户外监测的无线传感器网络部署策略

需积分: 9 27 浏览量更新于2024-07-19 收藏 10.47MB PDF 举报

"《Wireless Sensor Networks Deployment Strategies for Outdoor Monitoring》是关于无线传感器网络在户外监控中的部署策略的专业书籍，由Fadi Al-Turjman撰写。这本书详细探讨了无线传感器网络在实际环境监测中的应用和部署技术。" 在无线传感器网络（Wireless Sensor Networks, WSNs）领域，部署策略是确保系统效率、可靠性和覆盖范围的关键因素。本书深入研究了这些策略，以适应户外监测的需求。户外监测通常涉及环境监测、灾害预警、野生动物保护等多个领域，WSNs在这种场景下能提供实时、分布式的数据采集能力。 MATLAB是书中提及的一种工具，它被广泛用于仿真和分析无线传感器网络的性能。尽管MATLAB公司并未对此书的内容准确性做出保证，但本书的作者可能利用MATLAB来演示和解释WSN的算法和模型。无线传感器网络的部署策略涵盖以下几个关键方面： 1. **覆盖优化**：确保网络能够覆盖目标区域，避免盲点，同时最大化数据收集的范围。 2. **能量效率**：WSN节点通常电池供电，因此部署时需要考虑能量效率，延长网络寿命。 3. **拓扑控制**：建立和维护有效的网络拓扑结构，以支持数据传输和路由。 4. **抗干扰与可靠性**：在户外环境中，可能受到多种干扰，如多径衰落、信号干扰等，需要设计抗干扰机制。 5. **自组织能力**：WSNs应具有自我配置、自我修复的能力，以应对节点故障或移动性问题。 6. **安全性**：在部署中要考虑网络安全，防止数据篡改和非法接入。 7. **动态适应性**：随着环境变化，网络需要具备动态调整能力，例如调整通信距离、功率等级等。本书可能会详细讨论这些策略的理论基础、实现方法以及实际案例。通过案例分析，读者可以了解如何将这些理论应用于实际部署中，解决户外监测中的各种挑战。《Wireless Sensor Networks Deployment Strategies for Outdoor Monitoring》是一本面向科研人员、工程师和研究生的重要参考书，它提供了关于无线传感器网络部署的深度见解和技术指南。对于那些致力于开发和应用WSNs的人来说，这本书将有助于提升他们在这个领域的专业知识和实践技能。

IntroductIon

Wireless networking and advanced sensing technology have enabled

the development of low-cost and power-ecient Wireless Sensor

Networks (WSNs) which can be used in various domains such as

health care, home intelligence, and Outdoor Environment Monitoring

(OEM). Devices in WSNs, called Sensor Nodes (SNs), are used to

sense certain properties of the surrounding environment, including

physical/chemical properties, and transmit the sensed data to a central

unit, called a Base Station (BS), either periodically or on demand.

According to dierent application requirements, a WSN may consist

of just a few or as many as thousands of wireless nodes, operating in

a collaborative and coherent manner for a few days or several years to

fulll a specic task [1].

Currently, WSNs are proving to be a promising wireless monitor-

ing technology in various OEM applications. In OEM applications,

sensor nodes are assumed to monitor and report the status of the sur-

rounding outdoor environments, as in air pollution, forest re, and

ood detection applications [2,3]. e wide use of WSNs in OEM is

due to their enormous potential benets and advantages. e direct

and continuous existence of WSNs surrounding the monitored phe-

nomena allows them to provide localized measurements and detailed

information that is hard to obtain through traditional instrumenta-

tion (i.e., traditional sensors). For example, the Moderate Resolution

Imaging Spectroradiometer (MODIS) [4,5], a traditional instrument,

was launched to provide forest re surveillance by capturing satel-

lite images and processing them every 1–2 days, which is too late for

the forest to survive. In contrast, in Reference [2], a WSN is used to

provide real time interaction with forest res due to the cooperative

eorts of sensor nodes. Another example is the redwood tree appli-

cation in Reference [6]. e entire surrounding of the redwood tree

is known to have substantial variations in temperature and moisture

2 WIRELESS SENSOR NETWORKS

along its length (~70 m). In order to study these variations, a winch

near the top is needed to carry a set of weather monitoring instru-

ments with a very long serial cable connected to a battery powered

data logger at the tree base. However, a few small nodes constructing

the WSN were able to collect the targeted data wirelessly at dierent

scales and resolutions.

