34 Abi SSW Y DON BB BREE OR SY 35 4 Performance evalwationg 37 R perimentaienvonmentseimm...-...-- 37 [2.1 Comparison between the proposed algorithm and an exact solver] 38 [2.2 Impac
Trang 1HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY
MASTER’S GRADUATION THESIS
Evolutionary algorithms to minimize the
number of energy depleted sensors in wireless
rechargeable sensor networks
NGO MINH HAI
hai.nm202661m(@sis.hust.eduxn
Thesis advisor: Dr Nguyen Phi Le Signature of advisor
Department: Department of Software engineering
Institute: School of Information and Communication ‘Technology
Hanoi, 2021
Trang 2HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY
MASTER’S GRADUATION THESIS
Evolutionary algorithms to minimize the
number of energy depleted sensors in wireless
rechargeable sensor networks
NGO MINH HAI
hai.nm202661m(@sis.hust.eduxn
Thesis advisor: Dr Nguyen Phi Le Signature of advisor
Department: Department of Software engineering
Institute: School of Information and Communication ‘Technology
Hanoi, 2021
Trang 3CONG LOA XA HOL CLIO NGIHA VII
Đôc lập - Tự đo - Hạnh phúc
BẢN XÁC NHẬN CHỈNH SỬA LUẬN VĂN THẠC SĨ
Họ và tên tác giá luận văn: Ngô Minh LIải
Đề tài luận văn (Tiếng Việt): Áp dụng giải thuật tí
số lượng cảm biến cạn kiệt năng lượng trong mạng cắm
hóa giải bài tuán tối thiểu
in sac khong day
Đề tài hận văn (Tiếng Anh): Evolutionary algorithms tu minimize the namber
of energy depleted sensors in wireless rechargeable sensor networks
Chuyên ngành: Khoa học dữ liệu và trí tuệ nhân tạo
Mã số HV: 20202661M
“Tấu giả, Người hướng đân khoa học và Hội đồng chẩm luận văn xác nhận tác gid
da sửa chưa, bố sung luận văn thcơ biên bản hụp Hội dóng ngày 24/12/2021 với
các nội đụng sau:
Sữa tiên dế phần 2.2 từ ”Problem formulation” thanh "Problem description”
{trang 18)
Giải thích rõ hơn về cách tính tham số ý (trang 19)
Ve lại hình để các kí hiệu hiển thị rõ ràng hơn (trang 39 - 42)
Sữa một số lỗi chính tá (trang 20)
Hiện chỉnh lại cách mô bình hóa bài toán (trang 18-19)
“Thêm hình vẽ nhằm mô tả rõ hơn các ký hiệu trong luận văn (trang 23)
"Thêm một số hướng nghiên cửu tương lai và phần Kết luận (trang 43)
Sửa Chương Š: Kết luận thành một phần không đánh số chương (trang 43)
Hanoi ngày - thắng - năm 2021
Giáo viên hướng dẫn Tác giá luận văn
CHỦ TỊCH HỘI ĐỒNG
Trang 4B An example offhe decodingprocedurd 31
[at _Different values of 7, impacts the distance between offspring and thei
ala XE% Taine M
Trang 5E parameters ofinput membershipy
B: je parameters of output memberships
B The parameters ofinput memberships
Trang 6E parameters ofinput membershipy
B: je parameters of output memberships
B The parameters ofinput memberships
Trang 7REQUIREMENTS OF THE THESIS
1 Studentsinformation:
Name: Ngo Minh Hai
Class: Tata Science (Blitech)
Affiliation: Hanvi University of Science and Technology
Phune: 0353 852 045 Email: hai.nm202661m@sis.hust.eduyn
Nguyen Phi Ly, Assoc Pref, Huynh Thi Thanh Binh and Mrs, Tran Thi
Jlueng, All of the results are genuine and are not copied from any other
sources, Every reference materials are clearly listed in the biblivgraphy L will
accept full responsibility for even one copy that violates school regulations
Hunvi,dute month — year 2021
Trang 8B Prob SWS we Bie a ew eee we wire eta %' 9/4696 1V š 18
Darl Sass l9
3 Hybrid Fuzzy logic and genetic-algorithm-based charging schemd 24
30
30
32
Trang 9B Prob SWS we Bie a ew eee we wire eta %' 9/4696 1V š 18
Darl Sass l9
3 Hybrid Fuzzy logic and genetic-algorithm-based charging schemd 24
30
30
32
Trang 10FGA Full-charging Genetic Algorithm based charging scheme [,
FIS Fuzzy logic Inference System [} 23
FLCDS Fuzzy Logic-based Charging Decision Support [} 24} D3} 23
GA Genetic Algorithm [}§ §} 57
GACS Genetic Algorithm based Charging Scheme [I B}.83 [O47]
HFLGA Hybird Fuzzy Logic and Genetic Algorithm based charging scheme [] 57-413] HPSOGA Hybird PSO and GA algorithm [J 57,53, (047
INMA Invalid Node Minimized Algorithm [} § 57} 53, FO
loT Internet of Thing [}]
MC Mobile Charger [5 8-} [307-20 23 2 BS) 50-3
MILP Mixed Integer Linear Programming, [} [923,57 59
