The recent spate of natural disasters such as earthquakes and floods destroyed buildings and caused loss of lives. Many times, the loss of life is attributed to slow response and not being able to reach the survivors. In such scenarios, the staggering number of deaths in the aftermath of a disaster can be reduced if information about survivors under debris is available to first responders and rescue workers. Large-scale destruction of roads and other communication infrastructure makes it hard to deploy advanced technologies for life detection. We explore the possibility of using low-cost, low-power, short-range communication technologies to assist rescue personnel in locating life under debris. We have designed and prototyped a thermopile-based sensor and communication device that provides information about the presence of survivors. The system weighs under 20 gm and costs US $30 per unit. The device can easily be fitted on battery-powered toy bugs and robots that can autonomously maneuver under the debris. We have proposed three simple algorithms, which together detect humans with 100% to 88% accuracy for 0.5 to 4.5 m range with fewer false alarms. Our evaluation shows that the detection is robust enough under several harsh ambient conditions, temperature ranges as well and partial exposure of the human body.@en

The next frontier in communications is teleoperation - manipulation and control of remote environments with haptic feedback. Compared to conventional networked applications, teleoperation poses widely different requirements, ultra-low latency (ULL) is primary. Realizing ULL communication demands significant redesign of conventional networking techniques, and the network infrastructure envisioned for achieving this is termed as Tactile Internet (TI). The design of meaningful performance metrics is crucial for seamless TI communication. However, existing performance metrics fall severely short of comprehensively characterizing TI performance due to their inability to capture how well sensed signals are reproduced. We take Dynamic Time Warping(DTW) as the basis of our work and identify necessary changes for characterizing TI performance. Through substantial refinements to DTW, we design Effective Time- and Value-Offset (ETVO) - a new method for measuring the fine-grained performance of TI systems. Through an in-depth objective analysis, we demonstrate the improvements of ETVO over DTW. Through subjective experiments, we demonstrate that existing QoS and QoE methods fall short of estimating the TI session performance accurately. Using subjective experiments, we demonstrate the behavior of the proposed metrics, their ability to match theoretically derived performance, and finally, their ability to reflect user satisfaction in a practical setting.

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Mapping the spatial distribution of soil organic carbon (SOC) is crucial for monitoring soil health, understanding ecosystem functions, and contributing to global carbon cycling. However, few studies have directly compared the influence of hybrid models and individual models with varying spatial resolutions on SOC prediction at a national scale. In this study, by combining remote sensing data, we utilized the LUCAS 2018 soil dataset to evaluate the potential capacities of hybrid models for predicting SOC content at different spatial resolutions in Germany. The hybrid models PLSRK and RFK consisted of partial least square regression (PLSR) with residual original kriging (OK) models, and random forest (RF) models with residual OK models, respectively. Individual PLSR and RF models were used as reference models. All these models were applied to estimate SOC content at 10 m, 50 m, 100 m, and 200 m spatial resolutions. Sentinel-2 bands, band indices, and topography variables were as predictors. The results revealed that hybrid models had a more accurate prediction of SOC content with higher explanations and lower prediction errors compared with individual models. The RFK model at the spatial resolution of 100 m was the fittest model with R² = 0.416, RMSE = 0.545, and RPIQ = 1.647, which enhanced 3.74% of explanation compared with the performance of RF model. The results also showed that hybrid models at a relatively coarse resolution (100 m) had better accuracy instead of those at high spatial resolution (10 m, 50 m). Sentinel-2 remote sensing data showed significant predictive capabilities for estimating SOC content. The predicted spatial distribution of SOC content revealed that the high SOC concentrated in the northwest grassland, central and southwestern mountains, and the Alps in Germany. Our study provided a benchmark SOC map in Germany for monitoring the changes resulting from land use and climate impacts, and we illustrated the accuracy of hybrid models and the effects of spatial resolutions on SOC predictions at a national scale.

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Urban greenery supports cities in achieving Sustainable Development Goals, but it is increasingly affected by multiple stressors impacting its health. Owing to the high costs of greenery inspection and monitoring, local governments often lack adequate data to effectively manage their urban greenery and prevent damage. In this Review, we present an overview of technology-supported methods and tools to measure the health of urban greenery and discuss the space–time resolution trade-offs associated with the various methods presented. To inform researchers and policymakers in global cities, we highlight how high-resolution urban greenery health data can support in achieving Sustainable Development Goals at scale.@en

Deep reinforcement learning (DRL) and evolution strategies (ESs) have surpassed human-level control in many sequential decision-making problems, yet many open challenges still exist. To get insights into the strengths and weaknesses of DRL versus ESs, an analysis of their respective capabilities and limitations is provided. After presenting their fundamental concepts and algorithms, a comparison is provided on key aspects, such as scalability, exploration, adaptation to dynamic environments, and multiagent learning. Current research challenges are also discussed, including sample efficiency, exploration versus exploitation, dealing with sparse rewards, and learning to plan. Then, the benefits of hybrid algorithms that combine DRL and ESs are highlighted.

