High-bandwidth memory (HBM) is regarded as a promising technology for fundamentally overcoming the memory wall. It stacks up multiple DRAM dies vertically to dramatically improve the memory access bandwidth. However, this architecture also comes with more severe reliability issues, since HBM not only inherits error patterns of the conventional DRAM, but also introduces new error causes. In this article, we conduct the first systematical study on HBM errors, which cover over 460 million error events collected from 19 data centers and span over two years of deployment under a variety of services. Through error analyses and methodology validations, we confirm that the HBM exhibits different error patterns from conventional DRAM, in terms of spatial locality, temporal correlation, and sensor metrics which make empirical prediction models for DRAM error prediction ineffective for HBM. We design and implement Calchas , a hierarchical failure prediction framework for HBM based on our findings, which integrate spatial, temporal, and sensor information from various device levels to predict upcoming failures. The results demonstrate the feasibility of failure prediction across hierarchical levels.

In high-performance ultra-scale cloud computing, heterogeneous clusters consisting of x86 and ARM architecture platforms have become increasingly common to boost performance and energy efficiency. Ensuring high availability in these environments is crucial for meeting service-level agreements. However, DRAM failures, a primary cause of server downtimes, present significant challenges to reliability, availability, and serviceability. This paper provides an in-depth analysis of memory failure characteristics across cross-architecture platforms in large-scale heterogeneous clusters. We introduce MemSeer, an AIOps-integrated tool that utilizes a multi-grained memory failure prediction approach for x86/ARM heterogeneous clusters. MemSeer improves the F1-score by 17.3% and increases recall by an average of $27 \%$ across different lead times compared to state-of-the-art methods. These advancements show great promise in reducing memory failures in cluster environments, decreasing VM interruptions by up to 42.7% and averaging 24.2% in real-world implementations.

Federated learning is a distributed learning framework with privacy protection; however, data heterogeneity poses significant challenges for federated training. Due to heterogeneous data, client models tend to overfit their local datasets during local training, leading to client drift. Variance reduction-based methods are currently the most popular solutions for addressing client drift; however, their performance deteriorates significantly under low client participation rates. We analyze the limitations of existing variance reduction techniques and attribute them to control variate drift and imbalanced global correction. To address these issues and further constrain client drift, we propose an easy-realization yet effective solution: Federated Proportional Variance Reduction (FedPVR). Specifically, we scale both client gradients and client control variates by a factor that is matched to the client participation rate. This can be viewed as replacing the outdated gradient information associated with each client in the global control variate with their most recent gradients, thereby deriving a closer overall update direction. This approach helps mitigate both client drift and the additional variance introduced by the staleness of control variates. Extensive experiments demonstrate the effectiveness of our approach under partial client participation. Specifically, under limited client involvement, our method achieves an average accuracy improvement of 2–3% compared to baseline methods and further accelerates the convergence rate.

Power delivery issues are increasingly evident in cyber-physical smart grid systems as energy transactions frequently overlook the physical constraints of distribution, leading to transmission congestion and compromising network security and reliability. This article presents a novel and cost-effective solution to power delivery challenges by utilizing electric vehicles (EVs) with dynamic transportation capabilities as free carriers. Unlike traditional approaches, a deep reinforcement learning (DRL)-based optimization framework is designed to effectively manage incomplete information in real-time. Our method first introduces an investment-free model that leverages existing EV routes to transport energy during congestion, operating in a “free-riding” transmission mode. This not only enhances network reliability but also curtails costs. Then, we develop a Markov decision process (MDP) for sequential decision-making of 24-h optimal control, aimed at minimizing operational losses including load shedding and battery degradation. To deal with the stochastic nature of energy requests and EV routes in the control problem, we employ a model-free DRL algorithm to tackle the challenge of incomplete information. An Actor-Critic network, combining value-based and policy-based approaches, helps discover approximately optimal strategies in a continuous action space. Finally, the simulation results numerically demonstrate the performance of the proposed method.

