Nghiên cứu các giải pháp sử dụng năng lượng hiệu quả trong mạng cảm biến không dây

Approach, research methods ...cscscssecssereerreeesssesessessseessseeneseeereenerernerscess 12 Mobile Robots Support Collecting Data in Wireless Sensor Networks.... | 30 | WPANS Wireless

ĐẠI HỌC THÁI NGUYÊN TRƯỜNG ĐẠI HỌC KỸ THUẬT CÔNG NGHIỆP

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NGHIÊN CỨU CÁC GIẢI PHÁP SỬ DỤNG NĂNG LƯỢNG HIỆU QUÁ TRONG

MANG CAM BIEN KHONG DAY

ĐẠI HỌC THÁI NGUYÊN

TRƯỜNG ĐẠI HỌC KỸ THUẬT CÔNG NGHIỆP

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NGHIÊN CỨU CÁC GIẢI PHÁP SỬ DỤNG NĂNG LƯỢNG HIỆU QUA TRONG

MANG CAM BIEN KHONG DAY

Mã số: T2020-B30_

Danh sách những thành viên tham gia nghiên cứu đề tài

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10 Nguyễn Tiến Duy Bộ môn Tin học công nghiệp - Khoa

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Mobile Robots Support Collecting Data in Wireless Sensor Networks 34

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Chapter 6: Blockchain Technology in Wireless Network: Benefits and

———————6.4-ConclusiofS= -=-

List of Tables, Figures (Danh mục bảng biểu và hình vẽ)

Figure 3.1, Architecture ofa WSN-ME with relocatable nodes Sf Figure 3.2 Architectures of WSN-MEs with MDCs: (a) mobile sinks and (b) EñöBI1Ì€:É€lãV S840 na S01 HbfnnbistedfitlSsdfi 10100980 0861001áyaea

Figure 3.3 Architecture of a WSN-ME with mobile peers

———————— Figure4.5 Multi-tier UAWSN network Tưng bon Si

Figure 4.6 Particle swarm optimization (PSO)-based shortest-path selection 61 Figure 4.7 General architecture of a UAWSN system cccccccsssseseesseereeeseeeseeees 62 Figure.5 1 Ene system architecture, «ss crseenasareenvertanrcinnecesmn oevereveieevsevesss 66 Figure 5.2, Graph representattOny ssresecccosseccstaesiesttersctassasacrseaterancasserecaeneesseenenenen 7 Figure 5.3 Ten UAVs with different predicted values reach a consensus 71 ElðfifE 2.4 TTOCKIHDTd SLAHG EHĐGL7 ri iccnnnniiiiini in iao aninoiooinioaniing T2 Figure 5.5 Gathering at the taFB€( St t v21 2 1 113 101 111111181111111 11 te, 72 Figure 5.6 Snapshots of initial positions of UAVs (a), avoiding obstacles and while moving to targets (b,c) and tracking the moving target (d,e) 73 Figure 6.1 WSN network types, data flow in WSN, data flow in WSN with Blockchaii technology ssiscuusommrnsrestenes cence 77

Table 6.I Table 6.1 COMPARISION BETWEEN TYPES OF W§N: cccccc, 78

List of Abbreviations (Danh muc cac chữ viết tắt)

STT Abbreviations

1 Al Artificial Intelligence

3 CELVNET Cellular Vehicular Networks

4 CH Cluster Head

q 7 |DSRC Dedicated Short-Range Communication

i 8 |DTNS | Delay-Tolerant Networks

9 | FL Federated Learning

— 11 |MAC —— |Medium Access Control ——

12 | MANETS Mobile Ad Hoc Networks

13 | MBDC Multimedia Big Data Collection

14 | MDCS Mobile Data Collectors

21 | UAIOT Uav-Aided Iot

22 | UAVS Unmanned Aerial Vehicle

23 | UAWSNS UAV-Aided Wireless Sensor Networks

24 |1V2I Vehicle-To-Infrastructure 25_ | V2P Vehicle-To-Pedestrian Links

26 | V2V Vehicle-To- Vehicle Links

| 27 |V2X Vehicle-To-Everything

| 28} VLC Visible Light Communication

| 29 | WLANS Wireless Local Area Networks

| 30 | WPANS Wireless Personal Area Networks

31 | WSN-MES Wireless Sensor Networks With Mobile Elements

32 | WSNS Wireless Sensor Networks

TRƯỜNG ĐẠI HỌC

THONG TIN KET QUA NGHIÊN CỨU

1 Thong tin chung:

- Tên để tài: Nghiên cứu các giải pháp sử dụng năng lượng hiệu quả trong

mạng cảm biến không dây

- Mã số: T2020-B30

- Chủ nhiệm dé tài: PGS.TS Nguyễn Tuần Minh

- Cơ quan chủ trì: Trường ĐH Kỹ thuật Công nghiệp - ĐH Thái Nguyên

2 Mục tiêu:

Mục tiêu của để tài là hướng tới những giải pháp nâng cao hiệu quả sử dụng năng

eer cua mạng cảm biến không dây Các phương án tập trung vào phương pháp/thuật —

———— toán thu thập dữ liệu, các công nghệ truyền thông, quản lý hệ thống xử lý dữ liệu hiệu

quả, để tối ưu cho mạng cảm biến không dây

3 Kết quả nghiên cứu:

Nghiên cứu để xuất một số giải pháp sử dụng các hệ thống truyền thông không dây hỗ trợ và mang lại hiệu quả cho mạng cảm biến không dây

Giảm thiểu được dữ liệu truyền trong quá trình thu thập dữ liệu, dẫn tới tiết kiệm năng

lượng, băng thông trong quá trình truyền

6 Khả năng áp dụng và phương thức chuyển giao kết quả nghiên cứu:

Ngày 27 tháng 09 năm 2021

Cơ quan chủ trì Chủ nhiệm đề tài

KT HIỆU TRƯỞNG PHÓ HIỆU TRƯỞNG

INFORMATION ON RESEARCH RESULTS

1 General information:

Project title: Research on energy efficient solutions in wireless sensor networks

