Number Of Retransmissions Research Articles

On the Interplay between Deadline-Constrained Traffic and the Number of Allowed Retransmissions in Random Access Networks.

In this paper, a network comprising wireless devices equipped with buffers transmitting deadline-constrained data packets over a slotted-ALOHA random-access channel is studied. Although communication protocols facilitating retransmissions increase reliability, a packet awaiting transmission from the queue experiences delays. Thus, packets with time constraints might be dropped before being successfully transmitted, while at the same time causing the queue size of the buffer to increase. To understand the trade-off between reliability and delays that might lead to packet drops due to deadline-constrained bursty traffic with retransmissions, the scenario of a wireless network utilizing a slotted-ALOHA random-access channel is investigated. The main focus is to reveal the trade-off between the number of retransmissions and the packet deadline as a function of the arrival rate. Towards this end, analysis of the system is performed by means of discrete-time Markov chains. Two scenarios are studied: (i) the collision channel model (in which a receiver can decode only when a single packet is transmitted), and (ii) the case for which receivers have multi-packet reception capabilities. A performance evaluation for a user with different transmit probabilities and number of retransmissions is conducted. We are able to determine numerically the optimal probability of transmissions and the number of retransmissions, given the packet arrival rate and the packet deadline. Furthermore, we highlight the impact of transmit probability and the number of retransmissions on the average drop rate and throughput.

CMAF: Context and Mobility-Aware Forwarding Model for V-NDN

Content dissemination in Vehicular Ad hoc Networks (VANET) is a challenging topic due to the high mobility of nodes, resulting in the difficulty of keeping routing tables updated. State-of-the-art proposals overcome this problem by avoiding the management of routing tables but resort to the so-called table of neighbors (NT) from which a next-hop is selected. However, NTs also require updating. For this purpose, some solutions resort to broadcasting beacons. We propose a Context- and Mobility-Aware Forwarding (CMAF) strategy that resorts to a Short-Term Mobility Prediction—STMP—algorithm, for keeping the NT updated. CMAF is based in Named Data Networking (NDN) and works in two modes, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2V CMAF leverages the overheard packets to extract mobility information used to manage NT and feed the STMP algorithm. V2I CMAF also uses a controlled and less frequent beaconing, initially from the Road-Side Units (RSUs), for a further refinement of the predictions from STMP. Results from extensive simulations show that CMAF presents superior performance when compared to the state of the art. In both modes, V2V and V2I (with one beacon broadcast every 10 s) present 5–10% higher Interest Satisfaction Ratio (ISR) than those of CCLF for the same overhead, at a cost of 1 s of increased Interest Satisfaction Delay (ISD). Moreover, the number of retransmissions of CMAF is also comparatively low for relatively the same number of hops. Compared to VNDN and Multicast, CMAF presents fewer retransmissions and 10% to 45% higher ISR with an increased overhead of about 20%.

Evaluation of BLE-based audio broadcasting under probabilistic interference

Wireless Bluetooth audio communication has become an inherent part of everyday life, from listening to a podcast on our headphones to streaming music on multiple speakers. Up until now, broadcasting audio to multiple receivers has always required a proprietary implementation. Therefore, recent advances in the specification allow Broadcast Audio streams to be set up on top of Bluetooth Low Energy (BLE). To cope with the unpredictability of wireless media, audio frame retransmission opportunities are provided. However, determining the applicable number of retransmissions for a broadcast stream that is exposed to volatile environmental conditions, is a complex research challenge. This paper presents a model that is capable of simulating a BLE broadcast stream schedule, exposed to various environmental conditions while using a variable number of audio frame retransmissions. The evaluation employs several existing Packet Loss Concealment (PLC) techniques to cope with audio frame losses. The results provide insights into the impact of various frame loss patterns on the audio quality and intelligibility of broadcasted speech. The more advanced PLC techniques can handle a higher frame loss rate threshold. The analysis also shows that large audio frames requiring fragmentation exhibit a higher amount of frame loss for the same BLE packet loss rate and that overlap with in-use 802.11 channels can lead to a large variability in frame loss behavior. The model provides a baseline for the next research challenge related to continuous management of Broadcast Audio streams operating under volatile environmental conditions.