With the rise of federated learning (FL) in the realm of machine learning for data privacy protection, its unique distributed data processing characteristics have garnered widespread attention. However, the implementation of FL faces many challenges, as achieving a balance between data privacy, model security, and system efficiency is difficult, often requiring the sacrifice of efficiency for privacy and security. Moreover, this process typically assumes the existence of a trusted server for coordination. Addressing these challenges, this article proposes a privacy-preserved and efficient FL framework with blockchain (PEFL). PEFL utilizes blockchain and differential privacy techniques to coordinate privacy protection among clients, and filters out anomalous model parameters through an aggregation-side detection algorithm to resist poisoning attacks. Under the assumption of an untrusted server, we design the model-validated fault-tolerant federation (MFF) consensus mechanism based on a committee, balancing efficiency expectations to regulate the server and ensure the reliability of the training process. Through experiments on the MNIST and CIFAR10 datasets, and comparison with typical FL schemes, PEFL demonstrates better defense against various attack models. Besides, it achieves higher training efficiency while ensuring privacy security.

Federated edge learning (FEL) enables Industrial Internet of Things (IIoT) devices to collaboratively train machine learning models without exposing their local data. However, insecure communication environments pose significant threats to the security of model transmission in FEL. Signcryption, as a novel cryptographic primitive, can provide confidentiality, integrity, and other security guarantees for model transmission. Nevertheless, most existing signcryption schemes are designed for a single recipient and therefore cannot meet the multi-recipient requirements of FEL. Additionally, these schemes lack mechanisms for revoking signcryption privileges, which allows malicious edge nodes to continue participating in model transmission and disrupt the training of the global model. To address these challenges, this paper proposes the Lightweight and Efficient Signcryption for Federated Edge Learning in IIoT (LES), which achieves efficient one-to-many signcryption by leveraging bilinear pairings and Lagrange interpolation. LES balances computational and communication overhead while ensuring security. Moreover, the LES scheme incorporates blockchain technology and the Chinese Remainder Theorem to enable revocation of signcryption privileges, preventing compromised edge nodes from continuing to engage in model transmission. Formal security proofs of the LES scheme are provided. A comprehensive comparison between LES and eight representative signcryption schemes proposed in recent years highlights the feasibility of LES and its clear advantages in terms of computational and communication overhead. Under partial participation, LES achieves at least a 29.1% reduction in computational cost and an 81.8% reduction in communication cost compared with the most efficient single recipient scheme among the eight evaluated.

Consensus mechanism is the foundational protocol for achieving distributed consistency among replicas in a blockchain network. A well-designed consensus mechanism needs to balance performance such as complexity, consensus mechanism initiative and dynamic adaptability. Based on Hot-Stuff (a BFT-like consensus with O(n) complexity), we propose a practical and robust consensus mechanism, namely Practical and Robust HotStuff, denoted as PRO-HotStuff. Firstly, PRO-HotStuff redesigns a pacemaker that simultaneously supports replica synchronization and quorum certificate caching, and thus achieves practical view change with O(n) communication complexity while avoiding the additional phase introduced by HotStuff. Secondly, PRO-HotStuff gives the leader election basis by introducing the theory of planed behavior (TPB) and a reputation mechanism. It facilitates restricting the malicious replicas while encouraging the trustworthy replicas, thus to enhance the robustness of PRO-HotStuff. Thirdly, the implementation design of PRO-HotStuff as well as its reconfiguration mechanism for dynamic adaptability is presented. Proofs of correctness of PRO-HotStuff are also provided. Finally, experiments demonstrate that compared to existing HotStuff-like consensus mechanisms, PRO-HotStuff has significant advantages in terms of consensus performance, security, and dynamic adaptability.

In this work, we study the problem of dispatching an unmanned aerial vehicle (UAV) for data collection of Internet of Things (IoT) devices, where a UAV departs from a data center, then visits some IoT devices for data collection and finally returns to the data center. Different from most existing works on UAV-enabled data collection, we assume that the UAV’s collection point, i.e., the location where the UAV stays during the data collection process, can be deployed anywhere within the communication range of each IoT device, rather than being assumed to be in a fixed position. This new assumption is motivated by the fact that the collection point has a great impact on both time and energy consumption of the UAV during its data collection tour. Thus, in this work, we focus on minimizing the UAV’s task completion time and energy consumption during a data collection tour, by jointly optimizing the UAV’s collection point for each IoT device, flight trajectory and flight speed. We formulate this problem as a multiobjective optimization problem, which is solved by executing the following three successive steps: 1) we first employ the ant colony optimization (ACO) algorithm to decide the UAV’s visiting order of all IoT devices; 2) we then reduce the searching space of the collection point for each visited IoT device by using geometric theory and reformulate the original problem; and 3) we finally develop an enhanced multiobjective particle swarm optimization (EMOPSO) algorithm by incorporating a novel gbest selection strategy to identify the optimal collection point for each visited IoT device, based on which the corresponding flight trajectory as well as the flight speed is calculated. We refer to the above three-step hybrid algorithm as ACO-EMOPSO-G. Extensive evaluations validate the superiority of ACO-EMOPSO-G in terms of the tradeoff between the UAV’s task completion time and energy consumption, compared with some other data collection approaches.

