Underwater wireless optical communication (UWOC) enables high-rate data offloading for underwater sensing systems, but its strong directionality makes multi-node networking vulnerable to misalignment, occlusion, and dynamic link disruptions. Existing control-plane-driven recovery is often too slow for such transient failures. We present A-SCAN, a data-plane self-healing mechanism that maintains neighbor-angle mappings and performs lightweight angle-guided recovery without triggering global routing updates. Based on the locally recovered topology, Q-SHARP performs quality-aware multi-hop path selection and backup optimization in the control plane. Together, they separate fast local link recovery from slow global routing optimization, enabling more stable self-healing communication in directional UWOC networks.

Generative diffusion models deliver remarkable synthesis quality but remain impractical for resource-limited Internet of Things (IoT) devices due to their substantial computational and memory demands. To bridge this critical gap, we present a comprehensive, multi-stage optimization framework that systematically reduces model size while meticulously preserving generative fidelity. The framework integrates an efficient backbone architecture designed for inherent lightness, a sensitivity-guided fine-grained pruning strategy that strategically removes redundant parameters to achieve high sparsity, and a novel distribution-aware quantization algorithm based on Gaussian Mixture Models (GMMs) to compress weights and activations with minimal quality degradation. Extensive validation across multiple diffusion architectures (DDPM, DDIM, SGM) and diverse datasets demonstrates the framework’s strong generalizability, achieving up to 79% model sparsity while preserving generative fidelity. To showcase practical utility, we demonstrate that our framework produces a compressed model compatible with standard mobile deployment toolchains, realizing a significant reduction in the on-device memory footprint required for inference. This work offers a robust and generalizable methodology for enabling advanced generative AI on a wide spectrum of edge and IoT platforms. Code is available at: https://github.com/mitchell-cheng/compress_diffusion.

WiFi-based human activity recognition (HAR) plays an essential role in various applications such as security surveillance, health monitoring, and smart home. Existing HAR methods, though yielding promising performance in indoor scenarios, highly depend on a massive labeled dataset for training which is extremely difficult to acquire in practical applications. In this paper, we present an automatic data labeling and HAR system, termed AutoDLAR. Taking a semi-supervised cross-modal learning framework with a hybrid loss function as the core, AutoDLAR transfers rich visual information to automatically label WiFi signals for WiFi-based HAR. Specifically, we devise a lightweight and multi-view WiFi sensing model with a parallel feature embedding method to accurately identify activities and accelerate recognition speed. Then, we exploit the video data to fine-tune a well-established visual HAR model, generating effective pseudo-labels for guiding the WiFi model’s training. We also build a synchronized Video-WiFi dataset with seven types of human activities under different scenarios to enable training and validating the semi-supervised HAR system. Extensive experiments on our collected activity dataset and the emotion recognition benchmark demonstrate that AutoDLAR attains an average accuracy of over 95.89% without manual labeling and only spends the inference time of 3.35 ms, outperforming the state-of-the-art (SOTA) methods.

Underwater Optical Wireless Sensor Networks (UOWSNs) are gaining an increasing demand in industrial and commercial applications as they can achieve high-speed communication. However, prior arts concentrate on promoting the performance of UOWSNs, while the reliability issue has not been fully addressed. In this paper, we propose a novel reliable data delivery scheme based on a cluster structure. First, we determine the orientation of each sensor for directional optical communication, which aims to establish reliable next-hop links among sensors. We formalize such an orientation problem into a submodular function maximization problem and propose a greedy method with an approximation ratio guarantee to solve it. Then, a cluster head designation scheme is developed to improve the data delivery success rate while minimizing the number of cluster heads. Finally, extensive simulations are conducted to demonstrate the effectiveness of the proposed scheme. The results reveal that compared with other algorithms, the proposed scheme can ensure a data delivery success rate of over 98.5 % while only keeping 45.3% fewer cluster heads. Furthermore, test-bed experiments are carried out to verify the applicability of the proposed scheme in practical applications.

