Successful Data Transmission Research Articles

Data Acquisition and Transmission Scheme for Large Projects Based on LoRa Internet of Things Using Improved Linear Integer Programming Model

Aiming at the problem that some security factors in large scale construction projects are difficult to be discovered in time and easily cause accidents, a low-power long-distance data acquisition and transmission scheme using LoRa wireless sensor network is proposed. Firstly, the improved linear integer programming (IILP) model is used to deploy sensor nodes, which is used to collect security risk data. Then, an ARM processor with a high-performance Cortex-M3 architecture is used by the LoRa node to interconnect the LoRa module via the SPI bus for data communication. Finally, the LoRa node sends the data collected by the sensor to the LoRa gateway through the wireless signal, and the data is stored in the cloud database through the TCP/IP protocol. Experimental tests are carried out in actual subway construction projects. The temperature and humidity sensors, deep horizontal displacement sensors, supporting shaft force sensors and groundwater level sensors are installed on the nodes to monitor real-time safety risk factors in the construction process. It is found that under the premise of ensuring more than 90% of the data transmission success rate, the proposed communication distance can be as far as 700 m. Compared with several other schemes, the proposed scheme can show better performance in terms of throughput and network delay. The simulation results of sensor deployment show that the improved ILP model can improve the performance of sensor deployment and save hardware cost to a certain extent. In addition, the proposed scheme combines the advantages of LoRa technology with the characteristics of low maintenance cost and long service life, and has a good reference significance for the application of Internet of Things technology in large-scale construction projects.

A Research on Clustering Based Optimized Routing Protocols in Wireless Sensor Network

Routing in wireless sensor network environment plays a critical role where it might be affected by various factors which would lead to data transmission failure/corruption. Energy is the most concerned factor which affects the routing task mostly due to their limited availability. Thus routers require most efficient routing protocol which can perform data communication with less energy consumption. This routing protocol should be capable of adapting critical conditions happening on routers. Another problem that is mostly found on routing task is security. The security issues created by the hackers would corrupt/steal the data when it is being transmitted to the destination nodes. The routing protocol should also capable of tolerating the security issues and needs to ensure the successful data transmission. In this analysis work, various hierarchical clustering algorithms that are proposed for grouping the wireless sensor nodes those is present in the environment is discussed. The clustering that can be applied on both heterogeneous and homogeneous wireless network environment is discussed. The operation and functionality of each clustering algorithms are given in depth to understand the routing skill of each nodes. The merits and demerits that are present in the every clustering algorithm are presented to find the better clustering approach. The experimental analysis of every algorithm are done and compared in terms of various performance measures to find the better clustering approach that can be utilized for further scenario.

Performance optimization of QoS-supported dense WLANs using machine-learning-enabled enhanced distributed channel access (MEDCA) mechanism

Quality of service (QoS) implementation in a wireless local area network (WLAN) enables the prediction of network performance and utilization of effective bandwidth for multimedia applications. In QoS-supported WLAN, enhanced distributed channel access (EDCA) adjusts back-off parameters to implement priority-based channel access at the medium access control (MAC) layer. Although conventional QoS-supported EDCA in WLANs can provide a certain degree of QoS guarantee, the performance of best effort data (low-priority) traffic is sacrificed owing to the blind use of a binary exponential back-off (BEB) mechanism for collision avoidance among WLAN stations (STAs). In EDCA, the BEB mechanism exponentially increases the contention window (CW[AC]) for any specific priority access category (AC) when collision occurs and resets it to its initial size after successful data transmission. This increase and reset of CW[AC] is performed regardless of the network density inference, i.e., a scarce WLAN does not require an unnecessary exponential increase in CW[AC]. Similarly, a dense WLAN causes more collisions if CW[AC] is reset to its initial minimum size. Machine-learning algorithms can scrutinize an STA’s experience for WLAN inference. Therefore, in this study, we propose a machine-learning-enabled EDCA (MEDCA) mechanism for QoS-supported MAC layer channel access in dense WLANs. This mechanism utilizes a Q-learning algorithm, which is one of the prevailing models of machine learning, to infer the network density and adjust its back-off CW[AC] accordingly. Simulation results show that MEDCA performs better as compared to the conventional EDCA mechanism in QoS-supported dense WLANs.

