Abstract: This paper presents a Sensing-as-a-Service run-time Service Oriented Architecture (SOA), called 3SOA, for the development of Internet of Things (IoT) applications. 3SOA aims to allow interoperability among various IoT platforms and support service-oriented modelling at high levels of abstraction where fundamental SOA theories and techniques are fully integrated into a practical software engineering approach. 3SOA abstracts the dependencies of the middleware programming model from the application logic. This abstraction allows the development efforts to focus on writing the application logic independently from hardware platforms, middleware, and languages in which applications are programmed. To achieve this result, IoT objects are treated as independent entities that may interact with each other using a well-defined message exchange sequence. Each object is defined by the services it provides and the coordination protocol it supports. Objects are then able to coordinate their resources to address the global objectives of the system. To practically validate our proposals, we demonstrate an intelligent transportation system and data privacy functional prototypes as proof of concepts. The use cases show that 3SOA and the presented abstraction language allow the amalgamation of macroprogramming and node-centric programming to develop real-time and efficient applications over IoT.

Abstract: Internet of Things (IoT) provides ubiquitous intelligence and pervasive interconnections to diverse physical objects. A key technology to seamlessly integrate different IoT devices into an IoT system is IoT middleware, a software system layer designed to be the intermediary between IoT devices and applications. However, two issues, communication gap and heterogeneous access , prevent the existing IoT middleware from effective application in an IoT system with heterogeneous standards and interfaces. To address this problem, inspired by a graph-based knowledge system for eliminating heterogeneity in business systems, we, in this article, propose a knowledge graph-based multilayer IoT middleware. The proposed multilayer IoT middleware introduces a new layer to bridge the gap between IoT devices with different communication protocols. It is able to uniformly manage all IoT devices by using an IoT knowledge graph. We evaluate the applicability of the proposed approach by a real-life IoT project, a remote monitoring project of rural sewage treatment stations located in Yunnan Province, China. We find that the proposed approach effectively resolves the communication gap and heterogeneous access problems that occurred in the system.

Abstract: In this chapter, we describe the role of the AUTOSAR standard in the development of automotive software/system architectures. AUTOSAR defines the reference architecture and the methodology for the development of automotive software systems built on top of the AUTOSAR platform (middleware). It also provides the language (meta-model) for their architectural models. The AUTOSAR platform comes in two flavors – the AUTOSAR Classic Platform designed for the development of traditional mechatronics systems such as climate control and doors and the AUTOSAR Adaptive Platform designed for the development of modern automotive software systems in the area of, e.g., autonomous drive and connectivity. For both of these platforms, we show the AUTOSAR’s reference architecture and describe the proposed development methodology. We also explain the role of the AUTOSAR meta-model in the development process of both the AUTOSAR Classic and Adaptive Platforms and show examples of the architectural models that instantiate this meta-model. Finally, we explain the use of the AUTOSAR meta-model for configuring the AUTOSAR platform modules in the middleware layer.

Abstract: Service Level Objectives (SLOs) guide the elasticity of cloud applications, e.g., by deciding when and how much the resources provisioned to an application should be changed. Evaluating SLOs requires metrics, which can be directly measured on the application or system, or, more elaborately, be composed from multiple low-level metrics. The implementation of such metrics and SLOs, the triggering of elasticity strategies, and allowing configurability by the user deploying an application, requires a flexible middleware. In this paper, we present a middleware that provides an orchestrator-independent SLO controller for periodically evaluating SLOs and triggering elasticity strategies, while decoupling SLOs from the elasticity strategies to increase flexibility, and provider-independent services for obtaining low-level metrics and composing them into higher-level metrics. We evaluate our middleware by implementing a motivating use case, featuring a cost efficiency SLO for an application deployed on Kubernetes.

