1,080 research outputs found
A Systematic Approach to Constructing Incremental Topology Control Algorithms Using Graph Transformation
Communication networks form the backbone of our society. Topology control
algorithms optimize the topology of such communication networks. Due to the
importance of communication networks, a topology control algorithm should
guarantee certain required consistency properties (e.g., connectivity of the
topology), while achieving desired optimization properties (e.g., a bounded
number of neighbors). Real-world topologies are dynamic (e.g., because nodes
join, leave, or move within the network), which requires topology control
algorithms to operate in an incremental way, i.e., based on the recently
introduced modifications of a topology. Visual programming and specification
languages are a proven means for specifying the structure as well as
consistency and optimization properties of topologies. In this paper, we
present a novel methodology, based on a visual graph transformation and graph
constraint language, for developing incremental topology control algorithms
that are guaranteed to fulfill a set of specified consistency and optimization
constraints. More specifically, we model the possible modifications of a
topology control algorithm and the environment using graph transformation
rules, and we describe consistency and optimization properties using graph
constraints. On this basis, we apply and extend a well-known constructive
approach to derive refined graph transformation rules that preserve these graph
constraints. We apply our methodology to re-engineer an established topology
control algorithm, kTC, and evaluate it in a network simulation study to show
the practical applicability of our approachComment: This document corresponds to the accepted manuscript of the
referenced journal articl
A Message Passing Strategy for Decentralized Connectivity Maintenance in Agent Removal
In a multi-agent system, agents coordinate to achieve global tasks through
local communications. Coordination usually requires sufficient information
flow, which is usually depicted by the connectivity of the communication
network. In a networked system, removal of some agents may cause a
disconnection. In order to maintain connectivity in agent removal, one can
design a robust network topology that tolerates a finite number of agent
losses, and/or develop a control strategy that recovers connectivity. This
paper proposes a decentralized control scheme based on a sequence of
replacements, each of which occurs between an agent and one of its immediate
neighbors. The replacements always end with an agent, whose relocation does not
cause a disconnection. We show that such an agent can be reached by a local
rule utilizing only some local information available in agents' immediate
neighborhoods. As such, the proposed message passing strategy guarantees the
connectivity maintenance in arbitrary agent removal. Furthermore, we
significantly improve the optimality of the proposed scheme by incorporating
-criticality (i.e. the criticality of an agent in its
-neighborhood).Comment: 9 pages, 9 figure
Fast Biconnectivity Restoration in Multi-Robot Systems for Robust Communication Maintenance
Maintaining a robust communication network plays an important role in the
success of a multi-robot team jointly performing an optimization task. A key
characteristic of a robust multi-robot system is the ability to repair the
communication topology itself in the case of robot failure. In this paper, we
focus on the Fast Biconnectivity Restoration (FBR) problem, which aims to
repair a connected network to make it biconnected as fast as possible, where a
biconnected network is a communication topology that cannot be disconnected by
removing one node. We develop a Quadratically Constrained Program (QCP)
formulation of the FBR problem, which provides a way to optimally solve the
problem. We also propose an approximation algorithm for the FBR problem based
on graph theory. By conducting empirical studies, we demonstrate that our
proposed approximation algorithm performs close to the optimal while
significantly outperforming the existing solutions
Minimal Driver Nodes for Structural Controllability of Large-Scale Dynamical Systems: Node Classification
This paper considers the problem of minimal control inputs to affect the
system states such that the resulting system is structurally controllable. This
problem and the dual problem of minimal observability are claimed to have no
polynomial-order exact solution and, therefore, are NP-hard. Here, adopting a
graph-theoretic approach, this problem is solved for general nonlinear (and
also structure-invariant) systems and a P-order solution is proposed. In this
direction, the dynamical system is modeled as a directed graph, called
\textit{system digraph}, and two types of graph components are introduced which
are tightly related with structural controllability. Two types of nodes which
are required to be affected (or driven) by an input, called \textit{driver
nodes}, are defined, and minimal number of these driver nodes are obtained.
Polynomial-order complexity of the given algorithms to solve the problem
ensures applicability of the solution for analysis of large-scale dynamical
systems. {The structural results in this paper are significant as compared to
the existing literature which offer approximate and computationally
less-efficient, e.g. Gramian-based, solutions for the problem, while this paper
provides exact solution with lower computational complexity and applicable for
controllability analysis of nonlinear systems.Comment: accepted at IEEE Systems Journa
Simplicial Homology for Future Cellular Networks
Simplicial homology is a tool that provides a mathematical way to compute the
connectivity and the coverage of a cellular network without any node location
information. In this article, we use simplicial homology in order to not only
compute the topology of a cellular network, but also to discover the clusters
of nodes still with no location information. We propose three algorithms for
the management of future cellular networks. The first one is a frequency
auto-planning algorithm for the self-configuration of future cellular networks.
