Mobile Edge Computing (MEC) networks have attracted significant attention for enabling users to offload computation-intensive tasks to edge servers. Task offloading scheduling is a critical challenge, especially when complete information about tasks and edge servers is only partially accessible in practice. In general, each edge server can only obtain its own information but has no access to the complete real-time information of other edge servers, resulting in information incompleteness. To address this issue, this paper investigates the problem of Energy Minimization through Offloading with Incomplete Edge Information (EMO-IEI). Specifically, to address the uncertainty of real-time computing resources caused by incomplete edge information, we adopt the Exact Convex Regularization (ECR) method to estimate resource availability based on known expectations and variances. Utilizing these estimations, we reformulate the problem as a collapsing multi-knapsack problem and propose the GAP-ESM algorithm for efficient solution. Theoretical analysis validate that the GAP-ESM algorithm achieves an approximation ratio of (1 + κ/κ−ρκ−ρ ), where κ is system parameter associated with the energy requirements of computing tasks, and ρ is a tunable design parameter balancing approximation quality and computational complexity. Extensive simulations demonstrate that the proposed GAP-ESM algorithm outperforms baseline schemes in terms of overall energy consumption and task completion rate.

Collaborative edge computing enables multiple edge servers to cooperate by sharing information and computing resources, achieving resource complementarity and task collaborative processing. In areas with dense mobile users, edge servers are often deployed and managed by different business entities. Therefore, in a heterogeneous edge environment with multiple business entities, the collaborative relationship between edge servers is often constrained by their respective management strategies and profit considerations. Resource sharing is no longer unconditional, but instead involves a certain degree of competition and self-interested behavior. Existing works often assume unconditional collaboration, overlooking the rational and self-interested behavior of edge servers belonging to different business entities in practice. This paper proposes a coalition-based collaborative scheduling framework tailored for Directed Acyclic Graph (DAG) tasks with inter-task dependencies. It designs a coalition formation algorithm for edge servers and a multi-DAG task scheduling algorithm within the coalition to achieve optimal resource collaboration and edge server utility. Extensive experiments are conducted to evaluate the performance of the proposed algorithms. Experimental results show that the proposed algorithms consistently outperform all baselines across different network scales, achieving at least a 46.34% improvement in cooperative utility.

Camouflaged object detection (COD) relies on multi-granularity structural information and fine-grained details to distinguish objects from highly similar backgrounds. Whereas low-resolution data lacks high-frequency cues such as textures and sharp edges, retaining only coarse structures. These not only weaken discriminative features but also introduce resolution-induced camouflage beyond natural blending. Existing COD methods assume high-resolution data and fail to address this dual-source ambiguity, resulting in significant performance degradation and underscoring the need for approaches that explicitly explore essential spatial priors under low-resolution constraints. Therefore, we propose KRNet, the first framework explicitly designed for COD in low-resolution settings. KRNet presents a Leader-Follower framework where the Leader extracts dual gold-standard distributions: conditional and hybrid, from supporting data to drive the Follower in rectifying knowledge learned from low-resolution data. The framework further benefits from a cross-consistency strategy, and a stronger time-prompt conditional encoder that improve the rectification of these distributions. Extensive experiments on benchmark datasets demonstrate that KRNet outperforms state-of-the-art COD methods and SR-assisted COD approaches, highlighting its effectiveness in tackling the challenges of low-resolution data in COD. Code: https://github.com/whyandbecause/KRNet/tree/main.

Collaborative Edge Computing (CEC) enables effective load balancing by decomposing tasks across edge servers. However, due to limited computing and storage resources in CEC networks, eliminating computational redundancies becomes particularly important for improving overall efficiency and conserving resources. To address this, we propose a novel compute-storage cooperation framework that jointly optimizes task offloading and computation result caching to minimize system-wide delay and caching cost. The optimization problem is decomposed into two subproblems: reusable task scheduling and reusable data caching. Accordingly, the CoRe-S algorithm and the VaRe-C algorithm along with a proactive pre-caching mechanism are proposed to solve these subproblems, respectively. By leveraging temporal and spatial correlations among computational tasks, the proposed framework directly caches computation results to reduce redundant processing. In addition, the age of data is incorporated into the evaluation metric to better assess the value of cached results, thereby enhancing reuse efficiency. Theoretical analysis and extensive simulations are conducted to validate the effectiveness and superiority of the proposed algorithms. Compared with state-of-the-art baselines, our method reduces the total cost by up to 41.89% and achieves a cache hit rate of 53.1%.

