ABSTRACT Adverse weather conditions such as haze, fog, and smog degrade image visibility, adversely affecting the performance of vision‐based systems. Existing dehazing methods often struggle with non‐uniform haze distributions, limited detail restoration, and poor generalization across diverse scenes. To overcome these limitations, this paper presents a deep learning‐based dehazing framework that jointly restores image clarity and detail. Unlike conventional algorithms that often neglect fine structure recovery, our architecture incorporates four specialized sub‐modules: (i) a noise attention module for enhancing noise suppression and feature preservation; (ii) an adaptive ConvNet module; (iii) a feature extraction module for capturing salient image features; and (iv) a detail refinement module to enhance spatial fidelity. The architecture is trained in an end‐to‐end manner to restore both structural integrity and colour consistency under challenging conditions. Extensive experiments conducted on synthetic and real‐world datasets, including indoor, outdoor, underwater, night‐time, and remote sensing scenarios, demonstrate superior generalization capability. In the SOTS indoor dataset, our method achieves a PSNR of 28.44 dB and an SSIM of 0.967, outperforming several state‐of‐the‐art methods. Evaluations using additional metrics such as CIEDE2000 and MSE confirm the effectiveness of the proposed method in handling dense and heterogeneous haze while preserving fine textures and visual fidelity.

Impressive technological advancements over the past decades commenced significant advantages in the maritime industry sector and elevated commercial, operational, and financial benefits. However, technological development introduces several novel risks that pose serious and potential threats to the maritime industry and considerably impact the maritime industry. Keeping in view the importance of maritime cyber security, this study presents the cyber security threats to understand their impact and loss scale. It serves as a guideline for the stakeholders to implement effective preventive and corrective strategies. Cyber security risks are discussed concerning maritime security, confidentiality, integrity, and availability, and their impact is analyzed. The proneness of the digital transformation is analyzed regarding the use of internet of things (IoT) devices, modern security frameworks for ships, and sensors and devices used in modern ships. In addition, risk assessment methods are discussed to determine the potential threat and severity along with the cyber risk mitigation schemes and frameworks. Possible recommendations and countermeasures are elaborated to alleviate the impact of cyber security breaches. Finally, recommendations about the future prospects to safeguard the maritime industry from cyber-attacks are discussed, and the necessity of efficient security policies is highlighted.

Internet of Things (IoT) networks are key to the realization of modern industries and societies. A key application of IoT is in smart-grid communications. Smart-grid networks are resource constrained in terms of computing power and energy capacity. Similarly, the wireless links between devices are typically associated with high packet-loss rates, low throughput, and instability. To provide a sustainable communication mechanism, an IoT network stack is proposed for these devices. However, each network stack layer has its own constraints. For example, to facilitate the operation of these low-power and lossy network (LLN) devices, the international engineering task force (IETF) standardized a network-layer protocol called a routing protocol for low-power and lossy networks (RPLs). RPL often creates an inefficient network in densely deployed and varying traffic load conditions. Future dense IoT-based networks are expected to automatically optimize the reliability and efficiency of communication by inferring the diverse features of both the environments and actions of the devices. Machine learning (ML) provides a promising framework for such a dense network environment. In this study, we examine the underlying perspective of ML for such systems. We utilize the multiarmed bandit (MAB)-based expected energy count (BEEX) technique, which provides nodes the ability to effectively optimize their operation. Using the proposed mechanism, nodes can intelligently adapt their network-layer behavior. The performance of the proposed (BEEX) algorithm is evaluated through a Contiki 3.0 Cooja simulation. The proposed method improves the energy consumption and packet delivery ratio and produces a lower control overhead than other state-of-the-art mechanisms.

