Catálogo de publicaciones - libros

Widely used positioning systems like GPS are not a valid solution in large networks with small size, low cost sensors, due both to their size and their cost. Thus, new solutions for localization awareness are emerging, commonly based on the existence of a few references spread into the network.

We propose a localization algorithm to reduce the number of transmitting nodes. The algorithm relies on self selecting nodes for location information disclosure. Each node makes a decision based on its proximity to the nodes in the area covered only by two of the references used for its own localization. We analyze different aspects of the location awareness propagation problem: communication overhead, redundant transmissions, network coverage.

In this paper we study the threshold behavior of the fixed radius random graph model and its applications to the key management problem of sensor networks and, generally, for mobile ad-hoc networks. We show that this random graph model can realistically model the placement of nodes within a certain region and their interaction/sensing capabilities (i.e. transmission range, light sensing sensitivity etc.). We also show that this model can be used to define key sets for the network nodes that satisfy a number of good properties, allowing to set up secure communication with each other depending on randomly created sets of keys related to their current location. Our work hopes to inaugurate a study of key management schemes whose properties are related to properties of an appropriate random graph model and, thus, use the rich theory developed in the random graph literature in order to transfer “good” properties of the graph model to the key sets of the nodes.

We consider the broadcasting problem in sensor networks where the nodes have no prior knowledge of their neighborhood. That is, to preserve power and bandwidth, no beacons or ’hello’ messages are sent. We describe several Area based Beaconless Broadcasting Algorithms (ABBAs). In 2D, upon receiving the packet, each node calculates the ratio of its perimeter, along the circle of transmission radius, that is not covered by this and previous transmissions of the same packet. The node then sets or updates its timeout to be inversely proportional to . We also consider an alternative random timeout function. If the perimeter becomes fully covered, the node cancels retransmissions. The protocol is reliable, assuming an ideal MAC layer. We also describe three 3D ABBAs, one of them being reliable, each with two choices of timeouts.These three protocols are based on covering three projections, covering particular points on intersection circles, and covering intersection points of three spheres. Our protocols are the first reliable broadcasting protocols, other than blind flooding. We compare 2D ABBAs with two other existing beaconless protocols, BPS and Geoflood, showing its superiority. We also consider a MAC layer with collisions and show that all of our methods still remain very robust, showing high delivery ratio during the broadcast.

Motivated by the need for agent classification in sensor networking and autonomous vehicle control applications, we propose a flexible and distributed stochastic automaton-based network partitioning algorithm that is capable of finding the optimal -way partition with respect to a broad range of cost functions, and given various constraints, in directed and weighted graphs. Specifically, we motivate the need for new algorithms for network partitioning and distributed (or self-) partitioning. We then review our stochastic automaton-based partitioning algorithm, and extend its use for network partitioning and self-partitioning problems. Finally, the application of the algorithm to mobile/sensor classification in ad hoc networks is pursued in detail, and other applications are briefly introduced.

This paper presents a simple distributed algorithm to determine the bridges, articulation points, and 2-connected components in asynchronous networks with an message delivery semantics in time () using at most 4 messages of length (lg ). The algorithm does not assume a FIFO rule for message delivery. Previously known algorithms either use longer messages or need more time. The algorithm meets the requirements of wireless senor networks and can be applied in several areas relevant to this field such as topology control, clustering, localization and virtual backbone calculations.

In the network localization problem the locations of some nodes (called anchors) as well as the distances between some pairs of nodes are known, and the goal is to determine the location of all nodes. The localization problem is said to be solvable (or uniquely localizable) if there is a unique set of locations consistent with the given data. Recent results from graph rigidity theory made it possible to characterize the solvability of the localization problem in two dimensions.

In this paper we address the following related optimization problem: given the set of known distances in the network, make the localization problem solvable by designating a smallest set of anchor nodes. We develop a polynomial-time 3-approximation algorithm for this problem by proving new structural results in graph rigidity and by using tools from matroid theory.

Applications of Ubiquitous Robot (UR) based on wireless sensor networks usually require the location information of all sensor nodes deployed on the sensor field. For this, every sensor node should be aware of its own geographical location, which forces us to pay an expensive cost and make the size of sensor node larger. In this paper, we propose a cheap solution for positioning all sensor nodes without necessitating all sensor nodes in the field equipped with GPS modules. In our method, only some sensor nodes equipped with GPS modules are initially deployed, and the other nodes without GPS modules will find out their locations through communications to GPS nodes. To show effectiveness of our method, we carried out a computer simulation, and observed that all nodes could successfully recognize their locations, which can considerably save the price for implementing wireless sensor networks.

Programming Wireless Networks of Embedded Systems (WNES) is notoriously difficult and tedious. To simplify WNES programming, we propose (DRN) to program WNES as a whole (i.e., ) instead of several networked entities. DRN allows for a set of resources to be declaratively described by their run-time properties, and for this set to be mapped to a variable. Using DRN, resource access is simplified to only variable access that is completely network-transparent. DRN provides both sequential and parallel accesses to the desired set. Parallel, or group, access reduces the total access time and energy consumption because it enables in-network processing. Additionally, we can associate each set with tuning parameters (e.g., timeout, energy budget) to bound access time or to tune resource consumption.

Lately, sensor networks have received much attention in the research community. One of the aims of these networks is to make them self-configuring and self-healing so that they can be easily deployed and self-sufficient. A fundamental limitation with sensor networks is their limited life span because of their battery operation. To that end, much research has been carried out to maximize the life span of sensor networks through energy conservation. In this paper; we investigate energy conservation from a different angle. We introduce RETT, a protocol that aims at equalising energy in such a way that the entire network remains operational at maximal time in case the application would so require. When the entire network is operational, it is certain that events from any sensor in the network will find a delivery path through the network and that actuation of actuators in response to sensed events will be carried out. We focus on the algorithms that enable this and provide experimental results that show how they save on energy compared with the well-known LEACH algorithm.

We design controllers that permit a network of mobile agents with distributed sensing capabilities to track (follow) desired trajectories, and identify what trajectory information must be distributed to each agent for tracking.