Underwater Optical Wireless Communication (UOWC) enables high-speed data transmission among Autonomous Underwater Vehicles (AUVs). However, due to cost and weight constraints, AUVs can only carry a limited number of directional optical transceivers. This implies that each AUV can communicate with only 1 to 2 neighbors simultaneously, complicating the establishment of an Underwater Optical Wireless Communication Network (UOWN). To address the networking problem with the degree constraint, we propose a topology generation method based on Hamiltonian paths. The topology achieves improved global connectivity at the cost of local optimality while satisfying the communication device limitations of AUVs. Preliminary results show that the generated topology can reduce the average communication overhead.

The marine Internet of things (MIoT) is the application of the Internet of things technology in the marine field. Nowadays, with the arrival of the era of big data, the MIoT architecture has been transformed from cloud computing architecture to edge computing architecture. However, due to the lack of trust among edge computing participants, new solutions with higher security need to be proposed. In the current solutions, some use blockchain technology to solve data security problems while some use federated learning technology to solve privacy problems, but these methods neither combine with the special environment of the ocean nor consider the security of task publishers. In this article, we propose a secure sharing method of MIoT data under an edge computing framework based on federated learning and blockchain technology. Combining its special distributed architecture with the MIoT edge computing architecture, federated learning ensures the privacy of nodes. The blockchain serves as a decentralized way, which stores federated learning workers to achieve nontampering and security. We propose a concept of quality and reputation as the metrics of selection for federated learning workers. Meanwhile, we design a quality proof mechanism [proof of quality (PoQ)] and apply it to the blockchain, making the edge nodes recorded in the blockchain more high-quality. In addition, a marine environment model is built in this article, and the analysis based on this model makes the method proposed in this article more applicable to the marine environment. The numerical results obtained from the simulation experiments clearly show that the proposed scheme can significantly improve the learning accuracy under the premise of ensuring the safety and reliability of the marine environment.

Wi-Fi-based indoor localization gained a lot of attention over recent years due to low cost and open access properties. However, existing schemes might not be applicable in the real environment if their robustness is low. This paper presents CLRS, a novel distributed Indoor Positioning System (IPS) with high robustness which uses Wi-Fi signals to divide the space twice based on Angle of Arrival (AoA) and Effective Channel State Information (ECSI). The proposed scheme trade the redundancy of Access Point (AP) quantity to improve the tolerance of data measurement error. We performed simulations as well as real-world experiments, in which simulation results proved that the theoretical average error is the least when the routers are placed vertically in our localization method while the real-world experiments proved the high accuracy and robustness of CLRS.

Big data technology has been developed rapidly in recent years. The performance improvement mechanism of targeted poverty alleviation is studied through the big data technology to further promote the comprehensive application of big data technology in poverty alleviation and development. Using the data mining knowledge to accurately identify the poor population under the framework of big data, compared with the traditional identification method, it is obviously more accurate and persuasive, which is also helpful to find out the real causes of poverty and assist the poor residents in the future. In the current targeted poverty alleviation work, the identification of poor households and the matching of assistance measures are mainly through the visiting of village cadres and the establishment of documents. Traditional methods are time‐consuming, laborious, and difficult to manage. It always omits lots of useful family information. Therefore, new technologies need to be introduced to realize intelligent identification of poverty‐stricken households and reduce labor costs. In this paper, we introduce a novel DBSCAN clustering algorithm via the edge computing‐based deep neural network model for targeted poverty alleviation. First, we deploy an edge computing‐based deep neural network model. Then, in this constructed model, we execute data mining for the poverty‐stricken family. In this paper, the DBSCAN clustering algorithm is used to excavate the poverty features of the poor households and complete the intelligent identification of the poor households. In view of the current situation of high‐dimensional and large‐volume poverty alleviation data, the algorithm uses the relative density difference of grid to divide the data space into regions with different densities and adopts the DBSCAN algorithm to cluster the above result, which improves the accuracy of DBSCAN. This avoids the need for DBSCAN to traverse all data when searching for density connections. Finally, the proposed method is utilized for analyzing and mining the poverty alleviation data. The average accuracy is more than 96%. The average F‐measure, NMI, and PRE values exceed 90%. The results show that it provides decision support for precise matching and intelligent pairing of village cadres in poverty alleviation work.

