There is a broad consensus that artificial intelligence (AI) will be a defining component of the sixth-generation (6G) networks. As a specific instance, AI-empowered sensing will gather and process environmental perception data at the network edge, giving rise to integrated sensing and edge AI (ISEA). Many applications, such as autonomous driving and industrial manufacturing, are latency-sensitive and require end-to-end (E2E) performance guarantees under stringent deadlines. However, the 5G-style ultra-reliable and low-latency communication (URLLC) techniques designed with communication reliability and agnostic to the data may fall short in achieving the optimal E2E performance of perceptive wireless systems. In this work, we introduce an ultra-low-latency (ultra-LoLa) inference framework for perceptive networks that facilitates the analysis of the E2E sensing accuracy in distributed sensing by jointly considering communication reliability and inference accuracy. By characterizing the tradeoff between packet length and the number of sensing observations, we derive an efficient optimization procedure that closely approximates the optimal tradeoff. We validate the accuracy of the proposed method through experimental results, and show that the proposed ultra-Lola inference framework outperforms conventional reliability-oriented protocols with respect to sensing performance under a latency constraint.

Edge artificial intelligence (AI) is expected to be a central part of 6G, where servers located at the edge of the network will support devices in performing inference using machine learning (ML) models. However, providing latency and accuracy guarantees needed by many 6G applications, such as automated driving and robotics, is challenging due to the black-box nature of ML models, the complexity of the tasks, and the random wireless channel. This paper proposes a novel framework leveraging conformal risk control to meet requirements on the expected loss under a strict deadline. To adapt to fluctuating channel conditions, our framework utilizes an ensemble of black-box encoder/decoder models and inference models of varying accuracy and complexity, and selects the model expected to yield the most informative prediction under the given requirements. We demonstrate the proposed framework on a deadline-constrained image classification task under a strict missed detection requirement. The results suggest that the proposed framework provides the required performance guarantees, making it a promising step toward achieving reliable real-time edge AI services in 6G.

Artificial intelligence (AI) is expected to be a defining component in sixth-generation (6G) wireless networks. One specific use is AI-empowered sensing, where sensor data will be processed at the network edge, giving rise to integrated sensing and edge AI (ISEA). Many sensing applications, such as autonomous driving and industrial manufacturing, are latencysensitive and require end-to-end (E2E) performance guarantees under stringent deadlines. However, data-agnostic ultrareliable and low-latency communication (URLLC) techniques designed for 5G fall short in achieving the optimal E2E sensing performance. In this work, we introduce an ultra-low-latency (ultra-LoLa) inference framework for perceptive networks that facilitates the analysis of E2E sensing accuracy in distributed sensing by jointly considering communication reliability and inference accuracy. By characterizing the tradeoff between packet length and the number of sensing observations, we derive an efficient optimization procedure that closely approximates the optimal balance. The experimental results show that the proposed approach outperforms conventional reliability-oriented protocols with respect to sensing performance under a latency constraint.

High-Altitude Platform Stations (HAPSs) enable wide-area coverage in 6G networks but introduce interference when sharing spectrum with terrestrial networks (TNs). Null-forming is a technique to mitigate this interference by directing low-power beams (nulls) toward terrestrial users. Traditional nullforming methods, such as Null-Sweeping, rely on changing null directions across the resource blocks (RB) to improve the impact of nullforming. Yet these null directions are predefined for uniform user distributions and may not fully account for nonuniform deployments. We propose a user-aware nullforming approach that leverages K-means clustering to adapt null positions to dense user regions in the terrestrial cells, while time-frequency resources are allocated proportionally to user density. Simulations show that our method reduces HAPS interference for terrestrial users and improves fairness in interference distribution.

