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.

Extending wireless connectivity to remote areas is essential for delivering intelligent services in critical sectors, i.e., healthcare, agriculture, and disaster management. Unmanned aerial vehicles (UAVs) have emerged as a promising solution due to their agile mobility, low deployment cost, and line-of-sight (LoS) communication capabilities. However, efficiently managing UAV resources while integrating sensing, communication, and computing (ISCC) functionalities presents significant challenges. In this paper, we propose a multi-UAV-assisted ISCC framework that simultaneously supports wireless communication links for computational task offloading, remote computing, and active target sensing. A comprehensive system model is developed, and a joint optimization problem is formulated to minimize the weighted sum energy consumption of UAVs and remote users, subject to constraints on latency, power budget, and UAV mobility. To solve the resulting non-convex problem, we design a decomposition-based solution that integrates a convex optimization technique with the deep deterministic policy gradient (DDPG) algorithm. Simulation results demonstrate the effectiveness of the proposed framework in achieving energy-efficient operation under practical system constraints.

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.

Achieving flexible and efficient wireless resource sharing across diverse applications and services is among the key goals of the sixth-generation of mobile systems (6G). This work investigates the performance of a real-time system coexisting with a broadband service in a frame-based wireless channel. Specifically, we consider a remote tracking device that monitors an information source and transmits updates to a base station (BS) for real-time source reconstruction, and potential remote actuation. We revise the common idealized assumptions in real-time remote tracking studies, such as instantaneous feedback and pervasive wireless resources, as they do not hold in practical scenarios. We consider a monitoring device and a broadband user communicating with the BS via a grant-free access mechanism over wireless resources defined for either orthogonal or non-orthogonal access, with feedback scheduled at the end of each frame. We analyze system performance using goal-oriented performance metrics for real-time remote reconstruction, alongside throughput and energy efficiency for the broadband user. Our results show that the ‘Idealistic’ model considered in conventional studies achieves better performance but incurs disproportionately high overhead compared to the Frame-Based model. Moreover, within the Frame-Based model, orthogonal resource sharing is preferable for maximizing broadband throughput, while non-orthogonal sharing significantly improves energy efficiency.

We study a scenario in which multiple uncoordinated devices aim to achieve reliable transmissions within a given time frame. The devices are intermittently active and access a shared pool of channel resources in a grant-free manner by utilizing multiple transmissions (K-repetition coding). This allows them to achieve diversity and improve the reliability within a certain latency constraint. We focus on two access methods: one where devices choose K slots at random and another one where the access patterns are deterministic and follow a specific code design, namely the Steiner System. We analyze the problem under two signal models that involve different complexity for the receiver. First, collision model is considered, where only interference-free transmissions can be used and combined. Second, a model treating interference as noise is analyzed, where the receiver is capable of utilizing all K replicas, applying maximum ratio combining (MRC). For both signal models, we investigate receivers with and without successive interference cancellation (SIC). We develop approximations and bounds for the outage probabilities that very closely match simulation results. Overall, we show that deterministic access patterns have the potential to significantly outperform random selection in terms of reliability. Furthermore, deterministic access patterns offer a simplified system design.

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.

The integration of graphics processing units through virtualization offers significant potential to optimize resource utilization in distributed systems. Particularly in edge computing scenarios, the architecture of edge devices can be simplified by consolidating GPU usage at the edge gateway, which offers the improvement of the GPU utilization by sharing the GPU by multiple edge client devices. However, the effectiveness of this approach is heavily contingent upon network communication, which in edge environments often lacks the high-speed and low latency links as well as specialized protocols available in traditional data center settings for high performance computing, especially if wireless networks are deployed. Therefore, our study aims to comprehensively analyze and evaluate the performance implications of using the conventional transmission control protocol (TCP) versus the emerging QUIC protocol in such environments. We specifically focus on assessing QUIC's unique features, including concurrent streams, stream cancellation, and stream prioritization, which are not inherently present in TCP. Furthermore, we investigate the potential performance gains achievable through the adoption of asynchronous API calls, aiming to provide insights into optimizing GPU utilization in edge computing. We show the advantages of most of QUIC's features and asynchronous API calls by an experimental evaluation.

In this paper, we investigate performance of a random access scheme that exploits binary-tree algorithm (BTA) with the free access. We assume a scenario where the receiver is capable to perform both multi-packet reception (MPR) and successive interference cancellation (SIC), where for the purpose of the latter only the last received and undecoded signal can be stored. We distinguish between two variants of the algorithm, where in the first the SIC can be triggered by a decoding event but also executed blindly among yet undecoded slots, while in the second the receiver can only execute the SIC after the decoding event. We analytically derive the maximum stable throughput (MST) of the scheme assuming Poisson arrivals. The evaluation shows that the scheme is able of achieving a favorable performance in comparison to the scenarios when only either MPR or SIC with single-slot memory is used, making it a suitable candidate for an access solution in applications that are characterized with a massive number of users and sporadic traffic arrivals. We also compare the performance of the scheme with the best performing BTA scheme that also exploits K-MPR and SIC and does not have memory limitations, showing that the relative difference in the MST’s of the two schemes diminishes with K.

