Abstract: Stream Processing (SP) has evolved as the leading paradigm to process and gain value from the high volume of streaming data produced, e.g., in the domain of the Internet of Things. An SP system is a middleware that deploys a network of operators between data sources, such as sensors, and the consuming applications. SP systems typically face intense and highly dynamic data streams. Parallelization and elasticity enable SP systems to process these streams with continuous high quality of service. The current research landscape provides a broad spectrum of methods for parallelization and elasticity in SP. Each method makes specific assumptions and focuses on particular aspects. However, the literature lacks a comprehensive overview and categorization of the state of the art in SP parallelization and elasticity, which is necessary to consolidate the state of the research and to plan future research directions on this basis. Therefore, in this survey, we study the literature and develop a classification of current methods for both parallelization and elasticity in SP systems.

Abstract: Adopting a mature AI strategy is fundamental for modern knowledge companies to govern the proliferation of smart AI-driven applications and to coordinate them within coherent knowledge workflows. We propose knowledge graphs as the reference technology for the enterprise AI context, i.e., the complex of entities, properties and relationships that shape a business domain and constitute a common backbone for all AI-driven applications. We contribute and discuss principles to design software architectures for AI-driven applications based on knowledge graphs. We focus on the Vadalog system, a successful knowledge graph middleware from the University of Oxford and show knowledge graphs in action in a number of use cases from the financial domain.

Abstract: #SmartME has been one of the first initiatives in Italy to realize a Smart City through the use of open technologies. Thanks to the use of low cost sensor-powered devices scattered over the city area, different “smart” services have been deployed having the Stack4Things framework as the common underlying middleware. In this paper, we present the results obtained after 2 years of project highlighting the vertical solutions that have been proposed in different areas ranging from environmental monitoring to parking management.

Abstract: Cloud computing has been one of the most popular distributed computing paradigms. Elasticity is a crucial feature that distinguishes cloud computing from other distributed computing models. It considers the resource provisioning and allocation processes can be implemented automatically and dynamically. Elasticity feature allows cloud platforms to handle different loads efficiently without disrupting the normal behavior of the application. Therefore, providing a resource elasticity analytical model can play a significant role in cloud resource management. This paper presents Controlling Elasticity (ControCity) framework for controlling resources elasticity through using “buffer management ” and “elasticity management ”. In the proposed framework, there are two essential components called buffer manager and elasticity manager in the application layer and middleware layer, respectively. The buffer management controls the input queue of the user's request and the elasticity management controls the elasticity of the cloud platform using learning automata technique. In the application layer, applications are received by cloud applications and, then, placed in the control of the buffer. Buffer manager controls the queue of requests, and elasticity manager of the middleware layer using the learning automata provides a solution for controlling the elasticity of the cloud platform. The experimental results indicate that the ControCity reduces the response time by up to 3.7%, and increases the resource utilization and elasticity by up to 8.4% and 5.4%, respectively, compared with the other approaches.

Abstract: As the Internet of Things (IoT) paradigm is maturing, innovative, and novel services are being envisioned. An upcoming trend is the depiction of services enacted through seamless integration of multiple vertical IoT services, termed as cross-vertical or unified IoT services in this paper. Traditional Cloud-based centralized network architectures cannot cater to real-time responses demanded by such unified IoT applications. Moreover, introducing Fog nodes within the network architecture, though a promising alternative, cannot sustain the burden of a huge number of applications that culminates in massive data handling. In this paper, we envision employing lessons learned from context-aware computing, specifically context sharing among interdependent vertical IoT applications to address this delay requirement of such unified IoT applications by enacting context sharing among Fog nodes for minimizing system delay. The detailed network model and context sharing mechanism have been presented and the service time minimization has been framed as an optimization problem. Algorithms for context sharing and delay tolerant load balancing have been presented and simulation results carried out demonstrate the efficacy of the proposed methodology.

Abstract: The use of robotics in disaster scenarios has become a reality. However, an Unmanned Surface Vehicle (USV) needs a robust navigation strategy to face unpredictable environmental forces such as waves, wind, and water current. A starting step toward this goal is to have a programming environment with realistic USV models where designers can assess their control strategies under different degrees of environmental disturbances. This paper presents a simulation environment integrated with robotic middleware which models the forces that act on a USV in a disaster scenario. Results show that these environmental forces affect the USV’s trajectories negatively, indicating the need for more research on USV control strategies considering harsh environmental conditions. Evaluation scenarios were presented to highlight specific features of the simulator, including a bridge inspection scenario with fast water current and winds.