Abstract: Traditional wireless technologies have evolutionary converged to Internet of Thing (IoT) for devices and service interactions. In the past decade, the academia, industry 4.0 and end-user interest has also grown drastically in IoT applications and their services. However, this increase in IoT services demand has witnessed a new challenge of seamless interaction among heterogeneous devices that are varied, diverse and dynamic in nature. In this connection, another challenge is tracing the footprint of these IoT interactions for trust-based interactions, that further becomes complex with the introduction of new applications. For instance, in many situations, IoT services are generally not self-contained and sufficient. They need to coordinate and interact with other IoT services held in the surroundings. The large-scale deployment of IoT based services is not conceivable without addressing interoperability and services coordination related challenges. In this regard, a comprehensive set of tools and techniques associated with IoT heterogeneity and interoperability have been explored. This article proposes a middleware framework to consider IoT heterogeneity and interoperability issues in different service interactions. Later, the article investigates experimentally, the trust measurements among IOTs, their decay, and the effectiveness of dynamic selection of trust parameters along with their thresholds. Appropriateness of time intervals have also been tested in various types of service interactions. To demonstrate middleware applicability, the trust-based algorithm has been applied in a service-oriented environment along with different types of services.

Abstract: Ubiquitous Computing Environment (UCE) has become increasingly crucial for a component-based Middleware for Ubiquitous Soft Computing Environment by using Fuzzy Agent Computing System (USCE–FACS) that can fully exploit interactive service characteristics to support user-centric services in UCE. It has posed some challenges, such as high Memory Consumption and high Component Building Time to users who are hiring services in a heterogeneous environment. To control the UCE for the user's benefit, FACS works non-rudely in an online deep-rooted learning way to get familiar with the user behavior. The point of incorporation of previously mentioned advances is to make more extensive the connection between individuals and data innovation gear through the use of an undetectable system of UCE devices making dynamic computational-environments equipped for fulfilling the users' prerequisites. Due to a large number of available services without ontology and Metadata Repository (MR), it becomes time-consuming for end-users to find appropriate services to satisfy their various needs. To help end-users obtain their desired services, the USCE–FACS proposed, it reduces MC and CBT in a heterogeneous environment. This paper concentrated on the communication from users to devices to enable a general and quick access to accessible element and administrations gave by the UCE.

Abstract: Internet of Things (IoT) offers complex networks of connected devices, which are used to serve in the real-time environment. Interestingly, Wireless Sensor Networks (WSNs) play as one of the major components to observe environment in real-time. Although, IoT is being used in diverse applications and services, which involve heterogeneity and interoperability that make it more complex as compared to traditional networks. Further, to take-care of these issues middleware coalesce as a software technology by supporting interoperability, heterogeneous computing, and communication. So, middleware can be used in various IoT applications by extending the WSNs as a services. With this reference, we provide a survey on WSNs middlewares towards IoT to provide a comparative view of various middlewares and how this middleware technology can be used to incorporate several problems arise in the development of IoT applications. This survey paper also highlights the various issues involved while developing the IoT applications.

Abstract: Perception algorithms in autonomous vehicles are vital for the vehicle to understand the semantics of its surroundings, including detection and tracking of objects in the environment. The outputs of these algorithms are in turn used for decision-making in safety-critical scenarios like collision avoidance, and automated emergency braking. Thus, it is crucial to monitor such perception systems at runtime. However, due to the high-level, complex representations of the outputs of perception systems, it is a challenge to test and verify these systems, especially at runtime. In this paper, we present a runtime monitoring tool, PerceMon that can monitor arbitrary specifications in Timed Quality Temporal Logic (TQTL) and its extensions with spatial operators. We integrate the tool with the CARLA autonomous vehicle simulation environment and the ROS middleware platform while monitoring properties on state-of-the-art object detection and tracking algorithms.

Abstract: Human Robot Interaction (HRI) is one of the biggest research field in the Robotics research world. Understanding the semantic meaning of the user instruction is very important to establish the communication between user and robot. When a user instructs to all heterogeneous service Robots with high level instructions who are working at different locations in smart house, all robots need to operate by understanding the semantic of the instruction and complete the task uniformly without considering underline software and hardware implementations. Ontology is used to extract the semantic meaning of the instructions. Each robot is assigned a specific task for specific time period for each day. User can issue commands to all robots at different time slots but the same command can be issued with different sentence by different users. We have used ontology to represents the semantic of the sentence with different commands. We have used three robots (TurtleBot, TiaGo and Husky) for our experiment in Gazebo simulator. Robot Operating System (ROS) is the middleware which is hiding more complex implementation of different functions for different robots and provide the interoperability for heterogeneous robot operations. The ROS nodes and topics can be different for different robots, therefore our implementation can solve this issue by autonomous robot registration algorithm which is working with Robot ontology.