Measuring natural species with a WSN can also enable long term

data collection at scales and resolutions that are dicult, if not impos-

sible, to obtain otherwise. Moreover, due to the local computing and

networking capabilities of WSNs, sensor nodes can be reprogrammed

to do dierent tasks after deployment on site, based on changes in

the conditions of the WSN itself and the targeted phenomena [1].

erefore, laboratories can be extended to the monitored site where

no human intervention is required, and real time interaction can be

provided remotely.

Nonetheless, deploying

WSNs in OEM is challenging and needs

further investigation due to harsh operational conditions, vast mon-

itored areas, limited energy budgets, and required 3-D setups. For

instance, most of the proposed OEM applications [2,3,6–9] are applied

in harsh and large-scale areas, such as forests and river surfaces, and

experience bad channel conditions as well as great chances of node

failures and damages. Furthermore, some applications required more

complicated deployment planning when 3-D setups [3,6,8,10], and

long lifetime periods [2,6,9] are required. erefore, we investigate

ecient deployment plans to address these challenges and ll the lit-

erature gap in this critical research direction. In the following sub-

sections, OEM deployment challenges and motivations behind this

work are detailed. And major research contributions are reviewed in

addition to outlining the remaining contents of this book.

1.1 Contributions

is work aims primarily at optimizing the WSN deployment in

OEM applications by exploiting static/mobile RNs in grid-based

deployments. We study the optimization problem when SNs’ posi-

tions are known in advance and static RNs are utilized. Afterward, we

Deployment is the process of identifying numbers and positions of the utilized

nodes (devices) in the WSN.

3IntroduCtIon

examine a more complicated scenario where SNs’ positions are anony-

mous and a hybrid of static and mobile RNs are in use. Accordingly,

we present novel deployment schemes (strategies) to place the set of

RNs/SNs on any grid model in these scenarios. e main contribu-

tions of this book are summarized as follows:

1. Generally, SNs/RNs can be placed on any vertex of the 3-D

grid which results in a huge search space in large-scale appli-

cations, such as an OEM. us, this leads to extreme com-

plexity in repositioning extra static or mobile RNs during the

run time. We overcome this challenge by nding a subset of a

relatively small number of grid vertices for these RNs without

aecting the solution optimality.

2. As

suming prepositioned SNs and static RNs only, we con-

sider them along with their communication links as a graph

to mathematically represent their connectivity property. And

thus, we formulate a solid mathematical optimization problem

that has the aforementioned limited search space and aims at

maximizing the network connectivity while maintaining cost

and lifetime constraints.

3. Key to our contribution is also the use of proper cost and

communication models in addition to a revised denition

for the network lifetime, which is more appropriate in OEM

applications.

4. Assuming the mobility feature in a subset of the available

RNs, we also prolong the network lifetime. We mathemati-

cally formulate this problem as an Integer Linear Program

(ILP) and provide a two-phase solution for it. We consider

an eective power level metric at the objective function of

the ILP, which is the minimum node residual energy along

with the total consumed energy. is consideration results

in more inuential movement for the mobile RNs, and thus

more equilibrium in the trac load is achieved.

We d

erive an upper bound for the maximum network life-

time under idealistic operational conditions in OEM. e

proposed two-phase solution shows signicant improvements

in terms of the network lifetime in comparison to this upper

bound.

4 WIRELESS SENSOR NETWORKS

6. We investigate mobile vs. static grid-based deployments in

order to overcome harsh operational conditions in OEM

applications while considering hexagon and square grid

shapes for these deployment solutions.

7. Genetic-based solutions, as well as solid mathematical opti-

mization, have been exploited in signicant OEM applica-

tions such as the emerging Smart City project.

In summary, this book provides guidelines for device deployment of

typical heterogeneous WSNs in OEM applications.