Reinforcement Learning for Decentralized Trajectory Design in Cellular UAV Networks With Sense-and-Send Protocol

Recently, the unmanned aerial vehicles (UAVs) have been widely used in real-time sensing applications over cellular networks. The performance of a UAV is determined by both its sensing and transmission processes, which are influenced by the trajectory of the UAV. However, it is challenging for the UAV to determine its trajectory, since it works in a dynamic environment, where other UAVs determine their trajectories dynamically and compete for the limited spectrum resources in the same time. To tackle this challenge, we adopt the reinforcement learning to solve the UAV trajectory design problem in a decentralized manner. To coordinate multiple UAVs performing real-time sensing tasks, we first propose a sense-and-send protocol, and analyze the probability for successful valid data transmission using nested Markov chains. Then, we propose an enhanced multi-UAV Q-learning algorithm to solve the decentralized UAV trajectory design problem. Simulation results show that the proposed algorithm converges faster and achieves higher utilities for the UAVs, compared to traditional singleand multi-agent Q-learning algorithms.

Relative Vehicle Displacement Approach for Path Tracking Adaptive Controller With Multisampling Data Transmission

This paper proposes a cyber-physical framework for vision-based automated vehicle path tracking. The framework has been developed in two parts: first, an intelligent relative vehicle displacement approach with an adaptive neural network controller; and second, multisampling data transmission architecture for any sensor–controller–actuator network. Uncertainties due to illumination effects, occlusion, and obscure images affect system performance drastically. The proposed relative vehicle displacement approach takes care of these uncertainties. The adaptive neural network controller generates precise control actions for stabilizing the system in minimum time. A reliable and robust data transmission architecture is of utmost importance for any Internet of Things application. Successful data transmission depends on several parameters, such as delay, communication channel behavior, and packet loss. A novel multisampling data transmission architecture addressing these issues has been proposed in this paper. Various cases of data transmission have been demonstrated on the time critical application of vision-based tracking by an automated guided vehicle. The results of real-time path tracking operation have been duly compared with other control techniques and data transmission architecture. The experimental results show the efficiency of the proposed system.

A High-Resolution Wireless Power Transfer and Data Communication System for Studying Gastric Slow Waves.

We present a wireless recording system designed for high-resolution mapping of gastric slow-wave signals. The system is composed of an implantable unit (IU), a wearable unit (WU), and a stationary unit (SU) connected to a computer. Two independent wireless data communication links consisting of IU-WU and IU-SU were developed based on near-field and far-field communication, respectively. Furthermore, the WU is capable to wirelessly recharge the IU's battery through an inductive link. For the IU-WU near-field communication, a differential pulse position data encoding algorithm with only 6.25% duty cycle, with load shift keying (LSK) modulation is developed to guarantee continuous power transmission and high data transfer rate, simultaneously. The IU sends the encoded data to the WU, and the WU can either store the data locally on a memory card or transmit them to the SU for real-time monitoring. In addition, the IU-SU far-field data communication was developed based on a RF transceiver in which the IU transmits the data directly to the SU. The benchtop validation of the system demonstrated successful IU-WU and WU-SU data transmission, while sample signals were recorded successfully at IU through saline solution and received by SU.

OCA-MAC: A Cooperative TDMA-Based MAC Protocol for Vehicular Ad Hoc Networks.

Cooperative communication is an effective method of improving the transmission performance for vehicular ad hoc networks. However, the rapid movement of vehicles leads to frequent changes in network topology and reduces the probability of successful data transmission on the medium access control (MAC) layer. In this paper, we propose an Optimal Cooperative Ad hoc MAC protocol (OCA-MAC) based on time division multiple access (TDMA). OCA-MAC utilizes multiple cooperative nodes to forward data, so as to improve the probability of successful data transmission. It chooses cooperative nodes according to direct successful transmission probability, communication range between potential helper node and destination node, and available time slot. Meanwhile, in order to avoid excessive transmission redundancy caused by multiple cooperative forwarding, the optimal number of cooperative forwarding nodes is obtained through analysis of a probabilistic model. Simulation results show that OCA-MAC improves the successful data transmission rate and reduces the number of transmission times and transmission delay compared to the multichannel TDMA MAC protocol (VeMAC) and the cooperative ad hoc MAC protocol (CAH-MAC).