Abstract: In recent years, Ethernet has been introduced into vehicular networks to cope with the increasing demand for bandwidth and complexity in communication networks. To exchange data between controller area network (CAN) and Ethernet, a gateway system is required to provide a communication interface. Additionally, the existence of networked devices exposes automobiles to cyber security threats. Against this background, a gateway for CAN/CAN with flexible data-rate (CANFD) to scalable service-oriented middleware over IP (SOME/IP) protocol conversion is designed, and security schemes are implemented in the routing process to provide integrity and confidentiality protections. Based on NXP-S32G, the designed gateway is implemented and evaluated. Under most operating conditions, the CPU and the RAM usage are less than 5% and 20 MB, respectively. Devices running a Linux operating system can easily bear such a system resource overhead. The latency caused by the security scheme accounts for about 25% of the entire protocol conversion latency. Considering the security protection provided by the security scheme, this overhead is worthwhile. The results show that the designed gateway can ensure a CAN/CANFD to SOME/IP protocol conversion with a low system resource overhead and a low latency while effectively resisting hacker attacks such as frame forgery, tampering, and sniffing.

Abstract: Enabling data processing at the network edge, as close to the actual source of data as possible, is a challenging, yet realistic goal to be achieved by the Internet of Things (IoT), which still primarily relies on the Cloud for data processing. By further extending the Fog and Edge computing principles, recent research advancements enabled aggregation of computing resources from multiple edge devices to support data-intensive task processing using Big Data clustering middleware. The use of these existing solutions, however, is hindered by the heterogeneous, dynamic, mobile, resource-constrained, and time-critical nature of IoT ecosystems. More specifically, a particularly challenging goal is to discover, select, and cluster suitable edge devices – on the one hand, and decompose and allocate data-intensive tasks with respect to discovered resources – on the other. To address this challenge, this paper introduces a novel decentralized architecture for clustering heterogeneous edge devices and executing data-intensive IoT workflows. The proposed approach first breaks down a complex workflow into simpler tasks, then discovers and selects suitable edge devices, and finally allocates the tasks to the selected nodes, connecting them to recompose the original workflow. The proposed approach benefits from an intelligent mapping algorithm that takes into account available cluster resources and processing demands to efficiently allocate fine-grained tasks to selected nodes. To support the clusterisation process, the proposed solution relies on a unified semantic knowledge base that provides a common vocabulary of terms for modelling task requirements and edge device properties, as well as enables automated task grouping and match-making for device discovery and selection, using built-in reasoning capabilities.

Abstract: This paper addresses the challenges in guaranteeing accurate time synchronization and deterministic data delivery over wireless using collaborative robotics as an example application. The paper describes a methodology to map application layer QoS requirements from the ROS2 (Robotics Operating System 2) and DDS (Data Distribution System) middleware, used to develop robotics applications, to the link layer transport based on Wireless Time-Sensitive Networking (TSN) capabilities built on Wi-Fi. The paper provides experimental results from a prototype implementation of a collaborative task between two robots enabled by WTSN time synchronization and traffic shaping over Wi-Fi. The results demonstrate how time synchronized time-aware scheduling over Wi-Fi can be configured to meet the QoS requirements of the robotics application even in the presence of background traffic sharing the same channel.

Abstract: Since the term Internet of Things (IoT) was coined by Kevin Ashton in 1999, a number of middleware platforms have been developed to cope with important challenges such as the integration of different technologies. In this context of heterogeneous technologies, IoT message brokers become critical elements for the proper function of smart systems and wireless sensor networks (WSN) infrastructures. There are several evaluations made on IoT messaging middleware performance. Nevertheless, most of them ignore crucial aspects of the IoT context that also need to be included, such as reliability and other qualitative aspects. Thus, in this article, we propose a methodology for classification and evaluation of IoT brokers to help the scientific community and technology industry on evaluating them according to their interests, without leaving out important aspects for the context of smart environments. Our methodology bases its qualitative evaluations on the ISO/IEC 25000 (SQuaRE) set of standards and its quantitative evaluations on Jain's process for performance evaluation. We developed a case study to illustrate our proposal with 12 different open‐source brokers, validating the feasibility of our methodological approach.