By deploying edge servers on the network edge, mobile edge computing network strengthens the real‐time processing ability near the end devices and releases the huge load pressure of the core network. Considering the limited computing or storage resources on the edge server side, the workload allocation among edge servers for each Internet of Things (IoT) application affects the response time of the application’s requests. Hence, when the access devices of the edge server are deployed intensively, the workload allocation becomes a key factor affecting the quality of user experience (QoE). To solve this problem, this paper proposes an edge workload allocation scheme, which uses application prediction (AP) algorithm to minimize response delay. This problem has been proved to be a NP hard problem. First, in the application prediction model, long short‐term memory (LSTM) method is proposed to predict the tasks of future access devices. Second, based on the prediction results, the edge workload allocation is divided into two subproblems to solve, which are the task assignment subproblem and the resource allocation subproblem. Using historical execution data, we can solve the problem in linear time. The simulation results show that the proposed AP algorithm can effectively reduce the response delay of the device and the average completion time of the task sequence and approach the theoretical optimal allocation results.

The foundation of urban computing and smart technology is edge computing. Edge computing provides a new solution for large-scale computing and saves more energy while bringing a small amount of latency compared to local computing on mobile devices. To investigate the relationship between the cost of computing tasks and the consumption of time and energy, we propose a computation offloading scheme that achieves lower execution costs by cooperatively allocating computing resources by mobile devices and the edge server. For the mixed-integer nonlinear optimization problem of computing resource allocation and offloading strategy, we segment the problem and propose an iterative optimization algorithm to find the approximate optimal solution. The numerical results of the simulation experiment show that the algorithm can obtain a lower total cost than the baseline algorithm in most cases.

Passive acoustic localization is an important technique in a wide variety of monitoring applications, ranging from health care over biological survey to structural health monitoring of buildings. However, the method obtains the location of an unknown sound source with low-cost and simple still is lacking. In this paper, we implement a passive sound source location system based on commercial off-the-shelf (COTS) mobile devices, typically a smartphone, are organized as Wireless Sensor Networks (WSNs). We use the Precise Time Protocol (PTP) in WLAN to achieve the time synchronization of mobile devices with a coarse grain, and then uses moving variance and linear interpolation to get the Time of Arrival (TOA) of the sound source signal. We also design and implement a robust Sequence-Based localization algorithm based on Linear Programming, i.e. LPSBL, which transforms the TOA information of the sound source signal arriving at these devices to a nodes sequence and estimate the location of the sound source by the nodes sequence. After plenty of experiments were carried out, it is verified that our system can provide sufficiently reasonable positioning accuracy and good robustness in an indoor environment.

Recent advances in Cross-Technology Communication (CTC) enable the coexistence and collaboration among heterogeneous wireless devices operating in the same ISM band (e.g., Wi-Fi, ZigBee, and Bluetooth in 2.4 GHz). However, state-of-the-art CTC schemes are vulnerable to spoofing attacks since there is no practice authentication mechanism yet. This paper proposes a scheme to enable the spoofing attack detection for CTC in heterogeneous wireless networks by using physical layer information. First, we propose a model to detect ZigBee packets and measure the corresponding Received Signal Strength (RSS) on Wi-Fi devices. Then, we design a collaborative mechanism between Wi-Fi and ZigBee devices to detect the spoofing attack. Finally, we implement and evaluate our methods through experiments on commercial off-the- shelf (COTS) Wi-Fi and ZigBee devices. Our results show that it is possible to measure the RSS of ZigBee packets on Wi-Fi device and detect spoofing attack with both a high detection rate and a low false positive rate in heterogeneous wireless networks.

Recently, the low-power, low-cost and reliable ZigBee technology have received significant research attention with the increasing popularity of applications such as smart home system, patient monitor in hospitals. Coexisiting with the WiFi devices on the crowded unlicensed ISM band, such as hotspots and mobile phones, ZigBee will receive significant performance influence. The throughput and Packet Reception Rate (PRR) of ZigBee will decrease with the increasing number of WiFi devices. Because of the incompatible PHY/MAC layer, ZigBee devices will suffer near 50% packet loss when coexisting with WiFi devices. The existing mechanism such as CSMA/CA is surprisingly inadequate for solving this problem. As the WiFi traffic typically appears bursty, the channel will be free for more than 60% of the time. In this paper, we propose a new metric called channel idle indicator (CII) which can quantify the channel quality. Based on the CII and logistic regression, we build a channel idle state prediction model which can help ZigBee devices to use the white space of WiFi channel efficiently. Particularly, our approach is light-weight, which can be easily implemented on the ability-limited commercial off-the-shelf (COTS) ZigBee devices. Extensive experiments show that our scheme can achieve over 91% of the PRR, which is near 40% higher than the B-MAC protocol. When the WiFi throughput is 3Mbps, our scheme achieves near 1.5x throughput over B-MAC. Carrying on further, our scheme consumes less energy via degrading packet loss rate in the energy consumption part.

