Mobile Cloud Computing (MCC) requires an infrastructure that is combining the capabilities of resource-constrained but mobile and context-aware devices with that of immovable but powerful resources in the cloud. Application execution shall be boosted and battery consumption reduced. Our goal is to relieve mobile devices by enabling applications to cooperate transparently with components provided by third parties remotely, locally, or both. For this purpose, we developed SAM - the Semantic-aware Middleware - and the SAM Store. The latter resembles a registry. It allows component providers to publish semantically enriched interfaces and to offer corresponding local and remote implementations. SAM makes use of the SAM Store to match local interprocess communication (IPC) messages transparently against local and remote components. This architecture facilitates the integration of powerful, but resource-intensive remote components, which may be prioritized over local ones, and fosters various patterns of cooperation that demonstrate useful solutions in the area of MCC. By means of a realistic scenario, our evaluation proves the efficiency with respect to latency and practicality.

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Geographic Information System (GIS) is the processes of managing, manipulating, analyzing, updating and presenting metadata according to its geographic location, to be effectively used in different aspects of life [1]. Cloud Computing allowed the utilization of all computing resources and software as required through the web in a virtual computing environment [2], [3]. Different Application software and data are provided at the (virtual) server side to be used. GIS Cloud is the future of Web GIS, with capabilities of easy and fast: collecting, processing, analyzing, updating, rectifying, publishing geospatial data through the internet. It is a web-based- GIS Application to enable all users fast, easy informed decision-making at fair price. It provides the power of desktop GIS on a web-based platform at fair cost anywhere. It offers a JavaScript Application Programming Interface (API) and a REST API, to provide GIS functionality into an application or website that can be hosted by GIS Cloud or by a third-party. In this work we are presenting a GIS-Cloud application in AL-Kamaliah region, small town of Amman suburbs to demonstrate its practicality and functionality.

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Cloud computing has evolved to emerge the most topical IT paradigm in recent times. Cloud computing is rapidly transforming the IT landscape. On a pay-as-you-use basis, cloud consumers can access resources, applications and infrastructure provided by cloud providers. Such access could be in form of applications already deployed by cloud providers for use by the cloud users. It could be in form of the capability to develop and deploy user applications using help of a cloud provider. In addition, massive storage infrastructure is available for database and data supplied by the user. The cloud has several unique architectures and many more are still evolving. The primary ones are the SaaS, PaaS and the IaaS that can be deployed on private, public, community and hybrid clouds. This paper examines present developments in the cloud computing architecture and presents guidance for additional research. Papers published in journals, conferences, white papers were analyzed. The objective of this present work is to identify, examine and explain the current trends and development in cloud computing architecture. However, only 13% of the papers examined discussed Others-as-a-Service, while only 26% of the papers reviewed considered issues relating to the major actors involved in cloud computing. This will beneficial to cloud providers, users, and researchers alike.

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In today’s world organizations outsource their data to cloud to enjoy number of advantages such as location independent data storage, ubiquitous data access and on demand high quality service. Data is collected by many of independent sources as micro data and published for decision making, predictions and to improve research. Outsourcing the data has an important side effect- the privacy of the individuals whose data are being collected and analyzed is at risk. It becomes the responsibility of the data curator i.e., the cloud service provider to preserve the privacy. Statistical Disclosure control or Statistical Disclosure Limitation seeks to alter the original microdata so that the utility of original microdata and modified microdata are at least same. Microaggregation is one of Statistical Disclosure Control methods used to protect the individual identification present in the microdata. This method works by partitioning the microdata into groups called cluster of k records and then replaces the record in each group with the centroid of the group. This paper defines microdata, provides the overview of various existing microaggregation methods for Statistical Disclosure Control. The paper also provides a novel model of microaggregation method in inter-cloud infrastructure.

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Publish-subscribe middleware is a popular technology for facilitating device-to-device communication in large-scale distributed Internet of Things (IoT) scenarios. However, the stringent quality of service (QoS) requirements imposed by many applications cannot be met by cloud-based solutions alone. Edge computing is considered a key enabler for such applications. Client mobility and dynamic resource availability are prominent challenges in edge computing architectures. In this paper, we present EMMA, an edge-enabled publish-subscribe middleware that addresses these challenges. EMMA continuously monitors network QoS and orchestrates a network of MQTT protocol brokers. It transparently migrates MQTT clients to brokers in close proximity to optimize QoS. Experiments in a real-world testbed show that EMMA can significantly reduce end-to-end latencies that incur from network link usage, even in the face of client mobility and unpredictable resource availability.

