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We analyze a remote sensing system in the Internet of things, where uncoordinated nodes send status updates to a common receiver to achieve information freshness, quantified through age of information. We consider a finite horizon scheduling over a random multiple access channel, where colliding messages are lost. We show that nodes must adopt a further randomization to deviate from identical schedules and escape collision deadlocks. Moreover, we discuss the impact of feedback availability if, due to, e.g., energy expenditure, it decreases the number of transmission opportunities.

DNA molecules, as a storage medium, possess unique advantages. Not only does DNA storage exhibit significantly higher storage density compared to electromagnetic storage media, but it also features low energy consumption and extremely long storage times. However, the integration of DNA storage into daily life remains distant due to challenges such as low storage density, high latency, and inevitable errors during the storage process. Therefore, this paper proposes constructing a DNA storage coding set based on the Levy Sooty Tern Optimization Algorithm (LSTOA) to achieve an efficient random-access DNA storage system. Firstly, addressing the slow iteration speed and susceptibility to local optima of the Sooty Tern Optimization Algorithm (STOA), this paper introduces Levy flight operations and propose the LSTOA. Secondly, utilizing the LSTOA, this paper constructs a DNA storage encoding set to facilitate random access while meeting combinatorial constraints. To demonstrate the coding performance of the LSTOA, this paper consists of analyses on 13 benchmark test functions, showcasing its superior performance. Furthermore, under the same combinatorial constraints, the LSTOA constructs larger DNA storage coding sets, effectively reducing the read–write latency and error rate of DNA storage. [ABSTRACT FROM AUTHOR]

With the increasingly dense deployment of base stations in the internet of things (IoT), the importance of interference management becomes ever more pronounced. In IoT environments, devices often employ random access, connecting to channels in a distributed manner. In scenarios involving massive numbers of devices, severe interference may arise between nodes, leading to significant degradation in the throughput performance of the network. To address interference control issues in networks with random access, a multi-base station slotted Aloha network based on cooperative reception was considered, the reinforcement learning techniques was leveraged to design adaptive transmission algorithms that effectively managed interference, optimized network throughput performance, and enhanced network fairness. Firstly, an adaptive transmission algorithm were devised based on Q-learning, which was verified to maintain high network throughput performance under varying traffic conditions through simulation. Secondly, to improve network fairness, the penalty function method was employed to refine the adaptive transmission algorithm. Simulations confirm that the fairness-optimized algorithm significantly enhances network fairness while preserving satisfactory network throughput performance.

Abstract FM-indexes are crucial data structures in DNA alignment, but searching with them usually takes at least one random access per character in the query pattern. Ferragina and Fischer [1] observed in 2007 that word-based indexes often use fewer random accesses than character-based indexes, and thus support faster searches. Since DNA lacks natural word-boundaries, however, it is necessary to parse it somehow before applying word-based FM-indexing. In 2022, Deng et al. [2] proposed parsing genomic data by induced suffix sorting, and showed that the resulting word-based FM-indexes support faster counting queries than standard FM-indexes when patterns are a few thousand characters or longer. In this paper we show that using prefix-free parsing—which takes parameters that let us tune the average length of the phrases—instead of induced suffix sorting, gives a significant speedup for patterns of only a few hundred characters. We implement our method and demonstrate it is between 3 and 18 times faster than competing methods on queries to GRCh38, and is consistently faster on queries made to 25,000, 50,000 and 100,000 SARS-CoV-2 genomes. Hence, it seems our method accelerates the performance of count over all state-of-the-art methods with a moderate increase in the memory. The source code for $$\texttt {PFP-FM}$$ PFP - FM is available at https://github.com/AaronHong1024/afm .

With the increasing demand for data exchange between nearby devices in proximity-based services, enhancing the security of wireless mutual broadcast (WMB) networks is crucial. However, WMB networks are inherently vulnerable to eavesdropping due to the open broadcast nature of their communication. This paper investigates the improvement of secrecy performance in random-access-based WMB (RA-WMB) networks by integrating physical layer security (PLS) techniques with hybrid duplex (HBD) operations under a stochastic geometry framework. The HBD method balances half-duplex (HD) receiving and full-duplex (FD) transceiving, utilizing self-interference cancellation (SIC) to enhance PLS performance. Key operational parameters, including transmission probability (TxPr), friendly jammer density, and conditions for FD operation, are designed to maximize secrecy performance. The analytical and numerical results demonstrate significant improvements in PLS performance, with SIC playing a critical role, particularly in scenarios with dense legitimate nodes, and with TxPr adjusted to balance HD receiving and FD transceiving based on SIC imperfections. The proposed design principles provide a comprehensive framework for enhancing the security of WMB networks, addressing the complex interplay of interference and SIC in various network configurations. [ABSTRACT FROM AUTHOR]

FM-indexes are crucial data structures in DNA alignment, but searching with them usually takes at least one random access per character in the query pattern. Ferragina and Fischer [1] observed in 2007 that word-based indexes often use fewer random accesses than character-based indexes, and thus support faster searches. Since DNA lacks natural word-boundaries, however, it is necessary to parse it somehow before applying word-based FM-indexing. In 2022, Deng et al. [2] proposed parsing genomic data by induced suffix sorting, and showed that the resulting word-based FM-indexes support faster counting queries than standard FM-indexes when patterns are a few thousand characters or longer. In this paper we show that using prefix-free parsing—which takes parameters that let us tune the average length of the phrases—instead of induced suffix sorting, gives a significant speedup for patterns of only a few hundred characters. We implement our method and demonstrate it is between 3 and 18 times faster than competing methods on queries to GRCh38, and is consistently faster on queries made to 25,000, 50,000 and 100,000 SARS-CoV-2 genomes. Hence, it seems our method accelerates the performance of count over all state-of-the-art methods with a moderate increase in the memory. The source code for PFP - FM is available at https://github.com/AaronHong1024/afm. [ABSTRACT FROM AUTHOR]