Currently, various Medium Access Control (MAC) protocols have been proposed for underwater Wireless sensor networks. Unlike terrestrial networks, underwater networks utilize acoustic waves, which have comparatively lower loss and longer range in underwater environments. However, the use of acoustic waves incurs long propagation delays that typically lead to low throughput especially in protocols that require receiver feedback such as multimedia stream delivery and the energy cost of transmission is much higher than receptions. Thus, collision and retransmission should be reduced in practice in order to reduce energy cost and improve throughput. Based on these motivations, we propose a novel MAC protocol called NCDC-MAC. NCDC-MAC leverages network coding and duty cycle, the combination of which is seldom explored, to solve these challenges. Heterogeneous wireless networks and node roles are considered while designing our algorithms. Meanwhile, fairness including schedule and service time assignment is supported in our approach. Extensive simulations show that our approach can achieve significantly better performance.

Restricted by finite battery energy, traditional wireless sensor networks (WSNs) can only maintain for a limited period of time, resulting in serious performance bottleneck in long-term deployment of WSN. Fortunately, the advancement in the wireless energy transfer technology provides a potential to free WSNs from limited energy supply and remain perpetual operational. A mobile charger called wireless charging vehicle (WCV) is employed to periodically charge each sensor node and keep its energy level above the minimum threshold. Aiming at maximizing the ratio of the WCV's vocation time over the cycle time as well as guaranteeing the perpetual operation of networks, we proposes a feasible and optimal solution to this issue within the context of a real-time large-scale deployed WSN.

In recent years, the problem of spectrum mobility in Cognitive Radio (CR) Networks has been widely investigated. In order to fully utilize spectrum resources, many spectrum handoff techniques based on game theory have been proposed, but most studies only concern how to achieve better payoffs for users, without paying much attention to the Quality of Service (QoS). Thus, we propose a new channel switching model based on game theory, using a prioritized approach to meet the diverse needs of users, such as bandwidth, delay, and jitter. Once the Nash equilibrium is achieved, our model will provide different QoSes by setting different priorities to different users. We also propose two acceleration methods to reach the Nash equilibrium more quickly. Finally, we evaluate the performance of the proposed schemes using real channel availability measurements. Experiments results show that our model can provide differentiated services and our algorithm is guaranteed to reach an approximate Nash equilibrium within polynomial time.

Indoor localization based on WiFi signal strength fingerprinting techniques have been attracting many research efforts in past decades. Many localization algorithms have been proposed in order to achieve higher localization accuracy. In this paper, we investigate Bayes learning algorithms and some common-used machine learning algorithms. We identify a general problem of Zero Probability (ZP) which may cause significant decrease of accuracy. In order to solve this problem, we propose an Improved Naive Bayes Simple learning algorithm, namely INBS, based on our data set characteristic. INBS is applicable even though Zero Probability problem occurs. We design experiments based on off-the-shelf WiFi devices, mobile phones and well-known machine learning tool Weka. Our experiments are conducted on a floor covering 560m2in a campus building and a laboratory covering 78m2. Experiment results show that INBS outperforms traditional Naive Bayes and k-Nearest Neighbors (k-NN) algorithms and two common-used machine learning algorithms in terms of accuracy.