1.2 Book Outline

e rest of this book is organized as follows. Chapter 2 highlights

relevant background and related work in the literature. In Chapter3,

we introduce a novel 3-D deployment strategy, called Optimized 3-D

Grid deployment with Lifetime Constraint (O3DwLC), for relay

nodes in environmental applications. e strategy optimizes network

connectivity while guaranteeing specic network lifetime and limited

cost. Key to our contribution is a very limited search space for the

optimization problem in addition to a revised denition for network

lifetime, which is more appropriate in environment monitoring. e

eectiveness of our strategy is validated through extensive simulations

and comparisons, assuming practical considerations of signal propa-

gation and connectivity. In Chapter 4, we propose two Optimized

Relay Placement (ORP) strategies with the objective of federating

disjoint WSN sectors with the maximum connectivity under a cost

constraint on the total number of RNs to be deployed. e perfor-

mance of the proposed approach is validated and assessed through

extensive simulations and comparisons assuming practical consid-

erations in outdoor environments. In Chapter 5, we propose a 3-D

grid-based deployment for relay nodes in which the relays are e-

ciently placed on grid vertices. We present a novel approach, named

Fixing Augmented network Damage Intelligently (FADI), based on

a minimum spanning tree construction to reconnect the disjointed

WSN sectors. e performance of the proposed approach is validated

and assessed through extensive simulations; comparisons with two

mainstream approaches are presented. Our protocol outperforms the

5IntroduCtIon

related work in terms of the average relay node count and distribution,

the scalability of the federated WSNs in large scale applications, and

the robustness of thetopologies formed. We elaborate further on this

strategy in Chapter 6 where the hexagonal virtual grid is assumed

instead of the square one. We propose the use of cognitive nodes

(CNs) in the underlying sensor network to provide intelligent infor-

mation processing and knowledge-based services to the end users. We

identify tools and techniques to implement the cognitive functionality

and formulate a strategy for the deployment of CNs in the underlying

sensor network to ensure a high probability of successful data recep-

tion among communicating nodes. From MATLAB

simulations, we

were able to verify that in a network with randomly deployed sensor

nodes, CNs can be strategically deployed at predetermined positions,

to deliver application-aware data that satises the end user’s qual-

ity of information requirements, even at high application payloads.

Chapter7 proposes a 3-D grid-based deployment for heterogeneous

WSNs (consisting of SNs, RNs, and MRNs). e problem is cast

as a Mixed Integer Linear Program (MILP) optimization problem

with the objective of maximizing the network lifetime while main-

taining certain levels of fault-tolerance and cost eciency. Moreover,

an Upper Bound (UB) on the deployed WSN lifetime, given that

there are no unexpected node/link failures, has been driven. Based on

practical/harsh experimental settings in OEM, intensive simulations

show that the proposed grid-based deployment scheme can achieve an

average of 97% of the expectedUB.

Additionally, a typical scenario has been discussed and analyzed in

smart cities while considering genetic based approaches in Chapter8.

In this chapter, we study the path planning problem for these DCs

while optimizing their counts and their total traveled distances. As

the total collected load on a given DC route cannot exceed its stor-

age capacity, it is important to decide on the size of the exchanged

data packets (images, videos, etc.) and the sequence of the targeted

data sources to be visited. We propose a hybrid heuristic approach for

public data delivery in smart city settings. In this approach, public

vehicles are utilized as DCs that read/collect data from numerously

distributed Access Points (APs) and relay it back to a central process-

ing base station in the city. We also introduce a cost-based tness

function for DCs election in the smart city paradigm. Our cost-based

剩余236页未读，继续阅读

yinkaisheng-nj

粉丝: 763
资源: 6231

户外监测的无线传感器网络部署策略

Deployment with Docker 无水印原版pdf

AngularJS Deployment Essentials 无水印原版pdf

Beginning Django Web Application Development and Deployment with Python 无水印原版pdf

Optimizing Multi-UAV Deployment in 3D Space to Minimize Task Completion Time in UAV-Enabled Mobile Edge Computing Systems

Warning: No artifacts marked for deployment

OCP deployment does not have minimum availability

kubectl get deployment 中deployment 缩写是什么

flowable6.7.2 No deployment resources were found for autodeployment

springboot flowable6.7.2 No deployment resources were found for autodeployment

flowable No deployment resources were found for autodeployment

最新资源