154 research outputs found
Coordination of Mobile Mules via Facility Location Strategies
In this paper, we study the problem of wireless sensor network (WSN)
maintenance using mobile entities called mules. The mules are deployed in the
area of the WSN in such a way that would minimize the time it takes them to
reach a failed sensor and fix it. The mules must constantly optimize their
collective deployment to account for occupied mules. The objective is to define
the optimal deployment and task allocation strategy for the mules, so that the
sensors' downtime and the mules' traveling distance are minimized. Our
solutions are inspired by research in the field of computational geometry and
the design of our algorithms is based on state of the art approximation
algorithms for the classical problem of facility location. Our empirical
results demonstrate how cooperation enhances the team's performance, and
indicate that a combination of k-Median based deployment with closest-available
task allocation provides the best results in terms of minimizing the sensors'
downtime but is inefficient in terms of the mules' travel distance. A
k-Centroid based deployment produces good results in both criteria.Comment: 12 pages, 6 figures, conferenc
A Distance-Based Data-Mule Scheduling Technique for Lesser Nodal Delay in Wireless Sensor Network
Nodal delay in wireless sensor network is an indisputable factor in the medium of communication. Factor such as changeability of communication devices, network topologies, packet-sizes, and transmission rate demands to develop data-mule queue scheduling technique. Our proposed data-mule scheduling technique accomplish this through simulations using standard software written in C# by controlling data-mule schedules that collects data from all the nodes connected to the hop. The scheme identifies the hierarchical positions of static source nodes and the distance of mobile source nodes from the hop with rescheduling based on the newly acquired distances. Source nodes applied with data-mule scheduling technique resulted to lower nodal delay. Transmission of packet-data is efficiently and effectively improved
Opportunistic data collection and routing in segmented wireless sensor networks
La surveillance reĢgulieĢre des opeĢrations dans les aires de manoeuvre (voies de circulation et pistes) et aires de stationnement d'un aeĢroport est une taĢche cruciale pour son fonctionnement. Les strateĢgies utiliseĢes aĢ cette fin visent Ć permettre la mesure des variables environnementales, l'identification des deĢbris (FOD) et l'enregistrement des statistiques d'utilisation de diverses sections de la surface. Selon un groupe de gestionnaires et controĢleurs d'aeĢroport interrogeĢs, cette surveillance est un privileĢge des grands aeĢroports en raison des couĢts eĢleveĢs d'acquisition, d'installation et de maintenance des technologies existantes. Les moyens et petits aeĢroports se limitent gĆ©nĆ©ralement aĢ la surveillance de quelques variables environnementales et des FOD effectueĢe visuellement par l'homme. Cette dernieĢre activiteĢ impose l'arreĢt du fonctionnement des pistes pendant l'inspection. Dans cette theĢse, nous proposons une solution alternative baseĢe sur les reĢseaux de capteurs sans fil (WSN) qui, contrairement aux autres meĢthodes, combinent les proprieĢteĢs de faible couĢt d'installation et maintenance, de dĆ©ploiement rapide, d'eĢvolutiviteĢ tout en permettant d'effectuer des mesures sans interfeĢrer avec le fonctionnement de l'aeĢroport. En raison de la superficie d'un aeĢroport et de la difficulteĢ de placer des capteurs sur des zones de transit, le WSN se composerait d'une collection de sous-reĢseaux isoleĢs les uns des autres et du puits. Pour gĆ©rer cette segmentation, notre proposition s'appuie sur l'utilisation opportuniste des vĆ©hicules circulants dans l'aĆ©roport considĆ©rĆ©s alors comme un type speĢcial de nÅud appeleĢ Mobile Ubiquitous LAN Extension (MULE) chargĆ© de collecter les donneĢes des sous-reĢseaux le long de son trajet et de les transfeĢrer vers le puits. L'une des exigences pour le deĢploiement d'un nouveau systeĢme dans un aeĢroport est qu'il cause peu ou pas d'interruption des opeĢrations reĢgulieĢres. C'est pourquoi l'utilisation d'une approche opportuniste basĆ© sur des MULE est privileĢgieĢe dans cette theĢse. Par opportuniste, nous nous reĢfeĢrons au fait que le roĢle de MULE est joueĢ par certains des veĢhicules deĢjaĢ existants dans un aeĢroport et effectuant leurs deĢplacements normaux. Et certains nÅuds des sous- reĢseaux exploiteront tout moment de contact avec eux pour leur transmettre les donneĢes Ć transfĆ©rer ensuite au puits. Une caracteĢristique des MULEs dans notre application est qu'elles ont des trajectoires structureĢes (suivant les voies de circulation dans l'aeĢroport), en ayant eĢventuellement un contact avec l'ensemble des nÅuds situeĢs le long de leur trajet (appeleĢs sous-puits). Ceci implique la neĢcessiteĢ de dĆ©finir une strateĢgie de routage dans chaque sous-reĢseau, capable d'acheminer les donneĢes collecteĢes des nÅuds vers les sous-puits et de reĢpartir les paquets de donneĢes entre eux afin que le temps en contact avec la MULE soit utiliseĢ le plus efficacement possible. Dans cette theĢse, nous proposons un protocole de routage remplissant ces fonctions. Le protocole proposeĢ est nommeĢ ACME (ACO-based routing protocol for MULE-assisted WSNs). Il est baseĢ sur la technique d'Optimisation par Colonies de Fourmis. ACME permet d'assigner des nÅuds aĢ des sous-puits puis de dĆ©finir les chemins entre eux, en tenant compte de la minimisation de la somme des longueurs de ces chemins, de l'Ć©quilibrage de la quantitĆ© de paquets stockĆ©s par les sous-puits et du nombre total de retransmissions. Le probleĢme est deĢfini comme une taĢche d'optimisation multi-objectif qui est reĢsolue de manieĢre distribueĢe sur la base des actions des nÅuds dans un scheĢma collaboratif. Nous avons dĆ©veloppĆ© un environnement de simulation et effectueĢ des campagnes de calculs dans OMNeT++ qui montrent les avantages de notre protocole en termes de performances et sa capaciteĢ aĢ s'adapter aĢ une grande varieĢteĢ de topologies de reĢseaux.The regular monitoring of operations in both movement areas (taxiways and runways) and non-movement areas (aprons and aircraft parking spots) of an airport, is a critical task for its functioning. The set of strategies used for this purpose include the measurement of environmental variables, the identification of foreign object debris (FOD), and the record of statistics of usage for diverse sections of the surface. According to a group of airport managers and controllers interviewed by us, the wide monitoring of most of these variables is a privilege of big airports due to the high acquisition, installation and maintenance costs of most common technologies. Due to this limitation, smaller airports often limit themselves to the monitoring of environmental variables at some few spatial points and the tracking of FOD performed by humans. This last activity requires stopping the functioning of the runways while the inspection is conducted. In this thesis, we propose an alternative solution based on Wireless Sensor Network (WSN) which, unlike the other methods/technologies, combines the desirable properties of low installation and maintenance cost, scalability and ability to perform measurements without interfering with the regular functioning of the airport. Due to the large extension of an airport and the difficulty of placing sensors over transit areas, the WSN might result segmented into a collection of subnetworks isolated from each other and from the sink. To overcome this problem, our proposal relies on a special type of node called Mobile Ubiquitous LAN Extension (MULE), able to move over the airport surface, gather data from the subnetworks along its way and eventually transfer it to the sink. One of the main demands for the deployment of any new system in an airport is that it must have little or no interference with the regular operations. This is why the use of an opportunistic approach for the transfer of data from the subnetworks to the MULE is favored in this thesis. By opportunistic we mean that the role of MULE will be played by some of the typical vehicles already existing in an airport doing their normal displacements, and the subnetworks will exploit any moment of contact with them to forward data to the sink. A particular characteristic of the MULEs in our application is that they move along predefined structured trajectories (given by the layout of the airport), having eventual contact with the set of nodes located by the side of the road (so-called subsinks). This implies the need for a data routing strategy to be used within each subnetwork, able to lead the collected data from the sensor nodes to the subsinks and distribute the data packets among them so that the time in contact with the MULE is used as efficiently as possible. In this thesis, we propose a routing protocol which undertakes this task. Our proposed protocol is named ACME, standing for ACO-based routing protocol for MULE-assisted WSNs. It is founded on the well known Ant Colony Optimization (ACO) technique. The main advantage of ACO is its natural fit to the decentralized nature of WSN, which allows it to perform distributed optimizations (based on local interactions) leading to remarkable overall network performance. ACME is able to assign sensor nodes to subsinks and generate the corresponding multi-hop paths while accounting for the minimization of the total path length, the total subsink imbalance and the total number of retransmissions. The problem is defined as a multi-objective optimization task which is resolved in a distributed manner based on actions of the sensor nodes acting in a collaborative scheme. We conduct a set of computational experiments in the discrete event simulator OMNeT++ which shows the advantages of our protocol in terms of performance and its ability to adapt to a variety of network topologie
Belief Space Scheduling
