Packet Retransmission Research Articles

Collision-Free Dynamic Convergecast in Low-Duty-Cycle Wireless Sensor Networks

Convergecast is a fundamental operation in wireless sensor networks (WSNs). To support long-term deployment of WSNs, sensor nodes normally operate at low-duty-cycles. However, the low-duty-cycle operation significantly reduces the communication chance between nodes. Consequently, the risk of data collisions significantly increases when multiple senders transmit packets to a receiver during its very short active period. This problem further causes not only wasted packet retransmissions, but also a large delivery latency. Under such conditions, collision-free medium access is more appealing than recovering after collision for low-duty-cycle WSNs. In this work, we propose an <u>i</u>ncast-<u>co</u>llision-f<u>re</u>e convergecast protocol, named iCore, to address the many-to-one collision problem in low-duty-cycle WSNs. iCore employs the dynamic forwarding technique, establishes a non-conflicting schedule for efficient convergecast, and improves the channel utilization by allowing senders to opportunistically transmit packets once detecting unused slots. Specifically, we design efficient forwarder assignment and forwarding optimization algorithms that ensure low end-to-end latency under diverse data traffic types. Through comprehensive performance evaluations, we demonstrate that, compared with the baseline protocol, iCore effectively minimizes the end-to-end delay by 25&#x0025; &#x007E; 57&#x0025; and maintains high delivery ratio and energy efficiency for different many-to-one convergecast scenarios.

Packet Re-Management-Based C-NOMA for URLLC: From the Perspective of Power Consumption

Cooperative non-orthogonal multiple access (C-NOMA) has been gradually discussed in the ultra-reliable and low-latency communication (URLLC) scenarios. The recent research indicates that when optimizing blocklength, selection combining the re-transmission packet cannot sufficiently exploit the potential of C-NOMA. To tackle this problem, we propose a packet re-management framework to transmit different packets through direct and relay links, therefore, propose an energy-efficient transmission scheme. By investigating power consumption of unequal-blocklength non-orthogonal multiple access with appropriate successive interference cancelation order, we jointly optimize blocklength, power, and re-managed packet size to minimize the power consumption, under URLLC requirements and the maximum power constraint. Simulation results verify that the proposed scheme can reduce power consumption.

A BER-Conscious Synthesis of Switched-Beam Antenna to Maximize Reliable Coverage in WSN Applications

A BER-conscious synthesis of switched multi-beam multi-sector access point (AP) antenna is proposed to enhance reliable communication coverage for WSN applications. The sector-based design of multi-beam AP antenna is necessary for a densely deployed WSN to achieve high system capacity with 360° azimuth coverage. However, the neighboring sectors cause high co-channel interference resulting in a low signal to interference (SIR) ratio and hence degrades the communication link quality. This leads to frequent packet re-transmissions resulting in increased energy consumption at the power deficient sensor nodes. The proposed BER-conscious array synthesis scheme incorporates projected power and SIR limits within the antenna design process to maximize the effective coverage area with acceptable BER. The optimized coverage with reliable communication implies energy saving at the sensor nodes. Based on the proposed process of coverage optimization, a multi-sector AP antenna is analytically synthesized. A prototype is fabricated, and the analytically obtained coverage results are verified through experiments. The maximum radial range of 2 – 13 m with 360° communication coverage is achieved. Moreover, high port isolation of 45.95 dB between adjacent sectors enables the generation of multiple simultaneous beams in the communication region resulting in high system capacity. The achieved performance indicates that the synthesized switched multi-beam multi-sector antenna using the proposed scheme is a good candidate for AP in WSN applications.