Abstract: Parallel Input output is an essential component of modern high-performance computing (HPC). Obtaining good I/O performance for a broad range of applications on diverse HPC platforms is a major challenge, in part, because of complex inter dependencies between I/O middleware and hardware. The parallel file system and I/O middleware layers all offer optimization parameters that can, in theory, result in better I/O performance. Unfortunately, the right combination of parameters is highly dependent on the application, HPC platform, problem size, and concurrency. Scientific application developers do not have the time or expertise to take on the substantial burden of identifying good parameters for each problem configuration. They resort to using system defaults, a choice that frequently results in poor I/O performance. We expect this problem to be compounded on exascale-class machines, which will likely have a deeper software stack with hierarchically arranged hardware resources.We present as a solution to this problem an autotuning system for optimizing I/O performance, I/O performance modeling, I/O tuning, and I/O patterns. We demonstrate the value of this framework across several HPC platforms and applications at scale.

Abstract: The fog computing is the emerging technology to compute, store, control and connecting smart devices with each other using cloud computing. The Internet of Things (IoT) is an architecture of uniquely identified interrelated physical things, these physical things are able to communicate with each other and can transmit and receive information. This research presents a framework of the combination of the Internet of Things (IoT) and Fog computing. The blockchain is also the emerging technology that provides a hyper, distributed, public, authentic ledger to record the transactions. Blockchains technology is a secured technology that can be a boon for the next generation computing. The combination of fog, blockchains, and IoT creates a new opportunity in this area. In this research, the author presents a middleware framework based on the blockchain, fog, and IoT. The framework is implemented and tested. The results are found positive.

Abstract: Fatalities due to human reckless driving and technological advancements makes all automobile companies to work towards autonomous vehicles. Making autonomous car platforms from scratch would be difficult therefore some middleware framework like Robot Operating System (ROS) which consists of pre-built software libraries can be used for self-driving. This paper throws light upon the main existing ROS based open software platforms, Autoware and Apollo and their algorithms required for scene understanding, path planning, and vehicle control.

Abstract: Internet of things (IoT) is pushing the integration of physical and virtual worlds. Sensor devices provide rich sensory information to context-aware services. Actuators implement in the physical world application needs. The role of software is increasing as more and more resources become virtualized and software-defined. Smart environment is an emerging concept that has the potential to address many of the humanity problems. In this context, what are the promising tools for building the smart environments of the future? This paper provides a review of platforms, middleware, and frameworks that can help in this big challenge, discussing their architectures, service life-cycling, digital twins, cloud-based operation, virtualization, security, privacy, communication model, support for AI and machine learning, among other aspects. The proposed revision innovates by employing previous work on future Internet key enablers as parameters for qualitative comparisons. The idea is to determine the degree of alignment among current initiatives for smart environments and the ones emerging from future Internet research. Among the main conclusions are: (i) heterogeneity come to stay; (ii) many contemporary proposals do not cover important aspects raised in future Internet research; (iii) publish/subscribe model is largely employed; (iv) many proposals are stuck to the limitations of current Internet model (another reason to explore the relationship among current platforms and previous future Internet research for IoT); (v) devices interoperability is a problem solved; (vi) ingredients from future Internet research, such as SDN/NFV, ICN and SCN are systematically being adopted for smart environments design. Many others are to come; (vii) AI, machine learning, and big data support are missing not only in TCP/IP-based approaches, but also in future Internet-based.

Abstract: Message-oriented middleware (MOM) allows applications to communicate and exchange data by sending and receiving messages. With advantages of asynchronous and multi-point transmission, loosely coupling between participants etc. MOM is widely recognized as the most promising solution for communication between heterogeneous systems. This paper firstly reviews current research in MOM, then introduces main features and architecture of MOM, further elaborates on a few main functional modules, including message, queue, transmission mode and security, and finally carries out a comparative analysis of several commonly used MOM products. According to high-throughput and highly reliable data service needs for IoT and smart traffic etc. In this context, this paper analyzes opportunities and challenges to MOM when facing with new requirements and adapting to a more complicated environment.

Abstract: Cyber-physical systems (CPSs) are nowadays an important component of most industrial infrastructures and materialize the integration of control systems with advanced information technologies. Commonly, they aggregate distinct communication platforms and networked devices or nodes with different capabilities and goals. The resulting increase in complexity has inevitably brought into playing new challenges, namely, on the physical world and cyber space. In these systems, the development and gaining of state awareness and how to deal with the inherent vulnerabilities of the overall system are essential and also challenging. In this paper, the problem of resilience enhancement in CPSs is addressed based on a hierarchical multi-agent framework that is implemented over a distributed middleware, in which each agent carries out specific tasks. Physical and cyber vulnerabilities are taken into account, and state and context awarenesses of the whole system are targeted. The proposed framework ensures a minimum level of acceptable performance in case of physical disturbances and malicious attacks, as demonstrated by experiments on an IPv6-based test-bed.

