This paper explores the utilization of Reconfigurable Intelligent Surfaces (RISs) within multi-cell vehicular open radio access networks to redirect signals toward users with wireless link blockages. To address this, a stochastic optimization problem is formulated to determine the optimal transmit precoding for Road Side Units (RSUs) and adjust the phase shifts of corresponding RISs, considering random obstacles in wireless links. The objective is to enhance long-term data throughput while guaranteeing the Quality of Service (QoS) for each user. To solve the problem, a federated learning framework is introduced that employs multi-agent Deep Reinforcement Learning (DRL) and aligns with the O-RAN architecture. Specifically, deep learning agents are deployed at network edge servers, integrated within the Near-Real-Time Radio Access Network Intelligent Controller (Near-RT RIC), where they gather network information, train local models, and perform online executions. A global model is constructed in the Non-Real-Time RIC, which resides on a central server, by aggregating the local models received from edge servers. Simulation results confirm that the proposed method significantly improves average network data rates while ensuring users receive adequate link quality.

The evolution of 6G networks has led to the development of open, intelligent, and decentralized architectures. Nevertheless, protecting data privacy and security across systems with multiple vendors remains a critical challenge. Although Secure Multi-Party Computation (SMPC) and the Open Radio Access Network (O-RAN) have emerged as promising candidates, existing articles have evaluated their performance in isolation. It implies that their combined impact on system performance has not yet been explored. Consequently, this article presents an integrated, deployment-oriented synthesis that maps lightweight and scalable SMPC protocols to O-RAN components such as the RAN Intelligent Controller (RIC), xApps, and open interfaces. Particularly, this article identifies four key research challenges: SMPC protocol scalability, architectural integration, secure AI optimization, and multi-vendor data confidentiality. To address these identified challenges, a comprehensive performance evaluation of state-of-the-art SMPC schemes is performed. The evaluation focuses on latency, bandwidth, and computational overhead of SMPC schemes in 6G-enabled O-RAN systems. Thus, by linking SMPC techniques with O-RAN architectures, this article provides practical insights and quantitative evidence to support the development of secure, efficient, and interoperable 6G wireless technologies.

OpenRAN is revolutionizing wireless telecommunications, enabling more flexible and innovative network architectures. Within this framework, near-real-time applications in RAN Intelligent Controllers (near-RT RIC) are pushing the boundaries of ultra-reliable low latency communications. However, security concerns challenge their adoption. This paper investigates vulnerabilities in near-RT RIC AI xApps through systematic experiments, focusing on a Handover AI xApp. Using four distinct attack strategies, we demonstrate that current security measures are inadequate, exposing these Ultra-Reliable Low Latency Communications (URLLC) AI xApps to various attacks. Our findings highlight the potential for malicious exploitation, emphasizing the need for robust security frameworks in OpenRAN deployments utilizing near-RT applications.

The contemporary era experiences an unprecedented dependence on data generated by individuals via an array of interconnected devices constituting the Internet of Things (IoT). The information amassed through IoT devices serves many objectives, including prescriptive analytics and predictive maintenance, preemptive healthcare measures, disaster mitigation, operational efficiency, and increased yield. In contrast, most applications or systems that rely on user-generated data to fulfill their business objectives face challenges in adhering to privacy protocols. Consequently, users are exposed to many privacy risks. Such infringements upon privacy provisions give rise to apprehensions regarding the authenticity of the processed data. Hence, this paper presents weaknesses and challenges in current practices and proposes “PrivySeC,” a distributed ledger technology (DLT) based framework for privacy-preserving and secure sharing of personally and non-personally identifiable information. The security analysis indicates that the proposed solution ensures data privacy by design and complies with most of the requirements mandated by various privacy regulations. Similarly, PrivySeC promises low transaction latency and provides high throughput. Although we have created a privacy-preserving solution for sharing smart farm data, it can be customized to meet the specific privacy requirements of individual applications.

The growth of wireless communications facilitates advanced technologies like Tactile Internet and robotics, requiring ultra-reliable low-latency communications (URLLC). Effective user equipment (UE) handover between access points (AP) is crucial for URRLC to enhance Quality of Experience (QoE). However, HO failures can occur due to various reasons, impacting URRLC use cases reliant on service reliability. This paper introduces HORLA, a multi-objective reinforcement learning model that enhances received signal strength and reduces outage probability during AP selection for UE. HORLA surpasses the conventional HO algorithm, reducing failure attempts by over 40% and cutting energy consumption for reattempted HO requests by nearly 57%.

