ABSTRACT The morphological characteristics of electrocardiograms (ECGs) serve as a fundamental basis for diagnosing arrhythmias. Convolutional neural networks (CNNs), leveraging their local receptive field properties, effectively capture the morphological features of ECG signals and have been extensively employed in the automatic diagnosis of arrhythmias. However, the variability in the duration of ECG morphological features renders single‐scale convolutional kernels inadequate for fully extracting these features. To address this limitation, this study proposes a multi‐scale parallel joint optimization convolutional neural network (MPJO_CNN). The proposed method utilizes convolutional kernels of varying scales to extract ECG features, further refining these features via parallel computation and implementing a joint optimization strategy to enhance classification performance. Experimental results demonstrate that on the MIT‐BIH arrhythmia database, this method not only achieved state‐of‐the‐art performance, with an accuracy of 99.41% and an F1 score of 98.09%, but also showed high sensitivity to classes with fewer samples.

Live video commenting is popular on video media platforms, as it can create a chatting atmosphere and provide supplementary information for users while watching videos. Automatically generating live video comments can improve user experience and enable human-like generation for bot chatting. Existing works mostly focus on short video datasets while ignoring other important video types such as long videos like movies. In this work, we collect a new Movie Live Comments (MovieLC) dataset to support research on live video comment generation for long videos. We also propose a knowledge enhanced generation model inspired by the divergent and informative nature of live video comments. Our model adopts a pre-training encoder-decoder framework and incorporates external knowledge. Extensive experiments show that both objective metrics and human evaluation demonstrate the effectiveness of our proposed model. The MovieLC dataset and our code will be released.

The barycentric linear localization (BLL) methods provide a lightweight, distributed way to calculate locations for resource-limited IoT devices. A crucial requirement for BLL is that the nodes participating in the iterative location propagation are localizable. Otherwise, the unlocalizable nodes will continuously pose error information in the location propagation process, making even the theoretically localizable nodes converge to the wrong locations. However, the research on node localizability in BLL is much lacked, greatly limiting the application scope of BLL. In specific, BLL node localizability is detected on a generated graph$\mathcal {G^{A}}$. For any node, its neighbors appear in$\mathcal {G^{A}}$only when the neighbors can form triangle(s), so that$\mathcal {G^{A}}$is much sparser than the original$\mathcal G$. Thus, the node localizability condition in BLL is harder to be satisfied than that in traditional localization methods. Moreover, the distributed algorithm to detect BLL localizable nodes is still open. This paper thoroughly investigates the node localizability conditions and distributed localizable node detection algorithms in BLL. At first, an efficient and fully distributed Negative Edge Inference (NEI) algorithm is proposed for each node to infer implicit edges in its neighborhood. NEI strengthens the distance graph by revealing more distance constraints so that enables more neighboring triangles. Then a new sufficient condition, i.e., the recursive three disjoint path condition (Recursive-3DP) on the strengthened distance graph is proposed to identify BLL localizable nodes much more accurately. Secondly, a distributed Path Extension and Pruning (PEP) algorithm is proposed for distributed localizable node detection. PEP is proved to detect all the theoretically Recursive-3DP nodes in the strengthened distance graph. A Fast-PEP algorithm is further proposed, which misses very limited Recursive-3DP nodes while bringing significant improvement in efficiency. PEP and Fast-PEP guarantee to identify BLL localizable nodes in$2H$rounds, where$H$is the maximum hop number of the node disjoint paths. Finally, by using NEI and PEP (Fast-PEP), a localizability-aware BLL (LABEL) method is proposed, which correctly identifies localizable nodes and guarantees their correct location convergence. Extensive analysis and experiments show the advantages in localizability and location accuracy of the proposed schemes over the state-of-the-art methods.

