With the development of intelligent transportation systems, vehicular edge computing (VEC) has played a pivotal role by integrating computation, storage, and analytics closer to the vehicles. VEC represents a paradigm shift towards real-time data processing and intelligent decision-making, overcoming challenges associated with latency and resource constraints. In VEC scenarios, the efficient scheduling and allocation of computing resources are fundamental research areas, enabling real-time processing of vehicular tasks and intelligent decision-making. This paper provides a comprehensive review of the latest research in Deep Reinforcement Learning (DRL)-based task scheduling and resource allocation in VEC environments. Firstly, the paper outlines the development of VEC and introduces the core concepts of DRL, shedding light on their growing importance in the dynamic VEC landscape. Secondly, the state-of-the-art research in DRL-based task scheduling and resource allocation is categorized, reviewed, and discussed. Finally, the paper discusses current challenges in the field, offering insights into the promising future of VEC applications within the realm of intelligent transportation systems.

As communication and computing technologies advance, vehicular edge computing emerges as a promising paradigm for delivering a wide array of intelligent services in 6G enabled Intelligent Autonomous Transport Systems. These service requests, are safety-oriented and typically require the fusion of processing results from multiple independent computation tasks generated by various onboard sensors, in which the computation tasks are delay-sensitive and computation-intensive. Consequently, the allocation of multiple tasks within a single service request while efficiently reducing request completion time and energy consumption presents a substantial challenge. In order to address the problem of multi-task simultaneous scheduling, this paper proposed to employ deep reinforcement learning and edge computing architecture to make task scheduling decisions for vehicles. Firstly, the Vehicle-Infrastructure Network (VINET) is designed, in which the vehicles can assign multiple tasks to the edge servers and other idle vehicles, thus extending the task processing capabilities for vehicles. Secondly, Fully-decentralized Multi-agent Proximal Policy Optimization (FMPPO) algorithm is proposed to make task scheduling decisions for autonomous driving, the large model trained via FMPPO is adaptable to different scenarios with various numbers of vehicles. Thirdly, by taking into account task characteristic, environmental status, and vehicle mobility, the proposed method can make task scheduling decisions in real-time and then dynamically distributes tasks based on the decisions. Finally, experimental results demonstrate that the designed method outperforms benchmark methods in terms of both completion time and energy consumption of computation tasks.

This paper proposes a flexible job-shop scheduling problem optimization method, which focuses on providing solutions for industrial production. First, in terms of model construction, the method further considers the cost of automated guided vehicles and the priority of workpiece production based on previous methods. Then, this method solves the model by non-dominated sorting genetic algorithm with self-cross and delete-mutation. It reduces the production time and energy by an average of 6.4% and 19.4%, which are 5.4% and 15.1% with the priority. Finally, the simulation verifies that the method effectively reduces the production cost while realizing the adjustment of the automated guided vehicle number and the workpiece production sequence.

With the development of Fifth Generation (5G)/Sixth Generation (6G) -enabled Internet of Things (IoT) networks, different user equipment (UE) dynamically generates massive raw data and delay-sensitive computation tasks to be offloaded and processed at the mobile edge computing (MEC) nodes. In this paper, we propose a comprehensive digital twin-empowered UAV-assisted edge intelligent IoT framework, which enables UEs to offload their delay-sensitive tasks to a UAV-assisted MEC node. We aim to minimise the maximum total service delay including the transmission delay and the processing delay among all UEs. A deep deterministic policy gradient-based offloading and resource allocation optimisation algorithm, named (DDPG-ORAO), is proposed to optimise task offloading decisions among all UEs, which jointly optimising the communication and computation resources allocation among all UEs and all UAV-assisted MEC nodes. Simulation results show that our proposed optimisation algorithm outperforms the benchmarks in terms of the total service delay of all UEs.

