Channel Access Mechanism Research Articles

Channel access mechanism for maximizing throughput with fairness in wireless sensor networks

The spectrum scarcity problem of wireless sensor networks (WSNs) is improved through amalgamation of cognitive radio networks (CRNs) into WSNs. However, spectrum allocation to secondary users (SUs) is challenging in cognitive radio wireless sensor networks (CR-WSNs) as channel is already crowded and at same time should not induce interference to primary users (PUs). In designing efficient spectrum access model for CR-WSNs recent work have adopted machine-learning game theory (GT) and statistical model. However, the major limitation of existing spectrum access model they fail to assure access fairness with maximal throughput with minimal collision. This work presents a maximizing channel access fairness model to handle the research challenges. To boost CR-WSN performance, the throughput maximization using channel access fairness (TMCAF) employs shared and non-shared channel access designs. Experiment outcome shows throughput is improved and collision in network is reduced in comparison with state-of-art channel access models.

The Distributed Multi Constrained Channel Access Mechanism By Using Framework For Mobile Adhoc Network

The Mobile Ad hoc Network (MANET) is an autonomous wireless communication system, and does not have any fixed infrastructure. The Network loads which arise due to application evolving using MANET environment has to balance using routing protocol. Networks are increasingly resource intensive to efficiently manage the bandwidth & Energy in the group communication on basis of addressing the routing issues to provide the enhanced functionality of the network on basis of channel utilization and channel conditioning against load balancing. In this research, proposed a novel unified framework for Distributed multi constrained Channel Access Mechanism for mobile Adhoc Network on inclusion of Scheduled Throughput Discriminant Multi-Data synchronization Strategies for Channel Allocation in Mobile Adhoc Network on various traffic patterns, Multi Trajectory Particle Swarm Optimization towards Optimal Velocity Control against load balancing and band width controlling and finally Self Adaptive Redundancy Elimination Clustering and Distributed Load Bandwidth Management Constraints for Mobile Ad Hoc Network.

Throughput maximization with channel access fairness model using game theory approach

The <span>adoption of cognitive radio (CR) technology into wireless sensor networks (WSNs) effectively addresses the spectrum scarcity problem of traditional unlicensed spectrum. Allocating and managing limited network channel to secondary user (SUs) considering dynamic behavior pattern of primary users (PUs) is a critical issue of CR-WSN. Recently, various channel access methodologies using statistical, reinforcement learning (RL), game theory (GT), and deep learning (DL) model have been presented for CR-WSN. However, the existing channel access methodologies has following two limitations: i) fails to assure balance between maximizing throughput of SUs with minimal interference to PUs considering multi-channel CR-WSNs environment; and ii) maximizing throughput with minimal collision assuring access fairness among SUs considering energy constraint CR-WSNs. In addressing the research issues, this paper present throughput maximization channel access fairness using game theory (TMCAF-GT) model. The TMCAF employ both shared and non-shared channel access mechanism employing GT model for assuring throughput maximization with minimal interference and access fairness. Experiment outcome shows the TMCAF-GT provides superior throughput with minimal collision.</span>

Inspecting VANET with Various Critical Aspects – A Systematic Review

Due to its strategic significance in the area of smart transportation to facilitate Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, Vehicular Ad Hoc Networks (VANETs) have grown very popular in recent years. The rapid increase in the number of vehicles on the road has also led to the development of heterogeneous, large-scale, and highly dynamic VANETs, which introduce difficulty to fulfill the stringent requirements, which include low latency, high mobility, top security, and massive connectivity of the 5G network. Research works in VANETs mainly focus on message transmission within strict delay requirements based on different applications, data privacy and security. In this respect, a number of studies have been carried out by the researchers that propose models and solutions linked to the enhancement of VANET from several aspects, including applications, Quality of Service (QoS), security, physical layer fading, Artificial Intelligence (AI) techniques, Medium Access Control (MAC), and routing protocols. These factors serve as the driving force for this study's thorough examination of VANETs, which includes information on specific applications, QoS, channel fading, MAC protocols, channel access mechanisms, routing protocols, security, and difficulties. None of the surveys in existence today address all critical aspects of VANET in a single survey. In this paper, a complete taxonomy of VANETs has been provided based on various issues. First, an overview of VANET is presented with different applications. Then, QoS in VANETs and different proposed MAC protocols are discussed. Channel fading and access mechanisms are presented. After that, routing protocols, security, and clustering in VANETs are provided. AI approaches proposed in VANET are also summarized. Finally, a discussion of future research direction for all aspects is presented. This article might be used as a guide or a point of reference while designing and creating applications, networking and communication systems, and data security for VANETs.