The reliability of any IoT system's operation depends upon the accuracy of sensor data. Numerous sensing devices are also embedded in autonomous systems. The adversary can manipulate the output of an IoT device, such as a speed sensor or a temperature sensor, by altering its hardware, software modules, network parameters, or device configuration. These unauthorized changes may affect the legitimate operation of the autonomous system and cause a malfunction or a safety hazard. In addition, the corrupt sensor data may also affect the machine learning models by introducing false training data or biasing the model towards certain decisions to cause the autonomous system to make incorrect decisions. The existing techniques mostly perform memory attestation or ensure the secure execution of an application. In addition, current approaches have unrealistic assumptions about adversaries’ capabilities and rely on trusted parties to initiate and run the attestation protocol. No existing technique provides all the required security features, including protection against return-oriented programming and network attacks, including interference, rainbow, and physical compromise. Hence, this research presents a secure and efficient version of a unique hybrid attestation scheme "I2Map" that detects a malicious or malfunctioning IoT device based on an integrity map. The performance analysis infers that I2Map performs better in transaction commit time and transaction costs with more device parameters than its predecessor.

Industrial Internet of Things (IIoT) applications generate data in varying amounts with diverse quality of service requirements. The adaptive network access approach and distributed resource management in IIoT networks can reduce the communication overheads caused by centralized resource management approaches. In this regard, statistical learning is a promising tool for addressing decision-making problems in a dynamic environment. This article considers uplink dominant IIoT networks in which massive devices generate delay-sensitive and delay-tolerant data and communicate over shared radio resources. We propose a novel grant-free access scheme using a statistical learning approach that enables IIoT entities to perform delay-sensitive and delay-tolerant transmissions over dynamically partitioned resources in a prioritized manner. In order to improve utilization of available radio resources, we design an adaptive network access mechanism operating in a semi-distributed manner. This mechanism enables end devices to use their transmission history to choose between static and dynamic resource allocation-based grant-free schemes in a dynamic environment. Simulation results show that average latency and resource utilization vary in grant-free access schemes employing static and dynamic resource allocations. Thus, compared to a single transmission scheme, the proposed adaptive network access offers better channel utilization while meeting the application-specific latency bound in IIoT networks.

This article presents a multiservice pixelated stacked antenna for dual-band application at 5.2 and 5.8-GHz bands. An efficient method for designing multilayer or stacked antennas for multistandard Internet of Things (IoT) applications is proposed in this article. The proposed antenna topology is based on two different pixel shapes and offers essential aspects, such as flexibilities in the design of low-profile single-band or multiband antennas for wireless systems. The antenna design consists of two pixelated layers of radiating patch, consisting of different shaped pixels. The V-shaped binary particle swarm optimization (VBPSO) algorithm has been implemented for the optimization of pixel positions. Only by considering the predefined regions, the antenna can be designed and optimized for various design goals. In the design procedure, triangular-shaped pixels are used to form the principal radiating patch, while square-shaped pixels are implemented on the stacked parasitic patch. Measurement and simulation results are in good agreement which proves the accuracy of the design and simulation procedure. The antenna operates at 5.16–5.21 GHz and 5.79–5.86 GHz. With a compact design, the proposed antenna achieves 4.7 and 4.4-dBi gain at 5.2 and 5.8 GHz, respectively. The proposed antenna design methodology offers great potentials to be aligned with specific design requirements of wireless low-profile IoT devices.

Nonnegative matrix factorization (NMF) is a powerful tool for parameter estimation applied in numerous robotics applications, such as path planning, motion trajectory prediction, and motion intention detection. In particular, NMF has been successfully used to extract simplified and organized movement primitives from myoelectric signal (MES) for robust control of multi-degree of freedom humanoid robots. However, MES is typically contaminated by complex noise sources. The system performance often degrades due to the simplified Gaussian assumption of the noise distribution in existing NMF methods. Furthermore, most existing NMF models are unable to automatically determine the rank of the latent matrices. To address these issues, this article presents a hybrid variational Bayesian Gaussian mixture and NMF (GMNMF) model with a finite Gaussian mixture model adopted to fit the mixed noise density function of MES. In addition, the automatic relevant determination criterion is applied to automatically infer the number of movement primitives. The coordinate descent update rules for the proposed model are formulated by mean-field variational Bayesian inference. We assess the model performance on five synthetic noise distribution functions and an experimental MES dataset to perform six wrist movements. The results demonstrate that GMNMF yields low error and high robustness in extracting the movement primitives over four competitive methods for robust cybernetic control.

