人口老齡化現象給社會帶來了諸多嚴峻挑戰，其中最為突出的便是醫療資源和護理人員的嚴重匱乏，以及日益高昂的護理費用。鑒於許多老年人更傾向於在家中享受晚年生活，並結合當前老齡化產業的實際情況，居家養老模式已逐漸成為多數政府優先推廣的養老方式。在居家養老中，如何對老年人進行即時、有效的健康監測和緊急回應變得尤為重要。然而，在緊急情況下，傳統的無線感測器網路卻面臨著諸多挑戰，如傳輸延遲、能耗過高、網路擁堵以及資料包碰撞等問題。這些問題不僅影響了資訊的即時性和準確性，更對老年人的健康與安全構成了潛在威脅。
為了解決這些問題，本文詳細介紹了兩項專為優化藍牙通信協議而設計的創新機制: Joint Energy conservation and Collision avoidance Path construction (JECP)和Improving Emergent Events Transmission (IEET)。
JECP機制的核心目標在於提升能源使用效率並最小化資料包碰撞，這對於實現可靠監控和迅速回應緊急事件具有不可或缺的作用。該機制巧妙地將感測器的資料包分為常規和緊急兩大類。對於常規資料包，如溫濕度資訊等，JECP採用節能傳輸路徑，透過降低能耗來延長整個網路的使用壽命。而針對由跌倒、高血壓等緊急事件觸發的緊急資料包，JECP則給予優先處理，透過專用的無碰撞路徑快速路由至閘道。這一設計確保了緊急資料能夠即時傳遞，對於有效應對老年人的緊急狀況至關重要，特別是在需要立即醫療干預的情況下。JECP機制不僅實現了能源的有效利用，更保障了關鍵警報資訊的及時傳遞，避免了任何可能的延誤。
為了進一步拓展藍牙通信在複雜網路環境中的應用能力，本文還提出了IEET機制。該機制創新地將樹狀和網狀拓撲相結合，構建了一個混合網路結構。具體而言，樹狀拓撲為緊急資料提供了直接且低延遲的傳輸路徑，確保在緊急情況下能夠實現快速回應。而網狀拓撲則透過提供多個備選路由增強了網路的健壯性和可靠性，有助於緩解網路擁堵和潛在的瓶頸問題。此外，IEET機制還包含一項子速連接策略，該策略能夠根據緊急程度動態調整資料傳輸間隔，從而在優化網路緊急回應能力的同時，也提升了能源使用效率。
模擬實驗結果表明，所提出的JECP和IEET機制在平均數據包延遲、網路壽命和藍牙感測器節點的能耗等方面優於現有機制。

The phenomenon of population aging presents numerous severe challenges to society, most notably the acute shortage of medical resources and caregivers, coupled with the rising costs of care. Given that many elderly individuals prefer to enjoy their later years in the comfort of their homes, and considering the current state of the aging industry, home-based elderly care has gradually become the preferred mode of care promoted by most governments. In home-based elderly healthcare, timely and effective health monitoring and emergency response for the elderly become increasingly critical. However, traditional wireless sensor networks face numerous challenges in emergencies, such as transmission delays, high energy consumption, network congestion, and packet collisions. These issues not only affect the timeliness and accuracy of information but also pose potential threats to the health and safety of the elderly.
To address these issues, this dissertation introduces two innovative mechanisms specifically designed to optimize Bluetooth communication protocols for elderly monitoring systems: Joint Energy conservation and Collision avoidance Path construction (JECP) and Improving Emergent Events Transmission (IEET). 
The core objective of the JECP mechanism is to enhance energy efficiency and minimize data packet collisions, both of which are indispensable for reliable monitoring and rapid response to emergencies. This mechanism cleverly categorizes sensor data into general and emergency types. For general data packets, such as temperature and humidity information, JECP employs energy-saving transmission paths to extend the overall network lifespan by reducing energy consumption. For emergency data packets, triggered by events such as falls or high blood pressure, JECP prioritizes and routes them through dedicated collision-free paths to the gateway. This design ensures that emergency data is transmitted promptly, which is crucial for effectively managing emergencies, especially in scenarios requiring immediate medical intervention. The JECP mechanism not only achieves efficient energy use but also ensures the timely delivery of critical alerts, avoiding any potential delays.
