984 research outputs found
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
A Survey on Wireless Sensor Network Security
Wireless sensor networks (WSNs) have recently attracted a lot of interest in
the research community due their wide range of applications. Due to distributed
nature of these networks and their deployment in remote areas, these networks
are vulnerable to numerous security threats that can adversely affect their
proper functioning. This problem is more critical if the network is deployed
for some mission-critical applications such as in a tactical battlefield.
Random failure of nodes is also very likely in real-life deployment scenarios.
Due to resource constraints in the sensor nodes, traditional security
mechanisms with large overhead of computation and communication are infeasible
in WSNs. Security in sensor networks is, therefore, a particularly challenging
task. This paper discusses the current state of the art in security mechanisms
for WSNs. Various types of attacks are discussed and their countermeasures
presented. A brief discussion on the future direction of research in WSN
security is also included.Comment: 24 pages, 4 figures, 2 table
In-situ health monitoring for wind turbine blade using acoustic wireless sensor networks at low sampling rates
PhD ThesisThe development of in-situ structural health monitoring (SHM) techniques represents a
challenge for offshore wind turbines (OWTs) in order to reduce the cost of the operation
and maintenance (O&M) of safety-critical components and systems. This thesis propos-
es an in-situ wireless SHM system based on acoustic emission (AE) techniques. The
proposed wireless system of AE sensor networks is not without its own challenges
amongst which are requirements of high sampling rates, limitations in the communication bandwidth, memory space, and power resources. This work is part of the HEMOW-
FP7 Project, âThe Health Monitoring of Offshore Wind Farmsâ.
The present study investigates solutions relevant to the abovementioned challenges.
Two related topics have been considered: to implement a novel in-situ wireless SHM
technique for wind turbine blades (WTBs); and to develop an appropriate signal pro-
cessing algorithm to detect, localise, and classify different AE events. The major contri-
butions of this study can be summarised as follows: 1) investigating the possibility of
employing low sampling rates lower than the Nyquist rate in the data acquisition opera-
tion and content-based feature (envelope and time-frequency data analysis) for data
analysis; 2) proposing techniques to overcome drawbacks associated with lowering
sampling rates, such as information loss and low spatial resolution; 3) showing that the
time-frequency domain is an effective domain for analysing the aliased signals, and an
envelope-based wavelet transform cross-correlation algorithm, developed in the course
of this study, can enhance the estimation accuracy of wireless acoustic source localisa-
tion; 4) investigating the implementation of a novel in-situ wireless SHM technique
with field deployment on the WTB structure, and developing a constraint model and
approaches for localisation of AE sources and environmental monitoring respectively.
Finally, the system has been experimentally evaluated with the consideration of the lo-
calisation and classification of different AE events as well as changes of environmental
conditions. The study concludes that the in-situ wireless SHM platform developed in the
course of this research represents a promising technique for reliable SHM for OWTBs
in which solutions for major challenges, e.g., employing low sampling rates lower than
the Nyquist rate in the acquisition operation and resource constraints of WSNs in terms
of communication bandwidth and memory space are presente
Localisation in wireless sensor networks for disaster recovery and rescuing in built environments
A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyProgress in micro-electromechanical systems (MEMS) and radio frequency (RF) technology has fostered the development of wireless sensor networks (WSNs). Different from traditional networks, WSNs are data-centric, self-configuring and self-healing. Although WSNs have been successfully applied in built environments (e.g. security and services in smart homes), their applications and benefits have not been fully explored in areas such as disaster recovery and rescuing. There are issues related to self-localisation as well as practical constraints to be taken into account.
The current state-of-the art communication technologies used in disaster scenarios are challenged by various limitations (e.g. the uncertainty of RSS). Localisation in WSNs (location sensing) is a challenging problem, especially in disaster environments and there is a need for technological developments in order to cater to disaster conditions. This research seeks to design and develop novel localisation algorithms using WSNs to overcome the limitations in existing techniques. A novel probabilistic fuzzy logic based range-free localisation algorithm (PFRL) is devised to solve localisation problems for WSNs. Simulation results show that the proposed algorithm performs better than other range free localisation algorithms (namely DVhop localisation, Centroid localisation and Amorphous localisation) in terms of localisation accuracy by 15-30% with various numbers of anchors and degrees of radio propagation irregularity.
In disaster scenarios, for example, if WSNs are applied to sense fire hazards in building, wireless sensor nodes will be equipped on different floors. To this end, PFRL has been extended to solve sensor localisation problems in 3D space. Computational results show that the 3D localisation algorithm provides better localisation accuracy when varying the system parameters with different communication/deployment models. PFRL is further developed by applying dynamic distance measurement updates among the moving sensors in a disaster environment. Simulation results indicate that the new method scales very well
A service-oriented middleware for integrated management of crowdsourced and sensor data streams in disaster management
The increasing number of sensors used in diverse applications has provided a massive number of continuous, unbounded, rapid data and requires the management of distinct protocols, interfaces and intermittent connections. As traditional sensor networks are error-prone and difficult to maintain, the study highlights the emerging role of âcitizens as sensorsâ as a complementary data source to increase public awareness. To this end, an interoperable, reusable middleware for managing spatial, temporal, and thematic data using Sensor Web Enablement initiative services and a processing engine was designed, implemented, and deployed. The study found that its approach provided effective sensor data-stream access, publication, and filtering in dynamic scenarios such as disaster management, as well as it enables batch and stream management integration. Also, an interoperability analytics testing of a flood citizen observatory highlighted even variable data such as those provided by the crowd can be integrated with sensor data stream. Our approach, thus, offers a mean to improve near-real-time applications
Predilection Perspective of Peremptory Evaluation of Wireless Sensor Networks with Machine Learning Approach
Data mining based information processing in Wireless Sensor Network WSN is at its preliminary stage as compared to traditional machine learning and WSN Currently researches mainly focus on applying machine learning techniques to solve a particular problem in WSN Different researchers will have different assumptions application scenarios and preferences in applying machine learning algorithms These differences represent a major challenge in allowing researchers to build upon each other s work so that research results will accumulate in the community Thus a common architecture across the WSN machine learning community would be necessary One of the major objectives of many WSN research works is to improve or optimize the performance of the entire network in terms of energy conservation and network lifetime This paper will survey Data Mining in WSN application from two perspectives namely the Network associated issue and Application associated issue In the Network associated issue different machine learning algorithms applied in WSNs to enhance network performance will be discussed In Application associated issue machine learning methods that have been used for information processing in WSNs will be summarize
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