To address the issue of severe network congestion in the scenario of massive machine-type communications (mMTC), where machine-type communication devices (MTCD) employing traditional random access schemes often encountered challenges leading to a large number of MTCD failing to access the network successfully, a novel approach called preamble retransmission-based dynamic access class barring (PRT-ACB) was proposed.By utilizing the number of MTCD preamble retransmissions, the MTCD attempting to initiate access in each random access opportunity (RAO) were categorized into different sets of high and low priority.In conjunction with an estimation model for the number of payloads in each RAO, high and low-priority limiting factors and available preamble pools were dynamically adjusted based on the access load in each RAO.This allowed more MTCD to successfully access the network before reaching the maximum number of preamble transfers.Simulation results have demonstrated that the proposed scheme effectively enhances the MTCD successful access rate and reduces the time delay required for MTCD to access the network.The proposed scheme can serve as a solution to alleviate the congestion caused by a massive influx of communication devices attempting simultaneous network access.

Rapid advancements in Internet of Things (IoT) technology have promoted numerous novel applications that are sensitive to information timeliness. With different applications coexisting in one wireless network, each of which has heterogeneous traffic characteristics, it is of paramount importance while challenging to address how to characterize and optimize the information freshness performance network-wide, particularly in the massive access scenario. This paper casts attention on the heterogeneous slotted Aloha network and uses the Peak Age of Information (PAoI) metric to quantify information freshness. By assuming that the sensors in each group are equipped with unit-sized buffers and Bernoulli packet arrival, we derive the network steady-state point and PAoI. Depending on whether one group or all groups are age-sensitive, we focus on single-group PAoI and global PAoI optimization respectively. When only one single group is age-sensitive, we derive its optimal transmission probability. When all groups are age-sensitive, we propose a heuristic algorithm based on the particle swarm optimization method, where the bi-stability of Aloha network is considered for avoiding the risk of rapid performance deterioration. Extensive simulation results are presented to verify our analysis and the effectiveness of the proposed algorithm.

Narrowband Internet of Things (NB-IoT) is one of the most promising technologies for enabling reliable communication among low-power, and low cost devices present in massive machine-type communications (mMTC). In NB-IoT, random access (RA) is implemented in the medium access control (MAC) layer to resolve access contention among massive IoT devices. Efficient network access techniques are required to effectively solve the massive access issues in NB-IoT, guaranteeing increased throughput and high spectrum utilization. In this paper, we present a comprehensive overview of NB-IoT towards supporting mMTC, with focus on the NB-IoT coexistence with 5G, as well the design challenges and requirements of RA in NB-IoT. Moreover, available literature is reviewed to highlight the RA congestion control schemes proposed during the past few years to alleviate RA collisions. While existing RA approaches mainly focus on conventional contention-based techniques for performing RA, intelligent learning based and grant-free Non-Orthogonal Multiple Access (NOMA) have been identified as a potential candidates to increase the transmission efficiency of mMTC applications.

The swift progress of 6G cellular networks responds to the urgent demand for seamlessly integrating Internet of Things (IoT) devices on a large scale. Among various emerging technologies, grant-free non-orthogonal multiple access (GF-NOMA) emerges as a standout, offering distinct advantages over traditional networks in accommodating extensive connectivity needs. GF-NOMA optimizes resource usage by reallocating each time-frequency slot to multiple devices with different power levels. Furthermore, it reduces the coordination burden typically associated with uplink communication by broadcasting random access channels. Nonetheless, GF-NOMA faces a significant challenge: the risk of multiple devices inadvertently selecting the same resource and power, resulting in data loss, particularly problematic during emergencies marked by uncoordinated communications. Additionally, the expanding deployment of IoT devices demands a proportional increase in resources, despite advancements in network technology. To address these challenges, this paper introduces an innovative architecture aimed at significantly boosting spatial capacity through the establishment of autonomous social interactions among IoT devices. The proposed SAFE-GF-NOMA aggregation scheme facilitates resource sharing in an independent and ad-hoc trust management environment by ensuring trustworthy sharing. The proposed Social IoT (SIoT) framework reduces uplink access by grouping devices based on trust metrics, resulting in a notable 50% reduction in collision probability, and over a 50% increase in success probability, and a threefold capacity increase compared to conventional systems. Additionally, our system achieves a substantial reduction in energy consumption, cutting it from 17 J to just 5 J per device within the cluster.

The requirement of the upcoming sixth-generation (6G) wireless communication systems to significantly elevate the services of enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC) necessitates the design and investigation of appropriate multiple access schemes. This paper investigates the impact of random source deployment on the performance of uplink systems, emphasizing the implications of non-orthogonality in contention-free and contention-based access schemes. For the latter scheme, we combine the strengths of slotted ALOHA and successive interference cancellation. Considering the advantages of breaking orthogonality in scenarios with random source deployment, we propose distinct policies tailored for mMTC, eMBB, and hybrid mMTC-eMBB scenarios by splitting the cell into rings. Furthermore, we derive closed-form expressions for the outage probability, which play a pivotal role in extracting the throughput of the sources in the considered scenarios. The paper offers a comprehensive analysis of the proposed approach, corroborated through simulations, shedding light on the potential of such protocols in future 6G wireless communication systems.

In the process of the vigorous development of satellite internet of things, random access protocol is regarded as the most suitable communication protocol due to its characteristics of low signaling overhead, high transmission efficiency, high flexibility and easy implementation.However, due to drastic load changes, conventional random access mode only has advantages in the specific load range.Dynamic allocation of random access resources requires the accurate number of time slot access applications.In view of the above problems, a random access adaptive scheme was proposed, which could adaptively selected the appropriate random access mode according to the future load size, so as to improved the system throughput.The load size could be obtained through the proposed load estimation and prediction scheme.When narrowband interference exists in the transmission environment, the spread-spectrum communication means was introduced to made the adaptive scheme of random access run successfully.Moreover, the proposed adaptive scheme also improved the system throughput.