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There is an increasing interest in applications where sensors or other devices (acting as publishers) may generate data that are processed and analyzed by specialized software components (acting as subscribers to this data) that extract useful information in a variety of scientific or industrial settings. As a result of the large volumes of data that are often generated, Cloud infrastructures may be used to handle the data links between publishers and subscribers. Assuming that a certain number of virtual machines of some given bandwidth have been booked for this purpose, the problem that this paper considers is how to allocate data links to the virtual machines so that the amount of data received by subscribers is maximized. An Integer Linear Programming formulation of the problem and two heuristics are presented, which are evaluated in a range of experiments.

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Copyright and Reprint Permission: Abstracting is permitted with credit to the source. Libraries are permitted to photocopy beyond the limit of U.S. copyright law for private use of patrons those articles in this volume that carry a code at the bottom of the first page, provided the per-copy fee indicated in the code is paid through Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923. For other copying, reprint or republication permission, write to IEEE Copyrights Manager, IEEE Operations Center, 445 Hoes Lane, Piscataway, NJ 08854. All rights reserved. Copyright ©2018 by IEEE.

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The emergence of Internet of Things (IoT) and the technological advancements of smart devices and wearable gadgets, are empowering new real-time applications such as health monitoring, traffic monitoring, tele-surveillance, online multi-player gaming and live video streaming. These applications require a low latency response in order to ensure the user's quality of service (QoS). Fog computing is a promising solution to the high latency drawback of cloud computing, it allows provisioning resources and help at the proximity of end users. In this paper we give a detailed overview about the definition of fog computing, its architecture and potentials as opposed to cloud computing. State of the art surveys published in the years 2017 and 2018 are presented to guide the readers towards the current trends. Moreover, this survey highlights the most significant applications of fog computing, and reviews twelve recently proposed works for applications in smart cities, smart transportation, tele-surveillance, health care and multimedia. Finally, fog computing challenges and future research opportunities are discussed.

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Crowdsensing for smart city applications utilizes cloud computing to send to, store and publish data. The amount of data stored in the cloud is large and this introduces cost and storage challenges, since the cloud has a pay-as-you-use model. In this paper, we propose, as part of the data management architecture, a partitioning method that helps the user examine the flexibility of data in order to decide which of the reduction help can be applied on data. The results of the proposed partitioning method and its associated help show a large amount of storage savings in the cloud, from 24% to more than 30% in our analysis.

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Before deciding to visit a public facility such as a library with limited seating, it is often convenient to know how crowded the location is before arriving. Various sensor systems on the market can detect the presence of people in particular areas of a building, but if we want to install such a system on the premises of a public space, there are limitations to consider such as cost, ease of installment, readjustments, removal, etc. In this paper, we describe our work in progress on the development of a compact crowd sensor system based on the Raspberry Pi microcontroller which can be outfitted with a camera for computing the approximate number of people appearing in a snapshot of a selected area and judging the general level of crowdedness at periodic intervals. To protect the privacy of individuals in public spaces, once the camera snapshots have been processed to identify human figures, they are discarded and only the timestamped crowd level results are posted on a cloud database for viewing by remote users. Unlike commercial products which are sometimes mounted firmly in a building structure, the camera and microcontroller unit of the prototype can be held in one hand and positioned in a convenient location on a wall, desk, shelf, etc. in library study areas and cafeteria facilities on a college campus.

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Internet of Things (IoT), the emerging computing infrastructure that refers to the networked interconnection of physical objects, incorporates a plethora of digital systems that are being developed by means of a large number of applications. Many of these applications administer data collection on the edge and offer data storage and analytics capabilities in the cloud. This raises the following problems: (i) the processing stages in IoT applications need to have separate implementations for both the edge and the cloud, (ii) the placement of computation is inflexible with separate software stacks, as the optimal deployment decisions need to be made at runtime, and (iii) unified fault tolerance is essential in case of intermittent long-distance network connectivity problems, malicious harming of edge devices, or harsh environments. This paper proposes a novel fault-tolerant architecture CEFIoT for IoT applications by adopting state-of-the-art cloud technologies and deploying them also for edge computing. We solve the data fault tolerance issue by exploiting the Apache Kafka publish/subscribe platform as the unified high-performance data replication solution offering a common software stack for both the edge and the cloud. We also deploy Kubernetes for fault-tolerant management and the advanced functionality allowing on-the-fly automatic reconfiguration of the processing pipeline to handle both hardware and network connectivity based failures.