It aims at minimizing the number of planned frequencies while maximizing the
usage of each one. Then, our energy conservation algorithm falls into the
self-optimization feature of future cellular networks. It optimizes the energy
consumption of the cellular network during off-peak hours while taking into
account both coverage and user traffic. Finally, we present and discuss the
performance of a disaster recovery algorithm using determinantal point
processes to patch coverage holes
A mobile assisted coverage hole patching scheme based on particle swarm optimization for WSNs
Wireless sensor networks (WSNs) have drawn much research attention in recent years due to the superior performance in multiple applications, such as military and industrial monitoring, smart home, disaster restoration etc. In such applications, massive sensor nodes are randomly deployed and they remain static after the deployment, to fully cover the target sensing area. This will usually cause coverage redundancy or coverage hole problem. In order to effectively deploy sensors to cover whole area, we present a novel node deployment algorithm based on mobile sensors. First, sensor nodes are randomly deployed in target area, and they remain static or switch to the sleep mode after deployment. Second, we partition the network into grids and calculate the coverage rate of each grid. We select grids with lower coverage rate as candidate grids. Finally, we awake mobile sensors from sleep mode to fix coverage hole, particle swarm optimization (PSO) algorithm is used to calculate moving position of mobile sensors. Simulation results show that our algorithm can effectively improve the coverage rate of WSNs
Secure and robust multi-constrained QoS aware routing algorithm for VANETs
Secure QoS routing algorithms are a fundamental part of wireless networks that aim to provide services with QoS and security guarantees. In Vehicular Ad hoc Networks (VANETs), vehicles perform routing functions, and at the same time act as end-systems thus routing control messages are transmitted unprotected over wireless channels. The QoS of the entire network could be degraded by an attack on the routing process, and manipulation of the routing control messages. In this paper, we propose a novel secure and reliable multi-constrained QoS aware routing algorithm for VANETs. We employ the Ant Colony Optimisation (ACO) technique to compute feasible routes in VANETs subject to multiple QoS constraints determined by the data traffic type. Moreover, we extend the VANET-oriented Evolving Graph (VoEG) model to perform plausibility checks on the exchanged routing control messages among vehicles. Simulation results show that the QoS can be guaranteed while applying security mechanisms to ensure a reliable and robust routing service
Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method
The deployment of the sensor nodes (SNs) always plays a decisive role in the
system performance of wireless sensor networks (WSNs). In this work, we propose
an optimal deployment method for practical heterogeneous WSNs which gives a
deep insight into the trade-off between the reliability and deployment cost.
Specifically, this work aims to provide the optimal deployment of SNs to
maximize the coverage degree and connection degree, and meanwhile minimize the
overall deployment cost. In addition, this work fully considers the
heterogeneity of SNs (i.e. differentiated sensing range and deployment cost)
and three-dimensional (3-D) deployment scenarios. This is a multi-objective
optimization problem, non-convex, multimodal and NP-hard. To solve it, we
develop a novel swarm-based multi-objective optimization algorithm, known as
the competitive multi-objective marine predators algorithm (CMOMPA) whose
performance is verified by comprehensive comparative experiments with ten other
stateof-the-art multi-objective optimization algorithms. The computational
results demonstrate that CMOMPA is superior to others in terms of convergence
and accuracy and shows excellent performance on multimodal multiobjective
optimization problems. Sufficient simulations are also conducted to evaluate
the effectiveness of the CMOMPA based optimal SNs deployment method. The
results show that the optimized deployment can balance the trade-off among
deployment cost, sensing reliability and network reliability. The source code
is available on https://github.com/iNet-WZU/CMOMPA.Comment: 25 page
Modelling and Design of Resilient Networks under Challenges
Communication networks, in particular the Internet, face a variety of challenges that can disrupt our daily lives resulting in the loss of human lives and significant financial costs in the worst cases. We define challenges as external events that trigger faults that eventually result in service failures. Understanding these challenges accordingly is essential for improvement of the current networks and for designing Future Internet architectures. This dissertation presents a taxonomy of challenges that can help evaluate design choices for the current and Future Internet. Graph models to analyse critical infrastructures are examined and a multilevel graph model is developed to study interdependencies between different networks. Furthermore, graph-theoretic heuristic optimisation algorithms are developed. These heuristic algorithms add links to increase the resilience of networks in the least costly manner and they are computationally less expensive than an exhaustive search algorithm. The performance of networks under random failures, targeted attacks, and correlated area-based challenges are evaluated by the challenge simulation module that we developed. The GpENI Future Internet testbed is used to conduct experiments to evaluate the performance of the heuristic algorithms developed
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