Transmission Collisions Research Articles

Beacon-Based Uplink Transmission for LoRaWAN Direct to LEO Satellite Internet of Things

Direct-to-satellite IoT (DtS-IoT) communication structure is a promising solution to provide connectivity and extend the coverage of traditional low-power and long-range technologies, especially for isolated and remote areas where deploying traditional infrastructure is impracticable. Despite their bounded visibility, the Low Earth Orbit (LEO) satellites complement the terrestrial networks, offering broader gateway coverage and terrestrial network traffic offloading. However, the dynamics of LEO and the nature of such integration come with several challenges affecting the efficacy of the network. Therefore, this paper proposes Beacon-based Uplink LoRaWAN (BU-LoRaWAN) to enhance satellite-terrestrial communication efficiency. The proposed scheme exploits the LoRaWAN class B synchronization mechanism to provide efficient uplink transmission from LoRaWAN devices placed on the ground to satellite gateways. BULoRaWAN proposes an uplink transmission slot approach to synchronize ground devices’ uplink traffic with LEO-based orbiting gateways. It also uses a queue data structure to buffer end devices’ ready-to-send packets until the appropriate moment. BU-LoRaWAN avoids possible transmission collision by optimizing a random transmission slot for an end device within the beacon window. The proposed system is implemented and evaluated using OMNeT++ network simulator and FLoRaSat framework. The result demonstrates the feasibility of the proposed system. BU-LoRaWAN achieves better performance compared to the standard LoRaWAN, which manages to deliver almost double the traffic delivered by the standard one.

ABM-V: An Adaptive Backoff Mechanism for Mitigating Broadcast Storm in VANETs

In vehicular ad hoc networks (VANETs), the broadcast storm problem may disable road safety applications and cause congestion or traffic accidents. Existing schemes propose broadcast storm mitigation by reducing the number of relay vehicles. Concurrently, in improving reachability, these schemes stipulate that vehicles expected to transmit packets to more next-hop neighbors have higher relay priority. However, the potential redundancy caused by duplicate reception by neighbors is ignored, which exacerbates the contention and collisions in transmission. This paper proposes an adaptive backoff mechanism for mitigating broadcast storm in VANETs (ABM-V). Specifically, the receiver estimates the expected benefit and redundancy by combining the distribution of neighbors. Then, the receiver adaptively adjusts the backoff time by utilizing Dempster-Shafer evidence theory. Finally, the receiver with the shortest backoff time becomes the relay and rebroadcasts the packet to its neighbors. Simulation results illustrate that ABM-V significantly reduces the rebroadcast ratio and redundancy ratio compared with the existing typical schemes while maintaining stable reachability.

MBC-SS: A multi-band cooperative sidelink scheme for NR V2X networks

Vehicle-to-everything (V2X) sidelink (SL) communication at mmWave frequency is envisioned to satisfy the exchange of massive sensor data among vehicles. However, directional deafness due to inherent highly directional transmissions results in transmission collisions. Moreover, the high mobility of vehicles incurs significant beam training overhead for beam alignment and tracking. These issues cause performance degradation and pose challenges to SL scheme design. In this paper, we propose a multi-band cooperative SL scheme (MBC-SS) with the concurrent support of resource scheduling and beamforming, which offloads partial mmWave control signals to sub-6 GHz control channels and exploits spatial correlations between multiple frequencies. To enable such multi-band cooperation, centralized and distributed modes are developed for in-coverage and out-of-coverage deployments, respectively, whose advantages are twofold. First, centralized scheduling or receiver-side directional sensing in certain directions is adopted to avoid potential collisions during resource selection phase. Second, we take into account the spatial congruence and temporal dependence of channels to adaptively design training beams and select alignment beams. In addition, the performances of the proposed protocol are investigated theoretically and verified by simulations. Compared with the existing sub-6 GHz assisted scheme, the proposed distributed MBC-SS has an improvement of transmission reliability by 15% and throughput by 12.5% in highly dense scenarios. Meanwhile, lower delay and training overhead are achieved.