Proportional Fairness for Long-Term Evolution-Licensed Assisted Access (LTE-LAA) with Wi-Fi Coexistence in Unlicensed Spectrum

Long-Term Evaluation-Licensed Assisted Access (LTE-LAA) has good potential for fair coexistence with Wi-Fi in an unlicensed spectrum. Though the LTE-LAA utilizes the same listen before-talk technique as the distributed coordination function in IEEE 802.11, it utilizes completely different parameters and transmission durations. For this reason, a fair coexistence between LTE-LAA and Wi-Fi leaves an open question on how the LTE-LAA network should choose its parameters. To tackle this issue, an analytical framework considering the effects of varying important parameters, which are the different initial back-off window sizes, the numbers of sensing durations, the maximum back-off stages, the number of retransmissions and the transmission opportunities for LTE-LAA and Wi-Fi nodes on their performances has been developed. The node and network airtime performances are evaluated by using the current standard parameters setting to see how this setting affects the node and network airtime performances. From there, optimal settings of the initial backoff window size and number of sensing durations are applied by using the obtained node airtime equation that is derived from the Markov renewal process and evaluated as functions of system parameters. The optimum initial backoff window size and number of sensing durations of LTE-LAA are evaluated and verified by the calculation and simulation using MATLAB software. The analysis shows that LTE-LAA maintained unfair coexistence with Wi-Fi by using the current standard parameter setting as its initial back off window size or sensing duration. But in contrast, using the optimum initial backoff window size and number of sensing durations of LTE-LAA shows that they can maintain proportional fairness. It is unconcealed that the initial backoff window size tuning of LTE-LAA carries a high possibility of achieving fairness because it needs less system data and achieves a higher exactness result.

Emergency message broadcasting scheme based on V2V and V2I

As an important part of the advanced Intelligent Traffic System (ITS) in the Internet of Vehicles (IoV), Vehicle to everything (V2X) communication technology provides reliable end-to-end connection and efficient packet transmission. Currently, the main challenge is design and implementation of the multi-hop broadcast protocol. In particular, the network topology is constantly changing because of the high-speed vehicle and diversified road structure, which troubles the selection of forward nodes. Therefore, we propose an Emergency Message Broadcast Protocol (EMBP). In this method, we select the optimal relay node according to the directionality and mobility, channel fading, and link connectivity and apply a suboptimal relay node selection mechanism to guarantee the reliability of broadcast. Simulation results show that EMBP is more efficient and stable than part classical protocols. Due to the complex communication environment of urban roads, to better improve the reliability of emergency message transmission, a broadcasting scheme Roadside Unit (RSU) Emergency Message Broadcast Protocol (REMBP), based on Vehicle to Vehicle (V2V) and Vehicle to Road (V2R) hybrid transmission of emergency messages is proposed on the basis of EMBP. Compared with EMBP, REMBP has better performance in terms of delivery rate, number of forwarding hops, end-to-end delay, average number of collisions, number of packet redundancy, and number of retransmissions.

An Adaptive Hybrid Automatic Repeat Request (A-HARQ) Scheme Based on Reinforcement Learning

V2X communication is susceptible to attenuation and fading caused by external interference. This interference often leads to bit error and poor quality and stability of the wireless link, and it can easily disrupt packet transmission. In order to enhance communication reliability, the 3rd Generation Partnership Project (3GPP) introduced the Hybrid Automatic Repeat Request (HARQ) technology for both 4G and 5G systems. Nevertheless, it can be improved for poor communication conditions (e.g., heavy traffic flow, long-distance transmission), especially in advanced or cooperative driving scenarios. In this paper, we propose an Adaptive Hybrid Automatic Repeat Request (A-HARQ) scheme that can reduce the average block error rate, the average number of retransmissions, and the round-trip time (RTT). It adapts the Q-learning model to select the timing and frequency of retransmission to enhance the transmission reliability. We also design some transmission schemes—K-repetition, T-delay and [T, K]-overlap—which are used to shorten latency and avoid packet collision. Compared with the conventional 5G HARQ, our simulation results show that the proposed A-HARQ scheme decreases the system’s average BLER, the number of retransmissions, and the RTT to 5.55%, 1.55 ms, and 0.97 ms, respectively.

