Time Division Multiple Access Research Articles

Analytical and Empirical Insights into Wireless Sensor Network Longevity: The Role MAC Protocols and Adaptive Strategies

Wireless Sensor Networks (WSNs) have become essential in various fields, such as corporate industries, environmental monitoring, military defense, and healthcare, due to their ability to monitor and transmit data from remote locations. However, the battery-powered nature of WSN nodes necessitates a strong emphasis on energy conservation to extend network lifespan. This paper evaluates the impact of different Medium Access Control (MAC) protocols on WSN performance, focusing on energy efficiency and data transmission reliability. We explore various MAC protocols, including contention-based protocols like Carrier Sense Multiple Access (CSMA), schedule-based protocols like Time Division Multiple Access (TDMA), and hybrid protocols that combine both approaches. The methodology integrates analytical modelling, empirical data collection, and expert interviews to comprehensively assess the performance and longevity of WSNs under different MAC protocols. Analytical models were developed to simulate WSN behavior, considering factors such as node density, traffic load, and energy consumption. Empirical data from real-world deployments and experimental setups were analyzed to validate the models and provide practical insights. Expert interviews further highlighted the challenges and considerations in MAC protocol design and implementation. This paper underscores the importance of context-specific MAC protocol selection and the potential of adaptive strategies, including machine learning, to enhance WSN efficiency and reliability. These insights can guide the development of more sustainable and high-performing WSN applications.

Delay sensitive and energy adaptive clustering hierarchy protocol using enhanced agglomerative hierarchy algorithm with time‐controlled jellyfish optimization

AbstractWireless sensor networks have sensing functions which made up of small sensor nodes that are processing and communication capabilities. However, the traditional approach of designating a single sensor node as the cluster head often leads to a faster depletion of its energy resources than another node. So, periodic reassignment of the cluster head role among different sensor nodes is crucial to extend operational lifetime and ensure sustained performance. Therefore, this research proposes an Enhanced Agglomerative Hierarchy Algorithm based on the Low Energy Adaptive Clustering Hierarchy (EAHALEACH) protocol, coupled with an enhanced time‐controlled optimization algorithm, to enhance energy efficiency by selecting the optimal cluster head from a candidate pool. The proposed method enhances energy efficiency by optimally selecting cluster heads, which reduces energy consumption and extends the lifespan of the network. Additionally, a lightweight energy‐aware cluster head rotation algorithm is introduced to efficiently rotate the cluster head role within the sensor node network, minimizing unnecessary data transmissions and extending the network lifetime. A hybrid approach combining Carrier Sense and Time Division Multiple Access (CSTDMA) optimizes packet forwarding by dynamically allocating time slots, reducing collisions and contention to improve packet forwarding efficiency. Comparative analysis with existing techniques demonstrates that the hybrid clustering‐based LEACH protocol achieves superior performance in terms of throughput 4600 bps by 2000 rounds, energy consumption 50 J, latency as 1 ms and network lifetime in 1000th node attains 3500 s. These advancements contribute to prolonged operational efficiency and sustained performance in wireless sensor network deployments.

Dynamically stabilized recurrent neural network optimized with intensified sand cat swarm optimization for intrusion detection in wireless sensor network