The Industrial Edge Computing (IEC) network has recently received considerable attention, where industrial devices offload their computation-intensive and delay-sensitive tasks to servers located at the network edge. Task offloading scheduling is a fundamental problem in IEC networks to achieve satisfactory quality of service. Many prior efforts have been devoted to scheduling task offloading for networks with complete information, while the complete information is hard or even infeasible to acquire by the scheduler. Therefore, their performance degrades in IEC networks with incomplete information. Scheduling task offloading for IEC networks with incomplete information is urgent and presents great technical challenges. This paper proposes a group-centric task offloading framework tailored for IEC networks with incomplete information, and models the minimum delay scheduling problem as a Partially Observable Markov Decision Process. Then, the SGOS algorithm integrating the Long Short-Term Memory with Soft Actor-Critic networks in reinforcement learning is proposed to devise online task offloading schedules for IEC networks with incomplete information. Extensive experimental results verify that the SGOS algorithm can achieve the best performance compared with base-line schemes in terms of major metrics, including convergence, delay, and workload balance.

Camouflaged objects often blend in with their surroundings, making the perception of a camouflaged object a more complex procedure. However, most neural-network-based methods that simulate the visual information processing pathway of creatures only roughly define the general process, which deficiently reproduces the process of identifying camouflaged objects. How to make modeled neural networks perceive camouflaged objects as effectively as creatures is a significant topic that deserves further consideration. After meticulous analysis of biological visual information processing, we propose an end-to-end prudent and comprehensive neural network, termed IdeNet, to model the critical information processing. Specifically, IdeNet divides the entire perception process into five stages: information collection, information augmentation, information filtering, information localization, and information correction and object identification. In addition, we design tailored visual information processing mechanisms for each stage, including the information augmentation module (IAM), the information filtering module (IFM), the information localization module (ILM), and the information correction module (ICM), to model the critical visual information processing and establish the inextricable association of biological behavior and visual information processing. The extensive experiments show that IdeNet outperforms state-of-the-art methods in all benchmarks, demonstrating the effectiveness of the five-stage partitioning of visual information processing pathway and the tailored visual information processing mechanisms for camouflaged object detection. Our code is publicly available at: https://github.com/whyandbecause/IdeNet.

Battery-free wireless sensor network (BF-WSN) (including energy harvesting network and energy rechargeable network) is a new network architecture that has been proposed in recent years to solve the lifetime limitation problem of conventional WSNs. Battery-free sensor nodes can harvest energy from environmental energy resources or from artificial power stations. Thus, the lifetime of a BF-WSN is unlimited in terms of energy. The specific properties of BF-WSNs have brought new challenges in fundamental issues, such as energy management, networking, and data acquisition, which means the existing algorithms in WSNs cannot be adopted directly. The BF-WSN can be regarded as a totally new topic in Internet of Things (IoT) and has attracted much attention from researchers. Many algorithms have been proposed to solve the fundamental problems in BF-WSNs. The objective of this survey is to comprehensively summarize and analyze the existing works. In this survey, we first introduce the existing algorithms from three fundamental aspects, including energy management, networking, and data acquisition. Then, we present some specific applications of BF-WSNs.

Age of Information (AoI) is a new metric for measuring the freshness of sensory data in wireless sensor networks. The Battery-Free Wireless Sensor Network (BF-WSN) is proposed to break through the lifetime limitation of battery-powered wireless sensor networks. However, the emerging BF-WSN also brings challenges to the minimization of AoI, on account of its energy characteristics. In this paper, we investigate the AoI minimization data collection scheduling problem for BF-WSNs. The off-the-shelf works for the AoI minimization data collection scheduling problem either focus on simple networks with no more than three nodes or assume that battery-free sensor nodes have specific energy harvesting process, such as Bernoulli process and Poisson process. Different from these works, we first consider the AoI minimization data collection scheduling for one-hop BF-WSNs with multiple battery-free sensor nodes transmitting their sensory data to the sink node, where the energy harvesting processes of battery-free sensor nodes are non-specific. We propose the optimal offline algorithm and the online algorithm for the problem, respectively. The optimality of the offline algorithm and the competitive ratio of the online algorithm are theoretical proved and analyzed. Numerical results are provided to verify the performances of the proposed algorithms.

Industrial Internet of Things (IIoT) has attracted increasing attention for improving the efficiency of manufacturing. Plenty of computation-intensive and latency-sensitive applications are required by IIoT networks, which pose significant challenges for the computation capacities of IIoT networks. To address these challenges, Edge-enabled Industrial Internet of Things (E-IIoT) emerges. Edge devices located at the edge of IIoT networks enlarge computation capacities of IIoT networks and improve their efficiency accordingly. How to schedule computation resources wisely is a major problem in E-IIoT networks. Since IIoT devices in an E-IIoT network monitor the industrial site collaboratively, tasks for processing sensory data collected by them are correlated accordingly. That means, scheduling highly correlated tasks to be processed at the same device can improve computation efficiency. Inspired by this fact, we propose a correlation aware scheduling (CAS) algorithm for E-IIoT networks in this article. In specific, computation model decision and processing order decision are made by considering computation resources of devices and correlations among tasks in the algorithm to minimize latency of E-IIoT networks. The NP-hardness of correlation aware latency minimization scheduling problem in E-IIoT networks is first proved. Theoretical analysis on approximation ratio of the CAS algorithm is provided, and simulation results demonstrate the effectiveness of the proposed algorithm in reducing latency.

