Out-of-tree kernel patches are essential for adapting the Linux kernel to new hardware or enabling specific functionalities. Maintaining and updating these patches across different kernel versions demands significant effort from experienced engineers. Large language models (LLMs) have shown remarkable progress across various domains, suggesting their potential for automating out-of-tree kernel patch migration. However, our findings reveal that LLMs, while promising, struggle with incomplete code context understanding and inaccurate migration point identification. In this work, we propose MigGPT, a framework that employs a novel code fingerprint structure to retain code snippet information and incorporates three meticulously designed modules to improve the migration accuracy and efficiency of out-of-tree kernel patches. Furthermore, we establish a robust benchmark using real-world out-of-tree kernel patch projects to evaluate LLM capabilities. Evaluations show that MigGPT significantly outperforms the direct application of vanilla LLMs, achieving an average completion rate of 72.59\% ($\uparrow 50.74\%$) for migration tasks.

Nowadays, there is no de facto programming language dedicated to the development of complex Internet of Things (IoT) applications containing different kinds of devices. JavaScript has great potential to become the mainstream application-development program language across IoT platforms, as JavaScript is supported by many engines which have different footprint sizes from the KB level to the MB level. However, there are still technical challenges in the coding efficiency and heterogeneous platform supports. In this paper, we propose a lightweight distributed JavaScript engine, WebletScript, to help IoT developers to build applications for a group of heterogeneous IoT devices. WebletScript can run on any device that supports normal C libraries. It enables automatic code slicing and distribution for an IoT application. Therefore, with one-time platform-independent code development, the application can be executed on multiple devices, such as a group of end devices, a smart gateway and the cloud system. Compared to the traditional solution, in which embedded application and cloud application are developed separately, our approach can reduce the lines of code effectively by 50% and reduce the cost of application update cost by 65%.

We propose a kernel-level energy profiling tool KLEP that can work with diverse APIs of Android. KLEP addresses the challenges of the tail energy problem and the complex interrelation between hardware components in the device energy consumption profile. KLEP collects energy-sensitive events in the kernel and measures real energy consumption of the device at the same time, and employs a LSTM neural-network-based model for energy profiling. The preliminary results show that the curves profiled by KLEP can match the actual energy consumption with low error and overhead.

The lightweight RESTful protocols, such as CoAP, have been proposed to provide web service on resource constrained devices. However, these devices normally operate in battery and work in sleeping mode most time to prolong lifetime. Consequently it is difficult to maintain persistent end-to-end connection and leads to the high access delay for web requests. In this paper, we propose a collaborative caching scheme to solve this problem. Initially we define sleep probability based on the feature of low-duty-cycle networks to decide what data to cache. Then we introduce the definition of node popularity, which focuses nodes that are on the request forwarding public paths and forward most web requests. A centralized algorithm is presented based on the theory of gravity center to find which nodes to cache. Finally, we propose a cache discovery method. Experiments show that our caching scheme significantly improves the performance of web data access in low-duty-cycle sensor networks.

With rapid development of the Internet of Things, more and more smart devices are deployed in the physical space.A physical application is composed by several smart devices which provide physical data.The physical applications need appropriate physical information processing systems to process the related data.However, the physical applications are dynamic because of the ever-changing demands in the IoT.So it is necessary to design an evolution mechanism for the dynamic physical applications to find appropriate physical information processing systems.We first analyze the changing types of dynamic physical applications.Then we conclude three relationships between the dynamic physical applications and physical information processing systems.In order to verify the correctness of the evolution mechanism, we use Communication Sequential Process to formalize the evolution mechanism and use Process Analysis Toolkit to verify deadlock-free, divergence-free and nonterminating of the evolution mechanism.

Model-driven data acquisition is one of the strategies utilized to save sensor node energy in Wireless Sensor Networks (WSNs), which suppresses data transmission by running one synchronized prediction model at both the sensor and sink node, and only when the predicted value deviates far from the real value should the sensor node transmit the sensed data to the sink node. In this paper, we propose a novel online model-driven data acquisition method which runs two prediction models on the sensor node simultaneously. Specifically, one model is updated online using stochastic gradient descent (SGD) learning algorithm once a new sensor data is available and the other is used to predict sensor value and updated with the former one when it is time for model re-training. The collaborative working of these two models, together with the SGD learning algorithm, solve two main problems of existing methods: data transmission during off-line model re-training and high resource requirement for model update. Extensive experiments are performed to verify the benefits of our method over two existing methods based on more than 20000 data records from three data sets. The experiment results demonstrate that up to 96% of data transmission are reduced by our method while remaining user defined data accuracy, which outperforms the compared methods in terms of energy usage and data accuracy.

The lightweight RESTful protocols, such as CoAP and SeaHttp, have been proposed for web-based sensor network (WBSN), which provides web service on resource-constrained devices. In general, because sensing data are spatially correlated in sensor network, it is efficient to request a group of devices located in nearby area. Thus group requesting is a typical way to provide web service for resource-constrained devices in WBSN. However, it is a critical problem that how to make an optimal assignment of nodes for a group of requests to maximize network lifetime. In this paper, we address this problem in the scenario where nodes have different initial energy, and they can process in-network group request with branch and combine methods supported by SeaHttp. We prove this problem is NP-complete and transform the problem into an edge-weighted semi-matching problem in bipartite graph using the fat tree construction algorithm. Finally we propose an approximation algorithm to solve the problem. Simulation results show that our approach prolong lifetime of the network by 29.11% on average, which is more competitive when it is applied in a high concurrency scenario compared with traditional methods.

