11 research outputs found
Determining Distributions of Security Means for WSNs based on the Model of a Neighbourhood Watch
Neighbourhood watch is a concept that allows a community to distribute a
complex security task in between all members. Members of the community carry
out individual security tasks to contribute to the overall security of it. It
reduces the workload of a particular individual while securing all members and
allowing them to carry out a multitude of security tasks. Wireless sensor
networks (WSNs) are composed of resource-constraint independent battery driven
computers as nodes communicating wirelessly. Security in WSNs is essential.
Without sufficient security, an attacker is able to eavesdrop the
communication, tamper monitoring results or deny critical nodes providing their
service in a way to cut off larger network parts. The resource-constraint
nature of sensor nodes prevents them from running full-fledged security
protocols. Instead, it is necessary to assess the most significant security
threats and implement specialised protocols. A neighbourhood-watch inspired
distributed security scheme for WSNs has been introduced by Langend\"orfer. Its
goal is to increase the variety of attacks a WSN can fend off. A framework of
such complexity has to be designed in multiple steps. Here, we introduce an
approach to determine distributions of security means on large-scale static
homogeneous WSNs. Therefore, we model WSNs as undirected graphs in which two
nodes connected iff they are in transmission range. The framework aims to
partition the graph into distinct security means resulting in the targeted
distribution. The underlying problems turn out to be NP hard and we attempt to
solve them using linear programs (LPs). To evaluate the computability of the
LPs, we generate large numbers of random {\lambda}-precision unit disk graphs
(UDGs) as representation of WSNs. For this purpose, we introduce a novel
{\lambda}-precision UDG generator to model WSNs with a minimal distance in
between nodes
A Dependable Slepian-Wolf Coding Based Clustering Algorithm for Data Aggregation in Wireless Sensor Networks
International audienceThis paper considers the Slepian-Wolf coding based data aggregation problem and the corresponding dependable clustering problem in wireless sensor networks (WSNs). A dependable Slepian-Wolf coding based clustering (DSWC) algorithm is proposed to provide dependable clustering against cluster-head failures. The proposed D-SWC algorithm attempts to elect a primary cluster head and a backup cluster head for each cluster member during clustering so that once a failure occurs to the primary cluster head the cluster members within the failed cluster can promptly switchover to the backup cluster head and thus recover the connectivity of the failed cluster to the data sink without waiting for the next-round clustering to be performed. Simulation results show that the DSWC algorithm can effectively increase the amount of data transmitted to the data sink as compared with an existing nondependable clustering algorithm for Slepian-Wolf coding based data aggregation in WSNs
10211 Abstracts Collection -- Flexible Network Design
From Monday 24.05.2010---Friday 28.05.2010, the Dagstuhl Seminar 10211 ``Flexible Network Design \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Characterizing Heterogeneity in Cooperative Networks From a Resource Distribution View-Point
© by International Press. First published in Communications in Information and Systems, Vol. 14, no. 1, 2014, by International Press.DOI: http://dx.doi.org/10.4310/CIS.2014.v14.n1.a1A network of agents in which agents with a diverse set of resources
or capabilities interact and coordinate with each other to accomplish various tasks constitutes a heterogeneous cooperative network. In this paper, we investigate heterogeneity in terms of resources allocated to agents within the network. The objective is to distribute resources in such a way that every agent in the network
should be able to utilize all these resources through local interactions. In particular, we formulate a graph coloring problem in which
each node is assigned a subset of labels from a labeling set, and a graph is considered to be completely heterogeneous whenever all the labels in the labeling set are available in the closed neighborhood of every node. The total number of different resources that can be accommodated within a system under this setting depends
on the underlying graph structure of the network. This paper provides an analysis of the assignment of multiple resources to nodes and the effect of these assignments on the overall heterogeneity of the network
Network-centric methods for heterogeneous multiagent systems
We present tools for a network topology based characterization of heterogeneity in multiagent systems, thereby providing a framework for the analysis and design of heterogeneous multiagent networks from a network structure view-point. In heterogeneous networks, agents with a diverse set of resources coordinate with each other. Coordination among different
agents and the structure of the underlying network topology have significant impacts on the overall behavior and functionality of the system. Using constructs from graph theory, a qualitative as well as a quantitative analysis is performed to examine an inter-relationship between the network topology and the distribution of agents with various capabilities in heterogeneous networks. Our goal is to allow agents maximally exploit heterogeneous resources available within the network through local interactions, thus exploring a promise heterogeneous networks hold to accomplish complicated tasks by leveraging upon the assorted capabilities of agents. For a reliable operations of such systems, the issue of security against intrusions and malicious agents is also addressed. We provide a scheme to secure a network against a sequence of intruder attacks through a set of heterogeneous guards. Moreover, robustness of networked systems against noise corruption and structural changes in the underlying network topology is also examined.Ph.D
