MOTIVATION: Developing segmentation models that remain reliable across diverse medical imaging domains and accurately delineate complex anatomical boundaries remains a persistent challenge for clinical deployment. Variations in imaging modalities, scanners, and acquisition settings introduce significant domain shifts, while fuzzy or overlapping tissue boundaries further complicate precise segmentation. Despite extensive research, most approaches address these challenges separately, leading to limited generalization and reduced robustness in real-world clinical scenarios. RESULTS: To overcome these limitations, we propose HyperSeg-DG, a novel medical image segmentation approach that integrates the WMamba backbone with the Multi-Scale Hyper Feature Context Block (HFCB). The HFCB addresses foreground-background uncertainty and boundary ambiguities by capturing multi-scale feature relations and long-range contextual dependencies. This enables the model to focus on relevant pathological features while helping reduce the influence of irrelevant co-occurring ones, such as similarly sized polyps, especially in low-contrast or poorly lit environments. WMamba further improves domain generalization by processing images in localized windows and using its selective 2D scanning mechanism to learn robust, transferable features that reduce feature misalignment under domain shift. Extensive experiments across multiple medical segmentation benchmarks demonstrate that HyperSeg-DG achieves consistent 2%-3% improvements over strong baselines, confirming its effectiveness in enhancing segmentation performance and generalization across diverse, unseen domains. AVAILABILITY AND IMPLEMENTATION: The code and datasets of HyperSeg-DG are available at https://github.com/Pollob001/HyperSeg-DG.

In order to meet unprecedented demands for reliable and massive connectivity (MC), sixth-generation (6 G) cellular Radio Access Networks (RANs) require architectural innovations. Conventional cellular RANs' scalability is limited by the tightly coupled control and user planes. Fully-Decoupled RANs (FD-RANs) are a promising architectural innovation that enables flexible plane separation. However, current architectures have limitations due to ineffective multiple access schemes. To this end, we introduce NomaFdRaN, an innovative Non-Orthogonal Multiple Access (NOMA)-optimized FD-RAN architecture in order to optimize reliability and MC. To achieve holistic system optimization, NomaFdRaN applies NOMA on all network planes, control plane and user plane, and transmission paths, uplink and downlink. To improve NOMA efficiency, we develop a user pairing optimization approach that minimizes total transmit power while maintaining linear computing complexity. Based on stochastic geometry, we develop analytical models to analyze NomaFdRaN's performance. Subsequently, we analytically derived closed-form expressions for key performance metrics. Our simulation results demonstrate the effectiveness of the NomaFdRaN architecture and provide insights into the deployment strategies for next-generation FD-RANs.

EEG-based automatic emotion recognition is crucial in the field of brain-computer interface (BCI) and mental disorder diagnosis. Due to the low signal-to-noise ratio and non-stationary nature of raw EEG signals, most existing methods require handcraft feature extraction and are computationally intensive, which restricts their practicality, especially in real-time and resource-constrained scenarios. This study proposes BCRNet, an efficient and lightweight end-to-end model designed for EEG-based emotion recognition. BCRNet directly captures spatial and temporal dependencies from raw EEG signals via a lightweight branched convolution module. The branching structure combined with depthwise separable convolution enhances feature representation while significantly reducing the number of parameters. Additionally, Riemannian manifold embedding and geometric computations further enhance feature discriminability and reduce the impact of outliers and noise. Comprehensive evaluations on SEED and SEED-IV datasets demonstrate that BCRNet surpasses existing methods with fewer parameters and lower computational requirements. Finally, visualization of EEG channel weights reveals that the temporal and frontal lobes contribute more significantly to emotional responses, offering additional insights into emotion processing mechanisms.

Digital Twins are virtual representations of physical assets and systems that rely on effective Data Management to integrate, process, and analyze diverse data sources. This article comprehensively examines Data Management challenges, architectures, techniques, and applications in the context of Digital Twins. It explores key issues such as data heterogeneity, quality assurance, scalability, security, and interoperability. The paper outlines architectural approaches like centralized, distributed, cloud-based, and blockchain solutions and Data Management techniques for modeling, integration, fusion, quality management, and visualization. Domain-specific considerations across manufacturing, smart cities, healthcare, and other sectors are discussed. Finally, open research challenges related to standards, real-time data processing, intelligent Data Management, and ethical aspects are highlighted. By synthesizing the state-of-the-art, this review serves as a valuable reference for developing robust Data Management strategies that enable Digital Twin deployments.

A data publishing deal conducted with anonymous microdata can preserve the privacy of people. However, anonymizing data with multiple records of an individual (1:M dataset) is still a challenging problem. After anonymizing the 1:M microdata, the vertical correlation can be exploited to launch privacy attacks. In this paper, a novel privacy preserving model$l_{c}, l_{s}$-ANGEL is proposed. To validate the new model, two privacy attacks are presented, namely, a Vertical correlation attack ($V_{c0}$) and a Vulnerable sensitive attribute attack ($V_{sa}$) on 1:M datasets, which breach the privacy of individuals. Furthermore, the proposed model is examined through High-Level Petri Nets (HLPNs). Our experiments on three real-world datasets;“INFORMS”,“YOUTUBE”, and “IMDb” demonstrate that the proposed model outperforms the state-of-the-art models. Our practices and lessons learned in this work can direct future concrete steps towards Multiple Sensitive Attributes, where we can expand the proposed model to dynamic datasets.

