Lightweight Index Research Articles

Image compression encryption algorithm combining two-dimensional modular hyperchaotic map and compressed sensing

Recently, to offer better ensure for image privacy security, numerous new image encryption algorithms have been proposed. However, these algorithms still suffer from the problems of chaotic performance scarcity, low encryption effect, and high consumption of computational resources. To solve the above issues, we first construct a two-dimensional modular hyperchaotic map (2D-MHM). Then, we further develop an image encryption algorithm based on 2D-MHM and compressed sensing (CS). Several chaotic metrics verify the randomness and validity of 2D-MHM. These metrics include bifurcation diagram, Lyapunov exponent, initial value sensitivity, 0–1 test, and NIST test. Specifically, CS significantly reduces the ciphertext image size thereby reducing its resource consumption during transmission. Reality-preserving fractional DCT (RP-Fdct) diffusion is utilized to transform pixels into the frequency domain to enhance the encryption effect. Subsequently, lightweight index confusion and XOR diffusion further improve the algorithm security. The security of the algorithm is verified through various experiments. It is able to encrypt grayscale and color images of different sizes with good results. Notably, this algorithm also implements the encryption requirements for binary images. Due to our designs, it outperforms recently reported encryption algorithms in several areas, especially in reconstruction performance.

PMDB: A Range-Based Key-Value Store on Hybrid NVM-Storage Systems

Emerging Nov-Volatile Memory (NVM) may replace DRAM as main memory in future computers. However, data will likely still be stored on storage due to the enormous large size of available data. We investigate how key-value stores can be efficiently designed and implemented in a hybrid system, called NVM-Storage system, consisting of NVM as memory and traditional storage. We first discuss the performance trade-offs among Put, Get, and Range Query of the existing designs. Then, we propose PMDB, a range-based key-value store on NVM-Storage systems. PMDB achieves good performance for Put, Get and Range Query at the same time by utilizing a range-based data management and deploying a light-weight index on NVM. We compare PMDB with the state-of-the-art schemes including SLM-DB [21] and MatrixKV [40] for hybrid NVM-storage systems. Evaluation results indicate that in workloads with mixed Put, Get and Range Queries, PMDB outperforms existing key-value stores by <inline-formula><tex-math notation="LaTeX">$1.16\times$</tex-math></inline-formula> – <inline-formula><tex-math notation="LaTeX">$2.49\times$</tex-math></inline-formula> .

Device-Oriented Keyword-Searchable Encryption Scheme for Cloud-Assisted Industrial IoT

Massive physical devices are deployed in the Industrial Internet of Things (IoT) to collect ambiance data while heavy storage and communication cost are imposed on these IoT devices. To overcome this constraint, cloud-assisted technologies are introduced to store and manage the collected data. In order to protect data quality and security, encryption is required before uploading data to remote clouds. Consequently, a search function is added to cloud services to find the specific data. However, traditional data searching schemes are constructed in user-oriented systems, where the search function is mainly involved with the relationship between data and users rather than data and devices. As a result, traditional search schemes are not suitable to find special IoT devices. On the other hand, the status of these devices is described by many attributes, e.g., temperature, clean water storage, and machine speed in an early warning system for industrial sewage disposal equipment. Hence, multi-keyword conjunctive queries for partial attributes should be introduced so as to find the target device more accurately and more efficiently. To address these challenges, we propose a new universal device-oriented keyword searchable encryption (Do-KSE) scheme for cloud-assisted IoT in this paper. Furthermore, the functions of a single and conjunctive keyword search are maintained to handle the device search requirement of partial attributes. We conduct extensive experiments to evaluate the proposed scheme. Experimental results show that our scheme has excellent performance because of the lightweight index and query trapdoor.

Efficient semi-external depth-first search

As graphs grow in size, many real-world graphs are difficult to load into the primary memory of a computer. Thus, computing depth-first search (DFS) results (i.e., depth-first order or DFS-Tree) on the semi-external memory model is important to investigate. Semi-external algorithms assume that the primary memory can at least hold a spanning tree T of a graph G and gradually restructure T into a DFS-Tree, which is nontrivial. In this paper, we present a comprehensive study for the semi-external DFS problem. Based on a theoretical analysis of this problem, we introduce a new semi-external DFS algorithm called EP-DFS with a lightweight index N+-index. Unlike traditional algorithms, we focus on addressing such a complex problem efficiently with fewer I/Os, simpler CPU calculations (implementation-friendly), and less random I/O accesses (key-to-efficiency). Extensive experimental evaluations are performed on both synthetic and real graphs, and experimental results confirm that the proposed EP-DFS algorithm markedly outperforms existing algorithms.