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Internet of Things (IoT) deployments rely on data exchange middleware to manage communications between constrained devices and cloud resources that provide analytics, data storage, and serve user applications. In this paper, we propose the Resilient IoT Data Exchange (Ride) middleware that enables resilient operation of IoT applications despite prevalent network failures and congestion. It leverages programmable Software-Defined Networking (SDN)-enabled infrastructure along with both localized edge and cloud help. The two-phase Ride middleware extends existing publish-subscribe oriented IoT data exchanges according to application-specified resilience requirements and without IoT device client modifications. The first phase, Ride-C, improves IoT data collection by gathering network-awareness via a novel resource-aware adaptive probing mechanism and dynamically redirecting IoT data flows across multiple public and local (edge) cloud data exchange connections. The second phase, Ride-D, uses this information to disseminate time-critical alerts via an intelligent network-aware resilient multicast mechanism. Results from our prototype smart campus testbed implementation, Mininet-based emulated experiments, and larger-scale simulations show that Ride enables network awareness for greater cloud connection up-times, timely fail-over to edge help, and more resilient local alert dissemination.

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The first wearables DevCon for developers of wearables was held in San Francisco 5-7 March 2014 and was greeted with great enthusiasm, with more than 1,000 people attending the three-day event. With the introduction of Google Glass, Epson Moverio, Pebble, and Fitbit, wearables have certainly captured the attention of many consumers and enterprises. While some of these wearables mainly provide a single function, such as fitness tracking, health monitoring, and message display, others have taken on the integration of multiple functions in the same device; the BASIS watch, for example, combines time, fitness, and health-monitoring functions into a single device. According to a new market report published by Transparency Market Research, “Wearable Technology Market-Global Scenario, Trends, Industry Analysis, Size, Share and Forecast, 2012-2018,? the global wearable technology market is expected to grow from US750millionin2012toUS5.8 billion in 2018. U.K.-based Juniper Research projects that the number of wearable devices shipped will rise from about 13 million in 2013 to 130 million in 2018, and the size of the market will jump from US1.4billionin2013toUS19 billion in 2018. Business Insider Intelligence projects shipments of 100 million units in 2014 and forecasts the market will ultimately be worth about US$12 billion per year. Such widely divergent forecasts by research firms are typical when industries are in their relative infancies and hypergrowth mode.

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The rapidly increasing complexity and scale of hybrid cloud environments requires improved service management capabilities in orchestration and automation. Current methods focus on provisioning infrastructure but lack functionality for consistently enabling and performing operational activities on managed help. We propose a data-driven approach to dynamically generate Orchestration Engine plugins from service descriptor metadata. Our approach extends Orchestration Engines by representing managed help as code within reusable blueprints in order to accelerate service deployments and ease management activities. In our work, we provide a data model and system architecture to allow service providers to easily author and publish resource definitions for a wide range of public and private help. These definitions may be combined into solution blueprints, forming a declarative and reusable representation of a managed workload. After provisioning a workload, administrators can view service instance data and invoke operational activities. For evaluation, we describe the authoring and orchestration of a hybrid cloud workload and discuss the strengths of our solution versus current methods.

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Cloud computing is a widespread technology, which has attracted much attention nowadays. Among the many criteria that must be considered for data storage in the cloud, access control plays a vital role. Role-based access control (RBAC) is a well-known technique for secure data storage in the cloud. Since the traditional RBAC models are improper for open and decentralized environments, few works have integrated the trust concept into the RBAC model. Nevertheless, they have not fully addressed the required security metrics of a trust-based system. Therefore, in this paper, we first introduce the security goals that should be considered in an efficient trust-based system. Second, we propose a novel trust and reputation based RBAC model that not only can properly withstand the security threats of trust-based RBAC models, but also is scalable as it has reasonable computational cost. Third, we evaluate the proposed model using the famous trust network of advogato dataset. Eventually, we compare the proposed model with recently-published ones in terms of mean absolute error, indirect trust computational cost, and provided features. The achieved results are indicative of the priority and betterment of the proposed model to be employed in real cloud environments.

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The Internet of Things (IoT) is a concept that envisions the world as a smart space in which physical objects embedded with sensors, actuators, and network connectivity can communicate and react to their surroundings. However, IoT devices and consumers of data from these IoT devices can be owned by different entities which makes IoT data sharing challenging. Sensing as a Service is a concept that is influenced by the cloud computing term Every Thing as a Service. The proposed Sensing as a Service middleware enables consumers to access data generated by IoT devices owned by other entities. Consumers are charged for the amount of sensor data used. This paper addresses the architectural design of a cloud-based Sensing as Service middleware where IoT applications (consumers) can collect, and analyze sensor data through the middleware API. We propose multitenancy algorithms to make effective use of computing resources. In addition, we propose a SQL-Like language that can be used by IoT applications for sensing service discovery, and sensor stream analytics. The evaluation of the middleware implementation shows the effectiveness of the algorithms.