Unlike terrestrial networks that mainly rely on radio waves for communications, underwater networks utilize acoustic waves, which have comparatively lower loss and longer range in underwater environments. However, acoustic waves incurs long propagation delays that typically lead to low throughput especially in protocols that require receiver feedback such as multimedia stream delivery. In addition, energy cost of transmission underwater is much higher than reception (almost 125:1 [1]). Thus, collision and retransmission should be reduced in order to reduce energy cost and improve throughput Receiver-based protocols, like RIPT and COS-TS, can significantly reduce collision and retransmission. But they are time and energy consuming because nodes are controlled to turn into receiver mode by control packets or a timer regardless of load. In this paper, we propose an underwater practical MAC protocol, called UPMAC. The main objective of UPMAC is to adapt to the network load conditions by providing two modes (high and low load modes) and switching between them based on different offered load. Turn-around time overhead is reduced and it is less vulnerable to control packet corruption, since we reduce the use of control packets by the technique of piggyback. UPMAC provides a low data collision rate in both one-hop and multi-hop situation because we use Receiver-based approach in high load mode. Extensive simulations show that our approach can achieve significantly better performance in both general and Sea Swarm (tree) topologies.

With the development of Internet of Things (IoT), bridging wireless sensor networks (WSNs) and other networks has become important. We divide bridging solutions into two categories: hardware solutions and middleware solutions. Hardware solutions have both low power short distance wireless interfaces and other types of transmission interfaces, e.g. GPRS, 3G/4G, via hardware implementations. This kind of solutions is more stable and more applicable for deployed sensor networks. In middleware solutions, the whole system processes appropriate protocol conversion. This kind of solutions is more independent of hardware, making it easier to be reused in different networks. This paper briefly presents key points of each solution and evaluates advantages and disadvantages of them in terms of different criteria. Eventually, we derive the most appropriate situation for each solution from our comparisons and discussions.

The design and analysis of routing algorithm is an important issue in Wireless Sensor Networks (WSNs). Most traditional geographical routing algorithms cannot achieve good performance in duty-cycled networks. In this paper, we propose a k-connected overlapping clustering approach with energy awareness, namely k-OCHE, for routing in WSNs. The basic idea of this approach is to select a cluster head by energy availability (EA) status. The k-OCHE scheme adopts a sleep scheduling strategy of CKN, where neighbors will remain awake to keep it k-connected, so that it can balance energy distributions well. Compared with traditional routing algorithms, the proposed k-OCHE approach obtains a balanced load distribution and consequently a longer network lifetime.

SUMMARY Wireless converge‐cast networks (WCNs), such as data collection‐based wireless sensor networks, exhibit certain phenomena called funneling effect, where the region close to the sink node is heavily overloaded. In this paper, we identify that the funneling effect occurs not only close to the sink but also within the network region where nodes have collision and induce heavy traffic to relay; we name it hot‐spot funneling effect. This paper aims to improve the throughput and fairness of WCNs by mitigating the micro funneling effect. We propose a new mechanism, the backoff differentiation for contention resolution (BDCR), which is targeted to a system‐wide high throughput on the basis of the contention resolution mechanism. To achieve high spatial reuses, BDCR divides the network into several regions and does backoff differentiation within each region. Within each backoff differentiation region, the backoff window range is adjusted according to the traffic rate, and at the same time, the backoff values are set with the awareness of the traffic intensity level. All regions share the same algorithm, which uses Kelly's rate control theory and method to allow each sensor to locally adjust its backoff value. One of the key advantages of BDCR is that it is extremely easy to implement. With extensive simulations and testbed experiments, BDCR is proved to achieve much higher throughput over the traditional carrier sense multiple access and some recent media access control protocols in literature, particularly when the network suffers intensive congestions. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper1, we present the architecture, design, and simulation of an intelligent system for Temperature Monitoring used in metropolitan heating. The system consists of several TelosB-compatible motes, a Nokia uSDCard, and a smart phone. We use TelosB Motes to collect temperature data, and to transport the data to the smart phone. The uSDCard, as the middle layer, connects the smart phone with the ZigBee compatible devices. The smart phone, as the terminal, processes data and manages the TelosB Motes. We use the smart phone as the final terminal because it has a rich set of user interfaces and has access to various kinds of networks, allowing our system to be extended more easily and more user-friendly. In real scenes, our system can reduce the temperature reading fluctuation, and ultimately save the energy consumption for heating companies, providing a better living environment for indoor users.

