Since the introduction of the Internet of Things (IoT), several researchers have been exploring its productivity to utilize and organize the spectrum assets. Cognitive radio (CR) technology is characterized as the best aspirant for wireless communications to augment IoT competencies. In the CR networks, secondary users (SUs) opportunistically get access to the primary users (PUs) spectrum through spectrum sensing. The multipath issues in the wireless channel can fluster the sensing ability of the individual SUs. Therefore, several cooperative SUs are engaged in cooperative spectrum sensing (CSS) to ensure reliable sensing results. In CSS, security is still a major concern for the researchers to safeguard the fusion center (FC) against abnormal sensing reports initiated by the malicious users (MUs). In this paper, butterfly optimization algorithm (BOA)-based soft decision method is proposed to find an optimized weighting coefficient vector correlated to the SUs sensing notifications. The coefficient vector is utilized in the soft decision rule at the FC before making any global decision. The effectiveness of the proposed scheme is compared for a variety of parameters with existing schemes through simulation results. The results confirmed the supremacy of the proposed BOA scheme in both the normal SUs’ environment and when lower and higher SNRs information is carried by the different categories of MUs.
Exponential growth in mobile devices and rising demand of data rates pose challenges to mobile network operators [
The researchers argue that in IoT, along with the connectivity, the objects should have the cognitive capability to learn and understand the environment by themselves. This entails the need to develop a new paradigm, named cognitive Internet of Things (CIoT) to empower the current IoT with Intelligence [
In the CRN, secondary users (SUs) perform spectrum sensing to dynamically access the primary users’ (PUs) channel when the PU is not active [
Individual SUs in CSS, report from distinct geographical locations observe distinct Rayleigh fading effects. Therefore, it is unfair to weight their sensing reports equally at the FC. Contrary to above mentioned studies who dealt the normal and MUs statistics in a similar manner, this work allocates weights to the sensing data considering the reliability of SU by utilizing butterfly optimization algorithm (BOA). The BOA is considered effective among the various optimization classes to best estimate the PU activity. The BOA is a nature-inspired global optimization algorithm that was inspired by the behavior of butterflies to find food/mating partners using their senses, sight, taste, and smell. The main contributions of this paper itemized as follows:
A centralized CSS is inspected in the normal users and MUs environment when sensing data is reposted to the FC. The working norms of the MUs’ considered in the proposed work is the yes always (YA), no always (NA), and opposite always (OA), and opposite random (OR). The BOA governs the optimum weighting coefficient vector for the normal SUs and various classes of MUs’ along with a dynamic (adaptive) threshold in contrast to the static threshold adjustment schemes investigated in [ The coefficient vectors determined by the proposed BOA-based SDF scheme are further employed in the SDF scheme at the FC to get concluding remarks of the channel availability. In this the received sensing reports of the normal and MUs are adjusted with the help of the optimum coefficient vector and matched with the BOA identified optimum threshold. Simulation outcomes of the error probabilities are collected at multiple variations of: (1) sensing samples; (2) population size of the optimization algorithms; (3) algorithm iterations; (4) total number of cooperative SUs. The results validated the improvement in, sensing response of the CSS with minimum sensing error, high detection, and low false alarms for the proposed BOA-based SDF scheme in comparison with the MGC-SDF, PSO-SDF, and GA-SDF schemes.
The rest of the paper is organized as follows. In Section 2, the system model of the paper is presented. In Section 3, the BOA-based SDF scheme is elaborated. Section 4 evaluates the suggested and conventional schemes through simulations. Finally, conclusion is furnished in Section 5.
