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Aiming at the received signal strength index (RSSI) in wireless positioning system, an improved weighted KNN fingerprint positioning algorithm is proposed in this paper. The algorithm pre-processes fingerprint data in offline stage that including eliminating outliers and Kalman filtering first, in order to improve the accuracy of data acquisition. Secondly, the fingerprint data is partitioned by using RSSI to attenuate obstacles such as walls. Then, points with significant RSSI differences in each region are selected as regional feature points, and the distance between RSSI of test points and feature points in each region is calculated respectively to determine the region in which the test points are located. Geometric method is used to analyse and define the correlation degree, and KNN is re-weighted to achieve accurate positioning in the region. Finally, experiments were carried out in the indoor environment to complete the establishment of the fingerprint database. Compared with the existing NN, KNN and WKNN, the experimental analysis results show that the accumulated error and average error are better than the traditional algorithm with the increase of measurement points, which has reference value for the complex environment positioning technology. [ABSTRACT FROM AUTHOR]

With the increasing number of terminal devices in the sensor network and the Internet of things, non-orthogonal multiple access (NOMA) will be widely used in the future to improve spectrum resource utilization. In this paper, we considered how to control massive devices in the downlink in of scenario of space-based Internet of Things (S-IoT). In the case of multiple base stations (BSs), user fairness is a difficult problem, which usually requires a traversal algorithm to achieve the optimal solution. However, the time complexity caused by this method is the product of the complexity of user grouping and power allocation, which is unbearable. We split the problem into two sub-problems changing complexity from product form to additive form, and solve them by using low time complexity algorithms respectively. Firstly, we use a threshold-based algorithm to implement user grouping, and then use a bisection iterative algorithm for power allocation. In the simulation, we compared design with several benchmarks and results show that the minimum user rate is significantly improved and closed to optimal value by using proposed algorithm in this paper. [ABSTRACT FROM AUTHOR]

In the multi hop communication of wireless sensor networks, relay stations are used to realize remote transmission. In order to avoid the air data repeating collision problem and multi-path retransmission problem of wireless signals sent by relay nodes, this paper proposes a relaying algorithm using dual buffer pool. The node buffer pool is added in the node to store the package source identifier and packet count pairs, the package buffer pool is added to the package to store paths in transmission. First, after the package is received from radio, the node compares the pairs of the SID and Count in the node buffer pool with the pairs of the package source identifier and package count in the package. If there are pairs, the package is discarded, otherwise the package source identifier and package count are added into the node buffer pool. Next step, if the node identifier is equal to the destination byte DID in package, the package will be processed in the node and terminated transmission, otherwise the package will be continued to process. Then the node compares the node identifier with the package buffer pool bytes. If there is the node identifier in the package pool, the package will be discarded, otherwise the relay node stores its own node identifier into the packet buffer pool to create a new package and transmit forwards. The algorithm is implemented on CC2530 chip and relaying experiments of wireless sensor network are carried out. The results show that the algorithm can achieve relay forwarding and avoid duplicating forwarding data collision and avoid multipaths duplicate forwarding. [ABSTRACT FROM AUTHOR]

Blockchain technology is widely used in various fields due to its high security and reliability. And Unmanned aerial vehicle (UAV)-assisted edge computing is easy to deploy, fast, and flexible. Combining blockchain technology with UAV-assisted edge computing is a hot research topic today. In order to improve the problem of the high overhead of node communication in this environment. In this paper, a primary node selection method based on node reputation evaluation is proposed. In the traditional Practical Byzantine Fault Tolerance (PBFT) algorithm, the efficiency of node communication is very low when the number of nodes becomes large. The algorithm proposed in this paper selects the primary node based on the reputation value, which not only reduces the probability that the primary node is the wrong node but also reduces the number of communications and the communication overhead during the communication process, based on the node identity determined by the node reputation. In the experimental results, three benchmarks are selected for comparison experiments, and it is concluded that the algorithm proposed in this paper can effectively reduce the communication overhead in the PBFT algorithm, reduce the communication delay and improve the efficiency of nodes. [ABSTRACT FROM AUTHOR]

Computation offloading and service migration are two major research hotspots in the mobile edge computing (MEC) environment. However, in the existing MEC architecture, the idle computing resources of offsite edge servers are not fully utilized, which leads to the problem of high overall system time and energy costs. In this paper, we propose a multi-edge collaborative computation offloading strategy for this problem. The strategy analyzes and calculates the energy consumption and latency cost of task execution for local terminals, edge servers and central cloud, constructs a computation offloading model with the weighted sum of latency and energy consumption as the optimization objective, and then solves the model using an improved genetic algorithm to obtain the best computation offloading decision. On the other hand, the mobility of users in the MEC environment leads to service migration, which leads to unbalanced load on the edge servers and network congestion, etc. This paper proposes an energy threshold-based task migration strategy. The strategy analyzes the time and energy consumption of service execution and data transmission, designs an edge server selection algorithm based on the energy consumption threshold, constructs a service migration model, and finally solves the optimal service migration strategy by improving the genetic algorithm. Experimental results show that the multi-edge collaborative computation offloading strategy proposed can significantly improve the performance of data transfer cost, energy consumption, and task completion time. The proposed migration strategy based on energy consumption threshold can significantly improve the performance of mobile server energy consumption, service completion time, and data transfer energy consumption. [ABSTRACT FROM AUTHOR]

To solve the significant performance degradation in the blind equalization for multi-level quadrature amplitude modulation (QAM) signals, an improved multimodulus algorithm based on the real part (or the imaginary part) of the transmitted signals is developed in this paper. Theoretical analysis illustrates that the proposed algorithm can roughly half the computational complexity and fundamentally suppress the well-known steady-state maladjustment of classical constant modulus algorithm and multimodulus algorithm. Moreover, the large maladjustment in the iteration process can be completely removed, thus the proposed algorithm can work well in impulsive noise environment. Finally, simulation results show the effectiveness of the proposed algorithm under both Gaussian and impulsive noise environments. [ABSTRACT FROM AUTHOR]

