Data Transmission Latency Research Articles

LPClass: Lightweight Personalized Sensor Data Classification in Computational Social Systems

The blend of topics in computational social science enhances the research complexity in developing efficient computational social systems (CSSs). Electronic health (E-health) is a critical branch of CSS. Artificial intelligence-based cognitive computing is especially appropriate for solving E-health problems in social science. The development of the Internet of Things (IoT) and sensor technologies is triggering data explosion in E-health CSS. The IoT-based edge computing has been applied in the field of E-health to reduce the latency of data transmission. However, small edge devices have limited resources (e.g., computational and storage resources). There is an urgent need to develop lightweight and efficient classification models to classify E-health sensor data in edge computing. Automatic health sensor data classification can help medical workers make correct clinical decisions. Also, patient-specific modeling in E-health is important. Using personalized classification model can achieve higher diagnose accuracy than the generic models trained based on historical datasets. To address the above problems, we propose a lightweight personalized sensor data classification model, called LPClass. It embeds the shallow recurrent neural network as a kernel, which makes it lightweight enough to be deployed on edge devices. In addition, a transfer learning algorithm is proposed to build personalized models for individuals. We conduct comprehensive experiments to evaluate LPClass from different aspects. Compared with the generic models, the personalized models in LPClass can achieve a fast convergence rate while maintaining high classification accuracy.

Edge–Artificial Intelligence-Powered Parking Surveillance With Quantized Neural Networks

The rapid development of urbanization has raised challenges for existing parking facilities to serve increasing parking demand. Being constrained by the limited urban land resources for newly constructed parking facilities, improving the efficiency of existing parking infrastructures relies more on an advanced parking management strategy. Video-based parking surveillance technology, with abundant information, easy installation, and powerful algorithms, is the most popular method to provide real-time parking information as the required data feed for a parking management system. Meanwhile, deep learning-based algorithms gradually replace traditional computer vision methods for video processing since the detection accuracy has been increased considerably by learning fine-grained features. However, the excessive computational complexity of deep learning-based algorithms occupies considerable computational resources, which certainly hurts the entire system’s efficiency. Due to the limited computing power of edge devices, most parking surveillance systems deploy video processing algorithms on a server or cloud platform, which raises concerns about data transmission latency and central computation pressure. Deploying efficient algorithms on an edge-side device is a potential solution to solve these problems. This article proposes an edge computing parking occupancy detection system with a quantized deep learning model. Model quantization is employed to boost the inference speed while maintaining accuracy. In addition, knowledge distillation is applied to improve the quantized model’s training performance. Experiments are conducted to demonstrate the model’s superiority compared to state-of-the-art algorithms and the feasibility of edge computing. The proposed method can improve the accuracy and efficiency of parking surveillance systems. It is a systematic solution for obtaining parking information with limited computational resources.

Power control and resource allocation for return on investment maximization in cross‐modal communications

AbstractMulti‐modal services will become the mainstream in future applications, including video, audio, and haptic flows. In multi‐modal services, video and audio primarily require high‐rate transmission, while haptic flow requires ultra‐high reliability and ultra‐low latency transmission, which imposes unprecedented challenges on the network. To improve the transmission efficiency of multi‐modal services, this article proposes a cross‐modal communication architecture in the cloud radio access network based on network slicing technology. First, we take the data rate and transmission latency of video, audio, and haptic flows contents as the quality of service (QoS) parameters, and send different modes of flows into varying slices for transmission. Second, to transmit the cross‐modal services efficiently while saving communication resources as many as possible, we define our optimization goal as the form of return on investment (ROI), that is, we aim to maximize the return of QoS parameters under the limited investment in communication resources. Third, we propose a two‐stage framework to solve this ROI maximization problem. In the first stage, we propose the power control gradient assisted binary search algorithm to solve the power control problem, which can achieve the optimization of data rate and transmission latency and obtain the suitable QoS. Then, in the second stage, we introduce the network slicing ant colony optimization algorithm to solve the slice resource allocation problem, and get the largest possible ROI. The simulation results verify the effectiveness of the proposed cross‐modal communication architecture, and prove the optimum of the proposed algorithm in terms of ROI compared to some existing algorithms, such as the reverse labeling Dijkstra algorithm, the delay acceptance algorithm, and the random searching algorithm.

