260 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
Efficient Data Gathering in Wireless Sensor Networks Based on Matrix Completion and Compressive Sensing
Gathering data in an energy efficient manner in wireless sensor networks is
an important design challenge. In wireless sensor networks, the readings of
sensors always exhibit intra-temporal and inter-spatial correlations.
Therefore, in this letter, we use low rank matrix completion theory to explore
the inter-spatial correlation and use compressive sensing theory to take
advantage of intra-temporal correlation. Our method, dubbed MCCS, can
significantly reduce the amount of data that each sensor must send through
network and to the sink, thus prolong the lifetime of the whole networks.
Experiments using real datasets demonstrate the feasibility and efficacy of our
MCCS method
Adaptive Hierarchical Data Aggregation using Compressive Sensing (A-HDACS) for Non-smooth Data Field
Compressive Sensing (CS) has been applied successfully in a wide variety of
applications in recent years, including photography, shortwave infrared
cameras, optical system research, facial recognition, MRI, etc. In wireless
sensor networks (WSNs), significant research work has been pursued to
investigate the use of CS to reduce the amount of data communicated,
particularly in data aggregation applications and thereby improving energy
efficiency. However, most of the previous work in WSN has used CS under the
assumption that data field is smooth with negligible white Gaussian noise. In
these schemes signal sparsity is estimated globally based on the entire data
field, which is then used to determine the CS parameters. In more realistic
scenarios, where data field may have regional fluctuations or it is piecewise
smooth, existing CS based data aggregation schemes yield poor compression
efficiency. In order to take full advantage of CS in WSNs, we propose an
Adaptive Hierarchical Data Aggregation using Compressive Sensing (A-HDACS)
scheme. The proposed schemes dynamically chooses sparsity values based on
signal variations in local regions. We prove that A-HDACS enables more sensor
nodes to employ CS compared to the schemes that do not adapt to the changing
field. The simulation results also demonstrate the improvement in energy
efficiency as well as accurate signal recovery
Optimized Clustering Protocol for Balancing Energy in Wireless Sensor Networks
While wireless sensor networks (WSNs) are increasingly equipped to handle more complex functions and in-network processing may require these battery powered sensors to judiciously use their constrained energy to prolong the effective network lifetime. Cluster-based Hierarchical Routing Protocol using compressive sensing (CS) theory (CBHRP-CS) divides the network into several clusters, each managed by a set of CHs called a header. Each member of the header compresses the collected data using CS. This paper proposes an optimized clustering protocol using CS (OCP-CS) to improve the performance of WSNs by exploiting compressibility. In OCP-CS, each cluster is managed by a cluster head (CH). CHs are selected based on node concentration and sensor residual energy, and performs data aggregation using CS to reduce the energy consumed in the process of data sampling and transmission. Simulations show that our proposed protocol is effective in prolonging the network lifetime and supporting scalable data aggregation than existing protocols
An objective based classification of aggregation techniques for wireless sensor networks
Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented
SEPCS: Prolonging Stability Period of Wireless Sensor Networks using Compressive Sensing
Compressive sensing (CS) is an emerging theory thatis based on the fact that a small number of linear projections of asparse data contains enough information for reconstruction. CScan break through the asymmetry between the data acquisitionand information processing that makes a great challenge to therestriction energy and computation consumption of the sensornodes. In this paper, we propose a routing protocol called SEPCSfor clustered wireless sensor networks (WSNs) using CS. SEPCScombines a new clustering strategy with CS theory for prolongingstability period and network lifetime in WSNs. Our simulationresults show that the proposed protocol can effectively prolongthe stability period and network lifetime compared with existingprotocols
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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