MNED ‘The problem of Minimizing the Number of Energy Depleted sensors [I B} [5-
Trang 11§.3 Genetic algorithm for optimizing the chargingtimd 34
Abi SSW Y DON BB BREE OR SY 35
4 Performance evalwationg 37
R perimentaienvonmentseimm - 37
[2.1 Comparison between the proposed algorithm and an exact solver] 38 [2.2 Impact of the Fuzzy logic preprocessing on charging decisiong 39
Trang 12B Prob SWS we Bie a ew eee we wire eta %' 9/4696 1V š 18
Darl Sass l9
3 Hybrid Fuzzy logic and genetic-algorithm-based charging schemd 24
30
30
32
Trang 13B An example offhe decodingprocedurd 31
[at _Different values of 7, impacts the distance between offspring and thei
ala XE% Taine M
Trang 14FGA Full-charging Genetic Algorithm based charging scheme [,
FIS Fuzzy logic Inference System [} 23
FLCDS Fuzzy Logic-based Charging Decision Support [} 24} D3} 23
GA Genetic Algorithm [}§ §} 57
GACS Genetic Algorithm based Charging Scheme [I B}.83 [O47]
HFLGA Hybird Fuzzy Logic and Genetic Algorithm based charging scheme [] 57-413] HPSOGA Hybird PSO and GA algorithm [J 57,53, (047
INMA Invalid Node Minimized Algorithm [} § 57} 53, FO
loT Internet of Thing [}]
MC Mobile Charger [5 8-} [307-20 23 2 BS) 50-3
MILP Mixed Integer Linear Programming, [} [923,57 59
MNED ‘The problem of Minimizing the Number of Energy Depleted sensors [I B} [5-
Trang 15B Prob SWS we Bie a ew eee we wire eta %' 9/4696 1V š 18
Darl Sass l9
3 Hybrid Fuzzy logic and genetic-algorithm-based charging schemd 24
30
30
32
Trang 16§.3 Genetic algorithm for optimizing the chargingtimd 34
Abi SSW Y DON BB BREE OR SY 35
4 Performance evalwationg 37
R perimentaienvonmentseimm - 37
[2.1 Comparison between the proposed algorithm and an exact solver] 38 [2.2 Impact of the Fuzzy logic preprocessing on charging decisiong 39
Trang 17REQUIREMENTS OF THE THESIS
1 Studentsinformation:
Name: Ngo Minh Hai
Class: Tata Science (Blitech)
Affiliation: Hanvi University of Science and Technology
Phune: 0353 852 045 Email: hai.nm202661m@sis.hust.eduyn
Nguyen Phi Ly, Assoc Pref, Huynh Thi Thanh Binh and Mrs, Tran Thi
Jlueng, All of the results are genuine and are not copied from any other
sources, Every reference materials are clearly listed in the biblivgraphy L will
accept full responsibility for even one copy that violates school regulations
Hunvi,dute month — year 2021
Trang 18§.3 Genetic algorithm for optimizing the chargingtimd 34
Abi SSW Y DON BB BREE OR SY 35
4 Performance evalwationg 37
R perimentaienvonmentseimm - 37
[2.1 Comparison between the proposed algorithm and an exact solver] 38 [2.2 Impact of the Fuzzy logic preprocessing on charging decisiong 39
Trang 19REQUIREMENTS OF THE THESIS
1 Studentsinformation:
Name: Ngo Minh Hai
Class: Tata Science (Blitech)
Affiliation: Hanvi University of Science and Technology
Phune: 0353 852 045 Email: hai.nm202661m@sis.hust.eduyn
Nguyen Phi Ly, Assoc Pref, Huynh Thi Thanh Binh and Mrs, Tran Thi
Jlueng, All of the results are genuine and are not copied from any other
sources, Every reference materials are clearly listed in the biblivgraphy L will
accept full responsibility for even one copy that violates school regulations
Hunvi,dute month — year 2021
Trang 20REQUIREMENTS OF THE THESIS
1 Studentsinformation:
Name: Ngo Minh Hai
Class: Tata Science (Blitech)
Affiliation: Hanvi University of Science and Technology
Phune: 0353 852 045 Email: hai.nm202661m@sis.hust.eduyn
Nguyen Phi Ly, Assoc Pref, Huynh Thi Thanh Binh and Mrs, Tran Thi
Jlueng, All of the results are genuine and are not copied from any other
sources, Every reference materials are clearly listed in the biblivgraphy L will
accept full responsibility for even one copy that violates school regulations
Hunvi,dute month — year 2021
Trang 21FGA Full-charging Genetic Algorithm based charging scheme [,
FIS Fuzzy logic Inference System [} 23
FLCDS Fuzzy Logic-based Charging Decision Support [} 24} D3} 23
GA Genetic Algorithm [}§ §} 57
GACS Genetic Algorithm based Charging Scheme [I B}.83 [O47]
HFLGA Hybird Fuzzy Logic and Genetic Algorithm based charging scheme [] 57-413] HPSOGA Hybird PSO and GA algorithm [J 57,53, (047
INMA Invalid Node Minimized Algorithm [} § 57} 53, FO
