12,401 research outputs found
Lightweight Synchronization Algorithm with Self-Calibration for Industrial LORA Sensor Networks
Wireless sensor and actuator networks are gaining momentum in the era of
Industrial Internet of Things IIoT. The usage of the close-loop data from
sensors in the manufacturing chain is extending the common monitoring scenario
of the Wireless Sensors Networks WSN where data were just logged. In this paper
we present an accurate timing synchronization for TDMA implemented on the state
of art IoT radio, such as LoRa, that is a good solution in industrial
environments for its high robustness. Experimental results show how it is
possible to modulate the drift correction and keep the synchronization error
within the requirements
Dynamic Voltage Scaling Techniques for Energy Efficient Synchronized Sensor Network Design
Building energy-efficient systems is one of the principal challenges in wireless sensor networks. Dynamic voltage scaling (DVS), a technique to reduce energy consumption by varying the CPU frequency on the fly, has been widely used in other settings to accomplish this goal. In this paper, we show that changing the CPU frequency can affect timekeeping functionality of some sensor platforms. This phenomenon can cause an unacceptable loss of time synchronization in networks that require tight synchrony over extended periods, thus preventing all existing DVS techniques from being applied. We present a method for reducing energy consumption in sensor networks via DVS, while minimizing the impact of CPU frequency switching on time synchronization.
The system is implemented and evaluated on a network of 11 Imote2 sensors mounted on a truss bridge and running a high-fidelity continuous structural health monitoring
application. Experimental measurements confirm that the algorithm significantly reduces network energy consumption
over the same network that does not use DVS, while requiring significantly fewer re-synchronization actions than a classic DVS algorithm.unpublishedis peer reviewe
Comparison of CSMA based MAC protocols of wireless sensor networks
Energy conservation has been an important area of interest in Wireless Sensor
networks (WSNs). Medium Access Control (MAC) protocols play an important role
in energy conservation. In this paper, we describe CSMA based MAC protocols for
WSN and analyze the simulation results of these protocols. We implemented
S-MAC, T-MAC, B-MAC, B-MAC+, X-MAC, DMAC and Wise-MAC in TOSSIM, a simulator
which unlike other simulators simulates the same code running on real hardware.
Previous surveys mainly focused on the classification of MAC protocols
according to the techniques being used or problem dealt with and presented a
theoretical evaluation of protocols. This paper presents the comparative study
of CSMA based protocols for WSNs, showing which MAC protocol is suitable in a
particular environment and supports the arguments with the simulation results.
The comparative study can be used to find the best suited MAC protocol for
wireless sensor networks in different environments.Comment: International Journal of AdHoc Network Systems, Volume 2, Number 2,
April 201
Secure Precise Clock Synchronization for Interconnected Body Area Networks
Secure time synchronization is a paramount service for wireless sensor networks (WSNs) constituted by multiple interconnected body area networks (BANs). We propose a novel approach to securely and efficiently synchronize nodes at BAN level and/or WSN level. Each BAN develops its own notion of time. To this effect, the nodes of a BAN synchronize with their BAN controller node. Moreover, controller nodes of different BANs cooperate to agree on a WSN global and/or to transfer UTC time. To reduce the number of exchanged synchronization messages, we use an environmental-aware time prediction algorithm. The performance analysis in this paper shows that our approach exhibits very advanced security, accuracy, precision, and low-energy trade-off. For comparable precision, our proposal outstands related clock synchronization protocols in energy efficiency and risk of attacks. These results are based on computations
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
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
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