On Wireless Sensor Network Models: A Cross-Layer Systematic Review
Abstract
:1. Introduction
- A cross-layer vision is used to analyze and group the proposed IoT network infrastructure models using a system-centric approach that includes metrics not typically considered in WSN model reviews.
- A simplified taxonomy of three categories is proposed by presenting and comparing the metrics of each category, allowing for a comprehensive understanding of the limitations and potential of the models categorized in this review.
- The most common computer design tools are presented, and their potential for model development from a cross-layer perspective is examined.
2. Related Surveys and Reviews
2.1. Surveys Related to Node Models
2.2. Surveys Related to Network Models
2.3. Surveys Related to System Models
2.4. Surveys Related to Modeling Approaches
3. Review Methodology
4. Proposed Taxonomy
- Node models: This category includes all models in which metrics are related to the physical layer of the OSI reference model. Moreover, the models in this category are divided into three subareas: energy efficiency, timing and coverage, which will be detailed later in Section 5.
- Network models: This category includes all models in which metrics are related to the data link, network, and transport layers of the OSI reference model. Furthermore, the models in this category are divided into three subareas: energy efficiency, radio propagation and coverage, which will be detailed later in Section 6.
- System models: This category includes all models with metrics from the OSI reference model’s session, presentation, and application layers. Furthermore, this category is divided into three subcategories: power estimation, network estimation and timing, which will be covered in greater detail in Section 7.
5. Node Models
5.1. Energy Efficiency Models
5.1.1. Data-Processing Models
5.1.2. Communication Models
5.1.3. Power Supply Models
5.1.4. Synthesizer and VCO Models
5.1.5. Sensor Energy Models
5.2. Timing Models
5.3. Coverage Models
6. Network Models
6.1. Energy Efficiency Models
6.1.1. Network Lifetime Models
6.1.2. Power-Management Models
6.1.3. Hopping Network Models
6.2. Coverage Models
6.2.1. Communication Models
6.2.2. Localization Models
6.3. Radio Propagation Models
7. System Models
7.1. Power Estimation
7.2. Network Estimation
7.2.1. Reliability Models
7.2.2. Coverage Models
7.3. Timing Estimation
8. Modeling Simulation Tools
9. Research Trends and Open Challenges
9.1. Node Level
9.2. Network Level
9.3. System Level
9.4. Open Challenges
10. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Scheme | Modeling Problem | Modeling Elements | Modeling Methodology | Model | Solution Methodology | Evaluation Approach | Complexity | Models |
---|---|---|---|---|---|---|---|---|
Cui et al., 2005 [32] | Power consumption node parameters (maximum transmission time) | Communication medium-related front-end elements | Physical model | Analytical | Optimization (Integer programming problem) | Source code (Solver) | High | Energy Efficiency |
Li et al., 2006 [5] | Communication node parameters | Communication medium-related front-end elements | Physical model | Analytical | Optimization (Numerical analysis) | Simulation (NS-2) and validation (Implementation) | Medium | Energy Efficiency |
Zhou et al., 2011 [27] | Power consumption of node components | MCU, RF transceiver, and sensor unit | Physical model | Analytical | System simulation | Simulation (OPENET) | Medium | Energy Efficiency |
Özkaya et al., 2021 [26] | Power consumption of node components | MCU, RF transceiver, actuator, and sensor unit | Physical model | Analytical | System simulation and validation (Circuit implementation) | Simulation (MATLAB/Simuling and ContikiOS) | High | Energy Efficiency |
Zhai et al., 2010 [54] | Power consumption node of 4 different front-end architectures | Communication medium-related front-end elements | Physical model | Analytical | Optimization (Numerical analysis) | Simulation (Simulation tools not reported) | Medium | Energy Efficiency |
Tehrani et al., 2010 [55] | Performance operation of a particular block of the node (PA) | Reference model (mapping PA parameters values) | Generalized memory polynomial (GMP) | Behavioral | Normalized mean square error (NMSE) and Adjacent channel error power ratio (ACEPR) | Simulation (Simulator tools not reported) | High | Energy Efficiency |
