Packet Collision Rate Research Articles

An Environment-Aware Adaptive Data-Gathering Method for Packet-Level Index Modulation in LPWA.

Low-power wide-area (LPWA) is a communication technology for the IoT that allows low power consumption and long-range communication. Additionally, packet-level index modulation (PLIM) can transmit additional information using multiple frequency channels and time slots. However, in a competitive radio access environment, where multiple sensors autonomously determine packet transmission, packet collisions occur when transmitting the same information. The packet collisions cause a reduction in the throughput. A method has been proposed to design a mapping table that shows the correspondence between indexes and information using a packet collision minimization criterion. However, the effectiveness of this method depends on how the probability of the occurrence of the information to be transmitted is modeled. We propose an environment-aware adaptive data-gathering method that identifies the location of factors affecting sensor information and constructs a model for the probability of the occurrence of sensor information. The packet collision rate of the environment-aware adaptive data-gathering method was clarified through computer simulations and actual experiments on a 429 MHz LPWA. We confirm that the proposed scheme improves the packet collision rate by 15% in the computer simulation and 30% in the experimental evaluation, respectively.

Reinforcement Learning Approach for SF Allocation in LoRa Network

LoRa technology is widely used to build wireless networks in various Internet of Things (IoT) applications. As the increased popularity of IoT, LoRa also gains tremendous attention in recent years. In most of the LoRa networks, Aloha protocol is employed to send packets which may easily lead to collisions. Thanks to the orthogonality of spreading factor (SF) in modulate technique, a potential solution is obtained for this collision issue in LoRa network. In this study, a reinforcement learning (RL) based method called LR-RL is proposed to assign SF properly to alleviate collisions. The idea of LR-RL is mainly derived from the mathematical model of SF-channel traffic equilibrium, which indicates that SF with higher data rate must undertake more packet loads. Based on the system model, several similar methods such as LR-opt-pro, LR-greedy and LR-RL are put forward successively. The LR-RL algorithm owns the best performance in terms of packet collision rate (PCR). In addition, we carry out simulations to evaluate the performance of LR-RL in both one-hop and LoRa-Mesh networks. Results show that LR-RL has lower PCR than other SF allocation methods. Moreover, practical experiments are also conducted to verify the performance. All the experiments exhibit that LR-RL is a desirable method to reduce packet collisions in LoRa network.

Delay Optimization in LoRaWAN by Employing Adaptive Scheduling Algorithm With Unsupervised Learning

Low Power Wide Area Network (LPWAN) technologies have been exponentially growing because of the tremendous growth of the Internet of Things (IoT) devices across the globe. Several LPWAN technologies have been utilized by the researchers to address certain issues like increased number of collisions, retransmissions, delay, and energy consumption. However, Long Range Wide Area Network (LoRaWAN) is the most suitable and attractive technology in terms of delay optimization, low cost and efficient energy consumption. The main issue which arises in LoRaWAN is because of its high packet drop rate due to collision. The reason behind this packet drop rate is the MAC scheme known as Pure Aloha used by LoRaWAN for the transmission of the frames. Long Range (LoRa) End Devices (EDs) initiate communication with Pure Aloha that leads to a high number of retransmissions. These retransmissions further enhance the delay in LoRa networks. This paper aims to optimize the delay in LoRaWAN by using an Adaptive Scheduling Algorithm (ASA) with an unsupervised probabilistic approach called Gaussian Mixture Model (GMM). By using ASA with GMM, the retransmissions are reduced which optimizes the delay in LoRaWAN. The results show that in our approach, Packet Collision Rate (PCR) is reduced by 39% as compared to conventional LoRaWAN. In addition, the Packet Success Ratio (PSR) is also increased by 39% as compared to the conventional LoRaWAN and Dynamic Priority Scheduling Technique (PST). Further, the delay is optimized by 91% and 79%. This research could be effective for the environments where the critical data of patients need to be sent with optimised retransmissions and minimum delay towards gateways.