Raptor-like Coded Broadcasting for Efficient V2X Communications

Broadcasting is a critical feature in V2X communication, allowing for the simultaneous dissemination of safety-critical messages to all nearby vehicles. However, the requirement for low latency in information dissemination and the need for reliable and efficient data transmission pose significant challenges to broadcasting in V2X communication systems. In this paper, we present a novel raptor-like coded broadcasting (RLCB) scheme for low-latency V2X communications. Firstly, we introduce feedback into a concatenated fountain code, and adjust its precoding and coding structure to achieve effective data deliverance under a limited number of retransmissions for low-latency transmission. Then, based on the raptor-like encoding and decoding structure, we propose a mutual exclusion-based network encoding (MENC) algorithm to enable retransmission in broadcasting scenarios. We also conduct a complexity analysis on the encoding and decoding process of our proposed scheme. Numerical results demonstrate the superior performance of our proposed scheme in reducing the packet error rate (PER) and improving spectral efficiency compared to the R10 code and hybrid automatic repeat request (HARQ) scheme.

Energy efficient congestion control scheme based on Modified Harris Hawks Optimization for heavy traffic Wireless Sensor Networks

The performance of Wireless Sensor Networks (WSNs), a subset of Wireless Ad-hoc Networks, is significantly influenced by the application, lifetime, storage capacity, processing power, changes in topology, communication medium, and bandwidth. These restrictions call for a strong data transport control in WSNs that takes into account quality of service, energy efficiency, and congestion management. Wireless networks face a significant difficulty with congestion which impacts on the loss rate, channel quality, link utilization, the number of retransmissions, traffic flow, network lifetime, latency, energy, and throughput are all negatively impacted by congestion in WSNs. Since the routing problem has been shown to be NP-hard and it has been realized that a heuristic based method delivers better performance than their traditional counterparts, routing is one of the most popular methods for reducing the energy consumption of nodes and increasing throughput in WSNs. This research provides a Rate Aware Congestion Control (RACC), an effective congestion avoidance method that enhances network performance by applying Modified Harris Hawks Optimization (MHHO). Nodes are initially clustered using the DBSCAN clustering algorithm. When compared to existing approaches, the simulation outcomes of the developed technique indicate superior service, low delay, high energy, packet delivery ratio and increased living nodes.

Software Defined Networking Controller (SDNC): a robust security architecture for SDN-based 5G networks

The efficacy of Wireless Sensor Networks (WSNs), a subset of Wireless Ad-hoc Networks, is significantly impacted by application, lifetime, storage capacity, processing power, topology changes, communication medium, and bandwidth. These constraints necessitate a robust data transport control in WSNs that considers service quality, energy efficiency, and congestion management. Congestion is a significant issue for wireless networks. Congestion in WSNs has deleterious effects on loss rate, channel quality, link utilization, number of retransmissions, traffic flow, network lifetime, latency, energy, and throughput. The predominance of WSNs necessitates the development of more efficient congestion control algorithms. Since it has been demonstrated that the routing problem is NP-hard and that heuristic-based methods outperform their traditional counterparts, routing is one of the most prevalent techniques for reducing the energy consumption of nodes and increasing throughput in WSNs. This study presents Rate Aware Congestion Control (RACC), an efficient method for avoiding congestion that improves network performance by employing Modified Harris Hawks Optimisation (MHHO). Initially, nodes are clustered utilizing the DBSCAN clustering algorithm. The simulation results of the developed technique indicate superior service, low latency, high energy, a high packet delivery ratio and an increasing number of living nodes when compared to existing approaches.

ReT-FTS: Re-transmission-based fault-tolerant scheduling in TSN

Time-Sensitive Networking (TSN) has become a popular technique for providing deterministic transmission policies in the modern Internet of Things (IoT). To simplify configuration complexity and enhance dynamic and flexible behavior in TSN, IEEE 802.1Qch (Cyclic Queuing and Forwarding, CQF) was released. However, messages may encounter transient faults during transmissions, which can influence schedulability and reliability. To protect against such faults, re-transmitting incorrectly received messages is a common approach. However, unlimited re-transmissions can cause a temporary and rapid increase in network load, leading to situations where some streams cannot be transmitted within their deadlines, decreasing the reliability of time-critical streams. This presents a new challenge for performing re-transmissions in TSN. In this paper, we address this issue and present a re-transmission-based fault-tolerant scheduling technique for TSN (called ReT-FTS). Each stream is limited to a minimum number of re-transmissions while maintaining reliability requirements. Therefore, we develop analysis techniques for quantifying network reliability in various scenarios, including those in which streams are dropped or not dropped. We then demonstrate that the reliability does not increase when streams are dropped. We also conduct extensive experiments with synthetic and realistic networks in order to demonstrate the effectiveness of our approach.

