Improving energy conservation using blockchain-enabled wireless cognitive networks for smart cities

It is crucial to use the node’s trust value as an essential measure to cooperate in CSS to improve the security of CWN. Therefore, incorporating the node trust value into the basic system design can improve detection accuracy while reducing power consumption. To avoid data ambiguity, the blockchain management center can be more efficient20. The proposed flowchart improves the detection accuracy and performance of CWN. This method therefore begins by estimating the coherence of the system. This estimate is based on accessible statistical information. When an accused node is identified, it generates an instant decision to isolate the node’s detection data. The method achieves system resilience but increases energy usage, and the effects of global variations on the node are not taken into account. The living conditions of the licensed user have an impact on the detection of nodes. For example, when the location of the licensed user changes, nodes with strong detection may become malicious the next instant, while nodes with low performance become trusted nodes. Therefore, to detect node state changes, a real-time node evaluation system must be implemented. When a node’s efficiency deteriorates, it can stop detecting real-time work, and when it improves, it can be moved to work in real-time.

This article establishes an interpretation of knots and an evaluation of the knot method to more effectively determine and identify knots. Before performing spectrum detection procedures, the CWN determines the consistency of each node, which is based on scientific data. The original goal will continue to work whenever the global environment is stable, but when the global environment changes, node consistency must be re-evaluated. To avoid problems, the reliability level of the node is calculated using the equation. (9), and the FC creates a list of nodes and transmits node data to the blockchain management center. The management center efficiently provides data from the nodes and is responsible for programming the nodes to engage in cooperative sensing based on the needs of the fusion center.

$$begin{aligned} y_u = frac{sum ^m_{a=1} |L_{u,a}| * l_{u,a}}{sum ^m_{a=1} |L_{u,a}|} end{aligned}$$


(y_u) represents the starting confidence value for the (u_{th}) node, (|L_{u,a}|) means the CSS in the ath detection cycle of the youth knot, (l_{u,a}) means the market value acquired in the ath detection cycle of the youth node. When the (l_{u,a} = 1); means the youth node in the ath detection cycle is reliable with FC, and (l_{u,a} = 0); means the youth node in the ath detection cycle is not reliable with the FC. The evaluation and interpretation of knots performed by Eq. (9) are used to store the value in the blockchain management center. The steps for evaluating and interpreting nodes are explained as follows:

  • First, check if the global environment has been changed, if yes, then re-evaluate the trust value of the nodes, if not, the detection nodes do not need to be changed.

  • Then FC will list the trust values ​​of the nodes.

  • Later, the blockchain management center is responsible for node management and scheduling.

  • Also, adjust the number of detection nodes and then call the nodes whose trust value is above the threshold value to engage in CSS.

Efficiency return value uhenergy usage return value EUglobal return value Whereefficiency correction coefficient (rho)energy use correction coefficient thisand global correction coefficient of oc are the three return values ​​and the three fixed correction coefficients. These are calculated in the given equation. (10) for the Efficiency return value uh:

$$begin{aligned} er = frac{1}{m} sum ^m_{a=1} [(1-beta _a)(alpha _a * W_C + (1-alpha _F)P_C)] + beta _a(gamma _a * W_C + (1-gamma _a) * P_C) end{aligned}$$


In the above equation. (10), the value of (beta a) is either 1 or 0, indicating that if its 1 means the licensed user is in sleep mode and 0 means the licensed user is in active mode, u represents the same as above, (TOILET) means the coefficient of value and (P_C) means the illegal coefficient. In this equation, (alpha _a) and (gamma _a) are the weighted coefficients which are represented in Eq. (11).

