Lightweight Mechanism Research Articles

The Lightweight Mechanism of Artificial Lightweight Aggregates Recycled with Anaerobically Digested Sewage Sludge and a Study of Process Optimization Using Machine Learning

The Lightweight Mechanism of Artificial Lightweight Aggregates Recycled with Anaerobically Digested Sewage Sludge and a Study of Process Optimization Using Machine Learning

The euBusinessGraph ontology: A lightweight ontology for harmonizing basic company information

Company data, ranging from basic company information such as company name(s) and incorporation date to complex balance sheets and personal data about directors and shareholders, are the foundation that many data value chains depend upon in various sectors (e.g., business information, marketing and sales, etc.). Company data becomes a valuable asset when data is collected and integrated from a variety of sources, both authoritative (e.g., national business registers) and non-authoritative (e.g., company websites). Company data integration is however a difficult task primarily due to the heterogeneity and complexity of company data, and the lack of generally agreed upon semantic descriptions of the concepts in this domain. In this article, we introduce the euBusinessGraph ontology as a lightweight mechanism for harmonising company data for the purpose of aggregating, linking, provisioning and analysing basic company data. The article provides an overview of the related work, ontology scope, ontology development process, explanations of core concepts and relationships, and the implementation of the ontology. Furthermore, we present scenarios where the ontology was used, among others, for publishing company data (business knowledge graph) and for comparing data from various company data providers. The euBusinessGraph ontology serves as an asset not only for enabling various tasks related to company data but also on which various extensions can be built upon.

Microservice Architecture for Scalability and Reliability in E-Commerce

Microservice structures offer little types of assistance that might be sent and scaled autonomously of one another, what's more, may utilize distinctive middleware stacks for their execution. Microservice designs expect to survive the deficiencies of solid structures where the entirety of the application's rationale and information are overseen in one deployable unit. Microservice architectural fashion is a method to developing a single utility as a suite of small services, each jogging in its own system and communicating with light-weight mechanisms, frequently an HTTP useful resource API.

Lightweight method of shuffling overlapped data-blocks for data integrity and security in WSNs

Wireless Sensor Networks (WSN) consist of devices with limited resources to explore and sense the environment in a cooperative way. Security, mainly in terms of guaranteeing the data integrity, is a primary issue for many applications, but with an extra energy cost. Thus, trade-off is required between security level and energy consumption in real applications. First of all, a brief survey about security methods, focusing in data integrity, in WSN is implemented. The objective of this paper is to propose a new data integrity method with medium security levels and low energy cost. Therefore, we propose a new and lightweight mechanism for data integrity with overlapping blocks in WSNs. Hence, an attacker will spend much time and effort to interpret and alter the packets. The experiments were performed using TinyOS 2.1 operating system and TelosB nodes for measuring the overhead in terms of energy consumption, memory, and packet size. Moreover, the receiver is able to detect tampering packets and request those retransmission data. An attacker would require huge amounts of memory and processing time to extract the original information, even for small-sized data blocks. Thus, this fact makes this approach a simple, yet effective, mechanism to protect data whilst enhancing the data integrity.

Security Vulnerabilities and Countermeasures in MPSoCs

This article deals with software attacks targeting multiprocessor systems-on-chip and proposes a set of countermeasures corresponding to lightweight mechanisms characterized by a small impact on the system performance and area, while still guaranteeing a good level of protection.

A lightweight model with spatial–temporal correlation for cellular traffic prediction in Internet of Things

Accurate cellular traffic prediction becomes more and more critical for efficient network resource management in the Internet of Things (IoT). However, high-accuracy prediction results are usually accompanied by high computational capacity requirements. Although many lightweight neural network models have been proposed, some lightweight mechanisms will easily destroy the features of the raw data. Not all lightweight mechanisms are suitable for network traffic prediction. Therefore, this study proposes and optimizes an input data conversion method to extract the features of spatio-temporal dependencies based on convolutional neural network (CNN) architecture. In addition, we also propose a lightweight neural network model to reduce the computational cost for cellular traffic prediction problem and use mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) to evaluate the prediction accuracy. The experimental results show that the proposed model is better than CNN, ConvLstm, and Densenet as well as can greatly reduce the parameters of the neural network while maintaining prediction accuracy.