Dynamic Probabilistic Broadcast based on Neighbour Discovery

Broadcasting in Ad hoc networks is an important information dissemination way. However, flooding may give rise to broadcast storm problem which causes severe contention, collision and delay. This paper proposes a dynamic probabilistic broadcast algorithm based on neighbour discovery that ensures the higher reliability at lower retransmission. The dynamic probabilistic broadcast algorithm forwards the information from the node (INode) that has obtained it to the node (UNode) that has not received it. By counting the number of UNode and INode of neigbors, INodes dynamically adjust the probability of broadcasting to reduce the number of information replication but cover more UNodes. Dynamic probabilistic algorithm can not only effectively reduce the number of retransmission, but also ensure the effectiveness and reliability. Simulation results show that the number of broadcasting of this algorithm is significantly lower than other algorithms.

Ensuring Data Freshness in Wireless Monitoring Networks: Age-of-Information-Sensitive Coverage and Energy Efficiency Perspectives

With the development of IoT, the sensor usage has been elevated to a new level, and it becomes more crucial to maintain reliable sensor networks. In this paper, we provide how to efficiently and reliably manage the sensor monitoring system for ensuring data freshness. A sensor transmits its sensing information regularly to the data center (DC), and the sensed information generally has the correlation over the space and the time. Hence, the accuracy of the estimated information from the sensed information decreases as the target location for information estimation is further away from the sensor or the AoI, the elapsed time from the information generation, increases. Therefore, by considering the effect of the AoI and the distance from the sensor on the estimation error, we newly define an error-tolerable sensing (ETS) coverage as the area that the estimated information at the DC is with smaller error than the target value. To analyze the impact of the AoI on the ETS coverage, we derive the η-coverage probability, which is the probability that the ETS coverage is greater than η ratio of the maximum sensor coverage, and the average ETS coverage. We also provide the optimal transmission power of the sensor, which minimizes the average energy consumption while guaranteeing certain level of the η-coverage probability. Numerical results validate the theoretical analysis and show the tendency of the optimal transmission power according to the maximum number of retransmissions. This paper can pave the way to efficient design of the AoI-sensitive sensor networks.

A new delay-based broadcast suppression mechanism for efficient emergency messages dissemination in CAVs environment

Network congestion is a major issue affecting communications in Connected and Autonomous Vehicles (CAVs) under high vehicle density scenarios. The common idea used by emergency message broadcast protocols to overcome this challenge is to reduce the number of retransmissions. This is achieved by suppressing redundant retransmissions while maintaining broadcast reliability. In this paper, we analyze the problem of inaccurate suppression of redundant retransmissions in delay-based broadcast protocols and propose uHBS (unHurried Broadcast Suppression), a new more efficient broadcast suppression mechanism. uHBS avoids inaccurate decisions to suppress or forward an emergency message by considering the occurrence of duplicate receptions of this message and using an indication about the channel’s busy status. Next, we use the proposed uHBS mechanism as a basis for designing uHBS-DP (uHBS based Dissemination Protocol), a novel delay-based protocol for broadcasting emergency messages in urban vehicular networks. The simulation results show that uHBS-DP, using the proposed broadcast suppression mechanism, significantly improves the efficiency of emergency message broadcasting by ensuring high reliability with low broadcasting overhead, compared to two other variants of uHBS-DP that use conventional suppression mechanisms. Furthermore, the results show a substantial improvement, compared to a well known protocol, in terms of reduced collision ratio (up to 36.57%), lower dissemination delay (up to 19.17%) and reduced broadcast overhead (up to 19.28%).

Age of Information for Short-Packet Relay Communications in Cognitive-Radio-Based Internet of Things With Outdated Channel State Information

It is crucial to consider time-critical communications in cognitive-radio-based Internet of Things (CR-IoT), which increasingly rely on the exchange of short-packet information to efficiently monitor and control. Therefore, the age of information (AoI) is exploited as a timeliness metric in this paper to investigate the freshness of received information at the remote server with dual-hop short-packet communications in underlay CR-IoT. Since short-packet communications are sensitive to time delay, the influence of outdated factors in the channel state information (CSI) cannot be ignored, so we develop the system under the condition of outdated CSI with Nakagami-m fading channels. Considering the impact of outdated CSI and packets error, we investigate the performance of dual-hop short-packet communications under the classical automatic repeat-request (ARQ) protocol and truncated ARQ (TARQ) protocol, and obtain the approximated expressions of the average AoI and the average peak AoI (PAoI), respectively. Accordingly, the blocklength and the number of retransmissions can be optimized by Fibonacci and iterative algorithms to minimize the average PAoI under two protocols, respectively. Simulation results substantiate the influence of each parameter on the system performance differently under two different protocols. Moreover, the AoI of the dual-hop is also better than that of the single-hop due to the interference threshold of the primary user.