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.

In this paper, we study binary tree-algorithms that exploit a combination of multi-packet reception (MPR) and successive interference cancellation (SIC), which so far has not been considered in the literature. Specifically, we assume that the receiver is capable of successfully decoding any collision of up to and including$K$concurrent packet transmissions and can perform SIC along the tree. We show a number of novel results for this type of tree algorithms. We first derive the basic performance parameters, which are the expected length of the collision resolution interval and the throughput normalized with$K$, conditioned on the number of contending users. We then analyze their asymptotic behaviour, identifying an oscillatory component that amplifies as$K$increases. In the next step, we derive the maximum stable throughput (MST) for the gated and windowed access assuming Poisson arrivals. We show that for windowed access, the bound on MST normalized with$K$increases with$K$. Finally, we discuss practical issues related to implementation of such scheme, as well as compare it to slotted ALOHA-based schemes that exploit both$K$-MPR and SIC.

The stringent timing and reliability requirements in mission-critical applications require a detailed statistical characterization of end-to-end latency. Teleoperation is a representative use case, in which a human operator (HO) remotely controls a robot by exchanging command and feedback signals. We present a framework to analyze the latency of a closed-loop teleoperation system consisting of three entities: an HO, a robot located in remote environment, and a Base Station (BS) with Mobile edge Computing (MEC) capabilities. A model of each component is used to analyze the closed-loop latency and optimize the compression strategy. The closed-form expression of the distribution of the closed-loop latency is difficult to estimate, such that suitable upper and lower bounds are obtained. We formulate a non-convex optimization problem to minimize the closed-loop latency. Using the obtained upper and lower bound on the closed-loop latency, a computationally efficient procedure to optimize the closed-loop latency is presented. The simulation results reveal that compression of sensing data is not always beneficial, while system design based on average performance leads to under-provisioning and may cause performance degradation. The applicability of the proposed analysis is much wider than teleoperation, including a large class of systems whose latency budget consists of many components.

In the above article, the authors propose$d$-ary SICTA and derive the expected conditional length of the collision resolution interval, optimal splitting probability and the maximum stable throughput (MST) for$d \geq 2$under stationary ergodic packet arrivals. In this correction, we show that the premise of the analysis for$d > 2$and consequentially the results presented for$d > 2$do not hold.

Currently, there is a plethora of low-power wide-area IoT networking solutions available, each targeting a specific niche of use-cases and deployment scenarios. Existing studies on reliability evaluations of IoT solutions rely on the assumption that a single technology is deployed in the service area, or different IoT technologies operate over dedicated spectrum bands. Here, we investigate the reliability performance of IoT communications in coexisting scenarios, where multiple competing radio-access technologies share spectrum resources. Our focus is on solutions exploiting grant-free communications, which are gaining traction due to their potential to lower the energy consumption, and have been adopted in recent IoT technologies like SigFox and LoRa. We first derive an analytical model of the interference, comprising both inter- and intra-technology interference sources. We then leverage the Poisson Cluster Process for modeling distribution of devices in the service area, and derive expressions for the communication reliability, energy consumption, and battery lifetime of IoT devices. Exploiting these expressions, we study the energy-reliability trade-offs and investigate strategies to maintain or improve communication reliability, while minimizing energy consumption in coexisting scenarios by proper adjustment of communications parameters at the device side and provisioning resources at the network side. We verify the analytical results via numerical evaluations, confirming their accuracy and performing optimization in some example networking setups.

Natural disasters occurring in inaccessible rural areas are on the rise, leading to the multiplication of first responders’ missions. However, engagement in fighting wildfires or participating in rescue missions includes risks for the well-being of the engaged first responders. Consequently, a system that monitors their actions and provides real-time and actionable information without obstructing their operational capacity is needed. The EU-funded SIXTHSENSE project aims to improve the efficiency and safety of first responders’ engagement in difficult environments by optimizing on-site team coordination and mission implementation. The project proposes an innovative wearable health monitoring system based on multimodal biosensor data that enables first responders to detect risk factors early on and allows real-time monitoring of all deployed responders. This paper is an introduction to the overall concept of the project, to the methodology and the system architecture, moreover details on Alpha version of SixthSense prototype are presented.

We present a framework to analyse the latency budget in wireless systems with Mobile Edge Computing (MEC). Our focus is on teleoperation and telerobotics, as use cases that are representative of mission-critical uplink-intensive IoT systems with requirements on low latency and high reliability. The study is motivated by a general question: What is the optimal compression strategy in reliability and latency constrained systems? We address this question by studying the latency of an uplink connection from a multi-sensor IoT device to the base station. This is a critical link tasked with a timely and reliable transfer of potentially significant amount of data from the multitude of sensors. We introduce a comprehensive model for the latency budget, incorporating data compression and data transmission. The uplink latency is a random variable whose distribution depends on the computational capabilities of the device and on the properties of the wireless link. We formulate two optimization problems corresponding to two transmission strategies: (1) Outage-constrained, and (2) Latency-constrained. We derive the optimal system parameters under a reliability criterion. We show that the obtained results are superior compared to the ones based on the optimization of the expected latency.

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.

