Resource Allocation Optimization in IoT-Enabled Water Quality Monitoring Systems
Abstract
:1. Introduction
- We propose the design of a NOMA-enabled protocol for IoT-enabled water quality monitoring systems;
- We propose the integration of edge computing with water quality monitoring systems;
- We propose resource allocation optimization methods, including a Dinkelbach algorithm-based optimization method for optimizing wireless energy transfer and wireless information transfer, as well as a dynamic resource allocation method for hybrid access point (HAP) resource allocation for data collection;
- We provide a comparison of the proposed method with a comparable baseline method.
2. Related Work
3. Proposed Method
3.1. System Architecture
3.2. System Model
4. Mathematical Model
4.1. Transformation of the Objective Function
4.2. Optimal Solution
Algorithm 1 Resource Allocation Algorithm |
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Algorithm 2 Proposed Dinkelbach-based Iteration Algorithm |
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4.3. Dynamic HAP Resource Allocation Algorithm
Algorithm 3 HAP allocation in the WIT Phase |
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5. Results and Discussions
5.1. Performance Comparison of Different Methods
5.2. Impact of Noise Power on Energy Efficiency
5.3. Effect of the Number of Power Sources on Energy Efficiency
5.4. Impact of Sensor Device Transmit Power on Energy Efficiency
5.5. Impact of QoS Data Requirements on the System EE
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Reference | Contribution of Related Works | Contribution of the Proposed Work |
---|---|---|
[20] | The authors designed a resource allocation algorithm to manage edge computation resource allocation in a network where all IoT devices participate in data transmission in the same cycle. | Unlike [20], we introduced a dynamic resource allocation method and an optimization-based method to jointly optimize energy harvesting and data transmission in a sequential multi-class WPCN, where each class of sensors operates sequentially to improve the overall system energy efficiency. |
[21] | The authors designed a wireless- powered network where IoT devices perform complex tasks. Additionally, IoT devices can only send their data to a single base station. | Contrary to [21], we shifted complex tasks from IoT devices to reduce energy consumption. Additionally, we contributed a dynamic resource allocation method to optimally allocate multiple hybrid access points to improve system energy efficiency. |
[22] | The authors designed a resource management scheme to offload computations in the network IoT devices concurrently. | Unlike [22], we introduced a sequential multi-class WPCN strategy for offloading computations in a sequential manner. Additionally, we contributed a dynamic resource allocation method to improve the overall system energy efficiency. |
[25] | The authors designed a game theory-based resource allocation method to improve energy efficiency in cooperative network settings. | Unlike [25], we proposed a dynamic resource allocation method and an optimization-based method to jointly optimize the allocation of system resources to improve the overall system energy efficiency in sequential multi-class WPCN settings. |
[26] | The authors designed a wireless- powered communication network with only one hybrid access point. | Different from [26], we contributed a sequential multi-class WPCN with dynamically allocated hybrid access points to improve energy efficiency. |
[27] | The authors designed a wireless- powered cooperative IoT network where devices transmit data in the same cycle. | Different from [27], we contributed a sequential multi-class WPCN where devices transmit data in different cycles to improve energy efficiency. |
[28] | The authors designed a wireless- powered communication network where IoT devices uses a multi-hop communication strategy to communi- cate with a single base station. | Contrary to [28], we contributed a sequential multi-class WPCN where IoT devices uses single-hop communication to communicate with dynamically allocated hybrid access points to improve energy efficiency. |
Requirement | Range |
---|---|
pH sensor | 0–14 |
Conductivity sensor | 100 μS/cm–200 mS/cm |
E. coli sensor | 1–1000 CFU/100 mL |
Residual chlorine sensor | 0–10 mg/L |
Dissolved oxygen sensor | 0–20 mg/L |
Transmitter/HAP | 1 W–3 W |
Edge node | ≤5 m from HAPs |
ZigBee radio | Above 100 m |
Acronymn | Definition |
---|---|
IoT | Internet of Things |
NOMA | Non-orthogonal multiple access |
HAP | Hybrid access point |
WEH | Wireless energy transfer |
WIT | Wireless information transfer |
Time slot for wireless information transfer | |
Time slot for wireless energy transfer | |
and | Downlink communication channel gains |
and | Uplink communication channel gains |
HAP transmission power | |
SIC | Successive interference cancellation |
B | System’s bandwidth |
DL | Downlink |
UL | Uplink |
Power sources | |
Energy harvested by K sensor devices | |
Energy harvested by L sensor devices | |
Energy used by each sensor device k for UL data transfer | |
Energy used by each sensor device l for UL data transfer |
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Olatinwo, S.O.; Joubert, T.H. Resource Allocation Optimization in IoT-Enabled Water Quality Monitoring Systems. Sensors 2023, 23, 8963. https://doi.org/10.3390/s23218963
Olatinwo SO, Joubert TH. Resource Allocation Optimization in IoT-Enabled Water Quality Monitoring Systems. Sensors. 2023; 23(21):8963. https://doi.org/10.3390/s23218963
Chicago/Turabian StyleOlatinwo, Segun O., and Trudi H. Joubert. 2023. "Resource Allocation Optimization in IoT-Enabled Water Quality Monitoring Systems" Sensors 23, no. 21: 8963. https://doi.org/10.3390/s23218963
APA StyleOlatinwo, S. O., & Joubert, T. H. (2023). Resource Allocation Optimization in IoT-Enabled Water Quality Monitoring Systems. Sensors, 23(21), 8963. https://doi.org/10.3390/s23218963