Determining whether nodes can be uniquely localized, called localizability detection, is a concomitant problem in network localization. Localizability detection under the traditional Non-Linear Localization (NLL) schema has been well explored, whereas localizability under the emerging Barycentric coordinate-based Linear Localization (BLL) schema has not been well investigated. Non-awareness of the node localizability in BLL may cause theoretically localizable nodes to converge to wrong locations because their locations are impacted by the wrong locations of the unlocalizable nodes through the iterative location propagation. In this article, the deficiency of existing localizability theories and algorithms in BLL is firstly investigated and then a necessary condition and a sufficient condition for BLL node localizability detection are proposed. Based on these two conditions, an efficient Iterative Maximum Flow (IMF) algorithm is designed to identify BLL localizable nodes, and only localizable nodes are selected to enable a Localizability Aware Barycentric Linear Localization (LABLL) algorithm, which can guarantee the locations of the localizable nodes converging correctly. The proposed IMF and LABLL algorithms are validated by both theoretical analysis and experimental evaluations.

A single strategy used in the firefly algorithm (FA) cannot effectively solve the complex optimal scheduling problem. Thus, we propose the FA with division of roles (DRFA). Herein, fireflies are divided into leaders, developers, and followers, while a learning strategy is assigned to each role: the leader chooses the greedy Cauchy mutation; the developer chooses two leaders randomly and uses the elite neighborhood search strategy for local development; the follower randomly selects two excellent particles for global exploration. To improve the efficiency of the fixed step size used in FA, a stepped variable step size strategy is proposed to meet different requirements of the algorithm for the step size at different stages. Role division can balance the development and exploration ability of the algorithm. The use of multiple strategies can greatly improve the versatility of the algorithm for complex optimization problems. The optimal performance of the proposed algorithm has been verified by three sets of test functions and a simulation of optimal scheduling of cascade reservoirs.

The increasing availability of autonomous small-size Unmanned Aerial Vehicles (UAVs) has provided a promising way for data gathering from Wireless Sensor Networks (WSNs) with the advantages of high mobility, flexibility, and good speed. However, few works considered the situations that multiple UAVs are collaboratively used and the fine-grained trajectory plans of multiple UAVs are devised for collecting data from network including detailed traveling and hovering plans of them in the continuous space. In this paper, we investigate the problem of the Fine-grained Trajectory Plan for multi-UAVs (FTP), in which m UAVs are used to collect data from a given WSN, where m ≥ 1. The problem entails not only to find the flight paths of multiple UAVs but also to design the detailed hovering and traveling plans on their paths for efficient data gathering from WSN. The objective of the problem is to minimize the maximum flight time of UAVs such that all sensory data of WSN is collected by the UAVs and transported to the base station. We first propose a mathematical model of the FTP problem and prove that the problem is NP-hard. To solve the FTP problem, we first study a special case of the FTP problem when m = 1, called FTP with Single UAV (FTPS) problem. Then we propose a constant-factor approximation algorithm for the FTPS problem. Based on the FTPS problem, an approximation algorithm for the general version of the FTP problem when m > 1 is further proposed, which can guarantee a constant factor of the optimal solution. Afterwards, the proposed algorithms are verified by extensive simulations.

Following the recent advances in the Internet of Things (IoT), it is drawing lots of attention to design distributed algorithms for various network optimization problems under the SINR (Signal‐to‐Interference‐and‐Noise‐Ratio) interference model, such as spanner construction. Since a spanner can maintain a linear number of links while still preserving efficient routes for any pair of nodes in wireless networks, it is important to design distributed algorithms for spanners. Given a constant t > 1 as the required stretch factor, the problem of our concern is to design an efficient distributed algorithm to construct a t‐spanner of the communication graph under SINR such that the delay for the task completion is minimized, where the delay is the time interval between the time slot that the first node commences its operation to the time slot that all the nodes finish their task of constructing the t‐spanner. Our main contributions include four aspects. First, we propose a proximity range and proximity independent set (PISet) to increase the number of nodes transmitting successfully at the same time in order to reduce the delay. Second, we develop a distributed randomized algorithm SINR‐Spanner to construct a required t‐spanner with high probability. Third, the approximation ratio of SINR‐Spanner is proven to be a constant. Finally, extensive simulations are carried out to verify the effectiveness and efficiency of our proposed algorithm.