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The rigidity of static configurations in the network devices, given by a list of commands or static scripts, is limiting current networks. They do not provide automatic resource provisioning and affects directly to the decrease of the efficiency. Furthermore, data centers have different traffic patterns because of the implementation of network virtualization and cloud computing technologies. Now the applications are executed in many virtual machines that must communicate, thus increasing the communication between servers. Software Defined Networks (SDNs) technology has appeared to allow higher programmable, automatic and flexible networks. They are able to divide the control plane from the data plane. In SDNs, the code is programmed centrally instead of programming node by node. The implementation can be done independently of the trademark or the model as long as it supports the communication protocol. SDN allows including new functions in the network devices. Moreover, it can easily include certain intelligent functions, in order to provide intelligence to the network. These features allow the system to self-adapt with the goal of improving it performance. The speech is focused on detailing and discussing some published Intelligent Systems for Multimedia delivery in SDN. Moreover, we will revise the most well-known artificial intelligence techniques that can be used in network data and communication networks. We will also revise the main network parameters and how they affect to the multimedia delivery. First we will detail the network architecture and the communication protocol design and development. We will also show how the information taken from the data frames, the users and servers behavior, and the traffic patterns (traffic changes, quality of service parameters, state of the frames, etc.) can be used to improve the multimedia delivery performance. It will self-adapt according to the state of the network. Network parameters and patterns are used by an intelligent network algorithm to estimate the behavior using the empirical data. Intelligent Systems for Multimedia delivery can be implemented in a wide range of multimedia applications.

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There are several concerns that legacy systems pose to their host organization. A major problem is the inability to support new functionality and innovations required by the organization. Consequently, organizations recognize the need to migrate to new technologies, such as cloud computing, to meet changing demands. Little has been published about the sociotechnical changes brought about by cloud computing and the important considerations for an IT department when migrating a legacy system to the cloud. This study investigates this issue through a case study. Interviews were used to collect qualitative data from participants, comprising systems engineers, project managers, and directors. The data was analyzed using thematic analysis to identify emergent themes. The findings suggest that changing user demands and the need for more functions were the major drivers for cloud adoption. Having redundant and reliable, fast Internet links was regarded as one of the most important considerations. Changing roles can arise: engineers will be required to be more versatile and develop new business analyst and management skills. Interestingly security was not regarded as an issue as most participants felt that the cloud is more secure than the traditional data center. The primary contribution of the paper is to highlight the economic and sociotechnical issues that decision makers should consider when migrating a legacy system to the cloud.

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Existing service ecosystems typically rely on some centralized service registries (e.g., ProgrammableWeb.com) as "middle people" to record service behaviors thus to provide service ranking and recommendation. Excessive centralization increasingly becomes the bottleneck and hinders the further growth of the service ecosystems. As the first attempt to apply the fundamental technique underneath the emerging Bitcoin network into the field of service oriented computing, this paper proposes to build a service ecosystem as a decentralized blockchain-oriented service network, called Decentralized Service Eco-System (DSES). Whenever any activity occurs in the system (e.g., APIs are used together in a published mashup), all involved parties will individually store and maintain a copy of the detailed record (provenance) at their local databases. Such a distributed database-oriented solution will enable help who do not fully trust each other to maintain a set of global states. In this way, service discovery and recommendation can be realized in a distributed manner that promises higher scalability and maintainability. As a proof of concept, a prototyping system of DSES is constructed using the real-world data from ProgrammableWeb.com, based on the INKchain, a newly open-source consortium blockchain mechanism extending the Hyperledger Fabric.

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In today's era of digitization, due to the improper structuring of huge data available from various sources leading to poor interlinking of data, the web is not being utilized to its full potential. So, there is a need for re-establishing the structures of openly published data on the web by giving various specifications or frameworks of data published in cyberspace. With the aim of better utilization of web data and transition from keyword-based search to contextual search, the Semantic web has emerged as a key milestone. In this direction, Tim Berners Lee has proposed the conventions namely RDF and SPARQL for linking and querying the machine-processable shared data on the web and also a 5-star linked data scheme to distinguish the type of data on web based on its linking properties. This paper presents a Framework for Linking and querying open datasets on government portals, embedding semantics in the data by RDF alignment using state-of-art tools such as OpenRefine and Twinkle tool. The 5-Star scheme has been discussed and practical demonstration of enriching data is given. This clearly puts forward the transition from web of documents to web of data. This paper also presents the framework for translating the data in datasets to the graph based RDF data in Triple format. This translated data is then queried using standard SPARQL Queries showcasing the linked data fetched in tuple format. The data-graph of the resultant linked data is also presented.

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By advent of multi cloud computing, abundant web help are published by several providers to their world-wide users. Web service composition technology attracted a lot of attention for the sake of reduction in software development cost. In multi cloud environment (MCE), each atomic web service published by any cloud provider with same functionality has different price and quality of service (QoS). Each cybersecurity attack on security tenets can make business financial loss or even failure. Literature review in this ambit indicates that the current techniques seldom solve a mission-critical business process since majority of them pay attention only on QoS and network parameters and do not take security tenets into consideration. A bi-objective genetic optimization algorithm is presented in which it solves web service composition problem in MCE by cost and risk viewpoints. The result of implementations show that the proposed bi-objective genetic algorithm is sustainable against a single objective algorithm which minimizes only service costs and disregards security risks.

