This paper presents a Markov-based system model for microfluidic molecular communication (MC) channels. By discretizing the advection-diffusion dynamics, the proposed model establishes a physically consistent state-space formulation. The transition matrix explicitly captures diffusion, advective flow, reversible binding, and flow-out effects. The resulting discrete-time formulation enables analytical characterization of both transient and equilibrium responses through a linear system representation. Numerical results verify that the proposed framework accurately reproduces channel behaviors across a wide range of flow conditions, providing a tractable basis for the design and analysis of MC systems in microfluidic environments.

DNA-based Molecular Communication (MC) has received significant attention for its potential in nanoscale bio-communication systems such as drug delivery, biosensing, and the Internet of Bio-Nano Things (IoBNT). This paper presents a theoretical model for DNA-based MC in spatially confined environments. In the proposed framework, the transmitter (TX) releases complementary DNA (cDNA) molecules in the channel, which reversibly bind to immobilized probe DNAs at the receiver (RX). The stochastic dynamics of diffusion, binding, and release are characterized by a Markov process. Closed-form channel characteristics, including the Channel Impulse Response (CIR), equilibrium distribution, and settling time, are derived. System performance is evaluated through both the proposed model and Particle-Based Simulation (PBS). The results demonstrate that the proposed model effectively describes the dynamic behavior of DNA hybridization-based MC, offering accurate predictions with significantly lower computational complexity than conventional simulation methods.

Evolving communication technologies and standards enable novel IoT use cases, such as real-time edge or fog assistance for distributed sensing and control in smart environments. Synchronizing the system state at centralized management entities with distributed components is a network resource-hungry task. Semantic communication is envisioned to cope with rapid network traffic increase and associated performance degradation by optimizing resource utilization for the application goal. Identification (ID) codes are a novel semantic technology that reduces network traffic for remote synchronization by representing a potentially multidimensional system state with a short tag. This work exploits ID codes in the intelligent transportation context. The vehicles in a truck platoon use ID codes to synchronize their distributed control decisions with the centralized edge server. We present one of the first frameworks demonstrating the advantages of using ID codes in practical scenarios. We show that the truck platoon management utilizing ID codes is$\sim 40 {\%}$more efficient than a fully distributed control scenario and never lags behind a fully centralized control. Moreover, ID codes reduce network traffic by$\sim 4$times compared to the latter.

As one of the most prevalent diseases worldwide, plaque formation in human arteries, known as atherosclerosis, is the focus of many research efforts. Previously, molecular communication (MC) models have been proposed to capture and analyze the natural processes inside the human body and to support the development of diagnosis and treatment methods. In the future, synthetic MC networks are envisioned to span the human body as part of the Internet of Bio-Nano Things (IoBNT), turning blood vessels into physical communication channels. By observing and characterizing changes in these channels, MC networks could play an active role in detecting diseases like atherosclerosis. In this paper, building on previous preliminary work for simulating an MC scenario in a plaque-obstructed blood vessel, we evaluate different analytical models for non-Newtonian flow and derive associated channel impulse responses (CIRs). Additionally, we add the crucial factor of flow pulsatility to our simulation model and investigate the effect of the systole-diastole cycle on the received particles across the plaque channel. We observe a significant influence of the plaque on the channel in terms of the flow profile and CIR across different emission times in the cycle. These metrics could act as crucial indicators for early non-invasive plaque detection in advanced future MC methods.

Stochastic network codes for consensus testing (CT) via a relay are proposed, where each of two or more parties knows a message and can find out if all these messages are equal, e.g. as an integrity check in a decentralized storage system or the control of mobile autonomous robots. The proposed codes achieve the CT capacity for memoryless uplinks channels when common randomness (CR) is available and no local randomness is used. With only local randomness at the edge nodes, upper and lower bounds for the capacity are given. The lower bound is achieved by CR generation via decode-and-forward transmission, and then using a common-randomness (CR)-assisted code. The upper bound is imposed by the CT over the uplink, when this consists of independent parallel channels to the relay. A recent derandomization result for encoders shows that, unlike deterministic encoding and CR shared between both encoders, the use of local randomness prevents the relay from successfully testing consensus. Therefore, in the proposed coding scheme, the relay recodes only to transmit random seeds and message hashes generated with these seeds. This scheme relies on an underlying CT code based on almost-universal hashing, where hashing is done with random seeds.