loT Internet of Thing [}]
MC Mobile Charger [5 8-} [307-20 23 2 BS) 50-3
MILP Mixed Integer Linear Programming, [} [923,57 59
MNED ‘The problem of Minimizing the Number of Energy Depleted sensors [I B} [5-
Trang 22B Prob SWS we Bie a ew eee we wire eta %' 9/4696 1V š 18
Darl Sass l9
3 Hybrid Fuzzy logic and genetic-algorithm-based charging schemd 24
30
30
32
Trang 23E parameters ofinput membershipy
B: je parameters of output memberships
B The parameters ofinput memberships
Trang 24REQUIREMENTS OF THE THESIS
1 Studentsinformation:
Name: Ngo Minh Hai
Class: Tata Science (Blitech)
Affiliation: Hanvi University of Science and Technology
Phune: 0353 852 045 Email: hai.nm202661m@sis.hust.eduyn
Nguyen Phi Ly, Assoc Pref, Huynh Thi Thanh Binh and Mrs, Tran Thi
Jlueng, All of the results are genuine and are not copied from any other
sources, Every reference materials are clearly listed in the biblivgraphy L will
accept full responsibility for even one copy that violates school regulations
Hunvi,dute month — year 2021
Trang 25B Prob SWS we Bie a ew eee we wire eta %' 9/4696 1V š 18
Darl Sass l9
3 Hybrid Fuzzy logic and genetic-algorithm-based charging schemd 24
30
30
32
Trang 26B An example offhe decodingprocedurd 31
[at _Different values of 7, impacts the distance between offspring and thei
ala XE% Taine M
Trang 27REQUIREMENTS OF THE THESIS
1 Studentsinformation:
Name: Ngo Minh Hai
Class: Tata Science (Blitech)
Affiliation: Hanvi University of Science and Technology
Phune: 0353 852 045 Email: hai.nm202661m@sis.hust.eduyn
Nguyen Phi Ly, Assoc Pref, Huynh Thi Thanh Binh and Mrs, Tran Thi
Jlueng, All of the results are genuine and are not copied from any other
sources, Every reference materials are clearly listed in the biblivgraphy L will
accept full responsibility for even one copy that violates school regulations
Hunvi,dute month — year 2021
Trang 28ABSTRACT
[fEeless Sensor Networls (WSNS]are one of the most core technologies of the
‘They have a wide range of applications and have attracted lots of atten-
tions from researchers However, a traditional (WSN) remains as an energy-constrained
network because of the limited energy of each sensor node As a result, prolonging net- work lifetime has become an urgent challenge that directly affects the network perfor-
mance In recent years, the appearance of a new sensor network generation, called [Wire]
has opened up a breakthrough in dealing with the energy issue, In we employ a Mobile Charger (MIC) equipped with a
charging device to charge the sensors that have rechargeable lithium battery inside wire-
lessly Therefore, an effective charging scheme can enhance the whole network's perfor- mance and minimize the energy depletion of sensor nodes Although the performance
of the charging scheme is decided by some essential factors including charging path and
charging time of the MC, most of the existing charging schemes only consider the MC's
charging path factor with a fully charging method Moreover, the previous works assume
that the MC's battery capacity is infinite or sufficient to charge all sensors in the networkin one charging cycle This hypothesis may lead to the energy depletion of the energy-hurry sensors and unnecessary visiting for energy-sufficient sensors The charging time has not
been considered thoroughly in the previous works
‘This dissertation aims to minimize the energy depletion in wireless rechargeable sen-
sor networks by optimizing both the MC’s charging path and charging time without the mentioned limitations Since the charging schedule optimization problem is NP-hard, the dissertation will propose an approximate algorithm to solve the investigated prob- lem.Specifically, it proposes a novel network model in which the MC|does not need to visit
and charge every sensor node Furthermore, it also proposes a hybrid genetic-algorithm- based charging scheme to achieve the problem’s aim
‘The thesis conducts various simulations and experiments to evaluate the proposed charg-
ing scheme performance Empirical evaluations have shown that the proposed charging
scheme outperforms the existing solutions by a substantial margin.