Summary Soft errors in network‐on‐chip (NoC) such as single bit upsets and multibit upsets cause hazardous effects such as congestion, deadlock, livelock, and corruption of data. Error‐correcting codes (ECCs) are the best choices to handle these soft errors in links and memory buffers of NoC, which is the need of all modern systems, including internet of thing (IoT) edge devices. Many of these ECCs cannot correct both random and burst errors. Specific codes possess the correction and detection capability at the cost of an increase in area, latency, and energy. In this article, a coding technique is proposed by using a single error correction double error detection‐triple adjacent error correction‐six adjacent error detection (SEC‐DED‐TAEC‐6AED) (24,16) I5, that provides both random and burst error fault tolerance for NoC. The proposed technique decreases the area, energy, and latency cost of the whole NoC. It also reduces the area overhead to 173.41% and 117.91% compare to joint crosstalk avoidance multiple error correction (JCAMEC) and joint crosstalk multiple error correction (JMEC), respectively. Besides, the delay overhead of the proposed technique reduces to 4.2% and 91.97% compared with JCAMEC and JMEC, respectively. The simulation results show that the proposed code possesses an enhanced ability of error correction and detection with 3.5 times less redundant bits and a 30% fast code rate compared with JMEC and JCAMEC. Hence, the proposed scheme can effectively be used for detecting and correcting single and multiple bit errors for on‐chip communication.

To sustain dense user equipment (UE) deployments, one of the difficult issues is to provide a proficient way to various channel access in the cellular communication networks, such as long-term evolution-Advanced (LTE-A) networks. In LTE-A, random access (RA) is the necessary process to set up the wireless connection between a UE and an evolved node B (eNB). The performance of the RA straightforwardly influences the performance of the whole system. Currently, a discrete uniform distribution (DUD) is used to avoid the potential collision in the network, while multiple UEs try to access the channel resources. However, in a DUD, every UE has an equal chance to choose similar contention preamble near to the expected value of the DUD, which may cause an increase in a collision among the UEs. In this work, we propose a method based on the existing alternatives, such as continuous exponential distribution (CED). CED distributes the random values between two bounds in a poison point process, in which random variables occur continuously and independently with a constant average rate. In this way, our proposed method can distribute the UEs in a parametric set of the probability distribution. Our proposed mechanism is named as CED RA (CED-RA), which replaces the DUD mechanism in RA with CED to show enhanced reliability and lower latency.

To enable the internet of things (IoT) devices with increased coverage and optimized power consumption, the 3rdgeneration partnership (3GPP) standardizes the idea of narrowband IoT (NB-IoT) technology in the fifth generation (5G) of cellular communication. Re-transmission of control and data packets due to a poor link between user equipment (UE) and the base station (BS), is considered as one of the key feature in NB-IoT to ensure the data delivery of delay-sensitive applications, e.g. ambulance services and body sensor networks (BSN). This phenomenon degrades the energy efficiency of the already resource constrained systems. One key solution for NB-IoT UE is to exploit the device-to-device (D2D) communication using two hops instead of transmitting on a direct uplink, due to which the system performance increases. In an attempt to transmit the NB-IoT UE uplink data packet, splendid researchers have focused towards developing a D2D communication based strategy, which typically optimizes the expected packet delivery ratio (EDR) and end-to-end delay (EED) through an opportunistic method. However, such methodology imposes an additional delay due to the unavailability of active relaying nodes and increases the overall energy consumption of the system. This necessitates us to design an intelligent deterministic D2D (2D2D) relay selection strategy for delay sensitive NB-IoT UEs. The EDR and EED have been improved using deterministic programming based algorithm. Simulations with various parameters are carried out, and results are presented. Simulation results show that the deterministic algorithm gives better performance with a 10% increase in EDR and overcomes the additional delay.

This paper proposes an adaptively scaled back-off (ASB) mechanism to mitigate the performance degradations in the Binary Exponential Back-off (BEB) of IEEE 802.11 CSMA/CA in the highly dense environment such as IEEE 802.11ax high efficiency WLAN (HEW). The proposed ASB mechanism selects the optimal CW size to achieve maximized network performance adaptively based on the measured conditional collision probability (pc) and the estimated number of contending stations. The ASB protocol can provide higher efficiency than the legacy Binary Exponential Back-off (BEB) that simply adjust the back-off contention window (CW) size by blind exponential increase at repeated collision avoidance and resetting to the minimum value (CWmin) at successful transmission. The performance analysis of the proposed ASB scheme with ns-3 network simulation shows that the proposed ASB scheme can achieve 21.14% higher throughput and take 32.45% less average interval between successful transmissions than the BEB mechanism in highly dense WLANs with saturated traffic environment.1