Reachability Queries with Label and Substructure Constraints on Knowledge Graphs

Since knowledge graphs (KGs) describe and model the relationships between entities and concepts in the real world, reasoning on KGs often correspond to the reachability queries with label and substructure constraints (LSCR queries). Specifically, for a search path p, LSCR queries not only require that the labels of the edges passed by p are in a certain label set, but also claim that a vertex in p could satisfy a certain substructure constraint. They are much more complex than existing label-constraint reachability (LCR) queries. LSCR queries on KGs can be addressed by two natural ways (EA-1) an online search algorithm and (EA-2) a combined search strategy, to the best of our knowledge. This paper presents two optimized algorithms for EA-1 and EA-2, but the optimized algorithms are still inefficient, since their efficiencies are highly dominated by their search directions as analyzed in this paper. Motivated by that, this paper presents an efficient informed search strategy on KGs, named INSK, with a lightweight index, named local index. An extensive experimental evaluation, on both synthetic and real KGs, illustrates that our INSK can efficiently process LSCR queries on KGs.

Speeding Up Reachability Queries in Public Transport Networks Using Graph Partitioning

Computing path queries such as the shortest path in public transport networks is challenging because the path costs between nodes change over time. A reachability query from a node at a given start time on such a network retrieves all points of interest (POIs) that are reachable within a given cost budget. Reachability queries are essential building blocks in many applications, for example, group recommendations, ranking spatial queries, or geomarketing. We propose an efficient solution for reachability queries in public transport networks. Currently, there are two options to solve reachability queries. (1) Execute a modified version of Dijkstra’s algorithm that supports time-dependent edge traversal costs; this solution is slow since it must expand edge by edge and does not use an index. (2) Issue a separate path query for each single POI, i.e., a single reachability query requires answering many path queries. None of these solutions scales to large networks with many POIs. We propose a novel and lightweight reachability index. The key idea is to partition the network into cells. Then, in contrast to other approaches, we expand the network cell by cell. Empirical evaluations on synthetic and real-world networks confirm the efficiency and the effectiveness of our index-based reachability query solution.

From magnesite directly to lightweight closed-pore MgO ceramics: The role of Si and Si/SiC

Partial/full replacement of traditional dense refractory aggregates (for furnace lining) with lightweight aggregates is considered to be an effective and promising strategy for energy saving and emission reduction. In this work, lightweight magnesia refractory ceramics with tailored closed porosity were prepared by one-step sintering at 1600 °C from high-purity magnesite added with silicon kerf waste in different ratios, with the emphasis on the evolution of their phase compositions, micromorphologies, and various properties. With the addition of additives, Mg2–xFexSiO4 solid solution was formed at the grain boundaries of the MgO matrix, and then the volumetric expansion effect (interfacial reaction) and activated sintering effect (vacancy defect) promote the decrease of apparent porosity and the increase of closed porosity, respectively. Consequently, MgO ceramics with apparent/closed porosity of 0.6%/6.5%, bulk density of 3.25 g cm−3, and lightweight index of 7.8% were successfully prepared, suitable for the working lining of metallurgical furnaces.

Energy-Efficient Scans by Weaving Indexes Into the Storage Layout in Computing Platforms for Internet of Things

Recent advances in computing are widely leveraged to design energy-efficient computing platforms for Internet of Things. Especially, scans is a crucial operation that consumes major energy in main-memory column-stores. Scanning a column involves returning a result bit vector to indicate whether each record satisfies a predicate (i.e., a filter condition) or not. ByteSlice, as an existing storage layout, chops the data into multiple bytes, thus possessing early-stop capability by comparing high-order bytes while scanning data. However, the width of columns is usually not multiples of byte, and in these cases, the last-byte of ByteSlice layout is padded with zeros, which wastes the computation energy and the memory bandwidth. To make the scan operation energy-efficient, a lightweight index is squeezed into the bits that originally padded with zeros. In this way, our proposed storage layout DIFusion integrates the data and the index in a unified structure. DIFusion enables an energy-efficient scan because it inherits the early-stopping capability from ByteSlice and possesses the data-skipping ability at the same time. The experimental evaluation on TPC-H, SSB and the real workload shows promising speedup for scans on DIFusion.

