2,420 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
No-Sense: Sense with Dormant Sensors
Wireless sensor networks (WSNs) have enabled continuous monitoring of an area
of interest (body, room, region, etc.) while eliminating expensive wired
infrastructure. Typically in such applications, wireless sensor nodes report
the sensed values to a sink node, where the information is required for the
end-user. WSNs also provide the flexibility to the end-user for choosing
several parameters for the monitoring application. For example, placement of
sensors, frequency of sensing and transmission of those sensed data. Over the
years, the advancement in embedded technology has led to increased processing
power and memory capacity of these battery powered devices. However, batteries
can only supply limited energy, thus limiting the lifetime of the network. In
order to prolong the lifetime of the deployment, various efforts have been made
to improve the battery technologies and also reduce the energy consumption of
the sensor node at various layers in the networking stack. Of all the
operations in the network stack, wireless data transmission and reception have
found to consume most of the energy. Hence many proposals found in the
literature target reducing them through intelligent schemes like power control,
reducing retransmissions, etc. In this article we propose a new framework
called Virtual Sensing Framework (VSF), which aims to sufficiently satisfy
application requirements while conserving energy at the sensor nodes.Comment: Accepted for publication in IEEE Twentieth National Conference on
Communications (NCC-2014
Energy efficient privacy preserved data gathering in wireless sensor networks having multiple sinks
Wireless sensor networks (WSNs) generally have a many-to-one structure so that event information flows from sensors to a unique sink. In recent WSN applications, many-tomany structures are evolved due to need for conveying collected event information to multiple sinks at the same time. This study proposes an anonymity method bases on k-anonymity for preventing record disclosure of collected event information in WSNs. Proposed method takes the anonymity requirements of multiple sinks into consideration by providing different levels of privacy for each destination sink. Attributes, which may identify of an event owner, are generalized or encrypted in order to
meet the different anonymity requirements of sinks. Privacy guaranteed event information can be multicasted to all sinks instead of sending to each sink one by one. Since minimization of energy consumption is an important design criteria for WSNs, our method enables us to multicast the same event information
to multiple sinks and reduce energy consumption
Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes
Time series forecasting is an important predictive methodology which can be
applied to a wide range of problems. Particularly, forecasting the indoor
temperature permits an improved utilization of the HVAC (Heating, Ventilating
and Air Conditioning) systems in a home and thus a better energy efficiency.
With such purpose the paper describes how to implement an Artificial Neural
Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous
intelligent wireless sensor network. The present paper uses a Wireless Sensor
Networks (WSN) to monitor and forecast the indoor temperature in a smart home,
based on low resources and cost microcontroller technology as the 8051MCU. An
on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs,
has been developed for real-time time series learning. It performs the model
training with every new data that arrive to the system, without saving enormous
quantities of data to create a historical database as usual, i.e., without
previous knowledge. Consequently to validate the approach a simulation study
through a Bayesian baseline model have been tested in order to compare with a
database of a real application aiming to see the performance and accuracy. The
core of the paper is a new algorithm, based on the BP one, which has been
described in detail, and the challenge was how to implement a computational
demanding algorithm in a simple architecture with very few hardware resources.Comment: 28 pages, Published 21 April 2015 at MDPI's journal "Sensors
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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