A trust evaluation scheme of service providers in mobile edge computing

Mobile edge computing (MEC) is a new computing paradigm that brings cloud services to the network edge. Despite its great need in terms of computational services in daily life, service users may have several concerns while selecting a suitable service provider to fulfil their computational requirements. Such concerns are: with whom they are dealing with, where will their private data migrate to, service provider processing performance quality. Therefore, this paper presents a trust evaluation scheme that evaluates the processing performance of a service provider in the MEC environment. Processing performance of service providers is evaluated in terms of average processing success rate and processing throughput, thus allocating a service provider in a relevant trust status. Service provider processing incompliance and user termination ratio are also computed during provider’s interactions with users. This is in an attempt to help future service users to be acknowledged of service provider’s past interactions prior dealing with it. Thus, eliminating the probability of existing compromised service providers and raising the security and success of future interactions between service providers and users. Simulations results show service providers processing performance degree, processing incompliance and user termination ratio. A service provider is allocated to a trust status according to the evaluated processing performance trust degree.

Analysis of Web-Based IoT through Heterogeneous Networks: Swarm Computing over LoRaWAN

The Internet of Things (IoT) leverages added valued services by the wide spread of connected smart devices. The Swarm Computing paradigm considers a single abstraction layer that connects all kinds of devices globally, from sensors to super computers. In this context, the Low-Power Wide-Area Network (LPWAN) emerges, spreading out connection to the IoT end devices. With the upsides of long-range, low power and low cost, LPWAN presents major limitations regarding data transmission capacity, throughput, supported packet length and quantity per day limitation. This situation makes LPWAN systems with limited interoperability integrate with systems based on REpresentational State Transfer (REST). This work investigates how to connect web-based IoT applications with LPWANs. The analysis was carried out studying the number of packets generated for a use case of REST-based IoT over LPWAN, specifically the Swarm OS over LoRaWAN. The work also presents an analysis of the impact of using promising schemes for lower communication load. We evaluated Constrained Application Protocol (CoAP), Static Context Header Compression (SCHC) and Concise Binary Object Representation (CBOR) to make transmission over the restricted links of LPWANs possible. The attained results show the reduction of 98.18% packet sizes while using SCHC and CBOR compared to HTTP and JSON by sending fewer packets with smaller sizes.

Evolutionary 2D organic crystals for optoelectronic transistors and neuromorphic computing

Brain-inspired neuromorphic computing has been extensively researched, taking advantage of increased computer power, the acquisition of massive data, and algorithm optimization. Neuromorphic computing requires mimicking synaptic plasticity and enables near-in-sensor computing. In synaptic transistors, how to elaborate and examine the link between microstructure and characteristics is a major difficulty. Due to the absence of interlayer shielding effects, defect-free interfaces, and wide spectrum responses, reducing the thickness of organic crystals to the 2D limit has a lot of application possibilities in this computing paradigm. This paper presents an update on the progress of 2D organic crystal-based transistors for data storage and neuromorphic computing. The promises and synthesis methodologies of 2D organic crystals are summarized. Following that, applications of 2D organic crystals for ferroelectric nonvolatile memory, circuit-type optoelectronic synapses, and neuromorphic computing are addressed. Finally, new insights and challenges for the field's future prospects are presented, pushing the boundaries of neuromorphic computing even farther.

Edge-Oriented Computing: A Survey on Research and Use Cases

Edge computing is a distributed computing paradigm such that client data are processed at the periphery of the network, as close as possible to the originating source. Since the 21st century has come to be known as the century of data due to the rapid increase in the quantity of exchanged data worldwide (especially in smart city applications such as autonomous vehicles), collecting and processing such data from sensors and Internet of Things devices operating in real time from remote locations and inhospitable operating environments almost anywhere in the world is a relevant emerging need. Indeed, edge computing is reshaping information technology and business computing. In this respect, the paper is aimed at providing a comprehensive overview of what edge computing is as well as the most relevant edge use cases, tradeoffs, and implementation considerations. In particular, this review article is focused on highlighting (i) the most recent trends relative to edge computing emerging in the research field and (ii) the main businesses that are taking operations at the edge as well as the most used edge computing platforms (both proprietary and open source). First, the paper summarizes the concept of edge computing and compares it with cloud computing. After that, we discuss the challenges of optimal server placement, data security in edge networks, hybrid edge-cloud computing, simulation platforms for edge computing, and state-of-the-art improved edge networks. Finally, we explain the edge computing applications to 5G/6G networks and industrial internet of things. Several studies review a set of attractive edge features, system architectures, and edge application platforms that impact different industry sectors. The experimental results achieved in the cited works are reported in order to prove how edge computing improves the efficiency of Internet of Things networks. On the other hand, the work highlights possible vulnerabilities and open issues emerging in the context of edge computing architectures, thus proposing future directions to be investigated.