Utilizing the millimeter-wave (mmWave) frequency is a promising solution to meet fast-growing traffic demand over wireless networks. However, mmWave communications are sensitive to physical obstructions on signal propagation. In this paper, the reconfigurable intelligent surfaces (RISs) are investigated to overcome the limitations of mmWave communications. Particularly, an RIS is deployed to reflect the mmWave signals towards vehicular users who experience direct link blockages that may occur due to static or dynamic obstacles. To this end, a risk-averse optimization problem is designed to optimize the Base Station (BS) precoding matrix and the RIS phase shifts under stochastic link blockages. A solution approach is developed in two phases: the BS precoding optimization and the RIS phase shift control phases. In the first phase, a Decomposition and Relaxation-based Precoding Optimization (DRPO) algorithm is developed to obtain the optimal precoding matrix. In the second phase, a learning-based method is introduced to dynamically adjust the direction of reflected signals under channel uncertainty. Extensive simulations are presented to validate the efficacy of the developed algorithms. The obtained results show that the developed algorithms can ensure reliable transmissions to users in non-LoS areas and improve network performance.

In this paper, we propose a novel mobility model providing train traffic traces essential for train-to-train communication models. As the proposed mobility model works only based on trip timetables and train timetables are currently available in real-time, the produced mobility traces will be also in real-time. Additionally, as no GPS module is used in this method, our proposed model can provide a practical solution when signal from GPS or Assisted GPS is poor or unavailable such as in urban area or inside tunnels. Furthermore, as we used an energy optimization function, the proposed mobility model will provide a guidance trajectory for trains to have an energy-optimized operation. We also develop an algorithm that can determine the specifications of contacts between trains based on the traffic traces obtained from the mobility model. Such specifications includes duration, rate and location of train contacts used for estimation of data exchange capacity between trains through train-to-train communications. We validate our proposed model using data collected from Sydney Trains of Australia. The results obtained from our proposed model show over 98 percent accuracy in comparison with the real data collected via a GPS module from Sydney Trains.

Future Internet of Things (IoT) devices are expected to be fully ubiquitous. To achieve this vision, a new generation of IoT devices needs to be developed, which can operate autonomously. To achieve autonomy, IoT devices must be completely wireless, both in terms of transmission and power. Further, accurate sensing is another crucial parameter of autonomy. Several wireless standards have been developed for improving the efficiency of IoT applications. However, the powering of IoT devices, sensor accuracy, and efficiency of electronic devices are open research problems in literature. With the advent of metamaterial perfect absorbers (MPAs), electromagnetic waves can be used as a source of energy, to enable sensing of the phenomenon and as a carrier for exchanging data. In this article, an extensive application-based investigation has been conducted on design principles and various methods of enhancing MPA characteristics. Moreover, the current applications that benefit from MPA, such as absorption of undesired frequencies, optical switching, energy harvesting, and sensing, are investigated. Finally, some implemented examples of MPA in industrial applications are provided along with possible directions for future work and open research areas.

The existing lottery-based consensus algorithms, such as Proof-of-Work, and Proof-of-Stake, are mostly used for blockchain-based financial technology applications. Similarly, the Byzantine Fault Tolerance algorithms do provide consensus finality, yet they are either communications intensive, vulnerable to Denial-of-Service attacks, poorly scalable, or have a low faulty node tolerance level. Moreover, these algorithms are not designed for the Internet of Things systems that require near-real-time transaction confirmation, maximum fault tolerance, and appropriate transaction validation rules. Hence, we propose "Pledge," a unique Proof-of-Honesty based consensus protocol to reduce the possibility of malicious behavior during blockchain consensus. Pledge also introduces the Internet of Things centric transaction validation rules. Initial experimentation shows that Pledge is economical and secure with low communications complexity and low latency in transaction confirmation.

Mission-critical machine type communication (MC-MTC) systems in which machines communicate to perform various tasks such as coordination, sensing, and actuation, require stringent requirements of ultra-reliable and low latency communications (URLLC). Edge computing being an integral part of future wireless networks, provides services that support URLLC applications. In this paper, we use the edge computing approach and present a statistical learning-based dynamic retransmission mechanism. The proposed approach meets the desired latency-reliability criterion in MC-MTC networks employing framed ALOHA. The maximum number of retransmissions Nr under a given latency-reliability constraint is learned statistically by the devices from the history of their previous transmissions and shared with the base station. Simulations are performed in MATLAB to evaluate a framed-ALOHA system's performance in which an active device can have only one successful transmission in one round composed of (Nr + 1) frames, and the performance is compared with the diversity transmission-based framed-ALOHA.

