Scalability Requirements Research Articles

Heterogeneous V2V Communications in Multi-Link and Multi-RAT Vehicular Networks

Connected and automated vehicles will enable advanced traffic safety and efficiency applications thanks to the dynamic exchange of information between vehicles, and between vehicles and infrastructure nodes. Connected vehicles can utilize IEEE 802.11p for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. However, a widespread deployment of connected vehicles and the introduction of connected automated driving applications will notably increase the bandwidth and scalability requirements of vehicular networks. This paper proposes to address these challenges through the adoption of heterogeneous V2V communications in multi-link and multi-RAT vehicular networks. In particular, the paper proposes the first distributed (and decentralized) context-aware heterogeneous V2V communications algorithm that is technology and application agnostic, and that allows each vehicle to autonomously and dynamically select its communications technology taking into account its application requirements and the communication context conditions. This study demonstrates the potential of heterogeneous V2V communications, and the capability of the proposed algorithm to satisfy the vehicles' application requirements while approaching the estimated upper bound network capacity.

Evaluating IoT service composition mechanisms for the scalability of IoT systems

The Internet of Things (IoT) is an emerging paradigm where practically every (physical and virtual) thing will be interconnected through innovative distributed services. Since the number of connected things is rapidly growing, IoT systems will require the composition of plenty of services into complex workflows. Thus, scalability in terms of the size of IoT systems becomes a significant concern. In this paper, we review and evaluate the fundamental semantics of existing IoT service composition mechanisms to determine how well they fulfil the scalability requirements of IoT systems. We identify scalability desiderata and, accordingly, our findings show that dataflows, orchestration and choreography do not fully satisfy such desiderata, unlike a novel composition mechanism called DX-MAN.

A Durable, Inexpensive and Scalable Redox Flow Battery Based on Iron Sulfate and Anthraquinone Disulfonic Acid

A new redox flow battery system based on iron sulfate and anthraquinone disulfonic acid (AQDS) is shown here to have excellent electrical performance, capacity retention, and chemical durability. While these redox couples, iron(II)/iron(III) and AQDS are well known individually, their combination in a redox flow battery is shown here for the first time to provide unique benefits for large-scale energy storage. Based on iron sulfate, a waste product of the steel industry, the active materials cost for this battery is anticipated to be $66/kWh. Cycling studies of over 500 cycles in the symmetric cell configuration show a negligibly low capacity fade rate of 7.6 × 10−5% per cycle. This symmetric cell also shows a notably high average coulombic efficiency of 99.63%. Using a graphite felt electrode modified with multi-walled carbon nanotubes (MWCNTs), we could achieve a peak power density of 194 mW cm−2. The major voltage losses are ascribed to the ohmic resistance of the electrode and electrolyte. Despite the lower cell voltage of the system relative to the vanadium flow battery, the iron–AQDS flow battery system presents a good prospect for simultaneously meeting the demanding requirements of cost, durability and scalability for large-scale sustainable energy storage.

Scrutinising internet banking security solutions

Internet-banking is a crucial service offered by financial Institutions and has gained popularity at a high pace. Owing to the increasing usage of this service, it is being frequently targeted by adversaries. The login process by the user is one of the main points that are at risk of this assault. Hence, a robust security mechanism is essential for warding off those risks. Among other security solutions, a typical arrangement presently employed is the one-time password (OTP), i.e., passwords that remain valid for a single exchange or session. However, the majority of these password generation and processing mechanisms do not fulfil the requirement of usability and/or scalability and hence can be considered as less reliable/fragile. This paper reviews the security mechanisms in E-banking. The pros and con of OTP as well as other non-OTP security solutions have been presented. Finally, the prominence of open issues have been elucidated.

Scrutinising internet banking security solutions

Internet-banking is a crucial service offered by financial Institutions and has gained popularity at a high pace. Owing to the increasing usage of this service, it is being frequently targeted by adversaries. The login process by the user is one of the main points that are at risk of this assault. Hence, a robust security mechanism is essential for warding off those risks. Among other security solutions, a typical arrangement presently employed is the one-time password (OTP), i.e., passwords that remain valid for a single exchange or session. However, the majority of these password generation and processing mechanisms do not fulfil the requirement of usability and/or scalability and hence can be considered as less reliable/fragile. This paper reviews the security mechanisms in E-banking. The pros and con of OTP as well as other non-OTP security solutions have been presented. Finally, the prominence of open issues have been elucidated.