Sensors are increasingly being deployed to monitor critical infrastructure. However, as the number of sensors being deployed increases, so does the amount of sensor data that must be transmitted, stored, and analyzed. Thus, a significant number of methods have been proposed to improve sensor data acquisition and analytics. However, the proposed strategies and methods generally focus exclusively on either sensor data acquisition or analytics, thus ignoring the possible optimization that can be performed by taking a holistic view. To explore this opportunity, this paper provides an overview of sensor data acquisition and analytics and an analysis of two very different use cases, specifically monitoring wind turbines and measuring utility consumption using smart meters. Based on this analysis, the Framework for joint Sensory Data Acquisition and Analytics (SENDAI) is proposed, an integrated framework that models sensor data acquisition and analytics together, thus enabling holistic reasoning about sensor data acquisition and analytics. To demonstrate how the information in SENDAI can be used to reason about sensor data acquisition and analytics together, we show how sensor data acquisition can be optimized to respond efficiently to query workloads.

Compared to the generations up to 4G, whose main focus was on broadband and coverage aspects, 5G has expanded the scope of wireless cellular systems toward embracing two new types of connectivity: massive machine-type communications (mMTCs) and ultrareliable low-latency communications (URLLCs). This article discusses the possible evolution of these two types of connectivity within the umbrella of 6G wireless systems. This article consists of three parts. The first part deals with the connectivity for a massive number of devices. While mMTC research in 5G predominantly focuses on the problem of uncoordinated access in the uplink for a large number of devices, the traffic patterns in 6G may become more symmetric, leading to closed-loop massive connectivity. One of the drivers for this type of traffic pattern is distributed/decentralized learning and inference. The second part of this article discusses the evolution of wireless connectivity for critical services. While latency and reliability are tightly coupled in 5G, 6G will support a variety of safety-critical control applications with different types of timing requirements, as evidenced by the emergence of metrics related to information freshness and information value. In addition, ensuring ultrahigh reliability for safety-critical control applications requires modeling and estimation of the tail statistics of the wireless channel, queue length, and delay. The fulfillment of these stringent requirements calls for the development of novel artificial intelligence (AI)-based techniques, incorporating optimization theory, explainable AI (XAI), generative AI, and digital twins (DTs). The third part analyzes the coexistence of massive connectivity and critical services. Specifically, we consider scenarios in which a massive number of devices need to support traffic patterns of mixed criticality. This is followed by a discussion about the management of wireless resources shared by services with different criticality.

Estimating the probability of rare channel conditions is a central challenge in ultra-reliable wireless communication, where random events, such as deep fades, can cause sudden variations in the channel quality. This paper proposes a sample-efficient framework for predicting the statistics of such events by utilizing spatial dependency between channel measurements acquired from various locations. The proposed framework combines radio maps with non-parametric models and extreme value theory (EVT) to estimate rare-event channel statistics under a Bayesian formulation. The framework can be applied to a wide range of problems in wireless communication and is exemplified by rate selection in ultra-reliable communications. Notably, besides simulated data, the proposed framework is also validated with experimental measurements. The results in both cases show that the Bayesian formulation provides significantly better results in terms of throughput compared to baselines that do not leverage measurements from surrounding locations. It is also observed that the models based on EVT are generally more accurate in predicting rare-event statistics than non-parametric models, especially when only a limited number of channel samples are available. Overall, the proposed methods can significantly reduce the number of measurements required to predict rare channel conditions and guarantee reliability.

Efficiently retrieving relevant data from massive Internet of Things (IoT) networks is essential for downstream tasks such as machine learning. This paper addresses this challenge by proposing a novel data sourcing protocol that combines semantic queries and random access. The key idea is that the destination node broadcasts a semantic query describing the desired information, and the sensors that have data matching the query then respond by transmitting their observations over a shared random access channel, for example to perform joint inference at the destination. However, this approach introduces a tradeoff between maximizing the retrieval of relevant data and minimizing data loss due to collisions on the shared channel. We analyze this tradeoff under a tractable Gaussian mixture model and optimize the semantic matching threshold to maximize the number of relevant retrieved observations. The protocol and the analysis are then extended to handle a more realistic neural network-based model for complex sensing. Under both models, experimental results in classification scenarios demonstrate that the proposed protocol is superior to traditional random access, and achieves a near-optimal balance between inference accuracy and the probability of missed detection, highlighting its effectiveness for semantic query-based data sourcing in massive IoT networks.