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Information Centric Networking (ICN) achieves high efficient via in-networking cache and direct object retrieval, i.e., content can be replicated and cached at any network nodes. However, the content is out of owner's control after published. With access control issues becoming more prominent, we propose combining the blockchain with traditional access control schemes in this paper. As all we know, blockchain has become one of the most popular technologies in recent years. This technology has been applied to various cryptographic currency, such as Bitcoin, Litecoin and so on. Due to its decentralized and untempered paradigm, using blockchain in access control is an ideal scheme. We utilize xor-based encoding/decoding scheme to realize fast encryption and decryption, which would save content provider plenty of time. Especially, when the content provider is off-line, our scheme could realize that authorized users also can obtain decryption information related contents. The security analysis proves that our access control framework is pretty safe.

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Recently, an accelerating number of studies are dedicated to deploying various IoT applications in the information centric network paradigm which has lower system complexity than traditional network architectures. However, such a paradigm poses a number of security challenges especially when it is applied in real-time e-health applications. Firstly, it is difficult to ensure security of sensitive data in such a distributed data caching environment because after the data is published in the form of a packet to the information centric network (ICN), it is no longer controlled by the data publisher. Secondly, in some real-time e-health applications, terminal medical sensors are usually resource-constrained, limiting the direct adoption of expensive cryptographic primitives. In order to address these challenges, a resource-efficient secure data sharing scheme in information centric e-health system is proposed, one that utilizes ciphertext-policy attribute based encryption (CP-ABE) and adapts it to the above-mentioned system with respect to necessary security requirements. It also exploits computation resources of fog nodes and employs outsourcing cryptography to improve system efficiency.The evaluation demonstrates that the scheme can significantly reduce the computation overheads of the resource-constrained terminal medical devices, and can better support the real-time e-health applications.

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The rigidity of static configurations in the network devices, given by a list of commands or static scripts, is limiting current networks. They do not provide automatic resource provisioning and affects directly to the decrease of the efficiency. Furthermore, data centers have different traffic patterns because of the implementation of network virtualization and cloud computing technologies. Now the applications are executed in many virtual machines that must communicate, thus increasing the communication between servers. Software Defined Networks (SDNs) technology has appeared to allow higher programmable, automatic and flexible networks. They are able to divide the control plane from the data plane. In SDNs, the code is programmed centrally instead of programming node by node. The implementation can be done independently of the trademark or the model as long as it supports the communication protocol. SDN allows including new functions in the network devices. Moreover, it can easily include certain intelligent functions, in order to provide intelligence to the network. These features allow the system to self-adapt with the goal of improving it performance. The speech is focused on detailing and discussing some published Intelligent Systems for Multimedia delivery in SDN. Moreover, we will revise the most well-known artificial intelligence techniques that can be used in network data and communication networks. We will also revise the main network parameters and how they affect to the multimedia delivery. First we will detail the network architecture and the communication protocol design and development. We will also show how the information taken from the data frames, the users and servers behavior, and the traffic patterns (traffic changes, quality of service parameters, state of the frames, etc.) can be used to improve the multimedia delivery performance. It will self-adapt according to the state of the network. Network parameters and patterns are used by an intelligent network algorithm to estimate the behavior using the empirical data. Intelligent Systems for Multimedia delivery can be implemented in a wide range of multimedia applications.

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Associate Prof. (Mrs.) Meera K. Joseph received the degrees of DPhil. Engineering Management from the University of Johannesburg (UJ) in April 2014, Masters in Computer Applications in 1998 from the Bangalore University, India and BSc. Chemistry (Maths, Physics - subsidiary) from University of Kerala in 1994. She worked as an Associate Professor at the DFC, Department of Electrical and Electronic Engineering Technology, School of Electrical Engineering, UJ and is a Professional Member of Institute of Information Technology Professionals South Africa (PMIITPSA) and Full Member of IEEE Computer Society (CS) and IEEE Computer Society SA chapter chair. 11 students completed Masters degree under her supervision and 2 doctoral students completed their thesis /passed from UJ in 2018 under her supervision. She has published over 52 papers, majority in IEEE International conferences, some local conferences, in many recognized peerreviewed journals and in a few book chapters over the past few years. She has over 18 years lecturing experience. She founded and leads the Information and Communication Technology for Development (ICT4D) research group at UJ and her research interests are - ICT4D, smart grids, cloud computing, computer networks / femtocells, renewable energy, ICT for Power research and ICT in Engineering Education.