Notation | Explanation |
---|---|
IoT | Internet of things |
CIoT | Cognitive internet of things |
SDR | Software defined radio |
CR | Cognitive radio |
CRN | Cognitive radio network |
PU, SU | Primary user, secondary user |
MU | Malicious user |
PUEA | Primary user emulation attacker |
CSS | Cooperative spectrum sensing |
FC | Fusion center |
HDF, SDF | Hard decision fusion, soft decision fusion |
MGC | Maximum gain combination |
EGC | Equal gain combination |
BOA | Butterfly optimization algorithm |
KL | Kullback-leibler |
PSO | Particle swarm optimization |
GA | Genetic algorithm |
YA, NA | Yes always, no always |
OA, OR | Opposite always, opposite random |
AWGN | Additive white gaussian noise |
SNR | Signal-to-noise-ratio |
For |
Energy reported by the |
End |
Initialize randomly weights |
Normalize weights |
Calculate |
For |
Investigate threshold against the |
Determine |
Estimate |
End |
Sort |
The vector at the top of |
For |
For |
Investigate threshold against the |
Determine |
Determine |
Estimate |
Calculate the fragrance of each butterfly as |
If |
Perform the global search phase to measure |
Else |
Perform the local search phase to measure |
End |
End |
Find new |
If |
Else |
End |
End iteration |
Select the optimal |
For |
End |
If |
Global Decision= |
Else |
Global Decision= |
End |
End sensing interval |
Parameters | Scenario-1 | Scenario-2 | Scenario-3 | Scenario-4 |
---|---|---|---|---|
Total users | 14 | 14 | 14 | |
Average SNRs | −13.5 dB | −11.5 dB | −11.5 dB | −11.5 dB |
Iterations | 50 | 50 | 50 | |
Sensing samples | 270 | 270 | 270 | |
Population | 30 | 20 | 20 |
The system model adopts a CSS consisting of a PU, normal SUs, MUs, and a common FC as shown in
The local sensing users in
where
In
The sensing energy conveyed to FC by the
where
In the given CSS model, the FC takes its global decision by combining soft energy reports with optimum weighting coefficient vector
where
In the above the variances of
A model of the proposed CSS scheme retaining the weighted SDF to reduce the impacts of abnormal sensing is shown in
The selection of the optimal coefficient vector
In these equations, the diagonalization practice is specified by
In
In
Nature-inspired optimization algorithms have got a high interest in various disciplines of engineering, where real-world problems are expressed as optimization problems. These optimization problems require enormous computational complexity that is difficult to solve using traditional methods [
In recent times, Arora introduced BOA as a promising metaheuristic algorithm, which is inspired by the butterfly’s food searching movements. The studies demonstrate that the BOA has superior results when compared with some other metaheuristic algorithms [
The BOA’s main strength lies in its fragrance modulating mechanism. To know fragrance modulation, it is first important to realize how any sense is processed by the stimulus of a living organism. The sensing basic concepts are dependent on three vital parameters such as power exponent (
This section illustrates the proposed BOA-based SDF scheme in the participation of the MUs’ to discover the optimum coefficient vector and threshold adjustments. The FC resolves the global decision by employing this weighted SDF approach to settle the PU channel detection based on the SUs’ sensing data.
The proposed BOA-SDF scheme consists of the following steps, while sensing the PU spectrum, such as (1) Initialization phase (2) Iteration phase (3) Final Phase [
The number of butterflies in the proposed work are fixed to
Similarly, the switching probability
The suitability of the coefficient vectors is based on
In the second phase of this algorithm,
where
Likewise, the local search phase is as follows
where
The BOA repeats step 2 if the fitness functions (
A general flowchart of the proposed BOA-based SDF scheme is shown in
In this section, we verify the performance of the proposed BOA-based SDF scheme in contrast to some other conventional schemes. The number of SUs in the CRN are set to 10 and 14 in this portion of the simulations. Out of the total SU, 4 of them are assumed as YA, NA, OA, and RO malicious. In the simulation, SNRs is kept at an average of −13.5 and −11.5 dB while determining sensing error. The 1
The proposed scheme results are evaluated in simulations and compared with the conventional PSO-SDF, GA-SDF, and MGC-SDF schemes. Furthermore, the results are distributed into 4 different scenarios as in
The graphical illustrations of scenario 1 are composed at the involvement of the normal NUs environment, lower SNRs subsidized by MU’s in CSS, and higher SNRs sensing contribution from MU’s. The average SNR values in respect of all users are kept at −13.5 dB with 270 sensing samples in each iteration. The population is 30 with total SUs varying from 10 to 22 users. In addition, the number of MUs are 4 that report sensing data along with the normally behaving users to the FC.
In
In
In scenario 2, the simulation parameters are selected with 14 cooperative users, fixed average SNRs at −11.5 dB, 20 population size and 270 sensing samples. The algorithm iterations in this case are kept changing from 50 to 110. The results in
In
In
In
In this scenario, the error probability results are shown for the increasing number of sensing samples varying in the range 270–335. Here the total number of the SUs are 14 with an average SNRs as −11.5 dB. The algorithm population size is kept as 20 with total 50 iterations. All other parameters are retained as identical to get the results in
In
In
In
This scenario discusses the results of error probability against population size ranging from 20 to 80. The SDF scheme results are collected in this case to investigate the performance by keeping the total number of cooperative users fixed at 14, average SNRs −11.5 dB, sensing iterations 50, and fixed sensing samples as 270.
In
The result in
In
The integration of CR with the IoT is anticipated to expand the devices connectivity and services in future. The CR increases spectrum utilization by getting accurate sensing information through the SUs’ cooperation. Although CSS can sense the PU spectrum opportunities more reliably, the deceitful sensing data stated by the MU’s in the CSS can essentially influence the FC decision. This paper proposed a BOA-based SDF scheme to determine a coefficient vector against SUs’, while making a global decision at the FC. The weighting coefficient vector in the proposed BOA-SDF scheme support in the PU channel identification with high detection, minimum false alarm and low error probabilities through the assignment of high weights to the normal SUs sensing in comparison with the MUs.
The simulation results at different SUs, average SNRs, and sensing samples confirm that the proposed BOA-based SDF essentially outperforms the traditional MGC-SDF, PSO-SDF and GA-SDF schemes.