Energy efficiency and information freshness are key requirements for sensor nodes serving Industrial Internet of Things (IIoT) applications, where a sink node collects informative and fresh data before a deadline, e.g., to control an external actuator. Content-based wake-up (CoWu) activates a subset of nodes that hold data relevant for the sink’s goal, thereby offering an energy-efficient way to attain objectives related to information freshness. This paper focuses on a scenario where the sink collects fresh information on top-kvalues, defined as data from the nodes observing thekhighest readings at the deadline. We introduce a new metric called top-kQuery Age of Information (k-QAoI), which allows us to characterize the performance of CoWu by considering the characteristics of the physical process. Further, we show how to select the CoWu parameters, such as its timing and threshold, to attain both information freshness and energy efficiency. The numerical results reveal the effectiveness of the CoWu approach, which is able to collect top-kdata with higher energy efficiency while reducingk-QAoI when compared to round-robin scheduling, especially when the number of nodes is large and the required size ofkis small.

Goal-oriented communication has become one of the focal concepts in sixth-generation communication systems owing to its potential to provide intelligent, immersive, and real-time mobile services. The emerging paradigms of goal-oriented communication constitute closed loops integrating communication, computation, and sensing. However, challenges arise for closed-loop timing analysis due to multiple random factors that affect the communication/computation latency, as well as the heterogeneity of feedback mechanisms across multi-modal sensing data. To tackle these problems, we aim to provide a unified timing analysis framework for closed-loop goal-oriented communication (CGC) systems over fading channels. The proposed framework is unified as it considers computation, compression, and communication latency in the loop with different configurations. To capture the heterogeneity across multi-modal feedback, we categorize the sensory data into the periodic-feedback and event-triggered, respectively. We formulate timing constraints based on average and tail performance, covering timeliness, jitter, and reliability of CGC systems. A method based on saddlepoint approximation is proposed to obtain the distribution of closed-loop latency. The results show that the modified saddlepoint approximation is capable of accurately characterizing the latency distribution of the loop with analytically tractable expressions. This sets the basis for low-complexity co-design of communication and computation.

This paper presents an experimental validation for prediction of rare fading events using channel distribution information (CDI) maps that predict channel statistics from measurements acquired at surrounding locations using spatial interpolation. Using experimental channel measurements from 127 locations, we demonstrate the use case of providing statistical guarantees for rate selection in ultra-reliable low-latency communication (URLLC) using CDI maps. By using only the user location and the estimated map, we are able to meet the desired outage probability with a probability between 93.6–95.6% targeting 95%. On the other hand, a model-based baseline scheme that assumes Rayleigh fading meets the target outage requirement with a probability of 77.2%. The results demonstrate the practical relevance of CDI maps for resource allocation in URLLC.

We investigate the coexistence of massive and critical Internet of Things (IoT) services in the context of the unsourced multiple access (UMA) framework introduced by Polyanskiy (2017), where all users employ a common codebook and the receiver returns an unordered list of decoded codewords. This setup is suitably modified to introduce heterogeneous traffic. Specifically, to model the massive IoT service, we assume that a standard message originates independently from each IoT device as in the standard UMA setup. To model the critical IoT service, we assume the generation of alarm messages that are common for all devices. This setup requires a significant redefinition of the error events, i.e., misdetections and false positives. We further assume that the number of active users in each transmission attempt is random and unknown. We derive a random-coding achievability bound on the misdetection and false positive probabilities of both standard and alarm messages on the Gaussian multiple access channel. Using our bound, we demonstrate that orthogonal network slicing enables massive and critical IoT to coexist under the requirement of high energy efficiency. On the contrary, we show that nonorthogonal network slicing is energy inefficient due to the residual interference from the alarm signal when decoding the standard messages.

