Digital twins (DTs) are envisioned as a key enabler of the cyber-physical continuum in future wireless networks. However, efficient deployment and synchronization of DTs in dynamic multi-access edge computing (MEC) environments remains challenging due to time-varying communication and computational resources. This paper investigates the joint optimization of DT deployment and synchronization in dynamic MEC environments. A deep reinforcement learning (DRL) framework is proposed for adaptive DT placement and association to minimize interaction latency between physical and digital entities. To ensure semantic freshness, an update scheduling policy is further designed to minimize the long-term weighted sum of the Age of Changed Information (AoCI) and the update cost. A relative policy iteration algorithm with a threshold-based structure is developed to derive the optimal policy. Simulation results show that the proposed methods achieve lower latency, enhanced information freshness, and reduced system cost compared with benchmark schemes.

6G In-Factory Subnetworks (InF-S) have recently been introduced as short-range, low-power radio cells installed in robots and production modules to support the strict requirements of modern control systems. Information freshness, characterized by the Age of Information (AoI), is crucial to guarantee the stability and accuracy of the control loop in these systems. However, achieving strict AoI performance poses significant challenges considering the limited resources and the high dynamic environment of InF-S. In this work, we introduce a proactive radio resource allocation approach to minimize the AoI violation probability. The proposed approach adopts a de-centralized learning framework using Bayesian Ridge Regression (BRR) to predict the future AoI by actively learning the system dynamics. Based on the predicted AoI value, radio resources are proactively allocated to minimize the probability of AoI exceeding a predefined threshold, hence enhancing the reliability and accuracy of the control loop. The conducted simulation results prove the effectiveness of our proposed approach to improve the AoI performance where a reduction of 98% is achieved in the AoI violation probability compared to relevant baseline methods.

Device-to-device (D2D) communication has been introduced as an innovative approach in 5G networks to enhance network performance and coverage. D2D-assisted relay helps to further boost transmission performance where the relay nodes assist in wireless data offloading for users with compromised direct cellular links. Typically, D2D links are established between the source users and the relay nodes in a deterministic manner which is inefficient considering the dynamic nature of the wireless channel. Particularly, the selection of D2D relay is critical for time-sensitive applications where data freshness of a significant importance. In this work, we propose an intelligent relay selection mechanism to improve the Age of Information (AoI) in 5G-enabled smart grids. The problem of selecting the best relay is structured within a Multi-Arm Bandit (MAB) framework and solved using the Upper-Confidence-Bound (UCB) algorithm. Each source node compiles a set of possible relay options according to established criteria. Subsequently, the learning mechanism employed in the UCB algorithm progressively gravitates towards choosing a relay node that enhances a combined AoI metric that integrates both the average AoI and the probability of AoI violation probability. The performance of the proposed intelligent relay selection method is evaluated using extensive discrete-event simulations, and the results affirm the effectiveness of the proposed method over the deterministic relay selection method.

The integration of edge controllers into smart grid infrastructures facilitates advanced functionalities and high re-sponsiveness, thereby bolstering the overall efficiency of the energy grid. The freshness of the sensing information received at the edge controller, captured by the Age of Information (AoI) metric, is vital to maintain system stability, where outdated information may lead to incorrect responses to grid conditions, potentially causing inefficiencies or system disruptions. However, the timeliness of the transmitted updates is mainly compromised by the delay and congestion within the routing links in the Neighborhood Area Network (NAN). In this work, we develop an intelligent routing strategy to improve the AoI of the Routing Protocol for Low-power and lossy networks (RPL), which is the common routing protocol for smart grids. Our proposed method is based on an AoI-aware parent selection mechanism, by which a node becomes attached to the parent with the highest probability of delivering a packet within a predefined AoI threshold. The prediction is made based on a supervised machine learning model, trained using the collection of heterogeneous routing metrics. The performance of the proposed method is evaluated via extensive discrete-event simulations and the results show its potency to improve the peak AoI and the AoI violation probability compared to the standard RPL.

Age of Information (AoI) and delay are crucial performance metrics for Industrial Internet of Things (IIoT) applications not only to perform seamless actuation and control actions but also to enable self-organized and re-configurable manufacturing systems. A challenging task in heterogeneous IIoT networks is to minimize the AoI while maintaining a predefined delay constraint. In this work, we consider a Device-to-Device (D2D)-based heterogeneous IIoT network that supports two types of traffic flows, namely AoI-sensitive flow and delay-sensitive flow. First, we introduce a distributed backlog-aware random access protocol that allows the AoI-sensitive nodes to opportunistically access the channel based on the queue occupancy of the delay-sensitive node. Then, we develop an analytical framework to evaluate the average delay and the average AoI, and formulate an optimization problem to minimize the AoI under a given delay constraint. Finally, we provide numerical results to demonstrate the impact of different network parameters on the performance in terms of the average delay and the average AoI. We also give numerical solutions of the optimal parameters that minimize the AoI subject to a defined delay constraint.

