13,777 research outputs found
Efficient organization of nodes in wireless sensor networks (clustering location-based LEACH)
The rapid development of connected devices and wireless communication has enabled several researchers to study wireless sensor networks and propose methods and algorithms to improve their performance. Wireless sensor networks (WSN) are composed of several sensor nodes deployed to collect and transfer data to base station (BS). Sensor node is considered as the main element in this field, characterized by minimal capacities of storage, energy, and computing. In consequence of the important impact of the energy on network lifetime, several researches are interested to propose different mechanisms to minimize energy consumption. In this work, we propose a new enhancement of low-energy adaptive clustering hierarchy (LEACH) protocol, named clustering location-based LEACH (CLOC-LEACH), which represents a continuity of our previous published work location-based LEACH (LOC-LEACH). The proposed protocol organizes sensor nodes into four regions, using clustering mechanism. In addition, an efficient concept is adopted to choose cluster head. CLOC-LEACH considers the energy as the principal metric to choose cluster heads and uses a gateway node to ensure the inter-cluster communication. The simulation with MATLAB shows that our contribution offers better performance than LEACH and LOC-LEACH, in terms of stability, energy consumption and network lifetime
Improved Fair-Zone technique using Mobility Prediction in WSN
The self-organizational ability of ad-hoc Wireless Sensor Networks (WSNs) has
led them to be the most popular choice in ubiquitous computing. Clustering
sensor nodes organizing them hierarchically have proven to be an effective
method to provide better data aggregation and scalability for the sensor
network while conserving limited energy. It has some limitation in energy and
mobility of nodes. In this paper we propose a mobility prediction technique
which tries overcoming above mentioned problems and improves the life time of
the network. The technique used here is Exponential Moving Average for online
updates of nodal contact probability in cluster based network.Comment: 10 pages, 7 figures, Published in International Journal Of Advanced
Smart Sensor Network Systems (IJASSN
Secure Clustering in DSN with Key Predistribution and WCDS
This paper proposes an efficient approach of secure clustering in distributed
sensor networks. The clusters or groups in the network are formed based on
offline rank assignment and predistribution of secret keys. Our approach uses
the concept of weakly connected dominating set (WCDS) to reduce the number of
cluster-heads in the network. The formation of clusters in the network is
secured as the secret keys are distributed and used in an efficient way to
resist the inclusion of any hostile entity in the clusters. Along with the
description of our approach, we present an analysis and comparison of our
approach with other schemes. We also mention the limitations of our approach
considering the practical implementation of the sensor networks.Comment: 6 page
Overlapping Multi-hop Clustering for Wireless Sensor Networks
Clustering is a standard approach for achieving efficient and scalable
performance in wireless sensor networks. Traditionally, clustering algorithms
aim at generating a number of disjoint clusters that satisfy some criteria. In
this paper, we formulate a novel clustering problem that aims at generating
overlapping multi-hop clusters. Overlapping clusters are useful in many sensor
network applications, including inter-cluster routing, node localization, and
time synchronization protocols. We also propose a randomized, distributed
multi-hop clustering algorithm (KOCA) for solving the overlapping clustering
problem. KOCA aims at generating connected overlapping clusters that cover the
entire sensor network with a specific average overlapping degree. Through
analysis and simulation experiments we show how to select the different values
of the parameters to achieve the clustering process objectives. Moreover, the
results show that KOCA produces approximately equal-sized clusters, which
allows distributing the load evenly over different clusters. In addition, KOCA
is scalable; the clustering formation terminates in a constant time regardless
of the network size
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
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