888 research outputs found
Energy Efficiency in Two-Tiered Wireless Sensor Networks
We study a two-tiered wireless sensor network (WSN) consisting of access
points (APs) and base stations (BSs). The sensing data, which is
distributed on the sensing field according to a density function , is first
transmitted to the APs and then forwarded to the BSs. Our goal is to find an
optimal deployment of APs and BSs to minimize the average weighted total, or
Lagrangian, of sensor and AP powers. For , we show that the optimal
deployment of APs is simply a linear transformation of the optimal -level
quantizer for density , and the sole BS should be located at the geometric
centroid of the sensing field. Also, for a one-dimensional network and uniform
, we determine the optimal deployment of APs and BSs for any and .
Moreover, to numerically optimize node deployment for general scenarios, we
propose one- and two-tiered Lloyd algorithms and analyze their convergence
properties. Simulation results show that, when compared to random deployment,
our algorithms can save up to 79\% of the power on average.Comment: 11 pages, 7 figure
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
Optimization strategies for two-tiered sensor networks.
Sensor nodes are tiny, low-powered and multi-functional devices operated by lightweight batteries. Replacing or recharging batteries of sensor nodes in a network is usually not feasible so that a sensor network fails when the battery power in critical node(s) is depleted. The limited transmission range and the battery power of sensor nodes affect the scalability and the lifetime of sensor networks. Recently, relay nodes, acting as cluster heads, have been proposed in hierarchical sensor networks. The placement of relay nodes in a sensor network, such that all the sensor nodes are covered using a minimum number of relay nodes is a NP-hard problem. We propose a simple strategy for the placement of relay nodes in a two-tiered network that ensures connectivity and fault tolerance. We also propose two ILP formulations for finding the routing strategy so that the lifetime of any relay node network may be maximized.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .B37. Source: Masters Abstracts International, Volume: 45-01, page: 0348. Thesis (M.Sc.)--University of Windsor (Canada), 2006
Improving the lifetime of two-tiered sensor networks using genetic algorithm
Wireless sensor networks have been envisioned to have a wide range of applications which consist of many inexpensive and low-powered wireless nodes which are used to sense, gather, and transmit the data towards the base station. In Two-Tiered wireless sensor networks, nodes are grouped into clusters, with a minimum of one cluster-head to distribute the work load among the member nodes. In the recent years, higher-powered relay nodes have been proposed to act as cluster heads and these relay nodes form a network among themselves in order to improve the lifetime of the sensor networks. Since the nodes are generally energy constrained, efficient management of the network data communication scheme can maximize the lifetime of the networks. A Genetic Algorithm is the technique for randomized search and optimization which is based on Darwin\u27s Principal of Natural Selection. In this paper, we have proposed a Genetic Algorithm based solution for scheduling the data gathering of relay nodes that can significantly extend the lifetime of the relay node network. We have simulated our method on 15 different sizes of networks and measured the lifetime of the network as the number of rounds, until the first relay node runs out of battery power. For smaller networks, where the global optimum can be determined, our genetic algorithm based approach is always able to find the optimal solution with a lesser program run-time. For larger networks, we have compared our approach with traditional routing schemes and shown that our method leads to significant improvements
Integrated placement and routing of relay nodes for fault-tolerant hierarchical sensor networks
In two-tiered sensor networks, using higher-powered relay nodes as cluster heads has been shown to lead to further improvements in network performance. Placement of such relay nodes focuses on achieving specified coverage and connectivity requirements with as few relay nodes as possible. Existing placement strategies typically are unaware of energy dissipation due to routing and are not capable of optimizing the routing scheme and placement concurrently.
We, in this thesis, propose an integrated integer linear program (ILP) formulation that determines the minimum number of relay nodes, along with their locations and a suitable communication strategy such that the network has a guaranteed lifetime as well as ensuring the pre-specified level of coverage (ks) and connectivity (kr). We also present an intersection based approach for creating the initial set of potential relay node positions, which are used by our ILP, and evaluate its performance under different conditions. Experimental results on networks with hundreds of sensor nodes show that our approach leads to significant improvement over existing energy-unaware placement schemes
DESIGN OF MOBILE DATA COLLECTOR BASED CLUSTERING ROUTING PROTOCOL FOR WIRELESS SENSOR NETWORKS
Wireless Sensor Networks (WSNs) consisting of hundreds or even thousands of
nodes, canbe used for a multitude of applications such as warfare intelligence or to
monitor the environment. A typical WSN node has a limited and usually an
irreplaceable power source and the efficient use of the available power is of utmost
importance to ensure maximum lifetime of eachWSNapplication. Each of the nodes
needs to transmit and communicate sensed data to an aggregation point for use by
higher layer systems. Data and message transmission among nodes collectively
consume the largest amount of energy available in WSNs. The network routing
protocols ensure that every message reaches thedestination and has a direct impact on
the amount of transmissions to deliver messages successfully. To this end, the
transmission protocol within the WSNs should be scalable, adaptable and optimized
to consume the least possible amount of energy to suite different network
architectures and application domains. The inclusion of mobile nodes in the WSNs
deployment proves to be detrimental to protocol performance in terms of nodes
energy efficiency and reliable message delivery. This thesis which proposes a novel
Mobile Data Collector based clustering routing protocol for WSNs is designed that
combines cluster based hierarchical architecture and utilizes three-tier multi-hop
routing strategy between cluster heads to base station by the help of Mobile Data
Collector (MDC) for inter-cluster communication. In addition, a Mobile Data
Collector based routing protocol is compared with Low Energy Adaptive Clustering
Hierarchy and A Novel Application Specific Network Protocol for Wireless Sensor
Networks routing protocol. The protocol is designed with the following in mind:
minimize the energy consumption of sensor nodes, resolve communication holes
issues, maintain data reliability, finally reach tradeoff between energy efficiency and
latency in terms of End-to-End, and channel access delays. Simulation results have
shown that the Mobile Data Collector based clustering routing protocol for WSNs
could be easily implemented in environmental applications where energy efficiency of
sensor nodes, network lifetime and data reliability are major concerns
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
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