Sensing is envisioned as a key network function of thesixth-generation(6G) mobile networks.Artificial intelligence(AI)-empowered sensing fuses features of multiple sensing views from devices distributed in edge networks for the edge server to perform accurate inference. This process, known asmulti-view pooling, creates a communication bottleneck due to multi-access by many devices. To alleviate this issue, we propose a task-oriented simultaneous access scheme for distributed sensing calledOver-the-Air Pooling(AirPooling). The existingOver-the-Air Computing(AirComp) technique can be directly applied to enable Average-AirPooling, which exploits the waveform superposition property of a multi-access channel to implement fast over-the-air averaging of pooled features. However, despite being most popular in practice, the over-the-air maximization, called Max-AirPooling, is not AirComp realizable given the fact that AirComp addresses only a limited subset of functions. We tackle the challenge by proposing the novel generalized AirPooling framework that can be configured to support both Max- and Average-AirPooling by controlling a configuration parameter and extended to even other pooling functions. The former is realized by adding to AirComp the designed pre-processing at devices and post-processing at the server. To characterize theEnd-to-End(E2E) sensing performance in object recognition, the theory of classification margin is applied to relate the classification accuracy and the AirPooling error, which allows the latter to be a tractable surrogate of the former. Furthermore, the analysis reveals an inherent tradeoff of Max-AirPooling between the accuracy of the pooling-function approximation and the effectiveness of noise suppression. Using the tradeoff, we make an attempt to optimize the configuration parameter of Max-AirPooling, yielding a sub-optimal closed-form method of adaptive parametric control. Experimental results obtained on real-world datasets show that AirPooling provides sensing accuracies close to those achievable by the traditional digital air interface but dramatically reduces the communication latency, by up to an order of magnitude.

The Age of Incorrect Information (AoII) is a metric that can combine the freshness of the information available to a gateway in an Internet of Things (IoT) network with the accuracy of that information. As such, minimizing the AoII can allow the operators of IoT systems to have a more precise and up-to-date picture of the environment in which the sensors are deployed. However, most IoT systems do not allow for centralized scheduling or explicit coordination, as sensors need to be extremely simple and consume as little power as possible. Finding a decentralized policy to minimize the AoII can be extremely challenging in this setting. This paper presents a heuristic to optimize AoII for a slotted ALOHA system, starting from a threshold-based policy and using dual methods to converge to a better solution. This method can significantly outperform state-independent policies, finding an efficient balance between frequent updates and a low number of packet collisions.

As IoT devices become increasingly advanced and equipped with sensors such as cameras and microphones, the collection of massive data streams, produced in real-time, becomes challenging. In many cases only a small fraction of the collected data might be relevant, e.g., if cameras are used to search for a specific object. In this paper, we introduce and analyze a set of random access protocols in which the transmitting IoT devices are activated by semantic queries. This can be seen as a semantic data sourcing random access: each device computes a matching score that characterizes the relevance of its current observation and, if the matching score exceeds a threshold, the device transmits its observation over a random access collision channel to an edge node. We study two random access transmission policies. The first is the classical slotted ALOHA policy, while the other is able to exploit semantic correlation between the device observations. Furthermore, we show how the protocol can be integrated with machine learning-based query and matching score functions to capture the semantic content of, say, images. The numerical results show that the proposed protocol is able to effectively filter the device observations, such that mostly relevant data is received. Overall, the protocol is promising for collecting data in real-time from massive IoT networks based on the semantic content of sensor observations.

Taking inspiration from linguistics, the communications theoretical community has recently shown a significant recent interest inpragmatic, or goal-oriented, communication. In this paper, we tackle the problem of pragmatic communication with multiple clients with different, and potentially conflicting, objectives. We capture the goal-oriented aspect through the metric of Value of Information (VoI), which considers the estimation of the remote process as well as the timing constraints. However, the most common definition of VoI is simply the Mean Square Error (MSE) of the whole system state, regardless of the relevance for a specific client. Our work aims to overcome this limitation by including different summary statistics, i.e., value functions of the state, for separate clients, and a diversified query process on the client side, expressed through the fact that different applications may request different functions of the process state at different times. A query-aware Deep Reinforcement Learning (DRL) solution based on statically defined VoI can outperform naive approaches by 15-20%.

