Factors that May Influence the Performance of Wireless Sensor Networks

International audienc

Ternary and Hybrid Event-based Particle Filtering for Distributed State Estimation in Cyber-Physical Systems

The thesis is motivated by recent advancements and developments in large, distributed, autonomous, and self-aware Cyber-Physical Systems (CPSs), which are emerging engineering systems with integrated processing, control, and communication capabilities. Efficient usage of available resources (communication,computation, bandwidth, and energy) is a pre-requisite for productive operation of CPSs, where security, privacy, and/or power considerations limit the number of information transfers between neighbouring sensors. In this regard, the focus of the thesis is on information acquisition, state estimation, and learning in the context of CPSs by adopting an Event-based Estimation (EBE) strategy, where information transfer is performed only in the occurrence of specific events identified via the localized triggering mechanisms. In particular, the thesis aims to address the following identified drawbacks of the existing EBE methodologies: (i) At one hand, while EBE using Gaussian-based approximations of the event-triggered posterior has been fairly investigated, application of non-linear, non-Gaussian filtering using particle filters is still in its infancy, and; (ii) On the other hand, the common assumption in the existing EBE strategies is having a binary (idle and event) decision process where during idle epochs, the sensor holds on to its local measurements while during the event epochs measurement communication happens. Although the binary event-based transfer of measurements potentially reduces the communication overhead, still communicating raw measurements during all the event instances could be very costly. To address the aforementioned shortcomings of existing EBE methodologies, first, an intuitively pleasing event-based particle filtering (EBPF) framework is proposed for centralized, hierarchical, and distributed (iii)state estimation architectures. Furthermore, a novel ternary event-triggering framework, referred to as the TEB-PF, is proposed by introducing the ternary event-triggering (TET) mechanism coupled with a non-Gaussian fusion strategy that jointly incorporates hybrid measurements within the particle filtering framework. Instead of using binary decision criteria, the proposed TET mechanism uses three local decision cases resulting in set-valued, quantized, and point-valued measurements. Due to a joint utilization of quantized and set-valued measurements in addition to the point-valued ones, the proposed TEB-PF simultaneously reduces the communication overhead, in comparison to its binary triggering counterparts, while also improves the estimation accuracy especially in low communication rates

Distributed Target Tracking with Fading Channels over Underwater Wireless Sensor Networks

This paper investigates the problem of distributed target tracking via underwater wireless sensor networks (UWSNs) with fading channels. The degradation of signal quality due to wireless channel fading can significantly impact network reliability and subsequently reduce the tracking accuracy. To address this issue, we propose a modified distributed unscented Kalman filter (DUKF) named DUKF-Fc, which takes into account the effects of measurement fluctuation and transmission failure induced by channel fading. The channel estimation error is also considered when designing the estimator and a sufficient condition is established to ensure the stochastic boundedness of the estimation error. The proposed filtering scheme is versatile and possesses wide applicability to numerous real-world scenarios, e.g., tracking a maneuvering underwater target with acoustic sensors. Simulation results demonstrate the effectiveness of the proposed filtering algorithm. In addition, considering the constraints of network energy resources, the issue of investigating a trade-off between tracking performance and energy consumption is discussed accordingly.Comment: 12 pages, 6 figures, 6 table

Deep learning for internet of underwater things and ocean data analytics

The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes

Smart Wireless Sensor Networks

The recent development of communication and sensor technology results in the growth of a new attractive and challenging area - wireless sensor networks (WSNs). A wireless sensor network which consists of a large number of sensor nodes is deployed in environmental fields to serve various applications. Facilitated with the ability of wireless communication and intelligent computation, these nodes become smart sensors which do not only perceive ambient physical parameters but also be able to process information, cooperate with each other and self-organize into the network. These new features assist the sensor nodes as well as the network to operate more efficiently in terms of both data acquisition and energy consumption. Special purposes of the applications require design and operation of WSNs different from conventional networks such as the internet. The network design must take into account of the objectives of specific applications. The nature of deployed environment must be considered. The limited of sensor nodes� resources such as memory, computational ability, communication bandwidth and energy source are the challenges in network design. A smart wireless sensor network must be able to deal with these constraints as well as to guarantee the connectivity, coverage, reliability and security of network's operation for a maximized lifetime. This book discusses various aspects of designing such smart wireless sensor networks. Main topics includes: design methodologies, network protocols and algorithms, quality of service management, coverage optimization, time synchronization and security techniques for sensor networks

Optimal Quantization Scheme for Data-Efficient Target Tracking via UWSNs Using Quantized Measurements

Target tracking is one of the broad applications of underwater wireless sensor networks (UWSNs). However, as a result of the temporal and spatial variability of acoustic channels, underwater acoustic communications suffer from an extremely limited bandwidth. In order to reduce network congestion, it is important to shorten the length of the data transmitted from local sensors to the fusion center by quantization. Although quantization can reduce bandwidth cost, it also brings about bad tracking performance as a result of information loss after quantization. To solve this problem, this paper proposes an optimal quantization-based target tracking scheme. It improves the tracking performance of low-bit quantized measurements by minimizing the additional covariance caused by quantization. The simulation demonstrates that our scheme performs much better than the conventional uniform quantization-based target tracking scheme and the increment of the data length affects our scheme only a little. Its tracking performance improves by only 4.4% from 2- to 3-bit, which means our scheme weakly depends on the number of data bits. Moreover, our scheme also weakly depends on the number of participate sensors, and it can work well in sparse sensor networks. In a 6 × 6 × 6 sensor network, compared with 4 × 4 × 4 sensor networks, the number of participant sensors increases by 334.92%, while the tracking accuracy using 1-bit quantized measurements improves by only 50.77%. Overall, our optimal quantization-based target tracking scheme can achieve the pursuit of data-efficiency, which fits the requirements of low-bandwidth UWSNs