Novel MAC protocol for handling correlation and dynamics of medical traffic in WBANs

In Wireless Body Area Networks (WBANs), it is crucial to ensure reliable and timely delivery of gathered data, despite their intrinsic challenges related to limited resources at the sensor level. In fact, in the medical context, most real-time and life-critical applications are both delay-sensitive and loss-sensitive; the loss or corruption packets carrying critical data due to unreliable wireless networks could have severe consequences. In this regard, the concept of context awareness can be exploited; the more an application acquires additional information about users, sensors, and their context, the better it can adapt its functional behavior to improve its performance of delay, throughput and packet loss.Thus, acquiring some knowledge about the patient’s medical state over time may enhance the interpretation and intervention of the medical staff. In this context, we propose a dynamic context-aware MAC protocol (DMTM-MAC) that takes into account the dynamic and correlation aspects of medical traffic in order to adjust the channel access mechanisms and the general network behavior. We assign to each sensor a context that reflects the type of traffic generated and its QoS constraints. We aim to track specific biosensors, which are related to vital indicators relevant to a specific disease; the associated measurements may reflect the variation of the patient’s state over time. To such nodes, we assign a new node context, called dynamic context, which can help to predict the eventual health decline over time. The body node coordinator will have the ability to analyze the received data in order to detect abnormalities and disease evolution. Observed changes in collected measurements can be an early predictor of an advanced or complicated situation and can allow this BAN coordinator to handle dynamic context changes in these types of sensors by increasing their priority and transmission frequency to cope with the current situation. Performance evaluation showed that DMTM-MAC outperforms both MTM-MAC and IEEE 802.15.6 in terms of delay and packet delivery ratio. The proposed approach enhances the delay up to 80 % over IEEE 802.15.6.

IoT-MAC: A Channel Access Mechanism for IoT Smart Environment

IoT-MAC: A Channel Access Mechanism for IoT Smart Environment

Station Grouping Mechanism using Machine Learning Approach for IEEE 802.11ah

IEEE 802.11ah, also known as Wi-Fi HaLow, improves the scalability of the Internet of Things (IoT) by connecting a large number of low-power devices. IEEE 802.11ah has developed a novel feature called Restricted Access Window (RAW) that aims to address one of the main problems of the IoT: excessive channel contention in large sensor networks. All stations can access the channel from the same group when using RAW, which allows the Access Point (AP) to split stations into different groups. Existing station grouping schemes support only the homogeneous traffic scenarios, having the same data transmission interval, packet size, and Modulation and Coding Scheme (MCS). In this paper, we highlight two contributions to solving this problem. The first is a Machine Learning (ML) based model that predicts RAW configuration parameters and performance under specific network conditions. It predicts accurately and is very quick to train. Second, a technique for a channel access mechanism that uses an ML model to determine the optimal RAW station grouping configuration for dynamic traffic conditions and heterogeneous stations. We compare our ML-based model with the existing state-of-the-art solutions. We calculated the performance of our proposed model and compared it to traditional 802.11ah and the Traffic Adaptive RAW Optimization Algorithm (TAROA). The results show that our proposed model outperforms a significant improvement in terms of throughput, packet received ratio, delay, and execution time.