Zahhad et al., 2015 [56] | Power consumption node parameters | Communication medium-related front-end elements | Physical model | Analytical | Optimization (Numerical analysis) | Simulation (NS-2) and validation (Implementation) | Low | Energy Efficiency |
Zahhad et al., 2016 [35] | Power consumption parameters (Transmitted power) | Communication medium-related front-end elements | Physical models | Analytical | Optimization (Numerical analysis) | Simulation (NS-2) | Low | Energy Efficiency |
Hou et al., 2017 [57] | - | Phase-locked loop, transmitter and receiver blocks | - | - | Circuit simulation with simulation tool | Implementation on chip (CMOS) | Low | Energy Efficiency |
Shafique et al., 2017 [58] | Performance operation of a particular block of the node (LNA) | Reference model (mapping LNA parameters values) | Physical model | Behavioral | - | Simulation (MATLAB/Simulink) | Medium | Energy Efficiency |
Friesel et al., 2018 [59] | Node parameters configuration | Reference model (mapping parameters values) | Training | - | Standard deviation-least square regression | Simulation (Simulation tool not reported) | Low | Energy Efficiency |
Mahmood et al., 2018 [60] | Power consumption node parameters (Signal to noise ratio (SNR), payload size and modulation order) | Specific block of the transceiver (PA) | Physical model | Analytical | Optimization (Linear programming problem) | Source code (Solver) | High | Energy Efficiency |
Zhang et al., 2020 [44] | Skew estimation and energy conservation | Transceiver´s clock model | Physical model | Estimation | Source code | Simulation (NS-2) | Medium | Timing |
He et al., 2017 [45] | Clock synchronization and skew estimation | Transceiver´s clock | Physical model | Estimation | Algorithm | Implementation (Experimental testbed) | Medium | Timing |
Mahmood et al., 2019 [31] | Power consumption node parameters (Modulation order and transmission time) | Communication medium-related front-end elements | Physical model | Analytical | Optimization (Numerical analysis) | Simulation (Simulation tool not reported) | Medium | Energy Efficiency |
Jadaa et al., 2020 [61] | Localization | Sensor module | Probability model | Statistical approach | Source code | Simulation (NS-2) | Medium | Coverage |
Wang et al., 2021 [62] | Efficiency of low power consumption continuous phase frequency shift-keying (CPFSK) transceiver with a frequency-to-amplitude converter (FAC) | Digital controller oscillator (DCO), LNA, FAC, PA | Physical model | - | System simulation | Simulation (Verilog-A) | Low | Energy Efficiency |
Lacroix et al., 2021 [63] | Efficiency of a particular part of the node (PA) | Communication medium-related front-end elements | Physical model | Analytical | Optimization (Numerical analysis) | Simulation (Simulation tool not reported) | Medium | Energy Efficiency |
Rasool et al., 2021 [38] | Efficiency of a particular block of the node | Specific block of the node (battery) | Physical model | Analytical | Optimization (Numerical analysis) | Simulation (MATLAB/Simulink) | Low | Energy Efficiency |
Mini 2021 [46] | Network Coverage | Sensor module | Mathematical model | Binary and probability | Optimization (Numerical analysis) | Simulation (Simulation tool not reported) | Medium | Coverage |
Scheme | Modeling Problem | Modeling Elements | Modeling Methodology | Model | Solution Methodology | Evaluation Approach | Complexity | Model |
---|---|---|---|---|---|---|---|---|
Al-Farhani 2021 [81] | Network lifetime | Front-end components | Physical model | - | Algorithm | Simulation (Simulation tool not reported) | Medium | Energy Efficiency |
Choi et al., 2021 [82] | Network lifetime | Communication medium and node energy consumption parameters | Physical model | Analytical | Optimization (Linear programming problem) | Simulation (MATLAB) | Medium | Energy Efficiency |
Satyanarayana et al., 2021 [83] | Network Coverage | Sensor module | Mathematical model | - | Algorithm | Simulation (Simulation tool not reported) | Medium | Coverage |
Mini 2021 [46] | Network coverage | Sensor module | Mathematical model | Probability | Optimization (Numerical analysis) | Simulation (Simulation tool not reported) | Medium | Coverage |
Zhang et al., 2012 [36] | Energy consumption | RF front-end module | Operation state model | Stochastic | Optimization (Numerical analysis) | Simulation (Simulation tool not reported) | Medium | Energy Efficiency |
Li et al., 2020 [75] | Network coverage | Sensor module | Physical model | Probability | Probability analysis (Algorithm) | Simulation (Simulation tool not reported) | Low | Coverage |