Frost damage in broadacre cropping and horticulture (including viticulture) results in substantial economic losses to producers and may also disrupt associated product value chains. Frost risk windows are changing in timing, frequency, and duration. Faced with the increasing cost of mitigation infrastructure and competition for resources (e.g., water and energy), multiperil insurance, and the need for supply chain certainty, producers are under pressure to innovate in order to manage and mitigate risk. Frost protection systems are cyber-physical systems (CPSs) consisting of sensors (event detection), intelligence (prediction), and actuators (active protection methods). The Internet-of-Things communication protocols joining the CPS components are also evaluated. In this context, this article introduces and reviews existing methods of frost management. This article focuses on active protection methods because of their potential for real-time deployment during frost events. For integrated frost prediction and active protection systems, prediction method, sensor types, and integration architecture are assessed, research gaps are identified and future research directions proposed.

This paper introduces a new framework into future data-driven railway condition monitoring systems (RCM). For this purpose, we have proposed an edge processing unit that includes two main parts: a data classification model that classifies Internet of Things (IoT) data into maintenance-critical data (MCD) and maintenance-non-critical data (MNCD) and a data transmission unit that, based on the class of data, employs appropriate communication methods to transmit data to railway control centers. For the transmission of MNCD, we propose a travel pattern method that employs train stations as points of data offloading so that trains can deliver data as well as passengers at stations. The performance of our proposed solution is successfully validated via three various data sets under different operating conditions.

This paper presents a new Multi-hop Device-to-Device (MD2D) routing protocol, referred to as SMDRP (SDN-based Multi-hop D2D Routing Protocol), for SDN-based wireless networks. Our proposed protocol can be considered as a semi-distributed routing protocol, where an SDN controller manages and controls part of the overall MD2D routing functionality to increase scalability while enabling network operators to control and maintain the out-of-band packet forwarding network. This paper also extends prior work on the Hybrid SDN Architecture for Wireless Distributed Networks (HSAW) [1] and is adapted to the framework presented in this paper. In HSAW, since all link state information is flooded by the controller to the nodes, the network will experience scalability problem. In our approach, this problem is overcome by only passing the next hop for each active route to the mobile nodes. To investigate this, we performed a theoretical and simulation studies comparing HSAW with SMDRP. From our result, it can be seen that for larger density populated networks, SMDRP shows better scalability than HSAW. In addition, mobile nodes need less memory and energy for their communications.

Green cellular network helps to decrease environmental pollution. In contrast, massive connectivity and demand for higher data rate promise the presence of new generation of cellular system (5G) and small cell networks. Hence, expectation on increasing the number of base stations (BSs), which leads to increase in energy usage. One way to improve energy consumption is by shutting down the redundant BSs while sustaining the Quality-of-Service (QoS) for each user. In this paper, we propose a dynamic structural algorithm based on transportation problem, to switch on/off the BSs in cellular networks without compromising its coverage, and maintain the networks load by neighboring cells. We use weighted graphs to translate our problem as a transportation problem and then use linear programming to solve it. The cost of transport, turning a BS into sleep mode, is illustrated as a function of energy usage, coverage area and load on the BSs. Running the proposed method consecutively provides the maximum number of BSs whom are at sleep mode. The methodology explained in this paper reduces energy consumption to almost 40%, whereas maintaining all the existing loads in the network.

As a new research concept for both academia and industry, there are several challenges faced by the Network Function Virtualization (NFV). One such challenge is to find the optimal mapping and scheduling for the incoming service requests which is the focus of this study. This optimization has been done by maximizing the number of accepted service requests, minimizing the number of bottleneck links and the overall processing time. The resultant problem is formulated as a multi- objective optimization problem, and two novel algorithms based on genetic algorithm have been developed. Through simulations, it has been shown that the developed algorithms can converge to the near to optimal solutions and they are scalable to large networks.

In this paper, we propose an offloading scheme to transfer massive stored sensor data from rolling stock to railway data centers. We apply a delayed offloading strategy for non-critical stored data assuming that the critical data has been already separated through an appropriate edge processing task and has been sent via a real-time communication such as cellular networks. We propose train stations as potential and feasible spots for data offloading via available wireless local area networks (WLAN) such as existing WiFi network at stations. Thus, stations will not only be the places of passenger exchange but also data exchange. We develop an analytical model customized for the proposed offloading strategy in rail applications. Then we validate the performance of our model through simulation in various scenarios in Omnet. The simulation results shows an accuracy of %98.67 for the proposed analytical model with reference to the simulation results in Omnetpp. Additionally, by using our proposed scheme, we can theoretically offload up to 5.43 GB per each stopping station.

