Showing papers in "International Journal of Wireless Information Networks in 2020"

IoT Ecosystem: A Survey on Devices, Gateways, Operating Systems, Middleware and Communication

Abstract: In this era of research and technology, Internet of things (IoT) takes a prominent part in the evolution of applications of the various field like health, education, smart cities, homes, agriculture etc. This paper provides a survey of the IoT ecosystem. All the components of IoT and their significance has been elaborated. The smart sensors collaborate through wireless communication and internet, with zero human activity, to deliver automated intelligent applications. In this internet world, machine-to-machine (M2M) technologies are the first phase of the IoT. As IoT is expanding, it is bringing together vast technologies as in Big Data, Artificial Intelligent, Machine Learning to tackle the huge data and devices. This paper starts by providing an overview of the taxonomy of the IoT ecosystem. Then, it provides a technical overview of IoT enabling architectures, devices, gateways, operating systems (OS), middleware, platforms, data storage, security, communication protocols and interfaces for the data flow in an ecosystem. This paper also discusses the key hurdles that need to be tackled for expanding IoT. A relation between IoT and new technologies like big data, cloud and fog computing has been briefed. Finally, it presents the growing applications that IoT delivers.

A Survey of Device to Device and Cooperative Communication for the Future Cellular Networks

Abstract: Device to Device and Cooperative communication are the two new emerging technologies in the new era of communication technology which differ from the existing cellular technology. In review article we have enlisted different technologies which play a very important role in third Generation Partnership Project (3GPP). In this paper we have studied the various techniques of resource allocation, Mode selection for underlay communications in terms of device to device and cooperative communication techniques in terms of Long Term Evolution and Long Term Evolution-Advanced platform. A new technique LTE-Advanced Pro has also been introduced by 3GPP. Various simulators including Vienna LTE-Advanced have also been discussed. Better utilization of the spectrum is also depicts which is done on the basis of analysis if proper resource allocation whether it is power, frequency or time and mode selection is done in the programmed manner which would result in the reduction of interference and it will also lead to the secure system.

Wireless Communications for the Hospital of the Future: Requirements, Challenges and Solutions

Abstract: In this conceptual paper, we discuss the concept of hospital of the future (HoF) and the requirements for its wireless connectivity. The HoF will be mostly wireless, connecting patients, healthcare professionals, sensors, computers and medical devices. Spaces of the HoF are first characterized in terms of communicational performance requirements. In order to fulfil the stringent requirements of future healthcare scenarios, such as enhanced performance, security, safety, privacy, and spectrum usage, we propose a flexible hybrid optical-radio wireless network to provide efficient, high-performance wireless connectivity for the HoF. We introduce the concept of connected HoF exploiting reconfigurable hybrid optical-radio networks. Such a network can be dynamically reconfigured to transmit and receive optical, radio or both signals, depending on the requirements of the application. We envisage that HoF will consist of numerous communication devices and hybrid optical-radio access points to transmit data using radio waves and visible light. Light-based communications exploit the idea of visible light communications (VLC), where solid-state luminaries, white light-emitting diodes (LEDs) provide both room illumination as well as optical wireless communications (OWC). The hybrid radio-optical communication system can be used in principle in every scenario of the HoF. In addition to the hybrid access, we also propose a reconfigurable optical-radio communications wireless body area network (WBAN), extending the conventional WBAN to more generic and highly flexible solution. As the radio spectrum is becoming more and more congested, hybrid wireless network approach is an attractive solution to use the spectrum more efficiently. The concept of HoF aims at enhancing healthcare while using hospital resources efficiently. The enormous surge in novel communication technologies such as internet of things (IoT) sensors and wireless medical communications devices could be undermined by spectral congestion, security, safety and privacy issues of radio networks. The considered solution, combining optical and radio transmission network could increase spectral efficiency, enhancing privacy while reducing patient exposure to radio frequency (RF). Parallel radio-optical communications can enhance reliability and security. We also discuss possible operation scenarios and applications that can be introduced in HoF as well as outline potential challenges.

