Efficient Algorithms for Distributed Detection of Holes and Boundaries in Wireless Networks

We propose two novel algorithms for distributed and location-free boundary recognition in wireless sensor networks. Both approaches enable a node to decide autonomously whether it is a boundary node, based solely on connectivity information of a small neighborhood. This makes our algorithms highly applicable for dynamic networks where nodes can move or become inoperative. We compare our algorithms qualitatively and quantitatively with several previous approaches. In extensive simulations, we consider various models and scenarios. Although our algorithms use less information than most other approaches, they produce significantly better results. They are very robust against variations in node degree and do not rely on simplified assumptions of the communication model. Moreover, they are much easier to implement on real sensor nodes than most existing approaches.Comment: extended version of accepted submission to SEA 201

BOUNDARY DETECTION ALGORITHMS IN WIRELESS SENSOR NETWORKS: A SURVEY

Wireless sensor networks (WSNs) comprise a large number of sensor nodes, which are spread out within a region and communicate using wireless links. In some WSN applications, recognizing boundary nodes is important for topology discovery, geographic routing and tracking. In this paper, we study the problem of recognizing the boundary nodes of a WSN. We firstly identify the factors that influence the design of algorithms for boundary detection. Then, we classify the existing work in boundary detection, which is vital for target tracking to detect when the targets enter or leave the sensor field

Probabilistic approaches to the design of wireless ad hoc and sensor networks

The emerging wireless technologies has made ubiquitous wireless access a reality and enabled wireless systems to support a large variety of applications. Since the wireless self-configuring networks do not require infrastructure and promise greater flexibility and better coverage, wireless ad hoc and sensor networks have been under intensive research. It is believed that wireless ad hoc and sensor networks can become as important as the Internet. Just as the Internet allows access to digital information anywhere, ad hoc and sensor networks will provide remote interaction with the physical world. Dynamics of the object distribution is one of the most important features of the wireless ad hoc and sensor networks. This dissertation deals with several interesting estimation and optimization problems on the dynamical features of ad hoc and sensor networks. Many demands in application, such as reliability, power efficiency and sensor deployment, of wireless ad hoc and sensor network can be improved by mobility estimation and/or prediction. In this dissertation, we study several random mobility models, present a mobility prediction methodology, which relies on the analysis of the moving patterns of the mobile objects. Through estimating the future movement of objects and analyzing the tradeoff between the estimation cost and the quality of reliability, the optimization of tracking interval for sensor networks is presented. Based on the observation on the location and movement of objects, an optimal sensor placement algorithm is proposed by adaptively learn the dynamical object distribution. Moreover, dynamical boundary of mass objects monitored in a sensor network can be estimated based on the unsupervised learning of the distribution density of objects. In order to provide an accurate estimation of mobile objects, we first study several popular mobility models. Based on these models, we present some mobility prediction algorithms accordingly, which are capable of predicting the moving trajectory of objects in the future. In wireless self-configuring networks, an accurate estimation algorithm allows for improving the link reliability, power efficiency, reducing the traffic delay and optimizing the sensor deployment. The effects of estimation accuracy on the reliability and the power consumption have been studied and analyzed. A new methodology is proposed to optimize the reliability and power efficiency by balancing the trade-off between the quality of performance and estimation cost. By estimating and predicting the mass objects\u27 location and movement, the proposed sensor placement algorithm demonstrates a siguificant improvement on the detection of mass objects with nearmaximal detection accuracy. Quantitative analysis on the effects of mobility estimation and prediction on the accuracy of detection by sensor networks can be conducted with recursive EM algorithms. The future work includes the deployment of the proposed concepts and algorithms into real-world ad hoc and sensor networks

Boundary node selection algorithms in WSNs

Physical damage and/or node power exhaustion may lead to coverage holes in WSNs. Coverage holes can be directly detected by certain proximate nodes known as boundary nodes (B-nodes). Due to the sensor nodes' redundant deployment and autonomous fault detection, holes are surrounded by a margin of B-nodes (MB-nodes). If all B-nodes in the margin take part in the hole recovery processes, either by increasing their transmission power or by relocating towards region of interest (ROI), the probability of collision, interference, disconnection, and isolation may increase affecting the rest of the network's performance and QoS. Thus, distributed boundary node selection algorithms (BNS-Algorithms) are proposed to address these issues. BNS-algorithms allow B-nodes to self-select based on available 1-hop information extracted from nodes' simple geometrical and statistical features. Our results show that the performance of the proposed distributed BNS-algorithms approaches that of their centralized counterparts. © 2011 IEEE