High-accuracy time of arrival (TOA) estimation is essential for indoor acoustic localization, but traditional TOA estimation methods often fail to obtain accurate and robust TOA estimation performance due to severe multipath challenges, such as peak extraction by general cross correlation (GCC). To address the problem, this article presents a novel acoustic TOA estimation technique called FDME-RATE, a high-accuracy acoustic ranging system. The proposed method utilizes GCC to determine the coarse signal arrival time, followed by an enhanced frequency modulated continuous wave (FMCW) technique to convert the various arrival paths into distinct frequencies within the spectrum. In addition, we employ an adaptive multiple signal classification (AMUSIC) algorithm to achieve high-resolution TOA estimation. Through simulations and experiments conducted in a reverberant corridor, the results indicate that our method achieves an average TOA ranging error of just 0.16 and 0.19 m at the 95% confidence level across a 30-m span. Within a 20-m range, there is only a centimeter-level error. Additionally, in non line-of-sight (NLOS) scenarios, our proposed method further enhances TOA-based ranging accuracy by an additional 0.3 m over the same distance. The experimental results also demonstrate that our method outperforms traditional methods and recent research in terms of both estimation accuracy and robustness.

The global trend of population aging poses significant challenges to society and healthcare systems, particularly because of neurocognitive disorders (NCDs) such as Parkinson's disease (PD) and Alzheimer's disease (AD). In this context, artificial intelligence techniques have demonstrated promising potential for the objective assessment and detection of NCDs. Multimodal contactless screening technologies, such as speech-language processing, computer vision, and virtual reality, offer efficient and convenient methods for disease diagnosis and progression tracking. This paper systematically reviews the specific methods and applications of these technologies in the detection of NCDs using data collection paradigms, feature extraction, and modeling approaches. Additionally, the potential applications and future prospects of these technologies for the detection of cognitive and motor disorders are explored. By providing a comprehensive summary and refinement of the extant theories, methodologies, and applications, this study aims to facilitate an in-depth understanding of these technologies for researchers, both within and outside the field. To the best of our knowledge, this is the first survey to cover the use of speech-language processing, computer vision, and virtual reality technologies for the detection of NSDs.

High-accuracy Time of Arrival (TOA) estimation is a crucial requirement for indoor acoustic localization. However, conventional approaches to TOA estimation based on Generalized Cross Correlation (GCC) suffer from insufficient resolution in severe multipath environments, which hinders their ability to achieve robust ranging. In this paper, we propose a novel and robust acoustic TOA estimation method based on Frequency Modulated Continuous Wave (FMCW) that selects the first-path signal. Our method transforms multipath signals into the frequency domain using several novel signal processing techniques of FMCW, which enhances the resolution of multipath identification and makes the estimation more stable. Simulation and experimental results show that, compared to other methods, our method can achieve centimeter-level TOA ranging estimation over a distance range of 16 m. Furthermore, our proposed method can enhance the accuracy of TOA estimation-based ranging by approximately 4.2 cm in non-line-of-sight (NLOS) scenarios.

Blockchain can help edge IoT-enabled Maritime Transportation Systems (MTS) in solving its privacy and security problems. In this paper, a lightweight blockchain called LBlockchainE is designed for edge IoT-enabled MTS to guarantee the security of sensor data stored in an edge computing environment. To save the resources of edge servers on ship, a data placement strategy is proposed. To encourage edge servers to positively contribute to storing data generated by sensor devices, storage resource consumption is employed as an influencing parameter, and servers with abundant resources are selected for priority storage. The data placement strategy also takes care of the access delay between servers and selects the nodes with the least access and storage costs as the priority storage choice. LBlockchainE applies the low-energy-consumption characteristics of Proof of Stake to determine the ownership of bookkeeping rights through a small number of competitive calculations and the resources of the node. Experimental results indicate that compared with Ethereum, the consensus mechanism of LBlockchainE consumes less energy and occupies less storage space. On average, the new system uses 1.6% less time and consumes 78% less battery power compared with traditional blockchain systems. In comparison to the random storage, the best storage, and the optimal data storage strategies, the proposed strategy maintains the same message costs.