To further expand the capabilities of Bluetooth communication in complex network environments, this dissertation also proposes the IEET mechanism. This mechanism innovatively combines tree and mesh topologies to create a hybrid network structure. Specifically, the tree topology provides direct and low-latency paths for emergency data, ensuring a rapid response in emergencies. Meanwhile, the mesh topology enhances network robustness and reliability by providing multiple alternative routes, which help alleviate network congestion and potential bottlenecks. Additionally, the IEET mechanism includes a subrate connection strategy, which dynamically adjusts data transmission intervals based on urgency, optimizing both the network’s emergency response capabilities and energy efficiency. 
Simulation results demonstrate that the proposed JECP and IEET mechanisms outperform existing mechanisms in terms of average packet delay, network lifetime, and energy consumption for Bluetooth sensor nodes.

Table of Contents
List of Figures	IX
List of Tables	X
Chapter 1 Introduction	1
1.1 Background	1
1.2 Research Goals	4
1.3 Organization of the Dissertation	6
Chapter 2 Related Work	7
2.1 Non-Bluetooth Protocol for Improving Emergency Transmission	7
2.1.1 Emergency Transmission System Based on Wi-Fi	7
2.1.2 Emergency Transmission System Based on ZigBee	8
2.1.3 Emergency Transmission System Based on 5G	9
2.2 Bluetooth Protocol for Improving Emergency Transmission	10
Chapter 3 Network Environment and Problem Statement	14
3.1 Network Environment	14
3.2 Problem Statement	16
3.2.1 Communication Model	16
3.2.2 Energy Model	18
3.3 Problem Objectives	20
3.4 Constraints	21
Chapter 4 Proposed Joint Energy Conservation and Collision Avoidance Path Construction Mechanism	24
4.1 Proposed JECP Mechanism	26
4.1.1 Broadcasting Phase	27
4.1.2 Role Identification Phase	30
4.1.3 Topology Construction Phase	31
4.2 Performance Evaluation	41
4.2.1 Simulation Environment	42
4.2.2 Simulation Results	44
4.3 Summary	54
Chapter 5 Proposed Improving Emergent Events Transmission Mechanism	57
5.1 Proposed IEET Mechanism	59
5.1.1 Constructing a Tree Topology for Sensors Located Far Away from the Gateway	61
5.1.2 Constructing a Mesh Topology for Sensors Near the Gateway	71
5.2 Performance Evaluation	75
5.2.1 Simulation Environment	76
5.2.2 Simulation Results	77
5.3 Summary	84
Chapter 6 Conclusion and Future Work	85
References	87

List of Figures
Fig. 1.1 Construction of emergency events transmission utilizing the proposed mechanism.	3
Fig. 3.1 A scenario of the considered network.	15
Fig. 4.1 The temporary path construction process of the Broadcasting Phase.	29
Fig. 4.2 The monitoring region of the experiment.	42
Fig. 4.3 Quality of all different paths from the emergent sensor s_10 to the gateway G.	45
Fig. 4.4 Evaluation of the average retransmission ratio from the emergent sensors to the gateway.	47
Fig. 4. 5 Evaluation of the path length from the emergent sensors to the gateway.	47
Fig. 4.6 Comparison of average energy consumption using Friedman test.	48
Fig. 4.7 Evaluation of average energy consumption of all emergent sensors.	49
Fig. 4.8 Evaluation of transmission success ratio and lifetime for the emergent sensor s_10.	51
Fig. 4.9 Comparison of MRT-BLE, LBMMRE-AODMV, and JECP in terms of lifetime.	53
Fig. 4.10 Comparison of lifetime using Friedman test.	54
Fig. 5.1 The construction of tree topology.	60
Fig. 5.2 The selection process of optimal channels.	67
Fig. 5.3 The illustration of adjustment transmission intervals.	69
Fig. 5.4 The process of adjustment transmission intervals.	70
Fig. 5.5 Constructing mesh topology for the proposed IEET mechanism.	74
Fig. 5.6 Construction tree and mesh topologies of the proposed IEET mechanism.	75
Fig. 5.7 The simulated environment of the proposed IEET mechanism.	77
Fig. 5.8 Analyzing the average packet latency by varying traffic.	78
Fig. 5.9 Analyzing lifetime by varying polling timeout.	79
Fig. 5.10 Evaluating sensor lifetime under varying polling timeout and network throughput.	80
Fig. 5.11 Evaluating energy consumption varying network throughput and upper bound δ_max.	81
Fig. 5.12 Evaluating energy consumption varying network throughput.	82

List of Tables
Table 2.1 Comparison of the proposed mechanism with the related studies.	13
Table 4.1 The algorithm of the Collision Avoidance Strategy for emergency sensors.	36
Table 4.2 The algorithm of Energy Conservation Strategy for general sensors.	40
Table 4. 3 Simulation parameters.	43
Table 5.1 The algorithm of the proposed IEET mechanism	73
Table 5.2 An ablation study comparing IEET with Tree and Mesh topologies.	83

[1]	R. J. Aitken, “The changing tide of human fertility,” Human Reproduction, vol. 37, no. 4, pp. 629-638, Jan. 2022.