Effect of service differentiation on QoS in IEEE 802.11e enhanced distributed channel access: a simulation approach

The enhanced distributed channel access (EDCA) protocol is a supplement to IEEE 802.11 medium access control (MAC), ratified by IEEE 802.11e task group to support quality of service (QoS) requirements of both data and real-time applications. Previous research show that it supports priority scheme for multimedia traffic but strict QoS is not guaranteed. This can be attributed to inappropriate tuning of the medium access parameters. Thus, an in-depth analysis of the EDCA protocol and ways of tuning medium access parameters to improve QoS requirements for multimedia traffic is presented in this work. An EDCA model was developed and simulated using MATLAB to assess the effect of differentiating contention window (CW) and arbitration inter-frame space (AIFS) of different traffic on QoS parameters. The optimal performance, delay, and maximum sustainable throughput for each traffic type were computed under saturation load. Insight shows that traffic with higher priority values acquired most of the available channels and starved traffic with lower priority values. The AIFS has more influence on the QoS of EDCA protocol. It was also observed that small CW values generate higher packet drops and collision rate probability. Thus, EDCA protocol provides mechanism for service differentiation which strongly depends on channel access parameters: CW sizes and AIFS.

Cluster-Based Stable BSM Dissemination System for Safe Autonomous Platooning

Recently, the importance of vehicle safety supporting system has been highlighted as autonomous driving and platooning has attracted the researchers. To ensure driving safety, each vehicle must broadcast a basic safety message (BSM) every 100 ms. However, stable BSM exchange is difficult because of the changing environment and limited bandwidth of vehicular wireless communication. The increasing number of vehicles on the road increases the competition to access wireless networks for BSM exchange; this increases the packet collision rate. An increased packet collision rate impairs the transmission and reception of BSM information, which can easily cause a traffic accident. We propose a solution, the vehicular safety support system (V3S), which exchanges BSMs reliably even when many vehicles are on the road. The V3S uses a clustering scheme to decrease network traffic by reducing the amount of data exchanged between a vehicle and the roadside unit (RSU). In addition, the V3S reduces the collision rate of wireless network packets by broadcasting the vehicle's BSM in an allocated timeslot using the time division multiple access (TDMA) MAC protocol. The V3S also deals with insufficient bandwidth for dedicated short-range communications (DSRC) by changing DSRC channels according to traffic flow. In evaluating the packet error rate for stable BSM packet delivery, the V3S demonstrates an excellent packet error rate of less than 1%, compared to the 802.11p with its packet error rate of 82%.

A genetic fuzzy contention window optimization approach for IEEE 802.11 WLANs

The role of IEEE 802.11 wireless local area networks (WLANs) become vital due to its low cost deployment and the aspiration to improve its performance has become the need of the day. Among existing challenges the most prominent are success ratio, packet loss ratio, collision rate, fairness index, and energy consumption, advances in wireless technologies stress to surmount these challenges. To attain these, performance enhancement, genetic fuzzy-contention window optimization (GF-CWO) approach, is proposed which combined the fuzzy logic controller (FLC) and genetic algorithm (GA), through this way GA optimally tuned the FLC. For this purpose three alogrithms are proposed, namely Algorithm-1: GF-CWO for WLANs, Algorithm-2: GA for GF-CWO, and Algorithm-3: FLC for GF-CWO. Proposed GF-CWO approach is tested for binary exponential back-off (BEB), selected being a de facto standard algorithm, and for Channel Status based Sliding Contention Window (CS-SCW) algorithm, selected being a fuzzy logic based algorithm, implemented in MATLAB. Recorded the resultant values for 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 nodes in different files for BEB, CS-SCW, and GF-CWO, respectively. Later on these files were used to evaluate success ratio, packet loss ratio, collision rate, fairness index and energy consumption and also generated the results graphically. The results generated through simulated test confirmed that the GF-CWO has effectively enhanced the performance.