Code number: T2020-B30 Coordinator: Assoc Prof Dr Nguyen Tuan Minh Implementing institution: Thai Nguyen University of Technology

The study proposes a number of solutions using wireless communication systems to support

and bring efficiency to wireless sensor networks

PHẢN MỞ ĐẦU

1 TÔNG QUAN TÌNH HÌNH NGHIÊN CỨU THUỘC LINH VUC CUA DE

Mang cam biến không dây hiện nay đang được triển khai trong rất nhiều lĩnh vực, cả trong quân sự và dân sự và mang lại nhiều đóng góp đáng kể về mặt kinh tế, quốc phòng an nỉnh, Đặc điểm của mạng này là các thành phần nút

cảm biến luôn cần được cấp một mức độ năng lượng ôn định cho hoạt động lâu đài, duy trì được chất lượng phục vụ của mạng [1, 2]

te Ä "Hiện nay có rất nhiều các kết quả nghiên cứu nâng cao hiệu quả sử dụng năng

lượng cho mạng cảm biến không dây Các nhà nghiên cứu, các công ty trong các ngành công nghiệp, khoa học công nghệ liên tục tìm kiếm các giải pháp mới

Để đạt được hiệu quả hơn cho mạng cảm biến không dây trong việc sử dụng

| năng lượng, sẽ có nhiều bài toán tích hợp các công nghệ, chia sẻ sử dụng nguồn

tài nguyên từ việc liên kết các mạng với nhau thông qua internet, bài toán tối ưu,

bài toán mới, [Š]

2 TINH CAP THIET CUA DE TAI

Mạng cảm biến không dây (WSNs) nói chung, mạng cảm biến di dong (MSNs), mạng robot di động hiện đang là đối tượng nghiên cứu rất phổ biến trên thế giới

Những mạng trên có rất nhiều ứng dụng không chỉ trong dân dụng mà còn trong

quân sự [1, 2] Chức năng chính của những mạng trên là phát hiện, cảnh báo sự kiện, thu thập thông tin cảm biến, tạo ra những bản đồ cảm biến, phục cho nhiều

mục đích giám sát khác nhau

Sử dụng hiệu quả năng lượng luôn là vấn đề quan trọng được dé cập ở các mạng

trên [3, 4, 5] Các bộ cảm biến được thả xuống các nơi có điều kiện khắc nghiệt,

khó tiếp cận, không có điều kiện sạc lại pin Các phương thức truyền nhận dữ liệu, thuật toán di chuyển phải được sử dụng hiệu quả

10

5 CÁCH TIẾP CẬN, PHƯƠNG PHÁP NGHIÊN CỨU

5.1 Cách tiếp cận

- Từ những nghiên cứu tổng quát, chỉ tiết về các phương pháp, kỹ thuật, công nghệ điều khiển và truyền thông cho mạng robot đã được công bố,

để tài sẽ tiếp cận được các công nghệ, các giải pháp nền tảng và nâng cao

Từ đó đề tài sẽ triển khai các thuật toán và các phương pháp mới nhằm đáp ứng được các yêu cầu đề ra

- Trên các cơ sở lý thuyết và thực tiễn, các thành viên của đề tài sẽ tiếp tục phát triển, có thể đề xuất các thuật toán, phương pháp điều khiển và

truyền thông mới

5.2 Phuong phap nghiên cứu

- Neghién ciru tat cả các nội dung cơ bản liên quan: những công trình đã

—————— công bố: được nghiên cứu, xem xét tính ổn: định, tính mới được ghinhận ——————

thành những nội dung liên quan, nền móng, để từ đó có thể xây dụng ý

tưởng mới, dự kiến có kết qua tốt hơn, mang lại nhiều ưu điểm hơn ở một góc độ nào đó trong nghiên cứu Cơ sở dữ liệu online có uy tín như:

IEEE, Elsevier, Springer,

- Xây dựng hướng nghiên cứu mới, thuật toán mới: khi những hướng nghiên cứu mới được hình thành, tác giả có thể phác thảo những thuật

toán mới mang tính khả dụng cao có thể áp dụng đề thay đổi, mang lại kết quả tốt hơn so với những kết quả đã có được ghi nhận tại các nguồn cơ sở

dữ liệu uy tín đã nêu

- _ Xây dựng công thúc toán: đây là bước phức tạp đòi hỏi một số lượng kiến

thức toán học cơ bản và nâng cao Công thức toán được xây dựng phải

đảm bảo tính chính xác của toán học, đồng thời cũng phải đảm bảo tích

logic để có thé dua vào chương trình máy tính chạy được

- Xây dựng chương trình mô phỏng: dựa trên các ý tưởng, từ thuật toán đến

phương trình toán học, mà các chương trình mô phỏng được xây dựng để

đáp ứng hai mục tiêu: (1) thu được kết quả đúng như thuật toán đã mô tả

(kết quả có thể tốt hơn hoặc kém hơn so với các kết quả đã công bổ); (ii)

so sách kết quả mô phỏng với kết quả thực hiện bởi các phương trình toán

12

- Dé xuất giải pháp, các thuật toán mới trong thu thập dữ liệu cảm biến, xử

lý tín hiệu, điều khiển, truyền thông, sử dụng năng lượng hiệu quả cho

mạng truyền thông không dây

- _ Tiến hành mô phỏng, chạy chương trình trên máy tính các nội dung theo

thuật toán mới

- Thu thập dữ liệu, so sánh kết quả, hiệu chỉnh chương trình, thuật toán và

kết luận về kết quả mới

-_ Viết các bài báo khoa học công bố trên các tạp chí quốc tế

} - Báo cáo các bài báo tiếng Anh tại hội thảo (seminar) của đơn vị

Tổng kết, đánh giá: viế ết báo cáo tóm tắt, báo cáo tổng kế,

14

|

|

1.1 Introduction

Recent advances in micro-electro-mechanical systems (MEMS) technology,

wireless communications, and digital electronics have enabled the development

of low-cost, low-power, multifunctional sensor nodes that are small in size and communicate untethered in short distances These tiny sensor nodes, which

