7,677 research outputs found
Distributed Clustering in Cognitive Radio Ad Hoc Networks Using Soft-Constraint Affinity Propagation
Absence of network infrastructure and heterogeneous spectrum availability in cognitive radio ad hoc networks (CRAHNs) necessitate the self-organization of cognitive radio users (CRs) for efficient spectrum coordination. The cluster-based structure is known to be effective in both guaranteeing system performance and reducing communication overhead in variable network environment. In this paper, we propose a distributed clustering algorithm based on soft-constraint affinity propagation message passing model (DCSCAP). Without dependence on predefined common control channel (CCC), DCSCAP relies on the distributed message passing among CRs through their available channels, making the algorithm applicable for large scale networks. Different from original soft-constraint affinity propagation algorithm, the maximal iterations of message passing is controlled to a relatively small number to accommodate to the dynamic environment of CRAHNs. Based on the accumulated evidence for clustering from the message passing process, clusters are formed with the objective of grouping the CRs with similar spectrum availability into smaller number of clusters while guaranteeing at least one CCC in each cluster. Extensive simulation results demonstrate the preference of DCSCAP compared with existing algorithms in both efficiency and robustness of the clusters
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
Performance evaluation of two-fuzzy based cluster head selection systems for wireless sensor networks
Sensor networks supported by recent technological advances in low power wireless communications along with silicon integration of various functionalities are emerging as a critically important computer class that enable novel and low cost applications. There are many fundamental problems that sensor networks research will have to address in order to ensure
a reasonable degree of cost and system quality. Cluster formation and cluster head selection are important problems in sensor
network applications and can drastically affect the network’s communication energy dissipation. However, selecting of the
cluster head is not easy in different environments which may have different characteristics. In this paper, in order to deal with this problem, we propose two fuzzy-based systems for cluster head selection in sensor networks. We call these systems: FCHS
System1 and FCHS System2. We evaluate the proposed systems by simulations and have shown that FCHS System2 make a good selection of the cluster head compared with FCHS System1 and another previous system.Peer ReviewedPostprint (published version
Energy Efficient Clustering and Routing in Mobile Wireless Sensor Network
A critical need in Mobile Wireless Sensor Network (MWSN) is to achieve energy
efficiency during routing as the sensor nodes have scarce energy resource. The
nodes' mobility in MWSN poses a challenge to design an energy efficient routing
protocol. Clustering helps to achieve energy efficiency by reducing the
organization complexity overhead of the network which is proportional to the
number of nodes in the network. This paper proposes a novel hybrid multipath
routing algorithm with an efficient clustering technique. A node is selected as
cluster head if it has high surplus energy, better transmission range and least
mobility. The Energy Aware (EA) selection mechanism and the Maximal Nodal
Surplus Energy estimation technique incorporated in this algorithm improves the
energy performance during routing. Simulation results can show that the
proposed clustering and routing algorithm can scale well in dynamic and energy
deficient mobile sensor network.Comment: 9 pages, 4 figure
Secure and Privacy-Preserving Data Aggregation Protocols for Wireless Sensor Networks
This chapter discusses the need of security and privacy protection mechanisms
in aggregation protocols used in wireless sensor networks (WSN). It presents a
comprehensive state of the art discussion on the various privacy protection
mechanisms used in WSNs and particularly focuses on the CPDA protocols proposed
by He et al. (INFOCOM 2007). It identifies a security vulnerability in the CPDA
protocol and proposes a mechanism to plug that vulnerability. To demonstrate
the need of security in aggregation process, the chapter further presents
various threats in WSN aggregation mechanisms. A large number of existing
protocols for secure aggregation in WSN are discussed briefly and a protocol is
proposed for secure aggregation which can detect false data injected by
malicious nodes in a WSN. The performance of the protocol is also presented.
The chapter concludes while highlighting some future directions of research in
secure data aggregation in WSNs.Comment: 32 pages, 7 figures, 3 table
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