Wireless Network Environment Research Articles

BBR-Based Congestion Control and Packet Scheduling for Bottleneck Fairness Considered Multipath TCP in Heterogeneous Wireless Networks

Aiming at improving the performance of Multipath TCP (MPTCP) in heterogeneous wireless network environments, in this paper, by taking the advantages Bottleneck Bandwidth and Round-trip propagation time (BBR) and considering bottleneck fairness, we propose BBR-based Congestion Control and Packet Scheduling scheme, called BCCPS. The proposed BCCPS first considers a BBR-based congestion control algorithm, MPTCP-BBR, for MPTCP by adaptively adjusting the sending rate of each subflow according to the real probing rate rather than the loss information to enhance the goodput while keeping bottleneck fairness with regular TCP. Then, considering that the different sending rates of the subflows in heterogeneous wireless networks will result in the problem of out-of-order (OFO) delivery, a fine-grained packet scheduling scheme is proposed to keep in-order delivery and so as to reduce the application layer completion time. The performance of the proposed scheme is evaluated via both NS-3 and real network scenarios. Experimental results show that our proposed scheme far outperforms existing MPTCP schemes in heterogeneous wireless environment.

Internet of Things (IoT) based Network Integrated with Sensor Nodes for Intruder Detection and Low Energy Consumption

The applications in Internet of Things (IoT) for a large-scale Network which necessitates the storage resources and computing tasks, are gradually deployed in most of the wireless network environments. The computing model of traditional techniques when compared with features of cloud such as unlimited expansion, dynamic acquisition and payas-you-go are represented in different IoT architectures based on the conveniences of applications. Thus, one of the key challenges is to consider the service requirements when sensors are assigned to the tasks and the network performance is improved. In the presented work, the two-phase service system using Enhanced Bernoulli Vacation (EBV) scheduling algorithm and Intrusion Detection framework is proposed to minimize the energy consumed by the sensors while the service is provided. The performance variation of Virtual Machine (VM) and its achieved delay is considered, while first the tasks are divided into different tasks at different levels. The proposed work deals with a queuing system ‘M/G/1’ for Bernoulli Vacation scheduling model at one phase and intrusion detection technique at second phase. The sensing distance is also calculated with its density of network. The tasking scheduling algorithm is considered for execution cost and residual energy where the deadlines or threshold are proposed. The delay time, accuracy, detection rate and False Alarm Positive rate are evaluated during simulation time. Based on the work flows, experiments conducted are simulated for controlled tasks of IoT which demonstrates the algorithm achieving high success rate and that the network performs better when compared with the existing algorithms.

Joint Coordinate Optimization in Fingerprint-Based Indoor Positioning

Fingerprint-based indoor positioning uses pattern recognition algorithms (PRAs) to estimate the users’ locations in wireless local area network environments, where satellite-based positioning methods cannot work properly. Traditionally, the training phase of PRA is separately conducted for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> coordinates. However, the received signal strength from access points is a unique fingerprint for each measured point, not for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> coordinates separately. In this letter, we propose a method to jointly employ the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> coordinates during the training phase using a novel PRA-based Gaussian process regression (GPR), named 2D-GPR. Experimental results show that the proposed 2D-GPR improves the accuracy of positioning more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$40cm$ </tex-math></inline-formula> in limited data samples and has a lower calculation cost compared with conventional GPR.

Sniffing Only Control Packets: A Lightweight Client-Side WiFi Traffic Characterization Solution

The advancement of the Internet of Things (IoT) is bringing unprecedented convenience into our daily life. However, with the relentlessly increasing number of mobile devices connected to the Internet, the wireless network environment is becoming more crowded than ever before. Particularly, WiFi, with its evolving role in IoT, is shouldering a tremendous amount of traffic from IoT and other mobile devices. As a result, exploding numbers of competing devices, encroachment by cellular technology, and dramatic increases in content richness deliver a more variable Quality of Experience (QoE) on WiFi than desired. Moreover, such variance tends to occur both across time and space making it an extremely difficult problem to debug. Existing active approaches tend to be expensive or impractical while existing passive approaches tend to be too narrow. To conduct efficient and nonobtrusive WiFi traffic characterization, in this article, we propose a novel passive client-side approach that delivers efficient and accurate characterization by taking advantage of the properties of frame aggregation (FA) and block acknowledgment (BA). The devised approach requires only capturing and analyzing certain types of control packets thus making it feasible to deploy on IoT devices that have limited computation power. We show in this article that we can accurately derive important characterization metrics, such as airtime, queuing information, and transmission rates with only a minimal amount of observed BAs. We show through extensive experiments the validity of our approach and conduct validation studies in the dense environment of a campus tailgate.