DNA molecules, as a storage medium, possess unique advantages. Not only does DNA storage exhibit significantly higher storage density compared to electromagnetic storage media, but it also features low energy consumption and extremely long storage times. However, the integration of DNA storage into daily life remains distant due to challenges such as low storage density, high latency, and inevitable errors during the storage process. Therefore, this paper proposes constructing a DNA storage coding set based on the Levy Sooty Tern Optimization Algorithm (LSTOA) to achieve an efficient random-access DNA storage system. Firstly, addressing the slow iteration speed and susceptibility to local optima of the Sooty Tern Optimization Algorithm (STOA), this paper introduces Levy flight operations and propose the LSTOA. Secondly, utilizing the LSTOA, this paper constructs a DNA storage encoding set to facilitate random access while meeting combinatorial constraints. To demonstrate the coding performance of the LSTOA, this paper consists of analyses on 13 benchmark test functions, showcasing its superior performance. Furthermore, under the same combinatorial constraints, the LSTOA constructs larger DNA storage coding sets, effectively reducing the read–write latency and error rate of DNA storage.

As IoT services become more active, the density of IoT devices is increasing, and massive connectivity technology is needed to support numerous devices simultaneously. In addition, IoT devices are often battery-powered, and during random access, it is necessary to reduce the power consumption to extend the lifetime of the devices. In particular, devices with poor channels need to send at a very low transmission rate through a large number of repetitions, and longer packet lengths can increase the probability of collisions, increasing the power consumption while shortening the lifetime of the IoT system. Dividing devices into groups based on the number of repetitions and allocating different resources to each group can reduce collisions for bad-channel devices, but it can be difficult to support large connections, due to the inefficient use of resources. This paper proposes schemes to reduce the collision probability of bad-channel devices while allowing IoT devices to use shared resources, instead of dividing resources by groups. There are two versions of the proposed schemes. The first method reduces collisions by delaying the response of a bad-channel device, and in the meantime, eliminating interference from other devices, assuming that the bad-channel device is not sensitive to delay. Instead of checking the response, and then, performing a random backoff when no acknowledgement packet is received, the second proposed method reverses the order of response checking and random backoff, that is, it first performs a random backoff, and then, checks the response to decide whether to retransmit. The proposed method can increase the lifetime of the IoT system by reducing the collision probability of a bad-channel device, without degrading the performance of other devices. [ABSTRACT FROM AUTHOR]

Copyright of Space-Integrated-Ground Information Networks is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the significant propagation delay in underwater sensor networks, conflict retransmission in channel access protocols comes at a high cost. This poses a challenge in scenarios where multiple sensor nodes generate data frames with strong temporal correlations, such as in disaster warning applications. Traditional channel allocation and timeout-based retransmission mechanisms lead to considerable access delays, making it difficult to meet the requirements. To tackle this issue, we propose the asynchronous pattern-designed random access (APDRA) protocol. This protocol enhances the access probability by designing retransmission time intervals for data frames based on pattern design. Additionally, we introduce a successive interference cancellation (SIC) mechanism at the receiver for decoding. This mechanism facilitates the transformation of the conventional method of discarding conflicted data frames into iterative decoding, thereby enhancing transmission efficiency. Via the utilization of simulations, we compare the APDRA protocol conventional underwater medium access control (MAC) protocols and existing retransmission mechanisms. The results demonstrate that the APDRA protocol has the ability to improve both the transmission success ratio (TSR) and reduces the access delay to some extent. [ABSTRACT FROM AUTHOR]

Currently, network requirements are placed on the efficiency and size of the networks. These conditions can be ensured by modern converged networks that integrate the functions of both data and telecommunication networks. Line or router failures have always been a part of transmission networks, which is no different from converged networks. As a result of outages, which can take from ms to tens of seconds, packets are lost. These outages cause degraded transmission quality, which is undesirable when transmitting real-time multimedia services (Voice over IP, video). To solve the mentioned problems, the IETF organization has developed IP Fast Reroute mechanisms to minimise the time to restore the connection after a line or node failure and, consequently, less packet loss. The article reviews and compares the latest IP Fast Reroute mechanisms deployed in the last three years. First, we have Optimistic Fast Rerouting, which calculates optimistic and fallback scenarios. The second is Post-processing Fast Reroute, which decomposes the network according to metrics such as load and route length. Third, Local Fast Reroute focused on low congestion and random access.

Thanks to its capability of providing seamless massive access and extended coverage, satellite communication has been envisioned as a promising complementary part of the future 6G network. Due to the large satellite-to-ground propagation loss, noise mitigation is one of the most important considerations for implementing key interface technologies on boarding a satellite, e.g., random access (RA). This paper aims at developing an effective preamble detection method with noise suppression for 5G-ntegrated satellite RA systems. Specifically, according to the satellite ephemeris and user equipment location, we first perform the pre-compensation of timing and frequency offset before preamble transmission to determine all the possible correlation peak positions in advance. By leveraging the advantage of the wavelet transform in signal-to-noise separation, we further design a novel detection framework based on wavelet denoising, which can efficiently reconstruct preamble signature from the noisy power delay profile. Simulation results validate the feasibility of the proposed method, and show that our method can achieve a notably improved detection performance under extremely low signal-to-noise ratio conditions, in comparison with the conventional one. [ABSTRACT FROM AUTHOR]

Copyright of Frontiers of Information Technology & Electronic Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Background In recent years, advances in high-throughput sequencing technologies have enabled the use of genomic information in many fields, such as precision medicine, oncology, and food quality control. The amount of genomic data being generated is growing rapidly and is expected to soon surpass the amount of video data. The majority of sequencing experiments, such as genome-wide association studies, have the goal of identifying variations in the gene sequence to better understand phenotypic variations. We present a novel approach for compressing gene sequence variations with random access capability: the Genomic Variant Codec (GVC). We use techniques such as binarization, joint row- and column-wise sorting of blocks of variations, as well as the image compression standard JBIG for efficient entropy coding. Results Our results show that GVC provides the best trade-off between compression and random access compared to the state of the art: it reduces the genotype information size from 758 GiB down to 890 MiB on the publicly available 1000 Genomes Project (phase 3) data, which is 21% less than the state of the art in random-access capable methods. Conclusions By providing the best results in terms of combined random access and compression, GVC facilitates the efficient storage of large collections of gene sequence variations. In particular, the random access capability of GVC enables seamless remote data access and application integration. The software is open source and available at https://github.com/sXperfect/gvc/ .