An Adaptive Data Traffic Control Scheme with Load Balancing in a Wireless Network

The symmetric wireless network has been expected to be a revolutionary technology for mobile communications. Due to the limited resources of the microbase stations in the wireless network, the way to jointly optimize resource allocation, traffic throughput, latency, and other key performances is a hot research issue. In this paper, we introduce a joint optimization algorithm for improving the performance and balancing the traffic load of the wireless network. For the optimal traffic routing scheme, we transfer the problem to a mixed mathematical programming model. The model contains multiple traffic constraints and a single joint objective; the objective of the joint optimization are data transmission latency, energy consumption of wireless microbase stations, and throughput of links. Moreover, in order to approximately solve the optimization problem, we propose an efficient heuristic traffic transmission and migration scheme with load balancing, called an adaptive data traffic control scheme. The main idea of the proposed scheme is to split the traffic of overloaded microbase stations and links in the symmetric wireless network, so as to achieve load balancing and reduce the energy consumption of microbase stations. At last, the evaluations and simulations verify the proposed algorithm can efficiently optimize the energy allocation of microbase stations, and the network lifetime is increased to 210 rounds. Meanwhile, the network latency is reduced to 2–3 ms, and the network throughput is increased to 1000 Mb in our simulation environment. The constructed traffic control system for the traffic engineering-based wireless network in this paper can serve the intelligent system in the future.

Efficient and trusted autonomous vehicle routing protocol for 6G networks with computational intelligence

The Internet of Things (IoT) and wireless sensors have collaborated with many real-time environments for the collection and processing of physical data. Mobile networks with sixth-generation (6G) technologies provide support for emerging applications using Connected and Autonomous Vehicles (CAV) and observe critical conditions. Although, autonomous vehicle-based routing solutions have presented significant development toward reliable and inter-vehicle communications. However, there are numerous research obstacles in terms of data delivery and transmission latency due to the unpredictable environment and changing states of IoT sensors. Therefore, this work presents an efficient and trusted autonomous vehicle routing protocol using 6G networks, which aims to guarantee high quality of service and data coverage. Firstly, the proposed protocol establishes a routing process using a simulated annealing optimization technique and improves energy optimization between IoT-based vehicles, and under difficult circumstances, it statistically guarantees the optimal solution. Secondly, it provides a risk-aware security system due to reliable session-oriented communication with network edges among connected vehicles and avoids uncertainties in the autonomous system. The proposed protocol is verified using simulations for varying vehicles and varying iterations that indicates a green communication system for the autonomous system with authenticity and system intelligence.

Neighbor Coverage and Bandwidth Aware Multiple Disjoint Path Discovery in Wireless Mesh Networks

With the ease of appending new nodes without re-installing the whole network, the Internet of Things (IoT) builds several smart applications on Wireless Mesh Network (WMN). One of the important aspects of integrating WMN and smart IoT applications is to provide an energy-efficient and reliable routing protocol. Seeking the communication route that delivers the high-quality stream quickly over WMN is an important issue, but the maximum utilization of a single high-quality path leads to poor throughput and large communication delay, including route discovery and data forwarding delay. The broadcasting mechanism creates redundant transmissions of control packets into the network and reinitializes the blind route discovery process due to link disconnections leading to network resource constraints and high delay during the route discovery process. Moreover, the congestion in the communication route incurs data transmission latency. This paper proposes the Multiple Disjoint Path Determination (MDPD) mechanism based on-demand routing in WMN to formulate the path discovery and data transmission latency. Reducing the neighbor list into the uncommon neighbor set reduces the unnecessary latency in route discovery, and deriving high capacity multiple disjoint communication routes reduce the communication delay in the proposed work. The proposed work employs the queue dynamics in queuing delay, which mainly provides adaptability to the dynamics in network capacity and efficient diversity paths to the gateway node to infer the available bandwidth and optimize the network traffic. To fully utilize the advantage of heterogeneous routers, it disables the flooding of control packets across the stable mesh routers, excluding the initial route discovery process, because it enables the available route storage system in each mesh router. Hence, the proposed work efficiently supports wireless broadband internet access with reduced delay and control overhead. The simulation results demonstrate the fast detection of the multiple disjoint routes and data traffic optimization over the discovered disjoint routes in the proposed MDPD mechanism over WMN.