A novel framework for quality of service aware vertical handover process in heterogeneous wireless networks

The evolution of wireless communication technology and the growing number of mobile users with various applications together have formed a heterogeneous environment of wireless communication networks with real-time availability and high bandwidth preferences. Everyone in the world wants consistent mobility to connect seamlessly to the best available network anytime and anywhere. Therefore, an efficient and Quality of Service (QoS) aware Vertical Handover (VHO) techniques are needed when the mobile connections have to switch from one network to another network to provide effective mobility performance, seamless connectivity, and high availability of connections. Applying efficient VHO process in a heterogeneous wireless network is still a big topic of interest in research field. It has been observed that existing handover techniques are not much capable of providing user preference and QoS aware mobile communication and network selection process. This problem incorporates various unwanted factors such as communication delay, inconsistent mobility, security towards the communication process. This paper discusses various existing research works that have been carried out to improve the VHO process to boost overall communication performance and to raise QoS of wireless mobile communications in the heterogeneousnetworks.

Vertical Handoff Decision Algorithm combined Improved Entropy Weighting with GRA for Heterogeneous Wireless Networks

Future network scenario will be a heterogeneous wireless network environment composed of multiple networks and multimode terminals (MMT). Seamless switching and optimal connectivity for MMT among different networks and different services become extremely important. Here, a vertical handoff algorithm combined an improved entropy weighting method based on grey relational analysis (GRA) is proposed. In which, the improved entropy weight method is used to obtain the objective weights of the network attributes, and GRA is done to rank the candidate networks in order to choose the best network. Through simulation and comparing the results with other vertical handoff decision algorithms, the number of handoffs and reversal phenomenon are reduced with the proposed algorithm, which shows a better performance.

IoV Scenario: Implementation of a Bandwidth Aware Algorithm in Wireless Network Communication Mode

Wireless network communication has developed rapidly in recent years, especially in the field of Internet of vehicles (IoV). However, due to the limitations of traditional network architecture, resource scheduling in wireless network environment is still facing great challenges. We focus on the urgent need of users for bandwidth resources in the IoV scenario under virtual network environment. This paper proposes a bandwidth aware multi domain virtual network embedding (BA-VNE) algorithm. The algorithm is mainly aimed at the problem that users need a lot of bandwidth in wireless communication mode, and solves the problem of bandwidth resource allocation from the perspective of virtual network embedding (VNE). In order to improve the performance of the algorithm, we introduce particle swarm optimization (PSO) algorithm to optimize the performance of the algorithm. In order to verify the effectiveness of the algorithm, we have carried out simulation experiments from link bandwidth, mapping cost and virtual network request (VNR) acceptance rate. The final results show that the proposed algorithm is better than other representative algorithms in the above indicators.

Joint Optimal Multicast Scheduling and Caching for Improved Performance and Energy Saving in Wireless Heterogeneous Networks

Base station caching and multicast are two promising methods to support mass content delivery in future wireless network environments. However, existing scheduling designs do not take full advantage of the two methods. This article focuses on employing multicast scheduling and caching in a network architecture which involves both macro cell base stations (MBS) and small cell base stations (SBS) in order to achieve joint optimization of average delay and power consumption. We describe this co-optimization problem as the Multicast-Aware Caching Scheduling Problem (MACSP). This article proposes a novel pending request queue model, which aims to solve the problem of long waiting time for non-popular content, and transform this collaborative multicast-cache scheduling problem into a Markov Decision Process that can be solved using reinforcement learning methods. For actual deployment, the paper further introduces a Distributed Policy Gradient algorithm (DPG) with similar performance and lower complexity. The simulation-based testing results demonstrate that our model and algorithm have better performance and lower energy consumption than existing state-of-the-art approaches.