As a promising technology to achieve perpetual operation of battery-powered wireless sensor devices, wireless power transfer has attracted much attention recently. In wireless power transfer, the charger enables the energy to be wirelessly transmitted to the rechargeable sensor devices that are hungry for energy. Previous works mainly focus on maximizing the charging utility or minimizing the charging delay. This paper concerns a more practical issue of placing wireless chargers, which aims at minimizing the deployment cost of chargers while satisfying the overall requirement for charging utility. We investigate the above cost-minimum charger placement problem under two typical scenarios in which omni chargers and directional chargers are used, respectively. To resolve this problem under the two charging models, we first prove its NP-hardness and then propose two approximation algorithms with proven performance guarantees. Finally, we conduct extensive simulation experiments to validate our designs, and the experimental results demonstrate that the proposed algorithms significantly outperform the baselines.

One significant problem in exchange networks is finding the equilibrium. To solve this problem, the concept of stable outcome has been developed. However, there are few effective methods to solve it from the point of graph theory. In this paper, we propose a minimum cost stable outcome (MCSO) problem, which is to find a stable outcome whose total transaction cost is minimized. Two algorithms have been designed to solve this problem on unit and general profit networks respectively. For unit profit networks, we use minimum cost edge cover based method to give the optimal solution. For general profit networks, we develop an approximate algorithm and prove that performance ratio is no more than twice the optimal value. Moreover, we provide the probabilistic analysis. At last, extensive experiments have been conducted on synthetic and real-life datasets. Experimental results validate the performance of the proposed algorithms.

Accurate and robust localization is crucial for wireless ad-hoc and sensor networks. Among the localization techniques, component-based methods advance themselves for conquering network sparseness and anchor sparseness. But component-based methods are sensitive to ranging noises, which may cause a huge accumulated error either in component realization or merging process. This paper presents three results for robust component-based localization under ranging noises. (1) For a rigid graph component, a novel method is proposed to evaluate the graph's possible number of flip ambiguities under noises. In particular, graph's \emph{MInimal sepaRators that are neaRly cOllineaR (MIRROR) } is presented as the cause of flip ambiguity, and the number of MIRRORs indicates the possible number of flip ambiguities under noise. (2) Then the sensitivity of a graph's local deforming regarding ranging noises is investigated by perturbation analysis. A novel Ranging Sensitivity Matrix (RSM) is proposed to estimate the node location perturbations due to ranging noises. (3) By evaluating component robustness via the flipping and the local deforming risks, a Robust Component Generation and Realization (RCGR) algorithm is developed, which generates components based on the robustness metrics. RCGR was evaluated by simulations, which showed much better noise resistance and locating accuracy improvements than state-of-the-art of component-based localization algorithms.

The sensors in wireless sensor network (WSN) are vulnerable to malicious attacks due to the transmission nature of wireless media. Secure and authenticated message delivery with low energy consumption is one of the major aims in WSN. The identity‐based key authentication scheme is more suitable for the WSN. In this paper, the Hierarchical Matrix Decomposition‐based Signcryption (HMDS) algorithm was proposed, which is a kind of identity‐based authentication scheme. In HMDS scheme, three‐layer architecture, base station (BS), cluster head, and intracluster, is employed to adapt to the common structure of WSN. As the key generation center (KGC), the BS adopts matrix decomposition to generate the identification information and public key for cluster head, which not only reduces the cost of calculation and storage but also avoids the collusion attack. Experiments show that the HMDS algorithm has more advantages over other algorithms and is very suitable for the large‐scale WSN.

Device-to-device (D2D) communication is a promising method to reduce power consumption and improve the throughput of cellular networks. However, densely deployed D2D pairs could result in severe interference to cellular users without proper power control. Therefore, the discrete location-aware power control (DLPC) scheme is proposed in uplink D2D underlaid cellular networks. The entire cell area is divided into several regions, and a total power budget for each region is conducted with weighted allocation, to meet the constraint on the outage probability of cellular user. Then DLPC scheme requires only the locations of active D2D pairs rather than channel information or massive calculations, which is of low complexity in implementation. Simulation indicates that DLPC can improve the outage probability of D2D pairs and the network throughput compared with the traditional greedy power control scheme.