= consist of sensing, data processing, and communicating components, leverage

the idea of sensor networks based on collaborative effort of a large number of -

sensors, which are deployed in the following two ways

Chapter 1:

Introduction to Wireless sensor networks (WSNs)

- Sensors can be positioned far from the actual phenomenon, i.e.,something =

| known by sense perception In this approach, large sensors that use some

, complex techniques to distinguish the targets from environmental noise

| - Several sensors that perform only sensing can be deployed The positions

of the sensors and communications topology are carefully engineered

Ì They transmit time series of the sensed phenomenon to the central nodes

where computations are performed and data are fused

A sensor network is composed of a large number of sensor nodes, which are densely deployed either inside the phenomenon or very close to it

The position of sensor nodes need not be engineered or pre-determined This

allows random deployment in inaccessible terrains or disaster relief operations

must—possess- self-organizing capabilities Another unique feature of sensor networks is the cooperative effort of sensor nodes Sensor nodes are fitted with

an on-board processor Instead of sending the raw data to the nodes responsible for the fusion, sensor nodes use their processing abilities to locally carry out

simple computations and transmit only the required and partially processed data

The above described features ensure a wide range of applications for sensor networks Some of the application areas are health, military, and security For

16

One of the most important constraints on sensor nodes is the low power

consumption requirement Sensor nodes carry limited, generally irreplaceable,

power sources Therefore, while traditional networks aim to achieve high quality

of service (QoS) provisions, sensor network protocols must focus primarily on

power conservation They must have inbuilt trade-off mechanisms that give the

end user the option of prolonging network lifetime at the cost of lower throughput or higher transmission delay

1.2 Sensor Network Applications

Sensor networks may consist of many different types of sensors such as seismic, low sampling rate magnetic, thermal, visual, infrared, acoustic and radar, which

* the presence or absence of certain kinds of objects,

¢ mechanical stress levels on attached objects, and

« the current characteristics such as speed, direction, and size of an object

Sensor nodes can be used for continuous sensing, event detection, event ID,

_Jocation-sensing, -and_local control of actuators The concept of micro-sensing

and wireless connection of these nodes promise many new application areas We

categorize the applications into military, environment, health, home and other commercial areas It is possible to expand this classification with more categories such as space exploration, chemical processing and disaster relief

1.2.1 Military applications Wireless sensor networks can be an integral part of military command, control, communications, computing, intelligence, surveillance, reconnaissance and

18

Nuclear, biological and chemical attack detection and reconnaissance: In

chemical and biological warfare, being close to ground zero is important for

timely and accurate detection of the agents Sensor networks deployed in the friendly region and used as a chemical or biological warning system can provide the friendly forces with critical reaction time, which drops casualties drastically

1.2.2 Environmental applications

Some environmental applications of sensor networks include tracking the

movements of birds, small animals, and insects; monitoring environmental

conditions that affect crops and livestock; irrigation; macroinstruments for large- scale Earth monitoring and planetary exploration; chemical/ biological

detection; precision agriculture; biological, Earth, and environmental monitoring

in marine, soil, and atmospheric contexts; forest fire detection; meteorological or

geophysical research; flood detection; bio-complexity mapping of the

environment; and pollution study

Forest fire detection: Since sensor nodes may be strategically, randomly, and densely deployed in a forest, sensor nodes can relay the exact origin of the fire

to the end users before the fire is spread uncontrollable Millions of sensor nodes

can be deployed and integrated using radio frequencies/ optical systems Also,

they may be equipped with effective power scavenging methods, such as solar cells, because the sensors may be left unattended for months and even years

The sensor nodes will collaborate with each other to perform distributed sensing and overcome obstacles, such as trees and rocks, that block wired sensors’ line

these advances, the sensor nodes also have the ability to connect with the Internet, which allows remote users to control, monitor and observe the

biocomplexity of the environment

Although satellite and airborne sensors are useful in observing large

biodiversity, e.g., spatial complexity of dominant plant species, they are not fine

20

Tracking and monitoring doctors and patients inside a hospital: Each patient has small and light weight sensor nodes attached to them Each sensor node has

its specific task For example, one sensor node may be detecting the heart rate while another is detecting the blood pressure Doctors may also carry a sensor node, which allows other doctors to locate them within the hospital

Drug administration in hospitals: 1f sensor nodes can be attached to

medications, the chance of getting and prescribing the wrong medication to patients can be minimized Because, patients will have sensor nodes that identify their allergies and required medications Computerized systems as described have shown that they can help minimize adverse drug events

1.2.4 Home applications = Home automation: As technology advances, smart sensor nodes and actuators

can be buried in appliances, such as vacuum cleaners, micro-wave ovens, refrigerators, and VCRs These sensor nodes inside the domestic devices can

interact with each other and with the external network via the Internet or

Satellite They allow end users to manage home devices locally and remotely more easily

Smart environment: The design of smart environment can have two different perspectives, i.e, human-centered and technology-centered For human-

centered, a smart environment has to adapt to the needs of the end users in terms

of input/ output capabilities For technology-centered, new hardware

technologies, networking solutions, and middleware services have to be developed A scenario of how sensor nodes can be used to create a smart environment is described in The sensor nodes can be embedded into furniture

and appliances, and they can communicate with each other and the room server

The room server can also communicate with other room servers tolearn-about——

the services they offered, e.g., printing, scanning, and faxing These room

servers and sensor nodes can be integrated with existing embedded devices to become self-organizing, selfregulated, and adaptive systems based on control

theory models Another example of smart environment is the ‘‘Residential Laboratory’? at Georgia Institute of Technology The computing and sensing in this environment has to be reliable, persistent, and transparent