Ambient Intelligence deployments are very vulnerable to Cyber-Physical attacks. In these attacking strategies, intruders try to manipulate the behavior of the global system by affecting some key elements within the deployment. Typically, attackers inject false information, integrate malicious devices within the deployment, or infect communications among sensor nodes, among other possibilities. To protect Ambient Intelligence deployments against these attacks, complex data analysis algorithms are usually employed in the cloud to remove anomalous information from historical series. However, this approach presents two main problems. First, it requires all Ambient Intelligence systems to be networked and connected to the cloud. But most new applications for Ambient Intelligence are supported by isolated systems. And second, they are computationally heavy and not compatible with new decentralized architectures. Therefore, in this paper we propose a new decentralized security solution, based on a Blockchain ledger, to protect isolated Ambient Intelligence deployments. In this ledger, new sensing data are considered transactions that must be validated by edge managers, which operate a Blockchain network. This validation is based on reputation metrics evaluated by sensor nodes using historical network data and identity parameters. Through information theory, the coherence of all transactions with the behavior of the historical deployment is also analyzed and considered in the validation algorithm. The relevance of edge managers in the Blockchain network is also weighted considering the knowledge they have about the deployment. An experimental validation, supported by simulation tools and scenarios, is also described. Results show that up to 93% of Cyber-Physical attacks are correctly detected and stopped, with a maximum delay of 37 s. [ABSTRACT FROM AUTHOR]

This paper considers an asynchronous grant-free non-orthogonal multiple access (NOMA) systems which can be applied in massive machine-type communications (mMTCs) and underwater IoT acoustic communication scenarios due to its high spectral efficiency and low power consumption characteristics. In particular, the system's asynchronous reception of user signals can effectively reduce the additional overhead caused by synchronous reception. We investigate the joint activity and channel estimation in the asynchronous case, where an asynchronous frame structure is considered, and a pilot sequence designed by chaotic sequence is used to reduce the pilot storage space. The joint estimations are formulated as single measurement vector (SMV) and multiple measurement vector (MMV) problems for single-antenna and multiple-antenna systems. Different from the existing estimation algorithms, where prior information is considered for estimation, an adaptive alternating direction method of multiplier (ADMM)is proposed for the SMV problem and a two-stage ADMM is proposed for the MMV problem. In particular, an index set is first estimated in each iteration of our proposed adaptive ADMM, and a linear ADMM is performed based on the index set. The first stage of our proposed two-stage ADMM is to estimate the delay and the activity, and then the channel state information is estimated. Further, we analyze the complexity of the two algorithms and their sensitivity to the initial values of chaotic sequences. Finally, simulation results reflecting the detection performance of the algorithms are given. Based on the simulation results, the proposed two algorithms are computationally efficient, providing superior signal recovery accuracy and user activity detection performance. More importantly, the signal delay has a relatively small impact on the proposed algorithm. [ABSTRACT FROM AUTHOR]

With the rapid development of industrial IoT technology, a growing number of intelligent devices are being deployed in smart factories to digitally upgrade the manufacturing industry. The increasing number of intelligent devices brings a huge task request. Fog computing, which is an emerging distributed computing paradigm, is widely applied to process the device data generated in smart manufacturing. However, as fog nodes are resource limited and geographically widely distributed limitations, proper fog node placement strategies are critical to enhance the service performance of fog computing systems. In this paper, we study the problem of fog node placement in smart factories and divide it into two scenarios, fixed device and mobile device fog node placement, depending on the mobility of the devices. The fog node placement model and objective function are built in the two scenarios, and two improved heuristic algorithms are proposed to obtain the most optimal placement scheme. In addition, we perform simulation experiments based on existing intelligent production line prototype platforms and devices to evaluate the performance of the proposed algorithms. The IGA reduces latency by an average of 586.7 - 1089 ms over the benchmark algorithm, saving 18.3 - 39 % in energy consumption. The total latency of IMOA is reduced by 59.8 - 68.5 % , and the maximum latency is reduced by 48.8 - 69.2 % . The experimental results show that the proposed algorithms outperform other benchmark algorithms in terms of task response time and energy consumption. [ABSTRACT FROM AUTHOR]

Since non-orthogonal multiple access (NOMA) has become a promising technology to enhance network energy efficiency (EE) thanks to its low latency,high reliability and massive connectivity, the optimization of EE in NOMA-based uplink heterogeneous networks (HetNets) is considered in this paper. The optimization problem for EE maximization is formulated as a combination of user association (base station selection, sub-channel allocation) and power control, which is hardly tractable. To address it, a user association algorithm satisfied the many-to-one relationship between mobile users, base stations and sub-channels is firstly proposed based on matching theory. Then, an iterative power control algorithm evolved from fractional programming and difference of convex programming is presented by proving the concavity of the sum-rate of sub-channels. Finally, simulation results are shown that NOMA-based HetNets equipped with our designed algorithms yield much higher EE than that with the compared schemes. [ABSTRACT FROM AUTHOR]

In order to further improve the accurate detection signal, reduce interference between signals, this paper designs a new type of signal detection algorithm for satellite communication systems, using stochastic resonance technology improve the signal-to-noise ratio of the input signal, the signal by using energy detection, double threshold, accurate judgment. The first step in the conventional energy of double threshold detection, the second step into the energy detection method based on stochastic resonance detection process. The experimental results show that this algorithm under the condition of low SNR signals effectively detect, promoted the whole satellite communication system performance. [ABSTRACT FROM AUTHOR]