Security Issues in Fog Environment: A Systematic Literature Review

Abstract: The potent concept of fog computing is currently attracting many researchers as it brings cloud services closer to the end-user. It also roots out some of the major limitations of the cloud scenario where there exists the need for extremely low latency. Despite its compelling advantages, fog computing is still an evolving paradigm that demands further research. Among all the other issues prevalent in fog computing, security is one of the burning issues. Fog nodes, being at the edge of the network, pose several security threats. The authors have thus conducted a systematic literature review (SLR) on security issues in fog computing scenario. Initially, the prevalent security issues are identified through an in-depth survey and then the available literature per security issue is analyzed systematically. This SLR reveals that the security in/through fog scenario is being addressed by the researchers all over the globe. But, the approaches with respect to fog environment still lack methods to evaluate security aspects. Though most of the researchers are prioritizing the consideration of security aspects at fog level, there still exists a need to consider fog computing security as an area of serious concern.

A Survey on Congestion Control Protocols in Wireless Sensor Networks

Abstract: Wireless sensor networks (WSNs) are composed of several sensor nodes which are limited in terms of resources for computing, storage, communication bandwidth, and, most importantly of all, energy. One of the major challenges in these networks is congestion, which is triggered by factors such as packet collision, node buffer overflow, transmission channel contention, transmission rate, and many-to-one data transmission from several nodes to the sink node. Congestion affects energy consumption and various parameters of service quality in sensor nodes. Therefore, it is one of the most critical issues in WSNs that requires developing more advanced techniques for its avoidance, diagnosis, and control. In this paper, different methods of congestion control have been investigated; also, a novel taxonomy has been proposed based on combination of concentration of congestion control policies, different algorithms employed, and the effective parameter in detecting and controlling congestion.

A Network Intrusion Detection Method Based on Deep Multi-scale Convolutional Neural Network

Abstract: Network intrusion detection (NID) is an important method for network system administrators to detect various security holes. The performance of traditional NID methods can be affected when unknown or new attacks are detected. Compared with other machine learning methods, the intrusion detection method based on convolutional neural network (CNN) can significantly improve the accuracy of classification, but the convergence speed and generalization ability of CNN are not ideal in model training process resulting in a low true rate and a high false alarm rate. To solve the above problems, this paper proposes a deep multi-scale convolutional neural network (DMCNN) for network intrusion detection. Different levels of features in a large number of high-dimensional unlabeled original data are extracted by different scales convolution kernel. And the learning rate of network structure is optimized by batch normalization method to obtain the optimal feature representation of the raw data. We use NSL-KDD dataset as the benchmark thus we can compare the performance of our proposed method with other existing works. This dataset includes two testing sets which are the first one is KDDTest+ while the second one is $$\text {KDDTest}^{-21}$$ which is more difficult to be classified. The experimental results reveal that the AC and TPR are higher through our DMCNN model. Especially, in terms of DOS, the AC appropriately reaches to 98%. DMCNN has a high intrusion detection accuracy and a low false alarm rate, which overcomes the limitations of using the traditional detection methods and makes the new approach an attractive one for practical intrusion detection.

Internet of Things Management Based on Software Defined Networking: A Survey

Abstract: Nowadays, the huge number of smart connected objects and the massive data amount produced require intelligent approaches for their management and supervision. Accordingly, Software Defined Networking (SDN) is considered as a promising paradigm for Internet of things (IoT) management. The IoT is now involved deeply in our daily activities and influences considerably our life style; from the way we drive, to how we make purchases or even what we should eat or not to maintain our health etc. The diversity of IoT related applications necessitates flexible, agile and adaptable IoT architecture. To meet this requirement, the design of IoT architecture based on SDN involves decoupling control plane from data plane to integrating intelligent management functionalities. In this context, this survey highlights the IoT network management functionalities and studies the correlation between SDN and IoT with focuses the role played by SDN to improve the management effectiveness of IoT networks. Furthermore, deep investigation is elaborated related to the SDN-IoT architectures and applications and some open issues and future research directions are provided.