ReLoRaWAN: Reliable data delivery in LoRaWAN networks with multiple gateways

LoRa has emerged as a promising technology to provide low-power and long-range communication for IoT devices. LoRaWAN networks build on top of the LoRa physical layer and adopt a centralized network architecture that supports one or more gateways connected with a large number of end devices. Although LoRa communication is resistant to channel noises, it still frequently suffers data delivery failures in LoRaWAN networks due to packet collisions of the concurrent transmissions. The delivery failures trigger the retransmission procedure in LoRaWAN protocol, which causes additional power consumption on end devices and further exacerbates packet collisions. To solve this issue, we investigate the reliable data delivery mechanism that utilizes with the packet reception of multiple gateways to recover the distorted payload. Firstly, we present a ReLoRaWAN framework, where the central server aggregates distorted packet payload from multi-gateway reception of the same packet and executes the corresponding data recovery operations. Then, we design a Tri-operation Integrated Data Recovery (TIDR) algorithm for recovering the distorted packet payload, which involves exclusive-OR based bitwise inversion operation, majority voting based bitwise inversion operation, and weighted bitwise decision operation. Finally, we implement a ReLoRaWAN testbed and conduct real-world experiments to evaluate the performance of our solution. Compared with existing works, the ReLoRaWAN greatly optimizes the quality of service and power consumption in the network. It improves the packet delivery ratio by 35% and reduces the average power consumption of the end device by 30%.

The controlled fission, fusion and collision behavior of two species Bose–Einstein condensates with an optical potential

Abstract By using the Crank–Nicolson method, we investigate numerically the dynamical properties of bright–bright solitons in two species Bose–Einstein condensates (BECs) trapped in an optical lattice. We confirm that the soliton splitting behavior occurs at a critical depth of optical potential. The splitting behavior of solitons and the fusion behavior of condensates can be accurately controlled by adjusting the depth and lattice parameter of optical potential, the initial amplitude and position of solitons, and the interspecies interactions. When the lattice parameter is fixed but the interspecies interaction increased exponentially with the time, each soliton splits into two soltions with different amplitude, and partial fusion of two species BECs can be found. While the interspecies interaction remains unchanged but the lattice parameter increases exponentially with the time, interestingly, the bright solitons with zero initial velocity can pass through each other and accomplish a transmission collision. Furthermore, for the case of both the interspecies interaction and lattice parameter increased exponentially with the time, a head-on collision of the bright solitons in two species BECs occurred. After the collision, each soliton splits into two soltions with equal amplitude. Meanwhile, the complete fusion of two species BECs can be observed. The relevant results can provide help for the precise manipulation of BECs experiments.

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.

Relay Selection for Over-the-Air Computation Achieving Both Long Lifetime and High Reliability.

In a general wireless sensor network, a sink node collects data from each node successively and then post-processes the data to obtain useful information. However, conventional methods have a scalability problem: the data collection/processing time increases with the number of nodes, and frequent transmission collisions degrade spectrum efficiency. If only statistical values of the data are needed, using over-the-air computation (AirComp) can efficiently perform data collection and computation. However, AirComp also has its problems: when the channel gain of a node is too low, (i) the transmission power of that node will be high, decreasing the lifetime of that node and the entire network, and (ii) sometimes, the computation error still occurs even though the maximal transmission power is used. To jointly solve these two problems, in this paper we investigate the relay communication for AirComp and study a relay selection protocol. The basic method selects an ordinary node with a good channel condition as a relay node, considering both computation error and power consumption. This method is further enhanced to explicitly consider network lifetime in relay selection. Extensive simulation evaluations confirm that the proposed method helps to prolong the lifetime of the entire network and reduce computation errors as well.

