Free regions of sensor nodes (original) (raw)

Locating and Bypassing Holes in Sensor Networks

Mobile Networks and Applications, 2006

In real sensor network deployments, spatial distributions of sensors are usually far from being uniform. Such networks often contain regions without enough sensor nodes, which we call holes. In this paper, we show that holes are important topological features that need to be studied. In routing, holes are communication voids that cause greedy forwarding to fail. Holes can also be defined to denote regions of interest, such as the “hot spots” created by traffic congestion or sensor power shortage. In this paper, we define holes to be the regions enclosed by a polygonal cycle which contains all the nodes where local minima can appear. We also propose simple and distributed algorithms, the Tent rule and BoundHole, to identify and build routes around holes. We show that the boundaries of holes marked using BoundHole can be used in many applications such as geographic routing, path migration, information storage mechanisms and identification of regions of interest.

Topology Analysis of Wireless Sensor Networks Based on Nodes' Spatial Distribution

IEEE Transactions on Wireless Communications, 2014

In this paper 1 , we explore methods to generate optimal network topologies for wireless sensor networks (WSNs) with and without obstacles. Specifically, we investigate a dense network with n sensor nodes and m = n b (0 < b < 1) helping nodes, and assess the impact of topology on its throughput capacity. For networks without obstacles, we find that uniformly distributed sensor nodes and regularly distributed helping nodes have some advantages in improving the throughput capacity. We also explore properties of networks composed of some isomorphic sub-networks. For networks with obstacles, we assume there are M = Θ(n v) (0 < v ≤ 1) arbitrarily or randomly distributed obstacles, which block cells they are located in, i.e., sensor nodes cannot be placed in these cells and nodes' communication cannot cross them directly. We find that the overall throughput capacity is bounded by the transmission burden in areas around these blocked cells and introduce a novel algorithm of complexity O(M) to generate optimal sensor nodes' topologies for any given obstacles' distributions. We further analyze its performance for regularly distributed obstacles, which can be taken to estimate the lower bound of the algorithm's performance.

Graphical properties of easily localizable sensor networks

Wireless Networks, 2009

The sensor network localization problem is one of determining the Euclidean positions of all sensors in a network given knowledge of the Euclidean positions of some, and knowledge of a number of inter-sensor distances. This paper identifies graphical properties which can ensure unique localizability, and further sets of properties which can ensure not only unique localizability but also provide guarantees on the associated computational complexity, which can even be linear in the number of sensors on occasions. Sensor networks with minimal connectedness properties in which sensor transmit powers can be increased to increase the sensing radius lend themselves to the acquiring of the needed graphical properties. Results are presented for networks in both two and three dimensions.

Extracting the overlapped sub-regions in wireless sensor networks

In wireless sensor networks, the overlapped sub-regions (faces) are generated due to the intersections among the sensing ranges of nodes. The faces play a significant role in solving the three problems k-coverage (i.e., all the points in the interested field should be covered by at least k active nodes while maintaining connectivity between all active nodes), coverage scheduling and cover sets. To find the faces and discover their coverage degrees, this article presents a distributed algorithm that runs in three steps. First, a colored graph called Intersection Points Colored Graph (IPCG) is proposed, in which the vertices are defined by the range-intersections of nodes-devices and are colored according to the position of these intersections in relation to the ranges of the nodes. The vertex that located on perimeter of the node's range is colored by red, while the green vertex is an intersection of two ranges inside the range of a third node. The edge that joins two red vertices is colored by red and the edge that joins two green vertices is colored by green while the edge that joins two distinct colored vertices is colored by blue. Second, based on their properties and distinct features, the faces in IPCG are classified into five classes (simple, negative, red, green and positive). Third, based on faces classification, the Three Colored Trees algorithm is proposed to extract the faces in linear time in terms of the number of vertices and edges in IPCG.

Connectivity in Sensor Networks

2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07), 2007

We analyze the probability that an arbitrarily selected sensor node in a sensor network is connected to a specified number, m, of other sensor nodes; i.e., that a communication path exists between a selected node and at least m other nodes. Specifically, we consider the following problem: If given a collection of sensor nodes, each with a communication radius r, that are to be uniformly distributed over an area of size A, how many nodes must be deployed to assure that any node is connected to at least m other nodes with a specified probability? Alternatively, if we deploy a fixed number of nodes over a region, what is the probability that a selected node will have m neighbors? Such questions are particularly pertinent for unattended sensor networks that perform cooperative operations requiring the participation of at least m nodes.