Many end-to-end deep learning algorithms seeking to classify malicious traffic and encrypted traffic have been proposed in recent years. End-to-end deep learning algorithms require a large number of samples to train a model. However, it is hard for existing methods fully utilizing the heterogeneous multimodal input. To this end, we propose the SHAPE model (simultaneous header and payload encoding), which mainly consists of two autoencoders and a transformer layer, to improve model performance. The two auto encoders extract features from heterogeneous inputs—the statistical information of each packet and byte-form payloads—and convert them into a unified format; then, a lightweight Transformers layer further extracts the relationship hidden in simultaneous input. In particular, the autoencoder for payload feature extraction contains several depthwise separable residual convolution layers for efficient feature extraction and a token squeeze layer to reduce the computing overhead of the Transformers layer. Moreover, we train the SHAPE model using deep metric learning, which pulls samples with the same class label together and separates samples from different classes in the low-dimensional embedding space. Thus, the SHAPE model can naturally handle multitask classification, and its performance is approximately 5.43% better than the current SOTA on the traffic type classification of the ISCX-VPN2016 dataset, at the cost of 9.31 times the training time, and 1.45 times the inference time.

Fusion positioning technology requires stable and effective positioning data, but this is often challenging to achieve in complex Non-Line-of-Sight (NLoS) environments. This paper proposes a fusion positioning method that can achieve stable and no hop points by adjusting parameters and predicting trends, even with a one-sided lack of fusion data. The method combines acoustic signal and Inertial Measurement Unit (IMU) data, exploiting their respective advantages. The fusion is achieved using the Kalman filter and Bayesian parameter estimation is performed for tuning IMU parameters and predicting motion trends. The proposed method overcomes the problem of fusion failure caused by long-term unilateral data loss in traditional fusion positioning. The positioning trajectory and error distribution analysis show that the proposed method performs optimally in severe NLoS experiments.

Industry 5.0 requires intelligent self-organi- zed, self-managed, and self-monitoring applications with ability to analyze and predict the human as well as machine behaviors across interconnected devices. Tackling dynamic network behavior is a unique challenge for Internet of Things applications in Industry 5.0. Knowledge-defined networks (KDN) bridge this gap by extending software-defined networking architecture with knowledge plane, which learns the network dynamics to avoid suboptimal decisions. Cognitive routing leverages the sixth-generation (6G) self-organized networks with self-learning feature. This article presents a self-organized cognitive routing framework for a KDN which uses link-reliability as a routing metric. It reduces end-to-end latency by choosing the most reliable path with minimal probability of route-flapping. The proposed framework precalculates all possible paths between every pair of nodes and ensures self-healing with a constant-time convergence. An experimental test-bed has been developed to benchmark the proposed framework against the industry-stranded link-state and distance-vector routing algorithms SPF and DUAL, respectively.

Existing navigation services provide route options based on a single metric without considering user’s preference. This results in the planned route not meeting the actual needs of users. In this paper, a personalized route planning algorithm is proposed, which can provide users with a route that meets their requirements. Based on the multiple properties of the road, the Polychromatic Sets (PS) theory is introduced into route planning. Firstly, a road properties description scheme based on the PS theory was proposed. By using this scheme, users’ travel preferences can be quantified, and then personalized property combination schemes can be constructed according to these properties. Secondly, the idea of setting priority for road segments was utilized. Based on a user’s travel preference, all the property combination schemes can be prioritized at relevant levels. Finally, based on the priority level, an efficient path planning scheme was proposed, in which priority is given to the highest road segments in the target direction. In addition, the system can constantly obtain real-time road information through mobile terminals, update road properties, and provide other users with more accurate road information and navigation services, so as to avoid crowded road segments without excessively increasing time consumption. Experiment results show that our algorithm can realize personalized route planning services without significantly increasing the travel time and distance. In addition, source code of the algorithm has been uploaded on GitHub for this algorithm to be used by other researchers.