Trang 29§.3 Genetic algorithm for optimizing the chargingtimd 34
Abi SSW Y DON BB BREE OR SY 35
4 Performance evalwationg 37
R perimentaienvonmentseimm - 37
[2.1 Comparison between the proposed algorithm and an exact solver] 38 [2.2 Impact of the Fuzzy logic preprocessing on charging decisiong 39
Trang 30ABSTRACT
[fEeless Sensor Networls (WSNS]are one of the most core technologies of the
‘They have a wide range of applications and have attracted lots of atten-
tions from researchers However, a traditional (WSN) remains as an energy-constrained
network because of the limited energy of each sensor node As a result, prolonging net- work lifetime has become an urgent challenge that directly affects the network perfor-
mance In recent years, the appearance of a new sensor network generation, called [Wire]
has opened up a breakthrough in dealing with the energy issue, In we employ a Mobile Charger (MIC) equipped with a
charging device to charge the sensors that have rechargeable lithium battery inside wire-
lessly Therefore, an effective charging scheme can enhance the whole network's perfor- mance and minimize the energy depletion of sensor nodes Although the performance
of the charging scheme is decided by some essential factors including charging path and
charging time of the MC, most of the existing charging schemes only consider the MC's
charging path factor with a fully charging method Moreover, the previous works assume
that the MC's battery capacity is infinite or sufficient to charge all sensors in the networkin one charging cycle This hypothesis may lead to the energy depletion of the energy-hurry sensors and unnecessary visiting for energy-sufficient sensors The charging time has not
been considered thoroughly in the previous works
‘This dissertation aims to minimize the energy depletion in wireless rechargeable sen-
sor networks by optimizing both the MC’s charging path and charging time without the mentioned limitations Since the charging schedule optimization problem is NP-hard, the dissertation will propose an approximate algorithm to solve the investigated prob- lem.Specifically, it proposes a novel network model in which the MC|does not need to visit
and charge every sensor node Furthermore, it also proposes a hybrid genetic-algorithm- based charging scheme to achieve the problem’s aim
‘The thesis conducts various simulations and experiments to evaluate the proposed charg-
ing scheme performance Empirical evaluations have shown that the proposed charging
scheme outperforms the existing solutions by a substantial margin.
Trang 31ABSTRACT
[fEeless Sensor Networls (WSNS]are one of the most core technologies of the
‘They have a wide range of applications and have attracted lots of atten-
tions from researchers However, a traditional (WSN) remains as an energy-constrained
network because of the limited energy of each sensor node As a result, prolonging net- work lifetime has become an urgent challenge that directly affects the network perfor-
mance In recent years, the appearance of a new sensor network generation, called [Wire]
has opened up a breakthrough in dealing with the energy issue, In we employ a Mobile Charger (MIC) equipped with a
charging device to charge the sensors that have rechargeable lithium battery inside wire-
lessly Therefore, an effective charging scheme can enhance the whole network's perfor- mance and minimize the energy depletion of sensor nodes Although the performance
of the charging scheme is decided by some essential factors including charging path and
charging time of the MC, most of the existing charging schemes only consider the MC's
charging path factor with a fully charging method Moreover, the previous works assume
that the MC's battery capacity is infinite or sufficient to charge all sensors in the networkin one charging cycle This hypothesis may lead to the energy depletion of the energy-hurry sensors and unnecessary visiting for energy-sufficient sensors The charging time has not
been considered thoroughly in the previous works
‘This dissertation aims to minimize the energy depletion in wireless rechargeable sen-
sor networks by optimizing both the MC’s charging path and charging time without the mentioned limitations Since the charging schedule optimization problem is NP-hard, the dissertation will propose an approximate algorithm to solve the investigated prob- lem.Specifically, it proposes a novel network model in which the MC|does not need to visit
and charge every sensor node Furthermore, it also proposes a hybrid genetic-algorithm- based charging scheme to achieve the problem’s aim
‘The thesis conducts various simulations and experiments to evaluate the proposed charg-
ing scheme performance Empirical evaluations have shown that the proposed charging
scheme outperforms the existing solutions by a substantial margin.