Through the lens of the age-of-information (AoI) metric, this paper takes a fresh look into the performance of coexisting enhanced mobile broadband (eMBB) and ultra-reliable low-latency (URLLC) services in the uplink scenario. To reduce AoI, a URLLC user with stochastic packet arrivals has two options: orthogonal multiple access (OMA) with the preemption of the eMBB user (labeled as puncturing) or non-orthogonal multiple access (NOMA) with the ongoing eMBB transmission. Puncturing leads to lower average AoI at the expense of the decrease in the eMBB user's rate, as well as in signaling complexity. On the other hand, NOMA can provide a higher eMBB rate at the expense of URLLC packet loss due to interference and, thus, the degradation in AoI performance. We study under which conditions NOMA could provide an average AoI performance that is close to the one of the puncturing, while maintaining the gain in the data rate. To this end, we derive a closed-form expression for the average AoI and investigate conditions on the eMBB and URLLC distances from the base station at which the difference between the average AoI in NOMA and in puncturing is within some small gap$\beta$. Our results show that with$\beta$as small as 0.1 minislot, the eMBB rate in NOMA can be roughly 5 times higher than that of puncturing. Thus, by choosing an appropriate access scheme, both the favorable average AoI for URLLC users and the high data rate for eMBB users can be achieved.

Enabling real-time communication in Industrial Internet of Things (IIoT) networks is crucial to support autonomous, self-organized and re-configurable industrial automation for Industry 4.0 and the forthcoming Industry 5.0. In this paper, we consider a SIC-assisted real-time IIoT network, in which sensor nodes generate reports according to an event-generation probability that is specific for the monitored phenomena. The reports are delivered over a block-fading channel to a common Access Point (AP) in slotted ALOHA fashion, which leverages the imbalances in the received powers among the contending users and applies successive interference cancellation (SIC) to decode user packets from the collisions. We provide an extensive analytical treatment of the setup, deriving the Age of Information (AoI), throughput and deadline violation probability, when the AP has access to both the perfect as well as the imperfect channel-state information. We show that adopting SIC improves all the performance parameters with respect to the standard slotted ALOHA, as well as to an age-dependent access method. The analytical results agree with the simulation based ones, demonstrating that investing in the SIC capability at the receiver enables this simple access method to support timely and efficient information delivery in IIoT networks.

Age of information (AoI) and reliability are two critical metrics to support real-time applications in Industrial Internet of Things (IIoT). These metrics reflect different concepts of timely delivery of sensor information. Monitoring traffic serves to maintain fresh status updates, expressed in a low AoI, which is important for proper control and actuation actions. On the other hand, safety-critical information, e.g., emergency alarms, is generated sporadically and must be delivered with high reliability within a predefined deadline. In this work, we investigate the AoI-reliability trade-off in a real-time monitoring scenario that supports two traffic flows, namely AoI-oriented traffic and deadline-oriented traffic. Both traffic flows are transmitted to a central controller over an unreliable shared channel. We derive expressions of the average AoI for the AoI-oriented traffic and reliability, represented by Packet Loss Probability (PLP), for the deadline-oriented traffic using Discrete-Time Markov Chain (DTMC). We also conduct discrete-event simulations in MATLAB to validate the analytical results and evaluate the interaction between the two types of traffic flows. The results clearly demonstrate the tradeoff between the AoI and PLP in such heterogeneous IIoT networks and give insights on how to configure the network to achieve a target pair of AoI and PLP.

In this paper, we investigate communication performance of a system for remote health-monitoring of first responders over Terrestrial Trunked Radio (TETRA) radio links. The system features a smart garment that periodically records and sends physiological parameters of first responders to a remote agent, which processes the recordings and feeds back the health-status notifications and warnings in the form of electrotactile stimuli. The choice of TETRA as the connectivity solution is driven by its routine use by first responders, thus representing a convenient and economically-effective connectivity basis. However, the support for data communications in TETRA is limited and in practice reduced to the Short Data Service, which adversely affects the delay and failure probability of the messages exchanges in the system, as shown in the paper. Nevertheless, when the system is examined and optimized in terms of the peak Age-of-Information, a metric suitable to characterize the quasi-periodic nature of the considered monitoring process, we show that its performance becomes rather favorable, enabling timely insights into the first responders’ health status.

The innovative services empowered by the Internet of Things (IoT) require a seamless and reliable wireless infras-tructure that enables communications within heterogeneous and dynamic low-power and lossy networks (LLNs). The Routing Protocol for LLNs (RPL) was designed to meet the communication requirements of a wide range of IoT application domains. How-ever, a load balancing problem exists in RPL under heavy traffic-load scenarios, degrading the network performance in terms of delay and packet delivery. In this paper, we tackle the problem of load-balancing in RPL networks using a reinforcement-learning framework. The proposed method adopts Q-learning at each node to learn an optimal parent selection policy based on the dynamic network conditions. Each node maintains the routing information of its neighbours as Q-values that represent a composite routing cost as a function of the congestion level, the link-quality and the hop-distance. The Q-values are updated continuously exploiting the existing RPL signalling mechanism. The performance of the proposed approach is evaluated through extensive simulations and compared with the existing work to demonstrate its effectiveness. The results show that the proposed method substantially improves network performance in terms of packet delivery and average delay with a marginal increase in the signalling frequency.