Cooperative Learning for Spectrum Management in Railway Cognitive Radio Network

High-speed rail public broadband wireless access requires communication terminals with high mobility to perform seamless cell switching in a harsh and fast time-varying environment. However, frequent switching caused by fast motion inevitably leads to unpredictable high volatility of the available spectrum, resulting in inefficient wireless communication. The application of highly mobile cognitive radio technology may address this challenge. This paper first analyzes the physical structure of the existing railway wireless communication network, and determines the characteristics of chainlike distribution and cascade operation of the base stations along the railway. Furthermore, using Bayesian reinforcement learning and multi-agent theory, a distributed cognitive base station model suitable for railway wireless environment is constructed. Based on the joint spectrum management of cascaded base station groups, this model proposes a multi-agent coordination algorithm with the goal of global optimal communication performance of the entire rail line. Finally, this paper evaluates the network performance of various test scenarios, and proves that the cognitive base station multi-agent cascade cooperative system can significantly increase the probability of successful data transmission and greatly reduce wireless spectrum handovers. This proposed scheme provides a brand-new solution for solving the problem of low wireless spectrum efficiency in the typical railway wireless network.

Performance Analysis of MultiACK-SFAMA for Underwater Acoustic Networks

MultiACK-Slotted Floor Acquisition Multiple Access (MultiACK-SFAMA) is a medium access control (MAC) protocol that has been proposed for underwater acoustic networks to overcome the problems of repeating the entire Request To Send (RTS) and Clear To Send (CTS) contention and DATA transmission cycle. In Slotted Floor Acquisition Multiple Access (S-FAMA), when an acknowledgement (ACK) fails to reach the transmitter, the entire cycle repeats. Multi-ACK mechanism has been analyzed in Shahabudeen et al. (IEEE J Ocean Eng 39(1):74–89, 2014). We chose to incorporate this mechanism in S-FAMA to enhance its performance. MultiACK-SFAMA is a modified version of S-FAMA and is aimed to avoid that a successful data transmission is wasted because the acknowledgement is lost, thus saving energy, by using a train of ACKs instead of a single ACK for each DATA packet. By using MultiACK, the probability that an ACK would be received successfully increases. In underwater acoustic networks, due to high propagation delay the cost of losing an ACK is very high and significantly impacts the performance of the protocol. The MultiACK mechanism has been proposed and analyzed to improve the S-FAMA performance. The MultiACK mechanism increases the probability of receiving at least one ACK packet by replying with a train of ACK packets. In this paper we present a mathematical analysis of MultiACK-SFAMA. The paper presents analysis of the performance of MultiACK-SFAMA as a function of transmission range, number of nodes and bit error rates. The results show that the throughput improvement (%) achieved is improved by using a MultiACK train instead of a single ACK. The MultiACK train improves the probability of receiving an ACK by 65.05%, for a BER of 0.005 and saves energy.

Realizing Railway Cognitive Radio: A Reinforcement Base-Station Multi-Agent Model

Wireless communication plays a vital role in the operations of modern rail transportation. The rapid motion characteristics of the train make the wireless spectrum environment unstable and discontinuous. These uncertainties, coupled with the inherent scarcity of the spectrum, lead to inefficiencies in railroad wireless communications. The application of cognitive radio is becoming a cutting-edge research field in railway wireless communication. This paper first analyzes the physical infrastructure of the railway wireless communication network and determines base station as the key communication node in the railway environment, which can implement the cognitive radio technology. Reinforcement learning and agent theory are then used to construct a cognitive base-station model which is suitable for the railway wireless environment. Furthermore, according to the characteristics of the chain-like distribution and cascade operation of the cognitive base stations along the railway, the reinforcement base-station multi-agent system model is proposed, and the unique Dual ε - greedy mechanism is used to drive the learning of multi-agent system to avoid local optimization. Our experimental results prove that the model can significantly improve the probability of successful data transmission in the railway wireless communication network, and greatly reduce the number of wireless channel switching. In addition, the effect of Dual ε - greedy mechanism on communication performance is discussed. This reinforcement base-station multi-agent model in this paper provides a new idea for realizing the railway cognitive radio and comprehensively solves the problem of low spectrum efficiency of cognitive radio in rail transit.

