A Low Collision and High Throughput Data Collection Mechanism for Large-Scale Super Dense Wireless Sensor Networks

Super dense wireless sensor networks (WSNs) have become popular with the development of Internet of Things (IoT), Machine-to-Machine (M2M) communications and Vehicular-to-Vehicular (V2V) networks. While highly-dense wireless networks provide efficient and sustainable solutions to collect precise environmental information, a new channel access scheme is needed to solve the channel collision problem caused by the large number of competing nodes accessing the channel simultaneously. In this paper, we propose a space-time random access method based on a directional data transmission strategy, by which collisions in the wireless channel are significantly decreased and channel utility efficiency is greatly enhanced. Simulation results show that our proposed method can decrease the packet loss rate to less than 2 % in large scale WSNs and in comparison with other channel access schemes for WSNs, the average network throughput can be doubled

Feature Selection Approaches for Optimising Music Emotion Recognition Methods

The high feature dimensionality is a challenge in music emotion recognition. There is no common consensus on a relation between audio features and emotion. The MER system uses all available features to recognize emotion; however, this is not an optimal solution since it contains irrelevant data acting as noise. In this paper, we introduce a feature selection approach to eliminate redundant features for MER. We created a Selected Feature Set (SFS) based on the feature selection algorithm (FSA) and benchmarked it by training with two models, Support Vector Regression (SVR) and Random Forest (RF) and comparing them against with using the Complete Feature Set (CFS). The result indicates that the performance of MER has improved for both Random Forest (RF) and Support Vector Regression (SVR) models by using SFS. We found using FSA can improve performance in all scenarios, and it has potential benefits for model efficiency and stability for MER task

Millimeter Wave Radar-based Human Activity Recognition for Healthcare Monitoring Robot

Healthcare monitoring is crucial, especially for the daily care of elderly individuals living alone. It can detect dangerous occurrences, such as falls, and provide timely alerts to save lives. Non-invasive millimeter wave (mmWave) radar-based healthcare monitoring systems using advanced human activity recognition (HAR) models have recently gained significant attention. However, they encounter challenges in handling sparse point clouds, achieving real-time continuous classification, and coping with limited monitoring ranges when statically mounted. To overcome these limitations, we propose RobHAR, a movable robot-mounted mmWave radar system with lightweight deep neural networks for real-time monitoring of human activities. Specifically, we first propose a sparse point cloud-based global embedding to learn the features of point clouds using the light-PointNet (LPN) backbone. Then, we learn the temporal pattern with a bidirectional lightweight LSTM model (BiLiLSTM). In addition, we implement a transition optimization strategy, integrating the Hidden Markov Model (HMM) with Connectionist Temporal Classification (CTC) to improve the accuracy and robustness of the continuous HAR. Our experiments on three datasets indicate that our method significantly outperforms the previous studies in both discrete and continuous HAR tasks. Finally, we deploy our system on a movable robot-mounted edge computing platform, achieving flexible healthcare monitoring in real-world scenarios

BodyMAC : energy efficient TDMA-based MAC protocol for Wireless Body Area Networks

Wireless Body Area Networks (WBANs) enable placement of tiny biomedical sensors on or inside the human body to monitor vital body signs. The IEEE 802.15.6 task group is developing a standard to optimize WBAN performance by defining the physical layer (PHY) and media access control (MAC) layer specifications. In this paper an energy efficient MAC protocol (BodyMAC) is proposed. It uses flexible bandwidth allocation to improve node energy efficiency by reducing the possibility of packet collisions and by reducing radio transmission times, idle listening and control packets overhead. BodyMAC is based on a Downlink and Uplink scheme in which the Contention Free Part in the Uplink subframe is completely collision free. Three types of bandwidth allocation mechanisms allow for flexible and efficient data and control communications. An efficient Sleep Mode is introduced to reduce the idle listening duration, especially for low duty cycle nodes in the network. Simulation results show superior performance of BodyMAC compared to that of the IEEE 802.15.4 MAC.5 page(s

Channel Efficiency Aware Scheduling Algorithm for Real-Time Services in Wireless Networks

Abstract In this paper, we consider the problem of scheduling real time services over time-varying wireles

Channel Efficiency Aware Scheduling Algorithm for Real-Time Service in Wireless Networks

6 page(s

Research on the implementation of VoIP service in mobile Ad Hoc network

This paper presents a new way to design VoIP application on mobile Ad Hoc network, which can correctly solve the problem of variable IP addresses in MANET and allows the nodes to communicate with each other reliably.3 page(s

A Two-tier evolutionary game theoretic approach to dynamic spectrum sharing through Licensed Shared Access

In this paper, we propose a two-tier evolutionary game for dynamic spectrum sharing nsing Licensed Shared Access (LSA) to serve the additional capacity needs of Mobile Network Operators (MNOs). The proposed algorithm ensures fair and demand-driven allocation of spectrum resources to LSA licensees, guaranteeing spectrum availability for licensees unlike previous spectrum sharing techniques. Moreover, we extend the evolutionary game by modeling the dynamic price adjustment strategies adopted by incumbents achieving an improved total gain for the incumbents. We prove the stability of the proposed evolutionary algorithm using Lyapunov stability criteria. Additionally! we perform simulations to prove convergence and stability of the evolutionary algorithm, and highlight the effects of dynamic parameters offered by incumbents such as price on overall average licensee payoff.6 page(s