ETX-Aware Energy-Efficient Algorithm to Reduce Packet Retransmissions in the Internet of Things

Objectives: An energy-efficient optimum path selection to reduce the number of packet retransmissions in a path is proposed in this paper using a new metric New-Expected Transmission Count (N-ETX), Residual Energy (RE), and Path Objective Function (POF). Methods: Energy efficiency, packet retransmission, and RE is considered while making the best choice of the optimum path. The refined N-ETX metric is employed to measure the Packet Delivery Ratio (PDR) of a node. If the PDR ratio is higher, this node is included in the optimum path set. The proposed algorithm avoids the inclusion of critical nodes such as dead nodes if the RE of the nodes is lower than the predefined Energy Threshold (ETHR). The Unicast message reply to strategy significantly decreases the quantity of control message overhead. The T-test’s Degree of Variance and Degree of Independence is used to discover the energy-efficient optimum path. It is demonstrated through performance outcomes that the obtained Quality of Service (QoS) parameters for the proposed algorithm stands superior to the state-of-art protocols. Findings: An algorithm established on N-ETX and RE metrics is proposed, and the discussions have shown that this approach is energy efficient. This metric decreases the quantity of packet retransmission effectively for a path. As an outcome, the sensor’s higher energy consumption is reduced. The proposed algorithm outperforms the current algorithm by decreasing energy consumption, reducing the quantity of packet retransmission, improving the PDR ratio, and enhancing the network life expectancy. The simulation outcome established that energy consumed is decreased by 19.07%, RE of the nodes is increased by 10.51%, PDR ratio is increased to 98%, and network lifespan is increased by 30.54%. Novelty: Internet of Things (IoT) gadgets are bound by hardware limits regarding computation, memory, and energy proficiency. To effectively transmit data packets, every IoT device must have a routing communications protocol that is easy to create. IoT devices necessitate the demand for self-adaptive routing algorithms. During network operation, packet energy dissipation during the broadcast and reception process is significantly higher when assessed to otherenergy-consuming processes like sensing, data processing, etc. Keywords: LOADng; Energy Efficiency; ETX; Packet Retransmission; Residual Energy

An Opportunistic Routing for Energy-Harvesting Wireless Sensor Networks With Dynamic Transmission Power and Duty Cycle

Opportunistic routing (OR) is widely used in energy-harvesting wireless sensor networks (EH-WSNs). The transmission power of sensor nodes in EH-WSNs is usually adjusted dynamically to make full use of the harvested energy. Many OR algorithms with dynamic transmission power adjustment have been proposed for EH-WSNs. However, fewer studies consider the non-bidirectional communication and the increase in packet retransmission and delay caused by the heterogeneous transmission power/radii. To this end, we propose an opportunistic routing algorithm for EH-WSNs with dynamic transmission power and duty cycle (ORDPD). It adjusts the transmission power of the sensor nodes at the end of each time slot, according to the predicted available energy. It also adjusts the transmission power and duty cycle of the sensor nodes in time slots if the nodes receive packets from other nodes outside their current transmission range. ORDPD also adopts an improved transmission model and information exchange mechanism to dynamically update relay sets and forwarding paths. A series of experiments were conducted to verify the effectiveness of the proposed algorithm. The experimental results demonstrate that the proposed ORDPD reduces non-bidirectional communication and retransmission significantly and performs better than its corresponding competitors.

Real-Time In-Network Microburst Mitigation on Programmable Switch

Microbursts in the datacenter network (DCN) last for an extremely short time in switches and are difficult to discover from a coarse-grained perspective. Most prior works are dedicated to in-network detection of microbursts and have not yet attempted to mitigate them in real time. Therefore, a realtime microburst mitigator, called RIMM, is proposed in this work and mainly applies <b>orderly detours</b> with <b>flowlet intervals</b> for preventing packet loss and packet retransmission in the network. RIMM mainly consists of three key components: (1) detour launcher, (2) packet sequencer, and (3) post-detour handler, and can entirely work in-network on a programmable switch. Experimental results demonstrate that RIMM is capable of preventing packet loss, allowing the reduction of packet retransmissions, while effectively reducing microbursts, resulting in an enhanced data-center network performance.