Radio Access Networks (RAN) are anticipated to gradually transition towards Cloud RAN (C-RAN), leveraging the full advantages of the cloud-native computing model. While this paradigm shift offers a promising architectural evolution to improve scalability, efficiency, and performance, significant challenges remain in managing the massive computing requirements of physical layer (PHY) processing. To address these challenges and meet the stringent Service Level Objectives (SLOs) in 5G networks, hardware acceleration technologies are essential. In this paper, we aim to mitigate this challenge by offloading 5G modulation mapping, a critical yet demanding function to encode bits into IQ symbols, directly onto the switch ASICs. Specifically, we introduce NetMod, a 5G New Radio (NR) standard-compliant in-network modulation mapper accelerator. NetMod leverages the capabilities of new-generation programmable switches within the C-RAN infrastructure to offload and accelerate PHY modulation functions. We implemented a NetMod prototype on a real-world platform using the Intel Tofino programmable switch and commodity servers running the Data Plane Development Kit (DPDK). Through extensive experiments, we demonstrate that NetMod achieves modulation mapping at switch line rate using minimal switch resources, thereby preserving ample space for traditional switching tasks. Furthermore, comparisons with a GPU-based 5G modulation mapper show that NetMod is 2.2$\boldsymbol{\times}$to 3.3$\boldsymbol{\times}$faster using only a single switch port. These results highlight the potential of in-network acceleration to enhance 5G network performance and efficiency.

In 5G Radio Access Networks (RAN), Cyclic Redundancy Check (CRC) algorithms play a vital role in detecting accidental changes to digital data during transmission. However, due to the massive bandwidth demands in 5G networks, CRC computation is a resource-intensive process. To address this challenge, we propose performing CRC computation and verification directly in the network path. Specifically, we introduce NetCRC-NR, a 5G New Radio (NR) standard-compliant in-network CRC accelerator. NetCRC-NR implements the 5G NR CRC algorithms specified in 3GPP TS 38.212, including CRC24A, CRC24B, CRC24C, CRC16, CRC11, and CRC6. It leverages programmable switches to perform in-network CRC generation and validation for the Transport Blocks (TBs) and Code Blocks (CBs), aiming at providing high CRC computation throughput and alleviating the computational burden on General-Purpose Processors (GPPs). We design and implement NetCRC-NR on Intel Tofino programmable switch and commodity servers running the Data Plane Development Kit (DPDK). Extensive experiments demonstrate that NetCRC-NR performs CRC generation and verification at the switch line rate of up to 4+Tbps CRC throughput, showcasing its efficiency and potential in accelerating the 5G RAN error detection process.

Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) enable direct interactions between the brain and external environments, with applications in medical rehabilitation, motor substitution, gaming, and entertainment. Traditional methods that model the non-Euclidean characteristics of EEG signals demonstrate robustness and high performance, but they suffer from significant computational costs and are typically restricted to a single BCI paradigm. This article addresses these limitations by utilizing a diffeomorphism from Riemannian manifolds to the Cholesky space, which simplifies the solution process and enables application across multiple BCI paradigms. Our proposed Cholesky space-based model, CSNet, achieves state-of-the-art (SOTA) performance in motor imagery (MI) decoding and emotion recognition and demonstrates competitive performance in error-related negativity (ERN) decoding, all without the need for data preprocessing. Furthermore, our runtime comparison shows that the Cholesky space method is more efficient than the method based on the Riemannian manifold as the matrix dimension increases. To enhance the interpretability of CSNet, we perform t-distributed stochastic neighbor embedding (t-SNE) visualization for MI, frequency band energy visualization for emotion recognition, and temporal importance visualization for ERN. The results indicate that CSNet effectively learns discriminative features, identifies important frequency bands, and focuses on important temporal features. The CSNet effectively captures the non-Euclidean characteristics of EEG signals across various BCI paradigms, while mitigating high computational costs, making it a promising candidate for future BCI algorithms. The code for this study is publicly available at: https://github.com/XingfuWang/CSNet.

Wireless Power Transfer (WPT) is a disruptive technology that allows wireless energy provisioning for energy-limited IoT devices, thus decreasing the over-reliance on batteries and wires. WPT could replace conventional energy provisioning (e.g., energy harvesting) and expand to be deployed in many of our daily-life applications, including but not limited to healthcare, transportation, automation, and smart cities. As a new rising technology, WPT has attracted many researchers from academia and industry about WPT technologies and wireless charging scheduling algorithms. Therefore, in this paper, we review the most recent studies related to WPT, including classifications, advantages, disadvantages, and main domains of application. Furthermore, we review the recently designed wireless charging scheduling algorithms (schemes) for wireless sensor networks. Our study provides a detailed survey of wireless charging scheduling schemes covering the main scheme classifications, evaluation metrics, application domains, advantages, and disadvantages of each charging scheme. We further summarize trends and opportunities for applying WPT at some intersections.

This paper reviews three main aspects of authentication protocols of Internet of Things (IoT): classifications and limitations, current trends, and opportunities. First, we explore the significance of IoT authentication protocols in ensuring secure communication and the protection of transmitted and received data, focusing on the classifications and associated limitations. Second, we discuss the latest developments and trends, such as using blockchain technology and machine learning to enhance authentication protocols. Third, we highlight the future opportunities, including the development of human-centric authentication designs and improved platform interoperability. At the end of this paper, we provided some insights gained for the new researcher, offering analyses of the trends and challenges in this field, giving recommendations for improving IoT authentication protocols, and emphasizing the need for further research and cooperation to develop advanced security solutions.

Recently, a plethora of promising green energy provisioning technologies has been discussed in the orientation of prolonging the lifetime of energy-limited devices (e.g., sensor nodes). Wireless rechargeable sensor networks (WRSNs) have emerged among other fields that could greatly benefit from such technologies. Such an ad-hoc network comprises a base station(s) and multiple sensor nodes, which are primarily deployed in harsh environments, meeting the requirements of transmitting, receiving, collecting, and processing data. Unlike existing works, this survey paper focuses on energy provisioning technologies within the context of WRSNs by reviewing two interrelated domains. First, we introduce various energy provisioning techniques and their associated challenges, including conventional energy harvesting methods (e.g., solar, thermal, and mechanical). We highlight wireless power transfer (WPT) as one of the most applicable technologies for WRSNs, covering both radiative and non-radiative WPT. Additionally, we present radio frequency (RF) energy harvesting, including simultaneous wireless information and power transfer (SWIPT) and wireless powered communication networks (WPCNs), as well as backscatter communications. Furthermore, we compare hybrid energy harvesting techniques (e.g., solar-RF, vibro-acoustic, solar-thermal, etc.). Second, we introduce the fundamentals of wireless charging, reviewing various charger types (static and mobile), charging policies (including full and partial charging), charging modes (offline and online), and charging schemes (periodic and on-demand). We also present the collaborative charging mechanisms. Additionally, we address several key challenges facing WRSNs, such as energy consumption, multi-charger coordination, dynamic network recharging, monitoring & security threats, vehicle-to-vehicle (V2V) charging, and hybrid WRSNs Finally, we highlight trends and future directions for integrating advanced artificial intelligence (AI) technologies into WRSNs.