Secured Wireless Sensor Network Framework to Support Guaranteed Successful Data Transmission

Secured Wireless Sensor Network Framework to Support Guaranteed Successful Data Transmission

Efficient Content Delivery for Mobile Communications in Converged Networks

The mobile Internet is already playing a key role in people’s daily lives worldwide, resulting in the dramatic growth in the number of mobile devices. The size of the Internet and the amount of the traffic are being expanded rapidly, which poses various challenges. In particular, the Internet and mobile communications are entering a new era that demands faster communication services and uninterrupted content delivery. A new paradigm called content-centric networking (CCN) is considered as an appropriate way for efficient content delivery. In this paper, we propose a CCN-based efficient content delivery mechanism in the 4G network and also in the upcoming 5G network where various heterogeneous networks are converged. We also propose a novel mobility management scheme to support the content diversity and network diversity by leveraging the abundant computational resources in the mobile network. In addition, we analyze the existing approaches with respect to mobility and evaluate the performance of our seamless content delivery mechanism in terms of content transfer time, throughput, and data transmission success ratio. Simulation results are also presented to show that the content-centric wireless network with our mobility management scheme can improve the data delivery services significantly compared to the existing schemes.

Antenna-in-Package Design and Robust Test for the Link Between Wireless Ingestible Capsule and Smart Phone

An antenna-in-package is proposed for a wireless ingestible capsule operated at ISM (2.4–2.48 GHz) band and also experimentally tested in a system level. The proposed antenna is designed for data transmission between the ingestible capsule and a smart phone using Bluetooth. A modified planar inverted-F structure is proposed for the antenna design, whose end section is bent to be vertical with respect to the substrate, mainly for the considerations of frequency tuning, size reduction, and polarization diversity. First, a one-layer cube muscle phantom model is used for initial design and optimization. Then, a comprehensive evaluation method, with considerations of the polarization mismatch loss, is proposed to analyze the radiation performance using a CST human body model. The evaluated results indicate that the proposed design can ensure enough margins for the link budget despite the relative position between the capsule and the smart phone. In addition to the advantages stated above, the proposed antenna is not sensitive to different digestive organs. Reflection measurement is presented using a muscle-mimicking phantom. Radiation characteristics are presented using a simple human torso model filled with pork by measuring the received power in a system. Successful data transmission, between a transmitter using the proposed antenna and a receiver using a smart phone, is performed in a temperature monitoring system.

A Robust Energy Saving Data Dissemination Protocol for IoT-WSNs

In Wireless Sensor Networks (WSNs) for Internet of Things (IoT) environment, fault tolerance is a most fundamental issue due to strict energy constraint of sensor node. In this paper, a robust energy saving data dissemination protocol for IoT-WSNs is proposed. Minimized energy consumption and dissemination delay time based on signal strength play an important role in our scheme. The representative dissemination protocol SPIN (Sensor Protocols for Information via Negotiation) overcomes overlapped data problem of the classical Flooding scheme. However, SPIN never considers distance between nodes, thus the issue of dissemination energy consumption is becoming more important problem. In order to minimize the energy consumption, the shortest path between sensors should be considered to disseminate the data through the entire IoT-WSNs. SPMS (Shortest Path Mined SPIN) scheme creates routing tables using Bellman Ford method and forwards data through a multi-hop manner to optimize power consumption and delay time. Due to these properties, it is very hard to avoid heavy traffic when routing information is updated. Additionally, a node failure of SPMS would be caused by frequently using some sensors on the shortest path, thus network lifetime might be shortened quickly. In contrast, our scheme is resilient to these failures because it employs energy aware concept. The dissemination delay time of the proposed protocol without a routing table is similar to that of shortest path-based SPMS. In addition, our protocol does not require routing table, which needs a lot of control packets, thus it prevents excessive control message generation. Finally, the proposed scheme outperforms previous schemes in terms of data transmission success ratio, therefore our protocol could be appropriate for IoT-WSNs environment.

A Temporal Adaptive Access Mechanism for Data Fusion in an IoT Environment.

Data fusion in the Internet of Things (IoT) environment demands collecting and processing a wide variety of data with mixed time characteristics, both real-time and non-real-time data. Most of the previous research on data fusion was about the data processing aspect; however, successful data transmission is a prerequisite for high-performance data fusion in IoT. On the other hand, research on data transmissions in IoT mainly focuses on networking without sufficiently considering the special requirements of the upper-layer applications, such as the data fusion process, that are consuming the transmitted data. In this paper, we tackle the problem of data transmission for data fusion in an IoT environment by proposing a distributed scheduling mechanism VD-CSMA in wireless sensor networks, which considers the values for data fusion, as well as the delay constraints of packets when determining their priority levels for transmission. Simulation results have shown that VD-CSMA may enhance both throughput and delay performance of data transmission as compared to the typical scheduling schemes used for data fusion in IoT.