Energy-Efficient AP Selection Using Intelligent Access Point System to Increase the Lifespan of IoT Devices.

With the emergence of various Internet of Things (IoT) technologies, energy-saving schemes for IoT devices have been rapidly developed. To enhance the energy efficiency of IoT devices in crowded environments with multiple overlapping cells, the selection of access points (APs) for IoT devices should consider energy conservation by reducing unnecessary packet transmission activities caused by collisions. Therefore, in this paper, we present a novel energy-efficient AP selection scheme using reinforcement learning to address the problem of unbalanced load that arises from biased AP connections. Our proposed method utilizes the Energy and Latency Reinforcement Learning (EL-RL) model for energy-efficient AP selection that takes into account the average energy consumption and the average latency of IoT devices. In the EL-RL model, we analyze the collision probability in Wi-Fi networks to reduce the number of retransmissions that induces more energy consumption and higher latency. According to the simulation, the proposed method achieves a maximum improvement of 53% in energy efficiency, 50% in uplink latency, and a 2.1-times longer expected lifespan of IoT devices compared to the conventional AP selection scheme.

Secure Aggregation of Data in Connection-Less Networks

Reliable Minimal Energy Cost Routing (RMECR) and Reliable Minimum Energy Routing are two ingenious dynamism- conscious routing algorithms for wireless ad hoc networks (RMER). Ad hoc network conditions for dynamism effectiveness, responsibility, and dragging network continuance are all managed by RMECR. To develop dynamism-effective and reliable pathways that outstretch the network's functional continuance, it takes into account the bumps' dynamism operation, their remaining battery dynamism, and the tractability of their links. RMER, on the other phase, is a dynamism-effective routing algorithm that identifies rows that exercise the least quantum of dynamism altogether for packet traversal from end to end. For networks where end- to- end retransmissions or lope- by- dance retransmissions guarantee responsibility, RMER and RMECR are alluded. Simulation simulations demonstrate that RMECR is able of locating reliable and dynamism-effective rows analogous to RMER, while contemporaneously dragging the network's operating life. RMECR is thus a sophisticated system for dragging the dynamism- effectiveness, responsibility, and life of wireless ad hoc networks. We take into account nanosecond procurators while intending the RMECR, similar as the dynamism exercised by the transceiver's processing factors, the ultimate number of packet retransmissions suffered, packet sizes, and the sequel of mention packets. This increases the oneness of our work in comparison to other examinations.

Adaptive–Persistent Nonorthogonal Random Access Scheme for URLL Massive IoT Networks

Critical massive Internet of Things networks are emerging technologies that face new challenges in designing transmission protocols for beyond 5G communication systems. Conventional transmission schemes are ill-suited to provide ultrareliable, lowlatency, and scalability requirements of IoT networks with the massive number of nodes having sporadic data traffic behavior. This article overcomes such challenges with proposing a random access transmission scheme that exploits nonorthogonal multiple access (NOMA) with short-packet transmissions and automatic request and repeat (ARQ) strategy with the limited number of retransmissions. To utilize the spectrum further and to meet ultrareliable low-latency requirement, an adaptive–persistent technique is proposed in which each node distributively controls its transmission based on the number of active devices without extra signaling. Since the nodes’ data traffic behavior is assumed sporadic, NOMA-based clustering is performed dynamically at each frame, avoiding additional signaling overhead. Network metrics, such as reliability, effective sum rate, and the distribution of packet latency, are analytically derived. Furthermore, the effects of different network parameters, such as blocklength, the maximum number of packet replicas, number of nodes, and number of resource blocks on network metrics, are investigated and compared with S-ALOHA-ARQ. The analysis show that the proposed scheme outperforms conventional schemes in terms of effective sum rate, reliability, and average packet latency.