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We present HUM-High-frequency UAV Messaging: an acoustic side channel communication system we design for localized drone-to-drone communications. We generate Pulse Width Modulated (PWM) signals from drone motors to carry information and improve communication reliability by mitigating propeller noise interference through modifications to the propeller's physical design. These modifications reduce propeller noise in the designated acoustic spectrum by up to 7 dB. We deploy a custom ultrasonic microphone shield specifically designed for decoding in the receiver. HUM's improved signal-to-noise ratio enables up to 80x higher data rates compared to the existing design from the literature while providing better scalability. HUM supports simultaneous decoding across 16 drones within 8 m, range as seen in real flight tests. The cost of this performance is minimal; we experimentally demonstrate that HUM has a marginal impact on flight dynamics and battery life.

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Healthy urban greenery is a fundamental asset to mitigate climate change phenomena such as extreme heat and air pollution. However, urban trees are often affected by abiotic and biotic stressors that hamper their functionality, and whenever not timely managed, even their survival. While the current greenery inspection techniques can help in taking effective measures, they often require a high amount of human labor, making frequent assessments infeasible at city-wide scales. In this article, we present GreenScan, a ground-based sensing system designed to provide health assessments of urban trees at high spatio-temporal resolutions, with low costs. The system uses thermal and multispectral imaging sensors fused using a custom computer vision model to estimate two tree health indexes. The evaluation of the system was performed through data collection experiments in Cambridge, USA. Overall, this work illustrates a novel approach for autonomous mobile ground-based tree health monitoring on city-wide scales at high temporal resolutions with low costs.

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This chapter presents a summary of the description and preliminary results of the use case related to the implementation of artificial intelligence tools in the emerging technology called wireless avionics intra-communications (WAICs). WAICs aims to replace some of the cable buses of modern aircraft. This replacement of infrastructure leads to: (1) complexity reduction of future airplanes, (2) creation of innovative services where wireless links are more flexible than wireline links, and mainly (3) a considerable weight reduction, which in turn leads to fuel consumption efficiency, increase of payload, as well as range extension. Therefore, WAICs is expected to have a large impact on the aeronautics industry, propelling a new generation of greener, more efficient, and less expensive aeronautical services. However, there are still several reliability, trust, interoperability and latency issues that need to be addressed before this technology becomes commercial. It is expected that AI will boost the applicability of this technology, contributing to the realization of the concept of “fly-by-wireless”.

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The increasing popularity of helium-assisted blimps for extended monitoring or data collection applications is hindered by a critical limitation-single-point failure when the balloon malfunctions or bursts. To address this, we introduce Janus, a hybrid blimp-drone platform equipped with integrated balloon failure detection and recovery capability. Janus employs a triggered mechanism that seamlessly transitions the platform from a blimp to a standard quad-rotor drone. Utilizing multiple sensors and fusing their readings, we have developed a robust balloon failure detection system. Janus demonstrates omnidirectional mobility in blimp mode and transitions promptly into quadrotor mode upon receiving the signal. Our results affirm the successful recovery of the system from balloon failure, with a rapid response time of 66 ms to balloon failure detection. The drone morphs into a quadrotor and achieves recovery within 0.362 seconds in 90% of cases. By amalgamating the enduring flight capabilities of blimps with the agility of quad-rotors within a morphing platform like Janus, we cater to applications demanding both prolonged flight duration and enhanced agility.

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Tactile Internet (TI) envisions communicating haptic sensory information and kinesthetic feedback over the network and is expected to transfer human skills remotely. For mission-critical TI applications, the network latency is commonly mandated to be between 1-10 ms, due to the sensitivity of human touch, and the packet delivery ratio to be 99.99999%, failing which can lead to catastrophic outcomes. However, with humans-in-the-loop, their dexterity and adaptability to varying responses to stimuli under different network conditions, measuring the performance of a TI session only with latency and packet losses are insufficient and presents an incorrect representation of the experience of the TI application. To develop an objective measure of the quality of TI sessions, we propose a framework that models TI applications as networked control systems, including humans-in-the-loop. We derive a closed-form expression for measuring the difference between the application performance in ideal and non-ideal network conditions. Based on Weber’s law of Just Noticeable Difference, we provide a metric called TIM to estimate the impact of the network on haptic feedback. We implemented TIM on multiple applications on a TI testbed to show that our approach is feasible and TIM strongly follows real subjective measurements. Further, we propose a channel compensation spring based on TIM, to alleviate the network conditions’ negative effects. We demonstrate the efficacy of the channel compensation spring in improving the user experience. We also present implementation notes for TI application developers.