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In this paper, we investigate the publish/subscribe standard Message Queue Telemetry Transport (MQTT) with respect to its application in the tactical domain. We leverage open source software to generate a testbed for controlled and repeatable experiments. The work has been performed in the context of the NATO CSO/IST-150 “NATO Core help profiling for Hybrid Tactical Networks” group. Our findings highlight that MQTT is a lightweight approach to publish/subscribe in general, which warrants future, more elaborate experiments with the protocol applied to the tactical domain. Our experiments relied on a central, cloud-deployed MQTT broker. However, this broker-based nature of MQTT is problematic in disruptive environments (e.g., single point of failure in case of link loss to cloud), and so measures to mitigate this effect should be investigated further (e.g., using broker federation and tactical cloudlets).

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The Internet of Things (IoT) continues to expand; as daily new smart-devices are connected to Internet and adding to a deluge of data created by our society. Compounding the challenges is that this data is very heterogeneous, including device or human activity trace data, structured data, sensor information, and media data. The computing needs for the IoT continue to rise in terms of scalable compute power, storage, and complex data processing pipelines to accommodate these diverse sources of data. For this reason, it becomes essential to develop a flexible framework that is able to efficiently manage the IoT data in a real-time and scalable approach. In this paper, we propose a novel framework to handle IoT data. Our framework is dynamically extensible, lightweight, resources efficient, and has the ability to handle stream data as well as batch data. We leverage autonomous agents along with the publish-subscribe pattern to achieve a run-time extensible, event-driven, high-performance computational architecture. Additionally, we have incorporated localized and centralized databases into the framework to support structured and unstructured data for compute processing and analytical tasks. We have implemented the proposed framework and evaluated its performance using a visual object-detection case study on both a local cluster and within cloud-computing infrastructure. Our analysis shows that this framework utilizes the CPU, memory, and network resources efficiently. Additionally, the framework can scale horizontally as adding more processing nodes reduces the time and increases the goodput.

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Cultural hegemony is an authority with which the subject of cultural hegemony leads group members to form specific values, ideals and faiths, etc. by means of cultural leadership. According to Huang Cui's empirical research methodology of quantification of policy documents and through analysis of the quantity of releases, social network clustering and co-word analysis of 10986 culture-related policies published, this paper reaches a conclusion that the power of the subject of cultural hegemony, the power structure of the subject of cultural hegemony, and the main culture and information dissemination of cultural hegemony are the key influential factors on cultural hegemony construction.

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The paper presents a study on offboard machine learning through edge computing for robotic applications. Edge or ‘fog’ computing is being pursued as a supplement to cloud computing to provide the computing resources near the ‘edge’ where it is needed most. In this paper a framework of such an edge computing system is presented for robotic applications. The system consists of a recent machine learning platform (Jetson TX2) integrated within a heterogeneous robotic environment of UAVs and mobile robots operated through robot operating system (ROS). The UAVs and the mobile robots are equipped with cameras for providing visual feedback of the surrounding to the main processor (Jetson TX2) for image processing and object classification. The UAVs and the robots have their individual local processors capable enough to handle local processing of other sensor data and providing interface for transmitting images to and receiving identification results from the offboard processor. The system has been developed around ROS which provides the communication between hardware and system drivers for UAVs and mobile robots. The effectiveness of the approach is demonstrated using a UAV and a mobile robot to send images to the offboard process to identify a moving person and receive commands to follow him. The mobile robot follows the person and the UAV action is simulated using a simulator with parameters of a physical UAV.

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A new scheme using Field Programmable Gate Arrays (FPGAs) to secure IoT data processing in public clouds against various attacks (including attacks from insiders) is proposed. The proposed scheme supports various business models involving multiple parties and allow the data owner to give temporary access to IoT data to specific clients at a public market place (the cloud). The scheme achieves perfect forward secrecy, provides FPGA authentication, a secure way to establish a symmetric session key between the on-cloud FPGA, the IoT device and the client, and allows user’s configuration integrity check while running in the cloud FPGA. A symmetric proxy re-encryption (PRE) scheme is used to support the publish/subscribe mode of operation of IoTs. A complete prototype has been implemented to show the feasibility of the proposed scheme. Formal verification of the proposed protocol verified that it does not have any vulnerabilities. Experimental results showed that an FPGA implementation of the proposed PRE was 6x faster than the SW implementation in transforming a ciphertext of size 1 Gb.