Low power wide area network (LPWAN) technologies have become an integral part of Internet-of-Things (IoT) applications due to their ability to meet key requirements such as long range, low cost, massive device numbers, and low energy consumption. Among all available LPWAN technologies, LoRaWAN has garnered significant interest from both industry and academia. Due to wide communication range of LoRaWAN, the coverage of gateway (GW) may overlap and the packet transmissions from nodes in the overlapping area would collide, which deteriorates the network performance. How to tune the backoff parameter settings to reduce collision and maximize the network throughput in multi-GW LoRaWAN is still an open issue. To solve this issue, in this paper, we first propose a low-complexity model for multi-GW LoRaWAN, which divides nodes into different groups based on GWs they can communicate with. Key performance metrics, i.e., the network throughput, access delay and the probability of successful transmission, are derived as functions of backoff parameter and input rate. As the throughput performance crucially depends on whether each group is saturated or not, we propose an iterative algorithm to analyze the stability of the multi-GW LoRaWAN, based on which we further develop an iterative algorithm to tune the backoff parameter of each group iteratively for the network throughput optimization. The simulation results validate the effectiveness of the proposed algorithms and reveal the trade-off between fairness and efficiency.

In 5th generation (5G) systems, two-step random access has been introduced for machine-type communication (MTC) to lower signaling overhead. It is also shown that when a base station (BS) is equipped with a large number of antennas, the notion of massive multiple-input multiple-output (MIMO) can be exploited to improve the performance in terms of throughput and spectral efficiency. In this paper, we consider the case that a BS is equipped with a small number of antennas, in which a sufficiently high spatial selectivity cannot be obtained, and propose an approach to two-step random access based on beam selection that can perform well with a small number of antennas. In the proposed approach, spreading sequences are also used in conjunction with beam selection to mitigate interference due to limited spatial selectivity. To analyze the performance of the proposed approach, the distribution of the signal-to-interference-plus-noise ratio (SINR) is derived as a closed-form expression and the throughput is found. We compare the throughput of the proposed approach with those of conventional two-step random access approaches through analysis and simulations. While the theoretical results agree with simulation results, we can see that the proposed approach outperforms conventional ones when the number of antennas is small for a wide range of traffic intensity.

In order to satisfy the higher demand raised by 6G, the major part of which refers to further extension of the coverage and achievements of ubiquitously massive connections, the integration of Non-Terrestrial Networks (NTNs) and the evolved version of Narrow Bandwidth Internet of Things (NB-IoT) are introduced as a potential solution. However, one of the main challenges still open is the Random Access (RA) procedure which refers to preamble detection and uplink synchronization. With the large Time of arrival (ToA) and Carrier frequency offset (CFO), traditional methods designed for Terrestrial Networks (TNs) can not provide accurate estimation performance. Some studies adopt Global Navigation Satellite System (GNSS) to pre-compensate the large ToA and CFO but it may not work well for NB-IoT devices in practice. Consequently, a complete system without GNSS is designed for RA in this paper. A system-level method is firstly applied to pre-compensate the CFO. Then we focus on tackling the ToA and residual CFO. A pre-process method for received signal is proposed and its phase series are analyzed. Then we propose a novel random access scheme based on change point detection (CPD) of phase series with machine learning to estimate ToA and CFO. Simulation results demonstrate that the designed overall system is practical in NTNs. Meanwhile, the proposed random access scheme achieves higher estimation accuracy of ToA and CFO compared with existing methods with the same range of ToA and CFO under low signal-noise ratio (SNR) conditions.

In this paper, we analyze the throughput and reliability of an indoor optical wireless communication (OWC)-based Internet of Things (IoT) system based on Slotted ALOHA (SA) where IoT devices exchange data with an access point (AP). Assuming that the OWC receiver at the AP exploits the capture effect, we derive the error probability of decoding a short-length data packet that originates from a randomly selected OWC IoT device in the presence of interfering users. The analysis is based on the derivation of the signal-to-interference-plus-noise-ratio (SINR) statistics and the application of the finite block-length (FBL) information theory. Using these analytical results, we derive relevant performance parameters such as the system throughput and reliability expressed in terms of the outage probability of a user transmission. The main trade-offs between the system performance and the OWC system setup parameters are investigated, in particular, by stressing how the indoor OWC-based system geometry plays an important role in the system performance.Using extensive numerical results, we clearly describe how the presented results are used to optimize the SA-based indoor OWC IoT system design.

The importance of unlicensed spectrum is highlighted in terms of the flexibility of network deployment for various services envisioned in 5G and beyond. Since listen before talk is mandatory for channel access in unlicensed spectrum and it causes an unavoidable waste of resources due to collisions, an efficient random backoff mechanism is required. In the existing backoff schemes that impose waiting penalties on collided packets, a degraded fairness performance is observed. In this work, we analyze how prioritizing collided packets can improve performance compared to existing schemes. To this end, we devise a random backoff scheme called the Collision Priority Backoff (CPB) under the concept of giving priority to collided packets. We apply Bayesian optimization to carefully determine channel access parameters of the CPB to maximize network throughput. Since the optimized access parameters require the number of stations in the network, we also devise an adaptive version of the CPB called the Adaptive CPB (ACPB). We deal with an environment where the number of stations changes as a switching bandit problem, and employ a variant of upper confidence bound policy in the ACPB. Various simulation results validate that the proposed backoff scheme shows high throughput and fairness performance.