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Introducing Chirpy, a hardware module designed for swarm robots that enables them to locate each other and communicate through audio. With the help of its deep learning module (AudioLocNet), Chirpy is capable of performing localization in challenging environments, such as those with non-line-of-sight and reverb. To support concurrent transmission, Chirpy uses orthogonal audio chirps and has an audio message frame design that balances localization accuracy and communication speed. As a result, a swarm of robots equipped with Chirpies can on-the-fly construct a path (or a potential field) to a location of interest without the need for a map, making them ideal for tasks such as search and rescue missions. Our experiments show that Chirpy can decode messages from four concurrent transmissions with a Bit Error Rate (BER) of at a distance of 250 cm, and it can communicate at Signal-to-Noise Ratios (SNRs) as low as -32 dB while maintaining ≈ 0 BER. Furthermore, AudioLocNet demonstrates high accuracy in classifying the location of a transmitter, even in adverse conditions such as non-line-of-sight and reverberant environments.

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Surveillance and monitoring are highly critical in many application scenarios like wildlife conservation, restricted areas such as nuclear spillover, and border security. Moreover, in these scenarios, intrusions do not happen frequently thus, conventional surveillance is overkill and expensive that also requires extensive human involvement which can be arduous, expensive, and inefficient. To address these issues we propose an end-to-end smart acoustic surveillance solution for intrusion detection using a simple low-cost system called Balls for Walls (B4 W). The objective is to create a network of sensors that could also be remotely launched. The nodes responsible for surveillance employ audio sensors which are packaged within hard balls thus allowing the launch of these sensors from a distance of over 500 m. We use microphones for detecting human activity inferred through sensing the sound of footsteps against background noise. We evaluate the systems across five different terrain types. We propose a novel, low complexity detection algorithm called SEED which leverages signal energy and shape to distinguish humans from ambient noise. B4 W offers a maximum detection rate of 98.3% on dry leaves and a low false alarm rate of 0.9%. The system is energy efficient to last a maximum of 170 days and it is orientation agnostic. The proposed system has been extensively tested across varying terrains and ambient signal scenarios to demonstrate its efficacy.@en

Testing the aerodynamics of micro-UAVs (mUAVs) and nano-UAVs (nUAVs) without actually flying is highly challenging. To address this issue, we introduce Open Gimbal, a specially designed 3 degrees of freedom (DoF) platform that caters to the unique requirements of mUAVs and nUAVs. This platform allows for unrestricted and free rotational motion, enabling comprehensive experimentation and evaluation of these UAVs. Our approach focuses on simplicity and accessibility. We developed an open-source, 3-D printable electromechanical design that has minimal size and low complexity. This design facilitates easy replication and customization, making it widely accessible to researchers and developers. Addressing the challenges of sensing flight dynamics at a small scale, we have devised an integrated wireless batteryless sensor subsystem. Our innovative solution eliminates the need for complex wiring and instead uses wireless power transfer for sensor data reception. To validate the effectiveness of open gimbal, we thoroughly evaluate and test its communication link and sensing performance using a typical nanoquadrotor. Through comprehensive testing, we verify the reliability and accuracy of open gimbal in real-world scenarios. These advancements provide valuable tools and insights for researchers and developers working with mUAVs and nUAVs, contributing to the progress of this rapidly evolving field.

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Situational awareness of armed forces acting in an urban environment is a key factor determining the success of military operations. In 2021 the European Defence Agency established the project on Wireless Sensor Networks for Urban Local Areas Surveillance. The main goal of the project is to assess how the situational awareness in an urban environment can be enhanced with the application of heterogeneous, autonomous and reconfigurable sensors. The paper presents a novel comprehensive approach that takes into account modelling and management of heterogeneous sensors, energy harvesting techniques, planning and management of the communication backbone, network security for data transfer and authorization for secure information exchange. The architecture of the system and the information flow are presented. The topology aspects are discussed and the sensing part is described. The paper finally highlights new essential enhancements of C2 with particular emphasis on mission planning, data fusion and threat prediction.@en

The pioneering field of tactile Internet (TI) will enable the transfer of human skills over long distances through haptic feedback. Realizing this demands a roundtrip latency of sub-5 ms. In this work, we investigate the capability of Wi-Fi 6 and existing TI scheduling/multiplexing schemes in meeting this stringent latency constraint. Taking the concrete example of the state-of-the-art video-haptic multiplexer (VH-multiplexer), we highlight the pitfalls of relying on the existing Wi-Fi 6 systems for TI communication. To circumvent this, we propose video-tactile latency scheduler (ViTaLS) - a novel link layer framework for tuning the video-tactile frame transmissions to suit their heterogeneous Quality of Service requirements. We present a mathematical model to characterize the packet transmission duration of ViTaLS. Using a custom simulator, we validate our model and measure the objective performance improvement of ViTaLS over VH-multiplexer. We also present ViTaLS-optimal - a variant of ViTaLS, for further 4 reducing the tactile latency. Objectively, we show that ViTaLS-optimal yields a latency improvement of up to 82 %. Based on experiments conducted on a real TI testbed, we subjectively demonstrate that ViTaLS-optimal outperforms the VH-multiplexer.