Hussein et al., 2020 [84] | Network lifetime | Node energy consumption parameters | Physical model | Analytical | Optimization (Nonlinear multi-variable optimization problem) | Simulation (Mathematica and MATLAB) and validation (implementation) | High | Energy Efficiency |
Agarwal et al., 2017 [85] | Network lifetime | Sensor node states | Mathematical model | Stochastic | Optimization (Numerical Analysis) | Simulation (MATLAB) | Medium | Energy Efficiency |
Agrawal et al., 2020 [71] | Network lifetime | Reference model (mapping parameters battery values) | Training | - | Genetic algorithm | Simulation (CAD not reported) and Source code on JAVA | Medium | Energy Efficiency |
Jadaa et al., 2020 [61] | Localization | Sensor module | Probability model | Statistical approach | Source code | Simulation (NS-2) and implementation of an algorithm | Medium | Coverage |
Zhou et al., 2019 [86] | Network lifetime | Communication medium and node energy consumption parameters | Operation state model | Analytical | Optimization (Linear programming problem) | Simulation (Simulation tool not reported) | High | Energy Efficiency |
Kumar et al., 2018 [87] | Network lifetime | Communication medium-related front-end elements | Physical model | Analytical | Source code | Simulation (PEGASIS protocol) | Low | Energy Efficiency |
Liu et al., 2018 [78] | Localization | Sensor module | Dimensional plane distance | - | Implementation of an algorithm | Simulation | Low | Coverage |
Kakhandki et al., 2017 [74] | Hop selection | Node energy consumption parameters | Physical model | Analytical | Optimization (Optimal linear problem) | Simulation (SENSORIA) | Medium | Coverage |
Setiawan et al., 2017 [88] | Network Lifetime | Node energy consumption parameters | Physical model | Analytical | Optimization (Numerical analysis) | Validation (Implementation) | Medium | Energy Efficiency |
Shakhov et al., 2017 [89] | Network coverage | Sensor module | Physical model | Probability | Optimization (Numerical analysis) | Simulation (Simulation tool not reported) | Medium | Coverage |
Li et al., 2017 [90] | Network lifetime | Node energy consumption parameters | Estimating model | Probability | Game theory (Nash equilibrium) | Simulation (MATLAB) | Medium | Energy Efficiency |
Song et al., 2017 [91] | Energy consumption | Reference model (mapping parameters values) | Operation state model | Analytical | Optimization (Numerical analysis) | Simulation (Simulation tool not reported) | Medium | Energy Efficiency |
Tudose et al., 2013 [73] | Network lifetime | Communication medium-related front-end elements | Physical model | Analytical | Optimization (Numerical analysis) | Simulation (Simulation tool not reported) and implementation of an algorithm | Medium | Energy Efficiency |
Sharma et al., 2011 [40] | Network lifetime | Node energy consumption parameters | Physical model | Analytical | Optimization (Numerical analysis) | Simulation and validation (Simulation tool and implementation not reported) | Medium | Energy Efficiency |
Jagriti et al., 2018 [69] | Network Lifetime | RF front-end module and S-MAC protocol | Mathematical model | Analytical | Algorithm | Simulation (MATLAB) | Medium | Energy Efficiency |
Das et al., 2021 [76] | Coverage and connectivity | Sensor module (sensing and communication radius) | Mathematical model | Probabilistic | Algorithm | Simulation | Medium | Coverage |
Scheme | Modeling Problem | Modeling Elements | Modeling Methodology | Model | Solution Methodology | Evaluation Approach | Complexity |
---|---|---|---|---|---|---|---|
Mazloomi et al., 2022 [98] | Power estimation (Network estimation) | Reference model (mapping parameters values) | Vector regression method | - | Optimization (Multiple-objective optimal problem MSOG algorithm) | Simulation (MATLAB) | medium |
Boardman et al., 2021 [100] | Power estimation (Network lifetime) | Sink, sensor nodes and target nodes | Probabilistic graph | Probability | Optimization (Bi-objective optimal problem) | Simulation (simulation tool not reported), source code (solver) | High |
Yang et al., 2021 [101] | Power estimation (Network lifetime) | Sink, sensor nodes and target nodes | Stochastic process | Probability | Maximum Likelihood | Simulation (simulation tool not reported) | High |
Ozkaya 2021 [26] | Power estimation | Sensor nodes components | Physical model | Analytical | Optimization (Numerical analysis) | Simulation (MATLAB/Simulink) | Medium |
