In the context of 6G technology, the Internet of Everything aims to create a vast network that connects both humans and devices across multiple dimensions. The integration of smart healthcare, agriculture, transportation, and homes is incredibly appealing, as it allows people to effortlessly control their environment through touch or voice commands. Consequently, with the increase in Internet connectivity, the security risk also rises. However, the future is centered on a six-fold increase in connectivity, necessitating the development of stronger security measures to handle the rapidly expanding concept of IoT-enabled metaverse connections. Various types of attacks, often orchestrated using botnets, pose a threat to the performance of IoT-enabled networks. Detecting anomalies within these networks is crucial for safeguarding applications from potentially disastrous consequences. The voting classifier is a machine learning (ML) model known for its effectiveness as it capitalizes on the strengths of individual ML models and has the potential to improve overall predictive performance. In this research, we proposed a novel classification technique based on the DRX approach that combines the advantages of the Decision tree, Random forest, and XGBoost algorithms. This ensemble voting classifier significantly enhances the accuracy and precision of network intrusion detection systems. Our experiments were conducted using the NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets. The findings of our study show that the DRX-based technique works better than the others. It achieved a higher accuracy of 99.88% on the NSL-KDD dataset, 99.93% on the UNSW-NB15 dataset, and 99.98% on the CIC-IDS2017 dataset, outperforming the other methods. Additionally, there is a notable reduction in the false positive rates to 0.003, 0.001, and 0.00012 for the NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets.

Wireless multimedia sensor networks (WMSNs) generate a huge amount of multimedia data. Congestion is one of the most challenging open issues in WMSNs. Congestion causes low throughput, high packet loss and low energy efficiency. Congestion happens when the data carried by the network surpasses the available capacity. This article presents an energy-efficient distributed congestion control protocol (DCCP) to mitigate congestion and improve end-to-end delay. Compared to the other protocols, the DCCP protocol proposed in this article can alleviate congestion by intelligently selecting the best path. First, congestion is detected by using two congestion indicators. Second, each node aggregates the received data and builds a traffic congestion map. The traffic congestion map is used to calculate the best path. Therefore, the traffic is balanced on different routes, which reduces the end-to-end delay. Finally, a rate controller is designed to prevent congestion in the network by sending a congestion notification message to a source node. After receiving a congestion notification message, the source node immediately adjusts its transmission rate. Experimental results based on raspberry pi sensor nodes show that the proposed DCCP protocol significantly improves network performance and is superior to existing modern congestion control protocols.

The paradigm of dynamic shared access aims to provide flexible spectrum usage. Recently, Federal Communications Commission (FCC) has proposed a new dynamic spectrum management framework for the sharing of a 3.5 GHz (3550–3700 MHz) federal band, called a citizen broadband radio service (CBRS) band, which is governed by spectrum access system (SAS). It is the responsibility of SAS to manage the set of CBRS-SAS users. The set of users are classified in three tiers: incumbent access (IA) users, primary access license (PAL) users and the general authorized access (GAA) users. In this article, dynamic channel assignment algorithm for PAL and GAA users is designed with the goal of maximizing the transmission rate and minimizing the total cost of GAA users accessing PAL reserved channels. We proposed a new mathematical model based on multi-objective optimization for the selection of PAL operators and idle PAL reserved channels allocation to GAA users considering the diversity of PAL reserved channels’ attributes and the diversification of GAA users’ business needs. The proposed model is estimated and validated on various performance metrics through extensive simulations and compared with existing algorithms such as Hungarian algorithm, auction algorithm and Gale–Shapley algorithm. The proposed model results indicate that overall transmission rate, net cost and data-rate per unit cost remain the same in comparison to the classical Hungarian method and auction algorithm. However, the improved model solves the resource allocation problem approximately up to four times faster with better load management, which validates the efficiency of our model.