1.2.5 Other commercial applications

22

number of items in the same category If the end users want to insert new inventories, all the users need to do is to attach the appropriate sensor nodes to the inventories The end users can track and locate where the inventories are at

all times

Vehicle tracking and detection: There are two approaches as described to track and detect the vehicle: first, the line of bearing of the vehicle is determined locally within the clusters and then it is forwarded to the base station, and

second, the raw data collected by the sensor nodes are forwarded to the base

station to determine the location of the vehicle

2.23 .Sensor Profocolfor Inƒformation via Negotiadtion (SPIN) — — ~

sleeping schedules and fault tolerance schemes that can prolong the network

lifetime

2.2.2 Flooding and Gossiping

Flooding and gossiping [40, 41] are two classical methods to relay data in WSNs In the flooding method, each sensor keeps sending broadcast messages

to its neighbors within a sensor transmission range until it receives data packets

or the maximum number of hops for the packet is reached On the other hand, gossiping is a slightly enhanced version of flooding where the receiving node sends the packet to a randomly selected neighbor This neighbor will pick another random neighbor to forward the data to, and so on

meta-data are exchanged among sensors via a data advertisement mechanism

Each sensor upon receiving new data, advertises it to its neighbors and interested neighbors Sensors which do not have data retrieve the data by

sending a request message

Sensor nodes negotiate with each other The negotiations ensure that nodes only transmit data when necessary and never waste energy on useless transmissions

SPIN’s meta-data negotiation solves the problems of flooding such as redundant

information passing, overlapping of sensing area and resource blindness

One of the advantages of SPIN is that topological changes are localized since

each node needs to know only its neighbors with single-hop communications

However, the data advertisement mechanism cannot guarantee delivery of data

For example, in considering the application of intrusion detection where data should be reported over periodic intervals, and assume that nodes interested in the data are located far away from the source node, etc such data would not be

delivered to the destination

26

as opposed to directed diffusion where data can be routed through multiple paths

at low rates

Rumor routing can only perform well when the number of events is small For a

large number of events, the cost for maintaining agents and event tables in each node becomes infeasible if there is not enough interest in these events from the

BS In addition, the overhead associated with rumor routing is controlled by

different parameters used in the algorithm such as time to live pertaining to

queries and agents

2.2.6 Minimum Cost Forwarding Algorithms (MCFA)

In this method [46], each sensor should know the least cost path estimated from

every sensor initiates its least cost to the BS to infinity Each sensor node, upon receiving the broadcast message originated at the BS, checks to see if the

estimate in the message plus the link on which it is received is less than the current estimate for updating In this case, the nodes far away from the BS get

more updates than the ones closer to the BS Once the cost field is established,

any sensor can deliver the data to the sink along the minimum cost path Each intermediate node forwards the message only if it finds itself on the optimal path

for this message based on the message’s cost states

2.2.7 Gradient-Based Routing (GBR)

In GBR [47, 48], each sensor calculates a parameter called the height of the

node, which is the minimum number of hops to reach the BS The difference

; between a sensor’s height and that of its neighbor is considered the gradient on

| that link A packet is forwarded on a link with the largest gradient GBR uses

auxiliary techniques such as data aggregation and traffic spreading in order to

= itself to the BS The BS broadcasts a message with the cost set to zero, while = -

uniformly divide the traffic over the network

In GBR, three different data dissemination techniques have been discussed

Stochastic scheme, where a sensor picks one gradient randomly when there is

more than one next hops that have the same gradient In energy-based scheme,

the sensor increases its height when its energy drops below a certain threshold

In the stream-based scheme, new streams are not routed through nodes that are

28

information from their neighbors within a look ahead of “ hops Once the query

is resolved completely, it is sent back through either the reverse or shortest path

to the BS Hence, ACQUIRE can deal with complex queries by allowing many

sensors to send responses

ACQUIRE selects the next node to forward a query that has two options, chooses randomly or selects based on maximum potential query satisfaction

2.2.10 Energy-Aware Routing

The goal of this method [52] is try to find the minimum energy path to optimize

: energy usage at a node to increase the network lifetime It maintains a set of

—— paths instead of maintaining or enforcing one optimal path at higher rates These

paths are maintained and chosen by means of a certain probability =ije — probability depends on how low the energy consumption is that each path can

achieve By having paths chosen at different times, the energy of any single path will not deplete quickly In addition, the energy is dissipated equally among all

sensors that balances the energy consumption in the network and prolonging the

network lifetime

2.2 Location-Based Routing

| There are many data collection methods for WSNs that require location

information for sensors to calculate the data transmission distances between two

| sensors or between sensors and the BS Based on that, the energy consumption

for data transmission can be estimated The local information of sensors can be used in routing in an energy-efficient manner For instance, the query can be

diffused only to a particular region that can reduce the number of transmissions

between sensors

2.2.1 MECN and SMECN

Minimum energy communication network [53] sets up and maintains a

minimum energy consumption for a WSN by utilizing low power GPS [54] The paper describes a distributed network protocol optimized for achieving the minimum energy for randomly deployed ad-hoc networks The network protocol

not only maintain a globally connected network in spite of possible module failure, but also defines the major power management strategy based on low-

30

GEAR [57] uses energy-aware and geographically informed neighbor selection heuristics to route a packet toward the destination region The key idea is to restrict the number of interest in directed diffusion by only considering a certain region rather than sending the interest to the whole network GEAR can

In GEAR, each sensor keeps an estimated cost and a learning cost of reaching

the destination through its neighbors The estimated cost is a combination of residual energy and distance to destination There are two phases: (1) Forwarding packets toward the target region: Upon receiving a packet, a node

checks its neighbors to see if there is one neighbor that is closer to the target

the next hop If they are all further than the node itself, one of the neighbors is picked to forward the packet based on the learning cost function (2)