To realize carbon emission reduction, restricting regional carbon emissions while meeting electricity usage is a critical but not trivial problem. In this paper, we propose a game-based pricing scheme for joint carbon emission rights (CER) and electricity trading between the electricity prosumers within a microgrid. For modeling and theoretical analysis, we first introduce the utility functions of electricity producers and consumers, which are determined by CER and electricity prices in a coupled way. Then, the multi-leader multi-follower (MLMF) Stackelberg game and non-cooperative game are employed to formulate the electricity and CER pricing and trading, respectively. The game equilibriums convince that optimal prices for both electricity and CER exist to satisfy electricity usage while meeting the carbon emission restriction. For implementation, the blockchain with smart contracts is developed to undertake the CER and electricity trading in a transparent and credible way. A prototype system based on Fabric blockchain verifies the feasibility of the proposed scheme, which demonstrated a five-fold increase in the economics and electricity generation utility of the microgrid and achieved a 2% reduction in carbon emissions compared to the baseline model.

Linking medical data silos for medical model learning and sharing makes for better healthcare for humanity. Before that, two critical issues must be solved, i.e., patient privacy protection and data contributors’ rights and interests. This paper proposes an evolutionary medical data modeling and sharing (EMDMS) framework. Specifically, EMDMS adopts a federated learning scheme to coordinate the decentralized medical model learning and model aggregation without the leakage of raw data. A dual-loop federated learning mechanism with a tailored control strategy is developed for the realization of evolutionary model learning with the consideration of the ever-growing medical data. Then, a long-term pricing and revenue distribution strategy is designed for evolutionary model sharing, thus to make the medical model self-growth. It not only ensures fair benefits for data contributors but also enables low-cost sharing of models for public welfare. EMDMS runs on the sharded blockchain to support parallel tasks where dedicated smart contracts are implemented for EMDMS to guarantee security and trustworthiness. A prototype system with simulations on the Fed-ISIC2019 dataset demonstrates the effectiveness of EMDMS and its advantages over some existing typical solutions.

In this paper, we study an unmanned aerial vehicle (UAV)-enabled wireless rechargeable sensor network (WRSN), where a rotary-wing UAV travels along a fixed trajectory while providing wireless charging services for a set of sensor nodes deployed on the ground. Given the practical speed-related flight energy model, we focus on minimizing the UAV’s flight energy during a time-bounded charging tour by appropriately controlling the UAV’s travelling speed, such that the charging demand of each node is satisfied. We first investigate the optimal speed control with the minimized flight energy on arbitrarily-shaped trajectories in a 2D space. We adopt the spatial discretization to tackle the non-convexity of the formulated problem, which is then solved by interior-point method with the provable upper bound of the UAV’s flight energy. Next, we develop the optimal speed control for the UAV to travel along a 1D trajectory, i.e., a straight line, which is commonly seen in many UAV applications. Extensive evaluations validate the effectiveness of our speed control design in terms of the UAV’s flight energy minimization.

Blockchain has gained significant adoption in energy trading, offering benefits for both economy and environment. Consensus, in particular, is a decisive factor for blockchain-based energy trading systems to operate efficiently and securely. However, the consensuses currently applied have been criticized for being too energy intensive or not sufficiently decentralized, which counteracts the positive effect of energy trading. Besides, consensus and energy trading are treated separately in many energy trading blockchain-based studies. In this article, we propose a green and efficient consortium blockchain-enabled transaction system for energy trading, meeting the requirement of low energy consumption under security. We then design a two-stage consensus mechanism called proof-of-energy that is coupled to trading through “energy” and naturally uses the monetary rewards to stimulate prosumer participation. Specifically, it retains a strong degree of decentralization, which selects a dynamic delegation with high historical energy generation and motivates delegates to compete for new blocks by solving a meaningful puzzle. Furthermore, a variable block reward is investigated as the incentive to regulate trading and consensus behavior within a reasonable range of energy consumption. Finally, we design a two-layer iterative algorithm to obtain the optimal consensus strategy and block rewards, taking the noncooperative game approach with the consideration of the strategy effect on the pricing model. Our simulation results show that the proposed blockchain-enabled system has a high energy efficiency ratio that improves the social welfare and reduces the consensus overhead.