Towards Enabling Critical mMTC: A Review of URLLC Within mMTC

Massive machine-type communication (mMTC) and ultra-reliable and low-latency communication (URLLC) are two key service types in the fifth-generation (5G) communication systems, pursuing scalability and reliability with low-latency, respectively. These two extreme services are envisaged to agglomerate together into critical mMTC shortly with emerging use cases (e.g., wide-area disaster monitoring, wireless factory automation), creating new challenges to designing wireless systems beyond 5G. While conventional network slicing is effective in supporting a simple mixture of mMTC and URLLC, it is difficult to simultaneously guarantee the reliability, latency, and scalability requirements of critical mMTC (e.g., i 4ms latency, 10 6 devices/km 2 for factory automation) with limited radio resources. Furthermore, recently proposed solutions to scalable URLLC (e.g., machine learning aided URLLC for driverless vehicles) are ill-suited to critical mMTC whose machine type users have minimal energy budget and computing capability that should be (tightly) optimized for given tasks. To this end, our paper aims to characterize promising use cases of critical mMTC and search for their possible solutions. To this end, we first review the state-of-the-art (SOTA) technologies for separate mMTC and URLLC services and then identify key challenges from conflicting SOTA requirements, followed by potential approaches to prospective critical mMTC solutions at different layers.

Scalable IoT Platform for Heterogeneous Devices in Smart Environments

The Internet of Things (IoT) is envisioned as a ubiquitous computing infrastructure in which everything becomes connected, enabling gigantic information exchange among Things and people. These connected smart Things generate an enormous amount of data which need to be efficiently managed to form a unified global IoT. Unfortunately, due to the lack of acceptable open standards, communication protocols, and support for device/service discovery, the recent IoT deployments in smart environments (e.g., smart home, smart building, smart city) are posing imperative challenges related to interoperability, discovery, and the configuration of deployed objects, since the number of objects is expected to grow over time. Therefore, it is of utmost importance to provide open and scalable solutions for the discovery of devices (i.e., Things), their configuration, and data management. This paper introduces an open and scalable IoT platform by adopting the modular characteristics of edge computing for smart environments. This paper: (i) performs a systematic literature review of IoT-based infrastructures and analyzes the scalability requirements; (ii) proposes a layered IoT platform for smart environments that fosters heterogeneity, interoperability, discovery, and scalability; and (iii) demonstrates the applicability of the proposed solution by relying on a comprehensive study of a Vare smart building use case at Aalto University.

Decision making for autonomous vehicles: Combining safety and optimality

In this paper, we propose a novel decision maker design for an autonomous vehicle driving on a highway, considering safety and optimality, and which is scalable, i.e., remains computationally tractable for more complex situations. This is realized in two stages. First, all safe actions are found, and second, from these actions the optimal action is selected, according to (weighted) criteria that capture safety, comfort and efficiency. The design combines rule-based safety checks with the solution of a Markov decision process, found through a tree search algorithm, to fulfill the safe, smart and scalable requirements of the decision maker. The design is validated in simulation using eight different scenarios. The performance of the new design is compared to the performance of a rule-based controller. This comparison is done using three performance criteria that aim to capture safety, efficiency and comfort.