Network lifetime is one of the most critical issues for wireless sensor networks (WSNs) since most sensors are equipped with non-rechargeable batteries with limited energy. To prolong the lifetime of a WSN, one common approach is to dynamically schedule sensors' active/sleep cycles (i.e., duty cycles) with sleep scheduling algorithm. In this paper, we propose a new sleep scheduling algorithm, named EC-CKN (Energy Consumed uniformly-Connected K-Neighborhood) algorithm, to prolong the network lifetime. The algorithm EC-CKN, which takes the nodes' residual energy information as the parameter to decide whether a node to be active or sleep, not only can achieve the k-connected neighborhoods problem, but also can assure the k awake neighbor nodes have more residual energy than other neighbor nodes at the current epoch. Based on the algorithm EC-CKN, we can obtain the state transition probability at the n'th epoch, and upper bound and lower bound of the network lifetime by Markov chain and Markov decision chain.

In this poster, we present the architecture, design, and the preliminary simulation results of a system for Metropolitan Heating, via intelligent temperature monitoring. The system consists of several Crossbow TelosB-compatible motes, a Nokia uSDCard, and a smart phone. The motes are used to collect indoor temperature data, which is further relayed to the smart phone's uSD interface. The uSDCard connects the phone with IEEE 802.15.4/ZigBee compatible devices. We use the smart phone as the sink terminal because it has a rich set of user interfaces and can access to different kinds of networks, such as GPRS and 3G. In real scenes, our system can reduce the reading fluctuation, and ultimately save the energy consumed for heating company, achieving a better living condition in the houses.

User profiles backup and migration among social networking providers have become more urgent after the conflict between Tencent, the largest Instant Messaging(IM) service provider in China, and Qihu360, the largest antivirus company in China. So far, exchanging contact list among emails has been wildly used. Mainstream email service providers commonly make use of Comma Separated Value (CSV) files to export/import contact lists. Meanwhile, XML format is popular to transfer information among different platforms due to its rich hierarchical data structures. By using CSV files and XML files, we propose a new method to do relationship migration among social networking providers, which consists of three stages: 1) information retrieval from original social networking provider, 2) information storage and re-processing, and 3) relationship migration and recovery in the target provider. In order to evaluate our solution, we have designed and implemented a real experiment to test the migration from RenRen (the largest social network in China) to my Elgg (our test bed server).

In this paper, we focus on achieving better energy conservation for geographic routing algorithms in duty-cycled WSNs when there is a mobile sink. We simplify the problem as a topology coverage one, and propose a multi-metric geographic algorithm (MMGR) which uses multi-metric candidates (MMCs) for geographic routing. The analysis and extensive simulation results show that MMGR can achieve better energy conservations than McTPGF, while retaining good performance of end-to-end delay and hop counts.

With the development of real-time business, the original traditional WMN has been difficult to satisfy the needs of the real-time business. This article is in support of this delay sensitive type under the background of real-time application business in WMN. This improved mechanism is proposing an pre-scanning algorithm focusing on the higher delay in WMN link layer scanning which based on traditional WMN link layer switching mechanism.

Based on a fundamental understanding of a high-quality routing protocol called Better Approach To Mobile Ad-hoc Networking or BATMAN, we provide an experimental study of its performance considering a representative set of meaningful measures with a real Wireless Mesh Network (WMN) test bed deploying in our campus. Analysis and experiments results show that BATMAN is not only an efficient and stable routing protocol, but also can satisfy the requirements of multimedia communication in mesh networks.

To suit the needs of data collection, routing protocols in WSN are normally required to form a collection tree where data flows from the source nodes to the sink nodes. These protocols, such as CTP and Multihop LQI, generally target at reducing the packet delivery cost without considering load balancing issues. We argue that load balancing is crucial for WSNs because load imbalance may cause certain nodes, which we call hot spots, to run out of energy faster than expected. The load imbalance may lead to holes and prominently degrade the performance of the network. In this paper, we propose BCTP (Balanced Collection Tree Protocol), which enhances CTP by enabling the network to migrate the load of the node under heavy traffic. BCTP uses the average transmission rate as the metric to measure a node's long term traffic load. Once a node is found heavily loaded, BCTP adopts a stochastic routing strategy to balance the load. BCTP is evaluated by test bed experiments with 9 Telosb motes. Experiment result shows that BCTP can reduce the load of the hot spot by up to 61.9% in a densely deployed network.