Abstract: It is necessary to develop a flexible and extensible watershed modeling framework with the support of parallel computing to conduct long-term high-resolution simulations over large areas with diverse watershed characteristics. This paper introduced an open-source, modular, and parallelized watershed modeling framework called SEIMS (short for Spatially Explicit Integrated Modeling System) to meet this need. First, a flexible modular structure with standard interfaces was designed, in which each module corresponds to one simulation algorithm for a watershed subprocess. Then, a parallel-computing middleware based on an improved two-level parallel computing approach was constructed to speed up the computational efficiency. With SEIMS, users can add their own algorithms in a nearly serial programming manner and construct parallelized watershed models. SEIMS also supports model level parallel computation for applications which need numerous model runs. The effectiveness and efficiency of SEIMS were illustrated through the simulation of streamflow in the Youwuzhen watershed, Southeastern China.

Abstract: This paper presents the design and implementation of a Multi-level Time Sensitive Networking (TSN) protocol based on a real-time communication platform utilizing Data Distribution Service (DDS) middleware for data transfer of synchronous three phase measurement data. To transfer ultra-high three phase measurement samples, the DDS open-source protocol is exploited to shape the network's data traffic according to specific Quality of Service (QoS) profiles, leading to low packet loss and low latency by synchronizing and prioritizing the data in the network. Meanwhile the TSN protocol enables time-synchronization of the measured data by providing a common time reference to all the measurement devices in the network, making the system less expensive, more secure and enabling time-synchronization where acquiring GPS signals is a challenge. A software library was developed and used as a central Quality of Service (QoS) profile for the TSN implementation. The proposed design and implemented real-time simulation prototype presented in this paper takes in consideration diverse scenarios at multiple levels of prioritization including publishers, subscribers, and data packets. This allows granular control and monitoring of the data for traffic shaping, scheduling, and prioritization. The major strength of this protocol lies in the fact that it's not only in real time but it's time-critical too. The simulation prototype implementation was performed using the Real Time Innovation (RTI) Connext connectivity framework, custom-built MATLAB classes and DDS Simulink blocks. Simulation results show that the proposed protocol achieves low latency and high throughput, which makes it a desired option for various communication systems involved in microgrids, smart cities, military applications and potentially other time-critical applications, where GPS signals become vulnerable and data transfer needs to be prioritized.

Abstract: In this study, a real-time remote monitoring and fault diagnosis method has been developed based on the Internet of Things (IoT) frame perception, and successfully applied to a mine hoist system. The proposed method combines the sensor technology, online monitoring technology, wireless transmission technology, and fault diagnosis technology. The basic structure of the traditional IoT comprises a perception layer, a network layer, and an application layer, the proposed structure contains an additional middleware layer between the network layer and the application layer. This four-layer system is used in a mine hoist remote monitoring and fault diagnosis framework to process heterogeneous multi-source information. The sensors and parameters are connected in the perception layer, the characteristic parameters are obtained using the configuration software, and the mine local area network is saved to the data server, thereby synchronizing real-time data in the local area network. The network layer utilizes mature Internet and long-distance wireless transmission communication technologies, whereas the middleware layer comprises of a Service-Oriented Architecture (SOA)-based IoT data processing framework that integrates the multi-source heterogeneous data. Further, the fault diagnosis method is analyzed and verified based on the gray association rules. In the application layer, a human-computer interface is used for the remote monitoring and diagnosis of the mine hoist and to provide the diagnosis results as feedback to the user. The results using the aforementioned analyses are applied to the remote monitoring and diagnosis of a mine hoist system. In this study, experimental tests are conducted in this study to significantly improve the fault monitoring, diagnostic capabilities, and reliability of the mine hoist system, indicating the good application good prospects of the proposed method.

Abstract: RADICAL-Cybertools (RCT) are a set of software systems that serve as middleware to develop efficient and effective tools for scientific computing. Specifically, RCT enable executing many-task applications at extreme scale and on a variety of computing infrastructures. RCT are building blocks, designed to work as stand-alone systems, integrated among themselves or integrated with third-party systems. RCT enables innovative science in multiple domains, including but not limited to biophysics, climate science and particle physics, consuming hundreds of millions of core hours. This paper provides an overview of RCT systems, their impact, and the architectural principles and software engineering underlying RCT

Abstract: Our research concerns the coordination and control of robotic vehicles for upper water-column oceanographic observations. In such an environment, operating multiple vehicles to observe dynamic oceanographic phenomena, such as ocean processes and marine life, from fronts to cetaceans, has required that we design, implement and operate software, methods and processes which can support opportunistic needs in real-world settings with substantial constraints. In this work, an approach for coordinated measurements using such platforms, which relate directly to task outcomes, is presented. We show the use and operational value of a new Artificial Intelligence based mixed-initiative system for handling multiple platforms along with the networked infrastructure support needed to conduct such operations in the open sea. We articulate the need and use of a range of middleware architectures, critical for such deployments and ground this in the context of a field experiment in open waters of the mid-Atlantic in the summer of 2015.

