301 research outputs found
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 Cooja-based tool for coverage and fifetime evaluation in an in-building sensor network.
Contiki’s Cooja is a very popular wireless sensor network (WSN) simulator, but it lacks support for modelling sensing coverage, focusing instead on network connectivity and protocol performance. However, in practice, it is the ability of a sensor network to provide a satisfactory level of coverage that defines its ultimate utility for end-users. We introduce WSN-Maintain, a Cooja-based tool for coverage and network lifetime evaluation in an in-building WSN. To extend the network lifetime, but still maintain the required quality of coverage, the tool finds coverage redundant nodes, puts them to sleep and automatically turns them on when active nodes fail and coverage quality decreases. WSN-Maintain together with Cooja allow us to evaluate different approaches to maintain coverage. As use cases to the tool, we implement two redundant node algorithms: greedy-maintain, a centralised algorithm, and local-maintain, a localised algorithm to configure the initial network and to turn on redundant nodes. Using data from five real deployments, we show that our tool with simple redundant node algorithms and reading correlation can improve energy efficiency by putting more nodes to sleep
CHOP: Maximum Coverage Optimization and Resolve Hole Healing Problem using Sleep and Wake-up Technique for WSN
The Sensor Nodes (SN) play an important role in various hazardous applications environments such as military surveillance, forests, battlefield, etc. The Wireless Sensor Network (WSN) comprised multiple numbers of sensor nodes which are used to perform sensing the physical conditions and subsequently transmitting data to the Base Station (BS). The nodes have limited batteries. The random distribution of nodes in the hazardous areas causes overlapping of nodes and coverage hole issues in the network. The Coverage Optimization and Resolve Hole Healing (CHOP) Protocol is proposed to optimize the network's overlapping and resolve the coverage hole problem. The working phases of the proposed protocol are network initialization, formation of the cluster, Selection of Cluster Head, and sleep and wake-up phase. The issues are optimized, and maximum coverage is achieved for a specific sensing range. Using statistics and probability theory, a link is established between the radius of the node and the coverage area. The protocol used the sleep and wake phase to select optimal nodes active to achieve maximum coverage. The proposed protocol outperformed and showed improvements in the network's performance and lifetime compared to LEACH, TEEN, SEP, and DEEC protocols
LOCALIZED MOVEMENT CONTROL CONNECTIVITY RESTORATION ALGORITHMS FOR WIRELESS SENSOR AND ACTOR NETWORKS
Wireless Sensor and Actor Networks (WSANs) are gaining an increased interest
because of their suitability for mission-critical applications that require autonomous
and intelligent interaction with the environment. Hazardous application environments
such as forest fire monitoring, disaster management, search and rescue, homeland
security, battlefield reconnaissance, etc. make actors susceptible to physical damage.
Failure of a critical (i.e. cut-vertex) actor partitions the inter-actor network into
disjointed segments while leaving a coverage hole. Maintaining inter-actor
connectivity is extremely important in mission-critical applications of WSANs where
actors have to quickly plan an optimal coordinated response to detected events. Some
proactive approaches pursued in the literature deploy redundant nodes to provide fault
tolerance; however, this necessitates a large actor count that leads to higher cost and
becomes impractical. On the other hand, the harsh environment strictly prohibits an
external intervention to replace a failed node. Meanwhile, reactive approaches might
not be suitable for time-sensitive applications. The autonomous and unattended nature
of WSANs necessitates a self-healing and agile recovery process that involves
existing actors to mend the severed inter-actor connectivity by reconfiguring the
topology. Moreover, though the possibility of simultaneous multiple actor failure is
rare, it may be precipitated by a hostile environment and disastrous events. With only
localized information, recovery from such failures is extremely challenging.
Furthermore, some applications may impose application-level constraints while
recovering from a node failure.
In this dissertation, we address the challenging connectivity restoration problem while
maintaining minimal network state information. We have exploited the controlled
movement of existing (internal) actors to restore the lost connectivity while
minimizing the impact on coverage. We have pursued distributed greedy heuristics.