Mitigating the effect of negative link correlation on contention mechanism of MAC protocols in wireless sensor networks

The existence of link correlation has been empirically validated, and different exemplary works exploit the link correlation in the designing of various network protocols. In this work, the authors investigated the impact of this link correlation in contention mechanism of medium access control (MAC). They illustrated negative link correlation could deteriorate the contention mechanism designed to handle hidden terminal problems and, consequently, increase the packet collision rate between the neighbours. They also showed that negative link correlation could increase the chance of an exposed terminal problem. Therefore, ignoring negative link correlation could lead to overestimate overall network throughput and underestimate packet delay. Next, instead of designing a new contention mechanism exploiting the link correlation, they proposed a new routing tree which mitigates the negative effect of negative link correlation without altering the underlying MAC layer. They evaluated this routing tree on Indriya testbed with TelosB nodes and compared it to the minimum spanning tree based on link quality only. The results show improvement in end-to-end throughput and packet reception ratio at each node.

Performance evaluation of grouping and regrouping scheme for mitigating hidden station problem in IEEE 802.11ah network

An Access Point can support up to 8192 stations with a coverage area of 1 km in the IEEE 802.11ah network. Due to its large communication area, this network severely suffers from hidden station problem. The various grouping strategies are used to separate hidden pairs, but with more communication overheads and computational complexity. To mitigate this, the efficient Randomness Region-based Grouping (RRG) and Proximity-based Regrouping Scheme (PRS) are proposed in this paper. The RRG partitions the entire network into a number of regions based on their closeness to reduce the active hidden stations. The PRS further reduces hidden stations using distance matrix and regroups into existing or new groups based on the Centroid of the neighbour groups. The proposed Markov model and NS3 simulation results reveal that the proposed algorithms outperform the other algorithms in terms of throughput, packet loss, packet collision rate, and fairness index.

Fuzzy-based beaconless probabilistic broadcasting for information dissemination in urban VANET

Multi-hop broadcasting in Vehicular Ad Hoc Network (VANET) is the basic building block for renovating on-road travel experiences because it supports message dissemination among a large number of travelers in real-time. In order to make forwarding decisions, 1-hop neighbor table is suggested to be maintained at each vehicle by exchanging beacon packets on a periodic basis. However, a network crowded with both data packets and beacons gives rise to broadcast storms, resulting in insignificant resource consumption. Therefore, a beaconless approach would be a better solution that can control packet flow throughout the network, owing to minimizing packet collision and drop rate. Some recent literature that does not rely on beacon information broadcasts ample amounts of data packets, promoting wastage of bandwidth. To handle such issues, a Fuzzy-based Beaconless Probabilistic Broadcasting Algorithms (FBBPA) is proposed to notify vehicles about an event in less broadcasting. It is a receiver oriented broadcast suppression technique, where the forwarding probability of the packets in vehicles’ buffer is decided on the basis of their distance, angular orientation, movement direction and buffer load delay. The resultant probability is used to reschedule the packet. Among all the packets in the vehicle's buffer, the one having the highest priority at an instant is broadcasted first. For accident and advertisement packets, the analysis through simulation reveals that the proposed algorithm outperformed the compared protocols in terms of reachability (Information coverage), average delay and saved rebroadcast.

Improving Quality-Of-Service in LoRa Low-Power Wide-Area Networks through Optimized Radio Resource Management