Wireless Sensor Networks (WSNs) are susceptible to various security threats owing to its deployment in hostile environments. Intrusion detection system (IDS) contributes a critical role on securing WSNs by identifying malevolent activities and ensuring data integrity. Traditional IDS techniques often struggle with the dynamic and resource-constrained nature of WSNs. In this paper, Dynamically Stabilized Recurrent Neural Network Optimized with Intensified Sand Cat Swarm Optimization for Wireless Sensor Network Intrusion identification (DSRNN-ISCOA-ID-WSN) is proposed. Initially, the input data is amassed from WSN-DS dataset. After that, the pre-processing segment receives the data. In pre-processing stage, redundant and biased records are removed from input data with the help of Adaptive multi-scale improved differential filter (AMSIDF). Then the optimal are selected by utilizing Wolf-Bird Optimization Algorithm (WBOA). DSRNN is used to classify the data as Normal, Grey hole, Black hole, Time division multiple access (TDMA), and Flooding attacks. Then Intensified Sand Cat Swarm Optimization (ISCOA) is employed to optimize the weight parameters of DSRNN for accuracte classification. The proposed DSRNN-ISCOA-ID-WSN technique is implemented Python. The performance of the proposed DSRNN-ISCOA-ID-WSN approach attains 29.24 %, 33.45 %, and 28.73 % high accuracy; 30.53 %, 27.64 %, and 26.25 % higher precision when compared with existing method such as Machine Learning-Powered Stochastic Gradient Descent Intrusions Detection System for WSN Attacks (SGDA-ID-WSN), An updated dataset to identify threats in WSN (CNN-ID-WSN) and Denial-of-Service attack detection in WSN: a Low-Complexity Machine Learning Model (DTA-ID-WSN) respectively.

Learning efficiency maximization in UAV-and-RIS-aided mobile edge learning system

With the ever-increasing number of Internet of Things devices (IoTDs) and the rapid development of artificial intelligence (AI) technologies, mobile edge learning (MEL) has emerged as a new communication network paradigm that can deploy machine learning on mobile edge computing (MEC) platforms with abundant computational resources. Then how to fully exploit the potential of MEL and enhance its performance is an important issue. To address this issue, the paper proposes an unmanned aerial vehicle (UAV)-and-reconfigurable intelligent surface (RIS)-aided MEL system. In the system, the UAV equipped with a MEL server can fly close to the IoTDs to collect their data for training a deep learning model. And the RIS mounted on the building can improve the wireless channel environment between the UAV and IoTDs to assist the UAV in collecting data. In order to maximize the MEL learning performance while minimizing the system energy consumption, this paper also proposes a new optimization metric called learning efficiency. Then, a learning efficiency maximization problem based on the proposed system is formulated by jointly optimizing the minority class sample size, the transmit resource of the IoTDs, the phase shift of the RIS, and the trajectory of the UAV. Considering the intractability of the problem, we solve it using the alternating optimization (AO) algorithms based on the two types of UAV trajectory design, i.e., a time-division-multiple-access (TDMA) design with higher performance and a Flight-Hover design with lower complexity. The simulation results demonstrate that the proposed optimization metric and algorithms are effective and perform excellently compared to other baselines.

Load-adaptive MAC protocol for frontier detection in Underwater Mobile Sensor Network

This work proposes a load-adaptive Medium Access Control (MAC) protocol for the frontier/boundary detection application of underwater phenomena using Underwater Mobile Sensor Network (UWMSN). A leader-follower architecture of a swarm of underwater vehicles is proposed here. Autonomous Underwater Vehicles (AUVs) traverse a random mobility pattern beneath one Autonomous Surface Vehicle (ASV) (leader) in the proposed network. ASV has to guide multiple-follower AUVs in the event of interest. The vehicular swarm aims to explore the frontiers in the event to build the map. Load-adaptive MAC protocol is therefore proposed and implemented in this hybrid multi-vehicular network to ensure seamless vehicular communications. The ASV has navigational capabilities to aid the AUVs in navigation and data collection. The proposed MAC protocol can adjust the dynamic mobility and load in the network. The protocol aims to provide dynamic Time Division Multiple Access (TDMA) slots for the AUVs wirelessly linked in the vicinity of the ASV. These slots are used for ranging/navigation and data transmission. Additional urgent data from any AUVs can be transmitted in open Carrier Sense Multiple Access (CSMA) protocol following the TDMA duration. Results have been generated by comparing protocols like CSMA, ALOHA, and TDMA with the proposed Load-Adaptive MAC protocol. The protocols have been compared to the throughput vs number of nodes and throughput vs simulation time. It has been observed that the proposed MAC can perform better than ALOHA and CSMA protocols. Nevertheless, it can produce comparable results for TDMA protocol while supporting the dynamic mobility and load in the network meantime supporting urgent data transmission for nodes in demand.

