In the Internet of Vessels (IoV), it is difficult for any unmanned surface vessel (USV) to work as a coordinator to establish full communication connections (FCCs) among USVs due to the lack of ...communication connections and the complex natural environment of the sea surface. The existing solutions do not include the employment of some infrastructure to establish USVs’ intragroup FCC while relaying data. To address this issue, considering the high-dimension continuous action space and state space of USVs, we propose a multi-agent deep reinforcement learning framework strategized by unmanned aerial vehicles (UAVs). UAVs can evaluate and navigate the multi-USV cooperation and position adjustment to establish a FCC. When ensuring FCCs, we aim to improve the IoV’s performance by maximizing the USV’s communication range and movement fairness while minimizing their energy consumption, which cannot be explicitly expressed in a closed-form equation. We transform this problem into a partially observable Markov game and design a separate actor–critic structure, in which USVs act as actors and UAVs act as critics to evaluate the actions of USVs and make decisions on their movement. An information transition in UAVs facilitates effective information collection and interaction among USVs. Simulation results demonstrate the superiority of our framework in terms of communication coverage, movement fairness, and average energy consumption, and that it can increase communication efficiency by at least 10% compared to DDPG, with the highest exceeding 120% compared to other baselines.
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Localization is a critical issue for Underwater Acoustic Sensor Networks (UASNs). Existing localization algorithms mainly focus on localizing unknown nodes (location-unaware) by measuring their ...distances to beacon nodes (location-aware), whereas ignoring additional challenges posed by harsh underwater environments. Especially, underwater nodes move constantly with ocean currents and measurement noises vary with distances. In this paper, we consider a special drifting-restricted UASN and propose a novel beacon-free algorithm, called MAP-PSO. It consists of two steps: MAP estimation and PSO localization. In MAP estimation, we analyze nodes' mobility patterns, which provide the priori knowledge for localization, and characterize distance measurements under the assumption of additive and multiplicative noises, which serve as the likelihood information for localization. Then the priori and likelihood information are fused to derive the localization objective function. In PSO localization, a swarm of particles are used to search the best location solution from local and global views simultaneously. Moreover, we eliminate the localization ambiguity using a novel reference selection mechanism and improve the convergence speed using a bound constraint mechanism. In the simulations, we evaluate the performance of the proposed algorithm under different settings and determine the optimal values for tunable parameters. The results show that our algorithm outperforms the benchmark method with high localization accuracy and low energy consumption.
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Indoor pedestrian tracking has been identified as a key technology for indoor location-based services such as emergency locating, advertising, and gaming. However, existing smartphone-based ...approaches to pedestrian tracking in indoor environments have various limitations including a high cost of infrastructure constructing, labor-intensive fingerprint collection, and a vulnerability to moving obstacles. Moreover, our empirical study reveals that the accuracy of indoor locations estimated by a smartphone Inertial Measurement Unit (IMU) decreases severely when the pedestrian is arbitrarily wandering with an unstable speed. To improve the indoor tracking performance by enhancing the location estimation accuracy, we exploit smartphone-based acoustic techniques and propose an infrastructure-free indoor pedestrian tracking approach, called iIPT. The novelty of iIPT lies in the pedestrian speed reliability metric, which characterizes the reliability of the pedestrian speed provided by the smartphone IMU, and in a speed enhancing method, where we adjust a relatively less reliable pedestrian speed to the more reliable speed of a passing by "enhancer" based on the acoustic Doppler effect. iIPT thus changes the encountered pedestrians from an"obstacle" into an "enhancer." Extensive real-world experiments in indoor scenarios have been conducted to verify the feasibility of realizing the acoustic Doppler effect between smartphones and to identify the applicable acoustic frequency range and transmission distance while reducing battery consumption. The experiment results demonstrate that iIPT can largely improve the tracking accuracy and decrease the average error compared with a conventional IMU-based method.
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Recently location based services (LBS) have become increasingly popular in indoor environments. Among these indoor positioning techniques providing LBS, a fusion approach combining WiFi-based and ...pedestrian dead reckoning (PDR) techniques is drawing more and more attention of researchers. Although this fusion method performs well in some cases, it still has some limitations, such as heavy computation and inconvenience for real-time use. In this work, we study map information of a given indoor environment, analyze variations of WiFi received signal strength (RSS), define several kinds of indoor landmarks, and then utilize these landmarks to correct accumulated errors derived from PDR. This fusion scheme, called Landmark-aided PDR (LaP), is proved to be light-weight and suitable for real-time implementation by running an Android application designed for the experiment. We compared LaP with other PDR-based fusion approaches. Experimental results show that the proposed scheme can achieve a significant improvement with an average accuracy of 2.17 m.
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Time series data classification is a significant topic as its application can be found in a various domain. Recent studies have shown that data-driven approach based on deep learning is powerful for ...data mining tasks. A typical deep learning method, Artificial Neural Network (ANN), has been proven to be capable for match complicated functions thus leading to the popularity. Convolutional neural network (CNN) is a special kind of ANN that has been widely used in the area of image processing tasks as its ability for extracting spatial features. However, it remains a challenge for implementing CNN in time series data classification. Recurrent Neural Network (RNN) is popular for tackling time series data as it can effectively utilize temporal information. But it is time-consuming to train RNN. This paper proposes a Dual Path CNN-RNN Cascade Network (DPCRCN) that achieves an end-to-end learning for classification. We use a dual path CNN to achieve a multi-size receptive field for better feature extraction, then using RNN and the following fully-connected layers to learn the map between the given features and the output. We also use Region of Interest (RoI) pooling to make our model capable for a flexible shape of data. We evaluate our model on Activity Recognition system based on Multisensor data fusion (AReM) dataset and we compare with many popular algorithms. We also evaluate our model using different shape of data. The results show that our model outperforms the alternatives. In addition, we provide the details of training our model.