IBAC: An Intelligent Dynamic Bandwidth Channel Access Avoiding Outside Warning Range Problem

IEEE 802.11ax uses the concept of primary and secondary channels, leading to the Dynamic Bandwidth Channel Access (DBCA) mechanism. By applying DBCA, a wireless station can select a wider channel bandwidth, such as 40/80/160 MHz, by applying the channel bonding feature. However, during channel bonding, inappropriate bandwidth selection can cause collisions. Therefore, to avoid collisions, a well-developed media access control (MAC) protocol is crucial to effectively utilize the channel bonding mechanism. In this paper, we address a collision scenario, called Outside Warning Range Problem (OWRP), that may occur during DBCA when a wireless station interferes with another wireless station after channel bonding is performed. Therefore, we propose a MAC layer mechanism, Intelligent Bonding Avoiding Collision (IBAC), that adapts the channel bonding level in DBCA in order to avoid the OWRP. We first design a theoretical model based on Markov chains for DBCA while avoiding the OWRP. Based on this model, we design a Thompson sampling based Bayesian approach to select the best possible channel bonding level intelligently. We analyze the performance of the IBAC through simulations where it is observed that, comparing to other competing mechanisms, the proposed approach can enhance the network performance significantly while avoiding the OWRP.

Throughput analysis of frequency-domain contention under realistic conditions for hyperautomation

ABSTRACT Internet of Things (IoT) has widely been deployed in various industrial fields. To achieve hyperautomation in IoT-based applications, it calls for high-efficient wireless transmission technologies to improve the response speed of IoT nodes. Frequency-domain contention (FDC) is a promising contention-based channel access mechanism to meet this requirement. However, the nature of FDC’s multiple-transmission-multiple-reception and the limit of IoT chip’s synchronization accuracy will lead to carrier frequency offset (CFO) and hence frequent transmission collisions and null transmissions. Considering integer and fractional CFO, this paper theoretically analyzes the FDC throughput and verifies the accuracy of our model via extensive simulations.

A TDMA-Based Access Protocol for Dense Networks with Moving Nodes for IoT Applications

Low-power wide-area (LPWA) technologies have gained popularity in accordance with the explosive growth of the Internet of Things (IoT). Among others, LoRa is considered as the leading standard that can meet the needs of modern wireless networking, mainly offering energy efficiency and broad coverage as well as a massive amount of device support. In addition to the ALOHA protocol, which is the default channel access mechanism used by the standard, a number of alternatives have been proposed in the literature in an effort to ameliorate the overall network performance. Furthermore, with moving nodes gaining ground more and more in the IoT realm and the research being at a relatively premature stage, it is imperative to create innovative algorithms that support highly dense networks with fast moving nodes. Motivated by these reasons, this work proposes a novel medium access protocol that takes advantage of the increased capabilities of modern wake up radio (WuR) technology in order to achieve low latency and mitigate the risk of lost packets in IoT networks with moving nodes based on the LoRa technology. A number of simulation scenarios have been devised and the findings suggest that the proposed protocol achieves the set goals and improves existing solutions.

RE-MAC: A Hybrid MAC Protocol for Underwater Multimedia Communication System

In this article, we propose a reliable and efficient medium access control (MAC) protocol, RE-MAC, for resource-constraint underwater acoustic multimedia communication systems. RE-MAC achieves scalability by utilizing a hybrid MAC protocol smartly, which takes advantage of contention, reservation, and null data packet (NDP) based channel access mechanisms. The proposed scheme achieves multihop communication and synchronization for video streaming using a transmission opportunity (TXOP) based efficient channel access mechanism at the access point (AP) and relay nodes. TXOP time has been utilized by relay and AP for sending stream of frames with low overhead. To improve the efficiency in multihop communication, RE-MAC finds an optimal frame duration for relays and AP. RE-MAC efficiently places the NDP mechanism replacing traditional ACK-based control packet exchange. We incorporate the TXOP in AP and relay nodes for multimedia streaming and sleep mode for stations. Further, the video compression technique gives the efficiency of our system to improve the transmission of multimedia data. Our proposed schemes help communicate multimedia data without compromising the quality of service. We have demonstrated our proposed scheme experimentally in a prototype. After that, a simulation-based detailed performance analysis is provided to see the scalability of the proposed scheme. Compared to the existing state-of-the-art solutions, RE-MAC significantly delivers the throughput, energy consumption, delay, and packet loss.