This paper extends our previous algorithm, termed as Particle Swarm Optimization with Distributed Acceleration Constants (PSODAC), which has been introduced for mitigating congestion based on rescheduling in a deregulated environment. The proposed variant adopts a sequential hybridization of the PSODAC principle with Differential Evolution, which is a well-known evolutionary algorithm. The variant in this paper is termed as Sequentially Hybridized Differential Evolution with Particle Swarm Optimization (SH-DEPSO). The experimental investigations are carried out in the IEEE 14 bus system in two scenarios namely, single point congestion and multipoint congestion. Firstly, the performance investigation is carried out on mitigating the congestion using cost analysis, stability analysis, complexity analysis, and strategy analysis. Secondly, the characteristics of the algorithm are observed by performing convergence analysis and investigating the quality of the solution dynamics. The studies demonstrate the competing performance of SH-DEPSO over PSODAC and the traditional PSO. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks are booming with the development of computer technology, network communication technology, and sensor technology. However, the question of how to use fewer nodes to achieve maximum coverage still exists. In this paper, a two-layer Surrogate-Assisted Phasmatodea Population Evolution (SAPPE) is proposed for 3D coverage of wireless sensors by combining the characteristics of meta-heuristic algorithms and surrogate models. In this algorithm, Radial Basis Function Networks are used to construct the surrogate model, and the two surrogate models are global surrogate-assisted and local surrogate-assisted, respectively. The global-surrogate model is used to smooth the fitness function, and the local surrogate-assisted model is used to find the optimal value accurately. They use the same archive DataBase (DB) to store particle positions and true fitness values. However, the number of particles involved in the creation of the surrogate-assisted model is different. Seven benchmark functions are used to test and analyze the algorithm, and the results show that the algorithm has good performance. Also, the algorithm verified the significance of the algorithm using Wilcoxon rank test. The result shows that the proposed algorithm is effective compared with PPE, PSO, PPSO, FMO, and BA. This paper compares the number of nodes and coverage radius using different algorithms to ensure maximum coverage. The result shows that the SAPPE algorithm has better performance in terms of 3D coverage. [ABSTRACT FROM AUTHOR]

Internet connectivity is now considered to be a basic human right and in this era of information technology, more efficient ways of browsing the web are being found. The most popular search engine, Google uses its 'Page Rank Algorithm' for its searches. In this paper, we aim to find out a more efficient way of searching the web and better organising the search result based on the categories of the pages. The results of the proposed algorithm are compared with the existing Page Rank algorithms, which include the original PageRank Algorithm and a Weighted PageRank Algorithm. The results obtained in this paper have been experimentally compared with the data of the above-mentioned algorithms using comparison graphs. [ABSTRACT FROM AUTHOR]

Conventional wireless sensors have difficulty solving the problem of energy limitation, especially in sensor networks in hard-to-reach extreme areas. In order to solve the problem that it is difficult to charge wireless sensors in the field using conventional energy sources, the energy harvesting wireless senor is designed to use renewable energy sources for power supply. Considering the uncertainty and unknown nature of renewable energy generation, and the need for effective energy management of the sensor. In this paper, an Node Power Control Optimization (NPCO) power allocation algorithm is proposed to adjust the power allocation problem of wireless sensor nodes within each time slot. In addition, to address the unknown and random nature of energy arrival, this paper proposes a CLSTM model based on deep learning to predict the energy arrival. The continuous autonomous energy management of wireless sensor nodes is achieved by combining the CLSTM prediction results using the NPCO algorithm. The algorithm is applicable to continuous states and is able to show good performance in the verification of real solar data. The algorithm achieves better performance in terms of long-term average net bit rate compared to the current DDPG algorithm, AC algorithm, and Lyapunov optimization algorithm. [ABSTRACT FROM AUTHOR]

In the mobile edge computing scenario, a large number of computing tasks are offloaded to the edge computing server, which may cause the tasks not to be processed on time. In this paper, the response time of the computing tasks, the energy consumption of the mobile terminal device and the load balance of the server were regarded as three optimization objectives, a multi-objective optimization model were set up, and an offloading decision scheme based on multi-objective optimization immune algorithm was proposed. A large number of comparative experiments are done to verify the effectiveness of proposed scheme. Experimental results show that the proposed scheme can make the whole server system achieve a better load balancing state while meeting the requirements of response time and energy consumption. [ABSTRACT FROM AUTHOR]

Wireless Rechargeable Sensor Networks based on wireless energy transmission successfully solve the problem of node death caused by energy shortage in traditional wireless sensor networks. Existing studies on joint energy replenishment and data collection have planned charging path first and then perform data collection. This approach does not effectively extend network life and improve network performance in data collection. In this paper, we jointly consider the impact of energy replenishment and data collection on network performance and use the Least Squares Support Vector Machine based regression prediction with dynamically changing energy consumption rate and data generation rate of sensor nodes. To solve the above problem, we propose a multi-objective path planning model for joint energy replenishment and data collection with the optimization objectives of maximizing the remaining lifetime of sensor nodes, maximizing the data collection of MC, and minimizing the amount of data loss. The conventional exact algorithm is difficult to solve the path planning problem, so heuristic algorithms have gradually become the main algorithm to solve the problem. Based on discrete fireworks algorithm and co-evolutionary algorithm, we present a grid-based multi-objective cooperation fireworks algorithm. Simulation results show that the proposed algorithm performs better in convergence and diversity. In terms of the average remaining lifetime of sensor nodes, the proposed algorithm is 0.43%, 0.52%, 1.97%, and 0.81% higher than MPACO, MODFA, NSGA-II, and SPEA-II, respectively. Similarly, the target value of solutions in data collection increased by 1.87%, 1.22%, 4.49%, and 2.10%, respectively. [ABSTRACT FROM AUTHOR]