Efficient and Traceable Patient Health Data Search System for Hospital Management in Smart Cities

Smart city, as a new mode, is introduced to improve the level of city management for modern cities. In smart cities, a kernel field is health management for urban residents. Hospital management, as one of the most important components in health management, is concerned. To provide high-quality medical service for sick residents, accurate patient health data analysis is needed. Thus, data collection in patient health monitoring is necessary. To achieve this, massive Internet-of-Things devices are distributed; in general, they are resource-constrained devices. From this, lightweight index generation is needed. Furthermore, with the development of professional technologies in medical science, the hospital manager has to employ many different types of professional doctors. They need the shared patient health data to do a precise diagnosis and present an efficient therapeutic schedule for each patient. However, many secret details are recorded in the patient health data. Thus, data privacy of the shared patient health data should be maintained. In this article, we propose a new traceable patient health data search system for hospital management in smart cities. In this system, the system manager shares the encrypted patient health data to different doctors at the grain of hospital bed. Each doctor accurately finds a patient with a special feature from the patient health monitoring data. To prevent patient health data leakage, the functions of illegal search query blocking and inside malicious user tracing are designed. The performance analysis shows that our system is practical for lightweight data collecting devices.

Research and Application of Lightweight Index for Passenger Cars

Lightweight is an effective design strategy to conserve energy in automotive vehicles. It is a big challenge to evaluate the level of lightweight for passenger cars. This paper summarizes various evaluation methods for lightweight automotive vehicles. A lightweight index [Lv for internal combustion engine vehicles (ICEVs) and Lev for battery electric vehicles (BEVs)] is proposed to assess the lightweight of passenger cars. The proposed lightweight index is composed of the nominal density, weight-to-power ratio, and fuel consumption of footprint area (in the case of ICEVs) or electricity consumption of footprint area (in the case of BEVs). The validity and universality of the proposed lightweight index are demonstrated through a statistical analysis of 7018 ICEV and 326 BEV models. The calculation procedures of the standard partial regression coefficients of statistical multiple regression and elastic coefficients are employed in the proposed method. The results show that either Lv or Lev is most sensitive to the curb mass of the vehicles. The proposed lightweight index can help guide automakers in setting reasonable weight reduction targets during new product development. In addition, the proposed lightweight index can be applied to new hybrid electric vehicles with further efforts, to facilitate the development of lightweight automotive design.

L-Match: A Lightweight and Effective Subsequence Matching Approach

Many IoT (Internet of Things) applications, like the industrial internet and the smart city, collect data continuously from massive sensors. It is crucial to exploit and analyze the time series data efficiently. Subsequence matching is a fundamental task in mining time series data. Most existing works develop the index and the matching approach for the static time series data. However, IoT applications need to continuous collect new data and deposit huge historical time series data, which pose a significant challenge for the static indexing approach. To address this challenge, we propose a lightweight index structure, L-index, and a matching approach, L-match, for the constraint normalized subsequence matching problem (cNSM). L-index is a two-layer structure and built on the simple series synopsis, the mean values of the disjoint windows. It is easy to build and update as data grows. Moreover, to further improve the efficiency for the variable query lengths, an optimization technique, named SD-pruning, is proposed. We conduct extensive experiments, and the results verify the effectiveness and efficiency of the proposed approach.

Compression testing of additively manufactured continuous carbon fiber-reinforced sandwich structures

Abstract The novel, additive manufacturing technique, continuous lattice fabrication, combines the advantages of continuous fiber-reinforcement with those of additive manufacturing. This enables the generation of fiber-reinforcement within a single layer and especially along an out-of-plane load path inside all spatial dimensions. This study is a test-related evaluation of sandwich panels with lattice core structures modifying a compression test. The specimens were manufactured differentially via plug and bond and automatically using continuous lattice fabrication. Additionally, the spatial arrangement of the rods within the lattice core structure varied in terms of base area. The ultra-lightweight sandwich panels have lattice core structures with core densities &lt; 10 mg × cm−3. The material testing was performed by a modified compression test at room temperature. The damage analysis of the single rods shows current deficits and future potentials for optimization of lattice core structures. It could be shown that sandwich panels exhibit a compression strength of up to 0.30 MPa at a core density of 6.57 mg × cm−3. Using a dimensionless lightweight index demonstrates a mechanical performance on a level comparable with that of selected core materials.