We introduce the concept of locally homomorphic channels (LHCs) as a framework for analyzing the composition and decomposition of channels that simulate functions. We establish an equivalence between a specific class of LHCs and function computation codes for noisy channels. Further, we show for LHCs composed of multiple parts, e.g., an encoder, a noisy channel, and a decoder, that each component is independently locally homomorphic. A key implication is that stochastic decoding offers only very limited improvements in reliability. In scenarios where two messages from a large set are encoded independently, such as in K-identification, we prove that, in general, at most one of the encoders can compress the messages to logarithmic size. This result has significant consequences: for instance, it implies that consensus testing (CT) over discrete memoryless multiple-access channels becomes impossible when the message set has double-exponential size. In contrast, independent encoders can be reliable in such a setting, when the number of messages is only exponential. We demonstrate this for the example of deterministic consensus testing over a pair of binary symmetric channels.

This study demonstrates Functional Compression (FC) using a real-time control application. We deploy FC across the network using a ball-balancing robot with vision-based control. We exhibit seamless migration of visual computation between numerous edge cloud servers while the robot continues to balance the ball. The following demonstration showcases one of the earliest applications of FC while emphasizing the importance of beyond-Shannon technology.

Communication systems are delicate to deliberate jamming, which may ruin legitimate communication. Two kinds of jammers exist: partial knowledge and full knowledge jammers. Jammers with partial knowledge only know the encoding and decoding functions, whereas jammers with full knowledge also know the actual message. This paper studies the detectability of Denial of Service (DoS) attacks by jammers with partial knowledge. The theoretical framework that characterizes the detectability of a DoS attack is expressed in the literature for Turing machines with no computing capacity scarcity. Even though there is no computational limitation for Turing machines, they cannot decide if a DoS attack is possible. Conversely, by following the theoretical framework, Turing machines can recognize an Arbitrarily Varying Channel (AVC) which a DoS attack is not possible. In these circumstances, we present an algorithm that detects the scenarios where DoS attack is impossible. We provide a complexity analysis and perform Monte Carlo simulations to investigate the performance of the proposed algorithm in terms of time consumption. We observe that the simulation results are compatible with the complexity analysis.

Physical-layer network codes for consensus-testing (CT) via a relay are proposed, where each of two parties knows a message and can find out if all messages are equal, e.g. as an integrity check in a decentralized storage system or the control of mobile autonomous robots. By assumption, the encoders cannot randomize. The proposed codes achieve the CT capacity for channels with a memoryless uplink multiple-access channel that is a binary adder channel or a pair of q-ary symmetric or erasure channels. There, the capacity of noiseless uplinks can always be achieved, by using generalized deterministic identification (ID) codes for the uplink, testing consensus at the relay, and broadcasting the one-bit result using zero rate. For pairs of Gaussian channels and Gaussian adder channels, the capacity bounds equal those known for ID over certain noiseless uplinks, where the code sizes scale superexponentially in the block length. Using a recent derandomization result for decoders, it is shown that for general channels, the ID capacity of certain noiseless uplinks upper-bounds the CT capacity. In contrast, both for transmission coding for the uplink and additive linear network codes, the asymptotically achievable code sizes and necessary block lengths are shown to be suboptimal.

The Interplanetary File System (IPFS) is one of the largest peer-to-peer networks currently in use. In networks with heterogeneous peers, IPFS may take a long time to obtain all packets. Random Linear Network Coding (RLNC) can be added to IPFS to mitigate the effects of the suboptimal requesting strategy, as proposed by the Multi Access Recording System (MARS) protocol. We measure the power consumption, and the file processing and receiving delay for MARS in a test bed and find that MARS introduces significant delay overhead because it encodes its packets only after receiving a request for a file. As a modification of MARS, we propose Storing Coded Packets In-advance for IPFS (SPIFI) that addresses this issue by offloading the computation of the RLNC encoded packets to a point in time before the packets are requested. This way, the power consumption, and the delay incurred by the RLNC encoding does not deteriorate performance at run time. In additional measurements, we find that SPIFI reduces the energy consumption at run time and the data retrieval time compared to both MARS and IPFS. The encoding time can be selected freely, to perform the computation when power grids have spare capacity, avoiding peak loads in the grid. SPIFI works best for small files of a few MB and the benefit vanishes for larger file sizes due to the decoding complexity. If the client only requests single code packets to increase the file redundancy in the network, then SPIFI always outperforms IPFS and MARS.