Security in Microservices Architectures

Abstract A Microservice is a small or even micro independent process that communicates, acts, and returns via messages through lightweight mechanisms like Thrift, HTTP or RESTAPI. Microservices Architecture is amateur evolution of the Monolithic Architecture. Observing it in afunctional way, it is correct to claim that it breaks down complex applications into a simpler abstraction. As this research demonstrates, Microservices Architecture is intrinsically connected as a symbiosis with container-based deployment, because these containers have no need for embedded operating systems and calls are made for OS resources, via an application programming interface. It is safe to claim that this technology is currently the focus of modern developers nowadays. Semantically speaking, Microservices functionally deconstruct larger applications into smaller, discrete services, and containers are viewed as a natural compute platform for this architecture [1]. A single service is and can be represented by multiple containers in a Microservices cluster, each single service is designed to provide a specific set of functions while services act to makeup the entire application. It’s common in large application the decomposition into multiple arms of more than twenty services, although less can be commonly found as well [1]. The main objective of Microservices Architecture is to disassemble the core components of a given type of application [2]. This study could be done in multiple ways, all of them different because practically everybody has their specific way of looking at Microservices, but one aspect is the same cross mentalities, Security. So, the focus of this research is to expose the main security aspects of this specific architecture, in this cost-effective era.

Towards a Dynamic Virtual IoT Network Based on User Requirements

The data being generated by the Internet of Things needs to be stored, monitored, and analyzed for maximum IoT resource utilization. Software Defined Networking has been extensively utilized to address issues such as heterogeneity and scalability. However, for small-scale IoT application, sometimes it is considered an inefficient approach. This paper proposes an alternate lightweight mechanism to the design and implementation of a dynamic virtual network based on user requirements. The key idea is to provide users a virtual interface that enables them to reconfigure the communication flow between the sensors and actuators at runtime. The throughput of the communication flow depends on the data traffic load and optimal routing. Users can reconfigure the communication flow, and virtual agents find the optimal route to handle the traffic load. The virtual network provides a user-friendly interface to allow physical devices to be mapped with the corresponding virtual agents. The proposed network is applicable for all systems that lie in the Internet of Things domain. Results conclude that the proposed network is efficient, reliable, and responsive to network reconfiguration at runtime.

Morphing lattice boom for space applications

Structures used in space applications demand the highest levels of stiffness for their mass whilst also performing in a hostile environment. To partly address these requirements and so as to also pack efficiently for stowage during launch we propose a new type of compact telescopic morphing lattice space boom. This boom stows within a 1U CubeSat volume and is lightweight being only 0.4 kg. The boom has a total length of 2 m in its deployed state which is 20 times its stowed height. The device comprises two multi-stable cylindrical composite lattices that are joined telescopically. These lattices nest inside one another in the stowed configuration, with the objective of improving packaging efficiency. Notably, prestress and lamina orientation are used to smoothly change shape from being compact when stowed to being extended when deployed. The lattices in the boom have been designed to maximise deployment force and to be self-deploying by tuning manufacturing parameters. As a result, only a small, lightweight mechanism is required to regulate deployment speed of the lattice boom. By reversing its direction, this mechanism can be used to retract the lattice boom to its stowed configuration, thereby enabling two-way reconfigurability.

Unsupervised learning of depth and ego-motion with absolutely global scale recovery from visual and inertial data sequences

ABSTRACT In this paper, we propose an unsupervised learning method for jointly estimating monocular depth and ego-motion, which is capable to recover the absolute scale of global camera trajectory. In order to solve the general problems of scale drift and scale ambiguity of monocular camera, we fuse geometric movement data from inertial measurement unit (IMU), and use Bi-directional Long Short-Term Memory (BiLSTM) to extract temporal features. Besides, we add a lightweight and efficient attention mechanism, Convolutional Block Attention Module (CBAM), to Convolutional Neural Networks (CNNs) to complete the extraction of image features. Considering the scenes with severe illumination changes, ambiguous structures, moving objects and occlusions, especially scenes with progressively-variant textures, the geometric features can provide adaptive estimation results in the case of the degeneration of visual features. Experiments on the KITTI driving dataset reveal that our scheme achieves promising results in the estimation of camera pose and depth. Moreover, the absolute scale recovery for the global camera trajectory is effective.

Interactive WebVR visualization for online fire evacuation training

Online training is necessary for public fire escaping with the development of WebVR (Virtual Realization on Web). However, the online real-time fire training could not implemented in WebVR for the large-scale public scenario because of the huge size of VR fire scenario data, the low network transmission speed, and the slowly rendering ability of Web browser. To solve these bottlenecks, we propose a suite of solution for WebVR online fire training. Firstly, we implement a series of lightweight technologies to compress the large-scale scene data. The huge BIM (Building Information Modeling) volume data could be compressed by as much as 10 times. Then, we propose a kind of FDS (Fire Dynamic Simulator) smoking data lightweight mechanism to compress huge volume smoke data, which can be reduced by as much as 30 times. Thirdly, we adopt the multithread loading mechanism to accelerate WebVR fire scenario lightweight data. Finally, we collect the escape traces from history fire evacuation of the public, who wear the VR devices. Based on the valuable traces, we propose the TC-eACO algorithm to plan crowd optimal evacuation paths. At the same time, we implement a prototype system based on TC-eACO for WebVR fire evacuation training, with which the user just needs to surf the internet and take part in the fire evacuation training. The experimental results demonstrate that the solutions we proposed are feasible for online fire evacuation training not only in subway stations but also in other types of buildings.