Age of Information (AoI) has recently received much attention due to its relevance for IoT sensing and monitoring. In this paper, we consider the problem of minimizing the AoI in a system in which a set of sources are observed by multiple sensors in a many-to-many relationship, and the probability that a sensor observes a source depends on the source’s state. This model represents many practical scenarios, such as when multiple cameras or microphones are deployed to monitor objects moving in certain areas. We formulate the scheduling problem as a Markov Decision Process, and show how the age-optimal scheduling policy can be obtained. We further consider partially observable variants of the problem, and devise approximate policies for large state spaces. The evaluations show that the approximate policies work well in the considered scenarios, while the fact that sensors can observe multiple sources is beneficial, especially when there is high uncertainty of the source states.

This paper proposes exploiting the spatial correlation of wireless channel statistics beyond the conventional received signal strength maps by constructing statistical radio maps to predict any relevant channel statistics to assist communications. Specifically, from stored channel samples acquired by previous users in the network, we use Gaussian processes (GPs) to estimate quantiles of the channel distribution at a new position using a non-parametric model. This prior information is then used to select the transmission rate for some target level of reliability. The approach is tested with synthetic data, simulated from urban micro-cell environments, highlighting how the proposed solution helps to reduce the training estimation phase, which is especially attractive for the tight latency constraints inherent to ultra-reliable low-latency (URLLC) deployments.

A key challenge for random access communications arising in the monitoring of physical phenomena is optimizing the access policy. This is particularly the case when the activity of each sensor is correlated, contrasting with the independence assumption underpinning standard slotted ALOHA schemes. In this paper, we propose a stochastic resource allocation algorithm to reduce outages via maximization of the expected number of sensors that are able to reliably communicate with an access point. Allowing for devices to transmit data over multiple consecutive frames, we show that the proposed algorithm converges with probability one to a locally optimal solution. Moreover, our algorithm significantly outperforms existing methods in terms of the average number of successful transmissions when utilizing successive interference cancellation.

In this work, we present a novel traffic prediction and fast uplink (FU) framework for IoT networks controlled by binary Markovian events. First, we apply the forward algorithm with hidden Markov models (HMMs) in order to schedule the available resources to the devices with maximum likelihood activation probabilities via the FU grant. In addition, we evaluate the regret metric as the number of wasted transmission slots to evaluate the performance of the prediction. Next, we formulate a fairness optimization problem to minimize the Age of Information (AoI) while keeping the regret as minimum as possible. Finally, we propose an iterative algorithm to estimate the model hyperparameters (activation probabilities) in a real-time application and apply an online-learning version of the proposed traffic prediction scheme. Simulation results show that the proposed algorithms outperform baseline models, such as time-division multiple access (TDMA) and grant-free (GF) random-access in terms of regret, the efficiency of system usage, and AoI.

With the advent of 5G technology, the notion oflatencygot a prominent role in wireless connectivity, serving as a proxy term for addressing the requirements for real-time communication. As wireless systems evolve toward 6G, the ambition to immerse the digital into physical reality will increase. Besides making the real-time requirements more stringent, this immersion will bring the notions of time, simultaneity, presence, and causality to a new level of complexity. A growing body of research points out that latency is insufficient to parameterize all real-time requirements. Notably, one such requirement that received significant attention is information freshness, defined through the Age of Information (AoI) and its derivatives. In general, the metrics derived from a conventional black-box approach to communication network design are not representative of new distributed paradigms, such as sensing, learning, or distributed consensus. The objective of this article is to investigate the general notion of timing in wireless communication systems and networks, and its relation to effective information generation, processing, transmission, and reconstruction at the senders and receivers. We establish a general statistical framework oftimingrequirements in wireless communication systems, which subsumes both latency and AoI. The framework is made by associating a timing component with the two basic statistical operations: decision and estimation. We first use the framework to present a representative sample of the existing works that deal with timing in wireless communication. Next, it is shown how the framework can be used with different communication models of increasing complexity, starting from the basic Shannon one-way communication model and arriving at communication models for consensus, distributed learning, and inference. Overall, this article fills an important gap in the literature by providing a systematic treatment of various timing measures in wireless communication and sets the basis for design and optimization for the next-generation real-time systems.

