, and help alleviate the computational burden on the real smartphones. Recently, it has been proposed to organize the clones in a peer-to-peer network in order to facilitate content sharing among the mobile smartphones. We believe that P2P network of clones, aside from content sharing, can be a useful tool to solve critical security problems on the mobile network of smartphones. In particular, we consider the problem of computing an efficient patching strategy to stop worm spreading between smartphones. The peer-to-peer network of clones is used to compute the best strategy to patch the smartphones in such a way that the number of devices to patch is low (to reduce the load on the cellular infrastructure) and that the worm is stopped quickly. We consider two well defined worms, one spreading between the devices and one attacking the cloud before moving to the real smartphones; we describe CloudShield, a suite of protocols running on the peer-to-peer network of clones; and we show by experiments that CloudShield outperforms state-of-theart worm-containment mechanisms for mobile wireless networks.

The complexity of social mobile networks, networks of devices carried by humans (e.g. sensors or PDAs) and communicating with short-range wireless technology, makes it hard protocol evaluation. A simple and efficient mobility model such as SWIM reflects correctly kernel properties of human movement and, at the same time, allows to evaluate accurately protocols in this context. In this paper we investigate the properties of SWIM, in particular how SWIM is able to generate social behavior among the nodes and how SWIM is able to model networks with a power-law exponential decay dichotomy of inter contact time and with complex sub-structures (communities) as the ones observed in the real data traces. We simulate three real scenarios and compare the synthetic data with real world data in terms of inter-contact, contact duration, number of contacts, and presence and structure of communities among nodes and find out a very good matching. By comparing the performance of BUBBLE, a community-based forwarding protocol for social mobile networks, on both real and synthetic data traces, we show that SWIM not only is able to extrapolate key properties of human mobility but also is very accurate in predicting performance of protocols based on social human sub-structures.