A Survey and Performance Evaluation of Reinforcement Learning Based Spectrum Aware Routing in Cognitive Radio Ad Hoc Networks

Abstract: Cognitive radio technology is an assuring solution for under-utilization of licensed spectrum bands and overcrowding of unlicensed spectrum bands, in which secondary user is permitted to access the primary users’ spectrum in an opportunistic manner. Opportunistic access of the spectrum requires complex changes across all the layers of a network protocol stack. Cognitive radio has to be an autonomous agent in order to configure itself to dynamic spectrum environment. And, the characteristics of reinforcement learning, a subfield of artificial intelligence in which the agent learns the surrounding operating environment through continuous interaction and takes an optimum decision on the fly, is in compliance with features of self-organized cognitive radio ad hoc network. Therefore, reinforcement learning is an appropriate option for incorporating intelligence and self-adaptivity into cognitive radio. This paper provides a comprehensive survey on the application of reinforcement learning for efficient spectrum aware routing in cognitive radio ad hoc network. The preliminaries of cognitive radio ad hoc networks and reinforcement learning are first introduced, and a review is investigated in the proposed research area along with a discussion on open research challenges with an aim to promote research. From the survey, reinforcement learning incorporated cognitive radio can learn the unknown primary user network model and the learned model can be then used for finding a suitable route to meet the Quality of Service requirements. With this in mind, the paper also proposes a multi-objective reinforcement learning based spectrum aware routing protocol with an aim to maximize the probability of successful transmission using a minimum hop path. The simulated results prove the performance of the algorithm.

Energy-Saving Optimization and Matlab Simulation of Wireless Networks Based on Clustered Multi-hop Routing Algorithm

Abstract: Reducing the energy consumption of the wireless sensor network is an effective way to extend the lifetime of the wireless sensor network. This paper proposes a real-time routing protocol RRPBLC that combines location information and clustering technology. By dividing the monitoring area into cells, each cell is composed of a cluster, and the method of mixing cluster head elections and dynamically adjusting the forwarding transmission rate is adopted. The simulation results show that the clustering algorithm and cluster head election algorithm designed in this paper have good performance in balancing node load, which not only avoids the problem of “hot zone”, but also makes the node energy consumption uniform. At the same time, the performance of the algorithm in response to network performance degradation caused by node failure is also outstanding, even if 50% of nodes fail. The algorithm can still guarantee reliable monitoring data from the network, so it can greatly extend the network life cycle. The protocol not only can achieve energy balance of the network, extend the life cycle of the network, and has better real-time performance.

Acoustic-Based Security: A Key Enabling Technology for Wireless Sensor Networks

Abstract: Technological advances have proliferated in several sectors by developing additional capabilities in the field of systems engineering. These improvements enabled the deployment of new and smart products. Today, wireless body area networks (WBAN) are commonly used to collect humans’ information, hence this evolution exposes wireless systems to new security threats. Recently, the interest by cyber-criminals in this information has increased. Many of these wireless devices are equipped with passive speakers and microphones that may be used to exchange data with each other. This paper describes the application of the watermark-based blind physical layer security (WBPLSec) to acoustic communications as unconventional wireless link. Since wireless sensors have a limited computation power the WBPLSec is a valuable physical layer standalone solution to save energy. Actually, this protocol does not need any additional radio frequency (RF) connection. Indeed, it combines watermarking and a jamming techniques over sound-waves to create secure region around the legitimate receiver. Due to their nature, wireless communications might experience eavesdropping attacks. The analysis proposed in this paper, addresses countermeasures against confidentiality attacks on short-range wireless communications. The experiments over the acoustic air-gap channel showed that WBPLSec can create a region two meters wide in which wireless nodes are able to communicate securely. Therefore, the results favor the use of this scheme as a key enabling technology to protect the confidentiality in wireless sensor networks.

Distance Mapping Algorithm for Sensor Node Localization in WSNs

Abstract: The localization of sensing nodes is most pronounced in the application of wireless sensor networks. To address this issue, a node localization algorithm called the DMA is proposed in this paper. This algorithm identifies the node position by using the estimation matrix and distance matrix together with the optimized linear transforming function. With the integration of GA, the position of the node can be accurately determined. The conducted simulation outcomes and the corresponding analysis verify the high accuracy and low energy consumption of the proposed algorithm, which can outperform other widely used approaches. This study designs and deploys the proposed algorithm and shows its sensor node localization theory, which makes it a promising basis for the realization of positioning in WSNs.