Solving University Course Timetabling Problem Using Multi-Depth Genetic Algorithm

The University Course Timetabling Problem (UCTP) is a scheduling problem of assigning teaching event in certain time and room by considering the constraints of university stakeholders such as students, lecturers, and departments. The constraints could be hard (encouraged to be satisfied) or soft (better to be fulfilled). This problem becomes complicated for universities which have an immense number of students and lecturers. Moreover, several universities are implementing student sectioning which is a problem of assigning students to classes of a subject while respecting individual student requests along with additional constraints. Such implementation enables students to choose a set of preference classes first then the system will create a timetable depend on their preferences. Subsequently, student sectioning significantly increases the problem complexity. As a result, the number of search spaces grows hugely multiplied by the expansion of students, other variables, and involvement of their constraints. However, current and generic solvers failed to meet scalability requirement for student sectioning UCTP. In this paper, we introduce the Multi-Depth Genetic Algorithm (MDGA) to solve student sectioning UCTP. MDGA uses the multiple stages of GA computation including multi-level mutation and multi-depth constraint consideration. Our research shows that MDGA could produce a feasible timetable for student sectioning problem and get better results than previous works and current UCTP solver. Furthermore, our experiment also shows that MDGA could compete with other UCTP solvers albeit not the best one for the ITC-2007 benchmark dataset.

Scalable BEOL compatible 2D tungsten diselenide

Low-temperature synthesis of two-dimensional (2D) transition metal dichalcogenides (TMDs) is a key challenge for their integration with complementary metal-oxide-semiconductor (CMOS) technology at ‘back-end-of-line (BEOL)’. Most low-temperature synthesis utilizes alkali salts, oxide-based metals, and methyl-group based chalcogen precursors which do not meet current BEOL requirements for contaminant-free manufacturing and process scalability. In this study, we benchmark a carbon and alkali salt-free synthesis of fully coalesced, stoichiometric 2D WSe2 films on amorphous SiO2/Si substrates at BEOL- compatible temperatures (475 °C) via gas-source metal-organic chemical deposition. This work highlights the necessity of a Se-rich environment in a kinetically limited growth regime for successful integration of low-temperature 2D WSe2. Atomic-scale characterization reveals that BEOL WSe2 is polycrystalline with domain size of ~200 nm and band gap of 1.8 eV. Back-gated and electrolyte double layer gated field-effect transistors (FETs) exhibit increased ON currents as high as 4 µA µm−1 and ON/OFF ratios of ~106, demonstrating a 100× improvement compared to previously reported BEOL compatible TMDs.

T-PBFT: An EigenTrust-based practical Byzantine fault tolerance consensus algorithm

Blockchain with these characteristics of decentralized structure, transparent and credible, time-series and immutability, has been considering as a promising technology. Consensus algorithm as one of the core techniques of blockchain directly affects the scalability of blockchain systems. Existing probabilistic finality blockchain consensus algorithms such as PoW, PoS, suffer from power consumptions and low efficiency; while absolute finality blockchain consensus algorithms such as PBFT, HoneyBadgerBFT, could not meet the scalability requirement in a large-scale network. In this paper, we propose a novel optimized practical Byzantine fault tolerance consensus algorithm based on EigenTrust model, namely T-PBFT, which is a multi-stage consensus algorithm. It evaluates node trust by the transactions between nodes so that the high quality of nodes in the network will be selected to construct a consensus group. To reduce the probability of view change, we propose to replace a single primary node with a primary group. By group signature and mutual supervision, we can enhance the robustness of the primary group further. Finally, we analyze T-PBFT and compare it with the other Byzantine fault tolerant consensus algorithms. Theoretical analysis shows that our T-PBFT can optimize the Byzantine fault-tolerant rate, reduce the probability of view change and communication complexity.

Enhancing Privacy for Big Data in Healthcare Domain based on Cryptographic and Decentralized Technology Methods

Privacy is one of the biggest concerns that hinder most organizations to adopt the Big Data technology. Some mechanisms and systems have been set-up to handle huge databases. Nevertheless, the scalability requirements of Big Data are far beyond the conventional databases to handle. Therefore, it is trivial to set-up scalable privacy algorithms for conventional databases. Most data are stored in a single location, which means the records it keeps are open and effortlessly irrefutable to third parties. Centralized versions of this data make it too easy for hackers to attack. As such, in this paper, we present the opportunities and challenges of implementing cryptography and blockchain for privacy perseverance in Big Data, focusing in the healthcare domain. In addition, we also present some use cases of integrating Directed Acyclic Graph (DAG) into healthcare database framework for anchoring information security and privacy.

