Contributors |
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Chapter 1 Wireless sensor networks: Concepts, components, and challenges |
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1 | (28) |
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1 | (6) |
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1.1 Network design objective |
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3 | (1) |
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1.2 Technological background |
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4 | (1) |
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5 | (2) |
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1.4 Classification of WSN |
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7 | (1) |
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2 WSN communication pattern |
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7 | (7) |
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2.1 Protocol stack of WSN |
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7 | (3) |
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2.2 Medium access control at data link layer |
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10 | (1) |
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11 | (2) |
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13 | (1) |
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14 | (3) |
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4 Comparative analysis of optimized clustering algorithm |
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17 | (2) |
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4.1 Cluster formation scenario |
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17 | (1) |
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4.2 Optimized clustering strategy |
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18 | (1) |
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5 Evaluation of clustering methods for optimization |
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19 | (1) |
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20 | (1) |
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20 | (3) |
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7 Conclusion and future work |
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23 | (6) |
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24 | (5) |
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Chapter 2 Secure performance of emerging wireless sensor networks relying nonorthogonal multiple access |
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29 | (14) |
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1 Brief history of IoT communications related to multiple access technique |
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29 | (2) |
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2 Basic fundamentals of NOMA |
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31 | (1) |
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3 NOMA and application in cooperation network |
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32 | (1) |
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4 NOMA and cognitive radio-assisted IoT system |
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32 | (3) |
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4.1 System model of IoT relying NOMA and CR |
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32 | (2) |
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4.2 Outage probability analysis in case of partial relay selection |
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34 | (1) |
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5 Improving security at physical layer |
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35 | (3) |
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6 Validating achievable expressions of outage behavior and secure performance via numerical simulation |
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38 | (1) |
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38 | (5) |
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40 | (3) |
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Chapter 3 Security and privacy in wireless body sensor networks using lightweight cryptography scheme |
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43 | (18) |
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43 | (3) |
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2 Motivation and objective of research |
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46 | (1) |
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47 | (2) |
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49 | (6) |
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4.1 Sensor communication between the sensor nodes |
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50 | (3) |
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4.2 Encryption and decryption |
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53 | (1) |
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4.3 Secure communication between sensor head to remote server |
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53 | (1) |
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4.4 Secure data access in cloud server |
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54 | (1) |
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55 | (3) |
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58 | (3) |
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58 | (3) |
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Chapter 4 Impact of thermal effects on wireless body area networks and routing strategies |
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61 | (26) |
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Vimalathithan Rathinasabapathy |
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62 | (1) |
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2 Thermal-aware routing protocols |
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63 | (6) |
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2.1 Thermal-aware routing algorithm (TARA) |
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63 | (1) |
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2.2 Least temperature rise (LTR) |
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64 | (1) |
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2.3 Least total route temperature (LTRT) |
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65 | (1) |
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2.4 Hotspot preventing routing (HPR) |
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65 | (1) |
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66 | (1) |
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2.6 Thermal-aware shortest hop routing protocol (TSHR) |
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66 | (1) |
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66 | (1) |
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67 | (1) |
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67 | (1) |
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2.10 M2E2 multihop routing protocol |
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67 | (1) |
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2.11 Thermal aware-localized QoS routing protocol |
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68 | (1) |
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2.12 Trust and thermal-aware routing protocol |
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68 | (1) |
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2.13 Self-healing thermal-aware RPL routing protocol |
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68 | (1) |
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2.14 Multipath ring routing protocol |
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68 | (1) |
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3 Introduction about the thermal influence on medical WSN |
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69 | (6) |
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3.1 Problems faced by recent scenario |
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69 | (3) |
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3.2 Thermal influence on human tissue |
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72 | (1) |
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3.3 Wireless communication technologies for data transfer in WBAN |
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73 | (1) |
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3.4 Sources of energy consumption |
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74 | (1) |
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3.5 Specific absorption rate (SAR) |
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74 | (1) |
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4 Proposed protocol (OPOTRP) |
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75 | (3) |
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4.1 OPOTRP functional procedure |
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76 | (2) |
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4.2 Optimal temperature selection |
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78 | (1) |
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78 | (5) |
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5.1 Simulation parameters |
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78 | (1) |
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5.2 Variation in temperature at different times |
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78 | (2) |
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5.3 Average power consumption |
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80 | (1) |
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5.4 Network lifetime analysis |
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80 | (1) |
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5.5 Different data priority signal |
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81 | (1) |
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81 | (2) |
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83 | (1) |
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83 | (4) |
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84 | (3) |
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Chapter 5 Four-way binary tree-based data gathering model for WSN |
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87 | (18) |
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87 | (1) |
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88 | (2) |