Using Time-Location Tags and Watchdog Nodes to Defend Against Node Replication Attack in Mobile Wireless Sensor Networks

Abstract: Node replication attack is one of the well-known and dangerous attacks against Wireless Sensor Networks (WSNs) in which adversary enters the network, searches randomly and captures one or multiple normal nodes. Adversary extracts data and keying materials of the captured node and generates several copies of that node and deploys them in the network. In this paper, a novel algorithm using watchdog nodes is proposed to detect replica nodes in mobile WSNs. The main idea of the proposed algorithm is inspired by maximum predefined speed for sensor nodes and using time-location tags by the watchdog nodes to detect replica nodes. Watchdog nodes collaborate to measure sensor nodes’ speed in the environment and if they find that a node moves faster than a predefined threshold, they mark it as a malicious node, because such replica node in different regions of the network is moving faster than usual in different regions of the network. The proposed algorithm is implemented by J-SIM simulator and its performance is evaluated in terms of false detection and true detection rates through some experiments. Experiment results show that the proposed algorithm is able to detect 100% of replica nodes, while the false detection rate is less than 0.5%.

A Fish Swarm Inspired Holes Recovery Algorithm for Wireless Sensor Networks

Abstract: Wireless sensors networks (WSNs) have be applied to a number of fields such as environment monitoring, military surveillance, data collection and etc. However, the coverage holes problem are usually caused by some undesirable reasons, such as random deployment of sensors, energy consumption imbalance, unethical attack and hardware failures. The holes affect capabilities of WSNs greatly, so its recovery is one of the pivotal problems in WSNs. In order to enhance the performance of holes recovery, a robust approach based on an improved artificial fish swarm algorithm is presented in this paper. The movement of mobile nodes is analogized to the motion of artificial fish with the network coverage rate as objective function. Besides the classic artificial fish motion such as prey, follow and swarm, two novel fish motions called as leap and rebirth are also presented to enhance the convergence of this algorithm. An approach of self-adaptive visual range and step length for fish motion are adopted when updating the status of artificial fish. Simulation experiments show the effectiveness and robustness of the algorithm. The holes can be recovered efficiently without location information and holes detection using the least amount of mobile nodes. The network coverage is improved significantly with this proposed algorithm.

Long Link Wireless Sensor Routing Optimization Based on Improved Adaptive Ant Colony Algorithm

Abstract: Compared with the advantages and disadvantages of genetic algorithm, based on the ant colony algorithm, this paper combined with the selection, crossover and mutation operation of genetic algorithm, the search speed and optimization ability of ant colony algorithm are improved. The optimal path evaluation function considers nodes. The energy consumption and the residual energy of the node enable the nodes with more residual energy to participate in the data forwarding preferentially and balance the energy consumption between the nodes. The comparison with the classical ant colony algorithm and the genetic algorithm shows that as the number of data forwarding rounds increases, the improved The ant colony algorithm has low energy consumption, many residual energy, and the network life cycle is obviously prolonged. With the increase of the network running time, the improved ant colony algorithm, the node equalization energy consumption is good, and the success rate of the optimal path search is also significantly better than the other two algorithms.

An Adaptive Prediction Strategy with Clustering in Wireless Sensor Network

Abstract: In Wireless Sensor Network, sensed data reflects two types of correlations of physical attributes: spatial and temporal. In this paper, a scheme named, Adaptive Prediction Strategy with ClusTering (APSCT) is proposed. In APSCT, a data-driven clustering and grey prediction model is used to exploit both the correlations. APSCT minimizes the transmission of messages in the network. However, the use of prediction includes additional computation overhead. There is a trade-off between prediction accuracy and energy consumption in computation and communication in wireless networks. This paper also gives an approach to calculate the upper and lower bound of the prediction interval which is used to evaluate different confidence levels and provides an energy-efficient sensor environment. Simulation is carried out on real-world data collected by Intel Berkeley Lab and results are compared with existing approaches.