Forwarding the packets within the region: If the packet has reached the region,

it can be diffused in that region by either recursive geographic forwarding or

restricted flooding

GEAR is compared to GPSR [58] in solving the problem of holes GEAR not

| only reduces energy consumption for the route setup but also outperforms GPSR

- conserve more energy than directed diffusion

in terms of packet delivery

2.2.4 MFR, DIR, and GEDIR

These protocols deal with basic distance, progress, and direction-based methods

| [59] The key ideas are forward and backward directions A source node or any

| intermediate node will select one of its neighbors according to a certain

criterion The routing methods that belong to this category are Most Forward within Radius (MFR), Geographic Distance Routing (GEDIR) including a

| method, and DIR (a compass routing method)

| GEDIR-is-a greedy algorithm that always moves the packet to the neighbor of

the current vertex whose distance to the destination is minimized The algorithm fails when the packet crosses the same edge twice in succession In most case, MER and greedy methods have the same path to the destination In DIR, the best neighbor has the closest direction toward the destination GEDIR and MFR are

32

Chapter 3:

Mobile Robots Support Collecting Data in Wireless Sensor Networks

3.1 Introduction

In recent years wireless sensor networks (WSNs) have become an

established technology for a large number of applications, ranging from

monitoring (e.g., pollution prevention, precision agriculture, structures and

buildings health), to event detection (e.g., intrusions, fire/flood emergencies) and target tracking (e.g., surveillance), WSNs usually consist of a large number of

~~~ sensor nodes, which are battery-powered tiny devices These devices perform ——-

assumed to be static, and mobility is not considered as an option More recently,

similarly to the research trends in mobile ad hoc networks (MANETs) and

delay-tolerant networks (DTNs), mobility has also been introduced to WSNs In

fact, mobility in WSNs is useful for several reasons, as discussed in the following

Connectivity: As nodes are mobile, a dense WSN architecture may be not

a requirement In fact, mobile elements can cope with isolated regions, so that the constraints on network connectivity can be relaxed, also in terms

of nodes (re)deployment Hence a sparse WSN architecture becomes a feasible option

- Cost: Since fewer nodes can be deployed, the network cost is reduced in a mobile WSN Although adding mobility features to the nodes might be

34

—— and the speed or sojourn time of mobile nodes have to be defined =

predicted with a certain accuracy, sensor nodes can be awake only when they expect the mobile element to be in their transmission range

- Reliable data transfer: As available contacts might be scarce and short, there is a need to maximize the number of messages correctly transferred

to the sink In addition, since nodes move during data transfer, message exchange must be mobility-aware

- Mobility control: When the motion of mobile elements can be controlled,

a policy for visiting nodes in the network has to be defined To this end,

in order to improve (maximize) the network performance

3.2 Wireless sensor network with mobile elements (ME) or Mobile robots

(MRs)

To better understand the specific features of wireless sensor networks with mobile elements (WSN-MEs), let us first introduce the reference network

architecture, which is detailed according to the role of the MEs The main

components of WSN-MEs are the following

- Regular sensor nodes (or just nodes, for short) are the sources of

information Such nodes perform sensing as their main task They may also forward or relay messages in the network, depending on the adopted

- Special support nodes perform a specific task, such as acting as

intermediate data collectors or mobile gateways They are not sources nor

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3.2.1.1 Relocatable Nodes

These are mobile nodes which change their location to better characterize the sensing area, or to forward data from the source nodes to the sink In contrast with mobile data collectors (which are discussed in the next section), relocatable

nodes do not carry data as they move in the network In fact, they only change the topology of the network—which is assumed to be rather dense—for

connectivity or coverage purposes More specifically, after moving to the new

' location, they usually remain stationary and forward data along multihop paths

MEs (e.g., support nodes) are used

Although in theory ordinary nodes might be relocatable, in most cases special

A system with relocatable nodes targeted for topology management has been proposed to support WSNs In particular, special predefined, intelligent, lightweight topology management (PILOT) nodes are used to reestablish network connectivity for faulty links In detail, PILOT nodes move to regions

| where the connection between nodes is unstable or failing, and act as bridges

| As a consequence, they actively change the WSN topology in order to improve

| both communication reliability and energy efficiency Algorithms for placement

of relocatable nodes in the context of improving network connectivity have been investigated

Relocatable nodes can also be used to address the problem of sensing coverage In this case, the primary concern is not ensuring network connectivity,

but avoiding coverage holes—areas where the density of nodes is not adequate

to properly characterize a phenomenon or detect an event Approaches targeted

for sensing coverage can focus on sensor deployment, sensor relocation and dispatch, or both Relocatable nodes provide a mobility-assisted approach to WSNs, in the sense that MEs are not actively exploited for data collection

Therefore, in the following we will not discuss further solutions based on

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architecture is depicted in Figure 4.2(b) MR-based data collection in WSNs has been proposed in the data-MULE system In detail, the data-MULE system consists of a three-tier architecture, where the middle tier is represented by relays, called mobile ubiquitous LAN extensions (MULEs)

Figure 3.3 Architecture of a WSN-ME with mobile peers

Mobile peers can also be used for opportunistic data collection in urban

sensing scenarios Sample applications include personal monitoring (¢.g.,

physical exercise tracking), civil defense (¢.g., hazards and hotspot reporting to police officers), and collaborative applications (e.g., information sharing for tourism purposes) In this context, sensors are not used mainly for monitoring the environment, but are rather exploited to characterize people in terms of both

interactions and context (or state) information An example is represented by

handheld mobiscopes where handhelds devices—such as cell phones or PDAs—

gather data from the surrounding environment and report them to servers, which

provide services to remote users Since most of the issues in the context of

WSN-MEs based on mobile peers are similar to classic DINs, we will not focus

on these topics

3.3 Overview of data collection in WSN with MEs or MRs assisted