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90 | (7) |
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3.1 Energy dissipation radio model |
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90 | (1) |
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3.2 Network characteristics |
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91 | (1) |
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3.3 Proposed (virtual 4-way full binary tree) structure |
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91 | (2) |
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93 | (1) |
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3.5 Working of the proposed scheme |
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93 | (3) |
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96 | (1) |
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97 | (1) |
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5 Advantages of the proposed scheme |
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97 | (4) |
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101 | (4) |
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101 | (4) |
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Chapter 6 Routing protocols: Key security issues and challenges in IoT, ad hoc, and sensor network |
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105 | (28) |
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106 | (1) |
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2 The security issues, challenges and requirements |
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107 | (1) |
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2.1 Network security and requirements |
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107 | (1) |
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2.2 Security issues and challenges |
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108 | (1) |
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3 Classification of attacks |
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108 | (1) |
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3.1 Based on the attacker's location |
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109 | (1) |
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3.2 Based on tempering with data |
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109 | (1) |
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4 Attacks and countermeasures on different layers |
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109 | (3) |
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4.1 Physical-layer attacks |
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109 | (1) |
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4.2 Data-link layer attacks |
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110 | (1) |
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4.3 Networks layer attacks |
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110 | (1) |
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4.4 Transport layer attacks |
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111 | (1) |
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4.5 Application layer attacks |
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111 | (1) |
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112 | (1) |
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4.7 Denial of service (DoS) attacks |
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112 | (1) |
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5 Survey of security issues, threats and defense mechanisms in IoT |
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112 | (5) |
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5.1 Wormhole attack and it's counter measure |
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113 | (1) |
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5.2 Classification of wormhole attacks |
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113 | (1) |
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5.3 Wormhole detecting and avoiding models |
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114 | (3) |
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6 Proposed solutions and analysis |
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117 | (6) |
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6.1 Transmission time-based wormhole detection |
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117 | (3) |
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120 | (3) |
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7 Computation of RTT in TTWD |
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123 | (1) |
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8 Simulation and experimental evaluation |
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124 | (5) |
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8.1 Simulation environment |
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124 | (1) |
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8.2 Network simulator parameters |
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124 | (1) |
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125 | (4) |
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9 Conclusion and future directions |
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129 | (4) |
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130 | (3) |
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Chapter 7 Fault tolerance of cluster-based nodes in IoT sensor networks with periodic mode of operation |
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133 | (20) |
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133 | (3) |
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136 | (4) |
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3 Mathematical background and main symbols and definitions |
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140 | (1) |
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4 Models formulation and solution |
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141 | (6) |
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4.1 Model 1. Sensor with diagnostics in active mode |
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141 | (1) |
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4.2 Model 2. Sensor with diagnostics in active mode and periodical diagnostics in the sleep mode |
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142 | (3) |
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4.3 Model 3. Sensors with mix architecture of backup batteries |
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145 | (2) |
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5 Results and discussions |
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147 | (2) |
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149 | (4) |
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150 | (3) |
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Chapter 8 Lightweight cryptographic algorithms for resource-constrained IoT devices and sensor networks |
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153 | (34) |
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154 | (2) |
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155 | (1) |
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156 | (1) |
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156 | (1) |
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157 | (5) |
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3.1 Resource constrained environment-internet of things (IoT) |
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157 | (3) |
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3.2 Implementation of lightweight block ciphers for IoT applications |
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160 | (2) |
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4 Lightweight cryptographic primitives |
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162 | (7) |
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4.1 Lightweight block ciphers |
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164 | (1) |
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4.2 Lightweight stream ciphers |
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165 | (2) |
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4.3 Lightweight hash functions |
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167 | (1) |
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4.4 Lightweight message authentication codes |
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168 | (1) |
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169 | (10) |
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5.1 Hardware analysis metrics |
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169 | (2) |
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5.2 Algorithm of KLEIN lightweight block cipher |
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171 | (3) |
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174 | (1) |
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5.4 Results and discussions |
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174 | (5) |
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6 Performance comparison of conventional and lightweight cryptographic algorithms for IoT |
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179 | (1) |
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180 | (7) |
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180 | (7) |
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Chapter 9 EELC: Energy-efficient lightweight cryptography for IoT networks |
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187 | (24) |
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188 | (3) |
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188 | (1) |
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1.2 Security challenges in IoT |
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189 | (2) |
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191 | (1) |
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3 Energy-efficient lightweight cryptography (EELC) architecture |
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192 | (12) |
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3.1 Energy efficient subkey generation |
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193 | (3) |
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196 | (1) |
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3.3 Mathematical model for MAC |
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197 | (2) |
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199 | (1) |
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200 | (1) |