On-body Cavity-Backed Low-UWB Antenna for Capsule Localization

Abstract: The paper presents a novel antenna operating at the lower UWB band (3.75–4.25 GHz), defined originally in IEEE 802.15.6 standard for Body Area Networks (BAN) applications. The proposed antenna is designed for biomedical application, wireless capsule endoscopy localization. In other words, the concerned application is dedicated to track a capsule, by means of an external device, swallowed by the patient to provide captured images of the Small Intestine (SI), essential part of the GastroIntestinal (GI) tract, and transfer them in real-time to the external device. In this context, antenna with and without cavity-backed structures, are presented and compared with the requirements for a receiving antenna in terms of directivity and bandwidth coverage in question. It was revealed that the cavity approach improved the antenna gain up to 8 dBi, at the 4 GHz center frequency, compared to 6 dBi without the cavity presence. Simulations were carried out using CST Microwave Studio, and the results were validated by measurements in proximity to human body. The antenna safety issue was assessed with CST SAR (Specific Absorption Rate) calculation, in compliance with IEEE/IEC 62704-1 standard. Results showed a maximum SAR of 0.112 W/kg and 0.005 W/kg at 4 mm and 30 mm antenna-skin distance, in the range of the SAR limit guidelines defined by safety standards. The cavity-backed antenna ability to penetrate the human tissues, to reach the small intestine layer was studied by means of CST voxel model and compared to a multi-layer model emulating the dielectric properties of the human tissues at 4 GHz. This analysis was conducted using power flow results and completed by the power field probes at the several tissue interfaces.

Coverage Optimization and Simulation of Wireless Sensor Networks Based on Particle Swarm Optimization

Abstract: As a new type of measurement and control network, wireless sensor networks (WSNs) technology has the characteristics of diversity and wide practicability. Based on the characteristics of WSNs, wireless sensor networks are widely used in military and civil industries. However, due to the frequent changes in the basic topology of wireless sensor networks and the relatively small number of corresponding network nodes, the efficiency of the nodes is the reason for its further development. Therefore, the node distribution strategy and the corresponding network coverage optimization strategy of wireless sensor networks are of great significance to achieve energy saving and network life extension. In order to solve the above WSNs problems, this paper will analyze and simulate the coverage optimization of wireless sensor networks based on Improved Particle Swarm Optimization algorithm. Firstly, this paper will analyze the structure characteristics of wireless sensor networks, complete the WSNs network coverage model and get the corresponding functions. Then this paper innovatively proposes “external dispersion method” to solve the problem of local area overlap in WSNs network coverage. At the same time, it innovatively proposes “free-particle swarm optimization” to solve the local convergence of conventional particle swarm optimization. At the end of this paper, simulation experiments are carried out to compare the optimal particle swarm optimization algorithm with the traditional particle swarm optimization algorithm. The experiments show that the proposed algorithm has obvious advantages in convergence and coverage.

A Novel Important Node Discovery Algorithm Based on Local Community Aggregation and Recognition in Complex Networks

Abstract: There has been many recent researches on identifying local community structure in networks. However, most existing approaches require complete information of the graph structure, which is impractical for some network. And most of existing algorithms do not mine important node information in the network. In order to solve these problems, a novel important node discovery algorithm based on local community aggregation and recognition in complex networks is proposed in this paper. Firstly, the connections of the merged nodes with the local communities and their external nodes are comprehensively considered and compared so as to avoid introducing heterogeneous nodes directly from the external expansion of a given node in the process of local community aggregation; and then, edge node identification is adopted to control community clustering so as to determine the extent and size of the local community without intervention. Through computer-generated network and real-network experiments, it is shown that the proposed algorithm can effectively mine and identify its important node based on a given local community and improve the quality of community detection compared with other state-of-art algorithms.

Efficient Anti-collision Algorithm for RFID EPC Generation-2 Protocol Based on Continuous Detection

Abstract: To improve the efficiency of the identification process in RFID system, anti-collision protocol is crucial. EPC Class-1 Generation-2 standard (Gen2) employs ALOHA-based Q algorithm as anti-collision protocol. However, the same updating rate will cause unnecessary collided and idle slots. In 2010, we proposed a Fast Q algorithm (FQA) that adjusted the value of Q with different steps. Recently, we find that the Fast Q algorithm can be further improved since it did not consider the probabilities of collided/idle slots. Accordingly, this paper proposes an Enhanced Fast Q algorithm (EFQA). In EFQA, Q will be updated after two continuous collided or idle slots with different updating rates, which are calculated by synthesizing the duration time of collided/idle slots and their probabilities. We then quantitatively analyze the performance of EFQA by establishing a Discrete-Time Markov Chain (DTMC) model. Simulation results show that EFQA outperforms other protocols regardless of the number of tags.

