673 research outputs found
A Centralized Mechanism to Make Predictions Based on Data From Multiple WSNs
In this work, we present a method that exploits a scenario with
inter-Wireless Sensor Networks (WSNs) information exchange by making
predictions and adapting the workload of a WSN according to their outcomes. We
show the feasibility of an approach that intelligently utilizes information
produced by other WSNs that may or not belong to the same administrative
domain. To illustrate how the predictions using data from external WSNs can be
utilized, a specific use-case is considered, where the operation of a WSN
measuring relative humidity is optimized using the data obtained from a WSN
measuring temperature. Based on a dedicated performance score, the simulation
results show that this new approach can find the optimal operating point
associated to the trade-off between energy consumption and quality of
measurements. Moreover, we outline the additional challenges that need to be
overcome, and draw conclusions to guide the future work in this field.Comment: 10 pages, simulation results and figures. Published i
A Simple Flood Forecasting Scheme Using Wireless Sensor Networks
This paper presents a forecasting model designed using WSNs (Wireless Sensor
Networks) to predict flood in rivers using simple and fast calculations to
provide real-time results and save the lives of people who may be affected by
the flood. Our prediction model uses multiple variable robust linear regression
which is easy to understand and simple and cost effective in implementation, is
speed efficient, but has low resource utilization and yet provides real time
predictions with reliable accuracy, thus having features which are desirable in
any real world algorithm. Our prediction model is independent of the number of
parameters, i.e. any number of parameters may be added or removed based on the
on-site requirements. When the water level rises, we represent it using a
polynomial whose nature is used to determine if the water level may exceed the
flood line in the near future. We compare our work with a contemporary
algorithm to demonstrate our improvements over it. Then we present our
simulation results for the predicted water level compared to the actual water
level.Comment: 16 pages, 4 figures, published in International Journal Of Ad-Hoc,
Sensor And Ubiquitous Computing, February 2012; V. seal et al, 'A Simple
Flood Forecasting Scheme Using Wireless Sensor Networks', IJASUC, Feb.201
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
RTXP : A Localized Real-Time Mac-Routing Protocol for Wireless Sensor Networks
Protocols developed during the last years for Wireless Sensor Networks (WSNs)
are mainly focused on energy efficiency and autonomous mechanisms (e.g.
self-organization, self-configuration, etc). Nevertheless, with new WSN
applications, appear new QoS requirements such as time constraints. Real-time
applications require the packets to be delivered before a known time bound
which depends on the application requirements. We particularly focus on
applications which consist in alarms sent to the sink node. We propose
Real-Time X-layer Protocol (RTXP), a real-time communication protocol. To the
best of our knowledge, RTXP is the first MAC and routing real-time
communication protocol that is not centralized, but instead relies only on
local information. The solution is cross-layer (X-layer) because it allows to
control the delays due to MAC and Routing layers interactions. RTXP uses a
suited hop-count-based Virtual Coordinate System which allows deterministic
medium access and forwarder selection. In this paper we describe the protocol
mechanisms. We give theoretical bound on the end-to-end delay and the capacity
of the protocol. Intensive simulation results confirm the theoretical
predictions and allow to compare with a real-time centralized solution. RTXP is
also simulated under harsh radio channel, in this case the radio link
introduces probabilistic behavior. Nevertheless, we show that RTXP it performs
better than a non-deterministic solution. It thus advocates for the usefulness
of designing real-time (deterministic) protocols even for highly unreliable
networks such as WSNs
Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey
Growing progress in sensor technology has constantly expanded the number and
range of low-cost, small, and portable sensors on the market, increasing the
number and type of physical phenomena that can be measured with wirelessly
connected sensors. Large-scale deployments of wireless sensor networks (WSN)
involving hundreds or thousands of devices and limited budgets often constrain
the choice of sensing hardware, which generally has reduced accuracy,
precision, and reliability. Therefore, it is challenging to achieve good data
quality and maintain error-free measurements during the whole system lifetime.
Self-calibration or recalibration in ad hoc sensor networks to preserve data
quality is essential, yet challenging, for several reasons, such as the
existence of random noise and the absence of suitable general models.
Calibration performed in the field, without accurate and controlled
instrumentation, is said to be in an uncontrolled environment. This paper
provides current and fundamental self-calibration approaches and models for
wireless sensor networks in uncontrolled environments
Malicious node detection using machine learning and distributed data storage using blockchain in WSNs
In the proposed work, blockchain is implemented on the Base Stations (BSs) and Cluster Heads (CHs) to register the nodes using their credentials and also to tackle various security issues. Moreover, a Machine Learning (ML) classifier, termed as Histogram Gradient Boost (HGB), is employed on the BSs to classify the nodes as malicious or legitimate. In case, the node is found to be malicious, its registration is revoked from the network. Whereas, if a node is found to be legitimate, then its data is stored in an Interplanetary File System (IPFS). IPFS stores the data in the form of chunks and generates hash for the data, which is then stored in blockchain. In addition, Verifiable Byzantine Fault Tolerance (VBFT) is used instead of Proof of Work (PoW) to perform consensus and validate transactions. Also, extensive simulations are performed using the Wireless Sensor Network (WSN) dataset, referred as WSN-DS. The proposed model is evaluated both on the original dataset and the balanced dataset. Furthermore, HGB is compared with other existing classifiers, Adaptive Boost (AdaBoost), Gradient Boost (GB), Linear Discriminant Analysis (LDA), Extreme Gradient Boost (XGB) and ridge, using different performance metrics like accuracy, precision, recall, micro-F1 score and macro-F1 score. The performance evaluation of HGB shows that it outperforms GB, AdaBoost, LDA, XGB and Ridge by 2-4%, 8-10%, 12-14%, 3-5% and 14-16%, respectively. Moreover, the results with balanced dataset are better than those with original dataset. Also, VBFT performs 20-30% better than PoW. Overall, the proposed model performs efficiently in terms of malicious node detection and secure data storage. © 2013 IEEE
Secure and Privacy-Preserving Data Aggregation Protocols for Wireless Sensor Networks
This chapter discusses the need of security and privacy protection mechanisms
in aggregation protocols used in wireless sensor networks (WSN). It presents a
comprehensive state of the art discussion on the various privacy protection
mechanisms used in WSNs and particularly focuses on the CPDA protocols proposed
by He et al. (INFOCOM 2007). It identifies a security vulnerability in the CPDA
protocol and proposes a mechanism to plug that vulnerability. To demonstrate
the need of security in aggregation process, the chapter further presents
various threats in WSN aggregation mechanisms. A large number of existing
protocols for secure aggregation in WSN are discussed briefly and a protocol is
proposed for secure aggregation which can detect false data injected by
malicious nodes in a WSN. The performance of the protocol is also presented.
The chapter concludes while highlighting some future directions of research in
secure data aggregation in WSNs.Comment: 32 pages, 7 figures, 3 table
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