2,372 research outputs found
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
Energy efficient clustering using the AMHC (adoptive multi-hop clustering) technique
IoT has gained fine attention in several field such as in industry applications, agriculture, monitoring, surveillance, similarly parallel growth has been observed in field of WSN. WSN is one of the primary component of IoT when it comes to sensing the data in various environment. Clustering is one of the basic approach in order to obtain the measurable performance in WSNs, Several algorithms of clustering aims to obtain the efficient data collection, data gathering and the routing. In this paper, a novel AMHC (Adaptive Multi-Hop Clustering) algorithm is proposed for the homogenous model, the main aim of algorithm is to obtain the higher efficiency and make it energy efficient. Our algorithm mainly contains the three stages: namely assembling, coupling and discarding. First stage involves the assembling of independent sets (maximum), second stage involves the coupling of independent sets and at last stage the superfluous nodes are discarded. Discarding superfluous nodes helps in achieving higher efficiency. Since our algorithm is a coloring algorithm, different color are used at the different stages for coloring the nodes. Afterwards our algorithm (AMHC) is compared with the existing system which is a combination of Second order data CC(Coupled Clustering) and Compressive-Projection PCA(Principal Component Analysis), and results shows that our algorithm excels in terms of several parameters such as energy efficiency, network lifetime, number of rounds performed
Energy efficient clustering and secure data aggregation in wireless sensor networks
Communication consumes the majority of a wireless sensor network\u27s limited energy. There are several ways to reduce the communication cost. Two approaches used in this work are clustering and in-network aggregation. The choice of a cluster head within each cluster is important because cluster heads use additional energy for their responsibilities and that burden needs to be carefully distributed. We introduce the energy constrained minimum dominating set (ECDS) to model the problem of optimally choosing cluster heads in the presence of energy constraints. We show its applicability to sensor networks and give an approximation algorithm of O(log n) for solving the ECDS problem. We propose a distributed algorithm for the constrained dominating set which runs in O(log n log [triangle]) rounds with high probability. We show experimentally that the distributed algorithm performs well in terms of energy usage, node lifetime, and clustering time and thus is very suitable for wireless sensor networks. Using aggregation in wireless sensor networks is another way to reduce the overall communication cost. However, changes in security are necessary when in- network aggregation is applied. Traditional end-to-end security is not suitable for use with in-network aggregation. A corrupted sensor has access to the intermediate data and can falsify results. Additively homomorphic encryption allows for aggregation of encrypted values, with the result being the same as the result as if unencrypted data were aggregated. Using public key cryptography, digital signatures can be used to achieve integrity. We propose a new algorithm using homomorphic encryption and additive digital signatures to achieve confidentiality, integrity and availability for in- network aggregation in wireless sensor networks. We prove that our digital signature algorithm which is based on Elliptic Curve Digital Signature Algorithm (ECDSA) is at least as secure as ECDSA. Even without in-network aggregation, security is a challenge in wireless sensor networks. In wireless sensor networks, not all messages need to be secured with the same level of encryption. We propose a new algorithm which provides adequate levels of security while providing much higher availablility [sic] than other security protocols. Our approach uses similar amounts of energy as a network without security --Abstract, page iv
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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