Underwater Wireless Sensor Networks (UWSNs) are vulnerable to attack because of the broadcast nature of the transmission. The sensor nodes in UWSN are highly constrained in terms of computational capabilities and communication bandwidth. Authentication schemes for ground WSNs might not be applicable for UWSNs due to their less computation and communication capacity. Thus, it is necessary to design special schemes tailored to underwater environments. In this paper, a low computational complexity authentication scheme is proposed. By using Vandermonde matrix, we replace the matrix multiplication by matrix addition to greatly reduce the computation overhead. Moreover, our scheme is self-correctable and irreversible which further enhances the security of the UWSNs. Experiment results indicate our algorithm has advantages in energy and time consumption over traditional RSA and Blom's scheme.

Traditional cellular network standards require users downlink(DL) and uplink(UL) associated to the same BS. However, due to the power gap between BSs and users and different transmission environment(e.g. the path gain) of DL and UL, a serving BS with the strongest downlink would yield to a non-serving BS in the uplink. In this paper we propose the DL/UL splitting user association framework under a two-tier heterogeneous network, in which femtocell base stations are randomly located over a macro base station in a specific area. Downlink and Uplink network ergodic throughput are analysed according to our DL/UL splitting association model. Simulation results show a great throughput promotion compared with the traditional DL/UL coupled association manner. We believe downlink and uplink splitting as a flexible connection mode between users and base stations would be an essential approach to realize the high capacity requirements, which is a evolution of the network connection art.

A wireless sensor network is said to form a belt-barrier for a region if it is able to detect any object moving from outside the region to inside. Recently, Cheng and Tsai found if camera sensors are used to form a belt-barrier, the breadth of the barrier becomes an important quality factor to ensure high quality of monitoring (QoM). Then, they proposed the minimum β-breadth belt-barrier construction problem ((β,1)-B3CP) whose goal is to select a minimum number of camera sensors to form a β-breadth belt-barrier, which ensures the width of the picture of any object which moves through the barrier is at least β. In this paper, we perform more thorough investigation of the problem and introduce a new polynomial time exact algorithm for the problem under the assumption that the angle of each camera is fixed. Our simulation result shows our algorithm outperforms Cheng and Tsai's algorithm. We also introduce a variation of (β, 1)-B3CP, namely (β, k)-B3CP, which aims to construct k node-disjoint β-breadth belt-barrier for fault-tolerance purpose, propose a new heuristic algorithm for it, and conduct simulations to evaluate its performance.

Mobile phones with a rich set of embedded sensors have been applied in various collaborative sensing applications. In some applications, to encourage mobile phone users performing collaborative sensing tasks, the data demanders may pay mobile phone users. However, none of the existing works takes into account it. In this paper, we study the Minimum Cost of Attaining the Required Data with mobile phones (MCARD) problem in collaborative sensing network. Given sensing regions R = {R1, R2, ..., Rm}, the set of requisite data Difor each sensing region Riand a set of mobile phones M, the MCARD problem is how to select mobile phones to get all the required data such that the total cost on paying mobile phone users is minimized. We first formally define the MCARD problem. Then, we propose an approximation algorithm for the MCARD problem with the determinate trajectories of mobile phones and a heuristic algorithm for that trajectories are unknown respectively. Simulation results demonstrate our algorithms are efficient.

This paper introduces two new coverage problems in mission-driven camera sensor networks, namely the target temporal effective-sensing coverage with non-adjustable cameras (TEC-NC) problem and the target-temporal effective-sensing coverage with adjustable cameras (TEC-AC) problem. Given a mission period, the objective of the problems is to find a sleep-wakeup schedule of the camera sensor nodes such that the overall target-temporal coverage is maximized. We formally introduce a method called Identifiability Test to check if a target with a face direction is effectively-covered by a camera sensor, and prove the problems are NP-hard. For TEC-NC, we propose a 2-approximation algorithm and two heuristic algorithms. We also design a greedy strategy which can be combined with our solutions for TEC-NC to solve TEC-AC. The simulation results indicate the quality of the outputs of our algorithms are much better than that of the existing alternative as well as close to the theoretical optimum on average.