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With the increasing usage of robots in numerous vertical industries, the need for standardised solutions has intensified. A number of research and industrial communities and other Standards Developing Organisations (SDOs) have been actively working to identify areas in the robotics and automation sector that are suitable for standardisation. On top of that, each generation of mobile communications brings in new features and a wealth of new standardisation possibilities. While the deployment of 5G New Radio (5G NR) networks is expanding, the race for 6G is already starting to take shape with a number of proposed enabling technologies such as the integrated communication and sensing (ISAC) which can revolutionize the robotic technology. Since the two sectors of robotic and communication technologies advancements have overlapping areas for research and development, the SDOs’ activities need to address the commonalities, to allow a harmonized and unified platform that formally adopts and confirms best practices in 6G and robotics as standards. In this work, we present relevant industry standardisation, associations, and fora for both mobile communication and robotic industries based on the ETSI Classification of SDOs. We present the standardisation landscape for 6G and robotics and highlight the gap in this multidisciplinary standardisation, sharing key recommendations.@en

UAVs are becoming versatile and valuable platforms for various applications. However, the main limitation is their flying time. We present BEAVIS, a novel aerial robotic platform striking an unparalleled trade-off between the maneuverability of drones and the long-lasting capacity of blimps. BEAVIS scores highly in applications where drones enjoy unconstrained mobility yet suffer from limited lifetime. A nonlinear flight controller exploiting novel, unexplored, aerodynamic phenomena to regulate the ambient pressure and enable all translational and yaw degrees of freedom is proposed without direct actuation in the vertical direction. BEAVIS has built-in rotor fault detection and tolerance. We explain the design and the necessary background in detail. We verify the dynamics of BEAVIS and demonstrate its distinct advantages, such as agility, over existing platforms including the degrees of freedom akin to a drone with 11.36 increased lifetime. We exemplify the potential of BEAVIS to become an invaluable platform for many applications.@en

Long-range wide-area network (LoRaWAN) is an energy-efficient and inexpensive networking technology that is rapidly being adopted for many Internet-of-Things applications. In this study, we perform extensive measurements on a new LoRaWAN deployment to characterise the spatio-temporal properties of the LoRaWAN channel. Our experiments reveal that LoRaWAN frames are mostly lost due to the channel effects, which are adverse when the end-devices are mobile. The frame losses are up to 70 percent, which can be bursty for both mobile and stationary scenarios. Frame losses result in data losses since the frames are transmitted only once in the basic configuration. To reduce data losses in LoRaWAN, we design a novel coding scheme for data recovery called DaRe that works on the application layer. DaRe combines techniques from convolutional and fountain codes. By implementing DaRe, we show that 99 percent of the data can be recovered with a code rate of 1/2 when the frame loss is up to 40 percent. Compared to the repetition coding scheme, DaRe provides 21 percent higher data recovery and can save up to 42 percent of the energy consumed on a transmission for 10-byte data units. We also show that DaRe provides better resilience to bursty frame losses.

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Tactile Internet (TI) enables the transfer of human skills over the Internet, enabling teleoperation with force feed-back. Advancements are being made rapidly at several fronts to realize a functional TI soon. Generally, TI is expected to faithfully reproduce operator's actions at the other end, where a robotic arm emulates it while providing force feedback to the operator. Performance of TI is usually characterized using objective metrics such as network delay, packet losses, and RMSE. Pari passu, subjective evaluations are used as additional validation, and performance evaluation itself is not primarily based on user experience. Hence objective evaluation, which generally minimizes error (signal mismatch), is oblivious to subjective experience. In this paper, we argue that user-centric designs of TI solutions are necessary. We first consider a few common TI errors and examine their perceivability, The idea is to reduce the impact of perceivable errors and exploit the imperceivable errors to our advantage, while the objective metrics may indicate that the errors are high. To harness the imperceivable errors, we design Adaptive Offset Framework (AOF) to improve the TI signal reconstruction under realistic network settings. We use AOF to highlight the contradictory inferences drawn by objective and subjective evaluations while realizing that subjective evaluations are closer to ground truth. This strongly suggests the existence of 'blind spots of objective measures'. Further, we show that AOF significantly improves the user grade, up to 3 points (on a scale of 10) compared to the standard reconstruction method.

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