Contention-based Medium Access Control Research Articles

A novel distributed multi-slot TDMA-based MAC protocol for LED-based UOWC networks

Underwater optical wireless communication (UOWC) networks are promising for many civilian and industrial applications due to underwater high-speed data transmission demand. This paper focuses on the distributed time division multiple access (TDMA) protocol design for UOWC networks. Unlike existing research, we focus on a more general underwater communication scenario where nodes are scattered, and node mobility is considered. We propose a distributed TDMA-based medium access control(MAC) protocol, called cluster-based cross-layer multi-slot MAC(CCM-MAC), which can assign multiple slots to each node according to the slot-occupying information. When using CCM-MAC, the network is divided into clusters. Each node maintains knowledge of the cluster topology and slot-occupying information of surrounding nodes through routing techniques; by updating this knowledge, collisions can also be detected and eliminated dynamically. Analysis results are presented to evaluate the performance of CCM-MAC, and simulations show that high network throughput and low collision rate can be realized in comparison with existing contention-based MAC protocols.

The Upper Bounds of Cellular Vehicle-to-Vehicle Communication Latency for Platoon-Based Autonomous Driving

Cellular vehicle-to-vehicle (V2V) communications can support advanced cooperative driving applications such as vehicle platooning and extended sensing. As the safety critical applications require ultra-low communication latency and deterministic service guarantee, it is vital to characterize the latency upper bound of cellular V2V communications. However, the contention-based Medium Access Control (MAC) and dynamic vehicular network topology brings many challenges to model the upper bound of cellular V2V communication latency and assess the link capability for quality of service (QoS) guarantee. In this paper, we are motivated to reduce the research gap by modelling the latency upper bound of cellular V2V with network calculus. Based on the theoretical model, the probability distribution of the delay upper bound can be obtained under the given task features and environment conditions. Moreover, we propose an intelligent scheme to reduce upper bound of end-to-end latency in vehicular platoon scenario by adaptively adjusting the V2V communication parameters. In the proposed scheme, a deep reinforcement learning model is trained and implemented to control the time slot selection probability and the number of time slots in each frame. The proposed approaches and the V2V latency upper bound are evaluated by simulation experiments. Simulation results indicate that our network calculus based analytical approach is effective in terms of the latency upper bound estimations. In addition, with fast iterative convergence, the proposed intelligent scheme can significantly reduce the latency by about 80% compared with the conventional V2V communication protocols.

A Multilevel Scheduling MAC Protocol for Underwater Acoustic Sensor Networks(UASN)

Underwater acoustic sensor networks (UASNs) have attracted great attention in recent years and utilizes as a part of oceanic applications. This network has to deal with propagation delay, energy constraints and limited bandwidth which are strenuous for designing a Medium Access Control (MAC) protocol for underwater communication. There also exists an idle channel listening and overhearing problem which sets down the energy into starvation in the contention-based MAC protocols. Alternatively, lengthy time slots and time synchronization equated by schedule-based MAC protocols, outcomes the variable transmission delay and degrades the network performances. To iron out these problems, we propose a cluster-based MAC protocol, tagged as Multilevel Scheduling MAC (MLS-MAC) protocol for UASN in the paper. The cluster head is a decision maker for packet transmission and aids to inflate the lifetime of sensor nodes. To reinforce the channel efficiency, the multilevel scheduling in data phase is initiated with two queues depending on the applications fixed by the cluster head. The simulation result shows that the MLS-MAC has increased the network throughput and has decreased energy consumption.

Energy-Efficient Collision Avoidance MAC Protocols for Underwater Sensor Networks: Survey and Challenges