Community Question Answering (CQA) provides a platform to share knowledge for users. With the increasing number of users and questions, askers have to wait a long time for an answer with high quality while responders may not be interested in assigned questions. Current methods usually try to address this issue based on text or link analysis. However, most of them suffer from delayed answers or low coverage of best answer. In this paper, we design a novel expert recommendation mechanism by incorporating the deep structured semantic model (DSSM) [20] with our proposed graph-based algorithm, a topic sensitive answerer rank algorithm (TSAR). In the process of constructing transition probability matrix, we not only take into account both the number of questions answered by the user and question difficulty, but also consider the user's average response time for providing the answer. The experiments carried out on Yahoo! Answers and Stack Overflow datasets demonstrate that the proposed mechanism outperforms the current typical algorithms [9] on multiple metrics and achieves the best answer coverages, which are 61.5% and 53.8%, respectively.

Location-aware recommendation is considered as one of human behavior cognitive analyses in the world of human-machine-environment system. The development of 5G technology and ubiquitous mobile devices has led to the emergence of a new online platform, location-based social networks (LBSNs), which allows users to share their locations. The essential feature of LBSNs is to provide users with location recommendations that help them explore new places and also to make LBSNs more prevalent to users. Most of the existing research is focusing on the introduction of new features and how these new features affect the check-in behaviors of the users. In addition, the dependencies between each feature and the probability of a user visiting the site is always a principle to follow. However, a user’s decision could be determined by considering several features at the same time. When a full model is applied by considering all the features, an overfitting problem could be occurred owing to the lack of sufficient data for each individual user. In this article, an intermediate solution was proposed to address all of these problems by fragmenting the model into several partial models, where each partial model is responsible for a few features. An additive strategy was also implemented to support the development of personalized partial models. Furthermore, a partition-based approach was introduced to explore the hidden patterns from the geographically clustered check-in data. The performance of the approaches has been evaluated by using the data sets from Foursquare and it demonstrates that the proposed approach outperforms the state-of-the-art approaches.

Deep neural network with multi-scale feature fusion has achieved great success in human pose estimation. However, drawbacks still exist in these methods: 1) they consider multi-scale features equally, which may over-emphasize redundant features; 2) preferring deeper structures, they can learn features with the strong semantic representation, but tend to lose natural discriminative information; 3) to attain good performance, they rely heavily on pretraining, which is time-consuming, or even unavailable practically. To mitigate these problems, we propose a novel comprehensive recalibration model called Pyramid GAting Network (PGA-Net) that is capable of distillating, selecting, and fusing the discriminative and attention-aware features at different scales and different levels (i.e., both semantic and natural levels). Meanwhile, focusing on fusing features both selectively and comprehensively, PGA-Net can demonstrate remarkable stability and encouraging performance even without pre-training, making the model can be trained truly from scratch. We demonstrate the effectiveness of PGA-Net through validating on COCO and MPII benchmarks, attaining new state-of-the-art performance. https://github.com/ssr0512/PGA-Net

With the prevalent application of Internet of Things (IoT) in real world, services have become a widely used means of providing configurable resources. As the number of services is large and is also increasing fast, it is an inevitable mission to determine the suitability of a service to a user. Two typical tasks are needed, which are service recommendation and service selection. The prediction for Quality of Service (QoS) is an important way to accomplish the two tasks, and there have been a series of methods proposed to predict QoS values. However, few methods have been used to study the QoS prediction in IoT environments, where contextual information is vital. In this article, we develop a holistic framework to attack the QoS prediction in the IoT environment, which is based on neural collaborative filtering (NCF) and fuzzy clustering. We design a fuzzy clustering algorithm that is capable of clustering contextual information and then propose a new combined similarity computation method. Next, a new NCF model is designed that can leverage local and global features. Sufficient experiments are implemented on two real-world data sets, and the experimental results verify the effectiveness of the proposed framework.