Ultra-wide Band Positioning in Sport: How the Relative Height Between the Transmitting and the Receiving Antenna Affects the System Performance

Abstract: This work presents the performance evaluation of an ultra-wide band (UWB) positioning system for monitoring athletes in sports. Focusing on a five-a-side football scenario, the objective of this paper is to evaluate the performance of the proposed UWB positioning system by considering three different heights of the receiver antenna: 1 m, 1.6 m and 2 m. The system consists of four static UWB receivers placed at the corners of the pitch, while a player is equipped with an UWB transmitter worn on the upper back of the body. The UWB communication link from the on-body transmitter to the receivers can be influenced by the relative height between the transmitting and the receiving antenna. In order to assess the performance of the positioning system, on-field tests have been performed with five players. The players were asked to run at a variable speed while traveling a predefined path inside the pitch. Three metrics have been considered to assess the system performance: the accuracy of the position for each player, the percentage of packets lost by each receiver with respect to the total number of transmitted packets, and the percentage of packets respectively received by one, two, three, and four anchors, with respect to the total number of transmitted packets. The experimental results show that the intermediate height of 1.6 m for the receiver antenna presents the best results: the lowest value of position RMSE (equal to 31 cm), an average of 28% of the lost transmitted packets and an average of 63% of the transmitted packets being received at least by 3 anchors.

Efficient Hybrid Linear Massive MIMO Detector Using Gauss–Seidel And Successive Over-Relaxation

Abstract: The initial solution of a massive multiple-input multiple-output (M-MIMO) detector for uplink (UL) is greatly influence the balance between the bit error rate (BER) performance and the computational complexity. Although the maximum likelihood (ML) detector obtains the best BER performance, it has an extremely high computational complexity. Iterative linear minimum mean square error (MMSE) detector based on the Gauss–Seidel (GS), the successive over-relaxation (SOR), and the Jacobi (JA), obtains a good performance-complexity profile when the base station (BS)-to-user-antenna-ratio (BUAR) is large. However, when the BUAR is small, the system suffers from a considerable performance loss. In this paper, a hybrid detector based on the joint GS and SOR methods is proposed where the initial solution is determined by the first iteration of GS method. Numerical results show a considerable complexity reduction and performance enhancement using the proposed GS-SOR method over all methods when the BUAR is small.

An Anti-Gray Hole Attack Scheme in Mobile Ad Hoc Network

Abstract: Dynamic variation of network topology in mobile ad hoc networks (MANET) forces network nodes to work together and rely on each other for routing. Considering the lack of a central control node, some nodes appear maliciously and selectively drop the data packets instead of transmitting to the next hop node, which is known as the gray hole attack. In order to prevent and identify the gray hole attack, an intrusion detection system (IDS) is proposed on nodes to maintain network efficiency. In addition to establishing security, the proposed anti gray hole attack mechanism extracts abnormal differences between routing packets in the route discovery phase of the ad hoc on-demand distance vector (AODV) routing protocol. It then assigns a suspicious value to each participating node and monitors the exchange of data packets between neighboring nodes in the data transfer phase using a data transmission information table. If the suspicious value reaches a predetermined threshold, IDS nodes send an alert message to other nodes about the identity of the malicious node, in order to prevent its activity on the network. Simulation results indicate that the performance of the proposed scheme in terms of packet delivery rate, packet loss rate, and routing overhead outperforms those in the literature.

A Massive Multi-Modal Perception Data Classification Method Using Deep Learning Based on Internet of Things