FEATHER: A Proposed Lightweight Protocol for Mobile Cloud Computing Security

Ensuring security for lightweight cryptosystems in mobile cloud computing is challenging. Encryption speed and battery consumption must be maintained while securing mobile devices, the server, and the communication channel. This study proposes a lightweight security protocol called FEATHER which implements MICKEY 2.0 to generate keystream in the cloud server and to perform mobile device decryption and encryption. FEATHER can be used to implement secure parameters and lightweight mechanisms for communication among mobile devices and between them and a cloud server. FEATHER is faster than the existing CLOAK protocol and consumes less battery power. FEATHER also allows more mobile devices to communicate at the same time during very short time periods, maintain security for more applications with minimum computation ability. FEATHER meets mobile cloud computing requirements of speed, identity, and confidentiality assurances, compatibility with mobile devices, and effective communication between cloud servers and mobile devices using an unsafe communication channel.

Bidirectional hash chains generically enhances resilience of key predistribution schemes

Key predistribution is an adequate technique to solve the (symmetric) key management problem in distributed systems. Owing to the large network size and limited memory (z) in devices, a given key is shared by a number of nodes (r). This leads to a generic degradation of a network's resilience. Several works dedicate to reduce this deterioration by applications of lightweight mechanisms. Few such prominent approaches are the q- composite method by Chan et al., hashed random preloaded subset by Ramkumar et al. and one-way function iterates by Cichoń et al. or its special case of hash chains (HCs) by Bechkit et al. The authors introduce the generic concepts of bidirectional HCs (2HCs) and q -composite 2HCs. A thorough qualitative investigations of vital system parameters indicate trade-offs between significant improvement in a network's resilience and nominal increment in space and computational overheads in constituent devices. In the process, they correct an error in resilience computations in the works of Bechkit et al. Instantiations of their indigenous concepts to three prominent schemes that represent individual types of key predistribution schemes (one random and two deterministic) concretely quantify their analyses.

An adaptive, utilization-based approach to schedule real-time tasks for ARM big.LITTLE architectures

ARM big.LITTLE architectures are spreading more and more in the mobile world thanks to their power-saving capabilities due to the use of two ISA-compatible islands, one focusing on energy efficiency and the other one on computational power. This architecture makes the problem of energy-aware task scheduling particularly challenging, due to the number of variables to take into account and the need for having lightweight mechanisms that can be readily computed in an operating system kernel scheduler. This paper presents a novel task scheduler for big.LITTLE platforms, combining the well-known Constant Bandwidth Server algorithm with a power-aware per-job migration policy. This achieves real-time adaptation of the CPU islands' frequencies based on the individual cores' overall utilization, as available in the scheduler thanks to the use of the resource reservation paradigm. Preliminary results obtained by simulations based on modifications to the open-source RTSim tool show that the proposed technique is able to achieve interesting performance/energy trade-offs.

Personalized cloud service review analysis based on modularized ontology

PurposeThe purpose of this paper is to analyze cloud reviews according to the end-user context and requirements.Design/methodology/approachpropose a comprehensive knowledge base composed of interconnected Web Ontology Language, namely, modular ontology for cloud service opinion analysis (SOPA). The SOPA knowledge base will be the basis of context-aware cloud service analysis using consumers' reviews. Moreover, the authors provide a framework to evaluate cloud services based on consumers' reviews opinions.FindingsThe findings show that there is a positive impact of personalizing the cloud service analysis by considering the reviewers' contexts in the performance of the framework. The authors also proved that the SOPA-based framework outperforms the available cloud review sites in term of precision, recall and F-measure.Research limitations/implicationsLimited information has been provided in the semantic web literature about the relationships between the different domains and the details on how that can be used to evaluate cloud service through consumer reviews and latent opinions. Furthermore, existing approaches are lacking lightweight and modular mechanisms which can be utilized to effectively exploit information existing in social media.Practical implicationsThe SOPA-based framework facilitates the opinion based service evaluation through a large number of consumer's reviews and assists the end-users in analyzing services as per their requirements and their own context.Originality/valueThe SOPA ontology is capable of representing the content of a product/service as well as its related opinions, which are extracted from the customer's reviews written in a specific context. Furthermore, the SOPA-based framework facilitates the opinion based service evaluation through a large number of consumer's reviews and assists the end-users in analyzing services as per their requirements and their own context.

