7,582 research outputs found
Machine learning based lightweight interference mitigation scheme for wireless sensor network
The interference issue is most vibrant on low-powered networks like wireless sensor network (WSN). In some cases, the heavy interference on WSN from different technologies and devices result in life threatening situations. In this paper, a machine learning (ML) based lightweight interference mitigation scheme for WSN is proposed. The scheme detects and identifies heterogeneous interference like Wifi, bluetooth and microwave oven using a lightweight feature extraction method and ML lightweight decision tree. It also provides WSN an adaptive interference mitigation solution by helping to choose packet scheduling, Acknowledgement (ACK)-retransmission or channel switching as the best countermeasure. The scheme is simulated with test data to evaluate the accuracy performance and the memory consumption. Evaluation of the proposed scheme’s memory profile shows a 14% memory saving compared to a fast fourier transform (FFT) based periodicity estimation technique and 3% less memory compared to logistic regression-based ML model, hence proving the scheme is lightweight. The validation test shows the scheme has a high accuracy at 95.24%. It shows a precision of 100% in detecting WiFi and microwave oven interference while a 90% precision in detecting bluetooth interference
Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT
Wireless sensor networks (WSN) are fundamental to the Internet of Things
(IoT) by bridging the gap between the physical and the cyber worlds. Anomaly
detection is a critical task in this context as it is responsible for
identifying various events of interests such as equipment faults and
undiscovered phenomena. However, this task is challenging because of the
elusive nature of anomalies and the volatility of the ambient environments. In
a resource-scarce setting like WSN, this challenge is further elevated and
weakens the suitability of many existing solutions. In this paper, for the
first time, we introduce autoencoder neural networks into WSN to solve the
anomaly detection problem. We design a two-part algorithm that resides on
sensors and the IoT cloud respectively, such that (i) anomalies can be detected
at sensors in a fully distributed manner without the need for communicating
with any other sensors or the cloud, and (ii) the relatively more
computation-intensive learning task can be handled by the cloud with a much
lower (and configurable) frequency. In addition to the minimal communication
overhead, the computational load on sensors is also very low (of polynomial
complexity) and readily affordable by most COTS sensors. Using a real WSN
indoor testbed and sensor data collected over 4 consecutive months, we
demonstrate via experiments that our proposed autoencoder-based anomaly
detection mechanism achieves high detection accuracy and low false alarm rate.
It is also able to adapt to unforeseeable and new changes in a non-stationary
environment, thanks to the unsupervised learning feature of our chosen
autoencoder neural networks.Comment: 6 pages, 7 figures, IEEE ICC 201
Formal Probabilistic Analysis of a Wireless Sensor Network for Forest Fire Detection
Wireless Sensor Networks (WSNs) have been widely explored for forest fire
detection, which is considered a fatal threat throughout the world. Energy
conservation of sensor nodes is one of the biggest challenges in this context
and random scheduling is frequently applied to overcome that. The performance
analysis of these random scheduling approaches is traditionally done by
paper-and-pencil proof methods or simulation. These traditional techniques
cannot ascertain 100% accuracy, and thus are not suitable for analyzing a
safety-critical application like forest fire detection using WSNs. In this
paper, we propose to overcome this limitation by applying formal probabilistic
analysis using theorem proving to verify scheduling performance of a real-world
WSN for forest fire detection using a k-set randomized algorithm as an energy
saving mechanism. In particular, we formally verify the expected values of
coverage intensity, the upper bound on the total number of disjoint subsets,
for a given coverage intensity, and the lower bound on the total number of
nodes.Comment: In Proceedings SCSS 2012, arXiv:1307.802
PhyNetLab: An IoT-Based Warehouse Testbed
Future warehouses will be made of modular embedded entities with
communication ability and energy aware operation attached to the traditional
materials handling and warehousing objects. This advancement is mainly to
fulfill the flexibility and scalability needs of the emerging warehouses.
However, it leads to a new layer of complexity during development and
evaluation of such systems due to the multidisciplinarity in logistics,
embedded systems, and wireless communications. Although each discipline
provides theoretical approaches and simulations for these tasks, many issues
are often discovered in a real deployment of the full system. In this paper we
introduce PhyNetLab as a real scale warehouse testbed made of cyber physical
objects (PhyNodes) developed for this type of application. The presented
platform provides a possibility to check the industrial requirement of an
IoT-based warehouse in addition to the typical wireless sensor networks tests.
We describe the hardware and software components of the nodes in addition to
the overall structure of the testbed. Finally, we will demonstrate the
advantages of the testbed by evaluating the performance of the ETSI compliant
radio channel access procedure for an IoT warehouse
A Systematic Approach to Constructing Families of Incremental Topology Control Algorithms Using Graph Transformation
In the communication systems domain, constructing and maintaining network
topologies via topology control (TC) algorithms is an important cross-cutting
research area. Network topologies are usually modeled using attributed graphs
whose nodes and edges represent the network nodes and their interconnecting
links. A key requirement of TC algorithms is to fulfill certain consistency and
optimization properties to ensure a high quality of service. Still, few
attempts have been made to constructively integrate these properties into the
development process of TC algorithms. Furthermore, even though many TC
algorithms share substantial parts (such as structural patterns or tie-breaking
strategies), few works constructively leverage these commonalities and
differences of TC algorithms systematically. In previous work, we addressed the
constructive integration of consistency properties into the development
process. We outlined a constructive, model-driven methodology for designing
individual TC algorithms. Valid and high-quality topologies are characterized
using declarative graph constraints; TC algorithms are specified using
programmed graph transformation. We applied a well-known static analysis
technique to refine a given TC algorithm in a way that the resulting algorithm
preserves the specified graph constraints.
In this paper, we extend our constructive methodology by generalizing it to
support the specification of families of TC algorithms. To show the feasibility
of our approach, we reneging six existing TC algorithms and develop e-kTC, a
novel energy-efficient variant of the TC algorithm kTC. Finally, we evaluate a
subset of the specified TC algorithms using a new tool integration of the graph
transformation tool eMoflon and the Simonstrator network simulation framework.Comment: Corresponds to the accepted manuscrip
A Survey of Access Control Models in Wireless Sensor Networks
Copyright 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/)Wireless sensor networks (WSNs) have attracted considerable interest in the research community, because of their wide range of applications. However, due to the distributed nature of WSNs and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. Resource constraints in sensor nodes mean that security mechanisms with a large overhead of computation and communication are impractical to use in WSNs; security in sensor networks is, therefore, a challenge. Access control is a critical security service that offers the appropriate access privileges to legitimate users and prevents illegitimate users from unauthorized access. However, access control has not received much attention in the context of WSNs. This paper provides an overview of security threats and attacks, outlines the security requirements and presents a state-of-the-art survey on access control models, including a comparison and evaluation based on their characteristics in WSNs. Potential challenging issues for access control schemes in WSNs are also discussed.Peer reviewe
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