Efficient Congestion Control With Information Loss Minimization for Vehicular Ad Hoc Networks

The reduction of beacon message rates is of great significance to network congestion control in Vehicular Ad Hoc Networks (VANETs). However, this may cause large traffic information loss and serious traffic accidents. In this paper, we propose an efficient congestion control protocol, which not only alleviates channel congestion but also minimizes information loss by adaptively adjusting the beacon rates. Unlike conventional methods, this protocol investigates beacon transmission from a perspective of information loss, and adopts different beacon rates for different vehicles. First, we design an information loss factor to quantify the information deviation between the original information generated by a vehicle and the actual information received by its neighbor vehicles. Then, based on the information loss factor, we transform the beacon rate adjustment problem into an information loss minimization problem, which is addressed by a greedy beacon-rate adjustment algorithm. In addition, based on the derived different beacon rates, we design a periodicity-based time-slot selection scheme to allocate idle time slots to different vehicles, in order to alleviate transmission collisions. Extensive simulation results show the advantages of our solution in terms of multiple metrics.

Federated Deep Reinforcement Learning-Based Spectrum Access Algorithm With Warranty Contract in Intelligent Transportation Systems

Cognitive radio (CR) provides an effective solution to meet the huge bandwidth requirements in intelligent transportation systems (ITS), which enables secondary users (SUs) to access the idle spectrum of the primary users (PUs). However, the high mobility of users and real-time service requirements result in the additional transmission collisions and interference, which degrades the spectrum access rate and the quality of service (QoS) of users in ITS. This paper proposes a spectrum access algorithm (Feilin) based on federated deep reinforcement learning (FDRL) to improve spectrum access rate, which maximizes the QoS reward function with considering the hybrid benefits of delay, transmission power and utility of SUs. To guarantee the utility of SUs, the warranty contract is designed for SUs to obtain compensation for data transmission failure, which promotes SUs to compete for more spectrum resources. To meet the real-time requirements and improve QoS in ITS, a spectrum access model called FDQN-W is proposed based on federated deep Q-network (DQN), which adopts the asynchronous federated weighted learning algorithm (AFWLA) to share and update the weights of DQN in multiple agents to decrease time cost and accelerate the convergence. Detailed simulation results show that, in the multiuser scenario, compared with the existing methods, the proposed algorithm Feilin increases the spectrum access success rate by 15.1%, and reduces the collision rate with SUs and the collision rate with PUs by 46.4% and 6.8%, respectively.

A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challenges

Long-range (LoRa) technology is most widely used for enabling low-power wide area networks (WANs) on unlicensed frequency bands. Despite its modest data rates, it provides extensive coverage for low-power devices, making it an ideal communication system for many internet of things (IoT) applications. In general, LoRa is considered as the physical layer, whereas LoRaWAN is the medium access control (MAC) layer of the LoRa stack that adopts a star topology to enable communication between multiple end devices (EDs) and the network gateway. The chirp spread spectrum modulation deals with LoRa signal interference and ensures long-range communication. At the same time, the adaptive data rate mechanism allows EDs to dynamically alter some LoRa features, such as the spreading factor (SF), code rate, and carrier frequency to address the time variance of communication conditions in dense networks. Despite the high LoRa connectivity demand, LoRa signals interference and concurrent transmission collisions are major limitations. Therefore, to enhance LoRaWAN capacity, the LoRa Alliance released many LoRaWAN versions, and the research community has provided numerous solutions to develop scalable LoRaWAN technology. Hence, we thoroughly examine LoRaWAN scalability challenges and state-of-the-art solutions in both the physical and MAC layers. These solutions primarily rely on SF, logical, and frequency channel assignment, whereas others propose new network topologies or implement signal processing schemes to cancel the interference and allow LoRaWAN to connect more EDs efficiently. A summary of the existing solutions in the literature is provided at the end of the paper, describing the advantages and disadvantages of each solution and suggesting possible enhancements as future research directions.