The Medium Access Control (MAC) layer protocol is the most important part of any network, and is considered to be a fundamental protocol that aids in enhancing the performance of networks and communications. However, the MAC protocol’s design for underwater sensor networks (UWSNs) has introduced various challenges. This is due to long underwater acoustic propagation delay, high mobility, low available bandwidth, and high error probability. These unique acoustic channel characteristics make contention-based MAC protocols significantly more expensive than other protocol contentions. Therefore, re-transmission and collisions should effectively be managed at the MAC layer to decrease the energy cost and to enhance the network’s throughput. Consequently, handshake-based and random access-based MAC protocols do not perform as efficiently as their achieved performance in terrestrial networks. To tackle this complicated problem, this paper surveys the current collision-free MAC protocols proposed in the literature for UWSNs. We first review the unique characteristic of underwater sensor networks and its negative impact on the MAC layer. It is then followed by a discussion about the problem definition, challenges, and features associated with the design of MAC protocols in UWANs. Afterwards, currently available collision-free MAC design strategies in UWSNs are classified and investigated. The advantages and disadvantages of each design strategy along with the recent advances are then presented. Finally, we present a qualitative comparison of these strategies and also discuss some possible future directions.

Machine Learning for Medium Access Control Protocol Recognition in Communications Networks

The ability to recognize the medium access control protocol employed by a network can facilitate the incorporation of a cognitive radio into an existing network by elucidating an integral aspect of network behavior. Since the way in which users access the electromagnetic spectrum is one of the most prominent distinctions between reservation based and contention based medium access control protocols, the first part of this work exploits the regular timing of transmissions from networks utilizing reservation based time-division multiple access (TDMA) protocols to differentiate between transmissions governed by TDMA and by contention based carrier sense multiple access (CSMA) protocols. Our approach leverages modular arithmetic to identify periodicity in transmission timings and an unsupervised $k$ -means algorithm to generate distinct TDMA and CSMA clusters. Several supervised machine learning algorithms are explored to build a protocol classifier. We then present a method of distinguishing between transmissions from multi-channel frequency division multiple access (FDMA) based networks and single channel networks. This method uses an automated machine learning clustering algorithm to obtain an estimate of the actual center frequencies of channels utilized by a network. Such information can be used to determine whether the network is employing an FDMA protocol to access the electromagnetic spectrum.

A Time-Slotted Data Gathering Medium Access Control Protocol Using Q-Learning for Underwater Acoustic Sensor Networks

Contention-based medium access control (MAC) protocols for underwater acoustic sensor networks are designed to handle packet collisions that are caused by long propagation delays. However, existing protocols are known to suffer from relatively high collisions, which decrease system performance. To enhance system performance, we propose a contention-based MAC protocol that employs a widely-popular machine learning technique, namely, Q-learning. Using Q-learning, the proposed protocol allows the sensor nodes to intelligently select the back-off slots and accordingly schedule the transmission of data packets such that collisions are minimized at the receiver. Unlike in existing protocols, the sensor nodes are not required to exchange scheduling information, which implies that the proposed protocol has low complexity and overhead. Under varying traffic loads and node numbers, the proposed protocol is compared with the state-of-the-art ALOHA-Q for underwater environment (UW-ALOHA-Q), multiple access collision avoidance for underwater (MACA-U) and exponential increase exponential decrease (EIED) protocols. Results demonstrate the effectiveness of the proposed protocol in terms of energy efficiency, channel utilization, and latency.

A Q-Learning-Based Adaptive MAC Protocol for Internet of Things Networks

In Internet of Things (IoT) applications, sometimes the quality of service (QoS) of throughput for transmitting video or the QoS of bounded delay for control of a sensor node is required. A traditional contention-based medium access control (MAC) protocol cannot meet the adaptive traffic demands of these networks and confers delay-related constraints. Q-learning (QL) is one of the reinforcement learning (RL) mechanisms and can potentially be the future machine learning scheme for spectrum MAC protocols in IoT networks. In this study, a QL-based MAC protocol is proposed to facilitate adaptive adjustment of the length of the contention period in response to the ongoing traffic rate in IoT networks. The novelty of QL-based MAC lies in its use of RL to dynamically adjust the length of the contention period according to the traffic rate. The QL-based MAC will solve the models without additional input information to adapt to environmental variations during training. We confirm that the proposed QL-based MAC protocol with node contention is robust. In addition, we showed that our proposed QL-based MAC protocol has higher system throughput, lower end-to-end delay, and lower energy consumption in MAC contention than those of contention-based MAC protocols.