Wireless Sensor Networks (WSNs) are used to sense and monitor physical conditions in various services and applications. However, there are a number of challenges in deploying WSNs, especially those pertaining to energy replenishment. Using the current solutions, when a significant number of sensors need to replenish their energy, this would be costly in terms of time, efforts and resources. Thus, this paper aims to solve this problem by efficiently deploying wireless power transfer technologies and scheduling Mobile Charging Vehicles (MCVs) in WRSN. The proposed method deploys multi-criteria decision-making (i.e., Analytical Hierarchy Process (AHP)) to schedule the charging tasks. To the best of our knowledge, this paper is the first to depend solely on AHP in MCVs scheduling. The paper demonstrates the validity of the proposed method by illustrating that the matrices that are created are within the accepted values of consistency ratio. In addition, the paper proposes a method of partitioning the values of our criteria to avoid the problem of different criteria having different measurement units. Unlike existing works, the paper aims to schedule an MCV for charging based on both the distance and residual energy of the sensor. The proposed method exhibits superiority in terms of the average remaining energy available in the system, having the shortest queue length, shorter MCV response time, shorter charging duration, and shorter queue waiting time against the state-of-the-art methods. Our study paves the way for next generation efficient charging and MCV scheduling.

Vehicle-to-grid (V2G) energy trading based on distributed ledger technologies (DLT), such as blockchains, has attracted much attention due to its promising features, including ease of deployment, decentralization, transparency, and security. However, existing DLT-based models do not support microtransactions due to the low value of such transactions relative to the incentives offered to transaction verifiers. To address this issue, we propose an IOTA DLT-based efficient and secure energy trading model for V2G networks, where electric vehicles (EVs) and grids negotiate energy prices in an off-chain manner. The proposed model utilizes a privacy-preserving protocol to prevent real-time tracking of EV locations. We develop a Stackelberg game model to represent the interactions between the EVs and grids, from which we derive a pricing scheme and propose a deposit mechanism to prevent fake energy trading between the EVs and grids. Extensive simulations demonstrate that our proposed scheme outperforms existing V2G energy trading mechanisms regarding transaction efficiency, provides enhanced EV privacy, and improves resilience against fake energy trading. Offering robust computational performance and addressing computational complexity (time, space, and message), our model presents a comprehensive V2G energy trading solution, balancing efficiency, security, and privacy.

This paper studies distributed data dissemination in multiple mobile sinks wireless sensor networks. Previous studies employed separated paths to disseminate data packets from a given source to a given set of mobile sinks independently, which exhausts the constrained resources of the network. In this paper, we explore how the merged paths mechanism could rationalize utilizing network resources. To do so, we propose a protocol named Merged Path, which is implemented in four steps in a distributed manner. First, the bifurcation points (i.e., where the path is branched into multiple sub-branches) are discovered. Second, we developed a Discrete Cumulative Clustering algorithm (DCC) to divide the sinks into disjoint clusters at each bifurcation point. Third, we propose a Diagonal Virtual Line (DVL) structure to delegate the communication between thehigh-tierand low-tier nodes. Last, on top of DVL and DCC, we propose an opportunistic metric that captures multiple network-layer attributes to disseminate the data packet to the sinks through multiple branches. The simulation results showed that about 50% of the network energy could be saved by merging the paths versus the separate paths, considering an area of interest application with 20 mobile nodes each carrying a sink.

With the rapid expansion of Internet of Everything (IoE) devices and the increasing demand for high-speed data and reliable communication services, particularly within 6G cellular networks (CNs), the design of efficient and robust CNs has become a critical research area. Consequently, enabling massive connections, optimizing network resource utilization, and achieving cost-effective network operation pose significant challenges. To this end, integrated space-ground cellular networks based on control- and user-plane separation (ISGCN-CUPS) architecture has been proposed as a promising solution. Furthermore, it becomes an integral aspect of the broader paradigm of integrated space-air-ground CNs (ISAGCNs). However, scalability poses an issue when increasing the number of connected cellular users, especially when conventional orthogonal multiple access (OMA) is utilized. To address this challenge, this paper introduces the non-orthogonal multiple access (NOMA)-enabled ISGCN-CUPS architecture. Subsequently, we provide an analytical model to analyze the scenarios of proposed architecture. Utilizing stochastic geometry, we derive closed-forms for coverage probabilities over control and data channels, by considering the propagation channel models for control and data channels, both with and without interference. Furthermore, total area spectral and energy efficiencies are computed. The proposed architecture demonstrates significant enhancements in terms of the key evaluation metrics compared to conventional and OMA-enabled ISGCN-CUPS architectures.

Vehicular crowd sensing (VCS), emerging as a new paradigm within mobile crowd sensing, leverages vehicles as the participator, which can obtain broader sensing coverage and higher sensing flexibility. Previous works ignored the strong impact of environmental factors on workers' travel costs, as well as improper gains from speculative behavior (i.e. workers detour or delay to get more compensation), resulting in unfair income of workers. Moreover, these works focused solely on sensing tasks within specific domains, lacking generalization ability. Therefore, our work is dedicated to providing a fair and universal VCS platform, which is called Environment-cost-based Heterogeneous Task Allocation (EHTA) framework. Our work differs from previous works in the following aspects: 1) We introduce the Environment Cost (EC) based on the investigation of traffic conditions to accurately quantify workers' efforts, and propose a straightforward yet efficacious detection methods to identify speculative behavior of malicious workers, both of which could guarantee the fairness in workers' income. 2) We design a spatial-temporal fair incentive mechanism based on monetary reward to ensure the fair execution of tasks in both space and time dimensions. 3) We summarize the characteristics of three kinds of sensing tasks and propose a universal task allocation algorithm to assign multiple types of tasks simultaneously. The effectiveness of our framework was validated by simulations, which are conducted on a data set comprising 13,000 taxi trajectories from Shanghai in April 2015. We compared our framework against four baseline algorithms, and the results shows that EHTA framework outperforms in terms of task expenditure, task utility and fairness.