This thesis develops the belief space scheduling framework for scheduling under uncertainty in Stochastic Collection and Replenishment (SCAR) scenarios. SCAR scenarios involve the transportation of a resource such as fuel to agents operating in the field. Key characteristics of this scenario are persistent operation of the agents, and consideration of uncertainty. Belief space scheduling performs optimisation on probability distributions describing the state of the system. It consists of three major components---estimation of the current system state given uncertain sensor readings, prediction of the future state given a schedule of tasks, and optimisation of the schedule of the replenishing agents. The state estimation problem is complicated by a number of constraints that act on the state. A novel extension of the truncated Kalman Filter is developed for soft constraints that have uncertainty described by a Gaussian distribution. This is shown to outperform existing estimation methods, striking a balance between the high uncertainty of methods that ignore the constraints and the overconfidence of methods that ignore the uncertainty of the constraints. To predict the future state of the system, a novel analytical, continuous-time framework is proposed. This framework uses multiple Gaussian approximations to propagate the probability distributions describing the system state into the future. It is compared with a Monte Carlo framework and is shown to provide similar discrimination performance while computing, in most cases, orders of magnitude faster. Finally, several branch and bound tree search methods are developed for the optimisation problem. These methods focus optimisation efforts on earlier tasks within a model predictive control-like framework. Combined with the estimation and prediction methods, these are shown to outperform existing approaches
Should I send now or send later? A decision-theoretic approach to transmission scheduling in sensor networks with mobile sinks
Mobile sinks can significantly extend the lifetime of a sensor network by eliminating the need for expensive hop-by-hop routing. However, a sensor node might not always have a mobile sink in transmission range, or the mobile sink might be so far that the data transmission would be very expensive. In the latter case, the sensor node needs to make a decision whether it should send the data now, or take the risk to wait for a more favorable occasion. Making the right decisions in this transmission scheduling problem has significant impact on the performance and lifetime of the node. In this paper, we investigate the fundamentals of the transmission scheduling problem for sensor networks with mobile sinks. We first develop a dynamic programming-based optimal algorithm for the case when the mobility of the sinks is known in advance. Then, we describe two decision theoretic algorithms which use only probabilistic models learned from the history of interaction with the mobile sinks, and do not require knowledge about their future mobility patterns. The first algorithm uses Markov Decision Processes with states without history information, while the second algorithm encodes some elements of the history into the state. Through a series of experiments, we show that the decision theoretic approaches significantly outperform naive heuristics, and can have a performance close to that of the optimal approach, without requiring an advance knowledge of the mobility
Service Provisioning in Mobile Networks Through Distributed Coordinated Resource Management
The pervasiveness of personal computing platforms offers an unprecedented opportunity to deploy large-scale services that are distributed over wide physical spaces. Two major challenges face the deployment of such services: the often resource-limited nature of these platforms, and the necessity of preserving the autonomy of the owner of these devices. These challenges preclude using centralized control and preclude considering services that are subject to performance guarantees. To that end, this thesis advances a number of new distributed resource management techniques that are shown to be effective in such settings, focusing on two application domains: distributed Field Monitoring Applications (FMAs), and Message Delivery Applications (MDAs).
In the context of FMA, this thesis presents two techniques that are well-suited to the fairly limited storage and power resources of autonomously mobile sensor nodes. The first technique relies on amorphous placement of sensory data through the use of novel storage management and sample diffusion techniques. The second approach relies on an information-theoretic framework to optimize local resource management decisions. Both approaches are proactive in that they aim to provide nodes with a view of the monitored field that reflects the characteristics of queries over that field, enabling them to handle more queries locally, and thus reduce communication overheads.
Then, this thesis recognizes node mobility as a resource to be leveraged, and in that respect proposes novel mobility coordination techniques for FMAs and MDAs. Assuming that node mobility is governed by a spatio-temporal schedule featuring some slack, this thesis presents novel algorithms of various computational complexities to orchestrate the use of this slack to improve the performance of supported applications.
The findings in this thesis, which are supported by analysis and extensive simulations, highlight the importance of two general design principles for distributed systems. First, a-priori knowledge (e.g., about the target phenomena of FMAs and/or the workload of either FMAs or DMAs) could be used effectively for local resource management. Second, judicious leverage and coordination of node mobility could lead to significant performance gains for distributed applications deployed over resource-impoverished infrastructures
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