The fast growth of time-sensitive applications calls for the optimization of radio access network (RAN) scheduling. We consider the problem of RAN scheduling of a mix of periodic and burst traffic and design a reinforcement learning method for the age of information and throughput optimization. The periodic traffic is generated with a fixed frequency and the burst traffic is generated by the Poisson Pareto Burst Process. We firstly formulate the scheduling problem as a non-linear integer programming problem. Then, we focus on the reinforcement learning method modeling and solve it via the Proximal Policy Optimization algorithm. Our evaluations show that the suggested reinforcement algorithm outperforms the classical algorithms without any prior knowledge of the arriving traffic.

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.

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.

In this paper, we analyze binary-tree algorithms in a setup in which the receiver can perform multi-packet reception (MPR) of up to and including K packets simultaneously. The analysis addresses both traffic-independent performance as well as performance under Poisson arrivals. For the former case, we show that the throughput, when normalized with respect to the assumed linear increase in resources required to achieve K-MPR capability, tends to the same value that holds for the single-reception setup. However, when coupled with Poisson arrivals in the windowed access scheme, the normalized throughput increases with K, and we present evidence that it asymptotically tends to 1. We also provide performance results for the modified tree algorithm with K-MPR in the clipped access scheme. To the best of our knowledge, this is the first paper that provides an analytical treatment and a number of fundamental insights in the performance of tree-algorithms with MPR.

This paper presents a finite length analysis of multi-slot type frameless ALOHA based on a dynamic programming approach. The analysis is exact, but its evaluation is only feasible for moderate number of users due to the computational complexity. The analysis is then extended to derive continuous approximations of its key parameters, which, apart from providing an insight into the decoding process, make it possible to estimate the packet error rate with very low computational complexity. Finally, a feedback scheme is presented in which the slot access scheme is dynamically adapted according to the approximate analysis in order to minimize the packet error rate. The results indicate that the introduction of feedback can substantially improve the performance of frameless ALOHA.

Enabling autonomous driving and industrial automation with wireless networks poses many challenges, which are typically abstracted through reliability and latency requirements. One of the main contributors to latency in cellular networks is the reservation-based access, which involves lengthy and resource-inefficient signaling exchanges. An alternative is to use grant-free access, in which there is no resource reservation. A handful of recent works investigated how to fulfill reliability and latency requirements with different flavors of grant-free solutions. However, the resource efficiency, i.e., the throughput, has been only the secondary focus. In this work, we formulate the throughput of grant-free access under reliability-latency constraints, when the actual number of arrived users or only the arrival distribution are known. We investigate how these different levels of knowledge about the arrival process influence throughput performance of framed slotted ALOHA with K-multipacket reception, for the Poisson and Beta arrivals. We show that the throughput under reliability-latency requirements can be significantly improved for the higher expected load of the access network, if the actual number of arrived users is known. This insight motivates the use of techniques for the estimation of the number of arrived users, as this knowledge is not readily available in grant-free access. We also asses the impact of estimation error, showing that for high reliability-latency requirements the gains in throughput are still considerable.

The collision resolution mechanism in the Random Access Channel (RACH) procedure of the Long-Term Evolution (LTE) standard is known to represent a serious bottleneck in case of Machine-Type Communication (MTC). Its main drawbacks are seen in the facts that Base Stations (eNBs) typically cannot infer the number of collided User Equipments (UEs) and that collided UEs learn about the collision only implicitly, through the lack of the feedback in the later stage of the RACH procedure. The collided UEs then restart the procedure, increasing the RACH load and making the system more prone to collisions. In this paper, we leverage machine learning techniques to design a system that, besides outperforming the state-of-the-art schemes in preamble detection for the LTE RACH procedure, is able to estimate the collision multiplicity and thus gather information about how many devices chose the same preamble. This data can be used by the eNB to resolve collisions, increase the supported system load and reduce transmission latency. Besides LTE, the presented approach is applicable to novel 3GPP standards that target massive Internet of Things (IoT), e.g., LTE-M and NB-IoT, as well as 5G, since their RACH procedures are based on the same principles.

This paper proposes content-based control of wakeup receivers for data collection in wireless sensor networks. The wake-up procedure is designed with a goal of waking up only the subset of the sensor nodes which have the relevant data observations. This prevents the sensors with less relevant data from waking up and wasting energy, which is inevitable when employing conventional ID-based wake-up control. We apply the proposed content-based wake-up scheme to top- k query, where the sink attempts to collect information on the set of nodes that own top- k observations from the sensing field. Assuming medium access based on p-persistent CSMA, we design a content-based wake-up control scheme suited for the data collection of top- k query. We analyze the scheme theoretically in terms of data collection delay and energy-efficiency and compare it to the ID-based wake-up. The numerical results confirm the effectiveness of the proposed content-based wake-up control, especially when the number of sensor nodes is large.

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.

The future connectivity landscape, and notably, the 5G wireless systems will feature Ultra-Reliable Low Latency Communication (URLLC). The coupling of high reliability and low latency requirements in URLLC use cases makes the wireless access design very challenging, in terms of both the protocol design and of the associated transmission techniques. This paper aims to provide a broad perspective on the fundamental tradeoffs in URLLC, as well as the principles used in building access protocols. Two specific technologies are considered in the context of URLLC: massive MIMO and multi-connectivity, also termed interface diversity. This paper also touches on the importance of the proper statistical methodology for designing and assessing extremely high-reliability levels.

