In this section, the EDHC-SA energy model and EDHC-SA multi-channel sensing coverage model have been implemented and evaluated in Matlab. The EDHC-SA energy model is experimented and compared with stable energy protocol (SEP), and LEACH protocol. The performance efficiency of the proposed energy model is evaluated based on average residual or remaining energy of the sensor nodes. The efficiency of the EDHC-SA multi- channel sensing coverage model is experimented and compared with existing ZigBee sensor network model. The proposed multi-channel sensing coverage model is evaluated based on the following metrics: Coverage error probability or bit error rate (BER), SNR, and Latency. Table 6.5 presents simulations parameters for the models.
Fig. 6.4 shows the energy consumption analysis based on average residual energy per round of the EDHC-SA energy model compared with the existing SEP and LEACH Protocol. From the result, it confirms that the EDHC-SA energy model can effectively do the data aggregation from the sensor nodes sources to the sink with minimal energy consumption. This is depicted in Fig. 6.4 which shows higher average residual energy than the existing SEP and LEACH energy protocols. For the EDHC-SA multi-channel sensing coverage model, the results in terms of BER with respect to SNR are obtained in two different scenarios. Scenario 1 represents Fig. 6.5, and Scenario 2 represents Fig. 6.5. Scenario 1 is where all the six channels priori are available in the EDHC-SA CRSN model, which is in comparisons with the conventional ZigBee WSN in order to obtain the BER and SNR. Looking at Fig. 6.5 it can be seen that the error rate at a given SNR in EDHC-SA CRSN model is lower than the error rate in the conventional ZigBee WSN. For example, EDHC-SA CRSN model exhibit a minimum error rate of approximately 10−4at
SNR of 18 dB and maximum error rate of approximately 10−2at SNR of 0 dB whereas the conventional ZigBee WSN exhibits a minimum error rate of approximately 10−2at SNR of 18 dB and maximum error rate of approximately 10−1at SNR of 0 dB.
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Table 6.5: Simulation parameters
EDHC-SA Energy model parameters
Parameters Values
Network field size 200 x 200 M2 Total number of nodes 200
Initial effective energy of CH member 2 J Initial effective energy of CH 5 J Number of Clusters 14
Location of Sink (BS) (250, 200) M Data packet size 4000 Bits
EDHC-SA Multi-channel sensing coverage parameters
Number of available channel 6 Simulation runs 10,000 Total Area of Coverage 200 x 200 M2 Multipath Fading Nakagami-q
Shadow Fading Log-Normal Shadowing Modulation size 4QAM
SNR 0:18
VR 1:6
Figure 6.4: Average residual energy per round for EDHC-SA compared with the existing SEP and LEACH Protocol
This means that the conventional ZigBee WSN encounters more errors in excess of over 100% at a given SNR than the EDHC-SA CRSN model. Also, at a given BER, the EDHC-SA
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CRSN model experience a lower energy per bit to noise ratio (Eb/No) than the conventional ZigBee WSN. For example, at BER of 10−2, the EDHC-SA CRSN model has SNR of 4 dB
whereas the conventional ZigBee WSN has over 18 dB for the same BER of 10−2. This means that a less energy is expended at a given BER in the EDHC-SA CRSN, whereas more energy is consumed in the conventional ZigBee WSN. Consequently, it is obvious that it will take a lower energy to accomplish greater sensing coverage and data frame transmission with minimal BER in the EDHC-SA CRSN than in the conventional ZigBee WSN.
In addition, we further simulate EDHC-SA CRSN model with VR= 2 and VR= 4 res-
pectively in order to validate the behavior of the EDHC-SA CRSN model with respect to changes in the available channels. it is observed that as the available channels increases the BER reduces leading to the improvement of the network with increase in available channels.
Also, as the available channels reduces, the BER increases. However, in all cases, the performance of the EDHC-SA CRSN with opportunistic multi-channels access is better than the performance of the conventional ZigBee WSN in terms of BER and energy per bit to noise ratio as depicted in Fig. 6.6. Therefore, it is easier to reduce the energy consump- tion of data frame transmission in the EDHC-SA CRSN model by using a lower SNR while simultaneously satisfying a certain minimal BER.
Furthermore, from the simulation results in Fig. 6.5, equation (6.16) was implemented at a given SNR in order to obtained a relationship of BER with respect to delay as depicted Fig. 6.7 for the EDHC-SA CRSN model. From Fig. 6.7, it is obvious that both the SNR and latency reduces as the BER reduces. For example, with the SNR of 18 dB,12 dB,and 6 dB it has a maximum latency of 0.44 seconds, 0.29 seconds, and 0.14 seconds respectively. This means that at any given SNR, there is a corresponding decrease in the latency or delay as the BER reduces; and corresponding increase in the latency as the BER increases. Hence, an optimal data frame transmission can be made at a given SNR with minimal error rate and low latency. Therefore, EDHC-SA CRSN model satisfies both energy efficiency and latency issues.
Moreover, from Fig. 6.8, the BER and the latency take the same trend as that of Fig. 6.7, but however, with a higher error rate and latency at a given SNR. This means that conventional ZigBee WSN exhibits high latency and is not energy efficient when compared with the EDHC-SA CRSN model.
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Figure 6.5: Scenario1: Error Probability comparisons of Conventional ZigBee WSN and EDHC-SA CRSN
6.5
Chapter Summary
In this chapter, a DHC topology for ZigBee CRSN in SG was proposed. Potential difference of various topologies between conventional ZigBee WSN and ZigBee CRSN suitable for SG application was evaluated. Further, energy efficient distributed heterogeneous clustered spectrum aware (EDHC-SA) model was proposed. The model was supported by provid- ing a novel algorithm called Equilateral triangulation algorithm for guaranteed network connectivity in CRSN based SG were presented. CSMA/CA MAC protocol algorithm for alternation of data frame transmission of both event driven and data driven CRSN nodes were incorporated in order to save the network life time.