A Packet Delay Assessment Model in the Data Link Layer of the LTE

The issues of modeling and evaluating the characteristics of the LTE data link layer functioning are considered. Transmitting packets in the data link layer are represented by a probabilistic-temporal graph consisting of two subgraphs. The first subgraph describes the operation of the HARQ protocol, and the second subgraph describes the operation of the ARQ protocol. The first subgraph is nested within the second subgraph. The probabilities of correct reception, non-error detection, and retransmission of packets in the MAC and RLC layers and generating functions of the packet service time based on the HARQ and ARQ protocols are determined. With the help of generating functions, the average value, variance, and coefficient of variation of the packet service time are determined. To calculate the average packet delay time in the LTE data link layer, the type of queuing system is selected, taking into account the coefficient of variation of the packet service time. The analysis of packets' delay time in the network's data link layer is carried out for different values of the intensity of packet arrival and the probabilities of a bit error in the physical layer of the network. For the sustainable functioning of the data link layer of the network, the limit values of the intensity of the arrival of packets are determined for a given probability of a bit error in the physical layer of the network.

Coded Wireless Distributed Computing With Packet Losses and Retransmissions

In wireless distributed computing systems, mobile devices that are connected wirelessly to the Fog (e.g., small base stations) collaboratively solve a given computational task. Unfortunately, wireless distributed computing systems suffer from packet losses due to severe channel fading. Moreover, a wireless device can drop out of the system when leaving the coverage of a master node in the Fog layer. We model this unreliability between a device and a master node as a packet erasure channel. When a packet fails to be detected at the receiver, the corresponding packet is retransmitted, which would significantly increase the overall run-time to finish the task. We take a coding-theoretic approach to tackle this straggler-like problem in wireless distributed computing. We first investigate the expected latency using an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(n, k)$ </tex-math></inline-formula> maximum-distance separable (MDS) code. We obtain the lower and upper bounds on the latency in closed forms and provide guidelines to design MDS codes depending on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">channel</i> condition characterized by packet erasure probability. Then, we introduce another important performance metric called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minimum latency</i> , and also provide guidelines on designing optimal codes. Based on optimal codes, we obtain the performance curves of achievable minimum latency and achievable workload as functions of packet erasure probability.

RM-ADR: Resource Management Adaptive Data Rate for Mobile Application in LoRaWAN.

LoRaWAN is renowned and a mostly supported technology for the Internet of Things, using an energy-efficient Adaptive Data Rate (ADR) to allocate resources (e.g., Spreading Factor (SF)) and Transmit Power (TP) to a large number of End Devices (EDs). When these EDs are mobile, the fixed SF allocation is not efficient owing to the sudden changes caused in the link conditions between the ED and the gateway. As a result of this situation, significant packet loss occurs, increasing the retransmissions from EDs. Therefore, we propose a Resource Management ADR (RM-ADR) at both ED and Network Sides (NS) by considering the packet transmission information and received power to address this issue. Through simulation results, RM-ADR showed improved performance compared to the state-of-the-art ADR techniques. The findings indicate a faster convergence time by minimizing packet loss ratio and retransmission in a mobile LoRaWAN network environment.

Efficient configuration for multi-objective QoS optimization in wireless sensor network

Wireless sensor network (WSN) is a self-organizing network consisting of many sensor nodes situated in the monitoring area. One of the most important challenges of WSNs is to configure the network in a way to improve the quality-of-service parameters, and thus enhance the network performance and reduce energy consumption. Energy consumption increases due to several reasons such as unsuccessful delivery of packets to the receiver, re-transmission of packets, delay in packet delivery to the sink, low received signal strength, inadequate link quality, noise level, etc. Each sensor network consists of different configuration parameters including the distance between receiver and transmitter, packet arrival time, packet size, maximum queue size, maximum number of transmissions, transmission power level, etc. All the mentioned parameters will affect several indicators such as latency, received signal strength, link quality, successful/fail packet delivery, noise level, and the number of re-transmissions. Finding the relationship between these two sets of parameters and optimizing them reduce energy consumption in the network and increase network lifetime. To achieve this, an efficient and new method based on support vector regression and genetic algorithm called MSOG is presented in this paper to model and optimize wireless sensor network indicators (such as delay, received signal strength, packet arrival time, successful packet delivery). The simulation results show that MSOG has better outcomes in optimizing network parameters and indicators in comparison with a similar algorithm.

An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things.