Device-to-device (D2D) communications are set to form an integral part of future 5G wireless networks. D2D communications have a number of benefits such as improving energy efficiency and spectrum utilization. Until now much of the D2D research in LTE and 5G-type network scenarios have focused on direct (one-hop) communications between two adjacent mobile devices. In this paper, we propose a new routing framework called virtual ad hoc routing protocol (VARP). This framework introduces significant advantages such as better security, lower routing overheads, and higher scalability, when compared to conventional ad hoc routing protocols. It also reduces traffic overhead in LTE networks using multi-hop D2D communications under management of a software defined networking (SDN)controller. Further, it enables the development of various types of routing protocols for different networking scenarios. To this end, a source-routing based protocol was developed on top of VARP, referred to as VARP-S. We present a detailed analytical study of routing overhead in the VARP-S protocol, as compared to overhead analysis of our previous proposed hybrid SDN architecture for wireless distributed networks (HSAW) Our results show that VARP-S, compared to HSAW, achieves higher network scalability and lower power consumption for mobile nodes.

Research on routing in a network of intelligent, lightweight, micro and nano-technology sensors deployed in or around the body, namely Body Area Network (BAN), has gained great interest in the recent years. In this paper, we present an energy efficient, thermal and power aware routing algorithm for BANs named Energy Efficient Thermal and Power Aware routing (ETPA). ETPA considers a node's temperature, energy level and received power from adjacent nodes in the cost function calculation. An optimization problem is also defined in order to minimize average temperature rise in the network. Our analysis demonstrates that ETPA can significantly decrease temperature rise and power consumption as well as providing a more efficient usage of the available resources compared to the most efficient routing protocol proposed so far in BANs, namely PRPLC. Also, ETPA has a considerably higher depletion time that guarantees a longer lasting communication among nodes.

Each node in a Body Area Network (BAN) needs to be assigned with a free IP address before it may participate in any sort of communication. This paper evaluates the performance of an IP address allocation scheme, namely Prophet allocation to be used for BANs. This allocation scheme is a fully decentralized addressing scheme which is applicable to BANs as it provides low latency, low communication overhead and low complexity. Relative theoretical analysis and simulation experiments have also been conducted to demonstrate its benefits which also represent the reason for the choice of this allocation scheme. It also solves the issues related to network partition and merger efficiently.

Infostations provide an inexpensive and high speed wireless disseminator that features discontinuous coverage by bounding many low cost, limited transmission range and high-bandwidth local wireless stations over an extended terrain. In this paper, we provide a system model in which several infostations are placed within the coverage area of a low bandwidth wide-area cellular network to form an integrated infostation-cellular network (IICN). In this model, the infostation continuously broadcasts data to its clients, while cellular network provides information to clients via explicit requests from clients. Based on this new model, we proposed a prefetching scheme that is capable of selectively prefetching information at client's local storage. From experimental results, the proposed model and technique show a reduction in number of requests made via expensive cellular network, thereby alleviating cost of wireless data access. Moreover, since the number of requests is reduced, the load on the cellular system is reduced. Further the cost of wireless data access is significantly reduced from both client and server perspectives

This paper presents a number of different route discovery strategies for on-demand routing protocols, which provide more control to each intermediate node make during the route discovery phase to make intelligent forwarding decisions. This is achieved through the idea of self-selection. In self-selecting route discovery each node independently makes route request (RREQ) forwarding decisions based upon a selection criterion or by satisfying certain conditions. The nodes which do not satisfy the selection criterion do not rebroadcast the routing packets. We implemented our self-selecting route discovery strategies over AODV using the GloMoSim network simulation package, and compared the performance with existing route discovery strategies used in AODV. Our simulation results show that a significant drop in the number of control packets can be achieved by giving each intermediate node more authority for self-selection during route discovery. Furthermore, a significant increase in throughput is achieved as the number nodes in the network is increased

The performance of direct sequence code division multiple access (DS-CDMA) sensor networks is limited by multiple access interference (MAI). The paper proposes using frequency division to reduce the MAI in a DS-CDMA sensor network. We provide theoretical characterization of the mean MAI at a given node and show that a small number of frequency channels can reduce the MAI significantly. In addition, we provide a comparison of our proposed system to systems which do not use frequency division or which employ contention based protocols. Our study found that, by using only a small number of frequency channels, our system has less channel contention, lower packet latency, higher packet delivery ratio and lower energy consumption.