16,426 research outputs found
Coverage and Connectivity in Three-Dimensional Networks
Most wireless terrestrial networks are designed based on the assumption that
the nodes are deployed on a two-dimensional (2D) plane. However, this 2D
assumption is not valid in underwater, atmospheric, or space communications. In
fact, recent interest in underwater acoustic ad hoc and sensor networks hints
at the need to understand how to design networks in 3D. Unfortunately, the
design of 3D networks is surprisingly more difficult than the design of 2D
networks. For example, proofs of Kelvin's conjecture and Kepler's conjecture
required centuries of research to achieve breakthroughs, whereas their 2D
counterparts are trivial to solve. In this paper, we consider the coverage and
connectivity issues of 3D networks, where the goal is to find a node placement
strategy with 100% sensing coverage of a 3D space, while minimizing the number
of nodes required for surveillance. Our results indicate that the use of the
Voronoi tessellation of 3D space to create truncated octahedral cells results
in the best strategy. In this truncated octahedron placement strategy, the
transmission range must be at least 1.7889 times the sensing range in order to
maintain connectivity among nodes. If the transmission range is between 1.4142
and 1.7889 times the sensing range, then a hexagonal prism placement strategy
or a rhombic dodecahedron placement strategy should be used. Although the
required number of nodes in the hexagonal prism and the rhombic dodecahedron
placement strategies is the same, this number is 43.25% higher than the number
of nodes required by the truncated octahedron placement strategy. We verify by
simulation that our placement strategies indeed guarantee ubiquitous coverage.
We believe that our approach and our results presented in this paper could be
used for extending the processes of 2D network design to 3D networks.Comment: To appear in ACM Mobicom 200
Node placement in Wireless Mesh Networks: a comparison study of WMN-SA and WMN-PSO simulation systems
(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.With the fast development of wireless technologies, Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless Internet connectivity. In our previous work, we implemented a simulation system based on Simulated Annealing (SA) for solving node placement problem in wireless mesh networks, called WMN-SA. Also, we implemented a Particle Swarm Optimization (PSO) based simulation system, called WMN-PSO. In this paper, we compare two systems considering calculation time. From the simulation results, when the area size is 32 × 32 and 64 × 64, WMN-SA is better than WMN-PSO. When the area size is 128 × 128, WMN-SA performs better than WMN-PSO. However, WMN-SA needs more calculation time than WMN-PSO.Peer ReviewedPostprint (author's final draft
Impromptu Deployment of Wireless Relay Networks: Experiences Along a Forest Trail
We are motivated by the problem of impromptu or as- you-go deployment of
wireless sensor networks. As an application example, a person, starting from a
sink node, walks along a forest trail, makes link quality measurements (with
the previously placed nodes) at equally spaced locations, and deploys relays at
some of these locations, so as to connect a sensor placed at some a priori
unknown point on the trail with the sink node. In this paper, we report our
experimental experiences with some as-you-go deployment algorithms. Two
algorithms are based on Markov decision process (MDP) formulations; these
require a radio propagation model. We also study purely measurement based
strategies: one heuristic that is motivated by our MDP formulations, one
asymptotically optimal learning algorithm, and one inspired by a popular
heuristic. We extract a statistical model of the propagation along a forest
trail from raw measurement data, implement the algorithms experimentally in the
forest, and compare them. The results provide useful insights regarding the
choice of the deployment algorithm and its parameters, and also demonstrate the
necessity of a proper theoretical formulation.Comment: 7 pages, accepted in IEEE MASS 201
Implementation and evaluation of a simulation system based on particle swarm optimisation for node placement problem in wireless mesh networks
With the fast development of wireless technologies, wireless mesh networks (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless internet connectivity. This paper implements a simulation system based on particle swarm optimisation (PSO) in order to solve the problem of mesh router placement in WMNs. Four replacement methods of mesh routers are considered: constriction method (CM), random inertia weight method (RIWM), linearly decreasing Vmax method (LDVM) and linearly decreasing inertia weight method (LDIWM). Simulation results are provided, showing that the CM converges very fast, but has the worst performance among the methods. The considered performance metrics are the size of giant component (SGC) and the number of covered mesh clients (NCMC). The RIWM converges fast and the performance is good. The LDIWM is a combination of RIWM and LDVM. The LDVM converges after 170 number of phases but has a good performance.Peer ReviewedPostprint (author's final draft
Enabling High-Level Application Development for the Internet of Things
Application development in the Internet of Things (IoT) is challenging
because it involves dealing with a wide range of related issues such as lack of
separation of concerns, and lack of high-level of abstractions to address both
the large scale and heterogeneity. Moreover, stakeholders involved in the
application development have to address issues that can be attributed to
different life-cycles phases. when developing applications. First, the
application logic has to be analyzed and then separated into a set of
distributed tasks for an underlying network. Then, the tasks have to be
implemented for the specific hardware. Apart from handling these issues, they
have to deal with other aspects of life-cycle such as changes in application
requirements and deployed devices. Several approaches have been proposed in the
closely related fields of wireless sensor network, ubiquitous and pervasive
computing, and software engineering in general to address the above challenges.
However, existing approaches only cover limited subsets of the above mentioned
challenges when applied to the IoT. This paper proposes an integrated approach
for addressing the above mentioned challenges. The main contributions of this
paper are: (1) a development methodology that separates IoT application
development into different concerns and provides a conceptual framework to
develop an application, (2) a development framework that implements the
development methodology to support actions of stakeholders. The development
framework provides a set of modeling languages to specify each development
concern and abstracts the scale and heterogeneity related complexity. It
integrates code generation, task-mapping, and linking techniques to provide
automation. Code generation supports the application development phase by
producing a programming framework that allows stakeholders to focus on the
application logic, while our mapping and linking techniques together support
the deployment phase by producing device-specific code to result in a
distributed system collaboratively hosted by individual devices. Our evaluation
based on two realistic scenarios shows that the use of our approach improves
the productivity of stakeholders involved in the application development
Architectures for Wireless Sensor Networks
Various architectures have been developed for wireless sensor networks. Many of them leave to the programmer important concepts as the way in which the inter-task communication and dynamic reconfigurations are addressed. In this paper we describe the characteristics of a new architecture we proposed - the data-centric architecture. This architecture offers an easy way of structuring the applications designed for wireless sensor nodes that confers them superior performances
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