Breakthroughs in Wireless Energy Transfer technologies have revitalized Wireless Rechargeable Sensor Networks. However, how to schedule mobile chargers rationally has been quite a tricky problem. Most of the current work does not consider the variability of scenarios and how many mobile chargers should be scheduled as the most appropriate for each dispatch. At the same time, the focus of most work on the mobile charger scheduling problem has always been on reducing the number of dead nodes, and the most critical metric of network performance, packet arrival rate, is relatively neglected. In this article, we develop a DRL-based Partial Charging algorithm. Based on the number and urgency of charging requests, we classify charging requests into four scenarios. And for each scenario, we design a corresponding request allocation algorithm. Then, a Deep Reinforcement Learning algorithm is employed to train a decision model using environmental information to select which request allocation algorithm is optimal for the current scenario. After the allocation of charging requests is confirmed, to improve the Quality of Service, i.e., the packet arrival rate of the entire network, a partial charging scheduling algorithm is designed to maximize the total charging duration of nodes in the ideal state while ensuring that all charging requests are completed. In addition, we analyze the traffic information of the nodes and use the Analytic Hierarchy Process to determine the importance of the nodes to compensate for the inaccurate estimation of the node’s remaining lifetime in realistic scenarios. Simulation results show that our proposed algorithm outperforms the existing algorithms regarding the number of alive nodes and packet arrival rate.

Wireless Power Transfer (WPT) is a promising technology that can potentially mitigate the energy provisioning problem for sensor networks. In order to efficiently replenish energy for these battery-powered devices, designing appropriate scheduling and charging path planning algorithms is essential and challenging. Whilst previous studies have tackled this challenge, the conjoint influences of network topology, charging path planning, and energy threshold distribution in Wireless Rechargeable Sensor Networks (WRSNs) are still in their infancy. We mitigate the aforementioned problem by proposing novel algorithmic solutions to efficient sector-based on-demand charging scheduling and path planning. Specifically, we first propose a hexagonal cluster-based deployment of nodes such that finding an NP-Complete Hamiltonian path is feasible. Second, each cluster is divided into multiple sectors and a charging path planning algorithm is implemented to yield a Hamiltonian path, aimed at improving the Mobile Charging Vehicle (MCV) efficiency and charging throughput. Third, we propose an efficient algorithm to calculate theimportanceof nodes to be used for charging duration decision-making and prioritization. Fourth, a non-preemptive dynamic priority scheduling algorithm is proposed for charging tasks’ assignments and scheduling. Finally, extensive simulations have been conducted, revealing the significant advantages of our proposed algorithms in terms of energy efficiency, response time, dead nodes’ density, and queuing processing.

Recently, sparse arrays have received considerable attention owing to their capability of achieving increased degrees of freedom (DoFs) by exploiting the virtual sensors resulting from their difference or sum-difference coarrays. Mutual coupling is another factor that attracts interest to these kinds of arrays. In this paper, both the fundamental criteria of high DoFs and reduced mutual coupling are considered in the design of the proposed array configuration for the direction of arrival (DoA) estimation of non-circular signals. Simulation results are provided to verify the robustness of the proposed array against heavy mutual coupling.

This paper proposes CookingCLIP, which introduces the latest CLIP (Contrastive Language-Image Pre-training) embedding from the general domain into the specific domain of cooking understanding, and makes two adaption upon the original CLIP embedding for better customization to the cooking understanding problems: 1) from the upstream perspective, we extend the static multi-modal CLIPembedding with a temporal dimension, to facilitate context-aware semantic understanding; 2) from the downstream perspective, we introduce the concept of zero-shot embedding to sequence-to-sequence dense prediction domains, facilitating CLIPbeing not only capable of telling “Which” (cross-modal recognition), but also capable of telling “When” (cross-context localization). Experiments conducted on two challenging cooking caption generation benchmarks, YouCook and CrossTask, demonstrate the effectiveness of the proposed embedding.

The hierarchical structure-based data dissemination is the most popular technique in mobile sink wireless sensor networks (MS-WSNs). An ingenious virtual structure design combined with a precise routing management strategy is significant to attaining efficient data dissemination in hierarchical approaches. This article proposes a hierarchical protocol called territory-bound data delivery (TBDD) that divides the network into multiple partitions called Regions, and spots the location of the mobile sink (MS) according to these partitions. TBDD dynamically assigns a defined role to each division by adopting the mobility of the sink. Thus, the protocol takes advantage of the sink’s movement and the Regions’ flexible role in balancing energy consumption (EC) throughout the network. A Region is designated as active if it contains the sink node or passive otherwise. By using the territory of the active region as a temporal location of the MS, the proposed protocol hides the local movements (i.e., moves inside the active region) of the sink from the rest of the network. In such a way, regardless of the exact position of the sink, sensed data flows from different network ends to the sink’s temporal location. Therefore, TBDD reduces the query request and response burden employed to get the position of the sink. Besides, TBDD implements a spanning tree to report the location information of the MS. Last, we applied an opportunistic routing technique that captures multiple network criteria to elect packet forwarder nodes. The proposed protocol is mathematically analyzed and experimentally evaluated and shows outstanding performance in terms of the number of hops, EC, delay, network lifetime, and success ratio.

