In the steel manufacturing industry, the detection and analysis of surface defects are crucial for ensuring high production quality and operational efficiency. Undetected defects can result in increased material wastage, elevated costs, and compromised product reliability. Although deep learning techniques have significantly advanced automated defect detection, their effectiveness often depends on access to large, meticulously labeled datasets and high-performance computing resources–requirements that are not always feasible in practical industrial settings. To address these limitations, this study introduces a lightweight Multi-Scale Attention Network (MSANet) designed to enhance defect detection performance while maintaining computational efficiency. The proposed MSANet architecture effectively captures and emphasizes defect-relevant features at multiple spatial scales. It begins by extracting multi-scale feature maps using SqueezeNet as the backbone. These maps are then refined through a purpose-built attention module that selectively focuses on regions most indicative of defects, enhancing the network’s discriminative capacity. A key innovation of MSANet lies in its feature fusion strategy, which integrates information from multiple scales to form a unified and enriched representation. This fusion not only boosts classification accuracy but also reduces reliance on heavy computational resources, making MSANet particularly suitable for deployment in real-world industrial environments. Experimental evaluations on the NEU surface defect dataset validate the effectiveness of the proposed approach. MSANet achieves an outstanding classification accuracy of 99.72%, significantly outperforming conventional models. Moreover, the network maintains over 92% accuracy even under data-scarce conditions, highlighting its robustness and strong generalization ability across a diverse range of defect types and operational scenarios.

Renewable energy enables sensor networks with the capability to recharge and provide perpetual data services. Due to low recharging rates and the dynamics of renewable energy such as solar and wind power, providing services without interruptions caused by battery runouts is nontrivial. Most environment monitoring applications require data collection from all nodes at a steady rate. The objective of this paper is to design a solution for fair and high throughput data extraction from all nodes in the presence of renewable energy sources. Specifically, we seek to compute the lexicographically maximum data collection rate and routing paths for each node such that no node will ever run out of energy. We propose a centralized algorithm and two distributed algorithms. The centralized algorithm jointly computes the optimal data collection rate for all nodes along with the flows on each link, the first distributed algorithm computes the optimal rate when the routing structure is a given tree, and the second distributed algorithm, although heuristic, jointly computes a routing structure and a high lexicographic rate assignment that is nearly optimum. We prove the optimality for the centralized and the first distributed algorithm, and use real test-bed experiments and extensive simulations to evaluate both of the distributed algorithms.

Energy harvesting sensor platforms have opened up a new dimension to the design of network protocols. In order to sustain the network operation, the energy consumption rate cannot be higher than the energy harvesting rate, otherwise, sensor nodes will eventually deplete their batteries. In contrast to traditional network resource allocation problems where the resources are static, the time-varying recharging rate presents a new challenge. In this paper, We first explore the performance of an efficient dual decomposition and subgradient method based algorithm, called QuickFix, for computing the data sampling rate and routes. However, fluctuations in recharging can happen at a faster time-scale than the convergence time of the traditional approach. This leads to battery outage and overflow scenarios, that are both undesirable due to missed samples and lost energy harvesting opportunities respectively. To address such dynamics, a local algorithm, called SnapIt, is designed to adapt the sampling rate with the objective of maintaining the battery at a target level. Our evaluations using the TOSSIM simulator show that QuickFix and SnapIt working in tandem can track the instantaneous optimum network utility while maintaining the battery at a target level. When compared with IFRC, a backpressure-based approach, our solution improves the total data rate by 42% on the average while significantly improving the network utility.

In wireless sensor networks, the many-to-one data communication pattern induces high collision losses as multiple transmissions cause contention and interference along the paths from sources to the sink. This paper proposes a low-overhead MAC layer solution to address the high contention problem to improve system throughput and reduce energy consumption. Periods of bursty transmissions with reduced contention from neighboring nodes are exploited to efficiently clear up back- logged queues and improve the performance of CSMA. Through analytical modeling we characterize the expected performance improvement and show that it conforms to the simulation results. Using extensive simulations onns-2and experiments on the 49 node sensor network testbed (Kansei) running TinyOS, we evaluate the performance of our solution.