To break through the limitation of battery-powered wireless sensor networks, a novel kind of network, named battery-free wireless sensor network (BF-WSN), is proposed. Battery-free sensor nodes in BF-WSNs harvest energy from power sources in their ambient environment, such as solar power, wind power and radio frequency (RF) signal power,etc., instead of batteries. Therefore, the energy consumption of battery-free sensor nodes are not limited by the battery capacity anymore. However, they still have limited energy harvesting rates and energy capacities. Data aggregation is a fundamental operation in sensor networks where the sensory data gathered by the relay nodes can be merged by in-network computation, such as taking the maximum, average, or sum, etc., of them. Due to the energy features of BF-WSNs, the data aggregation scheduling problem in BF-WSNs is more complicated and the previous aggregation scheduling algorithms designed for battery-powered WSNs are no longer applicable. This paper investigates the Minimum-Latency Aggregation Scheduling problem in BF-WSNs, which is proved to be NP-hard. Then, we propose the Data Aggregation Scheduling algorithm to solve the problem. Finally, the theoretical analysis and extensive simulation results are provided to verify the performance of the proposed algorithm.

Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) are envisioned as two promising techniques to satisfy the increasing energy and computation requirements of latency-sensitive and computation-intensive applications installed on mobile devices. The integration of MEC and WPT introduces a novel paradigm named Wireless Powered Mobile Edge Computing (WP-MEC). In WP-MEC networks, edge devices located at the edge of radio access networks, such as access points and base stations, transmit radio frequency signals to power mobile devices and mobile devices can offload their intensive computation workloads to edge devices. In this paper, we study the Computation Completion Ratio Maximization Scheduling problem for WP-MEC networks with multiple edge devices, which is proved to be NP-hard. We jointly optimize the WPT time allocation and computation scheduling for mobile devices in a WP-MEC network to maximize the computation completion ratio of the WP-MEC network and propose approximation algorithms. The approximation ratio and computation complexity of the proposed algorithms are theoretically analyzed. Extensive simulations are conducted to verify the performance of the proposed algorithms.

The lifetime of battery-powered Wireless Sensor Networks (WSNs) are limited by the batteries equipped in sensors. The appearance of Battery-free Wireless Sensor Networks (BF-WSNs) breaks through this limitation, in which battery-free sensors harvest energy from sustainable but uncontrollable energy sources in ambient environment, such as solar power, wind power, radio frequency signal power, and so on. The energy characteristics of BF-WSNs make it more challenging for data collection scheduling in BF-WSNs. Latency of data collection is a crucial measurement to evaluate the performance of data collection schedules. In this article, we study the problem of generating data collection schedules with minimum latency for BF-WSNs and propose latency-efficient data collection scheduling algorithms for line BF-WSNs and general BF-WSNs, respectively. Theoretical analysis and extensive simulations are conducted to verify the efficiency and effectiveness of the proposed algorithms.

Mobile edge computing (MEC) is a new computing approach in which computation tasks carried by mobile devices (MDs) can be offloaded to MEC servers or computed locally. Since the MDs are always battery limited and computation tasks have strict deadlines, how to schedule the execution of each task energy effectively is important. Comparing with existing works, we consider a much more complexed scenario, in which multiple moving MDs sharing multiple heterogeneous MEC servers, and a problem named as minimum energy consumption problem in deadline-aware MEC system is formulated. Such problem is proved to be NP-hard, and two approximation algorithms are proposed focusing on single and multiple MD scenarios, respectively. The performances of these algorithms are varied by theoretical analysis and simulations.

Battery-Free Wireless Sensor Networks (BF-WSNs) are newly emerging Wireless Sensor Networks (WSNs) to break through the energy limitations of traditional WSNs. In BF-WSNs, the broadcast scheduling problem is more challenging than that in traditional WSNs. This article investigates the broadcast scheduling problem in BF-WSNs with the purpose of minimizing broadcast latency. The Minimum-Latency Broadcast Scheduling problem in BF-WSNs (MLBS-BF) is formally defined and its NP-hardness is proved. Three approximation algorithms for solving the MLBS-BF problem are proposed. The broadcast latency of the broadcast schedules produced by the proposed algorithms is analyzed. The correctness and approximation ratio of the proposed algorithms are also proved. Finally, extensive simulations are conducted to evaluate the performances of the proposed algorithms. The simulation results show that the proposed algorithms have high performance.