To enhance the massive machine-type communications (mMTC) random access (RA) performance, a pattern division random access (PDRA) scheme was proposed for massive multiple-input multiple-output (mMIMO) systems.In this scheme, the pilot contention space was expanded to the pattern-domain.To enlarge the size of contention space without resorting to increasing the physical resources, the pattern-domain pilot was constructed based on the superposition of L cyclically-shifted Zadoff-Chu (ZC) sequences.Simulation results illustrate that the PDRA scheme can reduce pilot collision probability significantly, and improve the access success probability compared with the conventional RA scheme, without compromising excessively on channel estimation and data detection performance.

The role of various internet-of-things (IoT) devices responsible for data collection and reporting becomes more important in the era of Industry 4.0. Due to the various advantages (e.g., wide coverage, robust security, etc.), the cellular networks have been continuously evolved to accommodate IoT scenario. In IoT scenario, connection establishment is essential and primary for enabling IoT devices to communicate with centralized unit (e.g., base station (BS)). This connection establishment procedure in cellular networks, random access procedure, is generally operated in a contention-based manner. So, it is vulnerable to simultaneous connection requests from multiple IoT devices to the BS, which becomes worse as the contention participants increase. In this article, we newly propose a resource-efficient parallelized random access (RePRA) procedure for resource-efficiently ensuring reliable connection establishment in cellular-based massive IoT networks. Key features of our proposed technique are twofold: (1) Each IoT device simultaneously performs multiple RA procedures in parallel to improve connection establishment success probability, and (2) the BS handles excessive use of radio resources based on newly proposed two types of redundancy elimination mechanisms. Through extensive simulations, we evaluate the performance of our proposed technique in terms of connection establishment success probability and resource efficiency under various combinations of control parameters. Consequently, we verify the feasibility of our proposed technique for reliably and radio-efficiently supporting a large number of IoT devices. [ABSTRACT FROM AUTHOR]

In this paper, an asynchronous collision-tolerant ACRDA scheme based on satellite-selection collaboration-beamforming (SC-ACRDA) is proposed to solve the avalanche effect caused by packet collision under random access (RA) high load in the low earth orbit (LEO) satellite Internet of Things (IoT) networks. A non-convex optimization problem is formulated to realize the satellite selection problem in multi-satellite collaboration-beamforming. To solve this problem, we employ the Charnes-Cooper transformation to transform a convex optimization problem. In addition, an iterative binary search algorithm is also designed to obtain the optimization parameter. Furthermore, we present a signal processing flow combined with ACRDA protocol and serial interference cancellation (SIC) to solve the packet collision problem effectively in the gateway station. Simulation results show that the proposed SC-ACRDA scheme can effectively solve the avalanche effect and improve the performance of the RA protocol in LEO satellite IoT networks compared with benchmark problems. [ABSTRACT FROM AUTHOR]

With the development of IoT technology and 5G massive machine-type communication, the 3GPP standardization body considered as viable the integration of Narrowband Internet of Things (NB-IoT) in low Earth orbit (LEO) satellite-based architectures. However, the presence of the LEO satellite channel comes up with new challenges for the NB-IoT random access procedures and coverage enhancement mechanism. In this paper, an Adaptive Coverage Enhancement (ACE) method is proposed to meet the requirement of random access parameter configurations for diverse applications. Based on stochastic geometry theory, an expression of random access channel (RACH) success probability is derived for LEO satellite-based NB-IoT networks. On the basis of a power consumption model of the NB-IoT terminal, a multi-objective optimization problem is formulated to trade-off RACH success probability and power consumption. To solve this multi-objective optimization problem, we employ the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) method to obtain the Pareto-front solution set. According to different application requirements, we also design a random access parameter configuration method to minimize the power consumption under the constraints of RACH success probability requirements. Simulation results show that the maximum number of repetitions and back-off window size have a great influence on the system performance and their value ranges should be set within [4, 18] and [0, 2048]. The power consumption of coverage enhancement with ACE is about 58% lower than that of the 3GPP proposed model. All this research together provides good reference for the scale deployment of NB-IoT in LEO satellite networks.

Abstract Background Over the past few decades, the emergence and maturation of new technologies have substantially reduced the cost of genome sequencing. As a result, the amount of genomic data that needs to be stored and transmitted has grown exponentially. For the standard sequencing data format, FASTQ, compression of the quality score is a key and difficult aspect of FASTQ file compression. Throughout the literature, we found that the majority of the current quality score compression methods do not support random access. Based on the above consideration, it is reasonable to investigate a lossless quality score compressor with a high compression rate, a fast compression and decompression speed, and support for random access. Results In this paper, we propose CMIC, an adaptive and random access supported compressor for lossless compression of quality score sequences. CMIC is an acronym of the four steps (classification, mapping, indexing and compression) in the paper. Its framework consists of the following four parts: classification, mapping, indexing, and compression. The experimental results show that our compressor has good performance in terms of compression rates on all the tested datasets. The file sizes are reduced by up to 21.91% when compared with LCQS. In terms of compression speed, CMIC is better than all other compressors on most of the tested cases. In terms of random access speed, the CMIC is faster than the LCQS, which provides a random access function for compressed quality scores. Conclusions CMIC is a compressor that is especially designed for quality score sequences, which has good performance in terms of compression rate, compression speed, decompression speed, and random access speed. The CMIC can be obtained in the following way: https://github.com/Humonex/Cmic .