Providing location recommendations has become an essential feature for location-based social networks (LBSNs), as it helps the users to explore new places and makes LBSNs more prevalent to them. Existing studies mostly focus on introducing the new features that affect users' check-in behaviours in LBSNs. However, despite the difference in the type of the features exploited, they mostly follow the same principle - characterizing dependencies between the probability of a user visiting a point-of-interest (POI) and each feature separately. The decision of a user on where to go in an LBSN, however, is driven by multiple features that act simultaneously. On the other hand, applying a full model which considers all the features jointly suffers from overfitting, as for each user there is limited available data. In this paper, we propose an intermediate solution by fragmenting the model into multiple partial models which each takes the subset of the features as the input. The proposed approach focuses on building the personalized partial models (PRMs) which are further combined by applying an additive approach. Experiments on two datasets from Foursquare show that our proposed method outperforms the state-of-the-art approaches in POI recommendation.

Through the availability of location-acquisition devices, huge volumes of spatio-temporal data recording the movement of people is provided. Discovery of the group of people who travel together can provide valuable knowledge to a variety of critical applications. Existing studies on this topic mainly focus on the movement of vehicles or animals with forcing the group members to stay always connected. However, the movement of people is different; people might belong to the same main group while they contribute in various sub-groups during their movement. In this paper, we propose a group pattern called loose travelling companion pattern (LTCP), which allows the members of a group to contribute to various sub-groups as long as the community of members does not change during the movement and all of the members stay connected for a few time-slots. In addition, we propose weakly continuous loose travelling companion pattern (WCLTCP) to relax the continuous time constraint in LTCP. Finally, three algorithms have been developed to discover the proposed group patterns: (i) straightforward approach, (ii) smart-and-fast method, and (iii) and opportunistic algorithm. Through the extensive experimental evaluation on both real and experimental datasets, the efficiency and effectiveness of the proposed group discovery approaches are proven.

Molecular Nanoscale Sensor Networks (MNSNs) introduce a new molecular routing paradigm where the molecular communication is enabled by activating the nanosensors on the routing path to release the molecules. This new molecular activation mechanism is a new many-to-many scheme where the transmitting nanosensors transmits molecules to multiple receiving nanosensors and the receiving nanosensors receives the molecules from multiple transmitting nanosensors. Molecular activation mechanism poses two new capacity constraints where the received molecules must be above a threshold to activate the receiving node and the molecules released from the transmitter should not exceed its molecular capacity. These two new criteria (many-to-many communication and capacity constraint) make the molecular activation and communication scheme a very challenging issue in an MNSN, which is totally different from the communication and routing scheme in existing wireless IP networks. In this paper, for the first time, we propose a sound mathematical model to capture the many-to-many communication scheme, activation capacity constraint and molecular capacity constraint in the MNSN. We then propose a novel QoS (cost and capacity) aware algorithm, CACAMA, to identify the cost efficient molecular activation and communication path in the MNSN. From the computational experiments, it shows that the CACAMA algorithm is superior to other two heuristics, SP and MCST, in all network settings.

Nowadays, web services have to accommodate a significant and ever-increasing number of requests due to high interactivity of current applications. Although the built-in elasticity offered by a cloud can mitigate this challenge, it is highly desirable that applications can be built in a scalable fashion. State-of-the-art Web Application Frameworks (WAFs) focus on the creation of application logic and do not offer integrated cloud scaling concepts. As the creation of such scaling systems is very complex, we proposed in our recent work the concept of Web Scaling Frameworks (WSFs) in order to offload scaling to another layer of abstraction. In this work, a detailed design for WSFs including necessary modules, interfaces and components is presented. A mathematical model used for performance rating is evaluated and enhanced on a computing cluster of 42 machines. Traffic traces from over 25 million real-world applications are analysed and evaluated on the cluster to compare the WSF performance with a traditional scaling approach. The results show that the application of WSFs can substantially reduce the number of total machines needed for three representative real-world applications-a social network, a trip planner and the FIFA World Cup 98 website-by 32, 63 and 92 percent, respectively.