[2]	T. Wu, et al, “A rigid-flex wearable health monitoring sensor patch for IoT-connected healthcare applications,” IEEE Internet of Things Journal, vol. 7, no. 8, pp. 6932-6945, Aug. 2020.
[3]	M. Dadkhah, M. Mehraeen, et al, “Use of internet of things for chronic disease management: An overview,” Journal of Medical Signals and Sensors, vol. 11, no. 2, pp. 138-157, May 2021.
[4]	J. Wannenburg, R. Malekian. “Body Sensor Network for Mobile Health Monitoring, a Diagnosis and Anticipating System,” IEEE Sensors Journal, vol. 15, no. 12, pp. 6839-6852, Dec. 2015.
[5]	T. D. P. MENDES, et al. “Smart home communication technologies and applications: Wireless protocol assessment for home area network resources,” Energies, vol. 8, no. 7, pp. 7279-7311, Mar. 2015.
[6]	J. Lloret, et al. “A smart communication architecture for ambient assisted living,” IEEE Communications Magazine, vol. 53, no. 1, pp. 26-33, Jan. 2015.
[7]	A. Pal, et al., “A smartphone-based network architecture for post-disaster operations using WiFi tethering,” ACM Transactions on Internet Technology, vol. 20, no. 1, pp. 1-27, Feb. 2020.
[8]	K. Doke, et al., “Improving Emergency Preparedness and Response in Rural Areas,” The ACM SIGCAS Conference on Computing and Sustainable Societies, New York, USA, pp. 66-78, Jun. 2021.
[9]	R. Damaševičius, N. Bacanin, and S. Misra, “From sensors to safety: Internet of Emergency Services (IoES) for emergency response and disaster management,” Journal of Sensor and Actuator Networks, vol. 12, no. 3, pp. 41, May 2023.
[10]	J. Ding, and W. Yong, “A WiFi-based smart home fall detection system using recurrent neural network,” IEEE Transactions on Consumer Electronics, vol. 66, no. 4, pp. 308-317, Sep. 2020.
[11]	K. G. Dangi, A. Bhagat, and S. P Panda, “Emergency vital data packet transmission in hospital centered wireless body area network,” Procedia Computer Science, vol. 171, pp. 2563-2571, Jun. 2020.
[12]	J. Bravo-Arrabal, P. Zambrana, et al, “Realistic deployment of hybrid wireless sensor networks based on ZigBee and LoRa for search and Rescue applications,” IEEE Access, vol. 10, pp. 64618-64637, Jun. 2022.
[13]	X. Yang, J. Guo, and J. Guo, “Multiple-dimension biohealth data acquisition system for 5G tele-healthcare application,” IEEE Transactions on Industrial Informatics, vol. 19, no. 6, pp. 7700-7708, Jun. 2023.
[14]	P. I. Tebe, G. Wen, et al, “5G-enabled medical data transmission in mobile hospital systems,” IEEE Internet of Things Journal, vol. 9, no. 15, pp. 13679-13693, Aug. 2022.