Development of a Knowledge Discovery Computing based wearable ECG monitoring system

ECG signals contain a lot of information related to cardiac activity and play a critical role in diagnosing of heart disease. However, relying on health information monitoring with simple ECG measurement can lead to errors in health information analysis. Therefore, for accurate diagnosis and analysis, an algorithm that can efficiently process user’s measurement environment and activity information is required. In this paper, we propose a wearable ECG monitoring system based on Knowledge Discovery Computing using 3-axis acceleration sensor. The proposed system measures real-time cardiac information and activity information simultaneously to minimize errors in health information analysis through Knowledge Discovery Computing between the user’s environment information and abnormal ECG according to the measurement environment in everyday life. In addition, we implemented a packet transmission protocol to effectively transmit large amounts of data analyzed through Knowledge Discovery Computing to the base station. First, arrhythmia detection was performed using R-peak detection preprocessing and pattern matching algorithm. Also, a classification algorithm was implemented to classify activity types by utilizing an accelerometer in order to recognize the context surrounding the user. Information on the user’s vital signs and activity information can be used for more accurately determine arrhythmia in daily life. Also, variable packet generation protocol was designed for an effective transmission of data packets increased exponentially by long-term measurements and wireless data transfer. The variable packet generation protocol is efficient in limited wireless network environments, because it generate packets of the entire data only with case of abnormal cardiac rhythm and transmits minimal information for normal cardiac rhythm. In order to evaluate the performance of ECG monitoring system based on Knowledge Discovery Computing, we designed a 2-lead ECG measurement belt manufactured with conductive fiber to minimize user discomfort, and assessed the system performance in data packet transmission, data recovery, and arrhythmia detection in dynamic states in daily life. In static states, the posture detection was 100%, heart rate detection 99.8%, and CR (Compression Ratio) was 193.99 and correlation coefficient of 0.95 with commercial systems. In dynamic states, 96% detection rate and 59.14 of CR is identified. If arrhythmia is determined based only on ECG signals, it is difficult to differentiate an actual abnormal cardiac rhythm from an ECG signal altered due to motion. The experiments conducted in this study confirmed that Knowledge Discovery is possible in the dynamic state through the proposed system in daily life.

New approach of adapttve secure transmission through flat fading channel based on three-pass protocol

The three-pass protocol is one of the frameworks in cryptography, which provides the privacy of exchanging information safely without distribution of secret or public keys between users. The three-pass protocol method has been put into use within a wireless network environment to transmit confidential data between two nodes with the presence of passive eavesdropper through a flat fading channel that affects the bit error rate. This led to the impact on the confidentiality of the transmission. Therefore, there are multiple challenges to ensure the confidentiality of the transmission and to improve the error rate. In this paper, the data has been encrypted using a One-Time Pad algorithm, and the channel of transmission is authenticated using Hash function. Moreover, the high values of signal to noise ratio have been increased in order to enhance the rate of bit error, so both the confidentiality of the transmission and its error rate are improved. The analysis of the flat fading channel performance is based on the perceptual quality at the rated Signal to Noise Ratio (SNR).

TAMEx framework as an alternative for e-exam implementation in wireless network

One of significant change in learning process is exam implementation applying a computer-based test (e-exam) rather than paper-based test. Many institutions currently use wireless network with bring your own device (BYOD) policy for their e-exam implementation, due to limitation of space and user terminals. Moreover it is hard to maintain the availability of reliable signal quality in wireless network for all users during e-exam process. Therefore, this research proposes a development of time adaptive for mobile exam (TAMEx) framework to provide all users with good quality of service of e-exam in wireless network environment when dealing with signal quality variations. The received signal strength indication (RSSI) is the main indicator of the signal quality. The framework supports e-exam implementation in wirelesslocal area network and the use of BYOD mechanism. The research shows that the framework successfully has provided time compensation for the users who experienced temporary connection loss due to bad signal quality. The result exhibits that tiny processing time has been needed for signal loss detection, user’s request of compensation time, and server’s execution. In conclusion, the TAMEx could guarantee good services for all users who doing e-exam in wireless network environment.