With the rapid development and popularization of the Internet, the dependency of human life on the Internet is increasing by degrees. The demand for video transmission is also rising with the emergence of portable and handheld devices such as mobile phones, tablets, laptops, etc. The scalability of Scalable Video Coding (SVC) determines that it can meet the needs of the newly emerged devices or terminals. Despite this advancement, security is still one of the major challenges. Based on the research of network security, this paper deeply discusses the encryption technology, and puts forward a new point multiplication algorithm using SVC and sliding window, which makes the algorithm more flexible, improves the efficiency of the algorithm to a certain extent, greatly reduces the expected calculation and storage of the algorithm, and makes the algorithm to adapt the memory limited environment such as smart card. Critical analysis of the proposed DES algorithm is carried out and different fragile keys are recommended which is not safe for secure encryption. This study also present the complete overview of the SVC technology and its multi-layered stream representation in the form of temporal, spatial, and SNR resolution. In addition, an encryption method based on the DES algorithm is also proposed. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) based channel estimation (CE) improves the utilization of spectrum resources effectively in orthogonal frequency division multiplexing (OFDM) system. Generally, the channel sparsity is assumed to be known in sparse reconstruction algorithms such as Basis Pursuit (BP) and Orthogonal Matching Pursuit (OMP). However, since the uncertainty of the environment, the channel sparsity is usually tricky to obtain. This paper proposes an improved OMP algorithm based on intersect operation (IOMP), which does not need a priori knowledge of channel sparsity. Initially, we use the OMP algorithm twice to estimate two different partial sparse delay path supports at partially different pilot positions, respectively. Then introduce the intersect operation to process these two partial supports. Besides, we discussed an optimized pilot allocation scheme based on discrete stochastic approximation and Iterative Exhaustive Search, both of which are employed to build the framework of CS-based channel estimation. Simulation results demonstrate that the proposed IOMP algorithm provides a better bit error ratio (BER) performance than the traditional OMP algorithm under unknown channel sparsity. [ABSTRACT FROM AUTHOR]

Aiming at the problem of offloading optimization of transmission power and edge cloud pricing in mobile edge computing systems, this paper proposes an Offloading Strategy-and Price Control(OSPC) algorithm based on Stackelberg game. First, the Stackelberg game is used to establish the edge cloud pricing and transmission power decision model, which realizes the optimization of the utility of edge cloud and each user under the task delay constraint and the edge cloud capacity constraint; Then, a relationship model between the optimal transmission power and the amount of offloading data is established, which simplifies the user's optimal transmission power offloading decision; Finally, an OSPC algorithm is studied, which achieves the optimal pricing of edge cloud utility maximization and the optimal offloading strategy for each user to maximize their own utility under this pricing. The simulation results show that the method proposed in this paper not only guarantees the utility of the edge cloud, but also maximizes the utility of users, and the algorithm has good convergence and scalability. [ABSTRACT FROM AUTHOR]

Modern 5G wireless cellular networks use massive multiple-input multiple-output (MIMO) technology. This concept entails using an antenna array at a base station to concurrently service many mobile devices that have several antennas on their side. In this field, a significant role is played by the precoding (beamforming) problem. During downlink, an important part of precoding is the power allocation problem that distributes power between transmitted symbols. In this paper, we consider the power allocation problem for a class of precodings that asymptotically work as regularized zero-forcing. Under some realistic assumptions, we simplify the spectral efficiency functional and obtain tractable expressions for it. We prove that equal power allocation provides optimum for the simplified functional with total power constraint (TPC). We propose low-complexity Intersection methods (IM) that improve equal power allocation in the case of per-antenna power constraints (PAPC). On simulations using Quadriga, the proposed IM method in combination with widely-studied water filling (WF) shows a significant gain in spectral efficiency while using a similar computing time as the reference equal power (EP) solution. [ABSTRACT FROM AUTHOR]

Nodes localization has been a critical subject in wireless sensor network (WSN) field. As far as existing localization algorithms are concerned, distance vector hop (DV-Hop) has the advantages of no extra hardware and implementation simplicity, however its localization accuracy cannot meet some specific requirements. In order to enhance the accuracy of WSN nodes localization, a DV-Hop based localization algorithm based on nodes negotiation and multi communication radii (NNMCR DV-Hop) is proposed in this paper. Firstly, the hop counts between WSN nodes is modified from an integer to a decimal by changing communication radius of anchor nodes through nodes negotiation. By refining the hop counts, the accuracy of the estimated distance from the unknown to the anchor nodes is improved. Secondly, the calculation of the average hop size of the anchor node is abstracted into a combinatorial optimization problem which is solved by using binary particle swarm optimization (BPSO) to improve the accuracy of the estimated distance which is between the anchor and the unknown node. Finally, when calculating the coordinates of unknown nodes, only the anchor nodes with smaller hop counts are selected to participate in the calculation. Simulation experiments show that compared with the original DV-Hop as well as other improved algorithms based on DV-Hop, NNMCR DV-Hop greatly improves the localization accuracy of unknown nodes without additional hardware. [ABSTRACT FROM AUTHOR]

The incredible increase in the volume of remote sensing data has made the concept of Remote Sensing as Big Data reality with recent technological developments. Remote sensing image processing is characterized with features of massive data processing and intensive computation, which makes the processes difficult. To optimize the remote sensing image processing for GPU, compute unified device architecture (CUDA) is widely used to implement remote sensing algorithms. However, the usage of GPU in remote sensing image processing has been constrained by the complexity of its implementation and configuration. Therefore, how to take full advantage of the parallel organization of GPU architecture is awfully challenging. In this paper, a dynamic adaptive acceleration (DAA) method is proposed to determine calculation parameters of GPU adaptively and preprocess the input remote sensing images on host dynamically. By this method, we determine calculation parameters according to the hardware parameters of GPU firstly. And then, the input remote sensing images are reconstructed based on the calculation parameters. Finally, the preprocessed image blocks are arranged to stream tasks and executed on GPU respectively. The effectiveness of the proposed DAA method in accelerating remote sensing algorithm with point operations was verified by experiments in this paper, and the experimental results indicated that the DAA method can obtain better performance than traditional methods. [ABSTRACT FROM AUTHOR]

For different network traffic and network state's time-varying characteristics, this paper studies the highly energy-efficient network selection algorithm under dynamic change. Network selection algorithm has an important impact on network performance and users' experience, while current network selection schemes depend on a prior. They cannot effectively select the appropriate network. Targeting users' quality of experience (QoE) and networks' energy consumption, this paper uses online dynamic learning property of Q-learning method, consider users' QoE, networks' energy consumption, and switch times together, and proposes a QoE based dynamic network selection algorithm. This algorithm can dynamically select the network, obtain the maximum users' QoE and optimize networks' energy consumption and switch times. Simulation results show that the proposed algorithm exhibits better performance. [ABSTRACT FROM AUTHOR]