Energy harvesting technology enables wireless sensor networks (WSNs) to be self-sustainable, for maintaining long-term key performance indicators, such as the data throughput and sensing coverage. Due to the highly dynamic and complex environment, energy sources (ES) cannot provide stable energy supply, which needs the efficient learning algorithm to enable system adaptations. This article reports on the development of reinforcement learning (RL) methodology to long-term data collection in self-sustainable WSNs. Specifically, we consider the WSN as a 2-D rectangular region, where a mobile sensor (MS) can harvest energy from ambient environments while transmitting the collected data to a fixed sink. Due to the changing environment and the mobility of the MS, the harvested energy by the MS at each slot presents spatiotemporal dynamics within the network, which severely affects the performance of data throughput from the MS to the sink. The MS’s trajectory is investigated to maximize the long-term average MS-to-sink data throughput. Due to the unknown energy arrival information as well as the locations of ESs, we formulate the problem as a Markov decision process, which is then solved with model-free RL. In particular, the deep deterministic policy gradient (DDPG) is applied to tackle the continuous and deterministic movement space. Results show that the MS can learn and optimize the moving trajectory by intelligently tracking the aggregated received energy over slots. Finally, the MS can identify and move to the optimal location where the maximized long-term average MS-to-sink data throughput is achieved. Extensive numerical evaluations are conducted to investigate the impact of various system parameters on the network performance.

Industrial wireless sensor networks (IWSNs) have been considered as promising technology to enhance target tracking and state monitoring in process industries with harsh environments. In this article, the codesign of IWSNs and consensus-based sequential estimation (CSE) for typical long-belt process industries is proposed. Specifically, a group-based IWSNs deployment strategy is first designed to cover the transportation belt. Over the group-based IWSNs strategy, the CSE algorithm is then proposed to conduct target tracking and state estimation using the distributed Kalman filter. To reveal the interrelation of IWSNs and CSE, the upper bound on the error violation probability of state estimation is first deduced from the sequential estimation process. Then, the algorithm is developed for the determination of IWSNs parameters (such as the number of groups of the IWSNs and communication design) and the CSE algorithm parameters (such as iterations of sequential estimation and estimation accuracy prediction). A case study of slab temperature monitoring over the hot strip milling process demonstrates the effectiveness of the proposed codesign of IWSNs and CSE.

Mobile chargers (MCs) are usually dispatched to deliver energy to sensors in wireless rechargeable sensor networks (WRSNs) due to its flexibility and easy maintenance. This paper concerns the fundamental issue of charging path DEsign with the Minimized energy cOst (DEMO), i.e., given a set of rechargeable sensors, we appropriately design the MC’s charging path to minimize the energy cost which is due to the wireless charging and the MC’s movement, such that the different charging demand of each sensor is satisfied. Solving DEMO is NP-hard and involves handling the tradeoff between the charging efficiency and the moving cost. To address DEMO, we first investigate how to identify a single charging position where the MC could stay to charge a set of sensors distributed within a small area with the maximized charging efficiency. Then, based on the result obtained in the case of optimizing a single charging position, we develop a computational geometry-based algorithm to deploy multiple charging positions within the whole network, by considering the fixed and finite charging range of the MC. We prove that the designed algorithm has the approximation ratio of$O\!\left ({\ln \!N}\right)$, where$N$is the number of sensors. Then we construct the charging path by calculating the shortest Hamiltonian cycle passing through all the deployed charging positions within the network. In addition, we investigate the impact of the network topology as well as the distribution of charging demands among sensors on the MC’s energy cost during a charging tour. Extensive evaluations validate the superiority of our path design in terms of the MC’s energy cost minimization, compared with existing main algorithms.

