SMART: Coordinated Double-Sided Seal Bid Multiunit First Price Auction Mechanism for Cloud-Based TVWS Secondary Spectrum Market

Spectrum trading is an important aspect of television white space (TVWS) and it is driven by the failure of spectrum sensing techniques. In spectrum trading, the primary users lease their unoccupied spectrum to the secondary users for a market fee. Although spectrum trading is considered as a reliable approach, it is confronted with a spectrum transaction completion time problem, which negatively impacts on end-users Quality of Service and Quality of Experience metrics. Spectrum transaction completion time is the duration to successfully conduct TVWS spectrum trading. To address this issue, this paper proposes simple mechanism auction reward truthful (SMART), a fast and iterative machine learning-assisted spectrum trading model to address this issue. Simulated results indicate thatSMART out-performs referenced VERUM algorithm in three key performance indicators: bit-error rate, instantaneous throughput, and probability of dropped packets by 10%, 5%, and 15%, respectively

Node Cooperation to Avoid Early Congestion Detection Based on Cross-Layer for Wireless Ad Hoc Networks

The resent application of wireless ad hoc networks (WANET) demands a high and reliable data load. The simultaneous transfer of large amounts of data different nearby sources to nearby destinations in a massive network under these circumstances results in the possibility of network congestion. Congestion is an extremely unwanted condition because it creates extra overhead to the already deeply loaded environment, which ultimately leads to resource exhaustion, and can lead to packet drops and retransmission at either the MAC or upper layers. We present a lightweight congestion control and early avoidance congestion control scheme, which can effective control congestion while keeping overhead to a minimum. This scheme is based on the Cross-layer between the MAC and network layers lead to early detection of congestion. With the help of node cooperation the sender node is triggered to find an alternative route based on TMT. This mechanism controls the network resources rather than the data traffic. Detailed performance results show enhancement in the throughput and packet delivery ratio, as well as a reduction in packet drop. Generally, network performance increases

Connected bicycles: Potential research opportunities in wireless sensor network

In the area of high‐performance cycling, cyclist‐performance monitoring system can be considered one of the most important applications. Wireless sensor networks (WSNs) have been identified as one of the technology candidates to meet the mobility model, energy model, and real‐time monitoring of a cyclist. A few key WSN technologies that have been utilized are Bluetooth, ZigBee, Wi‐Fi, and advanced and adaptive network technology (ANT). By utilizing the infrastructure of the mobile and Internet networks, the cyclist parameters can be transmitted to a remote location via a framework system that consists of the WSN protocol and the mobile phone device. The previous research works and commercial products on methods of measuring cycling performance focus on how to transfer the cycling parameters from the bicycle sensor nodes to the monitoring device. With the advanced development of the sensors technology, wireless communication technologies, and cloud computing, the bicycle wireless sensor network is expected to join the Internet of Things (IoT) hype. This chapter provides an overview of bicycle wireless sensor network (BWSN) for connection between the cyclist and a remote monitoring location. BWSN comes with a number of challenges such as limitation of energy resources, limitation of size and weight for mounting of the sensor node on the bicycle as well as varying distances and channel conditions between the cyclist and the monitoring node. A few methods to address these challenges focusing on energy‐efficient techniques are proposed such as sleep/wake strategy, radio optimization, energy‐efficient routing, and energy harvesting. The latest development and potential research topics related to the Internet of Bicycles are also highlighted in this work

Internet of Vehicles Based On Cellular-Vehicle-To-Everything (C-V2X)

In line with the development of automotive and traffic systems, high mobility and density in different road topologies cause scalability and delay issues due to frequent disconnection between communication nodes. From a safety aspect, Cellular-V2X (C-V2X) wireless technology was introduced by the Third Generation Partnership Project Organization (3GPP) to realise the transmission of emergency messages at critical times, anywhere. Specifically, Mode 4 C-V2X supports side-link communication without relying on a base station to provide network coverage. However, Mode 4 is susceptible to several limitations, which include half-duplex transmission, packet collision, and propagation errors that will cause intermittent connectivity issues. It is also difficult to determine appropriate parameter configurations that can increase the spectrum efficiency of dense networks to facilitate reliable and low-latency networks. The objective of this paper is to investigate the effectiveness of a Mode 4 C-V2X system under different road topologies and traffic scenarios. The study adopts a Krauss vehicular mobility model based on SUMO software to model normal and dense networks in a highway and a road intersection scenario, then perform simulation using OMNET++ software to analyse the impact of different physical layer (PHY) configurations such as modulation and coding scheme, packet size, number of resource block allocation, as well as the probability of resource reservation. The results show that the optimal configuration of parameters depends on the scenario. For highway scenarios, a lower MCS and a higher number of RBs are recommended. For road intersection scenarios, a higher MCS and a lower number of RBs are recommended. The packet size should also be in accordance with the requirements of the application used. The findings of this study can be used to assist in the design of an optimal intelligent transportation system using adaptive C-V2X parameters that can be automatically adjusted under different scenarios and network conditions