In wireless sensor networks (WSNs), it is inappropriate to use conventional unicast routing due to the broadcast storm problem and spatial diversity of communication links. Opportunistic Routing (OR) benefits the low duty-cycled WSNs by prioritizing the multiple candidates for each node instead of selecting one node as in conventional unicast routing. OR reduces the sender waiting time, but it also suffers from the duplicate packets problem due to multiple candidates waking up simultaneously. The number of candidates should be restricted to counterbalance between the sender waiting time and duplicate packets. In this paper, software-defined networking (SDN) is adapted for the flexible management of WSNs by allowing the decoupling of the control plane from the sensor nodes. This study presents an SDN based load balanced opportunistic routing for duty-cycled WSNs that addresses two parts. First, the candidates are computed and controlled in the control plane. Second, the metric used to prioritize the candidates considers the average of three probability distributions, namely transmission distance distribution, expected number of hops distribution and residual energy distribution so that more traffic is guided through the nodes with higher priority. Simulation results show that our proposed protocol can significantly improve the network lifetime, routing efficiency, energy consumption, sender waiting time and duplicate packets as compared with the benchmarks.

Many opportunistic routing (OR) schemes treat network nodes equally, neglecting the fact that the nodes close to the sink undertake more duties than the rest of the network nodes. Therefore, the nodes located at different positions should play different roles during the routing process. Moreover, considering various Quality-of-Service (QoS) requirements, the routing decision in OR is affected by multiple network attributes. The majority of these OR schemes fail to contemplate multiple network attributes while making routing decisions. To address the aforesaid issues, this paper presents a novel protocol that runs in three steps. First, each node defines aRouting Zone (RZ)to route packets toward the sink. Second, the nodes within RZ are prioritized based on the competency value obtained through a novel model that employs Modified Analytic Hierarchy Process (MAHP) and Fuzzy Logic techniques. Finally, one of the forwarders is selected as the final relay node after forwarders coordination. Through extensive experimental simulations, it is confirmed that FLORA achieves better performance compared to its counterparts in terms of energy consumption, overhead packets, waiting times, packet delivery ratio, and network lifetime.

Topic modelling (TM) has shown significant progress in boosting the effectiveness of image captioning in the last few years. Although important improvements have been shown in previous topic-guided image captioning models, some challenges remain unsolved, such as the independence of the topic predictors and the sentence generators, resulting in ineffective exploitation of semantic information. Also, all the predicted topics or the top-one topic are used throughout the whole captioning task without considering the current time step's linguistic context, which deviates the captioning network to focus on inaccurate image objects. To tackle these challenges, we propose a novel image captioning method consisting of four modules: enhanced topic predictor (ETP), retrieval-based topics re-weighting module (RTR), subsequent topic predictor (STP), and caption generation module. The prediction and generation modules are trained in an end-to-end manner to promote the efficient use of topics by predicting suitable topics at each time step. ETP predicts the topics using the image features, and is enhanced with topic embedding (TE). The RTR is only applied in the testing stage for re-weighting the topics predicted by ETP. In each time step, the STP automatically predicts concise topics subsets to alleviate the diversity of the image topics. Compared with the existing topic-based models, our model can automatically generate more accurate and diverse captions, boosting the explainability of how the topics influence the generated word in each time step. Extensive experiments on the MS-COCO and Flickr30K benchmark datasets show that our method enhances the overall image captioning's performance and the topic prediction task, and outperforms many recent image captioning approaches in terms of the evaluation metrics.

Opportunistic routing (OR) is widely adopted in Wireless Sensor Networks (WSNs) running asynchronous duty-cycled MAC protocols. In conventional routing, where packets are forwarded along predetermined routes, the sender may wait for the receiver to wake up for a long time. To reduce the sender waiting time, the OR protocols allow nodes to select multiple neighbors as the forwarders so that the packets could be forwarded by multi-path. Thus, the forwarders selection algorithm affects network performance seriously. However, an excessive number of forwarders increases the probability that more than one forwarders wake up simultaneously. This will consume more energy since each of them will receive the packet. To address the two issues, a Dynamic Opportunistic Routing protocol using Analytical Hierarchy Process (AHP) and Fuzzy Inference System (FIS) called DORAF is proposed in this paper. DORAF is implemented in three steps. First, multiple criteria (i.e., residual energy, distance, and angle) at the network layer are defined to evaluate the nodes where the importance of these criteria is determined by AHP. Second, the pairwise comparison matrices in AHP are generated by using mathematical functions (i.e., Boltzmann function and Logistic function) and FIS. Third, each node uses AHP and FIS to prioritize its neighbors based on the criteria and selects appropriate ones as the forwarders dynamically in a distributed manner. The experimental results demonstrate that our protocol performs better than other state-of-the-art in terms of network lifetime, energy consumption, and average redundant transmissions.

Smart Parking has gained so much popularity in recent years due to the increasing number of vehicles in big cities, resulting in traffic congestion in urban areas. Not only on the streets but also in places such as educational institutions, hospitals, commercial activities, special events, and entertainment uses. Finding a free parking lot in these places has evolved difficulty for the drivers. To solve such a problem, governments and researchers tried to find alternative solutions to overcome or mitigate the traffic congestion. Many solutions have been proposed, such as increasing the parking capacities, which takes much time or makes it hard to find a square area in crowded places. Most existing studies, do not consider the cost of deployment, energy, and time-to-market consideration which makes the available systems need further investigation. In this paper, we propose an intelligent parking system prototype that can be useful for the drivers to have a prior knowledge about the available parking lots in the area of interest. Our proposed system involves deploying mmWave Radar sensor nodes in each parking lot to detect the availability of parking vehicle through transmitting radio pulses periodically. The detected information can be sent to the gateway through multi-hop for further statistics and reports. We also give an intensive analysis and study about the challenges and consideration on the mmWave radar design aiming to improve the detection accuracy and avoid false-detection that occurs from objects near to the sensor. To ensure continuous operation and extend sensor life-time, we propose Bluetooth Low Energy BLE-enabled relay-feature as the communication protocol between the nodes.

