Keep Your Nice Friends Close, but Your Rich Friends Closer -- Computation Offloading Using NFC

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.Comment: 9 pages, 4 tables, 13 figure

Decentralizing indexing and bootstrapping for online applications

https://doi.org/10.1049/blc2.12001Abstract Peer-to-peer (P2P) networks utilize centralized entities (trackers) to assist peers in finding and exchanging information. Although modern P2P protocols are now trackerless and their function relies on distributed hash tables (DHTs), centralized entities are still needed to build file indices (indexing) and assist users in joining DHT swarms (bootstrapping). Although the functionality of these centralized entities are limited, every peer in the network is expected to trust them to function as expected (e.g. to correctly index new files). In this work, a new approach for designing and building decentralized online applications is proposed by introducing DIBDApp. The approach combines blockchain, smart contracts and BitTorrent for building up a combined technology that permits to create decentralized applications that do not require any assistance from centralized entities. DIBDApp is a software library composed of Ethereum smart contracts and an API to the BitTorrent protocol that fully decentralizes indexing, bootstrapping and file storing. DIBDApp enables any peer to seamlessly connect to the designed smart contracts via the Web3J protocol. Extensive experimentation on the Rinkeby Ethereum testnet shows that applications built using the DIBDApp library can perform the same operations as in traditional back-end architectures with a gas cost of a few USD cents.Peer reviewe

FedClean: A Defense Mechanism Against Parameter Poisoning Attacks in Federated Learning

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FedClean: A Defense Mechanism Against Parameter Poisoning Attacks in Federated Learning

Peer reviewe

Lightweight Modeling of User Context Combining Physical and Virtual Sensor Data

The multitude of data generated by sensors available on users' mobile devices, combined with advances in machine learning techniques, support context-aware services in recognizing the current situation of a user (i.e., physical context) and optimizing the system's personalization features. However, context-awareness performances mainly depend on the accuracy of the context inference process, which is strictly tied to the availability of large-scale and labeled datasets. In this work, we present a framework developed to collect datasets containing heterogeneous sensing data derived from personal mobile devices. The framework has been used by 3 voluntary users for two weeks, generating a dataset with more than 36K samples and 1331 features. We also propose a lightweight approach to model the user context able to efficiently perform the entire reasoning process on the user mobile device. To this aim, we used six dimensionality reduction techniques in order to optimize the context classification. Experimental results on the generated dataset show that we achieve a 10x speed up and a feature reduction of more than 90% while keeping the accuracy loss less than 3%

Emerging ExG-based NUI Inputs in Extended Realities : A Bottom-up Survey

Incremental and quantitative improvements of two-way interactions with extended realities (XR) are contributing toward a qualitative leap into a state of XR ecosystems being efficient, user-friendly, and widely adopted. However, there are multiple barriers on the way toward the omnipresence of XR; among them are the following: computational and power limitations of portable hardware, social acceptance of novel interaction protocols, and usability and efficiency of interfaces. In this article, we overview and analyse novel natural user interfaces based on sensing electrical bio-signals that can be leveraged to tackle the challenges of XR input interactions. Electroencephalography-based brain-machine interfaces that enable thought-only hands-free interaction, myoelectric input methods that track body gestures employing electromyography, and gaze-tracking electrooculography input interfaces are the examples of electrical bio-signal sensing technologies united under a collective concept of ExG. ExG signal acquisition modalities provide a way to interact with computing systems using natural intuitive actions enriching interactions with XR. This survey will provide a bottom-up overview starting from (i) underlying biological aspects and signal acquisition techniques, (ii) ExG hardware solutions, (iii) ExG-enabled applications, (iv) discussion on social acceptance of such applications and technologies, as well as (v) research challenges, application directions, and open problems; evidencing the benefits that ExG-based Natural User Interfaces inputs can introduceto the areaof XR.Peer reviewe