In Low-PowerWide-Area Networks (LPWANs) such as LoRa, the preamble is essential for detecting highly attenuated signals. Its repetitive pattern allows a receiver to identify the presence of the signal and its precise starting point. However, in ultra-low Signal-to-Noise Ratio (SNR) environments, the preamble becomes undetectable as it is buried in strong noise, causing the entire detection process to fail. Although existing methods, such as those based on preamble symbol energy accumulation or deep learning-based spectrogram restoration, have been proposed, their performance remains limited under these extreme conditions. To address this limitation, this paper proposes a novel two-stage preamble detection scheme. The first stage employs a Convolutional-Transformer Encoder-Deconvolutional network that leverages self-attention to capture the distinct linear patterns of chirp signals even in the presence of severe noise. In the second stage, a classifier determines the presence of the preamble. Experimental results demonstrate that our proposed method significantly outperforms conventional approaches, lowering the minimum required SNR for preamble detection. To validate its performance, we utilized metrics including True Positive Rate (TPR) and F-scores. Under these evaluations, our scheme achieves a detection accuracy of over 90% in the ultra-low SNR range of -21.7 dB to -24.3 dB, confirming its robustness and practical viability.

Multi-view video streaming systems encounter challenges in managing delays during view-switching, which are related to switching response and smooth quality transition. Both factors prevent multi-view streaming to achieve seamless viewpoint transition, reducing the overall quality of experience. To tackle viewpoint delay, we employ a viewpoint-based group segment request method that assigns specific user perceptual quality targets for different viewpoint priorities. While this method effectively addresses switching response delay, it struggles to maintain smooth quality transition, which results from a quality drop during view-switching. The quality drop occurs because the bandwidth limitation hinders the transmission of high-quality video content from all viewpoints. This paper investigates viewpoint delays in multi-view video streaming based on Dynamic Adaptive Streaming over HTTP service. Through a detailed analysis, we identify buffer management as a pivotal factor in mitigating viewpoint delays. Thus, we propose dynamic quality target adjustments to regulate buffer levels. Additionally, we introduce the concept of segment redownloading to enhance low-quality segments after view-switching. The implementation results demonstrate the effectiveness of adjusting quality targets to minimize rebuffering, while segment redownloading mitigates quality delay issues.

The integration of the Internet of Things (IoT) with blockchain technology offers a promising solution to tackle interoperability, privacy, and security issues in IoT applications. However, maintaining low latency is a significant challenge in IoT-blockchain systems, as blockchain relies on an underlying communication network to create an overlay network for transmission and synchronization. The random nature of blockchain’s broadcast mechanism results in an overlay network topology that is not optimized for low-latency transmission. This study examines the impact of overlay network topologies on latency performance within an Ethereum-based IoT system. We developed a novel method for generating various overlay topology configurations and implemented Ethereum clients to establish neighboring connections based on these configurations. Our findings reveal that the overlay network significantly influences latency metrics, specifically the delays in transmitting transactions or blocks. We observed that the trade-off between delays caused by network congestion and latency reduction from fewer hops in the overlay network is dependent on the number of connections. To investigate further, we implemented three models for overlay networks. None of these network models consistently exhibited superior latency performance, indicating that latency is affected by network dynamics. In response, we developed an efficient peer selection method for blockchain nodes to reduce latency in dynamic environments. Drawing inspiration from the Perigee algorithm’s success in peer selection for transaction propagation, we propose Dual Perigee, an extension of Perigee that also optimizes block propagation latency. Through experiments in an emulated 50-node IoT-blockchain system, we compared the effectiveness of Dual Perigee against Ethereum’s default peering method and the Perigee algorithm. The results demonstrate that Dual Perigee reduces block latency by 48.5 %. These latency reductions enable more real-time responsiveness in IoT-blockchain systems and support their deployment in latency-sensitive applications.

Blockchain holds significant potential in addressing the security, privacy, decentralization, and interoperability challenges prevalent in the Internet of Things (IoT), However, this advancement often comes at the expense of scalability. Therefore, enhancing the scalability of IoT blockchain systems while preserving other essential blockchain attributes is imperative. This paper aims to mitigate network latency in the blockchain network, a factor directly linked to blockchain scalability. Achieving this goal requires implementing an efficient peer selection method, moving beyond the reliance on default selection (i.e., the one in Bitcoin, Ethereum, etc.). In existing literature, Perigee has been introduced as a method that nearly optimizes the delay in the transaction transmission process. However, Perigee has not comprehensively addressed the complete transaction life cycle, which includes a crucial process-block transmission. In response to this limitation, we propose Dual Perigee, a solution that thoroughly considers and optimizes both transaction-oriented latency (TOL) and block-oriented latency (BOL). To show the effectiveness of Dual Perigee, we implemented and evaluated it within an emulated IoT-Blockchain system, comparing its performance with Perigee and the default peering method in Ethereum. The results reveal that Dual Perigee excels in reducing BOL compared to Perigee. Moreover, Dual Perigee exhibited a latency that was 43% and 80% lower than the default peering method and Perigee, respectively.

