44,604 research outputs found
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
Autonomous monitoring framework for resource-constrained environments
Acknowledgments The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub, reference: EP/G066051/1. URL: http://www.dotrural.ac.uk/RemoteStream/Peer reviewedPublisher PD
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 IT-Techniques for a Dynamic Emergency Management in Large Infrastructures
This deliverable is a survey on the IT techniques that are relevant to the three use cases of the project EMILI. It describes the state-of-the-art in four complementary IT areas: Data cleansing, supervisory control and data acquisition, wireless sensor networks and complex event processing. Even though the deliverable’s authors have tried to avoid a too technical language and have tried to explain every concept referred to, the deliverable might seem rather technical to readers so far little familiar with the techniques it describes
SystemC Model Generation for Realistic Simulation of Networked Embedded Systems
Verification and design-space exploration of today's embedded systems require the simulation of heterogeneous aspects of the system, i.e., software, hardware, communications. This work shows the use of SystemC to simulate a model-driven specification of the behavior of a networked embedded system together with a complete network scenario consisting of the radio channel, the IEEE 802.15.4 protocol for wireless personal area networks and concurrent traffic sharing the medium. The paper describes the main issues addressed to generate SystemC modules from Matlab/Stateflow descriptions and to integrate them in a complete network scenario. Simulation results on a healthcare wireless sensor network show the validity of the approach
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
City Data Fusion: Sensor Data Fusion in the Internet of Things
Internet of Things (IoT) has gained substantial attention recently and play a
significant role in smart city application deployments. A number of such smart
city applications depend on sensor fusion capabilities in the cloud from
diverse data sources. We introduce the concept of IoT and present in detail ten
different parameters that govern our sensor data fusion evaluation framework.
We then evaluate the current state-of-the art in sensor data fusion against our
sensor data fusion framework. Our main goal is to examine and survey different
sensor data fusion research efforts based on our evaluation framework. The
major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed
Systems and Technologies (IJDST), 201
A Structured Hardware/Software Architecture for Embedded Sensor Nodes
Owing to the limited requirement for sensor processing in early networked sensor nodes, embedded software was generally built around the communication stack. Modern sensor nodes have evolved to contain significant on-board functionality in addition to communications, including sensor processing, energy management, actuation and locationing. The embedded software for this functionality, however, is often implemented in the application layer of the communications stack, resulting in an unstructured, top-heavy and complex stack. In this paper, we propose an embedded system architecture to formally specify multiple interfaces on a sensor node. This architecture differs from existing solutions by providing a sensor node with multiple stacks (each stack implements a separate node function), all linked by a shared application layer. This establishes a structured platform for the formal design, specification and implementation of modern sensor and wireless sensor nodes. We describe a practical prototype of an intelligent sensing, energy-aware, sensor node that has been developed using this architecture, implementing stacks for communications, sensing and energy management. The structure and operation of the intelligent sensing and energy management stacks are described in detail. The proposed architecture promotes structured and modular design, allowing for efficient code reuse and being suitable for future generations of sensor nodes featuring interchangeable components
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