Abstract: Based on software-defined principles, we propose a holistic architecture for cyberphysical systems (CPS) and internet of things (IoT) applications, and highlight the merits pertaining to scalability, flexibility, robustness, interoperability, and cyber security. Our design especially capitalizes on the computational units possessed by smart agents, which may be utilized for decentralized control and in-network data processing. We characterize the data flow, communication flow, and control flow that assimilate a set of components such as sensors, actuators, controllers, and coordinators in a systemic programmable fashion. We specifically aim for distributed and decentralized decision-making by spreading the control over several hierarchical layers. In addition, we propose a middleware layer to encapsulate units and services for time-critical operations in highly dynamic environments. We further enlist a multitude of vulnerabilities to cyberattacks, and integrate software-defined solutions for enabling resilience, detection and recovery. In this purview, several controllers cooperate to identify and respond to security threats and abnormal situations in a self-adjusting manner. Last, we illustrate numerical simulations in support of the virtues of a software-defined design for CPS and IoT.

Abstract: X-RDMA is a communication middleware deployed and heavily used in Alibaba’s large-scale cluster hosting cloud storage and database systems. Unlike recent research projects which purely focus on squeezing out the raw hardware performance, it puts emphasis on robustness, scalability and maintainability of large-scale production clusters. X-RDMA integrates necessary features, not available in current RDMA ecosystem, to release the developers from complex and imperfect details. X-RDMA simplifies the programming model, extends RDMA protocols for application awareness, and proposes mechanisms for resource management with thousands of connections per machine. It also reduces the work for administration and performance tuning with built-in tracing, tuning and monitoring tools.X-RDMA has been deployed in several large-scale clusters with over 4000 servers in Alibaba cloud since 2016. It can save at least 70% development and maintenance time over RDMA, effectively improve performance and reduce network jitter especially when production servers are under pressure. It also helped locate over 30 issues in different layers of productions with over 5000 connections for each server on average.

Abstract: Increasingly, more manufacturing companies are equipping their products with smart capabilities which allow them to provide more informed services to customers. Unfortunately, most of these companies lack enough technical capabilities to build scalable platforms to process data collected by the deployed devices. As a result, these device manufacturers rely on IoT middleware companies to provide the needed processing capabilities and scalability. With the proliferation of these middleware services in handling data and the increase in the risk of data leakage and data breaches, we propose an approach that ensures data protection by leveraging trusted hardware-based technology from the recent Software Guard Extension (SGX) provided by Intel. SGX is a new technology that enforces strong isolation by running a process in a secure sandbox called enclave, and it offers remote attestation to ensure computations on an untrusted system are running within an enclave. By deploying SGX in the IoT gateway and the cloud service, we show that our approach prevents attacks on IoT data in transit as well as at rest by using key hashing to enforce message integrity. Our proposed framework ensures the protection of user data on third-party IoT middleware platforms by dividing the IoT data platform into trusted and untrusted modules and ensures the execution of all sensitive data processing in the trusted module which runs inside a hardware protected memory region called as enclave. Our approach enables the user to implement data access policy control within the enclave. Our proposed framework allows the user to verify that the application is running in an authenticated SGX machine and to ensure the application is not modified by a platform owner as a result of the remote attestation mechanism provided by SGX. Meanwhile, our approach defeats low-level attacks and keeps all data securely encrypted without introducing significant overhead.

Abstract: This paper presents a cloud-based building energy management system, underpinned by semantic middleware, that integrates an enhanced sensor network with advanced analytics, accessible through an intuitive Web-based user interface The proposed solution is described in terms of its three key layers: 1) user interface; 2) intelligence; and 3) interoperability The system’s intelligence is derived from simulation-based optimized rules, historical sensor data mining, and a fuzzy reasoner The solution enables interoperability through a semantic knowledge base, which also contributes intelligence through reasoning and inference abilities, and which are enhanced through intelligent rules Finally, building energy performance monitoring is delivered alongside optimized rule suggestions and a negotiation process in a 3-D Web-based interface using WebGL The solution has been validated in a real pilot building to illustrate the strength of the approach, where it has shown over 25% energy savings The relevance of this paper in the field is discussed, and it is argued that the proposed solution is mature enough for testing across further buildings