The SAS-CBRS framework is being tested to share the federally held spectrum with licensed users and opportunistic users to maximize the underutilized spectrum’s utility and overcome spectrum scarcity. In the SAS-CBRS framework, radio resources are assigned to the incumbent access (IA), primary access licensees (PAL), and general authorized access (GAA) users according to the given priority. The SAS-CBRS three-tier framework is different from the conventional cognitive radio networks (CRN) as it involves a central entity that acts as a server called a spectrum access system (SAS). The methods to assign the resources using the SAS are still in the research phase. Yet, no standard method is defined by the FCC for resource allocation. The current CRN methods cannot be directly applied because of the addition of the third tier and a central server. Moreover, strict rules are defined for using the 3.5 GHz spectrum band for communication. In this paper, a novel DDRQ-SAS algorithm integrated with the double auction (DA) algorithm is proposed that uses deep recurrent double Q-learning. The DDRQ-SAS is used by the SAS to hold a spectrum auction and create a spectrum pool to get information on PAL channels. PAL operators use the DA algorithm to generate the asking prices intelligently for their available idle channels and the GAA users will use the DA algorithm to intelligently bid for their preferred channels. The DDRQ-SAS-DA algorithm allows the GAA users to get the guaranteed QoS offered by the PAL operators in an auction. GAA users maintain the preference list of the PAL reserved idle channels and bid intelligently based on the available QoS. SAS completes the transaction by allocating the channels to the winning GAAs. The defined problem is also modeled using the double auction multi-winner multi-channel technique and the TDSA-PS algorithm. Numerical results show that the proposed DDRQ-SAS-DA algorithm provides up to 20% better QoS at higher loads for GAA users, generates 24% more revenue for PAL operators, and is 1.6 times more efficient in assigning 500 GAA users.

With the growing demand for delay-sensitive voice, video, and data-based applications, the challenge of efficient spectrum utilization is becoming more critical. To address the challenges of spectrum scarcity, the Federal Communications Commission (FCC) has authorized the sharing of the federally held Citizen Broadband Radio Service (CBRS) band, also known as the 3.5 GHz spectrum band, with commercial use. The 3.5 GHz spectrum band controlled by the spectrum access system (SAS) is shared with three types of users i.e., Incumbent users, Primary access licensee (PAL) users, and general authorized access (GAA) users as opportunistic users. In this paper we proposed the SAS-based channel allocation (SAS-CA) algorithm to optimize spectrum utilization and system performance, considering the potential risks of adding opportunistic GAA users. The proposed algorithm ensures low outage probability for PAL users and provides an optimized channel allocation strategy for GAA users when PAL users are inactive in PAL reserved channels. Additionally, a security module integrated with SAS enhances spectrum utilization and reduces interference levels. The algorithm’s efficiency is validated through multiple test sets, showing rapid convergence within 100 iterations. The integration of the security module results in a median spectrum utilization increase of 16 points and a notable reduction in interference levels, enhancing execution efficiency by approximately 37% compared to traditional methods. These findings demonstrate the substantial benefits of advanced allocation algorithms and robust security mechanisms in ensuring equitable access, stabilizing system performance, and optimizing spectrum utilization within the CBRS band.

Spectrum access system (SAS) is a three-tier layered spectrum sharing architecture proposed by the Federal Communications Commission (FCC) for Citizens Broadband Radio Service (CBRS) 3.5 GHz band. The available 150 MHz spectrum is dynamically shared among Incumbent Access (IA), Primary Access Licensees (PAL) and General Authorized Access (GAA) users. IA users are the highest priority federal military users, PAL users are the licensed users and the GAA users are the least priority unlicensed users. In this scenario, PAL operators are willing to give access to their idle spectrum to GAA users to generate extra revenue. SAS will ensure to protect IA users and PAL users from interference caused by lower-tier users. It is the responsibility of SAS to allocate resources to GAA users but the method to do so is left open. In this article, a novel auction algorithm based on Q-learning for dynamic spectrum access (SAS-QLA) is proposed. In SAS-QLA, multiple GAA users dynamically and intelligently bid using Q-learning to access PAL reserved idle channels. SAS will decide to allocate the channels to GAA users with maximum bidding offers. GAA users have their own quality of service (QoS) demands i.e., transmission rate, packet loss, bidding efficiency, and maintain the preference of available PAL reserved idle channels based on Q-learning considering the available QoS. The proposed scenario is also modeled as a knapsack NP-hard problem and solved using dynamic programming and distributed relaxation method. Numerical results demonstrate the effectiveness of the SAS-QLA algorithm in improving the bidding efficiency, maximizing the data rate per unit cost and spectrum utilization.