Improvement of table tennis technology based on data mining in the environment of wireless sensor networks

With the development of Internet of things technology, the combination of Internet of things technology and sports competition parameter collection technology, so as to carry out rapid and accurate retrieval and positioning of technology and tactics, has innovation in the current research field. In the high-level table tennis competition, the use of technology and tactics is closely related to the gain and loss of points. At present, the traditional table tennis video mining algorithm has some problems such as low efficiency and poor performance of optimization classification. Based on this, this article introduces the big data platform of the wireless sensor networks to construct the table tennis match database, realizing the real-time updating of table tennis match parameters and the call of historical data at any time. Then establishing a data mining model to realize the data and dynamic analysis of table tennis matches. Finally, based on this strategic analysis system, the data collected from two table tennis competitions are simulated, and the tactical recommendation of theoretical analysis is obtained, which provides a theoretical basis for the digitization of table tennis sports.

Selective blockchain system for secure and efficient D2D communication

With the advent of a hyper-connected society, expectations for blockchain technology and its potential have increased. Blockchain technology is emerging as a method to solve security vulnerabilities in the IoT and mobile devices that are already used in various industrial fields. However, the security problem is still one of the most critical issues. The conventional blockchain has a problem in which the performance decreases as the number of participants increases because all of the users participate in the verification. As a result, it is not suitable for traditional IoT and mobile environments in which resources are limited.This work presents an approach of applying a blockchain system in a D2D (Device-to-Device) environment and proposes solutions for the problems of conventional mobile device communication and blockchain consensus algorithms. After a blockchain system in a wireless network environment is separated into an off-blockchain and an on-blockchain, the divided blockchains are processed in three steps. In the first stage of 0ff-blockchain, our proposal confirms the safety of IoT devices that transfer confidential data. In the next stage, the devices reduce the risk of eavesdropping from a third party by calculating the security capacity using the resources of the mobile devices which register for membership on a private blockchain. In the last stage, the devices join the blockchain consensus process. The reliability of the consensus and the performance of the system are improved through calculation of the real participation probability of the participants by the communication elements and by determining whether each node is a selfish participant. As a result, our work proposes a consensus algorithm suitable for D2D communication and a SBC(Selective Blockchain) system considering environmental characteristics.

Traveler Acceptance of Mobile Reservation Services

Given the rapid spread of mobile devices and the wireless network environment, mobile hotel reservation services (MHRS) have become a mainstream tool for many travellers. This study explores the driving factors that promote customer intention to use MHRS. Based on a research model consisting of six constructs and the original technology acceptance model (TAM), this study employs structural equation modelling to validate and examine customer acceptance of MHRS. Data collected from 937 respondents in South Korea are used in the analysis. The research model demonstrates not only the validity of the TAM but also the influential roles of four positive factors and one negative factor on customer intention to use. Moreover, the results confirm the intermediate role of perceived value, which connects intention to use, with the other constructs. The findings of this study show that the research model validates the relationships among the proposed constructs with the TAM constructs.

Malfunction Detection and Localization Algorithm for Wireless Sensor Network

Recently, the graph signal processing paves a new way to analyze the data residing in the networks. In this letter, the graph signal processing theory is applied to detect and localize the malfunctioning sensors caused by the sheer quality and complicated deploying environment of wireless sensor networks. Based on the high-pass graph filter and the history data information, a two-channel graph filtering structure is constructed to not only detect the malfunctioning phenomenon but also determine the positions of the malfunctioning sensors. The proposed scheme is operated on vertex domain, in which the graph Fourier transform is not required. Numerical examples conducted on the real-world data on sensor networks and artificial attack on IEEE bus grids demonstrate the effectiveness of the proposed algorithm.

Efficient coverage greedy packet stateless routing in wireless sensor networks

Wireless sensor network is a collection of sensor nodes designed with different routing capabilities to operate on real-world applications. In extreme environments, real-time applications of wireless sensor network ensure exchange of data, a difficult one between the sensor nodes, when less resources are consumed. Therefore, researchers are developing a routing protocol including optimal routing procedures to increase the longevity of the networks. In this paper, an improved route extension architecture is developed in wireless sensor network environment. This Improved Greedy Perimeter Stateless Routing is proposed to offer an improved transmission coverage and reduced power consumption capability. It deploys a periodic broadcast of hello message (or control messages) including the positional information between the sensor nodes. The experimental result concludes that the Improved Greedy Perimeter Stateless Routing method achieves improved routing capabilities than the traditional hybrid protocols like LEACH and Greedy Perimeter Stateless Routing.