Background: In recent years, advances in high-throughput sequencing technologies have enabled the use of genomic information in many fields, such as precision medicine, oncology, and food quality control. The amount of genomic data being generated is growing rapidly and is expected to soon surpass the amount of video data. The majority of sequencing experiments, such as genome-wide association studies, have the goal of identifying variations in the gene sequence to better understand phenotypic variations. We present a novel approach for compressing gene sequence variations with random access capability: the Genomic Variant Codec (GVC). We use techniques such as binarization, joint row- and column-wise sorting of blocks of variations, as well as the image compression standard JBIG for efficient entropy coding. Results: Our results show that GVC provides the best trade-off between compression and random access compared to the state of the art: it reduces the genotype information size from 758 GiB down to 890 MiB on the publicly available 1000 Genomes Project (phase 3) data, which is 21% less than the state of the art in random-access capable methods. Conclusions: By providing the best results in terms of combined random access and compression, GVC facilitates the efficient storage of large collections of gene sequence variations. In particular, the random access capability of GVC enables seamless remote data access and application integration. The software is open source and available at https://github.com/sXperfect/gvc/. [ABSTRACT FROM AUTHOR]

Currently, network requirements are placed on the efficiency and size of the networks. These conditions can be ensured by modern converged networks that integrate the functions of both data and telecommunication networks. Line or router failures have always been a part of transmission networks, which is no different from converged networks. As a result of outages, which can take from ms to tens of seconds, packets are lost. These outages cause degraded transmission quality, which is undesirable when transmitting real-time multimedia services (Voice over IP, video). To solve the mentioned problems, the IETF organization has developed IP Fast Reroute mechanisms to minimise the time to restore the connection after a line or node failure and, consequently, less packet loss. The article reviews and compares the latest IP Fast Reroute mechanisms deployed in the last three years. First, we have Optimistic Fast Rerouting, which calculates optimistic and fallback scenarios. The second is Post-processing Fast Reroute, which decomposes the network according to metrics such as load and route length. Third, Local Fast Reroute focused on low congestion and random access. [ABSTRACT FROM AUTHOR]

With the emerging Internet of Things paradigm, massive Machine-Type Communication (mMTC) has been identified as one of the prominent services that enables a broad range of applications with various Quality of Service (QoS) requirements for 5G-and-beyond networks. However, it is very difficult to employ a monolithic physical network to support various mMTC applications with differentiated QoS requirements. Moreover, in ultra-dense mobile networks, the scarcity of the preamble and Physical Downlink Control CHannel (PDCCH) resources may easily lead to resource collisions when a large number of devices access the network simultaneously. To tackle these issues, in this paper, we propose a network slicing-enabled intelligent random access framework for mMTC. First, by tailoring a gigantic physical network into multiple lightweight network slices, fine-grained QoS provisioning can be accomplished, and the collision domain of Random Access (RA) can be effectively reduced. In addition, we propose a novel concept of sliced preambles (sPreambles), based on which the transitional RA procedure is optimized, and the issue of preamble shortage is effectively relieved. Furthermore, with the aim of alleviating PDCCH resource shortage and improving transmission efficiency, we propose a learning-based resource-sharing scheme that can intelligently multiplex the PDCCH resources in the naturally dynamic environment. Simulation results show that the proposed framework can efficiently allocate resources to individual mMTC devices while guaranteeing their QoS requirements in random access processes. [ABSTRACT FROM AUTHOR]

With the rapid development of the internet of things (IoT), the application of machine type communication (MTC) in life has grown substantially, and the deployment of machine type devices (MTD) has become more and more intensive.In this case, if there is an unexpected situation such as a power outage and a network failure, a large-scale MTD will initiate access to the base station at the same time when the device is powered on.When the device accesses the base station, it needs to complete the downlink synchronization, system message receiving, random access (RA) and other processes.However, the preamble resources that can be allocated by the base station are limited and cannot meet such a huge access demand, which causes network congestion and affects the access probability and access delay of the device.In order to alleviate the access conflict of RA, a dynamic random access occasion (RO) adjustment scheme was proposed based on the uplink and downlink subframes and physical random access channel (PRACH) configuration, and time-based part of the MTD with high delay requirements allocates specific preamble resources.The simulation results show that this scheme can effectively improve the throughput of the system and reduce the access delay of the device.

In the age of 6G internet of things, massive terminal devices are connected intelligently to explore environmental information to improve life quality and create a more intelligent world.With the development of economic society and industry, the number of industrial nodes in the industrial internet of things (IIoT) grows at an significant speed.The real-time transmission and exchange of information between these industrial nodes puts forward higher requirements for communication delay and reliability.Under limited time-frequency resources and strict communication quality requirements, in order to ensure ultra-reliable and low-latency communication (URLLC) of massive terminals in IIoT, it is necessary to analyze the existing key problems from the aspects of low delay, reliability, energy efficiency, spectrum efficiency and scalability.In the scenario of IIoT, combined with the characteristics and requirements of URLLC, the key issues of communication in IIoT were studied, including challenges and opportunities in active user detection, random access, resource allocation and convergence of communication and computation.After summarizing and analyzing the key issues and existing technologies, the future research direction was proposed.

The low earth orbit (LEO) satellite Internet constellation is a hot spot in current global research.And it is highly anticipated that the LEO satellite Internet constellation supports random access to remote sensing satellites and direct on-orbit information processing.However, due to the different orbital heights, the difficult access problems of the bidirectional highly dynamic heterogeneous constellation will be caused.Firstly, the random access scenario under different orbit characteristics and satellite numbers of LEO satellite Internet constellation was set up, and the impact of the spatiotemporal discontinuous visibility and Doppler frequency shift on the remote sensing satellite access performance was focused on.Secondly, based on the characteristics of the random access scenario, the process of on-orbit processing tasks with different delays and the allocation of satellite-ground functions were analyzed.Finally, the problems existing in the current on-orbit intelligent processing algorithm and the focus of future research were expounded, so as to provide reliable theoretical support for the development and applications of LEO satellite Internet constellation and remote sensing satellites in the future.

The Sixth-Generation (6G) standard for wireless communications is expected to realize ubiquitous coverage for massive Internet of Things (IoT) networks by 2030. Satellite-based communications are recognized as a highly promising technical enabler to satisfy IoT service requirements in the 6G era. This study analyzes multiple access technologies, which are essential for the effective deployment of satellite-based IoT. First, we thoroughly investigate the existing research related to massive access, including information-theory considerations as well as Non-Orthogonal Multiple Access (NOMA) and Random Access (RA) technologies.Then, we explore the influence of the satellite transmission environment on multiple access technologies. Based on this study, a Non-orthogonal Massive Grant-Free Access (NoMaGFA) scheme, which reaps the joint benefits of RA and NOMA, is proposed for asynchronous transmissions in satellite-based IoT to achieve improved system throughput and enhance the system robustness under varying traffics. Finally, we identify some important and interesting future developments for satellite-based IoT, including waveform design, transceiver design, resource allocation, and artificial intelligence-enhanced design.

