Cell-free massive multiple-input multiple-output (CF-mMIMO) is a key enabler for sixth-generation (6G) wireless systems, offering enhanced spectral efficiency and ubiquitous coverage. In such systems, the association of user equipments (UEs) to access points (APs) is a critical challenge, as it directly impacts scalability, interference suppression, and overall system performance. Conventional user association (UA) methods optimize communication throughput but overlook emerging 6G requirements from sensing and integrated sensing and communication (ISAC) applications. To address this, we propose a scalable user association (SUA) scheme for CF-mMIMO networks that explicitly considers heterogeneous UE service needs, including communication, sensing, and ISAC. The proposed SUA scheme integrates AP masking, link prioritization, and optimization-driven AP selection to balance system load and enhance service quality. Simulation results demonstrate that the proposed approach significantly reduces interference and computational runtime, while improving symbol error rate for communication UEs and probability of detection for sensing UEs.

The Internet of Things (IoT) aims to interconnect billions of heterogeneous devices across diverse applications. To ensure interoperability, OM2M stands out as a popular open-source middleware platform for integrating heterogeneous IoT devices; however, its deployment complexity and limited documentation pose significant barriers to broader adoption. Existing implementations of OM2M often rely on centralized architectures, which can lead to a single point of failure, compromising the IoT system's resilience and scalability. In this paper, we address this challenge by proposing a lightweight, automated approach to install and configure OM2M that simplifies both the deployment process and end-node interactions. Our solution allows for a decentralized implementation of OM2M, mitigating the risks associated with centralized configurations. The proposed solution uses Docker-based containerization compatible across IoT devices with various central processing unit (CPU) architectures. Additionally, we provide a Python-based abstraction, implemented in both CPython and MicroPython, which streamlines the creation, retrieval, and management of oneM2M resources on an OM2M common service entity (CSE). Implementation results show that our containerized approach reduces CPU usage, power consumption, and memory usage compared to the bash-script-based method. These improvements highlight its efficiency in resource-constrained IoT scenarios. The proposed solution is available open-source on GitHub to encourage broader adoption, collaboration, and long-term viability within the IoT community.

The Internet of Things (IoT) has become a foundation of Industry 4.0 with numerous industrial facilities relying on smart sensors and actuators to optimize the efficiency of processes. Nevertheless, as IoT networks are adopted in industrial facilities, several security concerns arise. In industrial settings, IoT networks often involve the sharing of resources and sensitive data among a diverse set of users with different roles, increasing the potential for security breaches. Given the critical nature of industrial IoT networks, it is of high importance that only authorized users with pre-defined roles can access and interact with these systems. In this paper, we explore the potential of the blockchain technology to address this access control challenge for sustainable and resilient industrial IoT networks. Specifically, we propose a Hyperledger Fabric-based role-based access control (HF-RBAC) framework tailored for direct access control to IoT devices in industrial facilities. The proposed HF-RBAC solution leverages role-based access control principles to safeguard against outsiders and unauthorized insiders, mitigates single points of failure and ensuring continuous operation even under the Denial of Service (DoS) attack on the industrial IoT network leader.

Sensor nodes’ localization in wireless Internet of Things (IoT) sensor networks is crucial for the effective operation of diverse applications, such as smart cities and smart agriculture. Existing sensor nodes’ localization approaches heavily rely on anchor nodes within wireless sensor networks (WSNs). Anchor nodes are sensor nodes equipped with global positioning system (GPS) receivers and thus, have known locations. These anchor nodes operate as references to localize other sensor nodes. However, the presence of anchor nodes may not always be feasible in real-world IoT scenarios. Additionally, localization accuracy can be compromised by fluctuations in received signal strength indicator (RSSI), particularly under non-line-of-sight (NLOS) conditions. To address these challenges, we propose UBiGTLoc, a Unified bidirectional long-short-term memory (BiLSTM)-Graph Transformer Localization framework. The proposed UBiGTLoc framework effectively localizes sensor nodes in both anchor-free and anchor-presence WSNs. The framework leverages BiLSTM networks to capture temporal variations in RSSI data and employs Graph Transformer layers to model spatial relationships between sensor nodes. Extensive simulations demonstrate that UBiGTLoc consistently outperforms existing methods and provides robust localization across both dense and sparse WSNs while relying solely on cost-effective RSSI data.

Information-centric Internet of Things (IoT) sensor networks allow users to access data directly from the sensing layer. This is done through cluster heads (CHs), which are selected as a result of IoT nodes’ clustering. To respond to users’ data requests, CHs aggregate, encrypt, and store locally sensed data in rounds. For data encryption, security resources are allocated to sensor nodes every round. To satisfy user security needs, very often, security resources are overutilized leading to higher energy consumption and shorter network lifetime. Meanwhile, sensor nodes’ and users’ mobility may result in link failures. Therefore, efficient clustering and security resource allocation is required to ensure users’ data and security needs are satisfied while optimizing network resource utilization. Graph convolution networks (GCNs) can help to address this challenge. GCNs perform learning on graphs while considering non-Euclidean nodes’ relations and features. Using GCNs, user awareness, and IoT nodes’ features can be incorporated into the cluster-based management of mobile information-centric IoT sensor networks. Therefore, this article proposes user-aware clustering with security resource allocation (USRA) using GCNs. In USRA, the proposed clustering algorithm improves communication reliability by optimizing users’ and nodes’ coverage. Meanwhile, the proposed security resource allocation plan prevents overutilization of security resources by considering user security needs in each cluster. Compared to existing works, USRA achieves lower energy consumption on security while ensuring high user security satisfaction. This promotes a longer network lifetime. USRA further contributes to higher communication reliability and throughput with stable data delivery latency to users.