Energy efficient multi-user task offloading through active RIS with hybrid TDMA-NOMA transmission

In this paper, we address the challenge of minimizing system energy consumption for task offloading within non-line-of-sight (NLoS) mobile edge computing (MEC) environments. Our approach integrates an active reconfigurable intelligent surface (RIS) and employs a hybrid transmission scheme combining time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) to enhance the quality of service (QoS) for user task offloading. The formulation of this problem as a non-convex optimization issue presents significant challenges due to its inherent complexity. To overcome this, we introduce an innovative method termed element refinement-based differential evolution (ERBDE). Initially, through rigorous theoretical analysis, we optimally determine the allocation of local computation resources, computation resources at the base station (BS), and transmit power of users, while maintaining fixed values for the offloading ratio, amplification factor, phase of the reflecting element, and the transmission period. Subsequently, we employ the differential evolution (DE) algorithm to iteratively fine-tune the offloading ratio, amplification factor, phase of the reflecting element, and transmission period towards near-optimal configurations. Our simulation results demonstrate that the implementation of active RIS-supported task offloading utilizing the hybrid TDMA-NOMA scheme results in an average system energy consumption reduction of 80.3%.

Joint optimization for energy efficient full-duplex UAV relaying with multiple user pairs

This paper investigates an unmanned aerial vehicle (UAV) assisted amplify-and-forward relaying, where a full-duplex (FD) fixed-wing UAV employs a time-division multiple access scheduling protocol to provide relay services for multiple source–destination user pairs. With the aim of maximizing energy efficiency (EE) of the system, a joint optimization problem is studied so as to jointly fulfill the communication scheduling of multiple user pairs, the transmit power control and the trajectory design of the UAV. Since the optimization variables of the problem are coupled, it is non-convex and hence hard to solve directly. To this end, the initial problem is decomposed into three subproblems corresponding to the optimization of communication scheduling, and transmit power and trajectory of the UAV, respectively. The three subproblems are solved by utilizing the linear programming, the successive convex approximation (SCA), and the Dinkelbach’s algorithm. Then an iterative algorithm based on the block coordinate descent technique is proposed to tackle the joint optimization problem by optimizing the three blocks of variables alternately. Simulation results demonstrate that the proposed algorithm converges efficiently, and the EE of the joint optimization scheme can be significantly improved compared to the benchmark schemes.

Design and Implementation of TDMA Scheduling Based on BRKGA

Abstract Messages in wireless Ad hoc networks may require intermediate multi-hop forwarding from the source to the target node. To find an optimum contend-free time slots scheduling solution in Ad hoc, a TDMA (Time Division Multiple Access) scheduling scheme based on BRKGA (Biased Random-key GA) is proposed. The algorithm divides the population of generation k into elite group and rest group. The elite solutions are copied directly to the next generation. One parent from the elite group and another parent from the rest population are randomly selected to crossover a new solution. The worst Pm solutions of the population of generation k are replaced by the mutants. The simulation results demonstrate that the TDMA based on BRKGA exhibits better performance in terms of lower frame length and higher channel utilization.

A 0.00179 mm2/Ch Chopper-Stabilized TDMA Neural Recording System with Dynamic EOV Cancellation and Predictive Mixed-Signal Impedance Boosting.

This article presents a digitally-assisted multi-channel neural recording system. The system uses a 16-channel chopper-stabilized Time Division Multiple Access (TDMA) scheme to record multiplexed neural signals into a single shared analog front end (AFE). The choppers reduce the total integrated noise across the modulated spectrum by 2.4× and 4.3× in Local Field Potential (LFP) and Action Potential (AP) bands, respectively. In addition, a novel impedance booster based on Sign-Sign least mean squares (LMS) adaptive filter (AF) predicts the input signal and pre-charges the AC-coupling capacitors. The impedance booster module increases the AFE input impedance by a factor of 39× with a 7.1% increase in area. The proposed system obviates the need for on-chip digital demodulation, filtering, and remodulation normally required to extract Electrode Offset Voltages (EOV) from multiplexed neural signals, thereby achieving 3.6× and 2.8× savings in both area and power, respectively, in the EOV filter module. The Sign-Sign LMS AF is reused to determine the system loop gain, which relaxes the feedback DAC accuracy requirements and saves 10.1× in power compared to conventional oversampled DAC truncation-error ΔΣ-modulator. The proposed SoC is designed and fabricated in 65 nm CMOS, and each channel occupies 0.00179 mm2 of active area. Each channel consumes 5.11 μW of power while achieving 2.19 μVrms and 2.4 μVrms of input referred noise (IRN) over AP and LFP bands. The resulting AP band noise efficiency factor (NEF) is 1.8. The proposed system is verified with acute in-vivo recordings in a Sprague-Dawley rat using parylene C based thin-film platinum nanorod microelectrodes.