In this section we will outline the different phases of the data collection

process and point out the main issues involved For convenience, without loss of

42

residual contact time, while minimizing the energy consumption ——————————]

exchange Finally, we indicate as routing the process of data forwarding toward

an ME, that is, the selection of the path or the sequence of pairwise message transmissions to the intended destination On the basis of the preceding _ discussion, three main phases associated with the data collection in WSN-MEs

emerge: discovery, data transfer, and routing to MEs Each phase has its own

issues and requirements which we briefly investigate in the following

- Discovery is the first step for collecting data in WSN-MEs, since the presence of the ME in the contact area is generally unknown at sensors

The goal of discovery protocols is to detect contacts as soon as they _

ene happen, and with a low energy expenditure In other words, discovery =

should try to maximize the number of detected contacts, and also the

- Data transfer immediately follows discovery The goal of data transfer protocols is to get the most out of the residual contact time, that is, to

maximize the throughput in terms of messages successfully transferred

per contact while minimizing the energy consumption

- Routing to MEs is actually possible only when the density of the network

is enough to allow (even partial) multihop routes This is true for dense WSN-MEs, where routing to ME is always possible Actually, this can

happen even with more sparse WSN-MEs, where nodes can organize as disconnected clusters In this case, routing is possible only when an ME is

in contact with at least one node in the cluster However, some nodes can

be elected as bridges and act as gateways between the cluster nodes and the ME In both cases, the goal of routing is to find the best multihop

paths in terms of both delivery ratio and low energy consumption toward

either the ME or a node

44

of one or more sensors to provide feedback to the commander [13], [17] If each

UAV contains a sensor, a system must be devised to assign the vehicles/sensors

to positions in space and in time, and to move efficiently within a predetermined

environment to ensure monitoring Monitoring is related to various problems

that have been studied in the past A particular case takes place when a group of mobile sensors can be used to cover an area This is the so-called coverage

problem [5]

In summary, the benefit of multi-UAV cooperation and control is twofold First,

multi-UAV cooperation significantly reduces the operating cost by transferring the operation mode from many-to-one (i.¢., many operators control one UAV) to

to some technical challenges of airborne optical devices using lasercom links

[12] Consequently, a fundamental but challenging problem to be addressed in multi-UAV cooperation and control is efficient networking of UAVs over the

wireless medium this is able to provide timely and reliable information flowing among the UAVs In this project, we refer to this problem as the UAV networking problem and focus on the networking issues with specific applications to UAV operation scenarios

4.2 Network Structure-Based Routing

In network structure-based routing, the way that nodes are connected and how

they exchange information depend on the network architecture Flat routing, a type of network structure-based routing, combines all the sensor nodes together

to perform sensing, and all nodes have the same role to play in the network On the other hand, hierarchical routing has sensor nodes that play different roles in

the network For example, some nodes may send data to the sink node, whereas others may perform the task of sensing only In tree-based routing, a routing tree

46

surveillance Owing to this concept, Laourira et al proposed a multilayer hybrid architecture to design a surveillance system for border monitoring by using cameras, scalar sensors, radars, and UAVs They also addressed an activation

scheduling strategy based on load balancing and energy saving The main

purpose and aim of the proposed scheme is to track and detect any border

intrusion in the possible human involvement There are three kinds of nodes

used in the network, They are scalar sensors, multimedia sensors, and UAVs

They collaborate with each other to build an efficient intrusion detection system

Scalar sensors form the first layer called the detection layer This layer is

responsible for intrusion detection Once the scalar sensors detect any kind of

“intrusion, an alert is sent to the multimedia sensor nodes which constitute the

second layer of the network Finally, UAVs are called upon to track the intruder after they receive necessary information from the multimedia sensor nodes The

lifetime Although the network is designed with three-layer hierarchy, data

transmission (i.e., routing) among the three layers is flat-based That is why

UAV-WSN is classified as flat-based routing

UAV-based automated sensor deployment for mobile sink (UAV-AS-MS):

Gomez et al proposed a new method that combines a heterogeneous WSN

based on a multi-agent system and a mobile agent (UAV) for data acquisition in

places that are inaccessible for a human being The goal is to automate the

network deployment and data acquisition The primary concern is to facilitate

data gathering beyond the communication range of sensor nodes in the network

An UAV is responsible for transporting and releasing sensors at a certain location that is considered dangerous to access through direct human

intervention The UAV collects data automatically, following an optimal route

————-UAV-based liner sensor network (ULSN): Routing the data in a multi-hop

fashion in LSNs that can extend even up to hundreds or thousands of kilometers can cause high-energy dissipation A UAV-based liner sensor network (ULSN) deals with such a problem using UAVs Four kinds of nodes are defined in the

system They are sensor nodes (SNs), relay nodes (RNs), UAVs, and sink nodes

SNs are basic sensor nodes assigned to gather data from the environment RNs

collect information from the nearest sink nodes, and UAVs move along the LSN

to collect data from the sink nodes Sink nodes are placed at both ends of the LSN SNs use a normal and non-complex algorithm to transmit data to the

TỶ nearest RN RNs act as CHs and transfer data to a moving UAV Finally, the —

UAV carries the data to the sink node This procedure of the ULSN decreases

the energy consumption significantly because of the reduction of transmission

ranges from the SN to the RN and the use of one-hop transmission from the RNs

————————— tothe UAV The ULSN can also reduce the interference among RNs caused by —————

| the hidden-terminal problem One drawback is that the total travel time that a

| UAV may take while traversing a big LSN was not considered

Cluster-Based Routing

In cluster-based networks, nodes are divided into several virtual groups In the

clustering schemes, nodes are allocated a different purpose such as CH, cluster gateway (CGW), and cluster member (CM)

The CH functions as a local coordinator for its cluster, performing inter-cluster communication and data forwarding CGW is a non-CH node with inter-cluster