In modern years, network edges have been explored by many applications to lower communication and management costs. They are also integrated with the internet of things (IoT) to achieve network design, in terms of scalability and heterogeneous services for multimedia applications. Many proposed solutions are performing a vital role in the development of robust protocols and reducing the response time for critical networks. However, most of them are not able to support the forwarding processes of high multimedia traffic under dynamic characteristics with constraint bandwidth. Moreover, they increase the rate of data loss in an uncertain environment and compromise network performance by increasing delivery delay. Therefore, this paper presents an optimization model with mobile edges for multimedia sensors using artificial intelligence of things, which aims to maintain the process of real-time data collection with low consumption of resources. Moreover, it improves the unpredictability of network communication with the integration of software-defined networks (SDN) and mobile edges. Firstly, it utilizes the artificial intelligence of things (AIoT), forming the multi-hop network and guaranteed the primary services for constraints network with stable resources management. Secondly, the SDN performs direct association with mobile edges to support the load balancing for multimedia sensors and centralized the management. Finally, multimedia traffic is heading towards applications in an unchanged form and without negotiating using the sharing of subkeys. The experimental results demonstrated its effectiveness for delivery rate by an average of 35%, processing delay by an average of 29%, network overheads by an average of 41%, packet drop ratio by an average of 39%, and packet retransmission by an average of 34% against existing solutions.

Modeling and Online Adaptation of ALOHA for Low-Power Wide-Area Networks (LPWANs)

Unslotted ALOHA protocol has been adopted as a channel access mechanism in commercial low-power wide-area networks (LPWANs), such as Sigfox and long-range (LoRa) alliance. This work examines the throughput and random access (RA) delay distribution of unslotted ALOHA systems by considering exponential random backoff (ERB) or uniform random backoff (URB) algorithm. We further characterize the operating region of the systems as unsaturated stable, bistable, and saturated regions in terms of the new packet arrival and retransmission rates. To run the system stably with the maximum throughput, we propose a Bayesian online backoff algorithm that estimates the number of backlogged devices. Its performance is compared with other algorithms, such as particle filter (PF)-based algorithm, binary exponential backoff (BEB) algorithm, and the algorithm of exploiting exact backlog size information. Through extensive simulations, it is demonstrated that the performance of the proposed algorithm is very close to the upper bound and robust to time-varying traffic condition.

Dynamic Programming based Low-Latency Schedule (DPLLS) for 6TiSCH networks

Internet of Things (IoT) is a technological concept bringing sustainability and sophistication to our lives and is a significant component of Industry 4.0. The main requirements of Industrial IoT are reliability, stringent latency and energy efficiency. The IEEE 802.15.4e standard has adapted a Medium Access Control (MAC) behavioural mode called Time Slotted Channel Hopping (TSCH) to address the requirements of Industry 4.0. The 6TiSCH (IPv6 over IEEE 802.15.4e TSCH) protocol stack enables us to schedule the transmissions in TSCH network to achieve application-specific guarantees. In this paper, we propose DPLLS, a conflict-free dynamic programming based low-latency scheduling algorithm for TSCH network. In particular, identifying non-interfering transmissions in the network is posed as a maximum weight independent set (MWIS) problem, which is solved using a dynamic programming method. We organise each slotframe into smaller parts called blocks in which the non-interfering transmissions are scheduled simultaneously using either a conservative or a greedy scheme. The blocks are repeated in a slotframe to accommodate the retransmissions of packets to ensure reliability and minimise the latency. The proposed scheme is evaluated using an example to find the suitable block length of the proposed schemes and compared with the existing scheduling algorithms.