We have made research achievements worth sharing on the complicated topic of text-to-image synthesis. Our analysis of popular articles shows that they often use stacked structures to construct and generate confrontation network models and usually introduce multiple sets of generators and discriminator pairs. The entanglement between different generators affects the quality of the final synthesized image. Some researchers have proposed a single-stage network model to avoid traps between multiple generators, But it lacks the use of unstructured natural language information with different granularity. To correct this serious defect, we propose a multi-granularity feature network MGF-GAN, which plays the role of text information with different granularity based on the advantages of the single-stage network. Specifically, we input the three granularity features of the text, including sentences, aspect words, and single words of text, into different stages of the model through spatial attention and channel attention mechanisms to gradually refine the synthetic image from global and local perspectives. In addition, we reconstruct the loss function based on the contrast concept to stabilize the training and ensure that the visual meaning between the synthesized image and the natural language is consistent. We conducted validity experiments on CUB bird and COCO. The significant effect is sufficient to prove the effectiveness and advancement of our MGF-GAN.

Video Question Answering (VideoQA) is a challenging multimodal task that requires the ability to recognize visual elements and reason relations in spatial and temporal dimensions according to the given video and question. Most existing GNN-based methods model the visual elements in a video as graph structures and reason relations between them. Despite the remarkable results of their work, they neglected that the question also has graph structure dependencies, which can be used to reason about relations between the video and the question. In this work, we propose a multimodal graph reasoning and fusion network that builds three graph neural networks for appearance, motion, and text sequences, respectively, and hierarchically reasons and fuses nodes from different modalities. Our proposed method achieves superior performance to several state-of-the-art methods on three benchmark datasets.

Data transmission in Vehicular Ad Hoc Networks (VANETs) often suffers from routing interruptions due to the unstable communication links between vehicles. Over the past decades, many traffic-aware routing protocols have been proposed to alleviate routing interruptions by sensing traffic conditions. However, in most traffic-aware routing protocols, vehicles must transmit a large number of control packets to accumulate traffic information, which may degrade network performance due to the resulting intense competition over the wireless medium. Instead of using control packets, we propose to leverage the beacon mechanism that has been widely used in VANETs to realize traffic awareness. Vehicles broadcast beacons to exchange necessary information with their neighbors periodically. We can leverage this information exchange process among vehicles to replace control packets. To realize this idea, first, a mathematical analysis is provided to demonstrate its feasibility. Then, we propose a concrete protocol to address the technical challenges of using beacons. Extensive simulation results show that our protocol performs better than the state-of-the-art counterparts regarding packet delivery ratio, average delivery time, and network overhead.

Wireless sensor networks (WSNs) with a static sink suffer from concentrated data traffic in the vicinity of the sink, which increases the burden on the nodes surrounding the sink, and impels them to deplete their batteries faster than other nodes in the network. Mobile sinks solve this corollary by providing a more balanced traffic dispersion, by shifting the traffic concentration with the mobility of the sink. However, it brings about a new expenditure to the network, where prior to delivering data, nodes are obligated to procure the sink's current position. This paper proposes Tuft, a novel hierarchical tree structure that is able to avert the overhead cost from delivering the fresh sink's position while maintaining a uniform dispersion of data traffic concentration. Tuft appropriates the mobility of the sink to its advantage, to increase the uniformity of energy consumption throughout the network. Moreover, we propose Tuft-Cells, a distributed dissemination protocol that models data routing as a multi-criteria decision making (MCDM) in three steps. To begin with, each criterion constitutes a random variable defined by a mass function. Each of these cirterion serves a proportionately distinguishable alternative, and hence, may conflict. Therefore, the analytic hierarchy process (AHP) quantifies the relationship between criteria. Finally, the final forwarding decision is derived by a weighted aggregation. Tuft is compared with state-of-the-art protocols, and the performance evaluation illustrates that our protocol adheres to the requirements of WSNs, in terms of energy consumption, and success ratio, considering the additional overhead cost brought by the mobility of the sink.

This work is devoted to solving the problem of multicriteria multihop routing in vehicular ad hoc networks (VANETs), aiming at three goals: 1) increasing the end-to-end delivery ratio; 2) reducing the end-to-end latency; and 3) minimizing the network overhead. To this end and beyond the state of the art, heuristic routing for vehicular networks (HERO), which is a distributed routing protocol for urban environments, encapsulating two main components, is proposed. The first component, road-segment selection, aims to prioritize the road segments based on a heuristic function that contains two probability distributions, namely, shortest distance distribution (SDD) and connectivity distribution (CD). The mass function of SDD is the product of three quantities: 1) the perpendicular distance; 2) the dot-production angle; and 3) the segment length. On the other hand, the mass function of CD considers two quantities: 1) the density of vehicles and 2) the interdistance of vehicles on the road segment. The second component, vehicle selection, aims to prioritize the vehicles on the road segment based on four quantities: 1) the relative speed; 2) the movement direction; 3) the available buffer size; and 4) signal fading. The simulation results showed that HERO achieved a promising performance in terms of delivery success ratio, delivery delay, and communication overhead.

This article models the multihop data-routing in vehicular ad-hoc networks as multiple criteria decision making (MCDM) in four steps. First, the criteria that have impact on the performance of the network layer are captured and transformed into fuzzy sets. Second, the fuzzy sets are characterized by fuzzy membership functions (FMFs), which are interpolated (curve fitting) based on the data collected from massive experimental simulations. Third, the analytical hierarchy process (AHP) is exploited to identify the relationships among the criteria. Fourth, multiple fuzzy rules are determined and the Takagi-Sugeno-Kang (TSK) inference system is employed to infer and aggregate the final forwarding decision. Through integrating techniques of MCDM, FMF, AHP, and TSK, we design a distributed and opportunistic data routing protocol, namely, vehicular environment fuzzy router which targets vehicle-to-vehicle (V2V) communication and runs in two main processes-road segment selection (RSS) and relay vehicle selection (RVS). RSS is intended to select multiple successive junctions through which the packets should travel from the source to the destination, while RVS process is intended to select relay vehicles within the selected road segment. The experimental results show that our protocol performs and scales well with both network size and density, considering the combined problem of end-to-end packet delivery ratio and end-to-end latency.

