34 research outputs found
Rule-based approach for context inconsistency management scheme in ubiquitous computing
No full textContext data are updated frequently due to the dynamic changes of the various sensor values and the situations of application entities. Without a proper management, the stored contexts will become different from those of the real-world. Those invalid contexts will cause context inconsistency problems and thus should be eliminated at the right time and in an appropriate manner. In this paper, we propose a context inconsistency management scheme based on context elimination rules that describe the semantics of context invalidity to solve context inconsistency problems. The proposed rule-based scheme will enable users to easily specify elimination conditions for inconsistent contexts. Our performance evaluation shows that the rule processing overhead is compensated for by virtue of the well-maintained repository of the stored contexts
SNQL: A Query Language for Sensor Network Databases
No full textDatabase management in the wireless sensor network requires features that are different from the traditional database management. Data are collected from a massively large number of battery-operated sensor nodes in a wireless network. A large amount of dynamically changing data should be temporally queried and collected from the sensor nodes of multiple layers. In this paper, we focus on devising a new query language, SNQL that is efficient in dynamic data collection and energy-saving for massively large sensor networks. To minimize the energy consumption SNQL reduces unnecessary query executions for nodes by adaptive query operations to dynamic environments. SNQL also introduces a querying mechanism of controlling the quality of collected data in association with node selection strategy to minimize the energy consumption for the entire sensor network. We show how the language constructs in SNQL achieve the intended energy efficiency and further show the performance evaluation result
OntoSNP: Ontology driven knowledgebase for SNP
No full textOntology-based knowledgebase system can provide great benefits for analysis of biological information. Currently, some ontology-driven information systems have been introduced in this filed. In most cases, however, the advantage of ontology which computer science technology can provide is not fully implemented. Even the well-known ontologies such as Gene Ontology (GO) include only limited number of properties for each term. To find meaningful information across complex relation chains among data, indispensable properties of each term should be described and relationship among data can be analyzed by reasoning. Ontology with rules can play a key role in bioinformatics area because, as well as interoperability, it finds new knowledge and checks validity of candidate knowledge automatically using a reasoning engine. In this paper, we propose an ontology-based information system for SNP analysis (OntoSNP) equipped with Web Ontology Language (OWL) and a reasoning engine. The proposed system provides finding of SNP-gene-disease relations, automatic data validity and knowledge conflict checking. OntoSNP is based on well-defined SNP-gene-disease ontology model in OWL and knowledge-finding and validation rules in Semantic Web Rule Language (SWRL)
Facile One-Pot Synthesis of Bimetallic Co/Mn-MOFs@Rice Husks, and its Carbonization for Supercapacitor Electrodes
No full text© 2019, The Author(s).Novel hybrid nanomaterials comprising metal-organic framework compounds carbonised in the presence of biomass material derived from rice husk have been investigated as a new class of sustainable supercapacitor materials for electrochemical energy storage. Specifically, two synthetic routes were employed to grow Co/Mn metal-organic framework compounds in the channels of rice husks, which had been activated previously by heat treatment in air at 400 °C to produce a highly porous network. Pyrolysis of these hybrid materials under nitrogen at 700 °C for 6 h produced metal-containing phases within the nanocarbon, comprising intimate mixtures of Co, MnO and CoMn2O4. The materials thus produced are characterized in detail using a range of physical methods including XRD, electron microscopy and X-ray photoelectron spectroscopy. The synthetic pathway to the metal-organic framework compound is shown to influence significantly the physical properties of the resulting material. Electrochemical evaluation of the materials fabricated revealed that higher specific capacitances were obtained when smaller crystallite sized bimetallic Co/Mn-MOFs were grown inside the rice husks channels compared to larger crystallite sizes. This was in-part due to increased metal oxide loading into the rice husk owing to the smaller crystallite size as well as the increased pseudocapacitance exhibited by the smaller crystallite sizes and increased porosit
Privacy-Preserving Federated Learning Using Homomorphic Encryption
No full textFederated learning (FL) is a machine learning technique that enables distributed devices to train a learning model collaboratively without sharing their local data. FL-based systems can achieve much stronger privacy preservation since the distributed devices deliver only local model parameters trained with local data to a centralized server. However, there exists a possibility that a centralized server or attackers infer/extract sensitive private information using the structure and parameters of local learning models. We propose employing homomorphic encryption (HE) scheme that can directly perform arithmetic operations on ciphertexts without decryption to protect the model parameters. Using the HE scheme, the proposed privacy-preserving federated learning (PPFL) algorithm enables the centralized server to aggregate encrypted local model parameters without decryption. Furthermore, the proposed algorithm allows each node to use a different HE private key in the same FL-based system using a distributed cryptosystem. The performance analysis and evaluation of the proposed PPFL algorithm are conducted in various cloud computing-based FL service scenarios
Frame-Selective Wireless Attack Using Deep-Learning-Based Length Prediction
No full textWireless attack refers to the malicious activity to generate a wireless jamming signal to interfere with the data transmission of legitimate users. If the jamming duration of a wireless attack is long, it can be easily detected; such attacks also consume more energy for generating the jamming signal. We propose a frame-selective jamming to attack shorter frames that are essential to data communication protocols such as media access control (MAC) acknowledgement frames. Once a wireless signal is detected, the proposed jammer predicts the duration of the signal using a deep learning technique and generates a jamming signal selectively if the duration is expected to be shorter than or equal to a certain threshold. © 2018 IEEE
Design and Implementation of a Full-Duplex Pipelined MAC Protocol for Multihop Wireless Networks
No full textIn multihop wireless networks, data packets are forwarded from a source node to a destination node through intermediate relay nodes. With half-duplex relay nodes, the end-to-end delay performance of a multihop network degrades as the number of hops increases, because the relay nodes cannot receive and transmit at the same time. Full-duplex relay nodes can reduce their per-hop delay by starting to forward a packet before the whole packet is received. In this paper, we propose a pipelined medium access control (PiMAC) protocol, which enables the relay nodes on a multihop path to simultaneously transmit and receive packets for full-duplex forwarding. For pipelined transmission over a multihop path, it is important to suppress both the self-interference of each relay node with the full-duplex capability and the intra-flow interference from the next relay nodes on the same path. In the PiMAC protocol, each relay node can suppress both the self- and intra-flow interference for full-duplex relaying on the multihop path by estimating the channel coefficients and delays of the interference during a multihop channel acquisition phase. To evaluate the performance of the PiMAC protocol, we carried out extensive simulations and software-defined radio-based experiments
