Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Miniature, all-solid-state ion-selective sensor as a detector in autonomous, deployable sensing device

Lowering of the detection limit of ion-selective electrodes (ISEs) as well as their simple construction, low production cost and low power requirements make ISEs an ideal candidate for detector systems that can be integrated into autonomous, deployable sensing devices. Routine analysis and early warning systems are applications that first spring to mind, however great added value can be obtained by integration of many such devices into a wireless sensing network. In this work we describe our work towards the miniaturization of ISEs and their integration of with all-solid-state reference electrode into an all-solid-state sensor with a view of integration in autonomous, deployable sensing device. This work has two avenues: 1) development of a platform that can house all-solid-state ISEs and reference electrodes and 2) development of electronic circuitry for data acquisition and wireless transmission of the data. The latter utilizes novel, in-house made motes (a node in a wireless sensor network that is capable of performing some processing, gathering sensory information and communicating with other connected nodes in the network) that operate at lower frequency and therefore consume lower power then other, commercially available ones. In addition, they are easier to program which bridges the gap of communication between chemists and computer scientists. Intensification of the work in producing all-solid-state reference electrodes has enabled us to work on development of a platform that houses all-solid-state ISEs and reference electrode. We will here describe our progress in this avenue of our research

Toward Sensor Modular Autonomy for Persistent Land Intelligence Surveillance and Reconnaissance (ISR)

Currently, most land Intelligence, Surveillance and Reconnaissance (ISR) assets (e.g. EO/IR cameras) are simply data collectors. Understanding, decision making and sensor control are performed by the human operators, involving high cognitive load. Any automation in the system has traditionally involved bespoke design of centralised systems that are highly specific for the assets/targets/environment under consideration, resulting in complex, non-flexible systems that exhibit poor interoperability. We address a concept of Autonomous Sensor Modules (ASMs) for land ISR, where these modules have the ability to make low-level decisions on their own in order to fulfil a higher-level objective, and plug in, with the minimum of preconfiguration, to a High Level Decision Making Module (HLDMM) through a middleware integration layer. The dual requisites of autonomy and interoperability create challenges around information fusion and asset management in an autonomous hierarchical system, which are addressed in this work. This paper presents the results of a demonstration system, known as Sensing for Asset Protection with Integrated Electronic Networked Technology (SAPIENT), which was shown in realistic base protection scenarios with live sensors and targets. The SAPIENT system performed sensor cueing, intelligent fusion, sensor tasking, target hand-off and compensation for compromised sensors, without human control, and enabled rapid integration of ISR assets at the time of system deployment, rather than at design-time. Potential benefits include rapid interoperability for coalition operations, situation understanding with low operator cognitive burden and autonomous sensor management in heterogenous sensor systems

MarinEye - A tool for marine monitoring

This work presents an autonomous system for marine integrated physical-chemical and biological monitoring – the MarinEye system. It comprises a set of sensors providing diverse and relevant information for oceanic environment characterization and marine biology studies. It is constituted by a physicalchemical water properties sensor suite, a water filtration and sampling system for DNA collection, a plankton imaging system and biomass assessment acoustic system. The MarinEye system has onboard computational and logging capabilities allowing it either for autonomous operation or for integration in other marine observing systems (such as Observatories or robotic vehicles. It was designed in order to collect integrated multi-trophic monitoring data. The validation in operational environment on 3 marine observatories: RAIA, BerlengasWatch and Cascais on the coast of Portugal is also discussed.info:eu-repo/semantics/publishedVersio

Heterogeneous Integration of Autonomous Systems in Package for Wireless Sensor Networks

AbstractThe concept of Energy Harvester in Package (EHiP) is focused on the vertical heterogeneous integration of a MEMS die, dedicated to scavenge energy, with another auxiliary chip which includes the control and power management circuitry, sensors and RF capabilities. Based on this concept, we have developed and characterized several approaches for piezoelectric and electrostatic transductions to extract energy from the harmonic motion generated by a permanent magnet attached to the EHiP and placed in the surroundings of a cable of the power grid, i.e. an alternate electromagnetic field, in addition to the ambient mechanical vibrations

Implementing system simulation of C3 systems using autonomous objects

The basis of all conflict recognition in simulation is a common frame of reference. Synchronous discrete-event simulation relies on the fixed points in time as the basic frame of reference. Asynchronous discrete-event simulation relies on fixed-points in the model space as the basic frame of reference. Neither approach provides sufficient support for autonomous objects. The use of a spatial template as a frame of reference is proposed to address these insufficiencies. The concept of a spatial template is defined and an implementation approach offered. Discussed are the uses of this approach to analyze the integration of sensor data associated with Command, Control, and Communication systems

Electrochemical impedance spectroscopy as a tool for probing the functionality of ion-selective membranes

Recent success in lowering of the detection limit of ion-selective electrodes (ISEs) to part-perbillion levels have opened up the possibility for their application in environmental analysis. Its simplicity, low cost, and low power requirement coupled with excellent selectivity and sensitivity make ISEs excellent detecting system in autonomous and deployable sensing devices for routine analysis and as early warning systems. However, the necessity for calibration of detecting systems implies the use of sometimes complicated and costly systems for calibration solution and waste handling, pumps and data acquisition including the labour for system maintenance. Reducing the need for sensor calibration (or its complete elimination) would not only simplify sensing devices and reduce their costs but would allow integration of chemical sensors into the emerging area of wireless sensing networks (WSNs). It is envisioned that this integration will bring new dimensions into chemical sensing and bring benefits in many aspects of human lives. Here, we describe our attempts to address the issue of reducing the need for sensor calibration. The functionality of a typical physical transducer is probed using electrical signals testing its resistance, impedance, conductance etc. We employ a similar strategy and apply relatively simple AC signals to an ion-selective membrane in order to probe its functionality after it has been subjected to conditions that simulate in-situ long-term deployments. For example, we observe the impedance spectra of membranes that have been physically damaged, biofouled and/or have components leached out. Comparing this information with the sensor's potentiometric behaviour, we can draw conclusions regarding the functionality of the devices and their suitability to continue serving as a reliable detectors, for example, in remote locations

On-Device Soft Sensors: Real-Time Fluid Flow Estimation from Level Sensor Data

Soft sensors are crucial in bridging autonomous systems' physical and digital realms, enhancing sensor fusion and perception. Instead of deploying soft sensors on the Cloud, this study shift towards employing on-device soft sensors, promising heightened efficiency and bolstering data security. Our approach substantially improves energy efficiency by deploying Artificial Intelligence (AI) directly on devices within a wireless sensor network. Furthermore, the synergistic integration of the Microcontroller Unit and Field-Programmable Gate Array (FPGA) leverages the rapid AI inference capabilities of the latter. Empirical evidence from our real-world use case demonstrates that FPGA-based soft sensors achieve inference times ranging remarkably from 1.04 to 12.04 microseconds. These compelling results highlight the considerable potential of our innovative approach for executing real-time inference tasks efficiently, thereby presenting a feasible alternative that effectively addresses the latency challenges intrinsic to Cloud-based deployments.Comment: 8 pages, 6 figures, 1 Table, Accepted by the 1st AUTONOMOUS UBIQUITOUS SYSTEMS (AUTOQUITOUS) WORKSHOP of EAI MobiQuitous 2023 - 20th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Service