ECM-MAC: An Efficient Collision Mitigation Strategy in Contention Based MAC Protocol

Medium Access Control (MAC) is the sub-layer of data link layer in the communication OSI model, whose basic function is to control the hardware that interacts with the transmission medium. In Wireless sensor network, the communication is done without any wired connectivity and the sensor nodes get energy through batteries. MAC layer protocol is classified as contention based and contention free. Over the years, these protocols have served efficiently in controlling the data transmission, yet they have some compromising and challenging issues, and data collision is one of them. Contention based protocols are reported to be more vulnerable to collisions, as they allow sharing of channels among transmitting nodes. Collisions result in message delay, data loss and retransmissions, resultantly demanding more energy. Researchers are trying their best to reduce the collisions during transmission. This research is aimed to evaluate the priority-based collision control in contention-based protocol. We present a new technique Energy efficient Collison mitigation MAC (ECM-MAC) protocol to handle the QoS and energy constraints for variety of applications. ECM-MAC uses different contention window and node priority mechanism to get the overall performance of the network. Nodes with most important data or with least residual energy will be allowed to transmit first, thereby minimizing the chances of collisions and maximizing the lifespan of the network. In case of high level of collision, the data traffic will be intelligently reduced to minimize the collisions. A contention-based MAC protocol is evaluated to compare the impacts of suggested mechanism with the standard protocol. It will be useful for designers to develop a better collision avoiding contention-based protocol. In wireless Sensor Networks (WSNs), a sensor node is typically comprising detection unit, transceiver unit (to send and receive the data packets), processing unit and an energy supply unit for energy provisioning. Enhancement in network lifetime is an important and major challenge in WSNs. Since the transceiver unit has more impact on the energy consumption of the node and its reduction results in significant improvement on network lifetime. In our work, we propose an efficient mechanism that prioritized the nodes to get the high performance in quality of service. MAC protocol is targeted to achieve this, as MAC layer has the authority access to the transmission channel. We prioritize the nodes with lower energy to increase the chances of that node to get access of the transmission channel before their power and battery ran out. Our proposed strategy outperforms other protocols in terms of content retrieval Time, retransmissions and energy efficiency.

Occupancy-balancing downlink transmission for enhancing scalability of LoRa networks

LoRa/LoRaWAN is growing rapidly as an underlying technology for the Internet of Things because of its long-range connectivity and low power. However, its limited scalability, owing to spread spectrum–based modulation and contention-based medium access control, impedes its use in emerging massive applications. In this study, we propose a downlink transmission scheme for enhancing the scalability of LoRa networks. The proposed scheme consists of two mechanisms. First, a modulation parameter is adjusted using the different transmission power limitations between the gateways and end devices to reduce downlink duration. Second, the timing of downlink traffic is selected based on uplink traffic concentration analysis. The proposed scheme reduces the uplink failure, and consequently, allows more end devices to participate in the network. The simulation results show that the proposed scheme is superior to the standard protocol in terms of the packet delivery ratio.

SDN-Based Centralized Channel Assignment Scheme Using Clustering in Dense WLAN Environments

IEEE WLAN 802.11 uses a contention-based medium access control protocol. Adjacent WLAN access points (APs) and stations (STAs) sharing the same channels causes interference, and affect network performance. Existing distributed channel selection schemes are prone to channel oscillation and ripple effect problems. Additionally, centralized schemes may provide optimal or sub-optimal performance but the computation time complexity increases with number of APs. In this paper, we propose a clustering-based centralized channel selection scheme implemented using software-defined networking architecture to overcome these limitations. In the proposed scheme, we first group the APs using a similarity-based clustering algorithm using proximity information. The channel assignment problem is then formulated using an integer linear programming model. Performance evaluation using simulations indicate that the proposed scheme outperforms the common least congested channel selection (LCCS), in terms of throughput and required number of channels switching. Additionally, for N number of APs, the computation complexity is reduced from $$\mathcal {O}\left( 3^{N}\right)$$ to $$\mathcal {O}\left( (N)^2\right)$$ , in comparison to the common centralized channel selection scheme. The test bed results validate simulation results by achieving better data rates for the STAs compared with the LCCS scheme.