Trang 32B Prob SWS we Bie a ew eee we wire eta %' 9/4696 1V š 18
Darl Sass l9
3 Hybrid Fuzzy logic and genetic-algorithm-based charging schemd 24
30
30
32
Trang 33§.3 Genetic algorithm for optimizing the chargingtimd 34
Abi SSW Y DON BB BREE OR SY 35
4 Performance evalwationg 37
R perimentaienvonmentseimm - 37
[2.1 Comparison between the proposed algorithm and an exact solver] 38 [2.2 Impact of the Fuzzy logic preprocessing on charging decisiong 39
Trang 34FGA Full-charging Genetic Algorithm based charging scheme [,
FIS Fuzzy logic Inference System [} 23
FLCDS Fuzzy Logic-based Charging Decision Support [} 24} D3} 23
GA Genetic Algorithm [}§ §} 57
GACS Genetic Algorithm based Charging Scheme [I B}.83 [O47]
HFLGA Hybird Fuzzy Logic and Genetic Algorithm based charging scheme [] 57-413] HPSOGA Hybird PSO and GA algorithm [J 57,53, (047
INMA Invalid Node Minimized Algorithm [} § 57} 53, FO
loT Internet of Thing [}]
MC Mobile Charger [5 8-} [307-20 23 2 BS) 50-3
MILP Mixed Integer Linear Programming, [} [923,57 59
MNED ‘The problem of Minimizing the Number of Energy Depleted sensors [I B} [5-
Trang 35FGA Full-charging Genetic Algorithm based charging scheme [,
FIS Fuzzy logic Inference System [} 23
FLCDS Fuzzy Logic-based Charging Decision Support [} 24} D3} 23
GA Genetic Algorithm [}§ §} 57
GACS Genetic Algorithm based Charging Scheme [I B}.83 [O47]
HFLGA Hybird Fuzzy Logic and Genetic Algorithm based charging scheme [] 57-413] HPSOGA Hybird PSO and GA algorithm [J 57,53, (047
INMA Invalid Node Minimized Algorithm [} § 57} 53, FO
loT Internet of Thing [}]
MC Mobile Charger [5 8-} [307-20 23 2 BS) 50-3
MILP Mixed Integer Linear Programming, [} [923,57 59
MNED ‘The problem of Minimizing the Number of Energy Depleted sensors [I B} [5-
Trang 36§.3 Genetic algorithm for optimizing the chargingtimd 34
Abi SSW Y DON BB BREE OR SY 35
4 Performance evalwationg 37
R perimentaienvonmentseimm - 37
[2.1 Comparison between the proposed algorithm and an exact solver] 38 [2.2 Impact of the Fuzzy logic preprocessing on charging decisiong 39
Trang 37ABSTRACT
[fEeless Sensor Networls (WSNS]are one of the most core technologies of the
‘They have a wide range of applications and have attracted lots of atten-
tions from researchers However, a traditional (WSN) remains as an energy-constrained
network because of the limited energy of each sensor node As a result, prolonging net- work lifetime has become an urgent challenge that directly affects the network perfor-
mance In recent years, the appearance of a new sensor network generation, called [Wire]
has opened up a breakthrough in dealing with the energy issue, In we employ a Mobile Charger (MIC) equipped with a
charging device to charge the sensors that have rechargeable lithium battery inside wire-
lessly Therefore, an effective charging scheme can enhance the whole network's perfor- mance and minimize the energy depletion of sensor nodes Although the performance
of the charging scheme is decided by some essential factors including charging path and
charging time of the MC, most of the existing charging schemes only consider the MC's
charging path factor with a fully charging method Moreover, the previous works assume
that the MC's battery capacity is infinite or sufficient to charge all sensors in the networkin one charging cycle This hypothesis may lead to the energy depletion of the energy-hurry sensors and unnecessary visiting for energy-sufficient sensors The charging time has not
been considered thoroughly in the previous works
‘This dissertation aims to minimize the energy depletion in wireless rechargeable sen-
sor networks by optimizing both the MC’s charging path and charging time without the mentioned limitations Since the charging schedule optimization problem is NP-hard, the dissertation will propose an approximate algorithm to solve the investigated prob- lem.Specifically, it proposes a novel network model in which the MC|does not need to visit
and charge every sensor node Furthermore, it also proposes a hybrid genetic-algorithm- based charging scheme to achieve the problem’s aim
‘The thesis conducts various simulations and experiments to evaluate the proposed charg-
ing scheme performance Empirical evaluations have shown that the proposed charging
scheme outperforms the existing solutions by a substantial margin.