Indoor intrusion detection based on deep signal feature fusion and minimized-MKMMD transfer learning

Indoor intrusion detection based on Wireless Local Area Network (WLAN) has been widely used for security monitoring, smart homes, entertainment games, and many other fields in the Beyond 5G (B5G) wireless communication network environment. Most existing indoor intrusion detection methods directly exploit the Received Signal Strength (RSS) data collected by Monitor Points (MPs) and pay little attention to the instability of WLAN signals in complex indoor environments. In response to this problem, this paper proposes a novel WLAN indoor intrusion detection method based on deep signal feature fusion and Minimized Multiple Kernel Maximum Mean Discrepancy (Minimized-MKMMD). Firstly, the multi-branch deep convolutional neural network is used to conduct the dimensionality reduction and feature fusion of the RSS data, and the tags are obtained according to the features of the offline and online RSS fusion features that are corresponding to the silence and intrusion states, and then based on this, the source domain and target domain are constructed respectively. Secondly, the optimal transfer matrix is constructed by minimizing MKMMD. Thirdly, the transferred RSS data in the source domain is utilized for training the classifiers that are applying in getting the classification of the RSS fusion features in the target domain in the same shared subspace. Finally, the intrusion detection of the target environment is realized by iteratively updating the process above until the algorithm converges. The experimental results show that the proposed method can effectively improve the accuracy and robustness of the intrusion detection system.

Mobility management for RPL protocol in internet of things

&lt;p&gt;The IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) was proposed for various applications of IPv6 low power wireless networks. While RPL supports various routing metrics and is designed to be suitable for wireless sensor network environments, it does not consider the mobility of nodes. Therefore, there is a need for a method that is energy efficient and that provides stable and reliable data transmission by considering the mobility of nodes in RPL networks. In this paper a new heuristic flabellum algorithm inspired by physical and biological behaviour of flabella in the sea is presented, and bottleneck and swarm problems are resolved through managing the moving nodes by flabellum algorithm. Finally, the proposed algorithm’s performance is evaluated using the Cooja simulator. The proposed algorithm;Flabellum RPL; shows significant improvements with regards to packet delivery, and convergence and lifetime.&lt;/p&gt;

Employing sensor network based opportunistic spectrum utilization for agricultural monitoring

In this work, a cognitive Wireless Regional Area Network (WRAN) technology-based sensor network for agricultural monitoring is proposed. In WRAN technology, a Consumer Premise Equipment (CPE) and a base station communicate through incumbent channels opportunistically without causing any harmful interference to incumbent operations. Because CPE in IEEE 802.22 WRAN is considered as a fixed device, CPE with sensing capability is suitable for wireless sensor network environment in agriculture monitoring where there is no need for node mobility. The proposed network model consists of terrestrial and underground networks. In terrestrial network, CPEs employ Time Division Multiple Access (TDMA) technique with the purpose of sensing environmental data such as temperature, pressure, etc. and transmitting them to the base station. In the underground network, underground sensor nodes use Carrier Sense Multiple Access (CSMA) technique with the aim of sensing underground data such as humidity, soil mineral, etc. and transmitting them to the base station via cluster heads. After obtaining the analytical model of the proposed network, the simulation model of the network is carried out using Riverbed Modeler. The graphical results prove the validity and applicability of the proposed network in terms of throughput and energy consumption without any spectrum cost for agricultural monitoring in rural areas.

SFSH: a novel smart factory SDN-layer handoff scheme in 5G-enabled mobile networks

A smart factory network integrates various wireless networks to provide administrators with advanced factory network management capabilities and optimized services. Supporting smooth handoff management is an important issue in network architecture. Existing handoff management protocols are not sufficient to ensure handoff support in smart factories. This work was conducted to achieve the agility required by the smart factory network. The Smart Factory SDN-layer Handoff (SFSH) technique is proposed to guarantee Quality of Service (QoS) by supporting efficient handoff of devices in a smart factory. We proposes the SDN framework called SFSH for smart factory and a handover algorithm considering and the Received Signal Strength (RSS) and speed for SFSH. Through simple simulation, the SDN framework can flexibly cope with the conditions required by the smart factory, and it can be seen that the performance of SFSH considering RSS is improved over the existing handover process. SFSH uses the location and handoff signaling-delay information of devices in the factory to support mobility management and improve handoff performance in a wireless network environment. The sensitivity of the link layer and network layer is analyzed for handoff performance. Theoretical analysis shows that SFSH works better performance than other solutions.