Cooperative Coevolution Design of Multilevel Fuzzy Logic Controllers for Media Access Control in Wireless Body Area Networks

Fuzzy logic techniques have been successfully employed in wireless body area networks (WBANs) for media access control (MAC). However, most of the existing research work considered only single-level fuzzy logic controls (FLCs) for either contention-based or contention-free MAC, neglecting the importance of utilizing both contention-based and contention-free MAC to improve the reliability and performance of the network. This paper introduces a two-level FLC control scheme at both the coordinator level and the sensor level to further enhance communication quality in WBANs. The sensor-level FLC controls contention-based channel access and the coordinator-level FLC controls contention-free channel access. This two-level FLC architecture can effectively improve the cooperation between sensors and the coordinator such that both the performance and reliability of the network can be significantly improved. We also proposed a cooperative coevolutionary approach to design of our proposed two-level control scheme automatically. With the goal of effectively designing useful FLCs, we have particularly developed two new collaborator selection methods for our cooperative coevolutionary approach, which enable us to effectively select collaborators while evaluating the candidate FLC design in each subpopulation. Specifically, we demonstrate that, by employing network knowledge, our collaborator selection methods can judge the suitability of potential collaborator designs and notably improve the effectiveness of our evolutionary design approach. Empirically the FLCs designed by our approach clearly outperformed several state-of-the-art MAC schemes from recent literature and the IEEE 802.15.4 standard.

MAC protocol for indoor optical wireless networks

Optical wireless communication has emerged as a promising candidate for future high data rate indoor applications such as virtual reality. Even though physical layer of optical wireless networks has rapidly developed during last decade, upper layer architecture that harness the physical layer capabilities has not yet been developed in the same pace. To this end, the authors develop a novel contention-based medium access control (MAC) protocol that accompanies a service differentiation mechanism and a dynamic contention window tuning algorithm. The proposed service differentiation mechanism can identify the diverse traffic types and facilitate their throughput and delay requirements. To add more robustness to the contention-based MAC protocol which depends on contention windows to avoid collisions, the authors also propose an algorithm that dynamically changes the contention window sizes to suit the congestion level. They analyse the performance of the proposed MAC protocol under diverse network configurations and they show that it is far more effective to use end-user network metrics such as throughput in dynamic adaptation algorithms in addition to collision rate due to the wide range of traffic types present in the network. The proposed results demonstrate that the proposed MAC protocol can handle next-generation traffic types and their stringent latency requirements in an effective manner.

A Q-Learning Approach With Collective Contention Estimation for Bandwidth-Efficient and Fair Access Control in IEEE 802.11p Vehicular Networks

Vehicular ad hoc networks (VANETs) are wireless networks formed of moving vehicle stations, that enable safety-related packet exchanges among them. Their infrastructure-less, unbounded nature allows the formation of dense networks that present a channel sharing issue, which is harder to tackle than in conventional WLANs, due to fundamental differences of the protocol stack. Optimizing channel access strategies is important for the efficient usage of the available wireless bandwidth and the successful deployment of VANETs. We present a Q-learning-based approach to wirelessly network a big number of vehicles and enable the efficient exchange of data packets among them. More specifically, this work focuses on a IEEE 802.11p-compatible contention-based medium access control protocol for efficiently sharing the wireless channel among multiple vehicular stations. The stations feature algorithms that “learn” how to act optimally in a network in order to maximize their achieved packet delivery and minimise bandwidth wastage. Additionally, via a collective contention estimation mechanism, which we embed on the Q-learning agent, faster convergence, higher throughput, and short-term fairness are achieved.

Offloading and Transmission Strategies for IoT Edge Devices and Networks.