Identification is a new paradigm introduced for goal-oriented communication that verifies the semantics of the intended message rather than just transmitting the message bits, which is traditionally done in classical communication systems. One of the key techniques to improve randomized identification capacity is by introducing Common Randomness (CR). Physical Layer Secret Key (PLSK) is one such CR generation technique, where terminals generate secret keys based on changes in channel reciprocity between the wireless terminals and are mainly used for PHY layer encryption. However, the traditional way of evaluating PLSK techniques through bit discrepancies, bit generation rate, and entropy is not sufficient for evaluating their usefulness for identification. This paper proposes three key novel information theory paradigms that can be used alongside other mentioned parameters to evaluate key PLSK techniques for their usefulness in identification, i.e., CR rate, CR capacity, and cost function, followed by a supporting implementation using Software Defined Radio (SDR)s.

In a communication scenario with two transceivers (TRXs) and one relay, four time slots are required to communicate one packet in each direction if the relay uses a store-and-forward technique. With digital network coding (NC) three time slots are needed, and if analog netwok coding (ANC) is used, only two time slots are necessary. Communication systems with a low transmission rate such as molecular communication (MC) systems can especially benefit from the traffic reduction of ANC. The available literature only considers theoretical studies on the application of NC in MC nanonetworks. In this paper, we present for the first time a macroscale MC testbed for ANC to fill the gap between theory and practice. By using our testbed, we demonstrate the influence of time synchronization on the bit error ratio (BER) performance of the network coded MC system. We also study the influence of the bit sequence length on the error ratio of the system. Furthermore, we implemented duplex NC and demonstrated that the error ratios of half duplex and full duplex NC are close, but full duplex NC results in a time gain of up to$(n-1)$time slots for a bit sequence length n.

The trade-offs between storage and repair traffic for replacing failed storage nodes with new nodes (newcomers) in data centers with an omniscient controller are well understood. However, in edge storage settings, newcomers are not readily available, necessitating resilient data storage (caching) without newcomers. Edge storage nodes can often communicate via a broadcast wireless medium, which can be exploited to reduce the transmitted repair traffic via network coding. Repairs for resilient distributed caching without newcomers over a broadcast medium with Random Linear Network Coding (RLNC), which does not require an omniscient controller, have not been previously studied. We develop an information-theoretic model to characterize the theoretically achievable trade-offs between stored data and transmitted repair data in the RLNC broadcast setting without newcomers. Specifically, we formulate an Information Flow Graph (FG) model and identify all cuts in the resulting FG. We validate the theoretical FG model with simulations that demonstrate that the practically achievable trade-offs are close to the theoretical trade-offs.

Identification via channels (ID) is a goal-oriented (Post-Shannon) communications paradigm that verifies the matching of message (identity) pairs at source and sink. To date, ID research has focused on the upper bound$\lambda $for the probability of a false-positive (FP) identity match, mainly through ID tagging codes that represent the identities through ID codeword sets consisting of position-tag tuples. We broaden the ID research scope by introducing novel ID performance metrics: the expected FP-error probability$\overline {p_{\mathrm {fp}}}$which considers distance properties of ID codeword sets in conjunction with the probability for selecting ID pairs, the threshold probabilities$p_{\epsilon }$that characterize quantiles of FP-probabilities, and the distance tail uplift ratio DiTUR giving the fraction of ID pairs whose distance is increased above the minimum distance (which corresponds to$\lambda $). We define a No-Code (NC) approach that directly conducts the ID operations with the messages (identities) without any additional coding as a baseline for ID. We investigate a concatenated Reed-Solomon ID code and a Reed-Muller ID code, and find that they do not always yield advantages over using no ID code. We analytically characterize the reduction of error-prone ID pairs through sending multiple tags. Overall, our insights point to investigating the distance distribution of ID codes and to incorporating the ID pair distributions of real ID systems in future ID research.

Digital Twin (DT) implementations can reduce latency drastically in future communication systems for steering and control of cyber-physical systems (CPS). When the CPS and its DT periodically exchange information to repair possible desynchronisations between the two systems, this can lead to large data traffic flooding the networks. This work proposes a goal-oriented communication approach based on message identification (ID), which only sends repair data when necessary, but adds control traffic and a probability of not repairing some desynchronisations between the two systems. We compare optimal ID codes and a hash function for detection whether desynchronisation correction is necessary. Our method can reduce the traffic to within 0.3% of its optimal value while repairing 99.97% of all desynchronisations for 4 kbit of data and grows even better for larger data.