Traditional wireless sensor networks (WSNs) can be integrated into Internet and be regarded as its sensing infrastructure, which supports development and running of multiple third-party applications simultaneously. Therefore, due to constrained resource of sensor nodes, it is necessary to establish a runtime framework to improve sensor sharing efficiency for concurrent third-party applications. This paper presents EasiCAE, a concurrent applications runtime framework, to enhance sensor sharing efficiency greatly by incorporating task allocation with redundancy elimination. In brief, EasiCAE decompose the applications into tasks and distributes tasks to the sensors which will bring the least energy to run them. EasiCAE has three salient features. Firstly, we define task-sensor correlation to indicate how many samplings of a sensor can be shared with the new task. Secondly, EasiCAE reduces energy consumption by assigning tasks to a sensor with higher task-sensor correlation. Finally, a light-weight merging algorithm is proposed to eliminate redundant samplings for the assigned sensors. Experimental results show that EasiCAE reduces energy consumption by 31% to 79% compared with existing methods, while introducing tolerable overheads. We also evaluate EasiCAE with various influencing parameters, showing that the performance of EasiCAE increases stably as the network scale and the number of concurrent applications increases.

Facing the still unclear growth regularity of blooms, we propose rapid lightweight estimating method for the discovery of algae blooms. An application-specific approximate algorithm for timely dispatching salvaging boats is designed to minimize the total cleanup time.

In this paper, we formulate coverage requirements of the sensor network as a minimum-cost connectivity guaranteed point k-coverage problem. An improved ant colony algorithm (EasiDesign) is proposed to achieve the approximate solution to this optimization problem. We made modifications in the convergence strategy and the ant state transition rule of the general ant colony optimization. Considering the practicability issues, we design the obstacle avoidance and the routing cost tradeoff strategies to ensure that EasiDesign can work efficiently. The simulation results show that EasiDesign uses less sensor nodes than the existing works in the same scenario. The performance is also demonstrated through a real sensor network system for the environment monitoring in the Forbidden City.

The energy consumption rates of nodes vary in different positions in sensor networks, leading to the early depletion of the nodes and the disconnection of a network. In this paper, we present a distributed mobile sink guiding scheme (DLING) based on the center of gravity theory in physics. DLING regards the sensor network as an object composed of particles. Sensor nodes act as particles and their residual energy is treated as their "weight". We use the traditional discovering method for the center of gravity to compute the "energy center" of a network. The nodes near the energy center have more residual energy and can afford more traffic load. Then we propose a novel scheme to guide the sink sojourning around the network's center of energy so that the energy consumption of the nodes will be balanced. The simulation results demonstrate that DLING can achieve further improvements on the network lifetime in comparison with previous moving schemes while having low control costs.

Nowadays, the harmful blue-green algae blooms on lakes or streams threaten the daily life of millions of people in China. In this paper, we demonstrate the sensor network system we built on Lake Tai for the surveillance and cleanup of the algae blooms which is at work in Wuxi City, Jiangsu Province. We designed the sensor device and algorithm to monitor the algae bloom and estimate the bloom area. When the bloom area goes beyond the threshold, the salvaging boats are automatically dispatched to the scene to clean up the bloom for recycling utilization according to a mechanism in a scalable coordination fashion. The dispatching mechanism includes to consider the locations of salvaging boats, the number of available salvaging boats and the facility status in eight algae harvesting factories around the lake. The system also balances workloads of factories to achieve an overall high working efficiency. We develop a GIS-based management website for the end user to monitor the running of the whole system. All the sensing stations, the real time performance of the algae harvesting factory, the locations of salvaging boats and the automatically generated cleanup schedule are displayed in the map.

In a realistic sensor network, in particular with a non-uniform deployment, sensor nodes inevitably have varying workloads. This causes a natural problem that some sensor nodes are subject to excessive power consumption and thus become hot. These hot nodes deplete much earlier resulting in system performance degradation. This paper proposes a systematic approach to design a hotness-aware sensor network where each node is able to obtain its own hotness information. Based on these vital information, the system is able to provide various technologies to protect the critical set of hot nodes. More specifically, we design a centralized optimal algorithm to derive the precise hotness of each node. In addition, we develop a completely distributed algorithm to estimate hotness with high accuracy. An effective hotness-aware MAC is developed to offer medium access priority to the nodes with higher hotness to protect and prolong their lifetimes. It is demonstrated, through both theoretical analysis and comprehensive simulations, that our approach is valuable to improving system performance of practical sensor networks.

This paper presents an overview of our demonstration of a real application of a WSN system for relic protection applied in the Forbidden City of China. We briefly introduce the EasiNet hardware platform we have developed and the corresponding mesh-architecture of the system constructed. We present a sensor deployment optimization tool based on ant colony optimization technology (DT-ACO) to meet the requirements of network connectivity, sensing coverage and a minimized overall cost of the system. We describe a novel power-aware cross-layer scheme (PACS) to facilitate adjustable system lifetime and surveillance accuracy. We also show some detailed results of the application case.