Ultra-Low-Power Sensor Nodes for Real-Time Synchronous and High-Accuracy Timing Wireless Data Acquisition.

This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 μs and ultra-low power consumption of 15 μW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ -4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant.

Task execution latency minimization for energy-sensitive IoTs in wireless powered mobile edge computing: A DRL-based method

Wireless powered mobile edge computing (MEC) has become a vital component of future 6G networks, offering efficient computational capabilities to internet of things (IoT) devices and ensuring a stable energy supply. In this paper, we propose an innovative design for a wireless powered MEC-assisted energy-sensitive IoT systems. The system integrates wireless power technology (WPT), allowing IoT devices to efficiently perform tasks under wireless power supply without consuming their battery energy. Therefore, we formulate a dynamic computational task execution latency minimization (DCTELM) problem to tackle the impacts of unpredictable energy requirements and potential communication latency on resource allocation. We also analyze the differences between time division multiple access and non-orthogonal multiple access communication protocols. Given the complexity and dynamic nature of this problem, we define it as a mixed integer nonlinear programming problem. To address this problem, we introduced an enhanced deep reinforcement learning (DRL) algorithm. First, the DCTELM problem is transformed into a Markov decision process (MDP), which simplifies the structure of the problem. Then, after determining the offloading decision, we reveal the convex relationship between resource allocation and task latency. Furthermore, the action space of MDP is further reduced, and a multi-head proximal policy optimization (PPO) algorithm is proposed to deal with the simplified MDP problem. This process reduces the complexity of problem solving, decreases the consumption of computational resources. Simulation results demonstrate that our method outperforms other baselines in terms of computation latency, particularly with the introduction of an exploration strategy during the training phase, which significantly reduces task latency.

Resource allocation for relaying cognitive network with energy harvesting

This paper investigates a relay-based cognitive wireless-powered communication network with multiple secondary receivers and a secondary relaying network. In the secondary network, a relay adopts an energy model to harvest energy from radio frequency signals of the primary and secondary sources and subsequently utilizes the harvested energy to cooperate with the source for information transmission to multiple Internet-of-things users over time-division multiple access. The research group formulated a novel optimization problem that jointly optimizes the power and time fraction for energy and information transmission to maximize the sum throughput of the secondary network. The authors first converted the non-convex problem to a more computationally tractable problem and then proposed an iterative algorithm in which closed-form solutions are obtained at each iteration. Thus, simulation results verify and demonstrate the effectiveness of the proposed approach.

A Hybrid Anti-Collision Protocol Based on Frequency Division Multiple Access (FDMA) and Time Division Multiple Access (TDMA) for Radio Frequency Identification (RFID) Readers

Radio Frequency Identification (RFID) technology plays a crucial role in various Internet of Things (IoT) applications, necessitating the integration of RFID systems into dense networks. However, the presence of numerous readers leads to collisions, degrading communication between readers and tags and compromising system performance. To tackle this challenge, researchers have proposed Medium Access Control (MAC) layer protocols employing different channel access methods. In this paper, we present a novel solution, the Distributed Time Slot Anti-Collision protocol (DTS-AC), which employs a new TDMA notification system to address Reader-to-Reader Interference (RRI), while incorporating FDMA-based frequency resource management to resolve Reader-to-Tag Interference (RTI) collision issues. Simulation results demonstrate that DTS-AC significantly improves performance in dense RFID networks by enhancing read rates, with scalability benefits based on the number of readers, channels, and Time Slots (TSs). Moreover, the cost-effectiveness of DTS-AC facilitates efficient deployment in RFID networks, emphasizing considerations of time delay and data sensitivity.