A First Look at Wi-Fi 6 in Action: Throughput, Latency, Energy Efficiency, and Security

This paper presents a first-of-its-kind performance measurement of Wi-Fi 6 (IEEE 802.11ax) using real experiments. Our experiments focus on multi-client scenarios. The results reveal the impact of the new channel access mechanisms (i.e., OFDMA and TWT) on the spectrum efficiency, energy consumption, latency, and network security. (i) A comparison with the legacy CSMA/CA scheme shows that the commodity Wi-Fi 6 achieves 3× overall throughput and dramatically reduce the latency (5×) when coexisting with legacy Wi-Fi network. (ii) However, the current OFDMA implementation significantly increases the power consumption (6×), implying a design tradeoff between throughput and latency gain versus the cost of energy consumption. (iii) Finally, TWT negotiating procedure is vulnerable to various malicious attacks. We believe that our findings provide critical insights for the scheduling algorithm design, power optimization, and security protection of the next-generation WLANs.

Performance Analysis of IEEE 802.11p Protocol in IoV under Error-Prone Channel Conditions

The complexity of the channel condition in the Internet of vehicles (IoV) may increase the bit error rate (BER) of the intelligent vehicle terminals, resulting in data transmission failures or errors. Therefore, it is necessary to improve the performance of communication protocols under error-prone channel conditions. This article investigates the influence of the access categories’ (ACs) performance with channel errors by using four A C s to cause service differentiation based on the enhanced distributed channel access (EDCA) mechanism of the IEEE 802.11p. To address the error-prone characteristics of the channel and unsaturated traffic conditions, a three-dimensional Markov model is developed first, followed by an analysis of the characteristics and mutual transition probabilities of the seven classes of states in the model, and then, the steady-state equations of the system are developed to derive the steady-state distribution to study system performance. The model considers the backoff phase, the freezing of the backoff counter, the retransmission limit, the probability of collisions occurring, the size of the maximum and minimum contention window, and the number of interframe intervals. These parameters are chosen to meet the requirements for the protocol to operate, while also preventing overestimation of throughput and avoiding having packets being served all the time. We derive expressions for the throughput and delay of A C s under the conditions of error channels and unsaturated traffic. The impact of channel errors on the throughput and delay of A C s is evaluated by numerical simulation. Numerical results show that too many stations in the system will increase the average access delay and decrease throughput. The increase in BER will seriously decrease the performance of high-priority A C s . The throughput of the A C s is also modeled to vary with frame length and BER, and the variation curves of the optimal frame lengths for A C 3 and A C 2 were obtained.

Performance analysis of NOMA in Rayleigh and Nakagami Fading channel

Cellular connectivity is expanding rapidly in the modern world. The multiple access strategy is one of the highly used methods for allocating the range of users in cellular network. Spectrum allocation is a crucial element to take into account since cellular communication is becoming more and more popular. NOMA is a channel access mechanism used in 5G mobile communication. It is also known as non-orthogonal multiple access. NOMA is a potential strategy for enhancing spectral efficiency and sum rate. Using the NOMA method, we evaluated the BER versus transmitted power of two users in rayleigh and nakagami fading channels. In this NOMA setup, a single antenna is shared by two users. Two users can accept the same frequency using 5G NOMA technology, but at different power levels. The results of the MATLAB simulation show that the two user NOMA in the Nakagami channel performs better than the Rayleigh channel in terms of Bit Error Rate vs. Transmitted Power.