Hybrid adaptive MAC scheme for the broadcast of vehicular safety beacons

SummaryVehicular safety applications are based on broadcasting periodic safety beacons on the IEE802.11p control channel. The CSMA MAC scheme adopted in the IEE802.11p cannot ensure the strict timeline and the high reliability when broadcasting these real time and critical messages since it does not consider the high mobility of vehicles, the limited network resources, and the variant traffic densities. Therefore, the design of an effective MAC scheme that takes in consideration these parameters represents a challenging task. Furthermore, the fixed beaconing rate results an overload on the commination channel and collisions especially in dense situations that delay beacons broadcast. In this paper, we propose a novel hybrid MAC scheme for fast and reliable broadcast of safety beacons. At first, it relies on a time slot reuse policy that considers the mobility features of vehicles while reducing transmission collisions. In addition, it adapts mathematically the beaconing rate to the traffic density based on vehicular velocity at the aim of optimizing the performance of the wireless channel. Simulation results show that our proposal outperforms significantly the IEEE802.11p CSMA MAC scheme and upgrades the performance of vehicular networks in broadcasting periodic safety beacons.

Optimal Channel Selection algorithm for CRahNs

As the data traffic is increasing, the spectrum bands are getting congested. It causes low latency and unreliable communication. Additional spectrum can be utilized to solve this problem but moving towards higher frequency means higher power requirement and increased cost. Cognitive radio network is another solution to this problem. It helps the nodes of a network to use the channels of the nearby bands which are not being used at that time. However, it has several challenges. One of these challenges is the transmission collision with the primary users of the network. Researchers have been working on this problem. However, it is still a major concern for the researchers. This paper proposes an algorithm that selects the optimal cognitive channel for the data transmission by the secondary user in such a way so that the transmission collision with the PU is minimized. After comparison with the existing latest similar protocol, the proposed protocol has shown 5.6% improvement in the throughput, 5.3% improvement in PDR. The delay is decreased by 0.6% and the transmission collision with PUs is reduced by 2.5%.

An Improved Approach for Wireless Sensor Networks With Mobile Sink Using Dynamic Minimum Spanning Tree

In wireless sensor networks (WSNs), finding the best transmission route from each node to the mobile sink is not a simple task because of the dynamic characteristics of sink node and cluster transmission. In order to balance energy consumption and reduce collisions in wireless transmissions, this paper proposes a Dynamic Spanning Tree with Mobile Sink (DSTMS) routing algorithm. Firstly, based on the Low Energy Adaptive Clustering Hierarchy Protocol (LEACH), a multi-layer transmission framework with dynamic minimum spanning tree (MST) is constructed to optimize data transmission route. In this framework, the selected rendezvous points, according to the motion parameters of mobile sink, build the dynamic rendezvous layer to constraint the hierarchical transmission of DSTMS. In addition, considering some factors of transmission energy consumption and residual energy distribution,the weight factor based on energy efficient function is introduced to evaluate the cost of transmission path within the MST. Moreover, the proposed algorithm not only reduces network energy consumption caused by frequent location updates of mobile sink, but also reduces data transmission congestion by constructing dynamic hierarchical transmission strategy. The simulation results show that the DSTMS algorithm can effectively prolong the network life, balance the network load and reduce collisions in wireless transmissions.

CPA-MAC: A Collision Prediction and Avoidance MAC for Safety Message Dissemination in MEC-Assisted VANETs

Vehicular ad hoc networks (VANETs) have been widely recognized as a promising solution to improve traffic safety and efficiency for the ability to provide situation awareness even though the potential dangers and traffic anomalies are out of the visual range. In VANETs, time-division multiple access (TDMA) based overlay protocols can prevent transmission collisions and play an important role in providing an efficient communication channel. However, due to high vehicle mobility and time-varying traffic flow, the existing TDMA-based slot allocation approaches cannot fully utilize the channel resources, resulting in high transmission delay and packet collision. To overcome these shortcomings, we propose a collision prediction and avoidance MAC (CPA-MAC) protocol that utilizes the capability of mobile edge computing (MEC) and machine learning in this paper. Specifically, we propose a new slot assignment method that aims to guarantee the high channel utilization and low delay of safety message under dynamic traffic conditions. Furthermore, we propose a new same-direction collisions prediction algorithm that combines the V2R communication and LSTM-based trajectory prediction algorithm. Finally, we conduct extensive experiments to demonstrate the effectiveness of the proposed protocol.