Age of Information (AoI) has become an important concept in communications, as it allows system designers to measure the freshness of the information available to remote monitoring or control processes. However, its definition tacitly assumes that new information is used at any time, which is not always the case: the instants at which information is collected and used may be dependent on a certain query process, and resource-constrained environments such as most Internet of Things (IoT) use cases require precise timing to fully exploit the limited available transmissions. In this work, we consider apull-based communication modelin which the freshness of information is only important when the receiver generates a query: if the monitoring process is not using the value, the age of the last update is irrelevant. We optimize the Age of Information at Query (QAoI), a metric that samples the AoI at relevant instants, better fitting the pull-based resource-constrained scenario, and show how this can lead to very different choices. Our results show that QAoI-aware optimization can significantly reduce the average and worst-case perceived age for both periodic and stochastic queries.

Massive random access plays a central role in supporting the Internet of Things (IoT), where a subset of a large population of users simultaneously transmit small packets to a central base station. While there has been much research on the design of protocols for massive access in the uplink, the problem of providing message acknowledgments back to the users has been somewhat neglected. Reliable communication needs to rely on two-way communication for acknowledgement and retransmission. Nevertheless, because of the many possible subsets of active users, providing acknowledgments requires a significant amount of bits. Motivated by this, we define the problem of massive ARQ (Automatic Retransmission reQuest) protocol and introduce efficient methods for joint encoding of multiple acknowledgements in the downlink. The key idea towards reducing the number of bits used for massive acknowledgments is to allow for a small fraction of false positive acknowledgments. We analyze the implications of this approach and the impact of acknowledgment errors in scenarios with massive random access. Finally, we show that these savings can lead to a significant increase in the reliability when retransmissions are allowed since it allows the acknowledgment message to be transmitted more reliably using a much lower rate.

Age of Information (AoI) has become an important concept in communications, as it allows system designers to measure the freshness of the information available to remote monitoring or control processes. However, its definition tacitly assumes that new information is used at any time, which is not always the case. Instead instants at which information is collected and used are dependent on a certain query process. We propose a model that accounts for the discrete time nature of many monitoring processes, by considering a pull-based communication model in which the freshness of information is only important when the receiver generates a query. We then define the Age of Information at Query (QAoI), a more general metric that fits the pull-based scenario, and show how its optimization can lead to very different choices from traditional push-based AoI optimization when using a Packet Erasure Channel (PEC).

The fifth generation (5G) of wireless systems has a platform-driven approach, aiming to support heterogeneous connections with very diverse requirements. The shared wireless resources should be sliced in a way that each user perceives that its requirements have been met. Heterogeneity challenges the traditional notion of resource efficiency, as the resource usage has to cater for, e.g., rate maximization for one user and a timeliness requirement for another user. This paper treats a model for radio access network (RAN) uplink, where a throughput-demanding broadband user shares wireless resources with an intermittently active user that wants to optimize the timeliness, expressed in terms of latency-reliability or Age of Information (AoI). We evaluate the trade-offs between throughput and timeliness for Orthogonal Multiple Access (OMA) as well as Non-Orthogonal Multiple Access (NOMA) with successive interference cancellation (SIC). We observe that NOMA with SIC, in a conservative scenario with destructive collisions, is just slightly inferior to that of OMA, which indicates that it may offer significant benefits in practical deployments where the capture effect is frequently encountered. On the other hand, finding the optimal configuration of NOMA with SIC depends on the activity pattern of the intermittent user, to which OMA is insensitive.