Design and Validation of Lifetime Extension Low Latency MAC Protocol (LELLMAC) for Wireless Sensor Networks Using a Hybrid Algorithm

As the battery-operated power source of wireless sensor networks, energy consumption is a major concern. The medium-access protocol design solves the energy usage of sensor nodes while transmitting and receiving data, thereby improving the sensor network’s lifetime. The suggested work employs a hybrid algorithm to improve the energy efficiency of sensor networks with nodes that are regularly placed. Every node in this protocol has three operating modes, which are sleep mode, receive mode, and send mode. Every node enters a periodic sleep state in order to conserve energy, and after waking up, it waits for communication. During the sleep mode, the nodes turn off their radios in order to reduce the amount of energy they consume while not in use. As an added feature, this article offers a channel access mechanism in which the sensors can send data based on the Logical Link Decision (LLD) algorithm and receive data based on the adaptive reception method. It is meant to select acceptable intermediary nodes in order to identify the path from the source to the destination and to minimize data transmission delays among the nodes in the network scenario. Aside from that, both simulation and analytical findings are used to examine the activity of the suggested MAC, and the created models are evaluated depending on their performance. With regard to energy consumption, latency, throughput, and power efficiency, the result demonstrates that the suggested MAC protocol outperforms the corresponding set of rules. The extensive simulation and analytical analysis showed that the energy consumption of the proposed LELLMAC protocol is reduced by 22% and 76.9% the end-to-end latency is 84.7% and 87.4% percent shorter, and the throughput is 60.3% and 70.5% higher than the existing techniques when the number of node is 10 and 100 respectively.

QoS-oriented media access control using reinforcement learning for next-generation WLANs

Orthogonal frequency division multiple access (OFDMA) is introduced in IEEE 802.11ax to satisfy massive transmission demands. However, the uplink OFDMA-based random access (UORA) mechanism provides poor quality of service (QoS), and restricts channel utilization efficiency, especially in the dynamic network environment. To solve these issues, in this study, the transmission period is decoupled into a contention stage and a transmission stage, and an intelligent media access control (MAC) algorithm with QoS-guaranteed for next-generation wireless local area networks (WLANs) is presented. Specifically, we consider stochastic and various traffic in time-varying wireless communication conditions and propose two contention window (CW) optimization mechanisms based on Q-learning (QL) and a deep Q-network (DQN) (referred to as QL-MAC and DQN-MAC) for static and dynamic network scenarios, respectively. Meanwhile, the access point (AP) acts as the reinforcement learning (RL) agent and centrally optimizes the CW for all stations to eventually maximize the system throughput. Furthermore, we provide the QoS-guaranteed channel access mechanism for different priority data traffic, in which high-priority traffic can obtain more channel access opportunities in the uplink contention stage. Simulation results demonstrate that the proposed algorithms can significantly improve the network performance in terms of convergence, throughput, delay, and fairness compared to the adaptive grouping-based two-stage mechanism (BTM) and double random access QoS-oriented OFDMA MAC (DRA-OFDMA) algorithm in various scenarios.

Reliability Optimization for Channel Resource Allocation in Multihop Wireless Network: A Multigranularity Deep Reinforcement Learning Approach

This article investigates the high-reliable data transmission for multihop and multichannel wireless sensor networks (WSNs), which jointly optimizes the channel allocation and channel access mechanisms. We propose a novel wireless paradigm empowered by mobile-edge computing (MEC) and deep reinforcement learning (DRL) to improve the data process ability of WSNs and formulate the joint resource allocation problem for reliability maximization as a partially observable Markov decision process (POMDP). Meanwhile, we introduce the distributed decision-making (DDM) framework to decouple channel optimization into two subproblems: 1) channel allocation and 2) channel access. Correspondingly, we present an asynchronous channel allocation algorithm for multiagent scenario and enable the neighbor cooperation to tackle the nonstationary problem, which can significantly improve the network convergence speed. Besides, we present a collision-free channel access algorithm including three submodules that can simultaneously eliminate vanishing nodes, hidden terminal, and exposed terminal problems in large-scale WSNs. Simulation results demonstrate that the proposed algorithm significantly improves network performance in terms of convergence, throughput, collision, and packet delivery ratio (PDR).