An Energy-Efficient Data Aggregation Mechanism for IoT Secured by Blockchain

The Internet of Things (IoT) is getting important and interconnected technologies of the world, consisting of sensor devices. The internet is smoothly changing from an internet of people towards an Internet of Things, which permits various objects to connect to another wirelessly. The energy consumption of the IoT routing protocol can affect the network life span. In addition, the high volume of data produced by IoT will result in transmission collision, security issues, and energy dissipation due to increased data redundancy because tiny sensors are usually hard to recharge after they are deployed. Generally, to save energy, data aggregation reduces data redundancy at each node by turning some nodes into sleep mode and others into wake mode. Therefore, it is important to group the nodes with high data similarity using the fuzzy matrix. Then, the data received from the member nodes at the Cluster Head (CH) are analyzed using a fuzzy similarity matrix for clustering. In the next step, after clustering, some nodes are chosen from all groups as redundant nodes. The sleep scheduling mechanism is then applied to reduce data redundancy, network traffic jamming, and transmission costs. We have proposed an Energy-Efficient Data Aggregation Mechanism (EEDAM) secured by blockchain, which uses a data aggregation mechanism at the cluster level to save energy. As edge computing is used to provide on-demand trusted services to IoT with minimum delay, blockchain is integrated inside a cloud server, so the edge is validated by the blockchain to provide secure services to IoT. Finally, we performed simulations to calculate the performance of the proposed mechanism and compared it with the conventional energy-efficient algorithms. The simulation results show that the proposed structural design can successfully reduce the amount of data, provide proper security to the IoT, and extend the wireless sensor network (WSN).

Time-Slot Reservation and Channel Switching Using Markovian Model for Multichannel TDMA MAC in VANETs

Efficient communication in the intelligent transport system requires the vehicles to transmit safety and non-safety messages timely without any loss. In multi-channel MAC, SCH channels have different properties in terms of the frequency range, frequency limit, power limit, distance covered, and channel parameters. Based on these channel properties, messages must be transmitted using the most appropriate channel. Existing multi-channel MAC approaches randomly choose the available SCH channel to transmit the messages. Random selection of the channel leads to overuse of the channel resources or lack of the resources due to inappropriate channel selection. The proposed approach provides a way to reserve the most appropriate SCH leading to optimized resource utilization. For collision-free allocation of resources to a large number of contending vehicles, we have proposed a variable size TDMA approach for SCH channel allocation. The paper also focuses on reducing the transmission collision and re-usability of the time slot for timely message transmission. Markovian modeling determines the probability of successfully reserving the time slot and switching to the required SCH. Simulation results prove that the probability of time slot reservation in the required SCH is approximately 0.6 for the proposed approach and approximately 0.2 for other approaches.

Improving Performance of C-V2X Sidelink by Interference Prediction and Multi-Interval Extension

Cellular Vehicle-to-Everything (C-V2X), as a next-generation V2X communication technique, has attracted much attention recently. Especially, SideLink (SL) of C-V2X in the mode 4 is a promising technique for disseminating various information in a non-line-of-sight area via direct inter-vehicle communication without requiring base stations. In order to better prevent accidents, it is important to improve the reliability and adaptability of SL. However, the Semi-Persistent Scheduling (SPS) method has a limited performance at short distances due to transmission collisions, and cannot well support applications with different transmission intervals. To solve these problems, in this paper, we propose a new packet collision avoidance method, Interference Prediction and Multi-Interval extension (IPMI), for the C-V2X SL mode 4, based on directly predicting the interference vehicles (defined as vehicles in the overlapping communication range of two vehicles using the same resource, and susceptible to packet collisions at reception), without causing extra overhead. Specifically, the proposed method selects for each vehicle a resource that has (i) a minimal number of interference vehicles and (ii) a maximal inter-vehicle distance, to reduce packet collisions. It is further extended to support applications with different transmission intervals. Simulation results confirm that compared with the conventional methods, the proposed method can achieve higher reliability and still have a promising performance even in times of partial deployment.