Abstract: This work presents the design of an efficient edge-empowered sensor-cloud architecture equipped with a smart virtual sensing scheme for precision agriculture. Traditionally, in agricultural sensor-cloud, sensor nodes send raw sensed data periodically to the cloud, resulting in higher latency and higher energy and bandwidth consumption. The environment-dependent nature of agricultural parameters also limits the serviceability of sensor-cloud in regions with damaged or unemployed sensors. Moreover, agricultural sensor-cloud suffers from privacy issues due to the sharing of sensitive farming data across third-party service providers. To address these drawbacks, we first propose a modified sensor-cloud architecture using edge devices as the middleware layer for sensor virtualization, thereby reducing service provisioning latency and resource consumption. Next, we propose DLSense , a novel intelligent virtualization scheme to aid in the design of virtual sensors in the absence of working sensor nodes in a region. DLSense utilizes correlation theory and distributed learning in the edge devices to predict sensor data and enables the sharing of information of the trained models instead of raw sensed data, thus imparting privacy. Finally, we evaluate the performance of the DLSense scheme through extensive simulations and an experimental case study of an agricultural application. Results demonstrate that our proposed scheme reduces latency and service cost by 81% and 66%, respectively, and increases service availability by 39% compared to the state-of-the-art methods.

Abstract: Robowflex is a software library for robot motion planning in industrial and research applications, leveraging the popular MoveIt library and Robot Operating System (ROS) middleware. Robowflex takes advantage of the ease of motion planning with MoveIt while providing an augmented API to craft and manipulate motion planning queries within a single program. Robowflex's high-level API simplifies many common use-cases while still providing access to the underlying MoveIt library. Robowflex is particularly useful for 1) developing new motion planners, 2) evaluation of motion planners, and 3) complex problems that use motion planning (e.g., task and motion planning). Robowflex also provides visualization capabilities, integrations to other robotics libraries (e.g., DART and Tesseract), and is complimentary to many other robotics packages. With our library, the user does not need to be an expert at ROS or MoveIt in order to set up motion planning queries, extract information from results, and directly interface with a variety of software components. We provide a few example use-cases that demonstrate its efficacy.

Abstract: Complex software is taking over the current existence of the technology. With the furtherance of software, the deepest levels of constituents have originated. Aspects like time, expenses, and, error solving has been expounded to at most levels of convolution. The purpose of Software is to plunge deep into the concepts and then perceive the enduring and deep-rooted solutions. The paper focuses on three major characteristics of software: middleware, frameworks, and APIs. The reinforcement of these strands has escalated the development of new magnitudes in this field. We will assimilate about Middleware which is software whose function is to bridge together the operating system or database with the applications over a network. Another predominant module is Framework that incorporates various libraries, compiler, and supplementary programs that can be used to fabricate unique applications. Beyond Middleware and Framework, APIs i.e. Application Programming Interface facilitates interactivity between two applications. It has been thoroughly explained about the conjunction of these emerging technologies into new applications and how they can change the working of the software.

Abstract: This paper presents iBatch, a middleware system running on top of an operational Ethereum network to enable secure batching of smart-contract invocations against an untrusted relay server off-chain. iBatch does so at a low overhead by validating the server's batched invocations in smart contracts without additional states. The iBatch mechanism supports a variety of policies, ranging from conservative to aggressive batching, and can be configured adaptively to the current workloads. iBatch automatically rewrites smart contracts to integrate with legacy applications and support large-scale deployment. For cost evaluation, we develop a platform with fast and cost-accurate transaction replaying, build real transaction benchmarks on popular Ethereum applications, and build a functional prototype of iBatch on Ethereum. The evaluation results show that iBatch saves 14.6%-59.1% Gas cost per invocation with a moderate 2-minute delay and 19.06%-31.52% Ether cost per invocation with a delay of 0.26-1.66 blocks.