Future 5G networks have stringent end-user requirements on data rate and error performance. In order to satisfy these requirements, innovative wireless networking technologies and models need be researched. One particular example is the two-way relaying channel, which can have as much as 100% higher theoretical data rate than current systems where transmissions are arranged in an orthogonal manner. However, benefits of this model cannot be achieved without the application of proper relaying protocols. This paper proposes a novel protocol that directly addresses the problems of existing protocols of two-way relaying models, e.g. analogy network coding and physical network coding, and has improved performance. By combining direct and differential demodulation-forward schemes based on wireless channel qualities and signal to noise ratio, a new hybrid protocol is created. Theoretical analysis and numerical experiments show that the proposed solution has lower error rate than the existing ones, and can thus be applied to support future 5G networks.

Two-Way Relay Channel (TWRC) plays an important role in relay networks, and efficient relaying protocols are particularly important for this model. However, existing protocols may not be able to realize the potential of TWRC if the two independent fading channels are not carefully handled. In this paper, a Hybrid DeModulate-Forward (HDMF) protocol is proposed to address such a problem. We first introduce the two basic components of HDMF - direct and differential DMF, and then propose the key decision criterion for HDMF based on the corresponding log-likelihood ratios. We further enhance the protocol so that it can be applied independently from the modulation schemes. Through extensive mathematical analysis, theoretical performance of the proposed protocol is investigated. By comparing with existing protocols, the proposed HDMF has lower error rate. A novel scheduling scheme for the proposed protocol is introduced, which has lower length than the benchmark method. The results also reveal the protocol's potential to improve spectrum efficiency of relay channels with unbalanced bilateral traffic.

The social web and huge growth of mobile smart devices dramatically increases the performance requirements for web services. State-of-the-art Web Application Frameworks (WAFs) do not offer complete scaling concepts with automatic resource-provisioning, elastic caching or guaranteed maximum response times. These functionalities, however, are supported by cloud computing and needed to scale an application to its demands. Components like proxies, load-balancers, distributed caches, queuing and messaging systems have been around for a long time and in each field relevant research exists. Nevertheless, to create a scalable web service it is seldom enough to deploy only one component. In this work we propose to combine those complementary components to a predictable, composed system. The proposed solution introduces a novel class of web frameworks called Web Scaling Frameworks (WSFs) that take over the scaling. The proposed mathematical model allows a universally applicable prediction of performance in the single-machine- and multi-machine scope. A prototypical implementation is created to empirically validate the mathematical model and demonstrates both the feasibility and increase of performance of a WSF. The results show that the application of a WSF can triple the requests handling capability of a single machine and additionally reduce the number of total machines by 44%.

Distributed average consensus (DAC) algorithm is widely used in many applications. It utilizes matrix iteration to find the dominant eigenvector. To minimize the required number of iterations, the algorithm needs to be optimized. However, this optimization needs the knowledge of network topology, which is very hard to obtain for an individual agent in distributed networks. Thus, optimal step length and forgetting factor need to be calculated offline and forwarded to every agent. To solve this problem, we proposed a distributed real-time optimization technique so that each node can estimate these optimal parameters individually. In addition, the method is based on constant first-order DAC itself, so it will not stop the consensus process. The result shows that a numerical error due to quantization would exist in the distributed solution. It will increase as the network becomes larger. Thus, a numerical technique is introduced to mitigate the error. The estimated parameters after mitigation do not obviously decline the performance of higher-order DAC when network size is smaller than a threshold.

The emerging compressed sensing (CS) holds considerable promise for continuously acquiring biomedical signals in body sensor networks (BSNs), which enables nodes to employ a much lower sampling rate than Nyquist while still able to accurately reconstruct signals. CS-based BSNs are expected to significantly enhance the quality of healthcare and improve the ability of prevention, early diagnosis, and treatment of chronic diseases. However, existing BSNs are still unable to support long-term monitoring in healthcare, as well as providing an energy-efficient low communication burden and inexpensive scheme. Capitalizing on the sparsity of biomedical signals in transfer domains, this paper develops a continuous biomedical signal acquisition system, which explores a sparsification model to find the sparse representation of biomedical signals. The sparsified measurements of signals are wirelessly transmitted to a fusion center through BSNs. Meanwhile, a weighted group sparse reconstruction algorithm is proposed to accurately reconstruct the signals at the fusion center. Simulation results show that, on random sampling over BSN, the proposed group sparse algorithm shows good efficiency, strong stability, and robustness.