This dissertation presents four novel approaches for recovering from node failure. In
the first approach, volunteer actors exploit their partially utilized transmission power
and reposition themselves in such a way that the connectivity is restored. The second
approach identifies critical actors in advance, designates them preferably as noncritical
backup nodes that replace the failed primary if such contingency arises in the
future. In the third approach, we design a distributed algorithm that recovers from a
special case of multiple simultaneous failures. The fourth approach factors in
application-level constraints on the mobility of actors while recovering from node
failure and strives to minimize the impact of critical node failure on coverage and
connectivity. The performance of proposed approaches is analyzed and validated
through extensive simulations. Simulation results confirm the effectiveness of
proposed approaches that outperform the best contemporary schemes found in
literature
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
Topologie algébrique appliquée aux réseaux de capteurs
Simplicial complex representation gives a mathematical description of the topology of a wireless sensor network, i.e., its connectivity and coverage. In these networks, sensors are randomly deployed in bulk in order to ensure perfect connectivity and coverage. We propose an algorithm to discover which sensors are to be switched off, without modification of the topology, in order to reduce energy consumption. Our reduction algorithm can be applied to any type of simplicial complex and reaches an optimum solution. For random geometric simplicial complexes, we find boundaries for the number of removed vertices, as well as mathematical properties for the resulting simplicial complex. The complexity of our reduction algorithm boils down to the computation of the asymptotical behavior of the clique number of a random geometric graph. We provide almost sure asymptotical behavior for the clique number in all three percolation regimes of the geometric graph. In the second part, we apply the simplicial complex representation to cellular networks and improve our reduction algorithm to fit new purposes. First, we provide a frequency auto-planning algorithm for self-configuration of SON in future cellular networks. Then, we propose an energy conservation fot the self-optimization of wireless networks. Finally, we present a disaster recovery algorithm for any type of damaged wireless network. In this last chapter, we also introduce the simulation of determinantal point processes in wireless networks.Simplicial complex representation gives a mathematical description of the topology of a wireless sensor network, i.e., its connectivity and coverage. In these networks, sensors are randomly deployed in bulk in order to ensure perfect connectivity and coverage. We propose an algorithm to discover which sensors are to be switched off, without modification of the topology, in order to reduce energy consumption. Our reduction algorithm can be applied to any type of simplicial complex and reaches an optimum solution. For random geometric simplicial complexes, we find boundaries for the number of removed vertices, as well as mathematical properties for the resulting simplicial complex. The complexity of our reduction algorithm boils down to the computation of the asymptotical behavior of the clique number of a random geometric graph. We provide almost sure asymptotical behavior for the clique number in all three percolation regimes of the geometric graph. In the second part, we apply the simplicial complex representation to cellular networks and improve our reduction algorithm to fit new purposes. First, we provide a frequency auto-planning algorithm for self-configuration of SON in future cellular networks. Then, we propose an energy conservation fot the self-optimization of wireless networks. Finally, we present a disaster recovery algorithm for any type of damaged wireless network. In this last chapter, we also introduce the simulation of determinantal point processes in wireless networks.La représentation par complexes simpliciaux fournit une description mathématique de la topologie d’un réseau de capteurs, c’est-à -dire sa connectivité et sa couverture. Dans ces réseaux, les capteurs sont déployés aléatoirement en grand nombre afin d’assurer une connectivité et une couverture parfaite. Nous proposons un algorithme qui permet de déterminer quels capteurs mettre en veille, sans modification de topologie, afin de réduire la consommation d’énergie. Notre algorithme de réduction peut être appliqué à tous les types de complexes simpliciaux, et atteint un résultat optimal. Pour les complexes simpliciaux aléatoires géométriques, nous obtenons des bornes pour le nombre de sommets retirés, et trouvons des propriétés mathématiques pour le complexe simplicial obtenu. En cherchant la complexité de notre algorithme, nous sommes réduits à calculer le comportement asymptotique de la taille de la plus grande clique dans un graphe géométrique aléatoire. Nous donnons le comportement presque sûr de la taille de la plus grande clique pour les trois régimes de percolation du graphe géométrique. Dans la deuxième partie, nous appliquons la représentation par complexes simpliciaux aux réseaux cellulaires, et améliorons notre algorithme de réduction pour répondre à de nouvelles demandes. Tout d’abord, nous donnons un algorithme pour la planification automatique de fréquences, pour la configuration automatique des réseaux cellulaires de la nouvelle génération bénéficiant de la technologie SON. Puis, nous proposons un algorithme d’économie d’énergie pour l’optimisation des réseaux sans fil. Enfin, nous présentons un algorithme pour le rétablissement des réseaux sans fil endommagés après une catastrophe. Dans ce dernier chapitre, nous introduisons la simulation des processus ponctuelsdéterminantaux dans les réseaux sans fil
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