As IoT services become more active, the density of IoT devices is increasing, and massive connectivity technology is needed to support numerous devices simultaneously. In addition, IoT devices are often battery-powered, and during random access, it is necessary to reduce the power consumption to extend the lifetime of the devices. In particular, devices with poor channels need to send at a very low transmission rate through a large number of repetitions, and longer packet lengths can increase the probability of collisions, increasing the power consumption while shortening the lifetime of the IoT system. Dividing devices into groups based on the number of repetitions and allocating different resources to each group can reduce collisions for bad-channel devices, but it can be difficult to support large connections, due to the inefficient use of resources. This paper proposes schemes to reduce the collision probability of bad-channel devices while allowing IoT devices to use shared resources, instead of dividing resources by groups. There are two versions of the proposed schemes. The first method reduces collisions by delaying the response of a bad-channel device, and in the meantime, eliminating interference from other devices, assuming that the bad-channel device is not sensitive to delay. Instead of checking the response, and then, performing a random backoff when no acknowledgement packet is received, the second proposed method reverses the order of response checking and random backoff, that is, it first performs a random backoff, and then, checks the response to decide whether to retransmit. The proposed method can increase the lifetime of the IoT system by reducing the collision probability of a bad-channel device, without degrading the performance of other devices.

Owing to the increasing demand for machine-to-machine (M2M) communications, random access is being intensively researched. With M2M communications, numerous devices can transmit random access packets simultaneously. This results in congestion in random access and degrades its performance. To control congestion scenarios, the packet arrival rate of random access must be obtained with a short delay and high accuracy. In this paper, a new estimator is proposed to obtain the packet arrival rate in a long-term evolution system. The packet arrival rate of random access is computed using the estimated number of random access packets in each slot. Using the proposed estimator, the number of random access packets is estimated based on the received energy vector for sequences and the number of detected sequences. Analysis and simulations show that the proposed estimator presents better performance for determining the number of random access packets than conventional approaches.

Many technological enhancements are being developed worldwide to enable the “Internet of Things” (IoT). IoT networks largely rely on distributed access of billions of devices, but are still lagging in terms of combined reliability and low latency. To mend that shortcoming, it is paramount to adapt existing random access methods for the IoT setting. In this article, we shed light on one of the modern candidates for random access protocols fitted for IoT: the “Irregular Repetition Slotted ALOHA” (IRSA). As self-managing solutions are needed to overcome the challenges of IoT, we study the IRSA random access scheme in a distributed setting where groups of users, with fixed traffic loads, are competing for ALOHA-type channel access. To that aim, we adopt a distributed game-theoretic approach where two classes of IoT devices learn autonomously their optimal IRSA protocol parameters to optimize selfishly their own effective throughput. Through extensive simulations, we assess the notable efficiency of the game based distributed approach. We also show that our IRSA game attains the Nash equilibrium (NE) via the “better reply” strategy, and we quantify the price of anarchy in comparison with a centralized approach. Our results imply that user competition does not fundamentally impact the performance of the IRSA protocol.

This article evaluates the impact of backoff time through the Backoff Indicator’s (BI) configuration on the random-access channel (RACH) under different massive traffic scenarios considering network performance metrics such as the probability of successful access, the delay in access, and the average number of preamble transmissions. A discrete-event simulation model of the contention-based random-access procedure was developed using MATLAB software. Based on the results, an optimal range of BI values was characterized for each scenario through different reliability conditions. It was observed that with a suitable configuration of the RACH parameters, the performance of the system network could reach a successful access probability more significant than 85% with a moderate increase in the access delay. It was concluded that dynamic modification of the backoff time could alleviate channel congestion in delay-tolerant applications with massive traffi

Machine-type communications (MTC) should account for half the connections to the internet by 2030. The use case massive MTC (mMTC) allows for applications to connect a massive number of low-power and low-complexity devices, leading to challenges in resource allocation. Not only that, mMTC networks suffer under rigid random access schemes due to mMTC ultra-dense nature resulting in poor performance. In this sense, this paper proposes a $Q$ -Learning-based random access method for massive machine-type communications, with device clustering and non-orthogonal multiple access (NOMA). The traditional NOMA implementation increases spectral efficiency, but at the same time, demands a larger $Q$ -Table, thus slowing down convergence, which is known to be a highly detrimental effect on massive networks. We use pre-clustering through short-range device-to-device technology to mitigate this drawback, allowing devices to operate with a smaller $Q$ -Table. Furthermore, the previous selection of partner devices allows us to implement a full-feedback-based reward mechanism so that clusters avoid time slots already successfully allocated. Additionally, to cope with the negative impact of system overload, we propose an adaptive frame size algorithm to run in the base station (BS). It allows adjusting the frame size to the network load, preventing idle slots in an underloaded scenario, and providing extra slots when the network is overloaded. The results show the great benefits in terms of throughput of the proposed method. In addition, the impact of the use of clustering and the size of the clusters, as well as the frame size adaptation, are analyzed.

With the advent and rapidly consolidating of the Internet of Things (IoT), multiple applications arise with different performance requirements, such as the timeliness of information, which can be quantified by the age-of-information (AoI) metric. Furthermore, allowing the coexistence of massive numbers of low-complexity IoT devices is often desirable, and modern random access methods have been explored as a way of achieving it. In this paper, we investigate the performance of a random access two-hop network with multiple relays, focusing on the AoI metric. We evaluate the average age of the network under two age-dependent approaches, namely ADRA-MRU, in which users evaluate their age against a threshold before transmitting, and ADRA-MRD, where this responsibility belongs to relays. As a result, we show through analytical and numerical results that ADRA-MRU can considerably reduce the average AoI of an age-independent random access scheme with multiple relays (AIRA-MR) by more than 60%, while the performance of ADRA-MRD is slightly better than that of AIRA-MR. Moreover, we also provide insights on the optimal number of relays as well as other parameters that influence the AoI.