Delivering content from a network via a client-server architecture is expensive not only for content owners but also for network operators. Moving content closer to the end user is already used in Content Delivery Networks (CDN). Multi-Access Edge Computing (MEC) enables us to shift the content even closer by using the storage of end users. But, due to the large media files, storage and transport costs for peers increase significantly. Network Coding can reduce these costs. However, peers in CDNs tend to be highly fluctuating and often need to be restored, making continuous availability of data at the network edge a problem. While for uncoded data, individual packets lost due to peer failures can be tracked to determine availability, the availability of coded data is currently distinguished only in two cases: either there are still enough linearly independent packets to decode the file, or there are not. However, we have found that the network’s combined coded cache loses quality over time due to recovery. This quality loss, which we refer to as grade, can be measured by very cost-effective monitoring. If the grade falls below a certain limit, we can intervene in the network by performing a cache refresh to prevent data becoming unavailable preemptively. In this paper, we present the cases in which such monitoring is useful, how the grade is calculated, and when a cache refresh is necessary. The results show that we can reduce network traffic by up to 34% with minimal storage costs through efficient monitoring.

Random Linear Network Coding (RLNC) has been proven to improve the performances of Internet of Things (IoT) devices, such as throughput and resilience. However, for latency-sensitive applications, sliding window RLNC (SWNC) outperforms the block-based RLNC because it allows instantaneous packet decodeability and reduces the in-order delay. However, most conventional SWNC approaches have been applied to single-hop communication networks since recoding at intermediate nodes destroy the properties of the coding windows. In this work, we consider multi-hop communication networks and propose SWNC-based recoding algorithms that can be applied at inter-mediate nodes. The simulation results show that the proposed recoding algorithms outperform block-based RLNC by 53% in terms of the per-packet delay performance.

Future communication systems will require low latencies, especially for the small packets used in control applications. In WiFi systems, the main contributors to delays are retransmissions and congestions. Many of the transmitted packets are received with errors at the destination, and they are dropped at the MAC layer. Even if the number of errors is small the packets are discarded. Dropped partial packets are retransmitted, increasing the delays of the system because retransmissions involve new access to a shared medium. We show in our measurement campaign of IEEE 802.11 networks that between 5% and over 50% of the received packets, depending on the Modulation and Coding Scheme (MCS), have errors. However, only a few bits are erroneous (between 2% and 10%). Therefore, we show that a partial packet recovery technique based on network coding can make use of 55% more packets that otherwise would be dropped, which reduces the number of retransmissions and delays. Furthermore, if the length of the packets is short (between 50 and 250 Bytes) the computational overhead is low, which makes the implementation feasible, in such networks, for the short packets typical of applications of control of cyber-physical systems.

Coding techniques have improved the performance of distributed cloud systems (DCS). On one hand, they increased the reliability while trading off storage and bandwidth costs, and on the other hand, they provide high download speeds of data without centralized protocols. The higher data rates and lower delays are achieved by increasing the redundancy stored in clouds with higher bandwidth, allowing them to send more information before running dry. However, storing much information in a small set of clouds can violate confidentiality of the data in the presence of an attacker that breaks into these clouds. In this paper, we propose a technique that uses light-weight secure network coding schemes to increase the download rates of DCS. We compare the secure network coding techniques in terms of delay and codecs throughput, and we show that in multi-cloud systems, it is possible to obtain gains of 379% over DCS with equally shared redundancy and 192% over SotA techniques that aim to maximize the data rates by distributing more redundancy to the clouds with highest bandwidth.

Fulcrum decoders can trade off between computational complexity and the number of received packets. This allows heterogeneous nodes to decode at different level of complexity in accordance with their computing power. Variations of Fulcrum codes, like dynamic sparsity and expansion packets (DSEP) have significantly reduced the encoders and decoders' complexity by using dynamic sparsity and expansion packets. However, limited effort had been done for recoders of Fulcrum codes and their variations, limiting their full potential when being deployed at multi-hop networks. In this paper, we investigate the drawback of the conventional Fulcrum recoding and introduce a novel recoding scheme for the family of Fulcrum codes by limiting the buffer size, and thus memory needs. Our evaluations indicate that DSEP recoding mechamism increases the recoding goodput by 50%, and reduces the decoding overhead by 60%-90% while maintaining high decoding goodput at receivers and small memory usage at recoders compared with the conventional Fulcrum recoding. This further reduces the resources needed for Fulcrum codes at the recoders.