links CGW is used to access neighboring clusters and exchange information between clusters Ordinary nodes are called as CMs It was shown that cluster

schemes perform better in a large area with a large number of nodes There are

tolerance, maximal network lifetime, and energy efficiency are the most

common objectives among them In a multi- UAV network, one ground station is used to control one or more UAVs The ground station controls the UAV directly without any air-to-air (A2A) communication links However, this

scheme does not perform well on a large scale A centralized-based scheme does

not scale well for large UAV networks In cluster-based schemes, deployed UAVs may be minimized, and area coverage may be maximized A cluster- 50

have the same capabilities of energy, communication resources, and computation power, and CHs are selected according to a random manner On the

other hand, in the heterogeneous network, nodes have unequal capabilities For

this reason, CH selection is important in heterogeneous networks Normally, the

node with more capabilities is selected as a CH in heterogeneous networks

UAV-assisted routing protocol (URP): URP is a crop health-monitoring system developed using a UAV-aided clustered WSN Collecting data from WSNs

deployed in farm fields is challenging because of extreme weather conditions,

# like high temperature, dry weather, sandstorms, and remote locations To

overcome these challenges, some authors proposed a dynamic data-collection

classifier In the routing phase, nodes are classified into three types, namely,

CMs, candidate clusters (CCs), and candidate CHs (CCHs) Finally, to nominate

a node as a CH, the UAV and CCHs participate in the CH selection process

parameters of the crops, soil, and environment The role of the UAV in the

system is to create a communication infrastructure between the end users and

52

Figure 4.10 UAV-aided data gathering system

Hybrid and energy-efficient distributed (rHEED): When a large number of sensor nodes are deployed in a geographical area, the energy-hole problem can

be an issue that can cause nodes closer to the static sink node to die faster A received signal-strength indication (RSSI)-based hybrid and energy-efficient

distributed (rHEED) protocol is a clustering scheme in WSNs that incorporates

UAV-based mobile sinks to overcome the energy-hole problem Its network

model consists of sensor nodes that collect data and transmit them to CHs Then, all the CHs forward their data to the mobile sink node, which is the UAV It is

assumed that the UAV can move in three dimensions with variable speeds The

protocol can successfully prevent unnecessary cluster formation using the UAV

path as a parameter to form the clusters rHEED does not consider wind effects

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Figure 4 11 UAV and WSN communication

Projection-based comprehensive data gathering (PCDG): Ebrahimi et al

proposed the use of UAVs to collect data from extremely dense WSNs using

projection-based comprehensive data gathering (PCDG) Comprehensive data

gathering (CDG) is used to aggregate data from cluster members to their CHs

This can successfully reduce the number of transmissions that lead tothe reduction of energy consumption The UAV is responsible for data transfer to a

remote sink node, avoiding the need for long-range transmissions or multi-hop

communication among sensors After an optimized forwarding tree per cluster is constructed, the sensed data are gathered from selected CHs based on

rojection-based CDG with minimized UAV flight distance The joint problem —

| of optimized node clustering, forwarding tree construction, CH selection per

| cluster, and UAV trajectory planning for energy-efficient data collection was

mathematically modeled and analyzed According to the performance results,

the PCGD outperformed other approaches for small-, medium-, and large-size

| network scenarios Tree-Based Routing

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Hybrid UAV-aided WSN routing (H-UAV-WSN): When a WSN is formed with

thousands of sensor nodes and a traditional immobile sink node, this may result

in inefficient area coverage and decreased network performance To deal with

this issue, some authors proposed a hybrid UAV-WSN (H-UAV-WSN) network that can be self-configured for the improvement of data gathering across large areas The routing scheme firstly establishes a decentralized multi-level architecture The UAV is fitted with a sink node that acts as a data collector In

addition, the UAV can behave as a relay to connect WSNs to a remote base

station depending on the application requirement Such a feature enhances the connection between ground WSNs and the base station Based on the trajectory, two stages are considered Firstly, a discovery trajectory is planned Secondly, a —

neighborhoods of CHs The trajectory is planned in a way that guarantees

between ground WSNs and UAVs is not taken into account in forming clusters

Topology-aware data aggregation (TADA): UAV-based data aggregation of WSNs was reported Traffic volume can be significantly reduced using

comprehensive sensing (CS) The existing approaches based on CS have

limitations such as excessive overhead in broadcasting and high errors in data reconstruction process Addressing these issues, Wang et al proposed a

topology-aware data aggregation (TADA) protocol that can sustain the

advantages of CS-based schemes while alleviating the previously mentioned

issues One of the main features of TADA is its ability to utilize the topology information to rebuild the raw data with higher precision The mechanism of

weight coding is identical to CS in TADA, but TADA can successfully achieve

a short weight vector which results in lower energy consumption The communication time and obstacle avoidance However, any collaboration —————-

energy efficiency of data aggregation, data reconstruction error rate, and storage requirement However, the protocol ignores the effect of link transmission

58

information and local timing compared to earlier algorithms One drawback of EEJLS-WSN-UAV is that it does not consider the time taken for a GPS-

equipped roaming UAV to empower other nodes in the sensor field Route

optimization of UAVs is necessary if huge numbers of sensor nodes are

= deployed In a future improvement of EEJLS-WSN-UAYV, it may be possible to

optimize the flight path to reduce the time required to cover the entire network

| Location-based UAV-aided WSN (LS-UAV-WSN): Event-based WSN requires

a faster response in data processing and offloading Location service in a UAV-

: based WSN (LS-UAV-WSN) is based on a distributed algorithm, which +

sensor nodes in a WSN for data acquisition Mobile sinks directly infer and compute the network trajectory In addition, all the nodes are assumed to be

position-aware When a node has data to transmit, it broadcasts a source

and exact position Nodes that receive the packet must check its ID and advertisement packet at a certain time interval that contains its identifier (ID)