Mobile chargers (MCs) are usually dispatched to deliver energy to sensors in wireless rechargeable sensor networks (WRSNs) due to its flexibility and easy maintenance. This paper concerns the fundamental issue of charging path DEsign with the Minimized energy cOst (DEMO), i.e., given a set of rechargeable sensors, we appropriately design the MC’s charging path to minimize the energy cost which is due to the wireless charging and the MC’s movement, such that the different charging demand of each sensor is satisfied. Solving DEMO is NP-hard and involves handling the tradeoff between the charging efficiency and the moving cost. To address DEMO, we first develop a computational geometry-based algorithm to deploy multiple charging positions where the MC stays to charge nearby sensors. We prove that the designed algorithm has the approximation ratio of O(lnN), where N is the number of sensors. Then we construct the charging path by calculating the shortest Hamiltonian cycle passing through all the deployed charging positions within the network. Extensive evaluations validate the effectiveness of our path design in terms of the MC’s energy cost minimization.

Federated learning is an emerging paradigm to conduct the machine learning collaboratively but avoid the leakage of original data. Then, how to motivate the data owners to participate federated learning and contribute high-quality data is the crucial issue. In this article, a blockchain-based federated learning (BFL) with a reputation mechanism for high-quality model aggregation is proposed. Specifically, the blockchain transforms the federated learning into a decentralized and trustworthy manner. Over the blockchain, federated learning tasks, undertaken by smart contracts, can be conducted transparently and fairly. Besides, a reputation-constrained data contribution and reward allocation mechanism is designed to encourage data owners to participate in BFL and contribute high-quality data. The noncooperative game is adopted to analyze the behavior strategies of data owners. The existence of the unique equilibrium is proved and the equilibrium point indicates that the data owners can acquire highest reward with the contribution of the highest quality data. Thus, the model quality of BFL is guaranteed. Finally, simulations on the public data sets (MNIST and CIFAR10) demonstrate that BFL with a reputation mechanism can well promote the high-quality model aggregation of federated learning as well as can prevent malicious nodes from corrupting the training task.

Recently, Siamese networks based tracking algorithms have shown favorable performance. Latest work focuses on better feature embedding and target state estimation, which greatly improves the accuracy. Nevertheless, the simple cross-correlation operation of the features between a fixed template and the search region limits their robustness and discrimination capability. In this paper, we pay more attention to learn an outstanding similarity measure for robust tracking. We propose a novel relation network that can be integrated on top of previous trackers without any need for further training of the siamese networks, which achieves a superior discriminative ability. During online inference, we utilize the feedback from high-confidence tracking results to obtain an additional template and update it, which improves the robustness and generalization. We implement two versions of the proposed approach with the SiamFC-based tracker and SiamRPN-based tracker to validate the strong compatibility of our algorithm. Extensive experimental results on several tracking benchmarks indicate that the proposed method can effectively improve the performance and robustness of the underlying trackers without reducing speed too much, and performs superiorly against the state-of-the-art trackers.

The abuse of personal identity information is one of the most serious problems worldwide. Most social services or businesses use the identity authorization to confirm their validity and legality and the copies of users' identity certification are usually recorded by the service providers. It is easy to leak the users' identity information due to the untrustworthy service provider or single-point security failure, and various social problems are then caused. To deal with such problems, this paper proposes a Blockchain-based Identity Authorization mechanism (BIA). First, an Identity Authorization Module (IAM) is devised, which reads the identity certificate and transform the identity plaintext to ciphertext under the authorization by the user's identity certificate entity and password. IAM guarantees the security of identity information by keeping its plaintext offline. Second, a Business Contract Module (BCM) is designed, which provides a general smart contract framework for identity authorization that can be adopted by most of social services or businesses. Third, a double-chain blockchain infrastructure is developed, whereby the encrypted identity information and service smart contracts are respectively recorded in the tamper-resistant, non-repudiable, and publicly verifiable way. Finally, a prototype system has been developed to verify the security, feasibility and effectiveness of the proposed BIA.

Industrial wireless sensor networks (IWSNs) are the fundamental components in the next-generation factories. Due to massive heterogeneous data generated from large-scale IWSNs, it is still challenging to achieve predictable, deterministic, and real-time transmission scheduling. In this article, a framework of priority-aware packet transmission scheduling (PPTS) in cluster-based IWSNs is proposed, where the PPTS strategy, the optimization theory, and the implementation design are systematically considered. In particular, the proposed PPTS strategy not only minimizes the transmission delay of high priority packets but also greatly improves the transmission delay of low priority packets. The optimization theory for end-to-end priority-aware scheduling in cluster-based IWSNs is formalized, which contributes to the optimal solution for multidimensional network resources allocation and achieves the minimum of average transmission delay. Finally, the advantages of the PPTS framework over some existing solutions are demonstrated by a case study.

