With the development of blockchain technology, participants need to have huge storage volumes to deal with the growing blockchain ledger size over time. This requirement leads to the conditional participation and verification of participants, thus weakening the decentralization of a blockchain system. Several compression schemes have been proposed to mitigate this storage problem by compressing a blockchain ledger based on redundancy, modular functions, and hash functions. However, these schemes have the limitation of accumulating the compression results to validate the retained blocks. The accumulation gradually reduces the storage volume for the blockchain ledger within the storage volume of nodes with limited resources, thus reducing the verification capability of the nodes. In this paper, a selective compression scheme using a checkpoint-chain is proposed to prevent the accumulation of compression results. The checkpoint-chain is a second blockchain that stores the checkpoints compressing existing blocks through a block Merkle tree. An update process is also proposed to prevent the accumulation of checkpoints by combining them. As numerous blocks can be verified with only a few updated checkpoints, blockchain nodes with limited resources can reduce the storage volume for the blockchain ledger and achieve high verification capabilities. Finally, compared with the existing compression schemes, the proposed scheme can achieve an average reduction in the storage overhead and an average increase in the verification capability of 76.02% and 13.90%, respectively. Moreover, the corresponding performance improvements are 86.14% and 15.44% when the update process is performed, respectively.

Space-air-ground integrated networks (SAGIN) are receiving a lot of attention as a candidate for an extension to non-terrestrial networks beyond the limit of terrestrial networks. SAGIN can be a means to satisfy various quality-of-service (QoS) by providing several communication links in different characteristics. To fully utilize the advantage of SAGIN, a protocol for informing user equipment (UE) of an appropriate base station layer of SAGIN is needed according to QoS requirements. In addition, the development of the protocol should consider the characteristics of the UE. In particular, when the UE is a machine-type communication device (MTCD) constituting a massive internet of things network, random access congestion issue should be considered. To solve the problems, we propose a random access protocol. We propose an indicator named virtual deadline indicator (VDI) to inform MTCD which layer to try for random access. MTCD attempts random access to one layer of SAGIN by comparing the remaining deadlines of traffic and the point of the VDI. To minimize the deadline expiration rate, we propose an algorithm to shift the VDI position. The base station estimates the number of the MTCDs and remaining deadline distribution by using the number of the idle preambles to find the VDI location. Finally, we compare the performance of the protocol through simulation with three benchmark schemes. We confirm that the proposed protocol reduces the deadline expiration rate without compromising the age of information and energy efficiency.

Cell-free massive multiple-input multiple-output (CF-mMIMO) can provide uniformly great service to user equipment (UE) by utilizing a large number of distributed access points (APs) connected to a single central processing unit (CPU). For addressing scalability and feasibility issues inherent in single-CPU CF-mMIMO systems, multiple-CPU CF-mMIMO (MC-CF-mMIMO) has been considered. However, the MC-CF-mMIMO system inevitably encounters performance degradation at the boundary area of the CPUs, referred to as the CPU edge. Cooperation among CPUs can improve the performance in the CPU edge region; however, scalability problems resurface owing to inter-CPU information sharing during the cooperation process. In this study, we propose a scalable CPU cooperation scheme that focuses on power control to address the performance degradation issue in the CPU edge region. Initially, we propose a policy estimation scheme for other CPUs to reduce the overhead of information sharing. Based on these estimated policies, each CPU can independently derive power control policies for the max-min optimization problem without simultaneous information sharing. Simulation results show that the proposed power control scheme achieves a performance improvement of up to 4.3234 dB in terms of CPU edge region performance and minimum spectral efficiency compared to baseline schemes based on the degree of CPU cooperation.

The mobility of low Earth orbit (LEO) satellites causes the LEO satellite network to experience topology changes. Topology change includes periodic topology change that occurs naturally and unpredictable topology change that occurs due to instability of the inter-satellite link between satellites. Periodic and unpredictable topology change causes frequent topology change, requiring massive communications throughout the network due to frequent route convergence. LEO satellites have limited onboard power because they operate on batteries. The waste of limited satellite onboard resources shortens the lifespan of the satellite, and achieving stable end-to-end transmission is challenging for the network. In this regard, minimizing communication overhead is a fundamental consideration when designing a routing scheme. In this paper, we propose a distributed detour routing scheme with minimal communication overhead. This routing scheme consists of a rapid detour, selective flooding, and link recovery procedures. When a link failure occurs in the network, a rapid detour can detect link failure using only a precalculated routing table. Subsequently, selective flooding searches for the optimal detour point within the minimum hop region and flood to detour point. After link recovery, a procedure is defined to traverse the pre-detour path and switch it back to the original path. The simulation results show that the proposed routing scheme achieves a reduction of communication overhead by 97.6% compared with the n-hop flooding approach.

This study addresses the challenge of high-power collision rates in grant-free nonorthogonal random access (GF-NORA) for ultramassive machine-type communication (umMTC) in ultradense networks (UDNs). We analyze the impact of power collision and intercell interference, defining the key factors affecting successive interference cancelation (SIC) decoding failure. To tackle power collision problem, we propose a multiagent reinforcement learning (MARL) framework, QMIX algorithm, with joint optimization of access control and power-level design. We evaluate the performance of the proposed scheme with extensive random access simulations in an umMTC environment. Our approach outperforms state-of-the-art schemes, achieving at most 10% increase in successful SIC decoding rate with lower access delay.

Owing to the nature of battery-operated sensors, the residual energy of their battery should be considered when a sink node collects sensory data from them. If a sink attempts to collect data from a sensor that does not have sufficient residual energy to transmit data, the utilization of wireless communication resources may be degraded. However, requiring a periodic residual energy report of the sensors considerably reduces the energy efficiency of them. Consequently, the sink node must be able to estimate the residual energy of the sensors without additional information exchange before attempting to collect data. To tackle this problem, we propose a data collection scheme for mobile sinks in cellular Internet-of-things (IoT) networks. The scheme consists of two phases. In the first phase, the mobile sink estimates the residual energy of the surrounding sensors. To reduce the complexity, we design a state diagram composed of three states based on the Markov chain of the sensor. The sink node calculates the communicable likelihood of each sensor based on the state diagram and collects data from the sensor with the highest likelihood. In the second phase, a cellular base station (BS) selects the appropriate mobile sink to improve the signal-to-interference-plus-noise ratio (SINR) of the signal from a cellular user. Each sink node delivers the bidding price to the BS based on the likelihoods of the sensors. The BS determines an appropriate sink node to allocate resources based on the received bidding prices. Moreover, we show the performance of the proposed method in terms of SINR and IoT network utilization through simulation.