Recently, geometric routing has emerged as an efficient routing strategy on wireless networks. An ideal geometric routing is memoryless and does not suffer from the drawbacks of traditional proactive/reactive routings. All existing geometric routings on 3-D wireless networks either work deterministically only on the networks with special properties or do not guarantee delivery. In this paper, we divide the memoryless requirement into two sub-requirements, node-memoryless-ness and message-memoryless-ness. Then, we propose a new node-memoryless geometric routing, which is still free from the drawbacks of traditional routings. Our algorithm partitions the 3-D space with regular cubes and converts the routing problem over nodes into a routing problem over cubes. With minimal information attached to the header of a message, our algorithm deterministically delivers a message to its destination in any connected 3-D wireless networks. The forwarding decision on the message is made in a completely localized manner. The simulation results indicate that our algorithm outperforms its competitors on average.

Barrier coverage is an important issue in wireless sensor network. In wireless camera sensor networks, the cameras take the images or videos of target objects, the position and angle of camera sensor impact on the sense range. Therefore, the barrier coverage problem in camera sensor network is different from scalar sensor network. In this paper, based on the definition of full-view coverage, we focus on the Minimum Camera Barrier Coverage Problem (MCBCP) in wireless camera sensor networks in which the camera sensors are deployed randomly in a target field. Firstly, we partition the target field into disjoint subregions which are full-view-covered regions or not-full-view-covered regions. Then we model the full-view-covered regions and their relationship as a weighted directed graph. Based on the graph, we propose an algorithm to find a feasible solution for the MCBCP problem. We also proved the correctness of the solution for the MCBCP problem. Furthermore, we propose an optimal algorithm for the MCBCP problem. Finally, simulation results demonstrate that our algorithm outperforms the existing algorithm.

In this paper, we studied the minimum latency conflict-free many-to-one data aggregation scheduling problem in multi-channel multi-hop wireless sensor networks: Given locations of all sensors and a base station, some sensors which are called as sources, find a schedule such that data from all sources can be transmitted to the base station without any conflict and the latency is minimized. In this model, each sensor has three parameters which are transmission range r, interference range ar and carrier sensing range βr where α, and β are constant. There are λ ≥ 1 available channels for communications. We designed an approximation algorithm with ratio (⌈a/λ⌉ + 11 ⌈b/λ⌉) This work improves our previous work when λ = 1. Extensive simulations valuate the performance of the algorithm.

Numerous algorithms on geometric networks has been studied, and most of them were based on 2-dimensional networks. But 2-dimensional geometric routing algorithms cannot be directly adapted to the 3-dimensional networks. In this paper, we propose routing algorithms based on the iteration of specific angles on the networks of Delaunay Triangulation in 3D space, and prove the certainty of data transmission of our routing algorithms. In the algorithms, the messages only need to carry information of O(1) nodes and each node just keeps 1-hop neighbors' information.

Traffic congestion is a very serious problem in large cities. With the number of vehicles increasing rapidly, especially in cities whose economy is booming, the situation is getting even worse. In this paper, by leveraging the techniques of Vehicular Ad hoc Networks (VANETs) we present a dynamic navigation protocol called VAN for individual vehicles to find the shortest-time paths toward their given destinations. Specifically, a vehicle initiates a number of queries, which are routed by VANETs along different paths toward its destination. During query forwarding, the real-time road traffic information in each road segment is aggregated from multiple participating vehicles and returned to the source after the query reaches the destination. This information enables the source to calculate the shortest-time path. We also propose two forwarding optimization methods to reduce communication costs and an error handling mechanism to deal with abnormal circumstances. To evaluate its performance, we use the real traffic data of Beijing, including 2,308 road segments at two different times. Our simulation results demonstrate that our protocol, on average, could save around 30% driving time, compared to traveling along the shortest distance paths.

Currently, a large number of ALM algorithms have been proposed to build the overlay networks and transmit data through spanning trees over the overlay networks. However, few works focus on the congestion of spanning tree, the key to the performance of application. This paper studies the impact of spanning tree congestion on ALM performance and proposes a model to quantify and evaluate the congestion. A statistical link model is introduced to describe congestion occurring in ALM networks and a dependency-degree factor is presented to express the dependence between adjacent links. Furthermore, two schemes, the inter-level redundant links (INTER-RL) scheme and the intra-level redundant links (INTRA-RL) scheme, are proposed to enhance the resilience of ALM spanning trees against congestion. Simulation results indicate that the proposed statistical model well describes the congestion in ALM networks and two redundant link schemes both significantly boost the robustness of ALM even in tough network environments.