broadcast it again after a certain timeout Mobile sinks also can send a sink advertisement after the expiration of a particular time interval The energy consumption is significantly reduced and, thus, the network lifetime is

prolonged

4.3 Protocol Operation-Based Routing

distinct categories: swarm intelligence routing and multi-path routing Network

L

|

As discussed earlier, protocol operation-based routing can be divided into two

management for UAV-based WSNs is gradually becoming more difficult

on swarm intelligenee This kind of algorithm depends on the interaction of a

multitude of simultaneously interacting agents Multi-path routing involves multiple alternative paths in a UAV-based network that can maximize the

benefit in terms of fault tolerance, bandwidth, and security

4.3.1 Swarm Intelligence Routing Particle swarm optimization (PSO)-based UAWSNs: In PSO-WSN-UAV, the issue of determining the network topology of a WSN and the use of UAVs for

60

This is done by using a priority-based frame-selection scheme Nodes within the

coverage of UAVs are classified into different frames based on their locations

The contention window value in IEEE 802.11 MAC is also adjusted for this purpose A lower contention window range is assigned to the frame with high priority in an urgent area, and a higher contention window range is assigned to frames with low priority in unimportant areas Based on this framework, FSRP aims to reduce the distance between senders and receivers to obtain better channel quality At least one CH is responsible for data communication with a

UAV This decreases the distance between sensors With a shorter distance, the

channel quality becomes better, thus saving energy

Activated sensor (ON)

The Ist path

Figure 4.14 General architecture of a UAWSN system

4.3.3 Shortest-Path Routing Energy efficient UAV routing for WSN (EFUR-WSN): In EFUR-WSN, the authors addressed the problem of energy consumption for data transmission in

UAV-enabled WSNs, where a UAV is dispatched to collect data from sensors

In EFUR-WSN, a Voronoi diagram-based algorithm is introduced tor efficient UAV routes in order to conserve the residual energy of sensors The optimization problem of data collection and UAV traveling distance is solved on the basis of two different methods Firstly, a feasible UAV routing path is

provided based on the Voronoi diagram, which provides UAV hovering locations with low computational complexity The Voronoi diagram is

extensively exploited by focusing on sensor energy information and UAV

hovering location in order to prolong network lifetime The shortest UAV route

62

decades, there has been tremendous progress on the development of UAV,

which enables multiple UAVs applications Cooperation of a UAVs group can

be considered as natural cooperation in which each member locally operates to reach a mutual mission for the group Cooperative UAVs can offer various benefits including shorter operating time [4], more energy-efficient [5], more

missiles As the defense mechanism becomes more modern, an attack by a

single missile is very complicated A group of cooperative missiles will increase the chance of successful attacks [9]

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Cooperative | See EE súc) Desired actions

2 Local controller ————> UAV Dynamics controller

decision-making In object detection and tracking applications, feature detection, feature description, and matching methods are widely employed In this section,

we provided a brief introduction of these methods with several popular

algorithms

Feature detection methods are used to detect objecfs from images Interest objects are determined using features Features are roughly categorized into three main types which are edges, corners, and blobs Edge detection is widely exploited to detect lines and planes In [10], the authors discussed several classical edge detection methods such as Canny, Sobel, Scharr edge detectors

The advantages and disadvantages of these classical detectors were compared in [11] Canny detectors have the highest accuracy and have been exploited in

66

high-resolution remotely sensed images collected from UAVs to assess forest coverage area The overall accuracy of classification of this method is over 90%

This result has shown that the deep learning approach is quite efficient in the

“classification of images obtained using UAV and could outperform other machine learning algorithms in the process of classification of very high- resolution images However, one major disadvantage of deep learning is that it requires better computing facilities compared with traditional machine learning

algorithms

Graph theory and Consensus algorithm for target synchronization Each UAV in a group has distinct observations on the desired target Under these different observations, objects classification algorithms may result in - different predictions [23] For example, assume a correct classification of a

person is 100%, A UAV with a full view of a person may have a percentage

score of 90% while a UAV only seeing a part of a person may give a result of

60% Multiple UAVs may give varied classification results, in which case it will

be difficult to determine an exact target for a UAV group Therefore, a decision-

making mechanism is required to synchronize UAVs in a formation The mechanism should be designed in a distributed form for saving resources and

scaling abilities In order to reach a consensus for UAVs, we have utilized a

consensus algorithm based on a graph theory

The communication topology of a UAV group is described by a graph A graph G=(N,) has a set of N nodes (a node presenting a UAV) and a set of E edges

If there exists a communication link between node i and node j, an edge is defined as “= J) It is said that node j receive information from node i The

adjacency matrix is defined as 4=[%] wịti % “lị£ OP otherwise %EỐ, The degree matrix is obtained as D=diag(4) The laplacian matrix of a graph

prediction results of the j" UAVs at time step t is presented by a n-vector

Then, each UAV updates its result as follows:

68

is used to lead the group to desired targets

NY ={keVy: [Pu — Pi|< ñ›Va = 1.2 K} (4)

where '® is an obstacle detection range, /° is the set of K obstacles, ? represents positions of UAV ith projecting on obstacle k

Flocking consists of three terms: (1) a formation connection, (2) obstacle avoidance, and (3) a navigation term The first term is used to maintain a

formation by generating attractive and repulsive forces among UAVs in a group

It also contains a consensus component to synchronize the velocities of the UAVs group The second term prevents collision with external obstacles by creating repulsive force among UAVs and their virtual neighbors The third term

The formation connection is defined as:

where “°° are positive constant, 6, is the potential function, is a vector

connecting ”' and P/ with a constant £€(0.),

The obstacle avoidance term is defined as:

ý =đ jen} d, (IPs ~ Pi l) Hụ +€; jen? Bi Vin =i) (6)

where * is the repulsive function, "# is is a vector connecting ”: and ?:, The navigation term is defined as:

where ?:is the position of a target which is obtain in a visual sensing stage,

“and “are constants

5.3 Simulation Results

5.3.1 Consensus algorithm

70