Energy efficient routing for critical physiological parameters in wireless body area networks under mobile emergency scenarios

In recent innovations, mobility networks have achieved a promising critical solution to smart healthcare monitoring systems. Focusing on different realistic scenarios, the mobility model with an energy-efficient communication model helps to find the position and efficient path to communicate with biosensors placed on the patients' body. The sensor data applies a prioritized mechanism to broadcast emergency data towards a sink with lesser propagation delays and path loss. Thus, the routing protocol solution integrates the concepts of critical, priority and hierarchical routing techniques for successful transmission of a patient's vital data to the destination. However, the proposed algorithm outperforms existing direct, in-direct and forwarder-based routing techniques concerning lifetime, average remaining energy and packets received or dropped in the network.

Protocol Architectures for IoT Domains

In this work we discuss architectural alternatives for the design of a proxy that interconnects IoT domains running CoAP with the rest of the Internet including micro datacenters and other domains building scalable hierarchical architectures. We assume that the CoAP domain is terminated by an IoT proxy with cache, and we investigate several design alternatives, assuming that the proxy autonomously maintains data freshness. Our analysis indicates that multicast and observe-based proxies perform better than the default POST/GET approach in terms of successful data transmission, round trip delay and energy consumption, with the multicast option having a slight advantage.

An Enhanced Paradigm for Cognitive Cooperation Networks: Two-to-One Energy and Spectrum Dual-Cooperation

In this paper, two-to-one energy and spectrum dual-cooperation (ESDC) is investigated for cognitive cooperation networks. Specifically, the energy and spectrum of two primary users (PUs) are both transferred or authorized to one multi-antenna secondary user (SU) in exchange for its aid in the signal relaying to guarantee the successful data transmission, whilst the SU, which originally owns no spectrum access privilege and limited energy storage, is also able to concurrently transmit its own data through spatial multiplexing. Moreover, network-coding is also adopted to further compress the data size and hence reduce the power consumption at SU. The formulated problem for the aforementioned two-to-one ESDC model is non-convex and intractable to solve directly. To solve the problem effectively, the Lagrangian dual methods plus fixed-point iteration methods and semidefinite relation methods are employed, and the optimal solution could be achieved through iterative optimization. Simulation results show that, compared with the traditional spectrum-only cooperation, the proposed two-to-one ESDC paradigms can greatly improve the successful transmission probability for PUs and achievable transmission rate for SU. Meanwhile, the proposed two-to-one dual-cooperation modes are significantly superior to the one-to-one cooperation mode, in terms of spectrum efficiency and energy efficiency.

Indoor Localization Using Barely Perceptible Audio Signals

This paper presents a new approach to an audio-based indoor localization system. By using audio signals emitted by a public address sound system, mobile devices may globally localize themselves in an indoor environment where global navigation satellite systems are not viable or reliable. The use of data hiding techniques such as spread spectrum coding or echo hiding has allowed to convey information to a receiver avoiding people’s perception of the added audio content. Results demonstrate a relatively quite good localization with centimetre accuracy and precision and successful data transmission using barely perceptible audio signals.

Characterizing Data Deliverability of Greedy Routing in Wireless Sensor Networks

As a popular routing protocol in wireless sensor networks (WSNs), greedy routing has received great attention. The previous works characterize its data deliverability in WSNs by the probability of all nodes successfully sending their data to the base station. Their analysis, however, neither provides the information of the quantitative relation between successful data delivery ratio and transmission power of sensor nodes nor considers the impact of the network congestion or link collision on the data deliverability. To address these problems, in this paper, we characterize the data deliverability of greedy routing by the ratio of successful data transmissions from sensors to the base station. We introduce $\eta$ -guaranteed delivery which means that the ratio of successful data deliveries is not less than $\eta$ , and study the relationship between the transmission power of sensors and the probability of achieving $\eta$ -guaranteed delivery. Furthermore, with considering the effect of network congestion, link collision, and holes (e.g., those caused by physical obstacles such as a lake), we provide a more precise and full characterization for the deliverability of greedy routing. Extensive simulation and real-world experimental results show the correctness and tightness of the upper bound of the smallest transmission power for achieving $\eta$ -guaranteed delivery.