Improved tag estimation method for TDMA anticollision protocols using CA-CFAR technique

Abstract Radio Frequency Identification (RFID) is considered a key element of the Internet of Things (IoT). It enables objects to be identified and tracked using radio waves. In RFID systems, several tags may be present within the range of the reader, leading to collisions and interference between signals that reduce system performance. Several anti-collision solutions have been proposed to solve this problem based on TDMA (Time-Division Multiple Access) technique, such as the ALOHA, Slotted ALOHA and Dynamic Framed Slotted ALOHA (DFSA) protocols. In this paper, we propose to improve TDMA anti-collision protocols based on the Cell Averaging Constant False Alarm Rate (CA-CFAR) scheme. The CA-CFAR technique is introduced to estimate the number of tags in the physical layer so that the reader can adjust its transmission parameters accordingly and avoid collisions. The obtained results show that this technique achieves good results in terms of system efficiency and the number of required slots to identify all the tags of the system.

Communication-aware formation control for networks of AUVs

We propose a distributed formation control algorithm augmented with communication awareness. We consider autonomous underwater vehicles (AUVs) that are able to communicate over an acoustic link using a Time Division Multiple Access (TDMA) protocol, and to measure the Signal-to-Noise Ratio (SNR) of incoming messages. Based on the measured SNR and packet loss, we endow them with a distributed formation control scheme that accounts for the time-varying nature of the acoustic communication channel. This scheme allows a network of N AUVs to follow a pre-determined, twice-differentiable path while adapting their formation. The size of the formation is dynamically scaled by a formation adaptation mechanism to stabilize the estimated packet loss probability at a desired level. A distributed packet loss estimator is then built on top of the same average consensus routines used by the formation control algorithm, and thus comes with a minimal communication overhead. We test the algorithm by means of high-fidelity simulators, and verify its efficacy in making the network of agents retain formation-wide communication capabilities in a range of cases.

Medium access control protocol based on time division multiple access scheme for wireless body area network

In recent years, the demand for wireless body area network (WBAN) technology has increased, driven by advancements in medical and healthcare applications. WBAN consists of small, low-power, and heterogeneous sensor devices attached inside or outside the body for continuous health monitoring. Medium access control (MAC) is pivotal in addressing WBAN challenges by ensuring reliability and energy efficiency under a dynamic environment caused by body movement. Therefore, to tackle these challenges, this paper presents a MAC protocol based on time division multiple access (TDMA) to enhance the WBAN performance. The proposed TDMA-MAC protocol employs a one-periodic scheduled-based access method to provide reliable data transmission while satisfying the WBAN requirements. The proposed protocol is compared to the IEEE 802.15.6 MAC, enhanced packet scheduling algorithm MAC (EPSA-MAC), and concurrent MAC (C-MAC) protocols based on the performance metrics of packet delivery ratio (PDR), network throughput, energy consumption, and average delay. The simulation results show that the TDMA-MAC protocol outperforms its competitors as it could achieve up to 98% PDR, 30% enhanced throughput, 30% energy optimization, and 20% improvement in average delay.

Channel utilization of media access control protocols for underwater acoustic networks with propagation delay and mobilitya).

This paper investigates the impact of mobility on underwater acoustic communication networks in which the propagation delay is comparable to or larger than the packet duration. An underwater acoustic wireless network, consisting of static and mobile nodes, is studied for its link-layer channel utilization. Synchronous and asynchronous media access control (MAC) protocols are employed with ALOHA, TDMA (time-division multiple access), and artificial intelligence (AI) agent nodes. The simulation results of a multi-node network show that the asynchronous MAC protocols achieve up to 6.66× higher channel utilization than synchronous protocols by allowing time slots to be shorter than the maximum propagation delay among nodes and permitting asynchronous transmission time. The high mobility of a few mobile nodes also favors asynchronous protocols and increases the overall channel utilization. However, node mobility causes more difficulties for the AI node to learn the environment, which may be ineffective to achieve higher gains in channel utilization.