We present a machine and deep learning method to offload trained deep learning model and transmit packets efficiently on resource-constrained internet of things (IoT) edge devices and networks. Recently, the types of IoT devices have become diverse and the volume of data has been increasing, such as images, voice, and time-series sensory signals generated by various devices. However, transmitting large amounts of data to a server or cloud becomes expensive owing to limited bandwidth, and leads to latency for time-sensitive operations. Therefore, we propose a novel offloading and transmission policy considering energy-efficiency, execution time, and the number of generated packets for resource-constrained IoT edge devices that run a deep learning model and a reinforcement learning method to find an optimal contention window size for effective channel access using a contention-based medium access control (MAC) protocol. A Reinforcement learning is used to improve the performance of the applied MAC protocol. Our proposed method determines the offload and transmission strategies that are better to directly send fragmented packets of raw data or to send the extracted feature vector or the final output of deep learning networks, considering the operation performance and power consumption of the resource-constrained microprocessor, as well as the power consumption of the radio transceiver and latency for transmitting the all the generated packets. In the performance evaluation, we measured the performance parameters of ARM Cortex-M4 and Cortex-M7 processors for the network simulation. The evaluation results show that our proposed adaptive channel access and learning-based offload and transmission methods outperform conventional role-based channel access schemes. They transmit packets of raw data and are effective for IoT edge devices and network protocols.

A Collision-Free Graph Coloring MAC Protocol for Underwater Sensor Networks

Employing contention-based medium access control (MAC) protocols in underwater sensor networks (UWSNs) is typically costly. This is due to the unique characteristics of underwater acoustic channels, such as long propagation delay, limited bandwidth, and high bit error rate. As a consequence, the contention-based (handshaking and random access-based) MAC protocols do not perform as efficiently as expected. The collision-free approach is therefore considered to achieve high performance by avoiding the collisions at the MAC layer in order to improve energy efficiency, throughput, and fairness. In this paper, we propose, inspired by the graph coloring techniques, a novel energy-conserving and collision-free reservation-based MAC protocol, called GC-MAC, for UWSNs. GC-MAC employs time-division multiple access (TDMA)-like approach by assigning separate time-slots, colors, to every individual sensor node in every two-hop neighborhood. Sensors with the same colors can thus transmit at the same time with no chance of collision. GC-MAC is also able to address the near-far effect, spatial-temporal uncertainty, and hidden/exposed node problems, without requiring code-division multiple access (CDMA) or power adjustment for collision avoidance. The network coverage and connectivity is then discussed to show the effectiveness of using cubes to cover a 3D underwater environment. Our extensive performance study shows that GC-MAC performs well by avoiding collisions to achieve better throughput and energy-efficiency performance compared with those of contention-based protocols. There is also a significant improvement in terms of packet delivery ratio and fairness among the nodes under different operational conditions.

MAC Layer Approaches for Mitigating the Spectrum Underutilization Due to Overlapping BSS Problem in WLAN

Overlapping basic service set (OBSS) interferences cause significant reductions in the network throughput as seen at the medium access control (MAC) layer. When the frequency bands of multiple OBSSs are partially overlapped, the reduction of throughput are worsened because in addition to the OBSS interference problem there arises the OBSS-induced spectrum underutilization problem which has not been addressed so far. In this paper, two complementary contention-based MAC layer schemes are proposed to solve the OBSS-induced spectrum underutilization problem by setting up a virtual-primary channel for contention. The proposed time-limited (TL) scheme is designed for transmitting long data packets, which is suitable for applications requiring high throughputs. In contrast, the multi-contention (MC) scheme is designed for transmitting short data packets, which is suitable for applications requiring short delays. By efficiently exploiting the OBSS-induced underutilized spectrum, simulation results verify that the proposed TL scheme can increase the throughput greatly and the proposed MC scheme can reduce the delay for short data packets significantly.

An Energy-Conserving Collision-Free MAC Protocol for Underwater Sensor Networks

An underwater sensor network (UWSN) has recently attracted considerable attention due to its ability to discover and monitor the aquatic environment. However, its acoustic communication has posed several inherent characteristics, such as high latency, low available bandwidth, and high bit error rate. These unique characteristics have made contention-based medium access control (MAC) protocols inefficient for UWSNs. They are most expensive and are not as effective as they are in terrestrial networks. Through this principle, a contention-free MAC protocol is, therefore, considered to be more reliable and flexible to overcome the consequences of applying acoustic signals and also to achieve a high performance (improving the energy efficiency and throughput across the network) by eliminating the chance of collision. In this paper, we propose a novel energy-conserving and collision-free depth-based layering MAC (DL-MAC) protocol for UWSNs. DL-MAC is able to deal with the underwater MAC challenges, such as the near-far effect, spatial-temporal uncertainty, and hidden/exposed terminal problems. It is able to efficiently schedule the transmission and reception operations in each side by using the concept of layering and a distributed clustering algorithm. By using a TDMA-based principle, DL-MAC can assign separate time slots to every sensor node individually to access the medium without any possibility of collision. Our extensive simulation study shows that DL-MAC outperforms other protocols in terms of throughput, packet delivery ratio, energy consumption, and packets lost under varying traffic rates and the numbers of nodes.