Coverage and Connectivity Aware Neural Network Based Energy Efficient Routing in Wireless Sensor Networks
There are many challenges when designing and deploying wireless sensor
networks (WSNs). One of the key challenges is how to make full use of the
limited energy to prolong the lifetime of the network, because energy is a
valuable resource in WSNs. The status of energy consumption should be
continuously monitored after network deployment. In this paper, we propose
coverage and connectivity aware neural network based energy efficient routing
in WSN with the objective of maximizing the network lifetime. In the proposed
scheme, the problem is formulated as linear programming (LP) with coverage and
connectivity aware constraints. Cluster head selection is proposed using
adaptive learning in neural networks followed by coverage and connectivity
aware routing with data transmission. The proposed scheme is compared with
existing schemes with respect to the parameters such as number of alive nodes,
packet delivery fraction, and node residual energy. The simulation results show
that the proposed scheme can be used in wide area of applications in WSNs.Comment: 16 Pages, JGraph-Hoc Journa
A Weakly-Robust PTAS for Minimum Clique Partition in Unit Disk Graphs
We consider the problem of partitioning the set of vertices of a given unit
disk graph (UDG) into a minimum number of cliques. The problem is NP-hard and
various constant factor approximations are known, with the current best ratio
of 3. Our main result is a {\em weakly robust} polynomial time approximation
scheme (PTAS) for UDGs expressed with edge-lengths, it either (i) computes a
clique partition or (ii) gives a certificate that the graph is not a UDG; for
the case (i) that it computes a clique partition, we show that it is guaranteed
to be within (1+\eps) ratio of the optimum if the input is UDG; however if
the input is not a UDG it either computes a clique partition as in case (i)
with no guarantee on the quality of the clique partition or detects that it is
not a UDG. Noting that recognition of UDG's is NP-hard even if we are given
edge lengths, our PTAS is a weakly-robust algorithm. Our algorithm can be
transformed into an O(\frac{\log^* n}{\eps^{O(1)}}) time distributed PTAS.
We consider a weighted version of the clique partition problem on vertex
weighted UDGs that generalizes the problem. We note some key distinctions with
the unweighted version, where ideas useful in obtaining a PTAS breakdown. Yet,
surprisingly, it admits a (2+\eps)-approximation algorithm for the weighted
case where the graph is expressed, say, as an adjacency matrix. This improves
on the best known 8-approximation for the {\em unweighted} case for UDGs
expressed in standard form.Comment: 21 pages, 9 figure
Connected Dominating Set Based Topology Control in Wireless Sensor Networks
Wireless Sensor Networks (WSNs) are now widely used for monitoring and controlling of systems where human intervention is not desirable or possible. Connected Dominating Sets (CDSs) based topology control in WSNs is one kind of hierarchical method to ensure sufficient coverage while reducing redundant connections in a relatively crowded network. Moreover, Minimum-sized Connected Dominating Set (MCDS) has become a well-known approach for constructing a Virtual Backbone (VB) to alleviate the broadcasting storm for efficient routing in WSNs extensively. However, no work considers the load-balance factor of CDSsin WSNs. In this dissertation, we first propose a new concept — the Load-Balanced CDS (LBCDS) and a new problem — the Load-Balanced Allocate Dominatee (LBAD) problem. Consequently, we propose a two-phase method to solve LBCDS and LBAD one by one and a one-phase Genetic Algorithm (GA) to solve the problems simultaneously.
Secondly, since there is no performance ratio analysis in previously mentioned work, three problems are investigated and analyzed later. To be specific, the MinMax Degree Maximal Independent Set (MDMIS) problem, the Load-Balanced Virtual Backbone (LBVB) problem, and the MinMax Valid-Degree non Backbone node Allocation (MVBA) problem. Approximation algorithms and comprehensive theoretical analysis of the approximation factors are presented in the dissertation.
On the other hand, in the current related literature, networks are deterministic where two nodes are assumed either connected or disconnected. In most real applications, however, there are many intermittently connected wireless links called lossy links, which only provide probabilistic connectivity. For WSNs with lossy links, we propose a Stochastic Network Model (SNM). Under this model, we measure the quality of CDSs using CDS reliability. In this dissertation, we construct an MCDS while its reliability is above a preset applicationspecified threshold, called Reliable MCDS (RMCDS). We propose a novel Genetic Algorithm (GA) with immigrant schemes called RMCDS-GA to solve the RMCDS problem.
Finally, we apply the constructed LBCDS to a practical application under the realistic SNM model, namely data aggregation. To be specific, a new problem, Load-Balanced Data Aggregation Tree (LBDAT), is introduced finally. Our simulation results show that the proposed algorithms outperform the existing state-of-the-art approaches significantly