The emerging compressed sensing (CS) theory can significantly reduce the number of sampling points that directly corresponds to the volume of data collected, which means that part of the redundant data is never acquired. It makes it possible to create standalone and net-centric applications with fewer resources required in Internet of Things (IoT). CS-based signal and information acquisition/compression paradigm combines the nonlinear \nreconstruction algorithm and random sampling on a sparse \nbasis that provides a promising approach to compress signal and data in information systems. This paper investigates how CS can provide new insights into data sampling and acquisition in wireless sensor networks and IoT. First, we briefly introduce the CS theory with respect to the sampling and transmission coordination during the network lifetime through providing a compressed sampling process with low computation costs. Then, a CS-based framework is proposed for IoT, in which the end nodes measure, transmit, and store the sampled data in the framework. Then, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and lower energy efficiency. Performance is evaluated with respect to network size using datasets acquired by a real-life deployment.

In this study, the authors have investigated a dynamic spectrum access method for cognitive radio (CR) networks by using polychromatic sets (PS) theory. First, a power control model is proposed in which the transmission power at a CR node can be calculated by considering both the primary radio (PR)-to-CR and PR-to-PR interference under a specific outage probability. In order to allocate the available spectrum among the CR nodes in a spectrum overlay scenario, the authors further propose a channel selection algorithm based on PS. This study also gives a framework of the PS-based method and concludes with future work describing the practical implementation of the proposed framework. Effectiveness of proposed method is demonstrated through an example application.

Adaptive signal identification has been an important issue in cognitive radio networks (CRNs). Most existing techniques require high-level signal-to-noise ratio (SNR) for signal identification. This study presents an intelligent technique that focuses on a theoretical and experimental study of the signal identification by using manifold learning algorithm in CRNs. The authors pose the problem of signal identification in CRNs as signal classification by using manifold learning on high dimensions, and a novel manifold learning algorithm named as SIEMAP is proposed, which is able to identify signals in a low-dimensional space. Simulation results indicate that SIEMAP outperforms classical methods in low dimensions and is capable of identifying signal types from the received signals.

Abstract In the IEEE 802.11 wireless LAN (WLAN), the fundamental medium access control (MAC) mechanism—distributed coordination function (DCF), only supports best‐effort service, and is unaware of the quality‐of‐service (QoS). IEEE 802.11e enhanced distributed channel access (EDCA) supports service differentiation by differentiating contention parameters. This may introduce the problem of non‐cooperative service differentiation. Hence, an incompletely cooperative EDCA (IC‐EDCA) is proposed in this paper to solve the problem. In IC‐EDCA, each node that is cooperative a priori adjusts its contention parameters (e.g., the contention window (CW)) adaptively to the estimated system state (e.g., the number of competing nodes of each service priority). To implement IC‐EDCA in current WLAN nodes, a frame‐analytic estimation algorithm is presented. Moreover, an analytical model is proposed to analyze the performance of IC‐EDCA under saturation cases. Extensive simulations are also carried out to compare the performances of DCF, EDCA, incompletely cooperative game, and IC‐EDCA, and to evaluate the accuracy of the proposed performance model. The simulation results show that IC‐EDCA performs better than DCF, EDCA, and incompletely cooperative game in terms of system throughput or QoS, and that the proposed analytical model is valid. Copyright © 2010 John Wiley & Sons, Ltd.

The distributed single and/or multiple user selection problems are strongly related to that of partitioning a sample with binary-type questions. Although several algorithms have been proposed for user selection, the comprehensive view of designing the optimal algorithm has not been fully investigated yet. In this paper, we reformulated a splitting based selection algorithm by introducing a new parameter, called the "selection factor". Asymptotic analysis of the algorithm leads to a functional equation, similar to that encountered in the analysis of the collision resolution algorithms. Consequently, we see that there is an intimate relation between the splitting based selection algorithms and collision resolution algorithms. A surprisingly simple solution to the functional equation, which provides a helpful method for optimally designing the algorithm, is found. By jointly optimizing the algorithm's parameters, numerical results show that the performance can be improved.