Narrowband Internet of Things (NB-IoT) is the latest IoT connectivity solution presented by the 3rd generation partnership project (3GPP). NB-IoT introduces coverage classes and offers a significant link budget improvement by allowing repeated transmissions by nodes that experience high path loss. However, those repetitions necessarily increase the energy consumption and the latency in the whole NB-IoT system. The extent to which the whole system is affected depends on the scheduling of the uplink and downlink channels. We address this question, not treated previously, by developing a tractable model of NB-IoT access protocol operation, comprising message exchanges in random-access, control, and data channels, both in the uplink and downlink. The model is then used to analyze the impact of channel scheduling as well as the interaction of coexisting coverage classes, through derivation of the expected latency and battery lifetime for each coverage class. These results are subsequently employed in investigation of latency-energy tradeoff in NB-IoT channel scheduling as well as determining the optimized operation points. Simulations results show validity of the analysis and confirm that channel scheduling and coexistence of coverage classes significantly affect latency and battery lifetime performance of NB-IoT devices.

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.

In this paper, we review the key figures of merit to assess the performance of advanced random access (RA) schemes exploiting physical layer coding, repetitions, and collision resolution techniques. We then investigate RA modeling aspects and their impact on the figures of merit for the exemplary advanced RA schemes: contention resolution diversity slotted ALOHA (CRDSA), irregular repetition slotted ALOHA (IRSA), coded slotted ALOHA (CSA) and enhanced spread-spectrum ALOHA (E-SSA). We show that the typical simplifications of the reception model when used to optimize RA schemes lead to inaccurate findings, both in terms of parameter optimization and figures of merit, such as the packet loss ratio (PLR) and throughput. We also derive a generic RA energy efficiency model able to compare the schemes in terms of the energy required to transmit a packet. The combination of achievable RA throughput at the target PLR and energy efficiency, for the same average user power investment per frame and occupied bandwidth, shows that E-SSA, which is an unslotted scheme, provides the best overall performance, while, in terms of the slotted schemes, CRDSA outperforms the more elaborated IRSA and CSA. This surprising results are due to the fact that the IRSA and CSA optimization has so far been performed using RA channel models that are not accurately reflecting the physical layer receiver behavior. We conclude by providing insights on how to include more accurate reception models in the IRSA and CSA design and optimization.

We consider a monitoring application where sensors periodically report data to a common receiver using time division multiplexing. The sensors are constrained by the limited and unpredictable energy availability provided by energy harvesting (EH), and by the channel impairments. To maximize the quality of the reported data, the packets transmitted contain newly generated data blocks together with up to r -1 previously unsuccessfully delivered ones, where r is a design parameter. These data blocks are compressed, concatenated, and encoded with a channel code. The scheme applies lossy compression, such that the fidelity of the individual blocks is traded off with the reliability provided by the channel code. We show that the proposed strategy outperforms the one in which retransmissions are not allowed. We also investigate the tradeoff between the value of r, the compression and the coding rates, under the constraints of the energy availability, and, once r has been decided, use a Markov decision process (MDP) to optimize the compression/coding rates. Finally, we implement a reinforcement learning algorithm, through which devices can learn the optimal transmission policy without knowing a priori the statistics of the EH process, and show that it indeed reaches the performance obtained via MDP.

We present a framework for random access that is based on three elements: physical-layer network coding (PLNC), signature codes, and tree splitting. In presence of a collision, physical-layer network coding enables the receiver to decode, i.e., compute, the sum of the packets that were transmitted by the individual users. For each user, the packet consists of the user's signature, as well as the data that the user wants to communicate. As long as no more than K users collide, their identities can be recovered from the sum of their signatures. This framework for creating and transmitting packets can be used as a fundamental building block in random access algorithms, since it helps to deal efficiently with the uncertainty of the set of contending terminals. In this paper, we show how to apply the framework in conjunction with a tree-splitting algorithm, which is required to deal with the case that more than K users collide. We demonstrate that our approach achieves throughput that tends to 1 rapidly as K increases. We also present results on net data-rate of the system, showing the impact of the overheads of the constituent elements of the proposed protocol. We compare the performance of our scheme with an upper bound that is obtained under the assumption that the active users are a priori known. Also, we consider an upper bound on the net data-rate for any PLNC-based strategy in which one linear equation per slot is decoded. We show that already at modest packet lengths, the net data-rate of our scheme becomes close to the second upper bound, i.e., the overhead of the contention resolution algorithm and the signature codes vanishes.