Flexible and Stretchable Memristive Arrays for in-Memory Computing

With the tremendous progress of Internet of Things (IoT) and artificial intelligence (AI) technologies, the demand for flexible and stretchable electronic systems is rapidly increasing. As the vital component of a system, existing computing units are usually rigid and brittle, which are incompatible with flexible and stretchable electronics. Emerging memristive devices with flexibility and stretchability as well as direct processing-in-memory ability are promising candidates to perform data computing in flexible and stretchable electronics. To execute the in-memory computing paradigm including digital and analogue computing, the array configuration of memristive devices is usually required. Herein, the recent progress on flexible and stretchable memristive arrays for in-memory computing is reviewed. The common materials used for flexible memristive arrays, including inorganic, organic and two-dimensional (2D) materials, will be highlighted, and effective strategies used for stretchable memristive arrays, including material innovation and structural design, will be discussed in detail. The current challenges and future perspectives of the in-memory computing utilizing flexible and stretchable memristive arrays are presented. These efforts aim to accelerate the development of flexible and stretchable memristive arrays for data computing in advanced intelligent systems, such as electronic skin, soft robotics, and wearable devices.

A Novel Design of Morlet Wavelet to Solve the Dynamics of Nervous Stomach Nonlinear Model

The present study introduces a novel design of Morlet wavelet neural network (MWNN) models to solve a class of a nonlinear nervous stomach system represented with governing ODEs systems via three categories, tension, food and medicine, i.e., TFM model. The comprehensive detail of each category is designated together with the sleep factor, food rate, tension rate, medicine factor and death rate are also provided. The computational structure of MWNNs along with the global search ability of genetic algorithm (GA) and local search competence of active-set algorithms (ASAs), i.e., MWNN-GA-ASAs is applied to solve the TFM model. The optimization of an error function, for nonlinear TFM model and its related boundary conditions, is performed using the hybrid heuristics of GA-ASAs. The performance of the obtained outcomes through MWNN-GA-ASAs for solving the nonlinear TFM model is compared with the results of state of the article numerical computing paradigm via Adams methods to validate the precision of the MWNN-GA-ASAs. Moreover, statistical assessments studies for 50 independent trials with 10 neuron-based networks further authenticate the efficacy, reliability and consistent convergence of the proposed MWNN-GA-ASAs.

Fused Contextual Data with Threading Technology to Accelerate Processing in Home UbiHealth

According to the ubiquitous computing paradigm, dispersed computers within the home environment can support the residents’ health by being aware of all the developing and evolving situations. The context-awareness of the supporting computers stems from the data acquisition of the occurring events at home. In some cases, different sensors provide input of identical type, thereby raising conflict-related issues. Thus, for each type of input data, fusion methods must be applied on the raw data to obtain a dominant input value. Also, for diagnostic inference purpose, data fusion methods must be applied on the values of the available classes of multiple contextual data structures. Dempster-Shafer theory offers the algorithmic tools to efficiently fuse the data of each input type or class. The employment of threading technology accelerates the computational process and carrying out benchmarks on publicly available data set, is shown to be more efficient. Thus, threading technology proved promising for home UbiHealth applications by lowering the number of required cooperating computers.

DESIGN OF A SERVERLESS OGC WPS BASED GEOPROCESSING SERVICE SOLUTION

Geospatial data and related technologies have increasingly become a crucial part of big data analysis processes and even a prominent player in most of them. Serverless architectures have become today's trending and widely used technology within the cloud computing paradigm. In this paper, we review the serverless paradigm advantages over traditional cloud architecture models and infrastructures. Moreover, we examined the deployment of Open Geospatial Consortium (OGC) Web Processing Service (WPS) specification based geoprocessing Application Programming Interface (API) with serverless architecture. In this context, we propose a system design and review it in detail together with the results discussed along with use cases.

Research on advanced technologies – design and development of cloud computing model

This article is a bibliographic review and focuses on a brief introduction to Cloud Computing as an innovative educational tool. Cloud technology is rapidly spreading in educational institutions, sometimes replacing the in-house infrastructure with cloud services. Education plays an important role in a country’s economy and today, the educational model in many countries has evolved with technology. Schools and universities in the world extensively use cloud Based Technology. Digital teaching – E-Learning and cloud-based technology are the latest models that have been widely adopted in the educational field. The identified trends in the use of cloud computing in education are clear, ranging from the design of cloud-oriented learning environments for future information technology specialists to the training of information technology specialists to enable them to obtain competencies in the use of cloud technologies. Cloud computing is a distributed computing paradigm, where, instead of acquiring information technology products, users access shared resources under various service models through a network, usually the Internet. The application of cloud computing is very broad and growing daily because of many advantages to the users, and is driven by the increasing use of various mobile devices (laptops, tablets and smartphones) and mobile Internet access being more available. The perspectives of its integration in the learning process are highlighted, as well as the factors that make its implementation difficult. Finally, some general conclusions presented on how technologies should be adopted to meet the educational needs of this new global challenge and some suggestions are made for more effective training through the Cloud Computing, as the need for further research emerges