Internet of Vehicles Based On Cellular-Vehicle-To-Everything (C-V2X)

In line with the development of automotive and traffic systems, high mobility and density in different road topologies cause scalability and delay issues due to frequent disconnection between communication nodes. From a safety aspect, Cellular-V2X (C-V2X) wireless technology was introduced by the Third Generation Partnership Project Organization (3GPP) to realise the transmission of emergency messages at critical times, anywhere. Specifically, Mode 4 C-V2X supports side-link communication without relying on a base station to provide network coverage. However, Mode 4 is susceptible to several limitations, which include half-duplex transmission, packet collision, and propagation errors that will cause intermittent connectivity issues. It is also difficult to determine appropriate parameter configurations that can increase the spectrum efficiency of dense networks to facilitate reliable and low-latency networks. The objective of this paper is to investigate the effectiveness of a Mode 4 C-V2X system under different road topologies and traffic scenarios. The study adopts a Krauss vehicular mobility model based on SUMO software to model normal and dense networks in a highway and a road intersection scenario, then perform simulation using OMNET++ software to analyse the impact of different physical layer (PHY) configurations such as modulation and coding scheme, packet size, number of resource block allocation, as well as the probability of resource reservation. The results show that the optimal configuration of parameters depends on the scenario. For highway scenarios, a lower MCS and a higher number of RBs are recommended. For road intersection scenarios, a higher MCS and a lower number of RBs are recommended. The packet size should also be in accordance with the requirements of the application used. The findings of this study can be used to assist in the design of an optimal intelligent transportation system using adaptive C-V2X parameters that can be automatically adjusted under different scenarios and network conditions

Assisted Car Platooning and Congestion Control at Road Intersections

Enhancing road safety and traffic efficiency are the important aspects and goals that automakers and researchers trying to achieve in recent years. The autonomous vehicle technology has been identified as a solution to achieve these goals. However, the adoption of fully autonomous vehicles in the current market is still in the very early stages of deployment. The objective of this paper is to develop a Cooperative Adaptive Cruise Control (CACC) model at a road intersection using platooning car-following mobility models, object detection at traffic light units, and Vehicle-to-Everything (V2X) communication through vehicular ad hoc networks (VANETs). The mobility model considers traffic simulation using the SUMO-PLEXE-VEINS platforms integration. Next, a prototype of an assisted car platooning system consisting of roadside unit (RSU) and on-board units (OBU) is developed using artificial intelligence (AI)-based smart traffic light for obstruction detection at an intersection and modified remote-control cars with V2X communication equipped with in-vehicle alert notification, respectively. The results show accurate detection of obstruction by the proposed assisted car platooning system, and an optimised smart traffic light operation that can reduce congestion and fuel consumption, improve traffic flow, and enhance road safety. The findings from this paper can be used as a baseline for the framework of CACC implementation by legislators, policymakers, infrastructure providers, and vehicle manufacturers

Assisted Car Platooning and Congestion Control at Road Intersections

Enhancing road safety and traffic efficiency are the important aspects and goals that automakers and researchers trying to achieve in recent years. The autonomous vehicle technology has been identified as a solution to achieve these goals. However, the adoption of fully autonomous vehicles in the current market is still in the very early stages of deployment. The objective of this paper is to develop a Cooperative Adaptive Cruise Control (CACC) model at a road intersection using platooning car-following mobility models, object detection at traffic light units, and Vehicle-to-Everything (V2X) communication through vehicular ad hoc networks (VANETs). The mobility model considers traffic simulation using the SUMO-PLEXE-VEINS platforms integration. Next, a prototype of an assisted car platooning system consisting of roadside unit (RSU) and on-board units (OBU) is developed using artificial intelligence (AI)-based smart traffic light for obstruction detection at an intersection and modified remote-control cars with V2X communication equipped with in-vehicle alert notification, respectively. The results show accurate detection of obstruction by the proposed assisted car platooning system, and an optimised smart traffic light operation that can reduce congestion and fuel consumption, improve traffic flow, and enhance road safety. The findings from this paper can be used as a baseline for the framework of CACC implementation by legislators, policymakers, infrastructure providers, and vehicle manufacturers

