In response to the demands and harsh conditions of underwater environments, developing sensor networks and underwater Internet of Things (IoT) has paved the way for wireless communication, ocean exploration, and vario...
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In recent years, the use of energy harvesting (EH) sensors has led to the proposal of activity sensing systems that are easy to install and do not require maintenance such as battery replacement. In this study, we aim...
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ISBN:
(纸本)9781665439299
In recent years, the use of energy harvesting (EH) sensors has led to the proposal of activity sensing systems that are easy to install and do not require maintenance such as battery replacement. In this study, we aim to construct a system that can not only sense but also recognize activities of daily living (ADLs) using only power generated by EH sensors. To achieve this goal, in this paper, we propose a fully EH-based ADL recognition system called Batterfly, which consists of EH analog PIR sensor nodes that can operate with indoor light, continuously senses human movement, and recognizes daily activities through machine learning. We applied the distributed execution method of the activity recognition model with five sensor nodes to five types of activities by five participants, and found that the system could recognize them with an average accuracy of 63.59%, comparable to the performance of the centralized model running on a gateway.
Wireless sensor networks are often distributed which makes detection of cyber-attacks or misconfiguration hard. Topology and data patterns change may result from attacks leading to the compromise of data and service a...
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ISBN:
(纸本)9781665439299
Wireless sensor networks are often distributed which makes detection of cyber-attacks or misconfiguration hard. Topology and data patterns change may result from attacks leading to the compromise of data and service availability or indicate operational problems. Graphs are often used to model topology and data paths to describe and compare state of a system. For anomaly detection, the definition of normal patterns, deviation from normal, and criteria when to declare anomaly are required. In this contribution the process of acquisition of normal patterns (ground truth), and criteria when to declare anomaly based on graph comparison are proposed. The anomaly detection is suitable for deployment at the edge of a network. Finally, the inability to define all security threats is addressed by a custom tree-based classifier which only requires normal patterns for training. A simulated wireless sensor network was used to acquire data and apply the method. Our experiments show that data and topology change can be detected at the edge of a network.
Underwater Optical Wireless sensor Networks (UOWSNs) are gaining an increasing demand in industrial and commercial applications as they can achieve high-speed communication. However, prior arts concentrate on promotin...
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ISBN:
(纸本)9798350339864
Underwater Optical Wireless sensor Networks (UOWSNs) are gaining an increasing demand in industrial and commercial applications as they can achieve high-speed communication. However, prior arts concentrate on promoting the performance of UOWSNs, while the reliability issue has not been fully addressed. In this paper, we propose a novel reliable data delivery scheme based on a cluster structure. First, we determine the orientation of each sensor for directional optical communication, which aims to establish reliable next-hop links among sensors. We formalize such an orientation problem into a submodular function maximization problem and propose a greedy method with an approximation ratio guarantee to solve it. Then, a cluster head designation scheme is developed to improve the data delivery success rate while minimizing the number of cluster heads. Finally, extensive simulations are conducted to demonstrate the effectiveness of the proposed scheme. The results reveal that compared with other algorithms, the proposed scheme can ensure a data delivery success rate of over 98.5% while only keeping 45.3% fewer cluster heads. Furthermore, test-bed experiments are carried out to verify the applicability of the proposed scheme in practical applications.
The effect of environmental change in WiFi signal is a major obstacle for generalized WiFi-based activity recognition. In this paper, we propose a novel method for adapting a WiFi-based activity classifier, that is tr...
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ISBN:
(纸本)9781665439299
The effect of environmental change in WiFi signal is a major obstacle for generalized WiFi-based activity recognition. In this paper, we propose a novel method for adapting a WiFi-based activity classifier, that is trained on a large-scale dataset for an environment, to a new environment with only one labeled sample per activity. To this end, we propose a novel representation extraction algorithm using the supervision of visual data during the training phase. Our proposed feature extraction explicitly learns the corresponding relation between WiFi signal and movement of human body parts. For environment adaptation, we propose a framework that relies on only one labeled sample per activity class in comparison with current state-of-the-art solutions which are not suitable for few-shot adaptation. We collect data from four volunteers from five different environments and show that our proposed solution is able to achieve 28% higher accuracy than state-of-the-art solutions for environmental adaptation.
Is it possible to build ultra-low power wireless sensor networks (WSN) that exploit the inherent parallel and distributed nature of powerful message passing/inference algorithms, embrace ultra-low power communication ...
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ISBN:
(纸本)9781665439299
Is it possible to build ultra-low power wireless sensor networks (WSN) that exploit the inherent parallel and distributed nature of powerful message passing/inference algorithms, embrace ultra-low power communication principles and make autonomous, in-network decisions, solely powered by the environment? While edge and cloud computing emerge, this work points towards the opposite direction, inspired by the fact that ambient energy, either from radio frequency (RF), sun, motion, temperature or even living organisms, has fixed (on average) density per surface (or volume). It is shown, perhaps for the first time in the literature (to the best of our knowledge), a proof of concept, where a WSN harvests energy from the environment and processes itself the collected information in a distributed manner, by converting the (network) inference task to a probabilistic, message passing problem. Examples from Gaussian Belief Propagation and Average Consensus are offered;ambient energy harvesting and availability are quantified, controling the probability of successful (or not) message passing. Such interrupted communication requires distributed algorithms robust to asynchrony, at the expense of increased overall delay. Simulation and experimental validation are offered in a WSN testbed with solar energy harvesting. Future work will focus on overall delay minimization.