Large-scale natural disaster or malicious attacks could cause serious damages to the communication network in smart grids. If the damaged network cannot be recovered timely, greater threat will be brought to the secure and stable operation of smart grids. In this paper, a link importance-based network recovery method for large-scale failures in smart grids has been proposed. Firstly, the link importance for the network can be obtained according to the link importance for the service type and the importance of the service type for the network; secondly, a network recovery model, which is 0–1 integer programming, has been established to recover more important communication services with limited recovery resources; finally, we propose a heuristic algorithm to solve the problem and reduce the computational expenditure. The simulation experiments are carried out to evaluate the performance of the proposed algorithm. The simulation results illustrate that our network recovery method is applicable to the large-scale failures and the link which carries the high-priority service can be first recovered in this paper, thus which further ensures the safe and stable operation of smart grids. [ABSTRACT FROM AUTHOR]

Wireless Ad hoc networks (WANETs) and Mobile Ad hoc networks are infrastructures less network structures made of energy-constrained nodes. These networks need to reduce their energy consumption for longer network lifetime. Clustering nodes play a vital role in WANETs to identify unattended nodes with a limited power source. Cluster Based Routing Protocols (CBRPs) in WANETs can help in minimizing the energy costs during the passing of messages. Many CBRPs have been proposed, but most of the existing approaches have their own limitations due to their Cluster Heads (CHs) workloads. This paper attempts to overcome this limitation by proposing Energy Efficient Weight Based Clustering Algorithm (EEWCA) scheme. In this approach, weights of nodes are assessed using links between nodes in terms of node stability; node neighborhoods; energy consumptions; distance between nodes, node densities, and residual energies. The proposed CH selections framework is based on preventing dead CHs when their power falls below a range by choosing an alternative cluster node as CH. Adaptive particle swarm optimization (APSO) is formulated with Ad hoc On-demand distance vector (AODV) for detecting breakages in links by assessing node energies, densities, and reduced overheads. The proposed EEWCA model is simulated on NS2 simulator and its efficiency is evaluated with different performance metrics by using 20 to 200 nodes in simulations. The proposed APSO–AODV protocol reduces network energy consumption and maximizes the network lifetime significantly. [ABSTRACT FROM AUTHOR]

Community detection in microblogging environment has become an important tool to understand the emerging events. Most existing community detection methods only use network topology of users to identify optimal communities. These methods ignore the structural information of the posts and the semantic information of users' interests. To overcome these challenges, this paper uses User Interest Community Detection model to analyze text streams from microblogging sites for detecting users' interest communities. We propose HITS Latent Dirichlet Allocation model based on modified Hypertext Induced Topic Search and Latent Dirichlet Allocation to distil emerging interests and high-influence users by reducing negative impact of non-related users and its interests. Moreover, we propose HITS Label Propagation Algorithm method based on Label Propagation Algorithm and Collaborative Filtering to segregate the community interests of users more accurately and efficiently. Our experimental results demonstrate the effectiveness of our model on users' interest community detection and in addressing the data sparsity problem of the posts. [ABSTRACT FROM AUTHOR]

Rendezvous control of multiple robots without losing network connectivity has important implications in multi-robot system including formation control, coordinated task assignments, and cooperative robotic missions. This paper introduces a new coordinate-free, bearing-based algorithm to enable rendezvous of distributed mobile robots at any designated leader robot node using hierarchical tracking of wireless network topology. An assumption is made that the robot can only sense and communicate with their neighbors (i.e., local sensing). The proposed approach preserves connectivity during the rendezvous task, adapts to dynamic changes in the network topology (e.g., losing or re-gaining a communication link), and is tolerant of mobility faults in the robots. We theoretically analyze the proposed algorithm and experimentally demonstrate the approach through simulations and extensive field experiments. The results indicate that the method is effective in a variety of realistic scenarios in which the robots are distributed in a cluttered environment. [ABSTRACT FROM AUTHOR]

Mobile network performance and user Quality of Experience have been negatively affected by the explosion of mobile data traffic. This paper proposes mobile edge caching to alleviate the problem. Recent research has focused on local caching at the wireless edge, as motivated by the 80/20 rule regarding content popularity. By caching popular contents at base stations (BSs) closer to users, backhaul congestion and content access latency can be dramatically reduced. To address the limited storage size of BSs in the context of the massive amount of available content, an algorithm optimizing cooperative caching has been highlighted. Contents requested by mobile users that cannot be obtained locally could be transferred by cooperative BSs. In this paper, we propose a cooperative caching algorithm based on BS content access patterns. We use tensor decompositions with distance constraint to analyze interaction between users, contents and base stations. Thus, BSs with small geographical distances and similar content access patterns constitute a cooperative caching domain. The distributed content placement and delivery algorithm is optimized based on simultaneous consideration of the caching hit ratio and cooperative cost. Simulation results based on a real dataset of usage detail records demonstrate the superior performance and promising practical gains in caching of the proposed caching method compared to user clustering and BS clustering. [ABSTRACT FROM AUTHOR]

This paper presents novel way to achieve fast and accurate Artificial Neural Network (ANN) modeling of Radio Frequency (RF) front end Low Noise Amplifier (LNA). Multilayer perceptron neural network is implemented to model high gain, ultra-low noise pseudomorhic High Electron Mobility Transistor (pHEMT) based LNA. Datasets are developed for input and output that is obtained from the Electro-Magnetic simulator. Different neural networks such as PatternNet, FitNet, and CascadeForwardNet with different algorithms are trained, tested, and compared to learn the behavior of the amplifier, and the best model is analyzed. Neural networks are modeled for LNA S-parameters and NF. It is observed that greater than 99% accuracy is achieved for PatternNet Bayesian regularization algorithm with less number of hidden layers and also it is found that the simulation results are almost similar to the developed ANN. [ABSTRACT FROM AUTHOR]