Fuzzy-MAC: An FIS based MAC protocol for a multi-constrained traffic in wireless body area networks

In this paper, we introduce Fuzzy-MAC protocol to facilitate the uniform transmission of the multi-constrained data generated by the sensor devices in a wireless body area network (WBAN). A physiological sensor device allows us to monitor various health-related parameters. In the process, the sensed data become associated with diverse Quality of Service (QoS)-specific constraints depending on the application requirements, thus imposing a challenge in front of the enabling communication protocols such as IEEE 802.15.4. To deal with these multi-constrained traffic data, Fuzzy-MAC first categorizes the sensor data according to the application-specific QoS requirements. Moreover, improved channel access mechanisms are applied according to the priority class value assigned to the different data types. Lastly, employing a Fuzzy Inference System (FIS), Fuzzy-MAC brings context-awareness to a health monitoring environment. We conduct rigorous simulations of the proposed Fuzzy-MAC to justify the competence with the existing literature. The results show that the proposed MAC protocol outperforms the benchmarks in terms of throughput, average delay, and overall reliability of a WBAN.

A Multi-Class Channel Access Scheme for Cognitive Edge Computing-Based Internet of Things Networks

Edge computing-based framework is capable of improving users’ quality of experience in cognitive Internet of Things (IoT) networks. To explore the advantages of this edge computing-based framework, possible offloading and processing delay resulting from computation bottlenecks, and the offloading latency caused due to inter-cell interference must be properly considered. This paper thus considered a multi-class channel access mechanism for cognitive edge computing-based IoT networks where IoT users were categorized based on their quality of experience requirements. Essential IoT devices are permitted to offload to the edge server at any time following the hybrid channel access model, while delay-tolerant IoT devices are only permitted to offload to the server when the channel is idle following the overlay channel access model. Analyses were obtained for transmission rate and offloading delay to demonstrate the performance of the proposed mechanism, while important metrics such as total offloading latency and total offloading cost were investigated. The total offloading costs were formulated through the mixed strategy Nash equilibrium method. The proposed mechanism achieves lower offloading latencies and costs for both type 1 and type 2 CUs when compared with existing methods. The obtained results showed that multi-class channel access mechanisms can reduce packet offloading delay in cognitive edge computing-based IoT networks.

Selfish Behavior in IEEE 802.11ah Networks: A Detection Algorithm and Mitigation Strategies.

One of the latest protocols developed for the Internet of Things networks is IEEE 802.11ah, proposed by the WiFi Alliance. The new channel access mechanism in IEEE 802.11ah, which is called Restricted Access Window, aims at reducing the contention between the stations by allowing only selected stations to transmit data at certain time slots. Stations may exhibit selfish behavior to maximize their own throughput. This will come at the cost of the overall network quality of service. In this paper, we first analyze the default behavior of the IEEE 802.11ah protocol in terms of fairness. We then introduce various percentages of selfish stations and observe how the network’s quality of service in terms of fairness, throughput and packet-loss are affected. After establishing the inherent fairness of IEEE 802.11ah, we analyze applicability of two existing selfish behavior detection algorithms designed for IEEE 802.11 to the IEEE 802.11ah protocol. Due to their poor performance, we propose a new definition of ’selfish behavior’ specifically for IEEE 802.11ah, based on which we present a new algorithm for detecting selfish behavior. To combat selfish behavior and to create a better fairness, throughput and lower packet loss, we consequently present a novel mitigation algorithm called Selfish Stations Quarantine Punishment Algorithm (SSQPA). The proposed algorithm takes advantage of the RAW grouping to isolate selfish stations from the honest stations, thus mitigating the effect of the selfish behavior. SSQPA comes in two variants: honest stations-centric and network-centric. Our experimental results show that both variants can successfully mitigate selfish behavior effects in IEEE 802.11ah networks and either one can be used depending on the goal of the network.