Recently, the Internet of Things (IoT) arose as paradigm to evolve the daily tasks and to deploy new intelligent services in the environments, turning them into Smart Environments (SEs). A SE is composed of heterogene...
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ISBN:
(纸本)9781665439299
Recently, the Internet of Things (IoT) arose as paradigm to evolve the daily tasks and to deploy new intelligent services in the environments, turning them into Smart Environments (SEs). A SE is composed of heterogeneous wireless devices, where part of them are mobile. These heterogeneous devices suffer a problem of cross-interference, since most of them use the 2.4 GHz Industrial, Scientific and Medical bands ISM for communication. The cross-interference problem affects directly the Quality of Service (QoS), mainly the low transmission power devices that provide the basis of the services running on the top of the network. Within this context, this paper presents the Scalable Cross-Interference Mitigation (SCIM), an algorithm to mitigate the cross-interference problem in smart environments. The SCIM algorithm considers the mobility of the devices and their communication capacity to perform a scalable wireless channels assignment. The results, from the experiments performed, suggest that the SCIM algorithm minimizes the total interference of the SE, while increases the packet delivery, when compared to existing approaches.
In this paper, we introduce PIMAP, an IoT-based system for continuous, real-time patient monitoring that operates in a fully autonomous fashion, i.e. without the need for human intervention. To our knowledge, PIMAP is...
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ISBN:
(纸本)9781665439299
In this paper, we introduce PIMAP, an IoT-based system for continuous, real-time patient monitoring that operates in a fully autonomous fashion, i.e. without the need for human intervention. To our knowledge, PIMAP is the first open system that integrates the basic patient monitoring workflow for continuous and autonomous operation and includes sensed data collection, storage, analysis, and real-time visualization. PIMAP's open design allows it to integrate a variety of sensors (custom and off-the-shelf), analytics, and visualization. Other novel features of PIMAP include its deployment flexibility, i.e., its ability to be deployed in different configurations depending on the specific application needs, setting, and resources, as well as PIMAP's self-profiling and self-tuning capabilities. While PIMAP can be applied to various patient monitoring applications and settings, in this paper we focus on the unsolved problem of preventing pressure injuries.
Wireless sensor networking is a key enabler of Industrial IoT. IETF (Internet Engineering Task Force) has standardized a protocol suite called 6TiSCH (IPv6 over the TSCH mode of ieee802.15.4e). 6TiSCH builds an IPv6 m...
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ISBN:
(纸本)9781665439299
Wireless sensor networking is a key enabler of Industrial IoT. IETF (Internet Engineering Task Force) has standardized a protocol suite called 6TiSCH (IPv6 over the TSCH mode of ieee802.15.4e). 6TiSCH builds an IPv6 multi-hop wireless network with the ieee802.15.4 radio, which achieves low energy consumption and high reliability. Although network formation time is one of key performance indicators of wireless sensor networks, it has not been studied well with 6TiSCH standard protocols such as MSF (6TiSCH Minimal Scheduling Function) and CoJP (Constrained Join Protocol). In this paper, we propose a scheduling function called SF-Fastboot which shortens network formation time of 6TiSCH. We evaluate SF-Fastboot by simulation comparing with MSF, the state-of-the-art scheduling function. The simulation shows SF-Fastboot reduces network formation time by 41 % - 80 %.
In this paper, we study a connected submodular function maximization problem, which arises from many applications including deploying UAV networks to serve users and placing sensors to cover Points of Interest (PoIs)....
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ISBN:
(纸本)9798350386066;9798350386059
In this paper, we study a connected submodular function maximization problem, which arises from many applications including deploying UAV networks to serve users and placing sensors to cover Points of Interest (PoIs). Specifically, given a budget K, the problem is to find a subset S with K nodes from a graph G so that a given submodular function f(S) on S is maximized while the induced subgraph G[S] by the nodes in S is connected, where the submodular function f can be used to model many practical application problems, such as the number of users within different service areas of the deployed UAVs in S, the sum of data rates of users served by the UAVs, the number of covered PoIs by placed sensors, etc. We then propose a novel 1-1/e/2h+2-approximation algorithm for the problem, improving the best approximation ratio 1-1/e/2h+2 for the problem so far, through estimating a novel upper bound on the problem and designing a smart graph decomposition technique, where e is the base of the natural logarithm, h is a parameter depends on the problem and its typical value is 2. In addition, when h = 2, the algorithm approximation ratio is at least 1-1/e/5 and may be as large as 1 in some special cases when K <= 21, and is no less than 1-1/e/6 when K >= 22, compared with the current best approximation ratio 1-1/e/7 (= 1-1/e/2h+3) for the problem. We finally evaluate the algorithm performance in the application of deploying a UAV network. Experimental results demonstrate the number of users within the service area of the deployed UAV network by the proposed algorithm is up to 7.5% larger than those by existing algorithms, and its empirical approximation ratio is between 0.7 and 0.99, which is close to the theoretical maximum value one.
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