The past decade has seen a growing interest in ocean sensor networks because of their wide applications in marine re- search, oceanography, ocean monitoring, offshore exploration, and defense or ...homeland security. Ocean sensor networks are gener- ally formed with various ocean sensors, autonomous underwater vehicles, surface stations, and research vessels. To make ocean sen- sor network applications viable, efficient communication among all devices and components is crucial. Due to the unique character- istics of underwater acoustic channels and the complex deployment environment in three dimensional （3D） ocean spaces, new effi- cient and reliable communication and networking protocols are needed in design of ocean sensor networks. In this paper, we aim to provide an overview of the most recent advances in network design principles for 3D ocean sensor networks, with focuses on de- ployment, localization, topology design, and position-based routing in 3D ocean spaces.
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Freehand exercises help improve physical fitness without any requirements for devices or places. Existing fitness assistant systems are typically restricted to wearable devices or exercising at ...specific positions, compromising the ubiquitous availability of freehand exercises. In this paper, we develop MobiFit, a contactless freehand exercise assistant using just one cellular signal receiver placed on the ground. MobiFit passively monitors the ubiquitous cellular signals sent by the base station, which frees users from the space constraints and deployment overheads and provides accurate repetition counting, exercise type recognition and workout quality assessment without any attachments to the human body. The design of MobiFit faces new challenges of the uncertainties not only on cellular signal payloads but also on signal propagations because the sender (base station) is beyond the control of MobiFit and located far away. To tackle these challenges, we conducted experimental studies to observe the received cellular signal sequence during freehand exercises. Based on the observations, we constructed the analytic model of the received signals. Guided by the insights derived from the analytic model, MobiFit segments out every repetition and rest interval from one exercise session through spectrogram analysis and extracts low-frequency features from each repetition for type recognition. Extensive experiments were conducted in both indoor and outdoor environments, which collected 22,960 exercise repetitions performed by ten volunteers over six months. The results confirm that MobiFit achieves high counting accuracy of 98.6%, high recognition accuracy of 94.1% and low repetition duration estimation error within 0.3 s. Besides, the experiments show that MobiFit works both indoors and outdoors and supports multiple users exercising together.
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Accelerometers have been widely embedded in most current mobile devices, enabling easy and intuitive operations. This paper proposes a Motion Gesture Recognition system (MGRA) based on accelerometer ...data only, which is entirely implemented on mobile devices and can provide users with real-time interactions. A robust and unique feature set is enumerated through the time domain, the frequency domain and singular value decomposition analysis using our motion gesture set containing 11,110 traces. The best feature vector for classification is selected, taking both static and mobile scenarios into consideration. MGRA exploits support vector machine as the classifier with the best feature vector. Evaluations confirm that MGRA can accommodate a broad set of gesture variations within each class, including execution time, amplitude and non-gestural movement. Extensive evaluations confirm that MGRA achieves higher accuracy under both static and mobile scenarios and costs less computation time and energy on an LG Nexus 5 than previous methods.
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Localization is one of the critical services in Underwater Acoustic Sensor Networks (UASNs). Due to harsh underwater environments, the nodes often move with currents continuously. Consequently, the ...acoustic signals usually propagate with varying speeds in non-straight lines and the noise levels change frequently with the motion of the nodes. These limitations pose huge challenges for localization in UASNs. In this paper, we propose a novel localization method based on a variational filtering technique, in which the spatial correlation and temporal dependency information are utilized to improve localization performance. In the method, a state evolution model is employed to characterize the mobility pattern of the nodes and capture the uncertainty of the location transition. Then, a measurement model is used to reflect the relation between the measurements and the locations considering the dynamics of the acoustic speed and range noise. After that, a variational filtering scheme is adopted to determine the nodes' locations, which consists of two phases: variational prediction and update. In the former phase, the coarse estimation of each node' location is computed based on its previous location; in the latter phase, the coarse location is optimized by incorporating the measurements from the reference nodes as precisely as possible. At last, an iterative localization scheme is applied, in which a node labels itself as a reference node if the confidence of its location estimation is higher than the predefined threshold. We conducted extensive simulations under different parameter settings, and the results indicate that the proposed method has better localization accuracy compared to a typical SLMP algorithm while maintaining relatively high localization coverage. Moreover, spatial⁻temporal variational filtering (STVF) is more robust to the change of the parameter settings compared to SLMP.
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This paper presents the design and implementation of an ISO-compliant ocean profiling observation system for wave-powered vertical profiler. This system aims to provide a comprehensive, scalable, and ...interoperable solution for high-resolution, real-time oceanic observation. As a part of this system, we introduce a wave-powered vertical profiler, known as “Wave Master,” designed to offer enhanced stability and reliability for long-term oceanic data collection. The core of the paper focuses on the Ocean Profiling Observation Complex Virtual Instrument (OPO-CVI), a comprehensive system developed in alignment with ISO 21851 standard. OPO-CVI seamlessly integrates data collection, transmission, storage, and visualization. Specifically, OPO-CVI addresses the challenges of information isolation, system rigidity, and lack of modularity in traditional ocean profiling methods by standardizing data formats and transmission protocols, allowing for seamless integration of new observation elements, and employing a modular architecture for enhanced scalability and reusability. By offering detailed technical insights into the OPO-CVI architecture and its compliance with ISO 21851 standard, this paper aims to contribute significantly to the advancement of standardized, efficient, and reliable oceanic observation systems.
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