The amount of consumer data transmitted over the internet will increase in the future. At the moment, this traffic is mainly handled by Content Delivery Networks (CDN) via Hypertext Transfer Protocol (HTTP). However, this server-based approach has the disadvantage that the servers themselves and their connections to the Internet are under a high load, which makes them a bottleneck. Therefore, new approaches for efficient content distribution are sought. One outstanding approach is the Interplanetary File System (IPFS), the synthesis of various successful Peer-to-Peer (P2P) approaches. Theoretically, the load on the server can be reduced by distributing the data to several nodes. To transmit data optimally, however, a high degree of coordination between nodes is necessary. Research on other content distribution schemes has shown that the cooperation effort can be avoided by using Random Linear Network Coding (RLNC). This has not been used in IPFS so far. In this work we present Multi Access Recoding System (MARS), a protocol which combines IPFS with RLNC. Simulations on the Interplanetary Testbed (IPTB) have shown that MARS is able to reduce the server load and download time by up to 50% compared to a transmission using only a single server and up to 45% compared to the same setup without RLNC. Even without coordination, this only causes an additional network load of 30%. In addition, MARS improves the download time by at least 15% even for very few peer nodes or peers with little storage.

Future communication systems, such as those enabling the Tactile Internet, will face disruptive changes compared to the state-of-the-art systems, which are 1) highly dynamic topology changes; 2) replacement of the end-to-end paradigm by real mesh topologies; and 3) a massive number of devices. To overcome these disruptive changes, future communication systems will substitute specialized hardware with generic hardware boxes and the softwarization paradigm. Furthermore, this approach will allow for a quick deployment of new services, which was known to the cloud service already. In this paper, we will introduce the most prominent candidates for softwarization such as software-defined networking (SDN) and network function virtualization (NFV) and explain the importance of these technologies for the upcoming 5G communication system and Tactile Internet applications realizing novel mobile edge computing, storage, and networking solutions. Specifically, we will discuss use cases of SDN/NFV such as network coding as a service, and ultrareliable distributed edge caching. Finally, we will describe our holistic testbed at the 5G Lab Germany as a fundamental step toward creating an experiment infrastructure that anticipates the 5G communication systems and Tactile Internet applications.

The 5G communication system will provide flexible and programmable networks by leveraging softwarization technologies such as NFV and SDN. That advanced feature of 5G networks will be demonstrated in the context of real-time video surveillance for public safety. Specifically, we demonstrate for the first time a practical deployment of Random Linear Network Coding (RLNC) with NFV and SDN technologies to improve video quality against packet loss due to congestion at the core network and signal impairment of lossy channels at network edges. The demonstration implements NFV and SDN applications on COTS devices to prove its flexibility and portability.

Random linear network coding (RLNC) has the potential to improve the performance of current and future Internet of Things (IoT) communication systems, but is computationally demanding due to matrix multiplications and inversions. Some single-core RLNC implementations achieve already sufficient coding speeds for contemporary multimedia streaming formats. However, advances in multimedia streaming formats and IoT applications will require the exploitation of heterogeneous multicore architectures, which are becoming common for a wide range of IoT nodes, including smartphones. In this paper, we introduce and evaluate efficient RLNC computing strategies for IoT node architectures, including the emerging heterogeneous big.LITTLE multicore architectures with multiple big (fast) cores and multiple LITTLE (slow) cores. In contrast to existing RLNC implementation strategies, we build on and adapt highly optimized dense matrix operations from the high performance computing field to RLNC on heterogeneous multicore IoT nodes. Our approach includes the optimization of RLNC matrix operations through optimized operations on matrix blocks with single instruction multiple data instructions. We schedule block operations on the heterogeneous cores through a directed acyclic graph that avoids artificial synchronization points while ensuring the data dependencies. We examine priority scheduling according to the number of outgoing dependencies of a task and data locality of cached blocks. Our extensive measurements with several heterogeneous big.LITTLE multicore IoT node and smartphone processor boards demonstrate higher RLNC encoding and decoding throughputs than existing approaches. Moreover, our measurements indicate that the utilization of more cores decreases energy consumption, which is an important goal for IoT nodes.

Although random linear network coding (RLNC) constitutes a highly efficient and distributed approach to enhance communication networks and distributed storage, it requires additional processing to be carried out in the network and in end devices. For mobile devices, this processing translates into energy use that may reduce the battery life of a device. This paper focuses not only on providing a comprehensive measurement study of the energy cost of RLNC in eight different computing platforms, but also explores novel approaches (e.g., tunable sparse network coding) and hardware optimizations for Single Instruction Multiple Data (SIMD) available in the latest generations of Intel and Advanced RISC Machines (ARM) processors. Our measurement results show that the former provides gains of two-to six-fold from the underlying algorithms over RLNC, while the latter provides gains for all schemes from 2× to as high as 20×. Finally, our results show that the latest generation of mobile processors reduce dramatically the energy per bit consumed for carrying out network coding operations compared to previous generations, thus making network coding a viable technology for the upcoming 5G communication systems, even without dedicated hardware.