From the perspective of the emerging Industrial Internet of Things (IIoT), the 6TiSCH working group has been created with the main goal to integrate the capabilities of the IEEE 802.15.4e time-slotted channel hopping (TSCH) with the IPv6 protocol stack. In order to support time-critical applications in IIoT, reliable real-time communication is a key requirement. Specifically, aperiodic critical traffic, such as emergency alarms, must be reliably delivered to the destination-oriented directed acyclic graph root within strict deadline bounds to avoid system failure or safety-critical situations. Currently, there is no mechanism defined in the 6TiSCH architecture for timely and reliably handling of such traffic and its prioritization over the noncritical one. In this article, we introduce REA-6TiSCH, a reliable emergency-aware communication scheme to support real-time communications of emergency alarms in 6TiSCH networks. In REA-6TiSCH, the aperiodic emergency traffic is opportunistically enabled to hijack transmission cells preassigned for the regular periodic traffic in the TSCH schedule. Moreover, we introduce a distributed optimization scheme to improve the probability that an emergency flow is delivered successfully within its deadline bound. To the best of our knowledge, this is the first approach to incorporate emergency alarms in 6TiSCH networks. We evaluate the performance of REA-6TiSCH through extensive simulations and the results show the effectiveness of our proposed method in handling emergency traffic compared to the Orchestra scheme. Additionally, we discuss the applicability of REA-6TiSCH and provide guidelines for real implementation in 6TiSCH networks.

The Routing Protocol for Low power and lossy network (RPL) is designed to support communication requirements in 6TiSCH networks in Industrial Internet of Things (IIoT) applications. RPL is mostly optimized for uplink communication, however, less attention is given to maintain connectivity for downlink communications. Supporting downlink communications is non-trivial task in process automation and control scenarios within the IIoT. RPL in its current definition is inefficient to support reliable downlink communications in terms of scalability and memory requirements leading to significant degradation in network performance. This paper introduces HyS-R, a Hybrid Subscription-Recovery method to maintain downlink connectivity and mitigate memory limitations in large-scale 6TiSCH networks. The proposed method is based on a relief group that is used as alternative route to unreachable destinations in the network. An intermediate node subscribes to the relief group when it fails to advertise a destination to its next-hop node. In addition, members of the relief group keep searching for alternative forwarders to keep the communication traffic to a minimum. Performance evaluations are carried out and the results demonstrate that the proposed HyS-R attains significant improvements in downlink communications compared to RPL storing and non-storing modes with a margin of energy cost.

In the context of Industrial Internet of Things (IIoT), the 6TiSCH working group has been created with the aim to enable IPv6 over the IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) mode. The Routing Protocol for Low power and lossy networks (RPL) is introduced as the de-facto routing protocol for 6TiSCH networks. However, RPL is primarily designed to handle moderate traffic loads, whereas, during specific events in industrial applications, high traffic rates cause congestion problems at particular intermediate nodes while other nodes are underutilized. Accordingly, packets are dropped due to buffer overflow, which in turn degrades the network performance in terms of packet loss and delay. In this paper, we introduce a congestion detection and control mechanism to reliably handle high traffic load in 6TiSCH networks. The proposed method comprises two parent selection mechanisms to adapt to dynamic traffic load in the network. Congestion is detected through monitoring of the queue backlog level of each node and new parent nodes are selected accordingly to balance the load in the network. Moreover, a new routing metric is defined that considers the queue occupancy while selecting the new parent node. Performance evaluations are carried out to prove the effectiveness of the proposed method and the results show that with a marginal increase in the average delay, our proposal improves the performance of the standard RPL under heavy traffic load conditions by at least 60% and 74% in terms of the packet delivery and queue loss, respectively.

A fundamental challenge in Mission-Critical Internet of Things (MC-IoT) is to provide reliable and timely delivery of the unpredictable critical traffic. In this paper, we propose an efficient prioritized Medium Access Control (MAC) protocol for Wireless Sensor Networks (WSNs) in MC-IoT control applications. The proposed protocol utilizes a random Clear Channel Assessment (CCA)-based channel access mechanism to handle the simultaneous transmissions of critical data and to reduce the collision probability between the contending nodes, which in turn decreases the transmission latency. We develop a Discrete-Time Markov Chain (DTMC) model to evaluate the performance of the proposed protocol analytically in terms of the expected delay and throughput. The obtained results show that the proposed protocol can enhance the performance of the WirelessHART standard by 80% and 190% in terms of latency and throughput, respectively along with better transmission reliability.

Priority-oriented packet transmission (PPT) has been a promising solution for transmitting time-critical packets in timely manner during emergency scenarios in Internet of Things (IoT). In this paper, we develop two associated discrete time Markov chain (DTMC) models to analyze performance of the PPT in an IoT network. Using the proposed DTMC models, we investigate the effect of traffic prioritization in terms of average packet delay for a synchronous medium access control (MAC) protocol. Furthermore, the results obtained from analytical models are validated via discrete-event simulations. Numerical results prove the accuracy of the models and reveal the behavior of priority based packet transmissions.