With the growing interest in the Internet of Things (IoT), research on massive machine-type communication (mMTC) services is being actively promoted. Because mMTC services are required to serve a large number of devices simultaneously, a lack of resources during initial access can be a significant problem when providing mMTC services in cellular networks. Various studies on efficient preamble transmission have been conducted to solve the random access problem of mMTC services. However, supporting a large number of devices simultaneously with limited resources is a challenging problem. In this study, we investigate code-expanded random access (CeRA), which extends the limited preamble resources to the code domain to decrease the high collision rate. To solve the existing CeRA phantom codeword and physical uplink shared channel (PUSCH) resource shortage problems, we propose an optimal preamble codeword set selection algorithm based on mathematical analysis. The simulation results indicate that the proposed code-expanded random access scheme to enhance success probability (CeRA-eSP) achieves a higher random access success rate with a lower access delay compared to the existing random access schemes. [ABSTRACT FROM AUTHOR]

In applications of remote sensing, estimation, and control, timely communication is critical but not always ensured by high-rate communication. This work proposes decentralized age-efficient transmission policies for random access channels with $M$ transmitters. We propose the notion of age-gain of a packet to quantify how much the packet will reduce the instantaneous age of information at the receiver side upon successful delivery. We then utilize this notion to propose a transmission policy in which transmitters act in a decentralized manner based on the age-gain of their available packets. In particular, each transmitter sends its latest packet only if its corresponding age-gain is beyond a certain threshold which could be computed adaptively using the collision feedback or found as a fixed value analytically in advance. Both methods improve age of information significantly compared to the state of the art. In the limit of large $M$ , we prove that when the arrival rate is small (below $\frac {1}{eM}$), slotted ALOHA-type algorithms are order optimal. As the arrival rate increases beyond $\frac {1}{eM}$ , while age increases under slotted ALOHA, it decreases significantly under the proposed age-based policies. For arrival rates $\theta $ , $\theta =\frac {1}{o(M)}$ , the proposed algorithms provide a multiplicative gain of at least two compared to the minimum age under slotted ALOHA (minimum over all arrival rates). We conclude that it is beneficial to increase the sampling rate (and hence the arrival rate) and transmit packets selectively based on their age-gain. This is surprising and contrary to common practice where the arrival rate is optimized to attain the minimum AoI. We further extend our results to other random access technologies such as Carrier-sense multiple access (CSMA). [ABSTRACT FROM AUTHOR]

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In 6G, one of the important application scenarios is ultra-massive machinetype communications, which is mainly oriented to the application scenarios of concurrent access of massive terminals such as the satellite Internet of Things.How to design an access mechanism that can meet the concurrent access service requirements of large-scale users in machine communication scenarios for the future 6G satellite communication network is an important problem to be solved.A random access protocol based on intra-frame interference cancellation was proposed.In this protocol, each access user who needs to transmit data sends multiple packet copies in one frame, and the receiver can successfully receive packets from multiple users in confl icting time slots by using interference cancellation algorithms in the frame.Compared with the traditional random access mechanism, this mechanism can greatly improve the throughput performance in the scenario of multi-user concurrent access.

Narrowband internet of things (NB-IoT) technology is widely used in data communications for a large number of low-speed and low-power devices, but it is diff cult to provide services for remote areas that cannot be covered by terrestrial networks.With its global coverage and low cost, the LEO satellite constellation can supplement and expand the terrestrial NB-IoT infrastructure, providing an eff ective solution for the space-based internet of things.Facing the application requirements of space-based internet of things, on the basis of reviewing the NB-IoT system architecture, the challenges of channel environment high dynamic delay and frequency off set faced by the application of NB-IoT technology in the LEO satellite constellation scenario were analyzed.For the uplink random access process, an eff ective user detection and timing synchronization method was designed, which had the advantages of high performance and low complexity, and provided an eff cient receiving method for the LEO satellite NB-IoT uplink random access synchronization.

Abstract Background Advances in sequencing technology have drastically reduced sequencing costs. As a result, the amount of sequencing data increases explosively. Since FASTQ files (standard sequencing data formats) are huge, there is a need for efficient compression of FASTQ files, especially quality scores. Several quality scores compression algorithms are recently proposed, mainly focused on lossy compression to boost the compression rate further. However, for clinical applications and archiving purposes, lossy compression cannot replace lossless compression. One of the main challenges for lossless compression is time complexity, where it takes thousands of seconds to compress a 1 GB file. Also, there are desired features for compression algorithms, such as random access. Therefore, there is a need for a fast lossless compressor with a reasonable compression rate and random access functionality. Results This paper proposes a Fast and Concurrent Lossless Quality scores Compressor (FCLQC) that supports random access and achieves a lower running time based on concurrent programming. Experimental results reveal that FCLQC is significantly faster than the baseline compressors on compression and decompression at the expense of compression ratio. Compared to LCQS (baseline quality score compression algorithm), FCLQC shows at least 31x compression speed improvement in all settings, where a performance degradation in compression ratio is up to 13.58% (8.26% on average). Compared to general-purpose compressors (such as 7-zip), FCLQC shows 3x faster compression speed while having better compression ratios, at least 2.08% (4.69% on average). Moreover, the speed of random access decompression also outperforms the others. The concurrency of FCLQC is implemented using Rust; the performance gain increases near-linearly with the number of threads. Conclusion The superiority of compression and decompression speed makes FCLQC a practical lossless quality score compressor candidate for speed-sensitive applications of DNA sequencing data. FCLQC is available at https://github.com/Minhyeok01/FCLQC and is freely available for non-commercial usage.