The meta-heuristic algorithms require a lot of fitness calculations to get good enough solutions, which constitutes an obstacle to solving computationally complex practical problems. Recently, it has been found that surrogate-assisted meta-heuristic algorithms show potential in solving expensive complex optimization problems. This paper proposes an efficient surrogate-assisted Taguchi salp swarm algorithm (SATSSA) to solve expensive complex optimization problems. The SATSSA uses a combination of the local surrogate-assisted model (LSAM), global surrogate-assisted model (GSAM), and k-means clustering surrogate-assisted model (KCSAM) to fit the fitness function. To enhance the prediction ability of the model, an improved salp swarm algorithm with the Taguchi method (TSSA) is proposed to update and predict the model. GSAM is mainly used to capture the entire landscape of the search space. KCSAM is designed to capture part of the search space to improve the exploration capability of the algorithm. LSAM is used to capture the contours around the optimal individual. The proposed SATSSA is compared with other four excellent algorithms on 30D, 50D, and 100D benchmark functions. In addition, SATSSA is also applied to intrusion detection. Simulation results show that SATSSA is effective in improving detection rate and reducing false alarm rate. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) as an emerging technology is widely used in various applications such as remote healthcare, smart environment, and intelligent transportation systems. It is necessary to address users' concerns about cost, ease of use, privacy, and comprehensive security to grow the popularity of this technology. Intrusion Detection System (IDS) plays an indispensable role in security and preventing unauthorized users to access authorized network resources through analyzing network patterns. Several techniques such as metaheuristic algorithms, machine learning, fuzzy logic, and artificial intelligence algorithms can be applied to increase the accuracy of IDS, feature selection, and network patterns classification. In this paper, a hybrid intrusion detection system based on Support Vector Machine (SVM) and Grey Wolf Optimization (GWO) is presented that utilizes the advantages of these algorithms. In the proposed approach, the support vector machine has been used to train and differentiate anomaly records from normal records and grey wolf optimization has been used to find the kernel function, feature selection, and adjust optimal parameters for the SVM in order to improve the classification. The conducted simulations prove that the proposed approach outperforms in terms of detection accuracy, precision, recall, and F-score on both NSL-KDD and TON_IoT datasets. [ABSTRACT FROM AUTHOR]

Recently, the unmanned aerial vehicle (UAV)-assisted edge computing is proposed to improve the quality of service in some scenarios within sparse or unavailable base stations (BSs). Meanwhile, the caching technology is adopted to reduce the wireless traffic load and the data transmission delay. However, due to the limited storage capacity of edge nodes, the edge nodes cannot store all of the contents required by user equipment (UE). So, how to select the reasonable contents for caching on edge nodes to reduce the content delivery delay becomes a challenge in the UAV-assisted edge computing environment. In this paper, the Dijkstra algorithm based cooperative caching strategy for UAV-assisted edge computing system is proposed. Specially, the content transmission delay between two nodes is computed. Then, for each requested content, the weighted edge-undirected graph (WEUG), in which one vertex represents one node, is built. Furthermore, Dijkstra algorithm is adopted to achieve the minimal content transmission delay from the edge node caching the requested content to UE. Finally, the optimization problem of content caching is built, and the corresponding cache strategy is achieved by solving the optimization problem. The experimental results imply that the proposed cooperative caching algorithm can achieve better performance on the average content transmission delay, the average cache hit rate, and the total of hops, respectively, comparing with the benchmark algorithms. [ABSTRACT FROM AUTHOR]

One of the wireless sensor networks applications is to sense a discrete set of targets lying on the field and maintain connectivity with the sink for data transmission. In addition, it needs to minimize energy consumption to maximize the coverage lifetime. One such solution for coverage maximization is to group sensor nodes into cover sets. Each cover set remains active at a time to keep track of all the targets in the field until one of its active nodes depletes energy completely. Therefore, maximizing the number of cover sets and enhancing each set's coverage lifetime is a challenging issue. In this paper, we propose a new energy-aware algorithm for the coverage and connectivity of the sensor nodes. In the algorithm, we devise an energy-efficient strategy to maximize the number of cover sets and energy-aware connectivity. Extensive simulation runs show that the proposed algorithm outperforms the existing ones. [ABSTRACT FROM AUTHOR]

Inspired by the collaborative mechanism among biological nervous, endocrine and immune systems, this paper proposes an algorithm of adaptive evolutionary based on biological cooperation (BCAE). This method can solve the dynamic multi-objective optimization problem of Industrial Internet of Things (IIoT) services to reduce the total service cost and service time. The BCAE algorithm consists of two parts: bottom level and top level. In the bottom level, different Pareto frontiers are obtained by coevolution of multiple subpopulations. In the top level, according to the distance between the service request and the service provider and the unit energy consumption of the service provider, the connection weight sequence is designed, and then the affinity matrix is constructed according to the connection weight sequence. Finally, the multi factor genetic algorithm (MFEA-II) is used to mate and imitate the service providers with different affinity, and the total service cost and total service time of the optimal solution are obtained, which are recorded in the top-level optimal antigen solution set. On the basis of single service strategy and collaborative service strategy, the IIoT services with dynamic requests are studied under different distributions. The obtained simulation results show that the performance of BCAE is better than the performance of the four existing algorithms, especially when solving high-dimensional problems. [ABSTRACT FROM AUTHOR]

In this paper, we present a general Gaussian N-relay network by allowing relays to communicate to each other and allowing a direct channel between source and destination as compared to the standard diamond network in Nazaroğlu et al. (IEEE Trans Inf Theory 60:6329–6341, 2014) at the cost of extra channel uses. Our main focus is to examine the min-cut bound capacities of the relay network. Very recently, the results in Uykan (IEEE Trans Neural Netw Learn Syst 31:3294–3304, 2020) imply that the GADIA in Babadi and Tarokh (IEEE Trans Inf Theory 56:6228–6252, 2010), a pioneering algorithm in the interference avoidance literature, actually performs max-cut of a given power-domain (nonnegative) link gain matrix in the 2-channel case. Using the results of the diamond network in Nazaroğlu et al. (2014) and the results in Uykan (2020), in this paper, we (i) turn the mutual information maximization problem in the Gaussian N-relay network into an upper bound minimization problem, (ii) propose a modified GADIA-based algorithm to find the min-cut capacity bound and (iii) present an upper and a lower bound to its min-cut capacity bound using the modified GADIA as applied to the defined "squared channel gain matrix/graph". Some advantages of the proposed modified GADIA-based simple algorithm are as follows: (1) The Gaussian N-relay network can determine the relay clusters in a distributed fashion and (2) the presented upper bound gives an insight into whether allowing the relays to communicate to each other pays off the extra channel uses or not as far as the min-cut capacity bound is concerned. The simulation results confirm the findings. Furthermore, the min-cut upper bound found by the proposed modified-GADIA is verified by the cut-set bounds found by the spectral clustering based solutions as well. [ABSTRACT FROM AUTHOR]