Research of NOSQL and SQL and Designing a Better Database Schema for Databases

The amount of data storage and querying is increasing day by day. The requirement of data-storage, security, scalability and management is an alarming issue. Structured Query Language (SQL) databases was designed to overcome most of the trouble faced over tradition file storage system. But the alarming rate of data storage leads to Not only Structured Query Language (NoSQL) databases. in this paper, various aspects of SQL and NoSQL are compared with respect to their data management and to develop a better schema for data management is attempted.

Melt-Process for the Preparation of Porous Composite Electrodes for Battery Application

Lithium-ion batteries have become the dominant technology for electrochemical energy storage. They have been used in electronic portable devices for decades and now taking the lead in the transportation sector in electrical vehicles. As it has long been known, the release of CO2 from combustion engine-powered constituted a significant proportion of our global emission of greenhouse gases. In order to reduce this proportion, efforts are being deployed to increase the market penetration of electric vehicles which puts a growing pressure on the production of lithium-ion batteries. As a result, this production is predicted to rapidly and sharply grow in the next few years. This fast development raises novel issues regarding the electrode production process and its impact on the environment. Currently, most of the electrode fabrication processes require organic solvent such as N-methyl-2-pyrrolidone (NMP), which is toxic and costly. It is therefore essential to find an alternative which is more sustainable that the solvent process yet that meets requirements of scalability, cost and productivity. In this context, we propose a new approach to electrodes fabrication process developed by our industrial partner Hutchinson (Patent US20130244098A1). It consists of a melt process which is well-known in the polymer industry to make composites. As such, our process does not require any solvent. In our study, we optimised the electrode formulation for power applications. In this presentation, we will report the different steps of the new melt process. The active materials and the conductive carbons used are the same as in the conventional wet process, but the binder needs to be chosen with care. Indeed, due to constraints in terms of temperature, shear forces and of the mixing process itself, the binder must be a heat-tolerant elastomer, to allow good mixing properties to the blend and flexibility to the resulting electrode. As an alternative to the most commonly-used binder, PVdF, a lower-cost commercial elastomer, Hydrogenated Nitrile Butadiene Rubber (HNBR), was used as the binder. To evaluate its compatibility and performance in lithium-ion battery composite electrodes, it was first compared to PVdF with the common wet process to make electrodes. After the validation of HNBR as a binder, it was used with the melt-process. Power testing and cycling performances of electrodes made with different active materials will be presented, showing the feasibility of electrodes and the versatility of the melt-process.

An efficient and scalable multi-dimensional indexing scheme for modular data centers

An efficient distributed indexing scheme plays an important role in improving the performance of cloud storage systems. To achieve concurrent query service and high manageability, the indexing scheme should meet the requirements of high scalability and low latency. In this paper, we propose RB-Index, an efficient and scalable multi-dimensional indexing scheme for modular data centers with the BCube topology. RB-Index is a two-layer indexing scheme integrating the BCube based routing protocol and the R-tree based indexing structure. In RB-Index, we build several distinct indexing spaces with dimensions selected according to query history. Each server takes responsibility for a portion of the indexing space according to a mapping scheme. A data pretreatment method and a publishing scheme are presented to uniformly distribute the global index across all the servers in the network. Index maintenance strategies are designed to keep the system cost at a low level. Efficient and complete query strategies are also introduced to support highly concurrent queries. We conduct experiments on Amazon EC2 platform to evaluate the performance of RB-Index and compare its performance with RT-CAN and FT-Index. Experiment results manifest the efficiency and scalability of our indexing scheme.