Abstract: In recent years, the Internet of Things (IoT) has developed rapidly due to its broad application prospects, and the types of sensing devices are becoming more and more abundant. In many applications, multi-dimensional attributes of monitoring objects are measured by deploying multiple independent heterogeneous data sources, thus heterogeneous multi-source multi-modal sensing data can be obtained. In this paper, we measure the data quality of multi-source and multi-modal data and make full use of the data quality information, a classification and detection method for heterogeneous multi-source and multi-modal sensing data was proposed. The purpose is to ensure the data quality and select some data sources for data transmission in order to save network resources as much as possible. Firstly, the problem caused by heterogeneous multi-modal data is solved by the feature transformation of deep neural network, and an innovative training method is used to extract the multi-modal shared feature expression with strong discriminant ability and low-dimensional characteristics from the original multi-modal high-dimensional data. Then, an algorithm combining deep learning with structural sparse multi-modal feature representation and mode selection is proposed, which innovatively uses deep learning to transform multi-modal data into modal-independent Abstract expressions. Finally, the structural sparse method is used to further select the feature dimension in the abstract expression to reduce the dimension of the final feature. Experiments show that the average false alarm rate of the proposed model is 2.2%, which is obviously superior to other similar algorithms.

Fractional Frequency Reuse in Multi-tier Networks: Performance Analysis and Optimization

Abstract: Frequency Reuse (FR) is an efficient approach to improve the network performance in a multi-cell cellular network. In this paper, we investigate the performance of heterogeneous networks utilising two well-known frequency reuse algorithms, called Strict FR and Soft FR, with a reuse factor of $$\varDelta$$ ($$\varDelta > 1$$). Based on a two-phase operation of the FR algorithm, we develop a new approach to analyse cellular networks based on the Poisson Point Process (PPP) model which successfully demonstrates the impact of the network parameters (such as the number of allocated Resource Blocks (RBs) and bias factor) and FR parameters (such as Base Station (BS) transmit power) on the performance of a user and overall network. Compared to related works, we propose the following novel approaches: (i) we investigate flexible FR networks in which users have connections with the BSs which deliver the highest performance; (ii) the BS observes SINR on the data channel to classify each user into either a Cell-Edge User (CEU) or Cell-Center User (CCU); (iii) the initial state of the network is considered to establish the initial network interference when new users arrive and request connections to the BSs. In the case of a single-tier network, it is proved that our analytical approach is more accurate than previous works. In the case of a two-tier network, the analytical results indicate that compared to the 3GPP model, our proposed model not only reduces up to 40.79% and 3.8% power consumption of a BS on the data channel but also achieves 16.08% and 18.63% higher data rates in the case of Strict FR and Soft FR respectively. Furthermore, the paper presents an approach to find an optimal value of SINR thresholds and bias factor to achieve the maximum network performance.

Hierarchical Information-Centric Networking Framework

Abstract: In information-centric networking, one content publishing operation involves a great number of content routers, and different name prefixes pollute forwarding tables and result in explosion of forwarding entries. Moreover, flooding is employed to achieve information-centric communications, and this flooding incurs huge costs. Taking these issues into account, this paper proposes a hierarchical information-centric networking framework and aims to reduce the content communication costs and latency. This framework proposes the hierarchical information-centric networking architecture and name structure to achieve forwarding aggregation and avoid forwarding table pollution. This framework is evaluated, and the data show that it reduces the content communication cost and latency by nearly 49.2% and 9.5%, respectively.

A Network Attack Detection Method Using SDA and Deep Neural Network Based on Internet of Things

Abstract: Aiming at the deficiency of network attack detection, a network attack detection method based on deep neural network is proposed. Firstly, the deep neural network technology is used to study the self-adaptive identification method of the security state, intelligently discriminate the security index of the network, recall comparative learning based on historical data, and establish the classification and identification database of network security. Then, according to the information of security classification and identification database, the corresponding state risk assessment system is mapped. Based on the risk intensity, different levels of early warnings are given. Finally, experimental simulation analysis is carried out to demonstrate the effectiveness of the proposed method. The simulation results show that the proposed method can actively send out early warning before the network is attacked, which obtains a high accuracy of early warning.

BER Performance of Signal Detection for Massive Multi User Spatial Modulation Systems