Discovery of Sensor Network Layout using Connectivity Information

2007

We propose a distributed algorithm to discover and recover the layout of a large sensor network having a complex shape. As sensor network deployments grow large in size and become non-uniform, localization algorithms suffer from ``flip'' ambiguities---where a part of the network folds on top of another while keeping all edge length measurements preserved. We explore the high-order topological information in a sensor field to prevent incorrect flips and accurately recover the shape of the sensor network. We select landmarks on network boundaries with sufficient density, construct the landmark Voronoi diagram and its dual combinatorial Delaunay complex on these landmarks. The key insight is that when the landmarks are dense enough to capture the local geometric complexity, the combinatorial Delaunay complex is globally rigid and has a unique realization in the plane. An embedding by simply gluing the Delaunay triangles properly derives a faithful network layout, which conseque...

Self-organization of connectivity and geographical routing in large-scale sensor networks

Unifying Themes in …, 2008

A large-scale sensor network (LSSN) is formed when a very large number of sensor nodes with short-range communication capabilities are deployed randomly over an extended region. The random distribution of nodes in an LSSN leads to regions of varying density, which means that if all nodes have an identical transmission radius, the effective connectivity would vary over the system. This leads to inefficiency in energy usage (in regions of unnecessarily high connectivity) and the danger of partitioning (in regions of low node density). In this paper, we propose a technique for adapting a node's transmission radius based on a node's local information. Through localized coordination and self-organization, nodes try to attain fairly uniform connectivity in the system to aid in efficient data messaging in the system. We study the benefits of network adaptation by incorporating it into an adaptive geographical routing algorithm called corridor routing. We present simulation results showing significant improvement in performance over routing algorithms that do not use network adaptation. We also propose and study several scenarios for network adaptation in the presence of node failures, and explore the effect of parameter variation.

FINDING MAXIMAL LOCALIZABLE REGION IN WIRELESS SENSOR NETWORKS BY MERGING RIGID CLUSTERS

Localization of Wireless Sensor Network (WSN) is the problem of finding the geo-locations of sensors in a sensor network deployed in various applications. Given the prolification of sensors in various applications, the localization and tracking of sensors have received considerable attention. Properties of rigidity and flexibility of the underlying graph of the WSN have been studied as a means of determining the localizability of the nodes in the WSN. In this paper, we present a new 3-merge technique for merging three rigid clusters of a network graph, into larger rigid cluster and we use this algorithm for finding maximal localizable regions within the WSN. We provide simulation results on random deployments of WSN to prove that this technique outperforms previously known algorithms for finding maximal localizable subregions. Moreover, simulation results show that the number of anchors needed to localize the entire WSN decreases due to finding large localizable regions.

A Modified Sensor Network Boundary Discovery Algorithm

International Journal of Computer Applications, 2013

A WSN computer network using sensors is used in important application like environmental monitoring warning dangers to human lives. WSN's may have several problems related to topology construction, maintenance, and connectivity. The deployed Sensor devices may have critical resource constraints in terms of energy consumption. Also the algorithms need to be memory-efficient. Algorithmic issues arise in sensor deployment and coverage, routing, and fusion. Applications which rely on timely sensor updates on environments may have unpredictable results due to sensor inefficiencies. WSN applications may be in operation for months without wired power supplies, thus requiring to meet the delay requirements at minimum energy cost. Though various algorithms and protocols have been proposed for deploying sensors This paper proposes a new Algorithm CN Algorithm for WSNs and discusses WSNs. Future work includes the extension of the algorithm to detect the coverage boundary of the network or area covered by the sensor nodes inside a network.

Effect of Neighborhood on In-Network Processing in Sensor Networks

Lecture Notes in Computer Science

Wireless sensor networks are growing from a few hand-placed devices to more large-scale networks in terms of coverage and node density. For various concerns, such as scalability, larger network sizes require some management of the large volume of data that a sensor network delivers. One way to manage this data is processing information in the network. This paper investigates how a sensor network's network architecture (specifically, the neighborhood structure) can influence the conclusions that a sensor network makes from its measurements. The results demonstrate that non-planar structures are infeasible for routing and some in-network processing applications. Structures with low average edge lengths give better quantitative results, while those with high edge densities give better qualitative results. and my neighbor?"), the network topology can be used to build a picture of a region.