An Efficient Scalable Scheduling MAC Protocol for Underwater Sensor Networks.

Underwater Sensor Networks (UWSNs) utilise acoustic waves with comparatively lower loss and longer range than those of electromagnetic waves. However, energy remains a challenging issue in addition to long latency, high bit error rate, and limited bandwidth. Thus, collision and retransmission should be efficiently handled at Medium Access Control (MAC) layer in order to reduce the energy cost and also to improve the throughput and fairness across the network. In this paper, we propose a new reservation-based distributed MAC protocol called ED-MAC, which employs a duty cycle mechanism to address the spatial-temporal uncertainty and the hidden node problem to effectively avoid collisions and retransmissions. ED-MAC is a conflict-free protocol, where each sensor schedules itself independently using local information. Hence, ED-MAC can guarantee conflict-free transmissions and receptions of data packets. Compared with other conflict-free MAC protocols, ED-MAC is distributed and more reliable, i.e., it schedules according to the priority of sensor nodes which based on their depth in the network. We then evaluate design choices and protocol performance through extensive simulation to study the load effects and network scalability in each protocol. The results show that ED-MAC outperforms the contention-based MAC protocols and achieves a significant improvement in terms of successful delivery ratio, throughput, energy consumption, and fairness under varying offered traffic and number of nodes.

DCO-MAC: A Hybrid MAC Protocol for Data Collection in Underwater Acoustic Sensor Networks.

In underwater acoustic sensor networks (UASNs), medium access control (MAC) is an important issue because of its potentially significant effect on the network performance. However, designing a suitable MAC protocol for the UASN is challenging because of the specific characteristics of the underwater acoustic channel and network, such as limited available bandwidth, long propagation delay, high bit-error-rate, and sparse network topology. In addition, as the traffic load is non-uniformly distributed in a UASN for data collection, it is essential to consider the application feature for the MAC protocol. In this paper, we propose a MAC protocol in a data-collection-oriented UASN, abbreviated as the DCO-MAC protocol. In the proposed protocol, the network is partitioned into two kinds of sub-networks according to the traffic load. A contention-based MAC protocol is used in the sub-network with a light traffic load, while a reservation-based MAC protocol is used in the sub-network with a heavy traffic load. Meanwhile, the DCO-MAC protocol supports the access of mobile nodes. The theoretical analysis and simulation results demonstrate that, in a UASN for data collection, the proposed MAC protocol outperforms the other existing MAC protocols, in terms of the network throughput, end-to-end packet delay, energy overhead, and fairness.

Hybrid ARQ-Based Fairness Enhancement in Uplink WLAN

Per-station fairness is one of the important and challenging issues in wireless local area network based on a contention-based medium access control protocol. The unfairness problem among stations having different channel qualities is caused by the imbalance in access probability and outage probability among them. In this paper, we investigate the per-station fairness by resolving the imbalance in the above two factors through the enhanced distributed coordination function (DCF) adopting hybrid automatic repeat request (HARQ), named the DCF-H. We suggest adopting the HARQ with Chase combining as a solution for the imbalance in the outage probability on top of the loss differentiation mechanism which resolves the imbalance in the access probability. We also analyze the performance of the DCF-H by proposing a new Markov chain model and derive a closed-form expression of system throughput, delay, and outage probability. We compare the performance of the DCF-H with that of the conventional DCF protocols and find out that the DCF-H not only guarantees the near-perfect per-station fairness but also improves the whole system performance, such as throughput, delay, and outage probability.