Low Power Wide Area Networks (LPWAN) enable a growing number of Internet-of-Things (IoT) applications with large geographical coverage, low bit-rate, and long lifetime requirements. LoRa (Long Range) is a well-known LPWAN technology that uses a proprietary Chirp Spread Spectrum (CSS) physical layer, while the upper layers are defined by an open standard—LoRaWAN. In this paper, we propose a simple yet effective method to improve the Quality-of-Service (QoS) of LoRaWAN networks by fine-tuning specific radio parameters. Through a Mixed Integer Linear Programming (MILP) problem formulation, we find optimal settings for the Spreading Factor (SF) and Carrier Frequency (CF) radio parameters, considering the network traffic specifications as a whole, to improve the Data Extraction Rate (DER) and to reduce the packet collision rate and the energy consumption in LoRa networks. The effectiveness of the optimization procedure is demonstrated by simulations, using LoRaSim for different network scales. In relation to the traditional LoRa radio parameter assignment policies, our solution leads to an average increase of 6% in DER, and a number of collisions 13 times smaller. In comparison to networks with dynamic radio parameter assignment policies, there is an increase of 5%, 2.8%, and 2% of DER, and a number of collisions 11, 7.8 and 2.5 times smaller than equal-distribution, Tiurlikova’s (SOTA), and random distribution, respectively. Regarding the network energy consumption metric, the proposed optimization obtained an average consumption similar to Tiurlikova’s, and 2.8 times lower than the equal-distribution and random dynamic allocation policies. Furthermore, we approach the practical aspects of how to implement and integrate the optimization mechanism proposed in LoRa, guaranteeing backward compatibility with the standard protocol.

Unsupervised Learning Clustering and Dynamic Transmission Scheduling for Efficient Dense LoRaWAN Networks

Long-Range (LoRa) communication technology is considered as a promising connectivity solutions for Internet of Things (IoT) dense applications. In particular, LoRa has drawn the interest due to its low power consumption and wide area coverage. Despite the benefits of LoRaWAN protocol, it still suffers from excessive random and simultaneous transmissions due to the adoption of ALOHA protocol. Therefore, resulting in severe packet collision rate as the network scales up. This leads to continuous retransmission attempts, which in return increase the transmission delay and energy consumption. Thus, this paper proposes a dynamic transmission Priority Scheduling Technique (PST) based on the unsupervised learning clustering algorithm to reduce the packet collision rate and enhance the network's transmission delay and energy consumption. Particularly, the LoRa gateway classifies the nodes into different transmission priority clusters. While the dynamic PST allows the gateway to configure the transmission intervals for the nodes according to the transmission priorities of the corresponding clusters. This work allows scaling up the network density while maintaining low packet collision rate and significantly enhances the transmission delay & the energy consumption. Simulation results show that the proposed work outperforms the typical LoRaWAN and recent clustering & scheduling schemes. Therefore, the proposed work is well suited for dense applications in LoRaWAN.

MAC Protocol for Underwater Sensor Networks Using EM Wave With TDMA Based Control Channel

Underwater communication (UWC) growing over the past few decades is the major tool for exploring the vast underwater space. Research and development of UWC networks are dominantly acoustic (Ac) wave based due to its long coverage capability. However, the demand of low latency and high throughput applications, and the emergence of short-range UWCs in modern times has drawn attention towards the development of electromagnetic (EM) wave based underwater sensor networks (UWSNs). In light of this, this paper proposes novel medium access control (MAC) protocols by integrating EM-based communications in UWSNs. Proposed protocols use one TDMA-based control channel and one or multiple data channel(s) for control and data packet transmissions respectively. The control channel uses EM wave, while the data channels use either EM or Ac wave for communications. Proposed MAC protocols are developed considering both protocol interference model and physical interference model by taking both propagation delay and underwater channel impairments into account. Performance of the protocols is evaluated through extensive simulations in terms of throughput, packet collision rate, waiting time, energy requirement and network coverage. Impact of system parameters including offered load, network size, data packet size, control packet power, water conductivity and channel bandwidth are analyzed thoroughly. A comprehensive feasibility study by comparing the proposed MAC protocols with the traditional protocols as well as with the full Ac-wave based counterpart is also presented for further validations.

Channel Status based Contention Algorithm for Non-safety Applications in IEEE802.11p Vehicular Network

Abstract The purpose of a vehicular network is to provide better safety and infotainment services to the users through an effective communication. To attain this, IEEE802.11p standard is developed with Enhanced Distributed Channel Access (EDCA) contention-based priority-oriented medium access method. In this method, the backoff algorithm selects the Contention Window (CW) size randomly from the range (0, CW). The lower bound for this range is always fixed as zero, irrespective of the backoff stages and congestion in the network. This causes the backoff procedure to select the smaller value even when the network is heavily congested thus increasing collisions and congestion. To resolve this unbalanced backoff interval problem, this paper proposes a Channel Status based Contention Algorithm (CSCA) which regulates the backoff interval based on the number of retransmission attempts by packets and vehicle density for the non-safety applications. The network simulation is carried out using NS3 along with SUMO for highway traffic and results are shown that the proposed algorithm outperforms the default EDCA and Adaptive EDCA (A-EDCA) methods in terms of packet collision rate, throughput and delay.