Basabaa et al., 2021 [101] | Power estimation (Harvesting) | Sink, sensor nodes and target nodes | Probabilistic graph | Probability | Algorithm | Simulation (simulation tool not reported) | High |
Liu 2021 [96] | Coverage estimation (Interference) | Sink, sensor nodes and target nodes | Basic probability assignment | Probability | D-S evidence theory | Algorithm, Simulation (simulation tool not reported) | High |
Nagar et al., 2020 [99] | Network estimation (Deployment) | Ideal characteristics of IoT devices | Probabilistic | Probabilistic | Optimization (Numerical analysis) | Simulation (MATLAB) | Medium |
Chakraborty et al., 2019 [97] | Network estimation (Shortest minimal path) | Sink, sensor nodes and target nodes | Probabilistic weight graphs | Probability | Multi-node state reliability evaluator (MNRE) | Simulation (MATLAB) | High |
Mahmood et al., 2019 [102] | Power estimation | Ideal characteristics of IoT devices (PA) and Gateway | Probabilistic | Probability (QoS) | Optimization (Bi-objective optimal problem) | Source code (Solver) | High |
Xu et al., 2019 [103] | Coverage estimation (Deployment) | Ideal characteristics of IoT devices and Gateway | Probabilistic node graph model | Analytical and probability | Optimization (Bi-objective optimal problem) | Simulation (MATLAB, Montecarlo method) | High |
Du et al., 2019 [104] | Power estimation | Ideal characteristics of IoT devices and Gateway | Probabilistic directed acyclic graph | Probability | Optimization (Optimal any path network sub-graph) | Simulation (MATLAB) | High |
Sarkar et al., 2019 [94] | Network estimation (Cluster-head) | Ideal characteristics of IoT devices and Gateway | Probabilistic | Probability | Optimization (Statistical analysis) | Simulation (MATLAB) | Medium |
Wang et al., 2018 [105] | Network estimation (Fault estimation) | Sink, sensor nodes and target nodes | Probabilistic functional dependence | Multistate fault tree | Multi-state multi-value decision diagram | Simulation (simulation tool not reported) | High |
Hasan et al., 2018 [72] | Network estimation (Fault estimation) | Sink, sensor nodes, target nodes | Markov discrete time | Multistate fault tree | Multi-state multi-value decision diagram | Simulation (MATLAB) | High |
Kassan et al., 2017 [106] | Power estimation (Energy harvesting and battery reliability) | Sensor, node battery, PV-WSN | Binary decision diagram | Probability | Optimization (Numerical analysis) | Simulation (simulation tool not reported) | High |
Suhonen et al., 2009 [107] | Timing estimation (QoS and duty cycle) | Coverage and deployment | Probabilistic graph | Probability | Optimization (Numerical analysis) | Simulation (NS-2) | Medium |
Simulation Tools | Node Models | Network Models | System Models | Core Language | References | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Circuit | Timing | Node Elements | MAC | Modulation | Topology | IEEE 802.1X Support | Channel | Environment | Routing Protocol | Low latency | Throughput | Time Synchronization | Failure Tolerance | Energy Balance | |||
Based on Spice | [47] | ||||||||||||||||
NS-2 | C++ and Otc | [49,108] | |||||||||||||||
NS-3 | C++ and Python | [109] | |||||||||||||||
MATLAB/Simulink | High-level programming language | [21] | |||||||||||||||
OMNET++ | C++ | [110,111,112,113] | |||||||||||||||
TOSSIM | Python and C++ | [114,115,116,117] | |||||||||||||||
COOJA (Contiki Os Java) | C++ | [20,118] | |||||||||||||||
Required Tool | Cross-layer model |
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Ojeda, F.; Mendez, D.; Fajardo, A.; Ellinger, F. On Wireless Sensor Network Models: A Cross-Layer Systematic Review. J. Sens. Actuator Netw. 2023, 12, 50. https://doi.org/10.3390/jsan12040050
Ojeda F, Mendez D, Fajardo A, Ellinger F. On Wireless Sensor Network Models: A Cross-Layer Systematic Review. Journal of Sensor and Actuator Networks. 2023; 12(4):50. https://doi.org/10.3390/jsan12040050
Chicago/Turabian StyleOjeda, Fernando, Diego Mendez, Arturo Fajardo, and Frank Ellinger. 2023. "On Wireless Sensor Network Models: A Cross-Layer Systematic Review" Journal of Sensor and Actuator Networks 12, no. 4: 50. https://doi.org/10.3390/jsan12040050
APA StyleOjeda, F., Mendez, D., Fajardo, A., & Ellinger, F. (2023). On Wireless Sensor Network Models: A Cross-Layer Systematic Review. Journal of Sensor and Actuator Networks, 12(4), 50. https://doi.org/10.3390/jsan12040050