NETEMBED: A Network Resource Mapping Service for Distributed Applications

Emerging configurable infrastructures such as large-scale overlays and grids, distributed testbeds, and sensor networks comprise diverse sets of available computing resources (e.g., CPU and OS capabilities and memory constraints) and network conditions (e.g., link delay, bandwidth, loss rate, and jitter) whose characteristics are both complex and time-varying. At the same time, distributed applications to be deployed on these infrastructures exhibit increasingly complex constraints and requirements on resources they wish to utilize. Examples include selecting nodes and links to schedule an overlay multicast file transfer across the Grid, or embedding a network experiment with specific resource constraints in a distributed testbed such as PlanetLab. Thus, a common problem facing the efficient deployment of distributed applications on these infrastructures is that of "mapping" application-level requirements onto the network in such a manner that the requirements of the application are realized, assuming that the underlying characteristics of the network are known. We refer to this problem as the network embedding problem. In this paper, we propose a new approach to tackle this combinatorially-hard problem. Thanks to a number of heuristics, our approach greatly improves performance and scalability over previously existing techniques. It does so by pruning large portions of the search space without overlooking any valid embedding. We present a construction that allows a compact representation of candidate embeddings, which is maintained by carefully controlling the order via which candidate mappings are inserted and invalid mappings are removed. We present an implementation of our proposed technique, which we call NETEMBED – a service that identify feasible mappings of a virtual network configuration (the query network) to an existing real infrastructure or testbed (the hosting network). We present results of extensive performance evaluation experiments of NETEMBED using several combinations of real and synthetic network topologies. Our results show that our NETEMBED service is quite effective in identifying one (or all) possible embeddings for quite sizable queries and hosting networks – much larger than what any of the existing techniques or services are able to handle.National Science Foundation (CNS Cybertrust 0524477, NSF CNS NeTS 0520166, NSF CNS ITR 0205294, EIA RI 0202067

A Multi-objective Evolutionary Algorithm to solve Complex Optimization Problems

Multi-objective optimization problem formulations reflect pragmatic modeling of several real-life complex optimization problems. In many of them the considered objectives are competitive with each other; emphasizing only one of them during solution generation and evolution incurs high probability of producing a one-sided solution, which is unacceptable with respect to other objectives. An appropriate solution to the multi-objective optimization problem is to investigate a set of solutions that satisfy all of the competing objectives to an acceptable extent, where no solution in the solution set is dominated by others in terms of objective optimization. In this work, we investigate well known Non-dominated Sorting Genetic Algorithm (NSGA-II), and Strength Pareto Evolutionary Algorithm (SPEA-II), to find Pareto optimal solutions for two real-life problems: Task-based Sailor Assignment Problem (TSAP) and Coverage and Lifetime Optimization Problem in Wireless Sensor Networks (CLOP). Both of these problems are multi-objective problems. TSAP constitutes five multi-directional objectives, whereas CLOP is composed of two competing objectives. To validate the special operators developed, these two test bed problems have been used. Finally, traditional NSGA-II and SPEA-II have been blended with these special operators to generate refined solutions of these multi-objective optimization problems

Mathematical Models and Algorithms for Network Flow Problems Arising in Wireless Sensor Network Applications