We present a novel access protocol for crowd scenarios in massive multiple-input multiple-output (MIMO) systems. Crowd scenarios are characterized by a large number of users with intermittent access behavior, whereas orthogonal scheduling is infeasible. In such scenarios, random access is a natural choice. The proposed access protocol relies on two essential properties of a massive MIMO system, namely, asymptotic orthogonality between user channels and asymptotic invariance of channel powers. Signal processing techniques that take advantage of these properties allow us to view a set of contaminated pilot signals as a graph code on which iterative belief propagation can be performed. This makes it possible to decontaminate pilot signals and increase the throughput of the system. Numerical evaluations show that the proposed access protocol increases the throughput by 36%, when there are 400 antennas at the base station, compared to the conventional method of slotted Additive Links On-line Hawaii Area. With 1024 antennas, the throughput is increased by 85%.

Radio access management plays a vital role in delay and energy consumption of connected devices. The radio access in existing cellular networks is unable to efficiently support massive connectivity, due to its signaling overhead. In this paper, we investigate an asynchronous grant-free narrowband data transmission protocol that aims to provide low energy consumption and delay, by relaxing the synchronization/reservation requirement at the cost of sending several packet copies at the transmitter side and more complex signal processing at the receiver side. Specifically, the timing and frequency offsets, as well as sending of multiple replicas of the same packet, are exploited as form of diversities at the receiver-side to trigger successive interference cancellation. The proposed scheme is investigated by deriving closed-form expressions for key performance indicators, including reliability and battery-lifetime. The performance evaluation indicates that the scheme can be tuned to realize long battery lifetime radio access for low-complexity devices. The obtained results indicate existence of traffic load regions, where synchronous access outperforms asynchronous access and vice versa.

One of the novelties brought by 5G is that wireless system design has increasingly turned its focus on guaranteeing reliability and latency. This shifts the design objective of random access protocols from throughput optimization towards constraints based on reliability and latency. For this purpose, we use frameless ALOHA, which relies on successive interference cancellation (SIC), and derive its exact finite-length analysis of the statistics of the unresolved users (reliability) as a function of the contention period length (latency). The presented analysis can be used to derive the reliability-latency guarantees. We also optimize the scheme parameters in order to maximize the reliability within a given latency. Our approach represents an important step towards the general area of design and analysis of access protocols with reliability-latency guarantees.

We propose a novel framework for secure and reliable authentication of Distributed Energy Resources to the centralized secondary/tertiary control system of a DC MicroGrid (MG), networked using the IEEE 802.11 wireless interface. The key idea is to perform the authentication using power talk - a powerline communication technique executed by the primary control loops of the power electronic converters. In addition, the scheme also promotes direct and active participation of the control system in the authentication process, a feature not commonly encountered in current networked control systems for MicroGrids. The FLECS®-based simulations verifies the viability of the proposed solution.

Random access protocols relying on the transmission of packet replicas in multiple slots and exploiting interference cancellation at the receiver have been shown to achieve performance competitive with that of orthogonal schemes. So far the optimization of the repetition degree profile, defining the probability for a user to transmit a given number of replicas, has mainly been performed targeting the collision channel model. In this paper the analysis is extended to a block fading channel model, also assuming capture effect at the receiver. Density evolution equations are developed for the new setting and, based on them, some repetition degree profiles are optimized and analyzed via Monte Carlo simulation in a finite frame length setting. The derived distributions are shown to achieve throughputs largely exceeding 1 [packet/slot].

We present a connection establishment protocol with integrated authentication, suited for Massive Machine-Type Communications (mMTC). The protocol is contention-based and its main feature is that a device contends with a unique signature that also enables the authentication of the device towards the network. The signatures are inspired by Bloom filters and are created based on the output of the MILENAGE authentication and encryption algorithm set, which is used in the authentication and security procedures in the LTE protocol family. We show that our method utilizes the system resources more efficiently, achieves lower latency of connection establishment for Poisson arrivals and allows a 87% signalling overhead reduction. An important conclusion is that the mMTC traffic benefits profoundly from integration of security features into the connection establishment/access protocols, instead of addressing them post-hoc, which has been a common practice.

Energy harvesting is a promising technology for the Internet of Things (IoT) towards the goal of self-sustainability of the involved devices. However, the intermittent and unreliable nature of the harvested energy demands an intelligent management of devices' operation in order to ensure a sustained performance of the IoT application. In this work, we address the problem of maximizing the quality of the reported data under the constraints of energy harvesting, energy consumption and communication channel impairments. Specifically, we propose an energy-aware joint source-channel coding scheme that minimizes the expected data distortion, for realistic models of energy generation and of the energy spent by the device to process the data, when the communication is performed over a Rayleigh fading channel. The performance of the scheme is optimized by means of a Markov Decision Process framework.

We study a communication framework for nonlinear multibus DC MicroGrids based on a deliberate modification of the parameters of the primary control and termed power talk. We assess the case in which the information is modulated in the deviations of reference voltages of the primary control loops and show that the outputs of the power talk communication channels can be approximated through linear combinations of the respective inputs. We show that the coefficients of the linear combinations, representing equivalent channel gains, depend on the virtual resistances of the primary control loops, implying that they can be modified such that effective received signal-to-noise ratio (SNR) is increased. On the other hand, we investigate the constraints that power talk incurs on the supplied power deviations. We show that these constraints translate into constraints on the reference voltages and virtual resistances that are imposed on all units in the system. In this regard, we develop an optimization approach to find the set of controllable virtual resistances that maximize SNR under the constraints on the supplied power deviations.