Wireless technologies are very often used simultaneously for different applications in the same deployment area. This is more and more the case with the massive use of the Internet of Things. Very few of these technologies are designed to coexist efficiently. We concentrate in this paper on two technologies: IEEE 802.15.4 and IEEE 802.11. We consider the case where these two technologies are available on the same communicating node. We propose backoff algorithms for IEEE 802.15.4 (WPAN) that take into account information collected at a multi-interface node to improve coexistence with IEEE 802.11 (WLAN). The proposed mechanisms benefit from a virtual Clear Channel Assessment given by the WLAN interface to improve both network throughput and overall energy efficiency. Our simulation results show that the proposed mechanisms improve the basic CSMA/CA mechanism of WPAN by up to 26% of more packet delivery rate under a traffic load in WLAN that does not exceed 40%. [ABSTRACT FROM AUTHOR]

Wireless powered communication network (WPCN) is a promising technique to resolve the power constraint issue faced by wireless nodes at the same time it also provides green and a safer solution. This paper studies, mutual sharing between WPCN and simultaneous wireless information and power transfer model where optimal resource allocation occurs between licensed/bandwidth group and an unlicensed/power group to enhance the system performance. The main aim of this paper is to maximize system weighted sum rate and minimize power consumption during transmission by optimal time allocation and energy beamforming vector allocation. These multiobjective combinatorial problems are solved using joint metaheuristic particle swarm optimization-cuckoo search algorithm to provide an optimal solution. The results are compared using a conventional mathematical optimization algorithm to set as a baseline for performance parameters. The obtained results demonstrate that the efficient joint metaheuristic approach provides a significant gain in the system performance and also offers a computationally less complex approach for solving a complicated non-convex multiobjective problem of wireless powered network. [ABSTRACT FROM AUTHOR]

Under the background of telemedicine, a new registration and mosaic algorithm for medical images is proposed in this paper to solve the problems of electronic noise, uneven illumination and ray scattering in the real-time medical process. The improved Retinex algorithm by trilateral filter and homomorphic filtering algorithm can enhance the image effectively in preprocessing. The improved phase correlation algorithm based on log polar transformation was used to calculate parameters, such as rotation, scaling and translation. Then, the SUSAN corner matching points were extracted in overlapping positions, the improved KD tree was used for enhancing matching efficiency. Later, matching points were purified by the improved RANSAC algorithm. Finally, images were processed by Laplacian pyramid decomposition algorithm to make the image joint seemed smooth and natural. The results of experiments and evaluation criteria confirm that the new method has high robustness in the process of medical image registration and stitching in the network. [ABSTRACT FROM AUTHOR]

The location of nodes is critical in underwater wireless sensor networks (UWSNs), which is an ocean monitoring platform. UWSNs are motivated by the popular usage of localization and play a major role in several technologies that depend primarily on innovations and localization of these nodes. Underwater node localization is a critical technology that enables the deployment of a variety of underwater applications. In this study, the underwater nodes are divided into two levels. Firstly, a clock asynchronous localization system (LS-AC) for base layer's node localization is presented. In order to eradicate the original ranging strategy's dependence on active nodes and address the problem of energy consumption, LS-AC performs in-network situation-based monitoring by relying on asynchronous clocks. Secondly, we propose a backtracking search algorithm (OTKL-BSA) based on optimal topology and knowledge learning. It is used to address the issues associated with traditional algorithms' lack of diversity and the imbalance between exploration and exploitation. Thirdly, to solve the problems that the traditional gray wolf optimizer (GWO) is prone to falling into local optimal values and has a low search efficiency, this paper proposes a GWO scheme based on hunting step size (GWO-HSS). Finally, simulation results show that the proposed algorithm outperforms SLMP, MCL-MP, MP-PSO, and MGP in aspects of localization performance. [ABSTRACT FROM AUTHOR]

The paper focused on cache optimization and service selection algorithms in the cloud-edge environment. In order to solve the problem of cached content in edge servers, factors such as energy consumption and cost are considered, and finally a cache optimization model based on popularity was proposed. As for service selection, a Q-learning-based service selection algorithm is proposed to address the problems of dynamic task allocation and the optimization of Qos in the edge computing environment. The experimental results show that the proposed cache optimization and service selection algorithms in cloud-edge environment can better improve the cache hit ratio, minimize the transmission overhead, and ensure the server load balancing in the cloud-edge environment. [ABSTRACT FROM AUTHOR]

In a distributed wireless network with a large number of nodes, competitive access of nodes may result in the deterioration of throughput and energy. Therefore, in this paper we propose an intelligent access algorithm based on the mean field game (MFG). First, we formulate the competitive access process between nodes as a game Query ID="Q1" Text="Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary." process by a stochastic differential game model, which maximizes the energy efficiency of nodes and obtain the optimal behavior strategy while meeting the requirements of channel access. However, as the number of nodes increases, the dimension of the matrix used to characterize the interaction between nodes becomes too large, which increases the complexity of the solution procedure. Therefore, we introduce the MFG and the interaction between nodes can be approximately transformed into the interaction between nodes and the mean field, which not only reduces the complexity, but also reduces the computational overhead. In addition, the HJB-FPK equation is solved to obtain the Nash equilibrium of the MFG. Finally, a backoff strategy based on the Markov model is proposed, and the node obtains the corresponding backoff strategy according to the network situation and its own state. Simulation results show that the proposed algorithm has good performances on optimizing network throughput and energy efficiency for a large scale multi-hop wireless network. [ABSTRACT FROM AUTHOR]