A State-Interactive MAC Layer TDMA Protocol Based on Smart Antennas

Mobile ad hoc networks are self-organizing networks that do not rely on fixed infrastructure. Smart antennas employ advanced beamforming technology, enabling ultra-long-range directional transmission in wireless networks, which leads to lower power consumption and better utilization of spatial resources. The media access control (MAC) protocol design using smart antennas can lead to efficient usage of channel resources. However, during ultra-long-distance transmissions, there may be significant transport delays. In addition, when using the time division multiple access (TDMA) schemes, it can be difficult to manage conflicts arising from adjacent time slot advancement caused by latency compensation in ultra-long-range propagation. Directional transmission and reception can also cause interference between links that reuse the same time slot. This paper proposes a new distributed dynamic TDMA protocol called State Interaction-based Slot Allocation Protocol (SISAP) to address these issues. This protocol is based on slot states and includes TDMA frame structure, slot allocation process, interference self-avoidance strategy, and slot allocation algorithms. According to the simulation results, the MAC layer design scheme suggested in this paper can achieve ultra-long-distance transmission without conflicts. Additionally, it can reduce the interference between links while space multiplexing. Furthermore, the system exhibits remarkable performance in various network aspects, such as throughput and link delay.

UAV-Mounted RIS-Aided Mobile Edge Computing System: A DDQN-Based Optimization Approach

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are increasingly employed in mobile edge computing (MEC) systems to flexibly modify the signal transmission environment. This is achieved through the active manipulation of the wireless channel facilitated by the mobile deployment of UAVs and the intelligent reflection of signals by RISs. However, these technologies are subject to inherent limitations such as the restricted range of UAVs and limited RIS coverage, which hinder their broader application. The integration of UAVs and RISs into UAV–RIS schemes presents a promising approach to surmounting these limitations by leveraging the strengths of both technologies. Motivated by the above observations, we contemplate a novel UAV–RIS-aided MEC system, wherein UAV–RIS plays a pivotal role in facilitating communication between terrestrial vehicle users and MEC servers. To address this challenging non-convex problem, we propose an energy-constrained approach to maximize the system’s energy efficiency based on a double-deep Q-network (DDQN), which is employed to realize joint control of the UAVs, passive beamforming, and resource allocation for MEC. Numerical results demonstrate that the proposed optimization scheme significantly enhances the system efficiency of the UAV–RIS-aided time division multiple access (TDMA) network.

Enhancing Quality of Service and Energy Efficiency in Wireless Sensor Networks through TDMA-Based Topology and Cross-Layer Approaches

It is necessary to continuously monitor the environment with the sensors in delay-sensitive wireless sensor network applications. The continuous monitoring operation results in higher energy consumption by the sensor nodes; wireless sensor networks are also subject to numerous difficulties, including latency, packet loss, and throughput. These are the most important constraints in wireless sensor networks (WSNs) and need to improve for better quality communication. In this research, quality of service is related to numerous systems that have very sensitive parameters such as end-to-end delay, packet loss, throughput, energy consumption, latency, and average delay. Therefore, it is necessary for the sensors to transmit reliable data within a reasonable timeframe. To find out better communication results, we have used the TDMA (Time Division Multiple Access) approaches in our paper to improve packet delivery ratio (PDR), latency, and throughput. The NS2 simulator is used to test communication between nodes. Latency, the packet delivery ratio (PDR) of transferring data packets, and throughput are measured. The TDMA approach is used in our proposed topology, and we can see a specific area where nodes are connected to communicate with one another. Each node is communicating with the other node using two different paths. Each node's bandwidth, latency, and PDR will be different. Hence, in one path, a TDMA approach is implemented, which results in improved node performance in terms of latency, packet delivery ratio, and bandwidth.