We examine multiple variations on two classical network flow problems, the maximum flow and minimum-cost flow problems. These two problems are well-studied within the optimization community, and many models and algorithms have been presented for their solution. Due to the unique characteristics of the problems we consider, existing approaches cannot be directly applied. The problem variations we examine commonly arise in wireless sensor network (WSN) applications. A WSN consists of a set of sensors and collection sinks that gather and analyze environmental conditions. In addition to providing a taxonomy of relevant literature, we present mathematical programming models and algorithms for solving such problems. First, we consider a variation of the maximum flow problem having node-capacity restrictions. As an alternative to solving a single linear programming (LP) model, we present two alternative solution techniques. The first iteratively solves two smaller auxiliary LP models, and the second is a heuristic approach that avoids solving any LP. We also examine a variation of the maximum flow problem having semicontinuous restrictions that requires the flow, if positive, on any path to be greater than or equal to a minimum threshold. To avoid solving a mixed-integer programming (MIP) model, we present a branch-and-price algorithm that significantly improves the computational time required to solve the problem. Finally, we study two dynamic network flow problems that arise in wireless sensor networks under non-simultaneous flow assumptions. We first consider a dynamic maximum flow problem that requires an arc to transmit a minimum amount of flow each time it begins transmission. We present an MIP for solving this problem along with a heuristic algorithm for its solution. Additionally, we study a dynamic minimum-cost flow problem, in which an additional cost is incurred each time an arc begins transmission. In addition to an MIP, we present an exact algorithm that iteratively solves a relaxed version of the MIP until an optimal solution is found

Algorithmen für Topologiebewusstsein in Sensornetzen

This work deals with algorithmic and geometric challenges in wireless sensor networks (WSNs). Classical algorithm theory, with a single processor executing one sequential program while having access to the complete data of the problem at hand, does not suit the needs of WSNs. Instead, we need distributed protocols where nodes collaboratively solve problems that are too complex for a single node. First we analyze a location problem, where the nodes obtain a sense of the network topology and their position in it. Computing coordinates in a global coordinate system is NP-hard in almost all relevant variants. So we present a completely new approach instead. The network builds clusters and constructs an abstract graph that closely reflects the topology of the network region. The resulting topology awareness suits the needs of some applications much better than the coordinate-based approach. In the second part, we present a novel flow problem, which adds battery constraints to dynamic network flows. Given a time horizon, we seek a flow from source to sink that maximizes the total amount of delivered data. As there is no prior work on this problem, we also analyze it in a centralized setting. We prove complexity results for several variants and present approximation schemes. The third part introduces the WSN simulator Shawn. By letting the user choose among different geometric communication models and data structures for the resulting graph, Shawn can adapt to many different setups, including mobile ones. Due to its design, Shawn is much faster than comparable simulation environments.Die vorliegende Arbeit beschäftigt sich mit algorithmischen und geometrischen Fragestellungen in Sensornetzwerken. Im Gegensatz zur klassischen Algorithmik, bei der ein einzelner Prozessor sequenziell Anweisungen abarbeitet und vollen Zugriff auf die Probleminstanz hat, werden hier verteilte Protokolle benötigt, bei denen die Knoten gemeinsam eine Aufgabe bewältigen, zu der sie allein nicht in der Lage wären. Zuerst untersuchen wir das grundlegende Problem, wie Sensorknoten ein Bewusstsein für ihre Position erlangen können. Motiviert daraus, dass das Problem, Koordinaten für ein globales Koordinatensystem zu bestimmen, in fast allen Varianten NP-schwer ist, wird ein vollkommen neuer Ansatz skizziert, bei dem das Netzwerk selbständig geometrische Cluster bildet und einen abstrakten Graphen konstruiert, der die Topologie des zugrunde liegenden Gebiets sehr genau widerspiegelt. Das sich daraus ergebende Positionsbewusstsein ist für einige Anwendungen dem klassischen euklidischen Ansatz deutlich überlegen. Der zweite Teil widmet sich einem Flussproblems für Sensornetzwerke, dass klassische dynamische Flüsse um Batteriebeschränkungen erweitert. Gesucht ist ein Fluss, der für gegebenen Zeithorizont die Datenmenge maximiert, die von einer Quelle zur Senke geschickt werden kann. Dieses Problem wird auch im zentralisierten Modell untersucht, da keine Vorarbeiten existieren. Wir beweisen Komplexitäten von Problemvarianten und entwickeln Approximationsschemata. Der dritte Teil stellt den Netzwerksimulator Shawn vor. Da der Benutzer zwischen verschiedenen geometrischen Kommunikationsmodellen wählen kann und das Speichermodell für den daraus resultierenden Graphen an den verfügbaren Speicher sowie an Simulationsparameter wie eventuell mögliche Mobilität der Knoten anpassen kann, ist Shawn hochflexibel und gleichzeitig deutlich schneller als vergleichbare Simulationsumgebungen