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Smart education systems and methodologies are based on the deep integration of information technologies in the educational process. Genuine platforms, concepts and methodologies such as cloud computing systems for collaborative learning, massive courses (MOOC) or flipped classrooms have been incorporated at different educational levels, although the European Higher Education Space allows an easier implementation at university level. Among all the tools belonging to the environment of teaching technologies, wikis have been reported as ideally suited for collaborative learning. Thus, nowadays, several Universities around the world are running Projects and events about the use of wikis in higher education. Therefore, in this paper the authors justify the use of wikis in the higher educational institutions, being focused on a special case: the Wikipedia. Wikipedia presents special characteristics which make it perfect to reach not only the specific learning objectives of a certain particular subject, but also the transversal abilities so demanded today (such as teamwork or social responsibility). Finally, the authors analyze in more depth the experience on the edition of the Wikipedia made by students and professors of the Universidad Politécnica de Madrid (UPM) in the course 2017 and the obtained results are presented.

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Social influence plays an essential role in spreading information within online social networks, and can be modeled or measured by analyzing various social networking data, such as published content, users' attributes or interactions among them. Because of the massive social data, researchers often fail to quantify user influence in an accurate and high efficient way. Big data technique can be adopted to alleviate this problem. In this paper, we introduce a kind of classical individual influence algorithm, and implement two parallel versions of this algorithm based on different big data processing framework. Experiment results on a large-scale real dataset demonstrate that the computational efficiency of influence algorithm can be improved significantly in massive data sets by virtue of big data processing framework.

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Mobile Edge Computing (MEC) has received much attention from the research community in recent years. A significant part of the published work has studied the telecom-centric MEC architecture, which assumes that the computing resource is located at the edge of the mobile access network (e.g., the Evolved Packet Core or ePC), typically at the first aggregation level. Many of these studies make a silent assumption that the latency at this stage of the network is negligible. We show not only that this assumption is false, but that in some common cases the latency of the first aggregation stage dominates the end-to-end latency. We further show that common probing metrics can easily fail when used to select the service site for an application and discuss possible improvements to server-selection reliability.

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This paper will present the performance results of a 5G millimeter wave (mmW) Fast Forward (FF) 70 GHz solution deployed as part of the European 5G-Crosshaul H2020 5G-PPP project. The Fast Forward solution was one of several different transports used to showcase Next Generation technologies on a unified Fronthaul and Backhaul data plane. 5G technologies such as Radio Access Network (RAN) split options were evaluated on a multi-transport network. Performance metrics such as throughput, packet loss and latency were recorded in-house and off-site for presentation in this paper. InterDigital provided two mmWave (mmW) solutions for the 5G-Crosshaul test bed at 5TONIC, an EdgeLink™ 60 GHz transport and a Fast-Forward 70 GHz transport to verify the feasibility of the RAN higher layer and lower layer splits respectively. With the Fast Forward technology, we were able to confirm two important goals: 1. The mmW transport can meet the stringent latency requirements of less than 250 μs for the MAC/PHY lower layer split requirement. 2. A mmW transport in certain circumstances is a comparable alternative or backup for fiber. Note that the performance results for EdgeLink™ [5] were captured as part of our previous participation in the 5G-Crosshaul experiments conducted on the 5G Berlin testbed in 2016 and have been published in a separate paper. Therefore, this paper will focus on the trial results related to the Fast Forward solution on the 5TONIC testbed.

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Persistent identifiers (PIDs) such as Digital Object Identifiers (DOIs) provide a unique and persistent way to identify and cite digital objects such as publications, media content and research data. They are widely used by data producers to catalogue and publish digital assets and research data. Nowadays, research infrastructures (RIs) offer help not only for accessing and publishing data objects, but also for processing data based on user demands, e.g., via scientific workflows or third party virtual research environments. However, efficiently retrieving and sharing digital objects in a shared data processing environment requires knowledge of application access patterns as well as the underlying network level distribution. As the number and size of data objects increases, optimizing data discovery and access among distributed partners on shared infrastructure emerges as an important challenge for infrastructure operators to maintain quality of service and user experience. In this paper, we propose a novel approach that utilizes Information Centric Networking (ICN) to retrieve content based on PIDs while optimizing data access on shared infrastructure.

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In the era of IoT and data analytics this paper has proposed system architecture for RFID based shipment tracking using Raspberry Pi equipped with RFID RC522 module. Light weight MQTT protocol is used for publishing real-time passive RFID tag data to CloudMQTT broker from Raspberry Pi. Further the logistics management and monitoring can be done using web based multiple platform GUI using MQTT subscribe message from MQTT broker. Using data analytics tool various logistic related data can be automated for better logistic management. Raspberry Pi uses python based Mosquitto MQTT client program to publish data to CloudMQTT broker using an IEEE 802.11 Wireless LAN NIC.