[15]	M. Poongodi, A. Sharma, et al, “Smart healthcare in smart cities: wireless patient monitoring system using IoT,” The Journal of Supercomputing, vol. 77, pp. 12230-12255, Nov. 2021.
[16]	K. Antevski, et al, “A 5G-based eHealth monitoring and emergency response system: Experience and lessons learned,” IEEE Access, vol. 9, pp. 131420-131429, Sep. 2021.
[17]	P. C. Ng and J. She, “Remote proximity sensing with a novel Q-learning in Bluetooth low energy network,” IEEE Transactions on Wireless Communications, vol. 21, no. 8, pp. 6156-6166, Aug. 2022.
[18]	F. Álvarez, L. Almon, et al, “Bluemergency: Mediating post-disaster communication systems using the internet of things and Bluetooth mesh,”  The 9th IEEE Global Humanitarian Technology Conference (GHTC), Seattle, WA, USA, pp. 17-20, Oct. 2019
[19]	T. Zhang, X. Zhou, et al, “QoE-driven data communication framework for consumer electronics in tele-healthcare system,” IEEE Transactions on Consumer Electronics, vol. 69, no. 4. Nov. 2023.
[20]	  Z. Guo, et al. “An on-demand scatternet formation and multi-hop routing protocol for BLE-based wireless sensor networks,” IEEE Wireless Communications and Networking Conference (WCNC), New Orleans, LA, USA, pp. 1590–1595, Sep. 2015.
[21]	G. Patti, et al. “A Bluetooth Low Energy real-time protocol for industrial wireless mesh networks”. Annual Conference of the IEEE Industrial Electronics Society (IECON), Florence, Italy, pp. 4627–4632, Oct. 2016.
[22]	L. Leonardi, G. Patti and L. Lo Bello, “Multi-hop real-time communications over Bluetooth low energy industrial wireless mesh networks,” IEEE Access, vol. 6, pp. 26505-26519, May 2018.
[23]	C. Sandeepa, C. Moremeda, et al, “An emergency situation detection system for ambient assisted living,” IEEE International Conference on Communications Workshops, Dublin, Ireland, pp. 1-6, Jun. 2020.
[24]	P. Pierleoni, et al. “Performance improvement on reception confirmation messages in Bluetooth mesh networks,” IEEE Internet of Things Journal, vol. 9, no.3, pp. 2056-2070, Feb. 2022.
[25]	D. Han, et al. “Anti-collision voting based on Bluetooth low energy improvement for the ultra-dense edge,” IEEE Access, vol. 9, pp. 73271-73285, May 2021.
[26]	P. Shabisha, et al. “Security enhanced emergency situation detection system for ambient assisted living,” IEEE Open Journal of the Computer Society, vol.2, pp. 241-259, July 2021. 
[27]	Á. Flor, et al. “Bluemergency: Mediating post-disaster communication systems using the internet of things and Bluetooth mesh,” IEEE Global Humanitarian Technology Conference (GHTC), Seattle, WA, USA, pp. 17-20, Oct. 2019.
[28]	A. Wang, W. B. Heinzelman, et al, “Energy-scalable protocols for battery-operated microsensor networks,” Journal of VLSI VLSI Signal Processing, vol. 29, pp. 223-237, Nov. 2001.
[29]	A., Saleh A, “Load balancing maximal minimal nodal residual energy ad hoc on-demand multipath distance vector routing protocol,” Wireless networks, vol.22, no.4, pp. 1355-1363, 2016.
[30]	Bluetooth core specification version 5.3, Bluetooth Special Interest Group, Kirkland, Washington, USA, Jul. 2021.
[31]	C. M. Yu, M. L. Ku, and L.c. Wang, “DTC-HSR: Distributed topology control and hierarchical self-routing for Bluetooth load balancing networks,” IEEE Internet of Things Journal, vol. 9, no. 20, pp. 19545-19560, Oct. 2022.
[32]	W. Cao, W. Xia, et al, “A hierarchical BLE mesh network for IoT and performance analysis,” IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, Dec. 2021.
[33]	X. Li, Q. Zhang, et al, “JECP: Joint energy conservation and collision avoidance for paths construction in Bluetooth networks,” IEEE Sensors Journal, vol. 23, no. 9, pp. 10168-10178, May 2023.