We present a random access method inspired on Bloom filters that is suited for Machine-Type Communications (MTC). Each accessing device sends a signature during the contention process. A signature is constructed using the Bloom filtering method and contains information on the device identity and the connection establishment cause. We instantiate the proposed method over the current LTE-A access protocol. However, the method is applicable to a more general class of random access protocols that use preambles or other reservation sequences, as expected to be the case in 5G systems. We show that our method utilizes the system resources more efficiently and achieves significantly lower connection establishment latency in case of synchronous arrivals, compared to the variant of the LTE-A access protocol that is optimized for MTC traffic. A dividend of the proposed method is that it allows the base station (BS) to acquire the device identity and the connection establishment cause already in the initial phase of the connection establishment, thereby enabling their differentiated treatment by the BS.

IEEE 802.11ah is a novel WiFi-based protocol, aiming to provide an access solution for the machine-to-machine (M2M) communications. In this paper, we propose an adaptive access mechanism that can be seamlessly incorporated into IEEE 802.11ah protocol operation and that supports all potential M2M reporting regimes, which are periodic, on-demand, and alarm reporting. The proposed access method is based on a periodically reoccurring pool of time slots, whose size is proactively determined on the basis of the reporting activity in the cell. We show that it is possible to both efficiently and reliably resolve all reporting stations in the cell, within the limits of the allowed deadlines. As a side result, we also provide a rationale for modeling the interarrival time in alarm events by using the Beta distribution, a model that is considered in the 3GPP standardization.

Power talk is a novel ultra narrow-band powerline communication (UNB-PLC) technique for communication among control units in MicroGrids (MGs). Unlike the existing UNB-PLC solutions, power talk does not require installation of additional dedicated communication hardware and, instead, uses only the power electronic converters through which the control units interface the common bus. This way the communication system has practically the same reliability as the power system. The information is transmitted by modulating the parameters of the primary control, incurring subtle power deviations that can be detected by other units. In this paper, we develop power talk communication strategies for direct-current (DC) MG systems with arbitrary number of control units that carry out all-to-all communication. We investigate two multiple access strategies: time-division multiple access, where only one unit transmits at a time, and full duplex, where all units transmit and receive simultaneously. We apply the concepts of signaling space, where the power talk symbol constellations are constructed, and detection space, where the demodulation of the symbols is performed. The proposed communication technique is challenged by the random changes of the bus parameters due to load variations. To this end, we investigate the performance of power talk when a solution based on training sequences that re-establishes detection spaces is employed. The presented evaluation shows that power talk has a potential to offer an effective and inexpensive solution for reliable communication among units in DC MGs.

While LTE has been widely rolled out for human-type services, it is also a promising solution for cost-efficient connectivity of the smart grid monitoring equipment. This is a type of machine-to-machine (M2M) traffic that consists mainly of sporadic uplink transmissions. In such a setting, the amount of traffic that can be served in a cell is not constrained by the data capacity, but rather by the signaling constraints in the random access channel and control channel. In this paper, we explore these limitations using a detailed simulation of the LTE access reservation protocol (ARP). We find that 1) assigning more random access opportunities may actually worsen performance and 2) the additional signaling that follows the ARP has very large impact on the capacity in terms of the number of supported devices; we observed a reduction in the capacity by almost a factor of 3. This suggests that a lightweight access method, with a reduced number of signaling messages, needs to be considered in standardization for M2M applications. Additionally we propose a tractable analytical model to calculate the outage that can be rapidly implemented and evaluated. The model accounts for the features of the random access, control channel, and uplink and downlink data channels, as well as retransmissions.

We introduce a novel communication strategy for DC Micro Grids (MGs), termed power talk, in which the devices communicate by modulating the power levels in the DC bus. The information is transmitted by varying the parameters that the MG units use to control the level of the common bus voltage, while it is received by processing the bus measurements that units perform. This implies that the communication does not require a dedicated modem, but instead it is piggybacked on top of the power electronics. The communication is challenged by the random fluctuations of the voltage level due to the random load variations in the MG. We develop the corresponding communication model and address the random voltage fluctuations by using coding strategies that transform the MG into some well- known communication channels. The performance analysis shows that it is possible to mitigate the random voltage level variations and communicate reliably over the MG bus.

Machine-to-Machine (M2M) communications are one of the major drivers of the cellular network evolution towards 5G systems. One of the key challenges is on how to provide reliability guarantees to each accessing device in a situation in which there is a massive number of almost-simultaneous arrivals from a large set of M2M devices. The existing solutions take a reactive approach in dealing with massive arrivals, such as non-selective barring when a massive arrival event occurs, which implies that the devices cannot get individual reliability guarantees. In this paper we propose a proactive approach, based on a standard operation of the cellular access. The access procedure is divided into two phases, an estimation phase and a serving phase. In the estimation phase the number of arrivals is estimated and this information is used to tune the amount of resources allocated in the serving phase. Our results show that the proactive approach is instrumental in delivering high access reliability to the M2M devices.

