The latest version of ZigBee offers improvements in various aspects, including its low power consumption, flexibility, and cost-effective deployment. However, the challenges persist, as the upgraded protocol continues to suffer from a wide range of security weaknesses. Constrained wireless sensor network devices cannot use standard security protocols such as asymmetric cryptography mechanisms, which are resource-intensive and unsuitable for wireless sensor networks. ZigBee uses the Advanced Encryption Standard (AES), which is the best recommended symmetric key block cipher for securing data of sensitive networks and applications. However, AES is expected to be vulnerable to some attacks in the near future. Moreover, symmetric cryptosystems have key management and authentication issues. To address these concerns in wireless sensor networks, particularly in ZigBee communications, in this paper, we propose a mutual authentication scheme that can dynamically update the secret key value of device-to-trust center (D2TC) and device-to-device (D2D) communications. In addition, the suggested solution improves the cryptographic strength of ZigBee communications by improving the encryption process of a regular AES without the need for asymmetric cryptography. To achieve that, we use a secure one-way hash function operation when D2TC and D2D mutually authenticate each other, along with bitwise exclusive OR operations to enhance cryptography. Once authentication is accomplished, the ZigBee-based participants can mutually agree upon a shared session key and exchange a secure value. This secure value is then integrated with the sensed data from the devices and utilized as input for regular AES encryption. By adopting this technique, the encrypted data gains robust protection against potential cryptanalysis attacks. Finally, a comparative analysis is conducted to illustrate how the proposed scheme effectively maintains efficiency in comparison to eight competitive schemes. This analysis evaluates the scheme’s performance across various factors, including security features, communication, and computational cost.

Road maintenance systems (RMS) are crucial for maintaining safe and efficient road networks. The impact of climate change on road maintenance systems is a concern as it makes them more susceptible to weather events and subsequent damages. To tackle this issue, we propose an RMSDC (Road Maintenance Systems Using Deep Learning and Climate Adaptation) technique to improve road maintenance systems based on Deep learning and Climate Adaptation. RMSDC aims to use the multivariate classification technique and divides the dataset into training and test datasets. The RMSDC combines Convolutional Long Short-Term Memory (ConvLSTM) techniques with road weather information and sensor data. However, in emerging nations, the effects of climate change are already apparent, which makes road networks particularly susceptible to extreme weather, floods, and landslides. Therefore, climate adaptation of road networks is essential, especially in developing nations with limited financial resources. To address this issue, we propose an intelligent and effective RMSDC that utilizes deep learning algorithms based on climate change predictions. The ConvLSTM block effectively captures the relationship between input features over time to calculate the root-mean deviation (RMSD). We evaluate RMSDC performance against frameworks for downscaling climate variables using two metrics: root-mean-square error (RMSE) and mean absolute difference. Through real evaluations, RMSDC consistently outperforms approaches with a reduced RMSE of 0.26. These quantitative results highlight how effective RMSDC is in addressing maintenance systems on road networks leading to proactive road maintenance strategies that enhance traffic safety, reduce costs, and improve environmental sustainability.

Software-defined networks (SDN) provide a centralized administration programming interface for managing the network infrastructure. This new approach replaced traditional networks by establishing a flexible connection between the control and data planes, managing network operations through a centralized controller. As a result, prioritizing the security of the SDN controller becomes imperative in SDN networks. In the recent wave of distributed denial-of-service (DDoS) attacks, attackers have shifted their strategy from directly targeting the SDN controller to concentrating on specific links or area, causing disruptions in connectivity. This attack, known as Link-flooding attack (LFA), represent a novel form of DDoS attack. LFA targets the SDN control channel, which transmits control traffic from the SDN controller to switches, taking advantage of shared links in both control and data traffic paths. This sharing exposes a vulnerability that attackers can exploit to disrupt the control channel, using malicious data traffic to execute LFA. Considering the control channel's responsibility for granting centralized control to the controller over each network switch, it becomes relatively easy for an attacker to compromise all network functions. To handle this problem, in this paper, we develop a novel approach based on SDN designed for security solutions against DDoS and LFA. Our proposed scheme utilizes hop-by-hop network measurement to identify and capture abnormal link performance, enabling effective detection of such attacks. Subsequently, a Machine Learning (ML) model is employed to determine whether the congested links indicate the presence of such attacks. Unlike conventional approaches in the literature that solely rely on automatic ML models, our method begins by measuring congestion in each link. If abnormalities are detected, the ML model is then executed to identify whether it is an attack or not. By adopting this approach, we achieve optimized utilization of controller resources. Our proposed scheme will be implemented as an application at the application layer of the Ryu controller. Through our evaluation, we have demonstrated that this approach can efficiently optimize the process of measuring link performance, optimizing the utilization of SDN controller resources, and detecting DDoS and LFA.

Blockchain-based forensic evidence management for Industrial Internet of Things (IIoT) environments requires tamper-evident custody records and accountable interorganizational access. Yet, Hyperledger Fabric evaluations often use generic workloads or treat retrieval as a passive query, limiting evidence on ordering behavior for audit-bearing transactions. This study compares Raft and SmartBFT in Fabric 3.1 through submitEvidence metadata registration and retrieveEvidenceAndLog accountable access. Hyperledger Caliper experiments vary batch/load, inter-orderer RTT, orderer count, and crash-stop orderer unavailability, evaluating outcome and successful throughput, successful-completion latency, transaction outcomes, CPU, memory, and orderer traffic. At batch 300 and 200 TPS, Raft provides approximately 22.4% and 18.8% higher successful throughput and 89.4% and 90.6% lower latency for submit and retrieve, while SmartBFT records 35.2% and 30.3% higher traffic per successful transaction. At ten orderers, Raft records 90.5% and 91.5% lower aggregate orderer CPU/TPS. SmartBFT is more sensitive to the 100 ms RTT profile and shows larger memory peaks under stress. Crash-stop behavior is heterogeneous and operation-dependent, with unavailable-node placement, timing, and protocol-state evolution affecting interpretation. Throughput and latency alone are therefore insufficient. Raft is suitable under strong administrative trust and crash-fault assumptions, whereas SmartBFT is appropriate when independently administered orderers face credible Byzantine threats.