Opportunistic routing (OR) is gaining popularity in low-duty wireless sensor network (WSN), so the need for efficient and reliable data transmission is becoming more essential. Reliable transmission is only feasible if the routing protocols are secure and efficient. Due to high energy consumption, current cryptographic schemes for WSN are not suitable. Trust-based OR will ensure security and reliability with fewer resources and minimum energy consumption. OR selects the set of potential candidates for each sensor node using a prioritized metric by load balancing among the nodes. This paper introduces a trust-based load-balanced OR for duty-cycled wireless sensor networks. The candidates are prioritized on the basis of a trusted OR metric that is divided into two parts. First, the OR metric is based on the average of four probability distributions: the distance from node to sink distribution, the expected number of hops distribution, the node degree distribution, and the residual energy distribution. Second, the trust metric is based on the average of two probability distributions: the direct trust distribution and the recommended trust distribution. Finally, the trusted OR metric is calculated by multiplying the average of two metrics distributions in order to direct more traffic through the higher priority nodes. The simulation results show that our proposed protocol provides a significant improvement in the performance of the network compared to the benchmarks in terms of energy consumption, end to end delay, throughput, and packet delivery ratio.

In wireless sensor networks, routing protocols with immutable network policies lacking the flexibility are generally incapable of maintaining effective performance due to the complicated and rapidly changing environment situations and application requirements. The proposed “Flexible Routing Computing Approach (FRCA)” is a novel distributed and probabilistic computing approach capable of modifying or upgrading routing policies on the fly with low cost, which effectively enhances the routing flexibility. FRCA models the routing metric as a forwarding probability distribution for routing decisions. This model depends on three elements, the physical quantities collected at sensor nodes, the built-in base math functions, and the routing parameters. These elements are all user-oriented and can be specified to implement multifarious complicated network policies meeting different performance requirements. More significantly, through distributing routing parameters from the sink to end nodes, operators are allowed to adjust network policies on the fly without interrupting the network services. Through extensive performance evaluation studies and simulations, the results demonstrate that routing protocols designed based on FRCA could achieve better performance compared to its state-of-the-art counterparts regarding network lifetime, energy consumption, and duplicate packets as well as ensure high flexibility during network policies modification or upgrade.

This article extends the promising software-defined networking technology to wireless sensor networks to achieve two goals: 1) reducing the information exchange between the control and data planes, and 2) counterbalancing between the sender's waiting-time and the duplicate packets. To this end and beyond the state-of-the-art, this work proposes an SDN-based architecture, namely MINI-SDN, that separates the control and data planes. Moreover, based on MINI-SDN, we propose MINI-FLOW, a communication protocol that orchestrates the computation of flows and data routing between the two planes. MINI-FLOW supports uplink, downlink and intra-link flows. Uplink flows are computed based on a heuristic function that combines four values, the hops to the sink, the Received Signal Strength (RSS), the direction towards the sink, and the remaining energy. As for the downlink flows, two heuristic algorithms are proposed, Optimized Reverse Downlink (ORD) and Location-based Downlink(LD). ORD employs the reverse direction of the uplink while LD instantiates the flows based on a heuristic function that combines three values, the distance to the end node, the remaining energy and RSS value. Intra-link flows employ a combination of uplink/downlink flows. The experimental results show that the proposed architecture and communication protocol perform and scale well with both network size and density, considering the joint problem of routing and load balancing.

This article modeled the data routing problem in Wireless Sensor Networks as an in-zone random process. The data packets are randomly routed from the source to the sink within the defined RoutingZone via any-path. The proposed “Zone Probabilistic Routing (ZPR)” is a distributed probabilistic and randomized anycast routing protocol. In ZPR, the forwarding probability distribution is defined by multiplying the Four Probability Distributions (4PD) namely: direction, transmission distance, perpendicular distance, and residual energy. In order to meet different performance requirements for different applications, these probability distributions are completely controllable via a set of exponential control-parameters (direction control, transmission distance control, perpendicular distance control, and residual energy control). This set of parameters is user-oriented and can be modified prior to nodes deployment to achieve different performances. Through extensive simulations and experimental results, the optimal values for these exponential control-parameters have been obtained to meet different performance requirements in terms of energy consumption, energy balancing, network lifetime, and delay. Furthermore, through an extensive performance evaluation study and simulation of large-scale scenarios, the results showed that our proposed ZPR protocol achieved better performance compared to the state-of-the-art solutions in terms of network lifetime, energy consumption, and data routing efficiency.

Opportunistic Routing (OR) is adapted to improve the performance of low Duty-cycled Wireless Sensor Networks by exploiting its broadcast nature. In contrast to traditional routing, where packets are transmitted along pre-determined paths, OR uses a prioritization metric to select a set of candidates as potential forwarders. This solves the sender's waiting time problem. However, too many candidates may simultaneously wake-up, generating more duplicate packets, occupying the restricted resources and hinder the packet delivery performance. Consciously, to restrict the number of candidates and to counterbalance between the waiting time problem and the duplicate packets problem, this paper proposed a new protocol that combines two main parts. First, each node defines a Candidates Zone (CZ) by a regular geometric shape of four corners. The packets generated by the node will be routed via any path within the CZ. Expressly, the nodes within the CZ are allowed to be selected as candidates. The size of CZ is controlled by the network density. Second, the candidates within the CZ are prioritized based on the OR metric, which is defined as the multiplication of four-distributions: direction distribution, transmission-distance distribution, perpendicular-distance distribution, and residual energy distribution. Through an extensive performance evaluation study and simulation of large-scale scenarios, the results demonstrated that our protocol achieved better performance compared to the state-of-the-art solutions in terms of network lifetime, energy consumption, routing efficiency, sender waiting time, and duplicate packets.