In heterogeneous cluster-based information-centric wireless sensor networks (ICWSNs), sensor nodes acquire different application-specific data. They are clustered based on proximity, where cluster heads (CHs) act as cache nodes. Meanwhile, grouping sensor nodes based on the information type gathered can improve the ICWSN performance. Motivated by the heterogeneous nature of ICWSNs, application-specific communities can be formed within each cluster, where community leaders (CLs) can be selected to cache application-specific data. In this case, CHs gather aggregated data from CLs rather than basic sensing nodes. However, this creates an issue of the energy–latency and security tradeoff and affects user security satisfaction. In this work, we propose solving this issue by studying cluster-based ICWSNs with heterogeneous communities and comparing them to conventional heterogeneous cluster-based ICWSNs. Based on the formulated analytical model, we then propose SLAC-H, a security-level-aware CHs’ and CLs’ selection algorithm for cluster-based ICWSNs with heterogeneous communities. SLAC-H addresses the energy–latency and security issue by optimizing energy and coverage supported by sensor nodes in a cluster-based ICWSN with heterogeneous communities subject to security constraints. Simulation results show that compared to existing works, SLAC-H achieves lower latency and energy consumption while fulfilling higher user security satisfaction.

In cluster-based information-centric wireless sensor networks (ICWSNs), mobile sensor nodes are grouped into clusters in rounds. In each cluster, a cluster head (CH) is selected, which collects, aggregates, and forwards locally sensed data to a sink node. CHs further store a copy of data for the round period to act as cache nodes and deliver data to mobile users upon requests. Nevertheless, clustering and securing mobile ICWSNs are challenging. This is because, in addition to sensor nodes’ and users’ mobility, sensor nodes are often resource constrained. Therefore, clustering and security resource allocation in mobile ICWSNs should be carefully redesigned to ensure efficient ICWSN operation, data security, and timely data access to mobile users. This article proposes a node embedding with security resource allocation (NESRA) clustering algorithm for mobile ICWSNs in rounds. NESRA allocates security resources to sensor nodes based on the location, mobility, and energy resources available in the first step. An optimization problem is formulated to select CHs that maximize network coverage and minimize data delivery delay to mobile users in the second step. In the third step, NESRA utilizes network representation learning that embeds sensor nodes’ location, mobility, and expected energy expenditure features into a 2-D space to form well-separated clusters of sensing nodes. Compared to existing works, NESRA achieves lower energy consumption, nodes’ death rate, and latency and allows higher throughput and cache nodes’ utilization with stable data security. Still, NESRA has some challenges to overcome in high-mobility networks.

Nodes in wireless Internet of Things (IoT) sensor networks are heterogeneous in nature. This heterogeneity can come from energy and security resources available at the node level. Besides, these resources are usually limited. Efficient cluster head (CH) selection in rounds is the key to preserving energy resources of sensor nodes. However, energy and security resources are contradictory to one another. Therefore, it is challenging to ensure CH selection with appropriate security resources without decreasing energy efficiency. Coverage and energy optimization subject to a required security level can form a solution to the aforementioned trade-off. This paper proposes a security level aware CH selection algorithm in wireless sensor networks for IoT. The proposed method considers energy and security level updates for nodes and coverage provided by associated CHs. The proposed method performs CH selection in rounds and in a centralized parallel processing way, making it applicable to the IoT scenario. The proposed algorithm is compared to existing traditional and emerging CH selection algorithms that apply security mechanisms in terms of energy and security efficiencies.

Multi-band cooperative spectrum sensing can provide access to a wide range of spectrum in cognitive radio networks (CRNs). The design of multi-band spectrum sensing is very challenging mainly due to scheduling of secondary users (SUs) to sense a subset of channels. In this paper, we propose a distributed learning-based multi-band multi-user cooperative spectrum sensing (M2CSS) scheme to select most appropriate SUs to sense channels. The proposed scheme allows SUs to sense multiple channels, and consists of two stages: 1) leader selection for each channel, and 2) selection of corresponding cooperative SUs to sense these channels. We formulate an optimization problem to select leaders that can effectively communicate with other SUs subject to the constraint that each SU can act as a leader for only one channel, and there will be only one leader for each channel. We then formulate another optimization problem to select corresponding cooperative SUs for each channel. After this stage, selected cooperative SUs sense channels, and use consensus learning to determine the availability of channels in a distributed manner. Simulation results show that the proposed M2CSS scheme can enhance detection performance, avoid the choice of redundant cooperative SUs, owning similar sensed information, and provide fair energy consumption for all channels compared to the existing schemes.

In this paper, we consider an overlay cognitive radio (CR) scenario where the primary transmitter (PT) and the primary receiver (PR) communicate via the help of a secondary users' (SUs) system. Under a worst-case scenario, we assume that the link between the primary users (PUs) is broken and the help of a selected secondary transmitter (ST) is required. Taking advantage of this opportunity, this ST will be able to transmit its own data. The communications of the PUs and the SUs take place over two phases. In the first phase, receive space shift keying (R-SSK) is employed at the PT in order to activate one ST for reception. This ST is scheduled to transmit its own data during the second phase using conventional SSK, which also allows the PR to decode the PT's message. The proposed scheduling scheme is initiated by the PT based on its incoming bits which provides fairness among STs. The proposed system comes with other advantages including the low receivers' complexity and the improved energy efficiency (EE) all gained by the use of SSK. We analyze the performance of the proposed scheme in terms of the average bit error probability (ABEP). We finally provide comparisons to existing schemes and we generate numerical results through which we confirm the derived analysis and we demonstrate the effectiveness of the proposed overlay cognitive scheduling scheme.