Edge Intelligent Agent Assisted Hybrid Hierarchical Blockchain for continuous healthcare monitoring & recommendation system in 5G WBAN-IoT

Wireless Body Area Network (WBAN) is an emerging technology that faces many challenges for monitoring patient health state information and early diagnosis of any disease (e.g. COVID-19). In addition, WBAN has many issues such as high energy consumption, overhead and latency due to the scarcity in resources, for that we bring edge assisted 5G environment in WBAN. The patient information is sensitive and easily traced by the attackers due to insecurity, for that, we proposed the EiA-H2B model (Edge intelligent Agent Hybrid Hierarchical Blockchain) which includes five phases. Firstly we proposed Biological PUF based sensor authentication, which is a strong credential that provides high security. All the sensors are authenticated by the virtual authenticator that is deployed by a blockchain ledger. Due to the mobility of WBAN, we focused on handover that is done by Bi-Partite One to N matching theory. Secondly, we proposed high voting based virtual clustering, in this phase high voting sensor is elected as a super node and that is used to create a virtual cluster. Thirdly, we proposed duty cycle MAC scheduling protocol is used to predict the original state of the sensors which is done by Attention Matrix based Gated Recurrent Unit (AM-GRU), and then the timeslots are allocated for avoiding packet loss and retransmission. Fourth, we perform relay selection and routing that is done by Global Optimization based Artificial Electric Field Algorithm (AEFA). And finally, we predict emergency data and generate an alert in EiA using Human Learning aided State Action Reward State Action (SARSA) algorithm that finds the current state and predicts the corresponding action. Based on that current state information SARSA provides an efficient recommendation to the patients. Experiments are conducted by OMNeT ++ network simulator and evaluate the performance of the proposed EiA-H2B model in terms of energy consumption, success rate, network throughput, end to end delay, packet loss rate, authentication time and processing time.

Delay and Peak-Age-of-Information of ALOHA Networks With Limited Retransmissions

This letter analyzes the impact of packet retransmissions on the performance of wireless random access protocols. In particular, we focus on the effects of two controllable parameters, namely the rate at which retransmissions occur and the number of allowed retransmissions. In a scenario where packets are generated according to a Poisson process and new packets substitute old ones upon arrival, we determine the conditions in which a given throughput or packet success probability constraint is achievable. We also prove, for a general packet success probability function of the traffic, that selecting the smallest feasible value of the number of allowed retransmissions along with an appropriate retransmission rate is optimal in terms of mean delay to transmit packets and mean <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Peak-Age-of-Information</i> . We illustrate this result with numerical examples.

Slotted ALOHA Protocol for Next Generation IoT

Abstract: Machine to Machine (M2M) communication is used to maintain the connectivity between the various devices in IoT. One of the major issues of Machine to Machine (M2M) communications is to enhance the network lifetime with the help of an efficient MAC protocols. Slotted ALOHA Protocol is simple random access technique used in low power applications like LoRaWAN (Long Range Wireless Area Network). This approach gives an improved result as compared to P-persistence slotted ALOHA. In this paper, we consider an IoT -M2M network comprises of a large no of devices that transmit data packets to a gateway. We have tried to reduce the collision, which directly reduce retransmission of data packets. We propose Adaptive pSlotted ALOHA Protocol using Successive Interference Cancellation (SIC). It offers high throughput and reduces the delay. It is the simplest way of the channel allotment among the users. The proposed p-Slotted ALOHA protocol using SIC is better than the p-persistence slotted ALOHA in term of delay and throughput. Keywords: MAC, M2M, Slotted ALOHA, LoRa-WAN, IoT, SIC

Improving the Convergence Period of Adaptive Data Rate in a Long Range Wide Area Network for the Internet of Things Devices

A Long-Range Wide Area Network (LoRaWAN) is one of the most efficient technologies and is widely adopted for the Internet of Things (IoT) applications. The IoT consists of massive End Devices (EDs) deployed over large geographical areas, forming a large environment. LoRaWAN uses an Adaptive Data Rate (ADR), targeting static EDs. However, the ADR is affected when the channel conditions between ED and Gateway (GW) are unstable due to shadowing, fading, and mobility. Such a condition causes massive packet loss, which increases the convergence time of the ADR. Therefore, we address the convergence time issue and propose a novel ADR at the network side to lower packet losses. The proposed ADR is evaluated through extensive simulation. The results show an enhanced convergence time compared to the state-of-the-art ADR method by reducing the packet losses and retransmission under dynamic mobile LoRaWAN network.