ACHO: A framework for flexible re-orchestration of virtual network functions

Network Function Virtualization enables network slicing as a novel paradigm for service provisioning. With network slicing, Virtual Network Functions (VNFs) can be instantiated at different locations of the infrastructure, choosing their optimal placement based on parameters such as the requirements of the service or the resources available. One limitation of state-of-the-art technology for network slicing is the inability to re-evaluate orchestration decisions once the slice has been deployed, in case of changing service demands or network conditions.In this paper, we present ACHO, a novel software framework that enables seamless re-orchestration of VNFs of any kind, including RAN and Core. With ACHO, VNFs and resources can be easily re-assigned to match, e.g., varying user demands or changes in the nodes’ load. ACHO uses lightweight mechanisms, such as splitting the engine of a VNF from the data it requires to perform its operation, in such a way that, when re-allocating a VNF, only the data is moved (a new engine is instantiated in the new location). We demonstrate the use of ACHO in a small scale testbed, showing that (i) the proposed re-orchestration is feasible, (ii) it results much faster than existing alternatives (especially for relocation), and (iii) the framework can be readily applied to existing VNFs after minimal changes to their implementation.

Authenticated secret key generation in delay-constrained wireless systems

With the emergence of 5G low-latency applications, such as haptics and V2X, low-complexity and low-latency security mechanisms are needed. Promising lightweight mechanisms include physical unclonable functions (PUF) and secret key generation (SKG) at the physical layer, as considered in this paper. In this framework, we propose (i) a zero round trip time (0-RTT) resumption authentication protocol combining PUF and SKG processes, (ii) a novel authenticated encryption (AE) using SKG, and (iii) pipelining of the AE SKG and the encrypted data transfer in order to reduce latency. Implementing the pipelining at PHY, we investigate a parallel SKG approach for multi-carrier systems, where a subset of the subcarriers are used for SKG and the rest for data transmission. The optimal solution to this PHY resource allocation problem is identified under security, power, and delay constraints, by formulating the subcarrier scheduling as a subset-sum 0−1 knapsack optimization. A heuristic algorithm of linear complexity is proposed and shown to incur negligible loss with respect to the optimal dynamic programming solution. All of the proposed mechanisms have the potential to pave the way for a new breed of latency aware security protocols.

Application deployment using containers with auto-scaling for microservices in cloud environment

A microservice-based application is composed of a set of small services that run within their own processes and communicate with a lightweight mechanism. Processing the microservices efficiently with minimum processing time and cost, while utilizing the computing resources efficiently, is a challenging task in a cloud environment. To address this challenge, in this paper, we propose a new container-aware application scheduling strategy with an auto-scaling policy. The proposed strategy deploys the requested applications on the best-fit lightweight containers, with minimum deployment time, based on the resource requirements. Another important issue of the container-aware cloud environment is the cold start effect, which is solved using a rule-based policy in the proposed work for minimizing deployment time and cost of the applications. Furthermore, a dynamic bin-packing strategy is designed for deploying the applications to the minimum number of physical machines (PMs) with efficient utilization of the computing resources. Finally, a heuristic-based auto-scaling policy has been designed for minimizing the wastage of the computing resources in the cloud data center. Through numerical evaluation, we have shown the superiority of the proposed method over the existing state-of-the-art algorithms in terms of processing time, processing cost, resource utilization, and required numbers of PMs.

New Detection Mechanism for Distributed Denial of Service Attacks in Software Defined Networks

Software defined networks (SDN) are a recently developed form for controlling network management by providing centralized control unit called the Controller. This master Controller is a great power point but at the same time it is unfortunately a failure point and a serious loophole if it is targeted and dropped by attacks. One of the most serious types of attacks is the inability to access the Controller, which is known as the distributed denial of service (DDoS) attack. This research shows how DDoS attack can deplete the resources of the Controller and proposes a lightweight mechanism, which works at the Controller and detects a DDoS attack in the early stages. The proposed mechanism can not only detect the attack, but also identify attack paths and initiate a mitigation process to provide some degree of protection to network devices immediately after the attack is detected. The proposed mechanism depends on a hybrid technique that merges between the average flow initiation rate, and the flow specification of the coming traffic to the network.

IoTP an Efficient Privacy Preserving Scheme for Internet of Things Environment

Many emerging fields are adopting Internet of Things technologies to incorporate smartness in respective areas. Several IoT based application area produces large volumes of real time data. Data aggregated through sensor nodes may contain highly sensitive information. An effective and successful IoT system must protect sensitive data from revealing to unauthorized persons. In this article, the authors present an efficient privacy-preserving mechanism called Internet of Things privacy (IoTp). The research simulates and analyzes the effectiveness of the proposed data aggregation and data access mechanism for a typical IoT system. Proposed IoTp scheme ensures privacy at data collection, data store and data access phases of the IoT system. The authors have compared proposed work with existing model. Results show that IoTp scheme is efficient and lightweight mechanism for data collection and data access. It is suitable for the resource constrained IoT ecosystems.