In recent IEEE 802.11 WLANs, an Access Point (AP) can use the Channel Bonding (CB) technique to increase the transmission bandwidth. Given limited bandwidth resources, it is not trivial to allocate channels for densely deployed APs to fairly meet their diverse traffic demands. In general, the non-overlapping channel allocation approach has been proven to be an ideal solution for maximizing the achievable throughput of WLANs using CB. However, we argue that the approach is no longer efficient because of two common situations: i) insufficient non-overlapping channels and ii) imbalanced traffic demands. Instead, the overlapping channel allocation is more suitable in such situations. In this study, we first apply a continuous-time Markov Chain (CTMC) model to analyze the effect of overlapping channel allocations for neighboring APs under unsaturated traffic conditions. Based on the analysis results, we propose a novel channel allocation algorithm based on the Bin-Packing Problem (BPP) that maximizes the fairness of the throughput-to-demand satisfaction ratio between APs. Through simulations, we demonstrated that the proposed algorithm can achieve near-optimal performance in various network settings. The algorithm also outperformed existing algorithms with the same overlapping channel approach.

LoRa has gained popularity in realizing IoT applications by facilitating long-range communication with low power consumption. However, LoRa faces scalability issues due to its unsophisticated random access (RA) protocol, Aloha, which is vulnerable to collision and not scalable in dense network scenarios. In contrast, distributed queuing (DQ), a collisionfree contention-based RA protocol, is a promising candidate for replacing Aloha because of its near-optimal performance and independence from the traffic load and pattern. However, DQ is not compatible with LoRa, as it was initially designed to operate on CableTV that supports the full-duplex (FDX) bidirectional link. In FDX, the contention feedback overhead is negligible. Most wireless networks, such as LoRa, support the half-duplex, for which feedback overhead is considerable. Furthermore, a reduction in efficiency is aggravated under a multi-channel environment because of an unbalanced traffic load. This study proposes a joint distributed queueing protocol to consider contention for LoRa that minimizes control overhead and achieves inter-channel load balancing. Our analysis shows that the proposed protocol reduces the control overhead by up to 70% compared to DQ. The protocol performance demonstrates near-optimum throughput and access delay, irrespective of the number of arrivals.

Delay- or Disruption-Tolerant Networking DTN is a communications approach that is utilized in easily disrupted or delayed networks. Examples of such networks are often found in heterogeneous networks, mobile or extreme terrestrial networks, and planned networks in space. In this paper, we examine the metropolitan bus network as a research target of DTN for a public transport network. We analyze the metropolitan bus network through spatial and temporal modeling using an existing Bus Information System BIS database. On the basis of the results of our analysis, we propose and design an appropriate DTN routing scheme called Hybrid Position-based DTN Routing. This scheme uses position-based routing instead of address-based routing by soliciting infrastructural help from nearby Access Points for the real-time BIS location service. We simulated our scheme using a WLAN for the wideband DTN communication and evaluated it by comparing it with traditional Ad hoc flooding, Epidemic routing, and strategic protocol steps in our own algorithm. The results indicate that our scheme achieves reasonably high performance in terms of packet delivery ratio, latency, and resource usage. Copyright © 2013 John Wiley & Sons, Ltd.

Abstract Recently, the IEEE 802.11 working group has announced a new distributed access mechanism called Enhanced DCF (EDCF) to provide service differentiation among traffic classes defined as access category. With the increasing demand for supporting Quality of Service (QoS) in IEEE 802.11 wireless LANs, the EDCF is now attracting many researchers' attention due to its practical worth as a standard mechanism. In this paper, we focus on the analytical approach to evaluate the performance of the EDCF. An analytical model is presented to estimate the throughput of the EDCF in saturation (asymptotic) conditions by substantially revising and extending the analytical model developed by Bianchi for the performance analysis of the DCF. Extensive simulation studies based on the NS‐2 simulator have been carried out for the validation of the analysis, and they show that it estimates the throughput of the EDCF accurately. By utilizing the analytical model, we evaluate the performance of the EDCF. Specifically, we concentrate on discovering the characteristics of the EDCF parameters, such as CWmin, CWmax and AIFS, in the way that they influence on the performance of the EDCF. Copyright © 2004 John Wiley & Sons, Ltd.