Energy-Efficient Topology Construction via Power Allocation for Decentralized Learning via Smart Devices With Edge Computing

Data privacy preservation has drawn much attention in emerging machine learning applications. Decentralized learning among smart devices over wireless networks is thus developed to guarantee data security and eliminate the involvement of parameter servers to avoid transmission bottlenecks. However, the previous research focuses on data compression and exchange rules of model parameters among smart devices. Still, it neglects the interplay between link cardinality, transmission power consumption, and collision in transmission. To jointly optimize these issues, in this paper, we first set collision and interference aside and formulate a new optimization problem, named GreenDL, and then extend GreenDL to be collision-aware, namely, GreenDL-CA, by restricting the maximum degree of each smart devices. We prove their hardness and propose two approximation algorithms dubbed as CoTRAIN and CoTRAIN-CA for GreenDL and GreenDL-CA, respectively. Experiment and simulation results manifest that both CoTRAIN and CoTRAIN-CA reduce more than 20% power compared with the other heuristics without sacrificing the convergence rate in the decentralized learning practices.

A Data Aggregation Privacy Protection Algorithm Based on Fat Tree in Wireless Sensor Networks

Wireless sensor network is the momentous part of the Internet of Things. Data aggregation technology is the most practical method to reduce the amount of communication among nodes. Adding a privacy protection mechanism to data aggregation is one of the important means for privacy protection and security in wireless sensor networks. Aiming at certain performance defects of the existing SMART (Slice-Mix-AggRegaTe) privacy protection algorithms, a fat tree-based data aggregation privacy protection algorithm is proposed in this paper, which is referred to as the FTSMART (Fat Tree Slice-Mix-AggRegaTe) algorithm. Concerning the innovative algorithm, the fat tree (FT) is introduced, the fat tree structure is adopted to optimize the data slicing scheme and the aggregation tree generation scheme, and the allocation of fixed time intervals is employed for nodes to reduce the transmission collision in the data aggregation process and to guarantee the completion of the data transmission. The simulation experiment results demonstrate that the FTSMART algorithm has presented the favorable performance in terms of the privacy protection, the network communication overhead, and the data aggregation accuracy.

R-SplitC: Collision minimization for cellular communication in unlicensed spectrum

The 3rd generation partnership project (3GPP) has standardized 5G new radio in unlicensed spectrum (NR-U) that uses a wide unlicensed spectrum as an alternative solution to the insufficient bandwidth problem of the existing NR. NR-U has a listen-before-talk (LBT) operation similar to the carrier sense multiple access with collision avoidance (CSMA/CA) operation of Wi-Fi. It allows nodes to transmit only after LBT success. NR-U suffers from the collision issue because its channel access mechanism is similar to that of Wi-Fi. Wi-Fi solves the collision problem through the request-to-send/clear-to-send (RTS/CTS) mechanism. However, NR-U has no way of solving the collision problem. As a result, NR-U suffers severe performance degradation due to collisions as the number of contending nodes increases. In this paper, we propose to use an extended and split reservation signal (RS) for reservation in NR-U that consists of front RS and rear RS and design a new collision minimization scheme, termed R-SplitC, that contains two components: new RS structure and contention window size (CWS) control. The new RS structure helps to minimize collisions in NR-U transmissions, and CWS control works to protect the performance of other communication technologies such as Wi-Fi. We mathematically analyze and evaluate the performance of our scheme and confirm that R-SplitC improves network throughput by up to 99.3% compared to the baseline RS scheme without degrading Wi-Fi performance.