Long Term Evolution (LTE) in unlicensed spectrum, Licensed Assisted Access (LAA), has been considered as an effective complement in offloading growing traffic. The LAA is expected to be deployed initially through the small cell integrated under LTE macro cell. Therefore, LTE users under the coverage of LAA small cell must decide to connect one of the co-located networks either LTE or LAA to maximize their performance. In this scenario, we model a network selection game for LTE users using mixed strategy game theoretic approach. Afterwards, we formulate behavior of strategic users toward network selection as a function of the number of nearby users. The simulation results show that increasing number of nearby users make a user less likely to switch to LAA small cell. Additionally, more users nearby make it less likely that LAA resources is being utilized.

This letter proposes a new optimal ordering method which minimizes error propagation in the vertical Bell-lab layered space-time (V-BLAST) by exploiting the whole filter output. A suboptimal ordering metric is also proposed which requires much reduced complexity compared to the optimal ordering metric. We also derive a simplified version of the suboptimal ordering metric which achieves a significant performance gain over the conventional ordering with minor additional complexity.

Even though multicast data transmissions in wireless communication standards doesn't require ARQ, to achieve reliable multicast over wireless channel, using ARQ is inevitable. However, unlike unicast, a sender needs to receive multiple acknowledgements (ACKs) from group members, and transmitting ACK from each group member degrades the network performance due to the overhead including multiple ACK packet transmissions and channel access process for ACKs and so on. An acknowledgement method with a minimum overhead is proposed in this paper, called OFDMA-based Multicast ACK (OMACK). The scheme uses one OFDM symbol for acknowledgements from all member stations (STAs), and each member STA indicates their packet reception status by allocating a symbol on previously allocated its own sub-carrier within the OFDM symbol. The proposed scheme is extensively evaluated by using simulation, and the results show that the scheme significantly reduces the aforementioned overhead comparing to legacy solutions and as a consequence improves the performance of the wireless networks.

In this paper, we present statistical fairness opportunistic scheduling (SFOS), a wireless scheduling algorithm with the objective of improving system throughput while providing statistical fairness guarantee. In particular, SFOS provides statistical fairness by using a virtual time variation, while improving system throughput by using our designed utility function which relates to the transmission rate. We develop a general analytical framework for SFOS, which shows that the fairness index is bounded by the utility. Further, we investigate the design rule of the utility function over Rayleigh fading channels and discrete transmission rates and present a reference design. Simulation results evaluate SFOS can significantly improve system throughput while providing statistical fairness guarantee.

The IEEE 802.11 standard defines two coordination functions: distributed coordination function (DCF) and point coordination function (PCF). These coordination functions coordinate the shared wireless medium. The PCF uses a centralized polling-based channel access method to support time-bounded services. To design an efficient polling scheme, the point coordinator (PC) needs to obtain information about the current transmission status and channel condition for each station. To reduce overhead caused by polling frames, it is better to poll all stations using one polling frame containing the transmission schedule. In this paper, we propose an efficient polling scheme, referred to as two-step multipolling (TS-MP), for the PCF in wireless local area networks (WLANs). In this new scheme, we propose to use two multipolling frames with different purposes. The first frame is broadcast to collect information such as the numbers of pending frames and the physical-layer transmission rates for the communication links among all stations. The second frame contains a polling sequence for data transmissions designed based on the collected information. This frame is broadcast to all stations. Extensive simulation studies show that TS-MP not only overcomes the aforementioned deficiencies, but also help to implement rate adaptation over time-varying wireless channel.

The IEEE 802.11 standard defines a centralized polling-based channel access method, the point coordination function (PCF), to support time-bounded services. In this paper, we propose an efficient polling scheme, referred to as two-step multipolling (TS-MP), for the PCF in WLANs. In this new scheme, we propose to use two multi-polling frames for different purposes. The first frame is broadcasted to collect information such as the numbers of pending frames and the physical layer transmission rates for the communication links among all stations. The second frame is broadcasted with a polling sequence for data transmissions designed by utilizing the collected information. Extensive simulation studies show that TS-MP not only overcomes PCFs deficiencies, but also helps to implement rate adaptation over a time-varying wireless channel.