Then was the introduction of a variator mechanism for varying the opportunistic multi- channel access with single data frame transmission. The mechanism was implemented with a derived coverage probability of sensing signal under multipath fading conditions. Finally, the simulations results obtained confirms that EDHC-SA CRSN model outperforms conventional ZigBee WSN in terms of bit error rate, end-to-end delay (latency), and energy consumption. Hence, the EDHC-SA CRSN model is suitable for SG harsh environ- mental condition, due to the fact that SG applications are mission critical applications that require low latency for real-time satisfactory sensed data delivery.
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Figure 6.6: Scenario2: Error Probability comparisons of Conventional ZigBee WSN and EDHC-SA CRSN with further channel changes
The spectrum aware cross-layer algorithm framework in the EDHC-SA is mainly based on lower layer communication protocols. Spectrum aware cross-layer algorithm in the upper communication layer protocols (transport and application layer) of CRSN for SG will be an interesting future research area.
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Figure 6.7: BER relationship with Latency at three different SNR values for EDHC-SA CRSN model
Figure 6.8: BER relationship with Latency at three different SNR values for conventional ZigBee WSN
Conclusion and Future Works
7.1
Conclusion
In this chapter, the overview of the research work together with all relevant contributions have been presented. The importance and benefits of SG in chapter one were elaborated. Reliable communication systems are keys to achieving maximal benefits of smart grids. However, the conventional communications systems are associated with problem of spec- trum inefficiencies and interferences which will affect the reliability of communication in SG. Consequently, cognitive radio technology has been proposed as a new paradigm to solve the spectrum inefficiencies and interferences problems.
To this end, this new paradigm (a combination of cognitive radios with WSNs) leading to CRSNs, are applied to SG for reliable communication and adequate monitoring and control. Furthermore, there exists some problems in the CRSN itself, such as limited energy life span of the sensor node, low complexity in handling huge/varying SG data traffics including problem of existing protocols which are not spectrum aware for DSA support. In order to address these problems, an organized approach was systematically adopted in the chapters. Chapter two investigated and explored the CRSN conceptual framework including survey of literature of related works, involving SG communication architecture with its applica- tions as well as the communication access technologies. Further, implementation design with quality of service (QoS) support and unified communication solution for SG was intro- duced and incorporated. Overall, various research gaps including integration of LPWAN for CRSN based SG deployment were highlighted.
Consequent upon the challenges associated with competitive sensor nodes and communica- tion devices in accessing and utilizing radio resources, the need for dynamic radio resource allocation has been projected as a capable solution in allocating radio resource to sensor nodes in CRSN based smart grid ecosystem. These challenges include: energy or power
constraints, poor quality of service (QoS), interference, delay, spectrum inefficiency, and excessive spectrum handoffs.
Accordingly, in chapter three, an investigation and survey in terms of optimization criteria such as energy efficiency, throughput improvement, QoS guarantee, fairness, priority, in- terference mitigation, etc., for RRA were conducted in order to address the aforementioned problems in CRSN based SG.
The problem of multi fading channels due to multi antenna correlation channels of the sensor nodes as well as very close spacing of sensor nodes deployment in a SG environment has been a big issue. This correlation can lead to degradation of the signals as well as co-channels interference. In addition, the signal-interference-noise-ratio (SINR), multipath fading, and shadowing peculiar to SG harsh environmental condition including interference from SG equipment also pose great challenges leading to degradation of communication link in CRSN based SG.
In order to mitigate these dual correlation challenges, chapter four presents a performance analysis of an MGF based M-QAM error probability over Nakagami-q dual correlated fad- ing channels with maximum ratio combiner (MRC) receiver technique including derivation and a novel algorithmic approach. The results from the MATLAB simulation experiments are provided as a guide for sensor node deployment to avoid problem of multi correlation in CRSN based SG.
A reliable communication network is required in order to actualize some important SG features, such as renewable energy integration, distributed energy resources, scalability, self-healing and efficient holistic monitoring and control capability. However, this com- munication network needs to comply with critical requirements. In order to achieve this, chapter five presents a novel communication architecture in CRSN based SG. The objective here is the design of a CRSN based smart grid communication architecture and implemen- tation model for guaranteed QoS of smart grid data delivery. The work involves virtualized network in the form of multi-homing comprising low power wide area network (LPWAN) devices such as LTE CAT1/LTE-M, and TV white space band device (TVBD). Simulation analysis show that the performance of the proposed modules architecture outperforms the legacy wireless systems in terms of latency and throughput in SG harsh environmental condition.
SGs application requires reliable and efficient communication with low latency in timely manner as well as adequate topology of sensor nodes deployment for guaranteed QoS. Also, an optimized protocol/algorithms are required for energy efficiency and cross layer spectrum aware for opportunistic spectrum access in the CRSN nodes. To this end, chapter six presents a novel energy efficient distributed heterogeneous clustered spectrum aware (EDHC-SA) model including a novel topology. In addition, novel algorithm called Equilat-
eral triangulation algorithm for guaranteed network connectivity in CRSN based SG was provided.
Finally, the work also presents a novel CSMA/CA MAC protocol algorithm for alternation of data frame transmission of both event driven and data driven CRSN nodes due to varying SG applications in order to save the network life time. The simulation results obtained confirm that EDHC-SA CRSN model outperforms conventional ZigBee WSN in terms of bit error rate, end-to-end delay (latency), and energy consumption. This thus validates the suitability of the model in SG harsh environmental condition.