The emergence of Machine-to-Machine (M2M) communication requires new Medium Access Control (MAC) schemes and physical (PHY) layer concepts to support a massive number of access requests. The concept of coded random access, introduced recently, greatly outperforms other random access methods and is inherently capable to take advantage of the capture effect from the PHY layer. Furthermore, at the PHY layer, compressive sensing based multi-user detection (CS-MUD) is a novel technique that exploits sparsity in multi-user detection to achieve a joint activity and data detection. In this paper, we combine coded random access with CS-MUD on the PHY layer and show very promising results for the resulting protocol.

The analogies between successive interference cancellation (SIC) in slotted ALOHA framework and iterative belief-propagation erasure-decoding, established recently, enabled the application of the erasure-coding theory and tools to design random access schemes. This approach leads to throughput substantially higher than the one offered by the traditional slotted ALOHA. In the simplest setting, SIC progresses when a successful decoding occurs for a single user transmission. In this paper we consider a more general setting of a channel with capture and explore how such physical model affects the design of the coded random access protocol. Specifically, we assess the impact of capture effect in Rayleigh fading scenario on the design of SIC-enabled slotted ALOHA schemes. We provide analytical treatment of frameless ALOHA, which is a special case of SIC-enabled ALOHA scheme. We demonstrate both through analytical and simulation results that the capture effect can be very beneficial in terms of achieved throughput.

We propose a contention-based random-access protocol, designed for wireless networks where the number of users is not a priori known. The protocol operates in rounds divided into equal-duration slots, performing at the same time estimation of the number of users and resolution of their transmissions. The users independently access the wireless link on a slot basis with a predefined probability, resulting in a distribution of user transmissions over slots, based on which the estimation and contention resolution are performed. Specifically, the contention resolution is performed using successive interference cancellation which, coupled with the use of the optimized access probabilities, enables throughputs that are substantially higher than the traditional slotted ALOHA-like protocols. The key feature of the proposed protocol is that the round durations are not a priori set and they are terminated when the estimation/contention-resolution performance reach the satisfactory levels.

This paper presents a novel approach to multiple access control called coded splitting tree protocol. The approach builds on the known tree splitting protocols, code structure and successive interference cancellation (SIC). Several instances of the tree splitting protocol are initiated, each instance is terminated prematurely and subsequently iterated. The combined set of leaves from all the tree instances can then be viewed as a graph code, which is decodable using belief propagation. The main design problem is determining the order of splitting, which enables successful decoding as early as possible. Evaluations show that the proposed protocol provides considerable gains over the standard tree splitting protocol applying SIC. The improvement comes at the expense of an increased feedback and receiver complexity.

Various applications of wireless Machine-to-Machine (M2M) communications have rekindled the research interest in random access protocols, suitable to support a large number of connected devices. Slotted ALOHA and its derivatives represent a simple solution for distributed random access in wireless networks. Recently, a framed version of slotted ALOHA gained renewed interest due to the incorporation of successive interference cancellation (SIC) in the scheme, which resulted in substantially higher throughputs. Based on similar principles and inspired by the rateless coding paradigm, a frameless approach for distributed random access in the slotted ALOHA framework is described in this paper. The proposed approach shares an operational analogy with rateless coding, expressed both through the user access strategy and the adaptive length of the contention period, with the objective to end the contention when the instantaneous throughput is maximized. The paper presents the related analysis, providing heuristic criteria for terminating the contention period and showing that very high throughputs can be achieved, even for a low number for contending users. The demonstrated results potentially have more direct practical implications compared to the approaches for coded random access that lead to high throughputs only asymptotically.

In this paper we present a statistical analysis of the search for a set of predefined sequences in non-equiprobable data streams, both random and framed, and derive a number of corresponding statistical parameters. Most importantly, we derive the probability of occurrence of some sequence from the set at any given position, from which all other statistical quantities of interest can be obtained. The presented analysis is based on descriptors called cross-bifices, which express similarities among sequences in the set. Based on the obtained results, we examine the problem of frame synchronization in synchronous transmission and derive parameters that could be used for the evaluation of the properties of frame-synchronization sequences. We introduce a survival probability of the search in a frame and a list-miss probability for the list synchronizer, which give exact bounds on the probability of correct frame synchronization when hard-correlation is used as a sequence detection metric.

In this paper we propose an end-to-end solution for urban infrastructure-to-vehicle traffic data delivery based on a class of unequal error protection (UEP) rateless codes called expanding window fountain (EWF) codes. The proposed solution relies on attractive features that rateless codes introduce to networks with unpredictable dynamics: the universal capacity approaching property which is well-matched to time-varying behavior of wireless links, and the innovative nature of each encoded packet which makes both time-consuming retransmission and content-reconciliation mechanisms unnecessary. Furthermore, usage of EWF codes allows separation of delivered data in importance classes with different error protection and recovery time guarantees, enabling mobile users to retrieve more important information more reliably and in shorter time span, thus making the proposed solution suitable for time-critical services. The addressed urban communication scenario consists of large number of sensors that sample and relay traffic flow information to network of Access Points (APs). APs use the existing underlying communication infrastructure, such as metropolitan area networks (MANs), to exchange traffic flow data, encode it using EWF coding principles, and finally disseminate it to roaming vehicles that join the network service in an ad-hoc manner in order to retrieve information regarding the surrounding environment. The proposed approach is suitable for real-time applications, such as frequent periodic reporting of urban traffic conditions, that could be used by on-board computers to provide improved navigation for end-users.