Process parameter estimation, to a large extent, determines the quality of the industrial production. Traditionally, limited sensors are deployed in production field by elaborate wiring, which cannot provide the accurate estimate in the hostile industrial environment. Recently, industrial wireless sensor network (IWSN) has been considered as one promising technology to improve the process parameter estimation by deploying more sensors flexibly and making them work collaboratively. In this paper, a sequential IWSN (Seq-IWSN) approach is provided for the temperature estimation of the steel slab during the hot strip milling process. In Seq-IWSN, the network deployment and scheduling strategies coupling with the process parameter estimation algorithm are involved. Simulation results based on NS3 network simulator show that Seq-IWSN can help to reduce the estimation error to less than 3°C, although the covariance of the sampling noise is as large as 100.

Industrial wireless sensor networks (IWSNs) are committed to bring the industry automation into the era of Industry 4.0 by providing the ubiquitous perception to improve the production efficiency. However, the proliferation of wireless devices in industrial applications makes the spectrum sharing in limited industrial, scientific, and medical (ISM) band a challenging problem. In this paper, it is concerned with the intrinsic impact of the evenness of spectrum usage on the spectrum sharing performance in terms of channel accessing probability, spectrum utilization, and fairness of spectrum usage. In order to explore the explicit relationship between the evenness and spectrum sharing performance, a new concept of equilibrium is first defined to represent the achievable best evenness of spectrum usage. Then, a set of rules called local equilibrium-guided autonomous channel switching (LEQ-AutoCS) is devised, with which each accessed sensor autonomously equalizes the local channel occupations within its range of spectrum sensing without overhead on exchanging the sensors' spectrum sensing reports. It is further proved that the equilibrium can be achieved by this concessive manner. Theoretical analysis and experimental results demonstrate that the proposed LEQ-AutoCS rules provide higher utilization and fairness of spectrum usage comparing to the existing spectrum access approaches. Moreover, it is shown that LEQ-AutoCS rules assist the system to reduce the spectrum access delay to 1/2 of CSMA-based systems and 1/50 of TDMA-based systems, respectively.

State estimation, which computes the best possible approximation for the system state based on the perceived information transmitted from sensors to the estimators, is vital for control system performance in industrial cyber-physical systems (ICPSs) with the integrated techniques of control, communication and computing. Thus the performance of state estimation relies on the communication reliability. In order to improve the reliability, redundant channels/slots are reserved for the data transmission in industrial wireless techniques, such as WirelessHART. However, the redundancy scheme burdens the increasingly over-crowded ISM spectrum band due to the envisioned emerging ubiquitous industrial wireless monitoring in the architecture of ICPS in the near future. The cognitive radio (CR) technology can intelligently explore the available spectrum opportunities on licensed channels, and it motivates this paper to consider the redundant transmission through the opportunistically available licensed channels to guarantee the transmission reliability for state estimation. Unfortunately, spectrum sensing takes extra energy consumption, thus it is necessary to take into account the energy efficiency for the battery-powered IWSN. Then a CR enabled energy- efficiency maximization problem is formulated by regarding the convergence of state estimation as a constraint of the resource allocation problem. In order to solve the non-convex and mixed integer programming, the Dinkelbach and Lagrangian relaxation techniques are adopted to transform the problem into a convex programming and furthermore reduce the computational complexity. Numerical results demonstrate that the CR technology can significantly release the spectrum for the redundancy design from the ISM band while guarantee the reliability for the effective state estimation.

Spectrum sharing is a promising technique responsible for providing efficient and fair spectrum allocation. Considering the unevenness phenomenon of spectrum usage in industrial wireless networks, a novel spectrum sharing scheme, in the framework of industrial cognitive radio network (ICRN), is proposed in this paper via an autonomous switching technique to equalize the spectrum usage and increase the spectrum access possibility of new requests. The autonomous switching technique borrows the idea from the fact that specific collective motion can be achieved by local actions of individuals in many biological systems. The accessed nodes sense the limited spectrum range around their central frequency and then make the decision of channel switching autonomously. Several sensing report based rules are presented to facilitate the switching decision in order to equalize the channel usage among the sensing range of each node. It is demonstrated that by using these rules the spectrum usage becomes more even, and thus the spectrum utilization and fairness are both improved. Numerical examples are given to show the effectiveness of the proposed spectrum sharing scheme.