Towards an efficient snapshot approach for virtual machines in clouds

High-availability of virtual machines is of significant importance for a cloud computing environment, because cloud services may be compromised due to the system maintenance, malicious attacks, and hardware and software failures. The virtual machine (VM) snapshot can effectively back up the state, disk data, and configuration of a machine at a specific time point. However, the existing VM snapshot methods suffer from performance issues such as long downtime and I/O performance degradation during a live snapshot. To address such issues, we proposed an efficient VM snapshot system, iROW (improved Redirect-on-Write), based on the qemu-kvm virtual block device driver. iROW employs the following techniques. (1) A bitmap-based light-weight index method is designed to reduce the query cost of the existing two-level index table structure compared with qcow2. (2) An index-free approach is used for VM state data to improve the performance of data saving and loading operations of VM state. (3) VM state data is separated from disk image data in a snapshot. (4) The free page detection (FPD) is designed using virtual machine introspection to identify and skip saving free pages in the guest OS during snapshotting, thus reducing the VM state snapshot creation time and the snapshot disk space usage. Our experimental results demonstrate that iROW is evidently advantageous over qcow2 in performance, in terms of the disk snapshot, the state snapshot and the disk I/O.

Design on Text Retrieval Algorithm in Cloud Computing Environment

This paper presents a lightweight index does not suspend services online update program, and demonstrate the performance of the index update program from the theoretical analysis and experimental data in two ways. A new method of MapReduce existing index methodology based on this design and further discussion are done in the paper, in the index MapReduce and Hadoop MapReduce feasibility aspects, the design flaws through experimentation.

FlashLight

A very promising approach for using NAND flash memory as a storage medium is a flash file system. In order to design a higher-performance flash file system, two issues should be considered carefully. One issue is the design of an efficient index structure that contains the locations of both files and data in the flash memory. For large-capacity storage, the index structure must be stored in the flash memory to realize low memory consumption; however, this may degrade the system performance. The other issue is the design of a novel garbage collection (GC) scheme that reclaims obsolete pages. This scheme can induce considerable additional read and write operations while identifying and migrating valid pages. In this article, we present a novel flash file system that has the following features: ( i ) a lightweight index structure that introduces the hybrid indexing scheme and intra-inode index logging , and ( ii ) an efficient GC scheme that adopts a dirty list with an on-demand GC approach as well as fine-grained data separation and erase-unit data allocation . We implemented FlashLight in a Linux OS with kernel version 2.6.21 on an embedded device. The experimental results obtained using several benchmark programs confirm that FlashLight improves the performance by up to 27.4% over UBIFS by alleviating index management and GC overheads by up to 33.8%.

Efficient probabilistic XML query processing using an extended labeling scheme and a lightweight index

Recently there is a growing interest in the data model and query processing for probabilistic XML data. There are many potential applications of probabilistic data, and the XML data model is suitable to represent hierarchical information and data uncertainty of different levels naturally. However, the previously proposed probabilistic XML data models and query processing techniques separate finding data matches with evaluating the probabilities of results. Therefore, they should repeatedly access the data and need to get full data of paths given in queries to calculate the probabilities of results.In this paper, we propose an extended interval-based labeling scheme for the probabilistic XML data tree and an efficient query processing procedure using the labeling scheme. Against previous researches, our method accesses only the labels of data specified in queries and finds data matches simultaneously with evaluating the probability of each data match. Also, we present an extended probabilistic XML query model with the predicates for the values of probabilities and a lightweight index for those probabilities in order to eliminate unnecessary access to data that will not be included in results.Experimental results show that our approach is efficient in probabilistic XML query processing and our index scheme significantly improves the performance of query processing when the predicates for the values of probabilities are given.

The body of the new BMW 5 series architecture between aesthetics and dynamics

With the longest wheelbase in its segment, a long bonnet, short overhangs and a flowing roof line, the new BMW 5 Series sets itself apart from the competition. The consistent combination of ultra high-strength lightweight materials and structural optimisation made it possible to significantly increase the lightweight index compared to the predecessor model. Aluminium is used not only for the door modules but also for structural components.