401 research outputs found
Dynamic distributed clustering in wireless sensor networks via Voronoi tessellation control
This paper presents two dynamic and distributed clustering algorithms for Wireless Sensor Networks (WSNs). Clustering approaches are used in WSNs to improve the network lifetime and scalability by balancing the workload among the clusters. Each cluster is managed by a cluster head (CH) node. The first algorithm requires the CH nodes to be mobile: by dynamically varying the CH node positions, the algorithm is proved to converge to a specific partition of the mission area, the generalised Voronoi tessellation, in which the loads of the CH nodes are balanced. Conversely, if the CH nodes are fixed, a weighted Voronoi clustering approach is proposed with the same load-balancing objective: a reinforcement learning approach is used to dynamically vary the mission space partition by controlling the weights of the Voronoi regions. Numerical simulations are provided to validate the approaches
Impact of Obstacles on the Degree of Mobile Ad Hoc Connection Graphs
What is the impact of obstacles on the graphs of connections between stations
in Mobile Ad hoc Networks? In order to answer, at least partially, this
question, the first step is to define both an environment with obstacles and a
mobility model for the stations in such an environment. The present paper
focuses on a new way of considering the mobility within environments with
obstacles, while keeping the core ideas of the well-known Random WayPoint
mobility model (a.k.a RWP). Based on a mesh-partitioning of the space, we
propose a new model called RSP-O-G for which we compute the spatial
distribution of stations and analyse how the presence of obstacles impacts this
distribution compared to the distribution when no obstacles are present.
Coupled with a simple model of radio propagation, and according to the density
of stations in the environment, we study the mean degree of the connection
graphs corresponding to such mobile ad hoc networks
Structure in the 3D Galaxy Distribution: I. Methods and Example Results
Three methods for detecting and characterizing structure in point data, such
as that generated by redshift surveys, are described: classification using
self-organizing maps, segmentation using Bayesian blocks, and density
estimation using adaptive kernels. The first two methods are new, and allow
detection and characterization of structures of arbitrary shape and at a wide
range of spatial scales. These methods should elucidate not only clusters, but
also the more distributed, wide-ranging filaments and sheets, and further allow
the possibility of detecting and characterizing an even broader class of
shapes. The methods are demonstrated and compared in application to three data
sets: a carefully selected volume-limited sample from the Sloan Digital Sky
Survey redshift data, a similarly selected sample from the Millennium
Simulation, and a set of points independently drawn from a uniform probability
distribution -- a so-called Poisson distribution. We demonstrate a few of the
many ways in which these methods elucidate large scale structure in the
distribution of galaxies in the nearby Universe.Comment: Re-posted after referee corrections along with partially re-written
introduction. 80 pages, 31 figures, ApJ in Press. For full sized figures
please download from: http://astrophysics.arc.nasa.gov/~mway/lss1.pd
Building Robust Distributed Infrastructure Networks
Many competing designs for Distributed Hash Tables exist exploring multiple models of addressing, routing and network maintenance. Designing a general theoretical model and implementation of a Distributed Hash Table allows exploration of the possible properties of Distributed Hash Tables. We will propose a generalized model of DHT behavior, centered on utilizing Delaunay triangulation in a given metric space to maintain the networks topology. We will show that utilizing this model we can produce network topologies that approximate existing DHT methods and provide a starting point for further exploration. We will use our generalized model of DHT construction to design and implement more efficient Distributed Hash Table protocols, and discuss the qualities of potential successors to existing DHT technologies
AoI-based Multicast Routing over Voronoi Overlays with Minimal Overhead
The increasing pervasive and ubiquitous presence of devices at the edge of
the Internet is creating new scenarios for the emergence of novel services and
applications. This is particularly true for location- and context-aware
services. These services call for new decentralized, self-organizing
communication schemes that are able to face issues related to demanding
resource consumption constraints, while ensuring efficient locality-based
information dissemination and querying. Voronoi-based communication techniques
are among the most widely used solutions in this field. However, when used for
forwarding messages inside closed areas of the network (called Areas of
Interest, AoIs), these solutions generally require a significant overhead in
terms of redundant and/or unnecessary communications. This fact negatively
impacts both the devices' resource consumption levels, as well as the network
bandwidth usage. In order to eliminate all unnecessary communications, in this
paper we present the MABRAVO (Multicast Algorithm for Broadcast and Routing
over AoIs in Voronoi Overlays) protocol suite. MABRAVO allows to forward
information within an AoI in a Voronoi network using only local information,
reaching all the devices in the area, and using the lowest possible number of
messages, i.e., just one message for each node included in the AoI. The paper
presents the mathematical and algorithmic descriptions of MABRAVO, as well as
experimental findings of its performance, showing its ability to reduce
communication costs to the strictly minimum required.Comment: Submitted to: IEEE Access; CodeOcean: DOI:10.24433/CO.1722184.v1;
code: https://github.com/michelealbano/mabrav
Enhancing Received Signal Strength-Based Localization through Coverage Hole Detection and Recovery
In wireless sensor networks (WSNs), Radio Signal Strength Indicator (RSSI)-based localization techniques have been widely used in various applications, such as intrusion detection, battlefield surveillance, and animal monitoring. One fundamental performance measure in those applications is the sensing coverage of WSNs. Insufficient coverage will significantly reduce the effectiveness of the applications. However, most existing studies on coverage assume that the sensing range of a sensor node is a disk, and the disk coverage model is too simplistic for many localization techniques. Moreover, there are some localization techniques of WSNs whose coverage model is non-disk, such as RSSI-based localization techniques. In this paper, we focus on detecting and recovering coverage holes of WSNs to enhance RSSI-based localization techniques whose coverage model is an ellipse. We propose an algorithm inspired by Voronoi tessellation and Delaunay triangulation to detect and recover coverage holes. Simulation results show that our algorithm can recover all holes and can reach any set coverage rate, up to 100% coverage
Coordinated control of mixed robot and sensor networks in distributed area exploration
Recent advancements in wireless communication and electronics has enabled the development of multifunctional sensor nodes that are small in size and communicate untethered in short distances. In the last decade, significant advantages have been made in the field of robotics, and robots have become more feasible in systems design. Therefore, we trust that a number of open problems with wireless sensor networks can be solved or diminished by including mobility capabilities in agents
- …