Abstract: Increasingly complex robotic platforms incorporate heterogeneous sensors and actuators. They are usually coupled with embedded computers but relying on software solutions not entirely suited for processing large amount of data concurrently and fast enough to keep real-time constrains. FPGAs are ideal candidates to enhance those systems computing capabilities while still being programmable. However, it is cumbersome to manually incorporate them into new or existing systems because providing accelerators with a specific integration capability limits their applicability. This work presents an approach to integrate multiple hardware accelerators into a robotic system. A model-based toolchain automatically generates the necessary hardware components (VHDL modules) from existing message specifications to exchange data with the accelerators. The goal is to seamlessly replace software components with FPGA-based ones while retaining the same communication interface. Instead of writing several hundred lines of VHDL, a dozen input specification lines are sufficient with our approach. The results are validated through an evaluation of all message specifications included in the latest Robot Operating System (ROS) versions. The 2295 messages evaluated show the robustness in our capabilities to support arbitrarily large ROS messages types, multiple data types, and nested messages. Moreover, our approach facilitates the extension from ROS1 to provide support for ROS2 easily. Finally, two use cases are shown to prove the feasibility of our approach on real applications. The first one incorporates a hardware accelerator for image processing obtained by High Level Synthesis (HLS) into an existing software architecture. The second one consists of a fully FPGA-based mobile platform with ROS features incorporated.

Abstract: Autonomous navigation is a complex problem that involves different tasks, such as location of the mobile robot in the scenario, robotic mapping, generating the trajectory, navigating from the initial point to the target point, detecting objects it may encounter in its path, etc. This paper presents a new optimal trajectory planning algorithm that allows the assessment of the energy efficiency of autonomous light vehicles. To the best of our knowledge, this is the first time in the literature that this is carried out by minimizing the travel time while considering the vehicle’s dynamic behavior, its limitations, and with the capability of avoiding obstacles and constraining energy consumption. This enables the automotive industry to design environmentally sustainable strategies towards compliance with governmental greenhouse gas (GHG) emission regulations and for climate change mitigation and adaptation policies. The reduction in energy consumption also allows companies to stay competitive in the marketplace. The vehicle navigation control is efficiently implemented through a middleware of component-based software development (CBSD) based on a Robot Operating System (ROS) package. It boosts the reuse of software components and the development of systems from other existing systems. Therefore, it allows the avoidance of complex control software architectures to integrate the different hardware and software components. The global maps are created by scanning the environment with FARO 3D and 2D SICK laser sensors. The proposed algorithm presents a low computational cost and has been implemented as a new module of distributed architecture. It has been integrated into the ROS package to achieve real time autonomous navigation of the vehicle. The methodology has been successfully validated in real indoor experiments using a light vehicle under different scenarios entailing several obstacle locations and dynamic parameters.

Abstract: Machine learning (ML) is actively finding its way into modern cyber-physical systems (CPS), many of which are safety-critical real-time systems. It is well known that ML outputs are not reliable when testing data are novel with regards to model training and validation data, i.e., out-of-distribution (OOD) test data. We implement an unsupervised deep neural network-based OOD detector on a real-time embedded autonomous Duckiebot and evaluate detection performance. Our OOD detector produces a success rate of 87.5% for emergency stopping a Duckiebot on a braking test bed we designed. We also provide case analysis on computing resource challenges specific to the Robot Operating System (ROS) middleware on the Duckiebot.

Abstract: Data center networks (DCNs) have widely deployed RDMA to support data-intensive applications such as machine learning. While DCNs are designed with rich multi-path topology, current RDMA (hardware) technology does not support multi-path transport. In this paper we advance Maestro- a purely software-basedmulti-path RDMA solution - to effectively utilize the rich multi-path topology for load balancing and reliability. As a "middleware" operating at the user-space, Maestro is modulaR@and software-defined:Maestro decouples path selection and load balancing mechanisms from hardware features, and allows DCN operators and applications to make flexible decisions by employing the best mechanisms as needed. As such, Maestro can be readily deployed using existing RDMA hardware (NICs) to support distributed deep learning (DDL) applications. Our experiments show that Maestro is capable of fully utilizing multiple paths with negligible CPU overheads, thereby enhancing the performance of DDL applications.