In (H_0) (right arrow) (alpha _a) (=) 1, (beta _a) (=) 0, (H_1) (right arrow) (alpha _a) (=) 0, (beta _a) (=) 0

$$begin{aligned} In H_0 rightarrow gamma _a = 0, beta _a = 1 quad and quad In H_1 rightarrow gamma _a = 1, beta _a = 1 end{aligned}$$


The representation for the calculation of the energy use eu is shown in the equation. (12)

$$begin{aligned} eu = frac{1}{m} sum ^m_{a=1} [E_WZ_a + E_P(1-Z_a)] end{aligned}$$


where (E_W) represents the power usage value which indicates that the node used for power usage is below the threshold value; (E_P) represents the punishable energy usage which indicates that the node used for energy usage is above the threshold value. (Z_a) is the energy return value for the weighted coefficient and its value is noted in Eq. (13) as: (Z_a = 1), (tau _0), (sum ^{I_v}_{a,u}) = 0

$$begin{aligned} Z_a = 0, tau _0 – sum ^{I_v}_{a,u}


where (tau _0) represents the threshold of power usage within a detection time. The overall return value for energy usage is calculated in the equation. (14) as:

$$begin{aligned} or = 0.3er + 0.7eu end{aligned}$$


This equation describes that 30% of the weight is attributed to the energy use feedback value and the remaining 70% of the weight is attributed to the efficiency feedback value. Thus, the authors focused on detection efficiency while considering the minimization of energy usage. The coefficient correction coefficient calculation equation (rho) is:

$$begin{aligned} rho _u = sum _a (mu _{a,u} – beta _a) end{aligned}$$


The total number of repetitions of the youthe node communicates incorrect information to the FC is represented by the correction coefficient (rho _u); (mu _a),u shows that in the ath detection cycle, the result provided by the youth node at merge center; (beta _a) reflects the result of the adecision of the th detection cycle. The energy use correction coefficient is denoted ec is calculated in Eq. (16).

$$begin{aligned} ec_u = sum _a S_{a,u} * J_{a,u} end{aligned}$$


The total number of repetitions of the youthe node increases the threshold value is represented by the use of energy; eq. (17) shows the importance of (J_{a,u}). In case (J_{a,u} = 1) means (v_{a,u} – tau _0 >= 0) and (J_{a,u} = 0) means (v_{a,u} – tau _0 . (tau _0) means that the threshold value has been increased for power usage and its value is calculated where I is the total number of nodes present in the CWN; and (v_{a,u}) says that the energy used for the youth node in the ath detection cycle is illustrated in Eq. (17).

$$begin{aligned} tau _0 = frac{sum ^I_{u=1} v_{a,u}}{I} end{aligned}$$


The preset value for the global correction coefficient α is given in the equation. (18).

$$begin{aligned} oc_u = 0.3rho _u + 0.7ec_u end{aligned}$$


(oc_u) is the global correction coefficient for youth node which was acquired by the weighted total number of the efficiency and energy use correction coefficient. The efficiency was rated at 30% and 70% for the efficiency and energy utilization correction coefficient, respectively. The trust value of the nodes is calculated as shown in the equation. (19).

$$begin{aligned} y_u^{r+1} = y_u^r + (omega oc – (1-varphi ) oc_u^r) y_u^r end{aligned}$$


In the above equation. (19), (y_u^r) displays the trust value of the nodes in the ath detection cycle for the youth node; (y_u^{r+1}) displays the current trust value of the nodes for the (u_{th}) node; (oc_u^r) is the overall return value of the detection cycle; the (varphi) denotes the value 1 or 0. The greater the value of (varphi) gives better efficiency for the use of energy. The flowchart for the evaluation and interpretation of nodes is shown in Fig. 4.

Figure 4

Flowchart for evaluation and interpretation of nodes.

The complexity of the proposed flowchart is O(I!) where O is denoted Big O Notation, I is the total number of nodes in the trust value. In the design flowchart, the main difficulty of blockchain-enabled CWN among IoT devices shows that this article includes blockchain system, CWNs and IoT devices. The FC is where users interact with the blockchain system. The IoT device provides node data to the FC, which looks up the node data in the blockchain system. The node then passes verified by the private key to the FC, which validates whether the detecting node has a matching private key pair. If so, send the request from the node to the blockchain system and the confirmation from the blockchain system to the detection node. The data verified by the detection node can verify the identity of CSS participants and ensure that their message has not been tampered with. The steps to follow for the design of CWN are:

  • First, check the detection nodes in the CWN. It passes the node information and then requests the cipher ID from the FC.

  • Second, review the verification request at the blockchain management center.

  • Third, the blockchain management center sends the verification information back to the FC and then sends the encrypted data back to CWN’s detection nodes.

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