Age of Information of Dual-Sensor Information Update System With HARQ Chase Combining and Energy Harvesting Diversity

A novel status update information system is designed by jointly considering wireless energy harvesting diversity, status update transmission diversity, and hybrid automatic repeat request with chase combining (HARQ-CC) diversity, which consists of two sensors and one destination. The two sensors, which have finite-sized batteries, harvest energy from a Bernoulli process, and cooperatively deliver the status update to the destination by sharing a common information source. In addition, the two sensors synchronously operate in half-duplex (HD) mode and are triggered by a common control center. We first propose the sensor energy harvesting model that can be represented by Markov chain (MC) and develop the construction method of the transition matrix of sensor battery energy states as well as the stationary distributions. Secondly, the average age of information (AoI) of the proposed dual-sensor status update system is derived in terms of a closed-form expression by accurately calculating the statistical descriptions of the maximal number of the retransmissions of an update packet and the service time of one packet. Compared with traditional single-sensor system, the proposed dual-sensor system not only harvests more energy, but also has a smaller average AoI.

D-OLIA: A Hybrid MPTCP Congestion Control Algorithm with Network Delay Estimation.

With the recent evolution of mobile technology, modern devices equipped with multiple communication interfaces have become popular. The multipath transmission control protocol (MPTCP) has evolved to facilitate multiple communication interfaces through a single TCP connection for faster Internet access. MPTCP congestion control algorithms (MPTCP-CCAs) control data flow by fulfilling three design goals, i.e., ensuring improvement over single-path flows, ensuring fairness, and balancing congestion. Current MPTCP-CCAs cannot fulfill these design goals. For example, the opportunistic-linked increase algorithm (OLIA), a well-known MPTCP-CCA in load balancing, often results in low throughput because it cannot properly utilize the underlying network. In addition, the current Internet has a rapidly changing characteristic due to a large amount of short-lived traffic, making it difficult for MPTCP-CCAs to cope. An awareness of prevailing network delay conditions might help MPTCP-CCAs to utilize the network capacity fully. Therefore, we propose dynamic OLIA (D-OLIA), a hybrid MPTCP-CCA that enhances the performance of OLIA by integrating an awareness of the current network delay condition for deciding the congestion window (CWND) decrease factor. We estimate the current network delay condition, i.e., less-congested or congested, by observing the changes in the round-trip-time (RTT). Based on the estimated network delay condition, we decide the CWND decrease factor in real-time for reducing the CWND during packet loss events. We implemented D-OLIA in the Linux kernel and experimented using the Mininet emulator. The emulation results demonstrate that D-OLIA successfully estimates current network delay conditions and results in approximately a 20% increased throughput compared to the original OLIA. Compared to certain MPTCP-CCAs, it also yields a highly improved performance in terms of throughput, RTT, packet retransmissions, and fairness among the MPTCP sub-flows.

Effective Energy Efficiency of Ultrareliable Low-Latency Communication

Effective capacity (EC) defines the maximum communication rate subject to a specific delay constraint, while effective energy efficiency (EEE) indicates the ratio between EC and power consumption. We analyze the EEE of ultrareliable networks operating in the finite-blocklength regime. We obtain a closed-form approximation for the EEE in quasistatic Nakagami- m (and Rayleigh as subcase) fading channels as a function of power, error probability, and latency. Furthermore, we characterize the quality-of-service constrained EEE maximization problem for different power consumption models, which shows a significant difference between finite and infinite-blocklength coding with respect to EEE and optimal power allocation strategy. As asserted in the literature, achieving ultrareliability using one transmission consumes a huge amount of power, which is not applicable for energy limited Internet-of-Things devices. In this context, accounting for empty buffer probability in machine-type communication (MTC) and extending the maximum delay tolerance jointly enhances the EEE and allows for adaptive retransmission of faulty packets. Our analysis reveals that obtaining the optimum error probability for each transmission by minimizing the nonempty buffer probability approaches EEE optimality, while being analytically tractable via Dinkelbach's algorithm. Furthermore, the results illustrate the power saving and the significant EEE gain attained by applying adaptive retransmission protocols, while sacrificing a limited increase in latency.