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

It is crucial to use the node trust value as an essential measure to cooperate in CSS to enhance the safety of CWN. As a result, integrating the node trust value with the basic system design can improve sensing accuracy while reducing energy utilization. To avoid data ambiguity, the blockchain management center can be more efficient20. The proposed flowchart enhances the accuracy of sensing and performance of the CWN so, this method begins by estimating the system’s consistency. This estimate is based on accessible statistical information. When an accused node is identified, it generates an instantaneous decision to isolate the node’s sensing data. The method achieves the system’s resiliency but boosts energy utilization, and the effects of global variations on the node are not taken into account. The licensed user’s living conditions have an impact on node sensing. For example, when the licensed user’s location changes, nodes with strong sensing may turn mischievous in the next instant, whereas nodes with low performance become a trusted nodes. As a result, to detect modifications in node status, a real-time evaluation system for nodes must be established. When a node’s efficiency worsens, it can cease detecting work in real-time, and when it improves, it can be moved to work in real-time.

This article establishes an interpretation of nodes and an evaluation of the nodes method to determine and identify nodes more effectively. Before executing spectrum sensing procedures, the CWN determines the consistency of each node, which is based on scientific data. The original aim will continue working whenever the global environment is stable, but when the global environment changes, the node’s consistency must be re-evaluated. To prevent issues, the node’s trustworthiness level is computed using Eq. (9), and the FC creates a nodes list and transmits node data to the blockchain’s management center. The management center effectively delivers node data and is in charge of scheduling nodes to engage in cooperative sensing based on the fusion center’s needs.

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

(9)

(y_u) represents the starting trust value for the (u_{th}) node, (|L_{u,a}|) signifies the CSS in the ath cycle of sensing of the uth node, (l_{u,a}) denotes the worth value acquired in the ath cycle of sensing of the uth node. When the (l_{u,a} = 1); signifies the uth node in the ath cycle of sensing is reliable with the FC, and (l_{u,a} = 0); signifies the uth node in the ath cycle of sensing is not reliable with the FC. The evaluation and interpretation of nodes achieved by Eq. (9) are used to store the value in the blockchain management center. The steps to evaluate and interpretation of nodes is explained as:

  • Firstly, check whether the global environment has been modified, if yes, then re-evaluate the trust value of the nodes, otherwise, sensing nodes need not be modified.

  • Then, FC will establish the list of nodes’ trust values.

  • Later, the blockchain management center is accountable for managing and scheduling nodes.

  • Further, adjust the number of sensing nodes and then call the nodes whose trust value is greater than the threshold value to engage in CSS.

Efficiency return value er, energy utilization return value eu, overall return value or, co-efficient of efficiency correction (rho), coefficient of energy utilization correction ec, and overall correction coefficient oc are the three return values and three correction coefficients set. These are computed in the given Eq. (10) for the Efficiency return value er:

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

(10)

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

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

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

(11)

The representation for computation of energy utilization eu is shown in Eq. (12)

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

(12)

where (E_W) represents the worth energy utilization which says that the node used for energy utilization is lower than the threshold value; (E_P) represents the punishable energy utilization which says that the node used for energy utilization is higher than the threshold value. (Z_a) is the energy return value for the weighted coefficient and its value is denoted in Eq. (13) as: (Z_a = 1), (tau _0), (sum ^{I_v}_{a,u}) = 0

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

(13)

where (tau _0) represents the threshold of energy utilization in a sensing duration. The overall return value for energy utilization is computed in Eq. (14) as:

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

(14)

This equation describes that 30% of the weight is assigned for the energy utilization return value and the rest 70% of the weight is assigned for the efficiency return value. Thereby, authors have focused on the sensing efficiency while taking into consideration the minimizing of energy utilization. The equation for calculating coefficient of coefficient correction (rho) is:

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

(15)

The total number of repetitions the uth node communicates incorrect information to the FC is represented by the correction coefficient (rho _u); (mu _a),u shows that in the ath sensing cycle, the outcome provided by the uth node to the fusion center; (beta _a) reflects the outcome of the ath sensing cycle’s decision. The energy utilization correction coefficient is denoted by ec is computed in the Eq. (16).

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

(16)

The total number of repetitions the uth node increases the value of threshold is represented by the energy utilization; the Eq. (17) shows the significance 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 < 0). (tau _0) means the threshold value has been raised for energy utilization and its value is computed where I is the total number of nodes present in the CWN; and (v_{a,u}) tells that the energy utilized for the uth node in the ath sensing cycle is shown in Eq. (17).

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

(17)

The predefined value for the overall correction coefficient oc is shown in Eq. (18).

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

(18)

(oc_u) is the overall correction coefficient for the uth node which has been acquired by the total weighted count of the efficient and energy utilization correction coefficient. The efficiency has been evaluated by 30% and 70% for the efficient and energy utilization correction coefficient respectively. The trust value of the nodes are computed as shown in Eq. (19).

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

(19)

In the above Eq. (19), (y_u^r) shows the nodes trust value in the ath sensing cycle for the uth node; (y_u^{r+1}) shows the present nodes trust value for the (u_{th}) node; (oc_u^r) is the overall return value of the sensing cycle; the (varphi) denotes the value either 1 or 0. More the value of (varphi) gives better efficiency for energy utilization. The flowchart for the evaluation and interpretation of nodes is shown in Fig. 4.

Figure 4

Flowchart for the 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 the IoT devices shows that this article consists of the blockchain system, CWN’s and IoT devices. The FC is where users interact with the blockchain system. The IoT device provides node data to the FC, which searches the blockchain system for node data. The node then transmits verified by the private key to the FC, which validates if the sensing node has a matching private key pair. If that’s the case, send the node’s request to the blockchain system, and the blockchain system’s confirmation to the sensing node. The data verified by the sensing node can verify the identity of those taking part in CSS and guarantee that their message has not been tampered with. The steps to follow for the designing of CWN are:

  • Firstly, check the sensing nodes in the CWN. It transmits the information of nodes and then requests for the identification of encryption to the FC.

  • Secondly, examine the verification request to the blockchain management center.

  • Thirdly, the blockchain management center returns the verification information to the FC and then returns the encrypted data to the sensing nodes of CWN.