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According to data published by Electric Power Research Institute (EPRI), USA, energy consumption of cloud computing centres keep growing at 10% per annum, to reach over 200 billion kWh by year 2020. If the efficiency of a server power supply could be enhanced by even 1 %, an astronomical amount of energy could be saved every year. Supercapacitor assisted low drop-out regulator (SCALDO), is a high efficiency linear DC-DC converter technique suitable for high current DC-supplies. SCALDO can be extended to any load current, has DC-UPS capability, and does not require RFI/EMI filtering. Given the advantages of a linear DC-DC converter based on SCALDO, this paper presents the design approaches used in developing an off-the-line DC power supply of 48 V DC output. In order to maintain the necessary electrical isolation, and to use the advantages of the connected load seeing the high quality low-noise output of a linear regulator, a supercapacitor is used in series with a transformer which can toggle between fly-back to forward mode in transferring energy. Early result indicating the potential of this new extended-SCALDO technique is presented in the paper.

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Cloud-RAN (C-RAN) is a centralized architecture which is able to solve most of the challenges of mobile operators and to fulfill the needs of ever-growing end-users in the fifth generation (5G) cellular network. The main idea behind C-RAN is to split the functionalities of the traditional base stations (BS) into a radio unit known as Remote Radio Head (RRH) and a Centralized and virtualized Baseband Unit (BBUs). The Virtualization of BBU provides more scalability and flexibility to the BBU unit, along with that this also gives a number of benefits in terms of cost and capacity. There are many research articles published earlier which described the C-RAN network architecture briefly, but no one describes the actual packet flow and processing in the C-RAN network. In this paper, we try to solve this issue in two consecutive steps. The first step is to classify the incoming packets, and then allocate that packet based on the destination address to the proper virtual baseband unit (VB). Then the second step is to schedule the packets based on the priority value and also provide the load balancing mechanism in VB if required.

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Microblogs sentiment analysis of people's attitudes, appraisals and emotions has become one of the most active research areas for business marketing, decision making, political campaigns, and alike. As people publish short snippets of texts through the social networks expressing their ideas, thoughts and opinions, an instant and reliable mining machine should be utilized. In this paper, we proposed an IoT mining machine for Twitter sentiment analysis. Firstly, we used Twitter's API for harvesting tweets in real time. Then, a mining engine was developed on the Raspberry Pi single-board microcomputer as an IoT platform due to its availability and connectivity. The IoT device was programmed for sentiment analysis and opinion mining using state-of-the-art Naïve Bayes classifier which after training was used to classify the trending tweets into either positive or negative. We used a gold standard dataset from SemEval 2017 for training our classifier which achieved 0.992 of accuracy. We aggregated the sentiments of tweets streamed in daily trend hashtags into visualized graphs. Finally, the visualized results from opinion mining were displayed on two-way smart mirror without any need for application installment. Our experimental results on the IoT mining machine demonstrate its feasibility and effectiveness.

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Structured publish/subscribe (pub/sub) is a promising technique adopted on kinds of vehicle applications of Internet of Industrial Vehicles (IoIV), which uses boolean expressions to capture the items with thousands of different attributes, values and spatial locations, and then processes and analyzes the vast amounts of data collected to obtain users' interests. However, existing pub/sub work with boolean expressions either ignores spatial requirement or focuses on Euclidean space. This paper aims to fill this gap by addressing the issue of fog-based spatial-textual pub/sub problem with boolean expressions in IoIV. A novel hybrid index called RnetBE is proposed, which exquisitely organizes traffic network structure, boolean expressions and spatial information of subscriptions. And RnetBE can prune huge numbers of unqualified subscriptions based on both spatial constraint and boolean expressions, thus achieves high efficiency in indexing and matching. Moreover, range-tree deletion and orderly group processing optimization techniques are proposed to save storage space and further improve the subscription pruning efficiency. Simulation results show that RnetBE and the proposed algorithm are efficient in terms of memory consumption and matching time.

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F2C (fog-to-cloud) enables service providers to rent the low-cost cloud/fog resources to publish their help, and the fog nodes, which are deployed at the edge, can provide short-latency service to users. However, new security threats come along with this new computing paradigm, where the access control and trusted payment are concerned in this work. We propose a privacy-preserving authentication scheme. By integrating k-times anonymous authentication (k-TAA) and attribute-based access control, in our proposed scheme, service providers can autonomously determine a fine-grained access policy and the maximal access times for authorized users. Thus, users who satisfy the access policy can receive benefits of this service for certain number of times without leaking any private information. Our authentication phase has a low-latency because it is offloaded to the fog as what the service does. This paper presents a lightweight and trusted billing mechanism using Merkle Hash Tree (MHT), which can detect the cloud's service forgery with high probability, without costing too much of service provider's bandwidth and computation. Rigorous security analysis proves that the proposed scheme is secure against malicious users, fogs, and cloud, and the experimental results show the significant performance advantage on both the delay reduction and service providers' cost saving.

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