优化无线传感器网络部署策略提升户外环境监测效率

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无线传感器网络（Wireless Sensor Networks, WSNs）是一种新兴的技术，它通过无线通信方式将大量小型、低成本的传感器节点部署在广阔的区域，用于环境监测等各种应用。在户外环境监测（Outdoor Environment Monitoring, OEM）场景中，WSNs展现出其独特的优势，如分布式数据收集、实时性和灵活性，尤其是在难以铺设有线基础设施的地区。然而，这些网络在实际部署时面临着诸多挑战，包括对极端环境的适应性、大面积覆盖需求、能源效率的限制以及三维空间的布置。首先，对于户外环境的特殊性，部署策略需要考虑以下几个关键因素： 1. **耐久性和可靠性**：WSNs在户外通常面临恶劣的气候条件，如雨、雪、高温和湿度变化，因此，选用具有抗干扰、耐候性强的传感器和通信模块至关重要。部署时应选择能够抵抗极端天气影响的设备，并设计合理的冗余机制以提高网络的生存能力。 2. **能量管理**：由于WSNs节点的电池寿命有限，部署策略必须考虑能量效率。通过优化数据传输、休眠模式、太阳能或节能硬件等方式来延长网络的生命周期。此外，能量 harvesting（能量采集）技术可以利用自然环境中的能量源，如光、热和震动，为网络提供额外电力。 3. **大范围覆盖**：为了监测广阔的区域，部署需要采用多级、层次化的结构，比如集群架构，这样既能保证信号强度，又可以减少节点间的通信负荷。同时，利用中继节点进行数据转发，确保信息的有效传播。 4. **3D空间部署**：在三维环境中，如森林、山区或者建筑密集区，部署可能涉及复杂的几何分布和角度调整。使用定向天线、智能路由算法以及三维建模可以帮助优化网络的覆盖范围，减少盲区。 5. **网络安全与隐私保护**：户外环境下的WSNs容易受到攻击，部署时需要实施强大的加密技术和安全协议，以防止数据被窃取或篡改。同时，尊重用户隐私，合理设置数据收集和存储规则。 6. **成本效益分析**：虽然初始投资可能较高，但考虑到WSNs的长期运行成本和其带来的自动化监测优势，必须进行经济效益分析，确保部署策略的合理性。《无线传感器网络部署策略》这本书深入探讨了针对户外环境监测的应用，提供了MATLAB®作为工具来模拟和优化部署方案。作者Fadi Al-Turjman强调，尽管MATLAB®软件在本书中被使用，但其使用并不构成对特定教学方法或软件使用的推荐，而是作为一种实用工具来帮助读者理解复杂的网络部署问题。无线传感器网络在户外环境监测领域的部署策略是多维度、技术密集型的工作，需要综合考虑环境特性、能源管理、网络拓扑和安全性等因素，以实现高效、可靠和经济的网络部署。随着科技的发展和优化算法的进步，未来WSNs在户外环境监测中的应用将会更加广泛和深入。

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

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