This paper presents a novel framework for traffic prediction of IoT devices activated by binary Markovian events. First, we consider a massive set of IoT devices whose activation events are modeled by an On-Off Markov process with known transition probabilities. Next, we exploit the temporal correlation of the traffic events and apply the forward algorithm in the context of hidden Markov models (HMM) in order to predict the activation likelihood of each IoT device. Finally, we apply the fast uplink grant scheme in order to allocate resources to the IoT devices that have the maximal likelihood for transmission. In order to evaluate the performance of the proposed scheme, we define the regret metric as the number of missed resource allocation opportunities. The proposed fast uplink scheme based on traffic prediction outperforms both conventional random access and time division duplex in terms of regret and efficiency of system usage, while it maintains its superiority over random access in terms of average age of information for massive deployments.

The established view on massive IoT access is that the IoT devices are activated randomly and independently. This is a basic premise also in the recent information-theoretic treatment of massive access by Polyanskiy [1]. In a number of practical scenarios, the information from IoT devices in a given geographical area is inherently correlated due to a commonly observed physical phenomenon. We introduce a model for massive access that accounts for correlation both in device activation and in the message content. To this end, we introduce common alarm messages for all devices. A physical phenomenon can trigger an alarm causing a subset of devices to transmit the same message at the same time. We develop a new error probability model that includes false positive errors, resulting from decoding a non-transmitted codeword. The results show that the correlation allows for high reliability at the expense of spectral efficiency. This reflects the intuitive trade-off: an access from a massive number can be ultra-reliable only if the information across the devices is correlated.

The emerging blockchain protocols provide a decentralized architecture that is suitable of supporting Internet of Things (IoT) interactions. However, keeping a local copy of the blockchain ledger is infeasible for low-power and memory-constrained devices. For this reason, they are equipped with lightweight software implementations that only download the useful data structures, e.g., state of accounts, from the blockchain network, when they are updated. In this paper, we consider and analyze a novel scheme, implemented by the nodes of the blockchain network, which aggregates the blockchain data in periodic updates and further reduces the communication cost of the connected IoT devices. We show that the aggregation period should be selected based on the channel quality, the offered rate, and the statistics of updates of the useful data structures. The results, obtained for the Ethereum protocol, illustrate the benefits of the aggregation scheme in terms of a reduced duty cycle of the device, particularly for low signal-to-noise ratios, and the overall reduction of the amount of information transmitted in downlink from the wireless base station to the IoT device. A potential application of the proposed scheme is to let the IoT device request more information than actually needed, hence increasing its privacy, while keeping the communication cost constant. In the conclusion, this paper is the first to provide rigorous guidelines for the design of lightweight blockchain protocols with wireless connectivity.

Blockchain is a technology uniquely suited to support massive number of transactions and smart contracts within the Internet of Things (IoT) ecosystem, thanks to the decentralized accounting mechanism. In a blockchain network, the states of the accounts are stored and updated by the validator nodes, interconnected in a peer-to-peer fashion. IoT devices are characterized by relatively low computing capabilities and low power consumption, as well as sporadic and low-bandwidth wireless connectivity. An IoT device connects to one or more validator nodes to observe or modify the state of the accounts. In order to interact with the most recent state of accounts, a device needs to be synchronized with the blockchain copy stored by the validator nodes. In this work, we describe general architectures and synchronization protocols that enable synchronization of the IoT endpoints to the blockchain, with different communication costs and security levels. We model and analytically characterize the traffic generated by the synchronization protocols, and also investigate the power consumption and synchronization trade-off via numerical simulations. To the best of our knowledge, this is the first study that rigorously models the role of wireless connectivity in blockchain-powered IoT systems.

Industry 4.0 introduces modern communication and computation technologies such as cloud computing and Internet of Things to industrial manufacturing systems. As a result, many devices, machines, and applications will rely on connectivity, while having different requirements to the network, ranging from high reliability and low latency to high data rates. Furthermore, these industrial networks will be highly heterogeneous, as they will feature a number of diverse communication technologies. Current technologies are not well suited for this scenario, which requires that the network is managed at an abstraction level, which is decoupled from the underlying technologies. In this paper, we consider network slicing as a mechanism to handle these challenges. We present methods for slicing deterministic and packet-switched industrial communication protocols, which simplify the manageability of heterogeneous networks with various application requirements. Furthermore, we show how to use network calculus to assess the end-to-end properties of the network slices.