Abstract: This paper deals with an intelligent data analytics platform - Jaison-Paul Data Analytics Platform (JP-DAP) - for metro rail transport systems. JP-DAP is intended to ensure smooth functioning, improved customer experience, ridership forecasting, and efficient administration of metro rail transportation systems by integrating and analysing its many data sources. It consists of a middleware which is built on the top of a Hadoop Distributed File System (HDFS) and Spark framework, along with a set of open-source software tools like Apache Hive, Pandas, Google TensorFlow and Spark ML-lib for real-time and legacy data processing. The benchmarking of JP-DAP was conducted using TestDFSIO and have found that it performs well according to industry standards. The specific use case for this project is Kochi Metro Rail Limited (KMRL). The analysis of Automated Fare Collection data from KMRL on JP-DAP framework have produced descriptive statistics visualisation of inflow and outflow analysis, travel patterns during weekdays and weekends, origin-destination matrix, etc.. Moreover JP-DAP framework is capable of producing short term passenger flow predictions using SVR machine learning algorithm with linear, radial basis function and polynomial kernels. Our experiments have shown that SVR linear kernel gives the most accurate results with the least errors in predicting the next day's passenger count using the previous five weekdays data. The station usage (one-to-all) prediction using Long Short-Term Memory (LSTM) is also integrated to this framework. The visualisation as well as analytical outcomes of JP-DAP framework have also been made available to the external world using a rich set of REST APIs and are projected on to a web-dashboard.

Abstract: The relational database has become one of the mainstream tools for data storage and management. However, there are two main types of threats to relational databases: external attacks and internal tampering threats. In this paper, we focus on the internal tampering threats and propose a tamper-proof detection middleware named TDRB to provide efficient tamper-proof detection for relational databases. Within the TDRB middleware framework, raw data is still stored and queried from the relational database, while the hash digest of the critical data in the relational database is synchronously migrated to the blockchain for tamper detection. Based on this method, we leverage blockchain’s immutability to detect data tamper and maintain the advanced features of relational databases to better support ease of data persistence, complex queries, and large storage capacity. We also propose a performance improvement mechanism that involves connecting the blockchain and relational database to improve throughput and mitigate performance impact. A series of experiments indicate that the TDRB middleware can accurately detect the tampering information during arbitrary tampering with the relational database and the cache database. Compare with the baseline, with the increase of cache hit rate, the TDRB middleware query speed increased by 93.1%, update speed increased by 16.3%, delete speed increased by 16.1%, and join operation average speed increased by 95.2%. Given its generality, the TDRB middleware can be flexibly and conveniently integrated into third-party platforms.

Abstract: Many enterprise software services are adopting a fully web-based architecture for both internal line-of-business applications and for online customer-facing applications. Although wireless connections are becoming more ubiquitous and faster, mobile employees and customers are often offline due to expected or unexpected network disruptions. Nevertheless, continuous operation of the software is expected. This article presents OWebSync: a web-based middleware for data synchronization in interactive groupware with fast resynchronization of offline clients and continuous, interactive synchronization of online clients. To automatically resolve conflicts, OWebSync implements a fine-grained data synchronization model and leverages state-based Conflict-free Replicated Data Types. This middleware uses Merkle-trees embedded in the tree-structured data and virtual Merkle-tree levels to achieve the required interactive performance. Our comparative evaluation with available operation-based and delta-state-based middleware solutions shows that OWebSync is especially better in operating in and recovering from offline settings and network disruptions. In addition, OWebSync scales more efficiently over time, as it does not store version vectors or other meta-data for all past clients.

Abstract: Machine learning (ML) is actively finding its way into modern cyber-physical systems (CPS), many of which are safety-critical real-time systems. It is well known that ML outputs are not reliable when testing data are novel with regards to model training and validation data, i.e., out-of-distribution (OOD) test data. We implement an unsupervised deep neural network-based OOD detector on a real-time embedded autonomous Duckiebot and evaluate detection performance. Our OOD detector produces a success rate of 87.5% for emergency stopping a Duckiebot on a braking test bed we designed. We also provide case analysis on computing resource challenges specific to the Robot Operating System (ROS) middleware on the Duckiebot.