Lightweight novel trust based framework for IoT enabled wireless network communications

For IoT enabled networks, the security and privacy is one of the important research challenge due to open nature of wireless communications, especially for the networks like Vehicular Ad hoc Networks (VANETs). The characteristics like heterogeneity, constrained resources, scalability requirements, uncontrolled environment etc. makes the problems of security and privacy even more challenging. Additionally, the high degree of availability needs of IoT networks may compromise the integrity and confidentially of communication data. The security threats mainly performed during the operations of data routing, hence designing the secure routing protocol main research challenge for IoT networks. In this paper, to design the lightweight security algorithm the use of Named Data Networking (NDN) which provides the benefits applicable for IoT applications like built-in data provenance assurance, stateful forwarding etc. Therefore the novel security framework NDN based Cross-layer Attack Resistant Protocol (NCARP) proposed in this paper. In NCARP, we designed the cross-layer security technique to identify the malicious attackers in network to overcome the problems like routing overhead of cryptography and trust based techniques. The parameters from the physical layer, Median Access Control (MAC) layer, and routing/network layer used to compute and averages the trust score of each highly mobility nodes while detecting the attackers and establishing the communication links. The simulation results of NCARP is measured and compared in terms of precision, recall, throughput, packets dropped, and overhead rate with state-of-art solutions.

A Set-Membership Approach to Event-Triggered Filtering for General Nonlinear Systems Over Sensor Networks

This paper is concerned with the distributed set-membership filtering problem for a class of general discrete-time nonlinear systems under event-triggered communication protocols over sensor networks. To mitigate the communication burden, each intelligent sensing node broadcasts its measurement to the neighboring nodes only when a predetermined event-based media-access condition is satisfied. According to the interval mathematics theory, a recursive distributed set-membership scheme is designed to obtain an ellipsoid set containing the target states of interest via adequately fusing the measurements from neighboring nodes, where both the accurate estimate on Lagrange remainder and the event-based media-access condition are skillfully utilized to improve the filter performance. Furthermore, such a scheme is only dependent on neighbor information and local adjacency weights, thereby fulfilling the scalability requirement of sensor networks. In addition, an optimization algorithm is developed to minimize the trace of the estimated ellipsoid set, and the effect from the adopted event-triggered threshold is thoroughly discussed as well. Finally, a simulation example is utilized to illustrate the usefulness of the proposed distributed set-membership filtering scheme.

Research Challenges and Technology Progress of Data Mining with Bigdata

'Big Data' has spread quickly in the framework of Data Mining as well as Business Intelligence. This brand-new circumstance can be de?ned by means of those troubles that can not be efficiently or ef?ciently resolved making use of the common computing resources that we currently have. We have to highlight that Big Data does not simply imply huge volumes of data but likewise the requirement for scalability, i.e., to make sure a response in an acceptable elapsed time. This paper discusses about the research challenges and technology progress of data mining with big data.

Distributed Resilient Filtering for Power Systems Subject to Denial-of-Service Attacks

This paper addresses the distributed resilient filtering problem for a class of power systems subject to denial-of-service (DoS) attacks. A novel distributed filter is first constructed to practically reflect the impact from both cyber-attacks and gain perturbations. For all possible occurrence of DoS attacks and gain perturbations, an upper bound of filtering error covariance is derived by resorting to some typical matrix inequalities. Furthermore, the desired filter gain relying on the solution of two Riccati-like difference equations is obtained with the help of the gradient-based approach and the mathematical induction. The developed algorithm with a recursive form is independent of the global information and thus satisfies the requirements of scalability and distributed implementation online. Finally, a benchmark simulation test is exploited to check the usefulness of the designed filter.

A Scalable Algorithm for Event-Triggered State Estimation With Unknown Parameters and Switching Topologies Over Sensor Networks

An event-triggered distributed state estimation problem is investigated for a class of discrete-time nonlinear stochastic systems with unknown parameters over sensor networks (SNs) subject to switched topologies. An event-triggered communication strategy is employed to govern the information broadcast and reduce the unnecessary resource consumption. Based on the adopted communication strategy, a distributed state estimator is designed to estimate the plant states and also identify the unknown parameters. In the framework of input-to-state stability, sufficient conditions with an average dwell time are established to ensure the boundedness of estimation errors in mean-square sense. In addition, the gains of the designed estimators are dependent on the solution of a set of matrix inequalities whose dimensions are unrelated to the scale of underlying SNs, thereby fulfill the scalability requirement. Finally, an illustrative simulation is utilized to verify the feasibility of the proposed design scheme.