Abstract: Massive spatial modulation (MSM) is considered as an attractive technique for multi antenna wireless communications. That is because, it gives higher energy efficiency and spectral efficiency than small scale multiple-input multiple-output (MIMO) systems. Massive SM-MIMO utilizes multiple transmit antennas for each user with only one transmit radio frequency (RF) chain and hundreds of receive antennas at base station (BS) with small number of RF chain. Where, each user can activate any one of its transmit antennas and the index of active transmit antenna (TA) can convey to information bits in addition to the information bits conveyed through classical modulation symbols (e.g. 16QAM). Owing to large number of TAs at the user and small number of RF chains at BS, multi user signal detection becomes challenging problem. To solve this matter, a joint grouped SM transmission scheme at users and group subspace pursuit (GSP) based signal detection at BS can be proposed to improve the signal detection performance. Owing to joint transmission scheme, SM signals in same transmission group exhibit group sparsity. Also, spatial signal composed of multiple users’ SM signals exhibits distributed sparsity. By utilizing these sparse features, the proposed GSP based signal detection can detect SM signals more reliability than other detection techniques. Additionally, the cyclic prefix single carrier (CPSC) is utilized to withstand the multipath channels. Simulation results prove that BER performance of the proposed GSP based signal detection outperforms classical SP based signal detection by 8 dB SNR gain at BER = 10−2. This gain can be improved by 2 dB by increasing the number of transmit antenna.

Time Slot Transmission Scheme with Packet Prioritization for Bluetooth Low Energy Devices used in Real-Time Applications

Abstract: Bluetooth Low Energy (BLE) is one of the most important technologies that feed the growing field of Internet of Things and Wireless Sensor Networks. Due to its flexibility and unique low power-consumption, an increasing number of industrial devices, household appliances and wearables are being designed using it. However, the real-time demands of these networks such as timing and Quality of Service are not fully covered by the protocol itself. To help improve and offer some control over these characteristics, this paper presents a time slot transmission scheme with packet prioritization. It is based on the division and allocation of the connection interval to two types of messages: real-time and ordinary. The goal is to offer the lowest packet loss and time guarantees for real-time messages, while providing acceptable throughput for ordinary ones. Since the probability of a BLE connection to close increases with the number of packets sent through it, the position where a real-time packet is being sent as well as the number of ordinary messages in a connection represent key factors. The use of the first and last slot for real-time packets with ordinary flow restricted to the space between them decreases the transmission delay uncertainty and allows probability tuning based on the number of ordinary messages. Simulations were performed using the proposed scheme and a reduction of more than 100 times in the delay variance was observed for real-time transmissions. Regarding reliability, around 5% of the packets were lost for a bit error rate of $${10^{-3}}$$ .

Improvement in the Detection of Passengers in Public Transport Systems by Using UHF RFID

Abstract: Automatic control for recognition of passengers in public transport systems has been a crucial point in mobility systems towards enhancements in passengers’ flow and overall system efficiency. It allows the recognition of passengers’ origins and destinations, so that the specific demands for specific periods of the day can be assessed for an effective system planning. However, this automatic control has to be efficient and smooth so that it does not incur in additional overhead to the entire system. This work presents a study on a passenger recognition system for public transport through the use of RFID technology using EPC Gen2 standard. Preliminary tests were performed with two different forms of voluntary order to evaluate different types of tags. These tests first evaluated the height and angle of the antennas using 1, 2, 3 and 4 antennas in the tag recognition. From the results of these first tests, a set up was defined and then applied to a second evaluation now with 10 volunteers, which evaluated repeatability and effectiveness of the system for recognition. Moreover, additional laboratory-based tests were performed to access the effectiveness of the proposed recognition system. The acquired results provide a basis for evaluate the suitability and applicability of the proposed system.

Compression Sensing Signal Detection Algorithm Based on Orthogonal Matching Pursuit

Abstract: At present, most of the detection algorithms used in our country take the iteration process of feature as the research object. This detection method is only suitable for the presence of perceptual signals, but not for all the signal measurement work. This paper introduces the basic principle of signal compression sensing, the construction of measurement matrix and the orthogonal matching pursuit algorithm. The orthogonal matching pursuit algorithm is applied to compressed sensing reconstruction of sparse signals in one-dimensional time domain and transform domain, and the reconstruction performance of the orthogonal matching pursuit algorithm is analyzed. Compared with the detection algorithm based on matching pursuit, this algorithm based on the idea of orthogonal matching pursuit corrects the feature quantities as the basis of decision. When the signal of interest exists, the feature quantities with smaller fluctuations are obtained, and better detection results are obtained. The experimental results show that the OMP detection algorithm proposed in this paper has better performance in improving detection success rate, sampling points required, noise suppression and so on compared with MP detection algorithm.