Joint Design of Access Point Selection and Path Planning for UAV-Assisted Cellular Networks

Unmanned aerial vehicle (UAV)-assisted communication is envisioned as a potential solution to the data traffic explosion in the massive machine-type communications (mMTC) scenario. In this article, we investigate the UAV-assisted cellular networks, where a UAV acts as a flying relay to offload part of the data traffic from the overloaded cell to another. We utilize the practical spatial distribution of data traffic and a convincing air-to-ground channel model. The quality of service (QoS) is defined as a UAV utility function which is designed based on a packet loss ratio (PLR)-related users’ cost function to represent the performance improvements brought by the UAV. We formulate a joint optimization problem to maximize the UAV utility function and then decompose it into the subproblems about the access point selection and the UAV path planning, which influence the PLR by influencing the packet collision rate and channel state. Since the access point selection subproblem is NP-hard, a game-theory-based distributed algorithm is proposed, instructing the users to select the base station (BS) or the UAV as the access point autonomously. To achieve the most superior channel state, we solve the UAV path planning subproblem by a deep reinforcement learning (DRL)-based approach, instructing the UAV to take the optimal action in each position. The simulation results show that the proposed access point selection scheme can significantly reduce the average cost of users and the proposed UAV path planning method can achieve a path with smaller average channel pathloss compared with other approaches.

An adaptive grouping scheme in ultra-dense IEEE 802.11ax network using buffer state report based two-stage mechanism

IEEE 802.11ax, which is an emerging WLAN standard, aims at providing highly efficient communication in ultra-dense wireless networks. However, due to a large number of stations (STAs) in the ultra-dense device deployment scenarios, the potentially high packet collision rate significantly decreases the communication efficiency of WLAN. In this paper, we propose an adaptive STA grouping scheme to overcome this dense network challenge in IEEE 802.11ax by using Buffer State Report (BSR) based Two-stage Mechanism (BTM). In order to achieve the optimal efficiency of BSR delivery, we analyze the functional relationship between STA number in group and Resource Unit (RU) efficiency. Based on this analysis results, an adaptive STA grouping algorithm with variable group size is proposed to achieve efficient grouping in BTM. The numerical results demonstrate that the proposed adaptive BTM grouping algorithm significantly improves the BSR delivery efficiency and the throughput of overall system and each STA in the ultra-dense wireless network.

Slotted ALOHA on LoRaWAN-Design, Analysis, and Deployment.

LoRaWAN is one of the most promising standards for long-range sensing applications. However, the high number of end devices expected in at-scale deployment, combined with the absence of an effective synchronization scheme, challenge the scalability of this standard. In this paper, we present an approach to increase network throughput through a Slotted-ALOHA overlay on LoRaWAN networks. To increase the single channel capacity, we propose to regulate the communication of LoRaWAN networks using a Slotted-ALOHA variant on the top of the Pure-ALOHA approach used by the standard; thus, no modification in pre-existing libraries is necessary. Our method is based on an innovative synchronization service that is suitable for low-cost wireless sensor nodes. We modelled the LoRaWAN channel with extensive measurement on hardware platforms, and we quantified the impact of tuning parameters on physical and medium access control layers, as well as the packet collision rate. Results show that Slotted-ALOHA supported by our synchronization service significantly improves the performance of traditional LoRaWAN networks regarding packet loss rate and network throughput.

Random access with adaptive packet aggregation in LTE/LTE-A.