Multi-Channel Assessment Policies for Energy-Efficient Data Transmission in Wireless Underground Sensor Networks

Wireless Underground Sensor Networks (WUGSNs) transmit data collected from underground objects such as water substances, oil substances, soil contents, and others. In addition, the underground sensor nodes transmit the data to the surface nodes regarding underground irregularities, earthquake, landslides, military border surveillance, and other issues. The channel difficulties of WUGSNs create uncertain communication barriers. Recent research works have proposed different types of channel assessment techniques and security approaches. Moreover, the existing techniques are inadequate to learn the real-time channel attributes in order to build reactive data transmission models. The proposed system implements Deep Learning-based Multi-Channel Learning and Protection Model (DMCAP) using the optimal set of channel attribute classification techniques. The proposed model uses Multi-Channel Ensemble Model, Ensemble Multi-Layer Perceptron (EMLP) Classifiers, Nonlinear Channel Regression models and Nonlinear Entropy Analysis Model, and Ensemble Nonlinear Support Vector Machine (ENLSVM) for evaluating the channel conditions. Additionally, Variable Generative Adversarial Network (VGAN) engine makes the intrusion detection routines under distributed environment. According to the proposed principles, WUGSN channels are classified based on the characteristics such as underground acoustic channels, underground to surface channels and surface to ground station channels. On the classified channel behaviors, EMLP and ENLSVM are operated to extract the Signal to Noise Interference Ratio (SNIR) and channel entropy distortions of multiple channels. Furthermore, the nonlinear regression model was trained for understanding and predicting the link (channel behaviors). The proposed DMCAP has extreme difficulty finding the differences of impacts due to channel issues and malicious attacks. In this regard, the VGAN-Intrusion Detection System (VGAN-IDS) model was configured in the sensor nodes to monitor the channel instabilities against malicious nodes. Thus, the proposed system deeply analyzes multi-channel attribute qualities to improve throughput in uncertain WUGSN. The testbed was created for classified channel parameters (acoustic and air) with uncertain network parameters; the uncertainties of testbed are considered as link failures, noise distortions, interference, node failures, and number of retransmissions. Consequently, the experimental results show that DMCAP attains 10% to 15% of better performance than existing systems through better throughput, minimum retransmission rate, minimum delay, and minimum energy consumption rate. The existing techniques such as Support Vector Machine (SVM) and Random Forest (RF)-based Classification (SMC), Optimal Energy-Efficient Transmission (OETN), and channel-aware multi-path routing principles using Reinforcement Learning model (CRLR) are identified as suitable for the proposed experiments.

Selfish Node Detection Scheme based on Bates Distribution Inspired Trust Factor for MANETs

The trustworthiness of mobile nodes is considered as the principal parameter for ensuring significant data dissemination in a Mobile Ad hoc NETwork (MANET). However, the selfish behaviour of nodes minimizes the trust by dropping a considerable amount of data packets in the network. The significant dropping of data packets by the selfish node introduces huge data overhead with increased latency and energy consumption, thus increasing the number of retransmissions. In this paper, Selfish Node Detection Scheme based on Bates Distribution Inspired Trust Factor (SNDS-BDITF) is propounded for predominant detection of selfish behaviour by investigating multiple levels of factors that contribute towards effective selfishness detection. The proposed SNDS-BDITF approach is also potent in enhancing the detection rate of selfishness by multi-perspective analysis of each monitored node’s forwarding characteristics considering the benefits of other cooperating mobile nodes. The simulation results of the propounded SNDS-BDITF method are enhanced on an average by 16% and 14% when compared to the existing selfish node segregation mechanisms prevalent in the literature.

One-Hop Listening Based ARQs for Low-Latency Communication in Multihop Networks

Inspired by emerging applications in vehicular networks, we address the problem of achieving high-reliability and low-latency communication in multi-hop wireless networks. We propose a new family of Automatic-Repeat-Requests (ARQs) based cooperative strategies wherein high end-to-end reliability is obtained using packet re-transmissions at each hop while the low-latency constraint is met by imposing an upper bound on the total number of packet retransmissions across the network. A hallmark of our strategies is the one-hop listening capability wherein nodes utilize the unused ARQs of their preceding node just by counting the number of failed attempts due to decoding errors. We further extend the idea of one-hop listening to multi-hop listening, wherein a set of consecutive nodes form clusters to utilize the unused ARQs of the preceding nodes, beyond its nearest neighbour, to further improve reliability. Thus, our strategies provide the high-reliability feature with no compromise in the original latency-constraint. For the proposed strategies, we solve non-linear optimization problems on distributing the ARQs across the nodes so as to minimize packet drop probability (PDP) subject to a total number of ARQs in the network. Through extensive theoretical results on PDP and delay profiles, we show that the proposed strategies outperform the best-known strategies in this space.