Mobile Ad hoc network (MANET) is a wireless network which is characterized by dynamic network topology, open medium, multi-hop communication etc. in which each device not only acts as a router but also as a host. Security in the mobile ad-hoc network is one of the most sought-after research topic. The performance of MANET is greatly affected by the presence of malicious nodes in the network. One of the most common attacks in MANET is a denial of service attack in which the source node is not able to communicate with the destination node due to malicious behaviour of the node. The malicious node which drops the packet is known as a black-hole node. In order to mitigate the black-hole attack problem, various techniques have been used by many researchers. In this paper, based on a dynamic sequence number threshold value, MBDP-AODV protocol is used. The main contribution of this paper is the performance evaluation of MBDP-AODV protocol under black-hole attack. Through simulation, it has been found that MBDP-AODV protocol is able to mitigate the impact of black-hole attack under different network density. Although it improves the network performance in terms of packet delivery, throughput and reduces the normalized routing load but it has high routing overhead. [ABSTRACT FROM AUTHOR]

The very limited sensor battery energy greatly hinders the large-scale, long-term deployments of wireless sensor networks. This paper studies the problem of scheduling the minimum charging vehicles to charge lifetime-critical sensors in a wireless rechargeable sensor network, by utilizing the breakthrough wireless charging technology. Existing studies still employ a number of charging vehicles to charge sensors. The purchase cost of a charging vehicle however is not inexpensive. To further reduce the number of employed charging vehicles, we propose a novel approximation algorithm, by exploring the combinatorial properties of the problem. The techniques exploited in this paper are essentially different from that in existing studies. Not only do we show that the approximation ratio of the proposed algorithm is much better than that of the state-of-the-art, but also extensive experimental results demonstrate that the number of scheduled charging vehicles by the proposed algorithm is at least 10% less than that by the existing algorithms and the total travel energy consumption of the charging vehicles is also smaller than that by the existing algorithms. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) has been proposed as a solution for the spectrum scarcity problem. This paper investigates single-carrier frequency division multiple access (SC-FDMA) for cognitive radios. Recently, SC-FDMA has been suggested as a candidate for multicarrier based CR systems. In this paper, we propose enhanced techniques to reduce the peak to average power ratio (PAPR) in SC-FDMA for Cognitive Radio Network (CRN). We start by showing the effect of two standard PAPR reduction techniques (interleaver based and selective mapping) to reduce the PAPR in SC-FDMA based CR. Then, a proposed joint algorithm interleaver with selective mapping in a way that is suitable for SC-FDMA based CR will be proposed that results in a lower PAPR than using them individually. Simulation results show that the SC-FDMA with proposed techniques has a lower PAPR when compared with the conventional SC-FDMA system for CRN, while the complexity of the system slightly increases. Results also reveal that the no. of interleavers and SLM sequences must be chosen carefully in order to limit the PAPR with slightly increases in the complexity of the system. [ABSTRACT FROM AUTHOR]

In the vehicular ad hoc network (VANET), due to the particularity of high-speed movement of vehicle nodes, there are higher challenges in link stability and network topology control overhead. In this paper, a new algorithm of clustering AODV based on edge computing strategy is proposed. Considering the vehicle node energy and speed, the AODV routing protocol based on the minimum hop number is optimized, which divided the communication mode into vehicle to vehicle (V2V) and vehicle to road (V2R) mode. Adding edge server in the road side unit (RSU) and using the idea of clustering, that is, the nodes in the cluster use V2V communication mode, and the nodes between clusters use V2V and V2R combined communication mode to select routes. The algorithm improves the routing efficiency in the high-speed mobile. Experiments show that the algorithm is feasible, reducing the network topology control overhead, lowering the end-to-end delay and improving the packet delivery rate comparing with others in different environment. [ABSTRACT FROM AUTHOR]

Sixth-generation wireless systems not only have more demanding communication requirements, they are also expected to have high-precision sensing capabilities and sufficient computing power. Integrated sensing, communication, and computation (ISCC) can meet the above system requirements and save spectrum resources. In this paper, we build a resource allocation and offloading decision problem in an ISCC scenario that makes considerations for user mobility and partial offloading policies. The established problem minimizes the average task cost when given constraints such as the typical sensing failure rate and task completion delay. We use Lyapunov optimization theory to transform the proposed problem and propose a two-level optimization algorithm based on matching theory to offer a solution for the transformed problem. The inner layer obtains the task offloading ratio through theoretical derivation, and the outer layer determines the base station access and channel assignment based on the inner layer results. The simulation results show that the average task cost can be effectively reduced while also guaranteeing high-quality sensing performance. [ABSTRACT FROM AUTHOR]

For many wireless sensor networks applications, a powerful localization algorithm is required to determine the exact positions of sensor nodes. In this paper, a new localization algorithm is presented which combines the distance vector hop (DV-Hop) algorithm with the shuffled shepherd optimization algorithm to determine the position for each unknown node within 3-D space. Studying the localization problem in 3-D space is more realistic, but more challenging due to the enlarged search space. Comparison of our proposed algorithm with its alternatives in literature shows that it offers improved accuracy and more stable performance with respect to changes in nodes density and communication range. [ABSTRACT FROM AUTHOR]

This paper proposes a quantitative evaluation algorithm based on a comprehensive analysis of material's shape, texture, color, style, and other characteristics using indoor wireless networking design and cloud data to improve the application effectiveness of ecological environment materials. It also adopts a data-driven method to quantitatively evaluate the application effectiveness of ecological environment materials using indoor wireless network design based on preliminary feature information. Specifically, this work reconstructs the feature space of ecological environment materials by extracting dimensionality features related to the quantitative evaluation, and analyzes the mutual information features of big data fusion. Moreover, a quantitative regression analysis and statistical model construction is used to evaluate the application effectiveness by using preliminary feature information as a constraint condition. In addition, this study employs the spatial effect element feature analysis method to quantitatively analyze and rigorously test the application effectiveness assessment. The empirical results show that the proposed quantitative evaluation algorithm has good accuracy and high reliability, which can effectively improve the application quality of ecological environment materials in indoor wireless network design. [ABSTRACT FROM AUTHOR]