520 research outputs found
Optimal fault-tolerant placement of relay nodes in a mission critical wireless network
The operations of many critical infrastructures (e.g., airports) heavily depend on proper functioning of the radio communication network supporting operations. As a result, such a communication network is indeed a mission-critical communication network that needs adequate protection from external electromagnetic interferences. This is usually done through radiogoniometers. Basically, by using at least three suitably deployed radiogoniometers and a gateway gathering information from them, sources of electromagnetic emissions that are not supposed to be present in the monitored area can be localised. Typically, relay nodes are used to connect radiogoniometers to the gateway. As a result, some degree of fault-tolerance for the network of relay nodes is essential in order to offer a reliable monitoring. On the other hand, deployment of relay nodes is typically quite expensive. As a result, we have two conflicting requirements: minimise costs while guaranteeing a given fault-tolerance. In this paper address the problem of computing a deployment for relay nodes that minimises the relay node network cost while at the same time guaranteeing proper working of the network even when some of the relay nodes (up to a given maximum number) become faulty (fault-tolerance). We show that the above problem can be formulated as a Mixed Integer Linear Programming (MILP) as well as a Pseudo-Boolean Satisfiability (PB-SAT) optimisation problem and present experimental results com- paring the two approaches on realistic scenarios
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
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
Quality-of-service in wireless sensor networks: state-of-the-art and future directions
Wireless sensor networks (WSNs) are one of today’s most prominent instantiations
of the ubiquituous computing paradigm. In order to achieve high
levels of integration, WSNs need to be conceived considering requirements
beyond the mere system’s functionality. While Quality-of-Service (QoS) is
traditionally associated with bit/data rate, network throughput, message delay
and bit/packet error rate, we believe that this concept is too strict, in
the sense that these properties alone do not reflect the overall quality-ofservice
provided to the user/application. Other non-functional properties
such as scalability, security or energy sustainability must also be considered
in the system design. This paper identifies the most important non-functional
properties that affect the overall quality of the service provided to the users,
outlining their relevance, state-of-the-art and future research directions
Energy Aware Design Strategies for Heterogeneous Sensor Networks
A sensor network is an interconnection of sensor nodes, each equipped with sensor(s), a micro-processor, some memory, and a wireless transceiver. Data from sensor nodes are usually collected at a central entity known as the base station or sink. Sensor nodes are powered by lightweight batteries, and it is often not feasible to replace or recharge these batteries. Therefore, the lifetime of a sensor network is considered to be over as soon as the batteries of critical nodes are depleted. For scalability and efficient data gathering, a hierarchical two-tier architecture has been proposed in the literature, where the sensor nodes constitute the lower-tier. The network is organized as a number of clusters, and, in each cluster, one node is assigned the role of the cluster head. The cluster heads constitute the upper-tier of the network. Each cluster head receives data from the sensor nodes in the corresponding cluster and communicates the data to the base station. The cluster heads may communicate with the base station either directly, using single-hop communication, or by forming a network among themselves using multi-hop communication. In recent years, a special node, provisioned with higher initial energy and communication capabilities, called the relay node, has been proposed in the literature to act as a cluster head in hierarchical sensor networks. The three major sub-problems when designing this type of network are i) to find a suitable placement of the relay nodes within the network, using the minimal number of relay nodes, so that each sensor node can communicate effectively with its cluster head, and the upper-tier network can tolerate fault(s), ii) to assign sensor nodes to clusters in an energy efficient manner, and iii) to compute a routing scheme for the relay nodes, such that the network lifetime is maximized. In this dissertation, we present two strategies for the placement of relay nodes, and five energy-aware strategies for the clustering and routing in a hierarchical, heterogeneous, two-tiered sensor network using relay nodes as cluster heads
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
Planning the deployment of fault-tolerant wireless sensor networks
Since Wireless Sensor Networks (WSNs) are subject to failures, fault-tolerance becomes an
important requirement for many WSN applications. Fault-tolerance can be enabled in
different areas of WSN design and operation, including the Medium Access Control (MAC)
layer and the initial topology design. To be robust to failures, a MAC protocol must be able
to adapt to traffic fluctuations and topology dynamics. We design ER-MAC that can switch
from energy-efficient operation in normal monitoring to reliable and fast delivery for
emergency monitoring, and vice versa. It also can prioritise high priority packets and
guarantee fair packet deliveries from all sensor nodes.
Topology design supports fault-tolerance by ensuring that there are alternative acceptable
routes to data sinks when failures occur. We provide solutions for four topology planning
problems: Additional Relay Placement (ARP), Additional Backup Placement (ABP),
Multiple Sink Placement (MSP), and Multiple Sink and Relay Placement (MSRP). Our
solutions use a local search technique based on Greedy Randomized Adaptive Search
Procedures (GRASP). GRASP-ARP deploys relays for (k,l)-sink-connectivity, where each
sensor node must have k vertex-disjoint paths of length ≤ l. To count how many disjoint
paths a node has, we propose Counting-Paths. GRASP-ABP deploys fewer relays than
GRASP-ARP by focusing only on the most important nodes – those whose failure has the
worst effect. To identify such nodes, we define Length-constrained Connectivity and
Rerouting Centrality (l-CRC). Greedy-MSP and GRASP-MSP place minimal cost sinks to
ensure that each sensor node in the network is double-covered, i.e. has two length-bounded
paths to two sinks. Greedy-MSRP and GRASP-MSRP deploy sinks and relays with minimal
cost to make the network double-covered and non-critical, i.e. all sensor nodes must have
length-bounded alternative paths to sinks when an arbitrary sensor node fails. We then
evaluate the fault-tolerance of each topology in data gathering simulations using ER-MAC
Connectivity, Coverage and Placement in Wireless Sensor Networks
Wireless communication between sensors allows the formation of flexible sensor networks, which can be deployed rapidly over wide or inaccessible areas. However, the need to gather data from all sensors in the network imposes constraints on the distances between sensors. This survey describes the state of the art in techniques for determining the minimum density and optimal locations of relay nodes and ordinary sensors to ensure connectivity, subject to various degrees of uncertainty in the locations of the nodes
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