Energy models of Zigbee-based wireless sensor networks for smart-farm

In this paper, we evaluated several network routing energy models for smart farm application with consideration of several factors, such as mobility, traffic size and node size using wireless ZigBee technology. The energy models considered are generic, MICA and Zigbee compliant MICAz models. Wireless sensor networks deployment under several scenarios are considered in this paper, taken into account commercial farm specification with varying complex network deployment circumstances to further understand the energy constraint and requirement of the smart farm application. Several performance indicators, such as packet delivery ratio, throughput, jitter and the energy consumption are evaluated and analysed. The simulation result shows that both throughput and packet delivery ratio increases as the nodes density is increased, indicating that, smart farm network with higher nodes density have a superior Quality of Service (QoS) than networks with sparsely deployed nodes. It is also revealed that traffic from the mobile nodes causes increase in the energy consumption, overall network throughput, average end-to-end delay and average jitter, compared to static nodes traffic. Based on the results obtained, the Generic radio energy models consumed the highest total energy, while MICAz energy consumption model offers the least consumption, having the lowest ‘Idle’ and ‘receive’ modes consumption. The MICAz model also has the lowest total consumed energy as compared with the other energy models, suggesting that it is the most suitable energy model that should be adopted for future smart farm deployment

Hybrid Indoor-Based WLAN-WSN Localization Scheme for Improving Accuracy Based on Artificial Neural Network

In indoor environments, WiFi (RSS) based localization is sensitive to various indoor fading effects and noise during transmission, which are the main causes of localization errors that affect its accuracy. Keeping in view those fading effects, positioning systems based on a single technology are ineffective in performing accurate localization. For this reason, the trend is toward the use of hybrid positioning systems (combination of two or more wireless technologies) in indoor/outdoor localization scenarios for getting better position accuracy. This paper presents a hybrid technique to implement indoor localization that adopts fingerprinting approaches in both WiFi and Wireless Sensor Networks (WSNs). This model exploits machine learning, in particular Artificial Natural Network (ANN) techniques, for position calculation. The experimental results show that the proposed hybrid system improved the accuracy, reducing the average distance error to 1.05 m by using ANN. Applying Genetic Algorithm (GA) based optimization technique did not incur any further improvement to the accuracy. Compared to the performance of GA optimization, the nonoptimized ANN performed better in terms of accuracy, precision, stability, and computational time. The above results show that the proposed hybrid technique is promising for achieving better accuracy in real-world positioning applications

Maximizing signal to leakage ratios in MIMO BCH cooperative beamforming scheme

Beamforming (BF) technique in cooperative multiple input multiple output (MIMO) antenna arrays improves signal to noise ratio (SNR) of the intended user. The challenge is to design transmit beamforming vectors for every user while limiting the co-channel interference (CCI) from other users. In this paper, we proposed cooperative beamforming based on Signal-to-Leakage Ratio (SLR) to exploit the leakage power as a useful power in the second time slot after user cooperation, for this purpose successive interference cancellation (SIC) is employed in each user to separate the leakage signal from the desired signal. Without increasing the complexity, Maximizing Signal-to-Leakage Ratio (SLR) subject to proposed power constraint instead of a unity norm is the way to achieve extra leakage power. To reduce the erroneous, Bose–Chaudhuri–Hocquenghem (BCH) codes employed in Beamforming of (SIC) cooperative scheme BF(CS-SIC-BCH). Maximum-likelihood (ML) estimator method is used at each user receiver. Simulation results show that the performance of the proposed scheme BF (CS-SIC-BCH) over Rayleigh and Rician fading channel is significantly better than the performance beamforming based on SLR in Non-cooperative system. More specifically to achieve a BER of about the required SNR for the proposed scheme is about 1 dB less than the Non-cooperative system