In this paper, we investigate the design of decentralized encoding procedure for distributed random linear coding (RLC) in resource-constrained wireless networks. We propose a novel distributed RLC scheme called “Fireworks”, analyze its performance and support it by simulation results. The presented results demonstrate design flexibility of the proposed scheme, where the design choices influence the trade-off between the coding efficiency and encoding communication costs.

This paper presents a data gathering scheme designed for an emergency monitoring Wireless Sensor Network (WSN), which employs distributed rateless encoding and dispersion of the encoded data toward the perimeter of the monitored area, where it is collected by a mobile collector (MC). Rate-less codes are low encoding/decoding complexity codes, suitable for resource constrained devices, such as sensor nodes, while the proposed distributed encoding and dispersion, performed using only local knowledge, are adapted to WSNs where the frequent changes in underlying topology and connectivity are expected. The proposed scheme allows for data persistence and efficient data gathering in the scenarios where decentralized WSNs are deployed in inaccessible locations with no infrastructure support, where nodes are prone to failures due to harsh operating conditions and where the access of the MC to the monitoring area is severely limited.

In this paper, we propose a novel approach for data gathering in wireless sensor networks (WSN) based on distributed rateless codes. Rateless codes are an efficient, lowcomplexity solution for coded data transmission over channels with packet erasures, which motivates their application in distributed network scenarios such as WSN. Recently proposed distributed rateless coding techniques for WSN are node-centric, i.e., collecting sufficient number of different sensor data packets and performing rateless encoding is the task of sensor nodes. In the proposed packet-centric approach, this task is assigned to encoded packets called rateless packets. While randomly moving through the network, rateless packets collect and encode into their content required number of uniformly sampled sensor data packets, completing their paths in randomly selected network nodes. Using this approach, any degree distribution of rateless codes can be exactly obtained. The problem of uniform combining of sensor data into rateless packets, and uniform dispersion throughout the network is addressed. The efficiency of the proposed scheme and comparison with the performance of centralized rateless codes are demonstrated by simulation results.

In this paper, we propose a simple and efficient two-stage list synchronizer for frame synchronization of low-density parity-check (LDPC) coded data transmitted over the additive white Gaussian noise (AWGN) channel. The proposed method uses both synchronization sequence and code constraints for frame synchronization. In the first stage, a list of the most likely frame starting positions is made using synchro-sequence, while in the second stage the code constraints are used to select the most likely position from the list. We analytically relate the synchro- sequence length, the frame length and the list length in order to keep the synchronization frame error rate (FER) lower than the error-correcting FER of the LDPC code, and we investigate the trade-off between the sequence length and the list length. We demonstrate that the proposed scheme outperforms both the frame synchronization methods based on synchro-sequence only and the recently proposed blind synchronization methods based on LDPC code constraints, the former in terms of the synchronization FER for the same sequence lengths and the latter in terms of complexity.

In this paper, we address the problem of distributed Raptor code design over information packets located across the network nodes. We propose a novel approach to this problem that consists of generating, encoding and dispersing Raptor packets across the network. Unlike recent node-centric proposals, where network nodes are responsible for collecting information packets and performing Raptor encoding, in the proposed packet-centric approach this task is assigned to Raptor packets. In a two-step encoding procedure that corresponds to precoding and LT-coding step of standard Raptor encoding, Raptor packets randomly traverse the network, collect and encode sufficient number of information packets following exactly a given degree distribution, and finish their paths in a random network node. The efficiency of the distributed Raptor coding scheme is confirmed by simulation results, where their performance is demonstrated to approach closely the performance of standard (centralized) Raptor codes.

In this paper, we propose a novel approach for data gathering in wireless sensor networks (WSN) based on distributed rateless codes. Rateless codes are an efficient, low-complexity solution for coded data transmission over erasure channels, which motivates their application in distributed network scenarios such as WSN. Recently proposed distributed rateless coding techniques for WSN are node-centric, i.e., the task of collecting sufficient number of different sensor data packets and performing rateless encoding is responsibility of sensor nodes. In the proposed packet- centric approach, this task is assigned to encoded data packets called rateless packets. While randomly moving through the network, rateless packets collect required number of uniformly sampled sensor data packets, finishing their paths in randomly selected network nodes. Using this approach, any degree distribution of rateless codes can be exactly obtained. The problem of uniform combining of sensor data into rateless packets, and uniform dispersion throughout the network is addressed. The efficiency of the proposed scheme and its comparison with the performance of centralized rateless codes are demonstrated by simulation results.

An analytical approach to a search process is a mathematical prerequisite for digital synchronization acquisition analysis and optimization. A search is performed for an arbitrary set of sequences within random but not equiprobable L-ary data. This paper derives in detail an expression for probability distribution function, from which other statistical parameters - expected value and variance - can be obtained. The probabilistic nature of (cross-) bifix indicators is shown and application examples are outlined, ranging beyond the usual telecommunication field