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201 | (1) |
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3.7 Energy efficient encryption |
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202 | (1) |
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203 | (1) |
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3.9 Energy efficient decryption |
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204 | (1) |
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204 | (3) |
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4.1 Time complexity analysis |
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204 | (1) |
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4.2 Analysis based on energy |
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204 | (2) |
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206 | (1) |
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207 | (4) |
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207 | (4) |
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Chapter 10 Blockchain as a solution for security attacks in named data networking of things |
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211 | (34) |
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211 | (2) |
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2 Security attacks in NDN of things (NDNoT) |
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213 | (3) |
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2.1 Cache misappropriation |
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213 | (1) |
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214 | (1) |
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215 | (1) |
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215 | (1) |
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2.5 Miscellaneous irruptions |
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216 | (1) |
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216 | (5) |
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216 | (1) |
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3.2 Blockchain architecture |
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217 | (3) |
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3.3 Key features of design of blockchain |
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220 | (1) |
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4 Security investigation of NDN blockchain of things |
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221 | (7) |
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227 | (1) |
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228 | (3) |
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6 The need for using a Blockchain in IoT |
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231 | (2) |
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233 | (1) |
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8 Existing issues of applications of blockchain IoT |
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234 | (4) |
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9 Further issues and recommendations of blockchain in IoT |
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238 | (1) |
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239 | (6) |
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239 | (6) |
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Chapter 11 A novel privacy-preserving healthcare information sharing platform using blockchain |
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245 | (18) |
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245 | (2) |
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247 | (1) |
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248 | (2) |
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250 | (7) |
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250 | (2) |
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4.2 Patient uploading medical data |
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252 | (1) |
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4.3 Provider uploading medical data of his/her patient |
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253 | (1) |
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4.4 Provider sharing medical data with another provider |
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254 | (1) |
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4.5 Provider querying medical data of patient |
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255 | (1) |
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4.6 Patient updating access given to provider |
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256 | (1) |
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257 | (1) |
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258 | (5) |
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259 | (4) |
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Chapter 12 Computational intelligent techniques for prediction of environmental attenuation of millimeter waves |
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263 | (22) |
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264 | (1) |
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264 | (1) |
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1.2 Composition of atmosphere |
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265 | (1) |
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2 Terrestrial and satellite links |
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265 | (4) |
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2.1 Attenuation due to gas |
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266 | (1) |
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2.2 Attenuation caused by snow |
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267 | (1) |
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2.3 Attenuation due to hail |
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268 | (1) |
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2.4 Attenuation due to dust |
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268 | (1) |
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2.5 Attenuation due to scintillation |
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269 | (1) |
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269 | (3) |
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3.1 Cloud attenuation model |
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269 | (2) |
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3.2 Work done by other researchers |
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271 | (1) |
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272 | (4) |
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273 | (1) |
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4.2 Simple attenuation model |
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274 | (1) |
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274 | (1) |
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4.4 Model proposed by Brazil |
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275 | (1) |
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275 | (1) |
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5 Rain attenuation in terrestrial links |
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276 | (3) |
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6 Implementation results of rain model |
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279 | (1) |
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7 Issues related to machine learning |
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280 | (1) |
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281 | (4) |
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281 | (4) |
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Chapter 13 The role of IoT in smart cities: Challenges of air quality mass sensor technology for sustainable solutions |
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285 | (24) |
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285 | (2) |
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2 Background of air quality monitoring sensors in urban environments |
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287 | (7) |
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2.1 Regulation-based air quality monitoring |
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288 | (2) |
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2.2 IoT-based air quality monitoring |
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290 | (2) |
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2.3 Evaluation of regulation- and IoT-based air quality sensor technology |
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292 | (1) |
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2.4 Applications of urban air quality data |
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293 | (1) |
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3 Challenges of air quality monitoring and management in urban environments |
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294 | (4) |
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3.1 Lack of spatial variability |
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295 | (1) |
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3.2 Environmental challenges |
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295 | (1) |
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3.3 Crowdsourcing and citizen science challenges |
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296 | (1) |
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297 | (1) |
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4 Applications, initiatives, and future direction |
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298 | (6) |
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4.1 Integration of sensor data and passive crowdsourced data |
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298 | (2) |
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4.2 Air quality data application framework |
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300 | (3) |
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4.3 Smart city digital twinning |
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303 | (1) |
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5 Discussion and findings |
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304 | (1) |
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304 | (5) |
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305 | (4) |
Subject Index |
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