While random access presents a promising solution for efficient uplink channel access, the preamble collision rate can significantly increase when massive number of devices simultaneously access the channel. To address this issue and improve the reliability of the random access, an adaptive packet aggregation method is proposed. With the proposed method, a device does not trigger a random access for every single packet. Instead, it starts a random access when the number of aggregated packets reaches a given threshold. This method reduces the packet collision rate at the expense of an extra latency, which is used to accumulate multiple packets into a single transmission unit. Therefore, the tradeoff between packet loss rate and channel access latency has to be carefully selected. We use semi-Markov model to derive the packet loss rate and channel access latency as functions of packet aggregation number. Hence, the optimal amount of aggregated packets can be found, which keeps the loss rate below the desired value while minimizing the access latency. We also apply for the idea of packet aggregation for power saving, where a device aggregates as many packets as possible until the latency constraint is reached. Simulations are carried out to evaluate our methods. We find that the packet loss rate and/or power consumption are significantly reduced with the proposed method.

Research on the Dynamic Power Control the S-MAC in the Wireless Sensor Network

Aiming to the Sensor-MAC (S-MAC) framework, the paper introduces the technology of controlling and ad- justing the Multi-Step power. When the transmitting nodes transmit the data packets to the receiving nodes, the receiving nodes can measure the Received Signal Strength Indication and then be transmitted back to the transmitting nodes, which are as the basis of disgusting the transmitting power, so that the power of the Received Signal is larger than the threshold set by the receiving terminal. The error rates of receiving the packets can the re-transmission times caused by the loss of the packets can be reduced, and the Throughput of the system can be increased. The simulated results show that the range of the Throughput and the packets' successful receiving rate which is added into the power control S-MAC in the different packet collision rate and the bounded packet re-transmission power is increased 3%~10% compared with that which is not added into the power control S-MAC in Multi-hops.

Development of Collision Alleviating DCF Protocol with Efficient Backoff Algorithm for Wireless Ad hoc Networks

Ad hoc networks have been proposed for emergency communication wherein the required infrastructure is unavailable. However, a major concern in Ad hoc networks is collisions. Even in infrastructure based wireless networks, when the number of contending nodes is high, more number of frame collisions occur which leads to drastic reduction in network performance. In all IEEE 802.11 based wireless and Ad hoc networks, the backoff algorithm dynamically controls the contention window of the nodes experiencing collisions. Even though several algorithms such as Binary Exponential Backoff, Double Increment Double Decrement backoff, Exponential Increase Exponential Decrease backoff, Hybrid Backoff, Binary Negative Exponential Backoff etc. have been proposed in the literature to enhance the performance of IEEE 802.11 Distributed Coordination Function (DCF) protocol, most of them have not been developed for real- traffic scenarios. Also the packet collision rate is high using these algorithms. So, in this paper, a new Contention Window based Multiplicative Increase Decrease Backoff (CWMIDB) algorithm is proposed for the DCF protocol to alleviate the number of collisions. Furthermore, the packet transmission procedure of the DCF protocol is modified to avoid channel capture effect and this is represented with a Markov chain model. A simple mathematical model is developed for transmission probability considering the non-saturated traffic and channel errors. Results show that the proposed CWMIDB algorithm provides superior quality-of-service parameters over existing backoff algorithms.

Unsupervised Learning for Robust Signal Classification

Cognitive Radio Networks (CRNs) have been proposed to increase the efficiency of channel utilization; presently the demand for wireless bandwidth is increased. Cognitive Radio Network can enable the sharing of channels among unlicensed and licensed users on a non-interference basis. An unlicensed user (i.e., secondary user) should monitor for the presence of a licensed user (i.e., primary user) to avoid interfering with a primary user. However to get more gain, an attacker also called self-ish secondary user may copy a primary user’s signal to cheat other secondary users. Therefore a primary user detection method is needed to detect the difference between a primary user’s signal and secondary user’s signal. In this paper, unsupervised learning methods such as K-means and SOM techniques are used to classify the signals and also measure the performance parameters such as throughput, end-to-end delay, energy consumption, packet delivery ratio and collision rate of the channel.