In this paper, we present a 3D indoor navigation service system based on smartphones. To achieve an accurate indoor navigation system in a multi-storey building, it is necessary to determine the storey where the user is. By attaching an atmosphere pressure sensor on smartphones, the altitude information of the user can be obtained, and then, the storey where the user is can be calculated indirectly. With the information of storey, we project localization anchor nodes, e.g. WiFi-AP, of different storeys onto the storey where the user is, and calculate their projection distances based on received signal strength (RSS). To utilize the projection distance in 2D localization algorithms, the systematic error deviation from the storey deviation can be reduced. In addition, we develop a software running in a smartphone to implement our localization method, and provide a smartphone navigation service in our office building. Experimental results show that our method is more accurate than the traditional triangle localization method.

We consider a contention based random access protocol for networks with multiple users sharing the same transmission medium. Specifically, communication channel between the base station and each user is modeled by a simplified finite-state Markov model to capture the correlation of the channel evolution in time. Under this scenario, we study a reservation-type protocol in channel aware ALOHA context. We formulate the Markovian model necessitates a state description consisting the outcome of the transmission in each slot. The average throughput of the scheme is obtained using the Markov Analysis technique, and we show that reservation help improve the system performance by reducing the number of collision in transmission. Numerical results show that the proposed scheme always improves the system performance when the user channels are more correlated over the time.

A quality of service (QoS) scheme for multimedia multicast communications in wireless mesh networks (WMNs) is proposed in this study. It uses a new bandwidth calculation scheme to provide rate-adaptive admission control. It relies on information it receives from the network and application layers to calculate the network bandwidth consumption and operates independently of the media access control (MAC) layer. Using the proposed QoS scheme, the network layer provides feedback on network congestion to the application layer. The multimedia multicast sender adapts the real-time data transmission rate based on the network congestion feedback it receives. In this study, the authors describe the detailed architecture of the proposed QoS scheme. Furthermore, the authors have implemented the QoS scheme in our previously developed uni-directional link aware multicast extension to AODV (UDL-MAODV) routing protocol. The authors present validation tests to ensure the correct functionality of the QoS algorithm using our SwanMesh WMN testbed. The authors have also performed simulation tests to evaluate the performance of the proposed scheme. The simulation results show the effectiveness of the proposed QoS scheme.

Wireless mesh networks (WMNs) facilitate both data transfer and real-time applications over wireless medium. Owing to the shared nature of wireless frequencies, bandwidth limitation is a major challenge facing WMNs. If real-time multimedia applications, such as live video streaming, are shared among multiple clients using unicast communications, it could result in network resources starvation. Multicast transmission saves network resources by replicating live multimedia transmitted data from one source to multiple destinations using the same stream. The authors have developed a novel implementation of a multicast extension to ad hoc on-demand distance vector (MAODV) routing protocol in Linux kernel 2.6 user space, which is referred to as unidirectional link-aware MAODV (UDL-MAODV). Multicast video transmissions use user datagram protocol, which does not use implicit handshaking dialogues for guaranteeing reliability of data. Therefore the authors propose and have implemented modifications to the MAODV route discovery process to improve the reliability of multicast video transmissions. These modifications enable UDL-MAODV to ensure reliable route establishment for multimedia multicast communications over WMNs in the presence of UDLs. The authors describe in this study the software architecture of the UDL-MAODV implementation in the Linux kernel 2.6 user space, and also present multicast validation and results of performance tests using the SwanMesh WMN testbed. Furthermore, UDL-MAODV has been cross-compiled and tests are presented to compare the performance of the implementation using X86 and ARM architecture-based SwanMesh nodes. The test results show that the proposed algorithm is reliable and efficient.