Budget allocation for multi-channel in advertising deals with distributing different sub-budgets to different channels under a fixed budget periodically. However, the issue of sequential decision making, with the goal of maximizing total benefits accrued over a period of time instead of immediate benefits, has rarely been addressed. Besides, there is a lack of explicit linking between the advertising actions taken in one channel and the responses obtained in another. What's more, the budget constraint restricts the feasible space of various optimal strategies. In this paper, we resolved these challenges by invoking a novel integrated algorithm based on both the Reinforcement Learning (RL) and Multi-Choice Knapsack Problem (MCKP), termed as Q-MCKP. Besides, we proposed some improvements such as a discretization method of discretizing the costs so as to decrease the complexity of the model. Moreover, the reward function of Q-learning was rebuilt by concerning an additional impact factor among channels. We conducted experiments using approximately two years of daily practical advertising data. Comparing to the state-of-arts, our experimental results demonstrated more effective in two angles.

The use of wireless sensor networks (WSN) in tracking applications is growing rapidly. In these applications, the nodes detect, monitor, and track a target, object, or event. In this paper, we consider the problem of tracking mobile objects in wireless sensor networks (WSN). We present a novel tracking model, named Grouping based Location Tracking (GLT), scaling well with the number of nodes and the number of mobile objects. GLT is based on the Grouping Hierarchy Structure, GHS. In GHS, nodes are partitioned into groups (not clusters) according to their maximum covered region (MCR) such that each group contains a number of nodes and a number of leaders. GLT consists of two tiers. The first tier, which is called the Notification Tree (NT), enhances the activation mechanism, the data cleaning mechanism, and the energy balancing mechanism. On the other hand, the second tier, which is called the Hierarchical Spanning Tree (HST), supports the data reporting mechanism and the lifetime prolonging mechanism. Simulations results show that GLT reduces the communication node selections overhead without diminishing object tracking accuracy and achieves a significant energy consumption reduction and network lifetime extension compared with the state-of-the-art approaches.

The security is a critical issue in wireless sensor networks (WSNs). A complex form of Denial of Service attack type is collaborative black hole attacks which challenge the security of wireless sensor networks. The purpose of this attack is to receive and drop all packets. Wireless sensor network nodes have limited energy and also have limited processing capability. WSNs devices have limited resources and they are particularly susceptible to the destruction and consumption of these limited resources. Collaborative black hole attacks are effective to partition the networks so the important data do not reach to base station. To secure wireless sensor network from collaborative black hole attacks, many security techniques have been proposed, most of them are energy inefficient and complex. In this paper, we discuss various techniques which detect and prevent the collaborative black hole attacks in WSNs and proposed a cluster-based energy preserving detection model of WSNs security against collaborative black hole attacks.

A basic (t,n)-secret sharing (SS) scheme allows a secret s to be divided into n shares and shared among n shareholders. In the scheme, any t or more than t shareholders can recover the secret while fewer than t shareholders cannot obtain the secret s. But an adversary without any valid share may obtain the secret if there are over t participants in the secret reconstruction. To address this type of attack, we first introduce the notion of randomized component (RC), which binds a share with all participants and protects the share from being exposed to outside without any computational assumption; at the same time, RCs can be used to reconstruct the secret. As one of the applications of RCs, a (t,m,n)-group oriented SS scheme is proposed to cope with the attack in basic (t,n)-SSs, in which once m (m ≥ t) participants form a tightly couple group by generating RCs, the secret can be recovered only if all m RCs are correct, which requires each participant to have a valid share in advance. Moreover, the scheme can secure the secret without any user authentication or share verification. Analyses show the proposed (t,m,n)-group oriented SS is asymptotically perfect and unconditionally secure. RCs can also be applied to build other schemes in a simple way, such as multi-SS, group authentication, and so on.

We propose an accurate acoustic direction finding scheme, Swadloon, according to the arbitrary pattern of phone shaking in a rough horizontal plane. Swadloon leverages sensors of the smartphone without the requirement of any specialized devices. Our Swadloon design exploits a key observation: the relative displacement and velocity of the phone-shaking movement corresponds to the subtle phase and frequency shift of the Doppler effects experienced in the received acoustic signal by the phone. Swadloon tracks the displacement of smartphone relative to the acoustic direction with the resolution less than 1 millimeter. The direction is then obtained by combining the velocity from the displacement with the one from the inertial sensors. Major challenges in implementing Swadloon are to measure the displacement precisely and to estimate the shaking velocity accurately when the speed of phone-shaking is low and changes arbitrarily. We propose rigorous methods to address these challenges, and apply Swadloon to several case studies: Phone-to-Phone direction finding, indoor localization and tracking. Our extensive experiments show that the mean error of direction finding is around 2.1 degree within the range of 32 m. For indoor localization, the 90-percentile errors are under 0.92 m. For real-time tracking, the errors are within 0.4 m for walks of 51 m.

Trusted Platform Module (TPM) is a coprocessor for detecting platform integrity and attesting the integrity to the remote entity. There are two obstacles in the application of TPM: minimizing trusted computing base (TCB) for reducing risk of flaws in TCB, for which a number of convincing solutions have been developed; formal guarantees on each level of TCB, where the formal methods on analyzing the application level have not been well addressed. To the best of our knowledge, there is no general formal framework for developing the TPM-based protocol applications, which not only guarantees the security but also makes it easier for design. In this paper, we make fine-grained refinement on TPM-based security protocols to illustrate our formal solution on the application level by using the Event-B language. First, we modify the classical Dolev-Yao attacker model, which assumes normal entity's compliance with the protocol even without TPM's protection. Thus, the classical security protocols are vulnerable in this modified attacker model. Second, we make stepwise refinement of the security protocol by refining the protocol events and adding security constraints. From the fifth refinement, we make a case study to illustrate the entire refinement and further formally prove the key agreement protocol from DAAODV, the TPM-based routing protocol, under the extended Dolev-Yao attacker model. The refinement provides another way of formal modeling the TPM-based security protocols and a more fine-grained model to satisfy with the rigorous security requirement of applying TPM. Finally, we prove all the proof obligations generated by Rodin, an Eclipse-based IDE for Event-B, to ensure the soundness of our proposal.