An Improved ID-Based Data Storage Scheme for Fog-Enabled IoT Environments.

In a fog-enabled IoT environment, a fog node is regarded as the proxy between end users and cloud servers to reduce the latency of data transmission, so as to fulfill the requirement of more real-time applications. A data storage scheme utilizing fog computing architecture allows a user to share cloud data with other users via the assistance of fog nodes. In particular, a fog node obtaining a re-encryption key of the data owner is able to convert a cloud ciphertext into the one which is decryptable by another designated user. In such a scheme, a proxy should not learn any information about the plaintext during the transmission and re-encryption processes. In 2020, an ID-based data storage scheme utilizing anonymous key generation in fog computing was proposed by some researchers. Although their protocol is provably secure in a proof model of random oracles, we will point out that there are some security flaws inherited in their protocol. On the basis of their work, we further present an improved variant, which not only eliminates their security weaknesses, but also preserves the functionalities of anonymous key generation and user revocation mechanism. Additionally, under the Decisional Bilinear Diffie–Hellman (DBDH) assumption, we demonstrate that our enhanced construction is also provably secure in the security notion of IND-PrID-CPA.

Fast Connectivity Construction via Deep Channel Learning Cognition in Beyond 5G D2D Networks

Along with the recent advance in wireless networking and data processing technologies, demands for low latency communication (LLC) are increasing in a wide variety of future-driven autonomous applications such as a smart factory, self-driving cars, and so on. The fifth generation of cellular mobile communications (5G) will cover this need as one of three key capacities in their usage scenarios: enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra-reliable low-latency communications (URLLC). The 5G systems are composed of mobile devices and various internet of things (IoT) devices for sensing, acting, and information services; they configure diverse networking topologies such as direct mobile-to-mobile, also known as device-to-device (D2D). In the 5G D2D network systems, the network topologies are easily broken because of the mobile devices such as smartphones, IoT devices, and so on. Thus, for the highly flexible and extensible 5G D2D network systems, mobility support for the mobile devices is necessary. In this paper, we first explore the mobility issues in beyond 5G D2D. Since there are static and mobile elements in the 5G application domains such as the smart factory, overall mobility would lead to highly frequent topology reconfiguration or connectivity reconstruction. Thus, latency-related problems derived from topology changes and connectivity failures due to the mobility are addressed. To handle the problems, a fast connectivity construction scheme, denoted by LMK, is proposed with a deep neural network dealing with learning on radio signal information in order to achieve the LLC. Evaluation results demonstrate that the proposed framework can provide reliable connectivity for the MAC layer link with a low latency data transmission.

Energy-Efficient Mobile Sink-Based Intelligent Data Routing Scheme for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have become an indistinguishable part of any Internet of Things (IoT) based system. In an IoT-based system, WSN monitors different physical objects and forwards the collected data to the base station or gateway node for further processing. Existing routing protocols designed for WSNs are not suitable for IoT-based systems. An intelligent and high-performance routing protocol is essential for real-time data analysis and decision-making. Therefore, this paper proposes a mobile sink-based intelligent data routing scheme for WSNs. Initially, Particle Swarm Optimization is used for optimal cluster formation. After cluster formation, the optimal number of rendezvous points are selected, and a data-gathering tour is planned for the mobile sink. Furthermore, a mobile sink-based intelligent data gathering scheme is proposed to collect the data from the cluster heads. Extensive simulations and mathematical analysis are performed to verify the effectiveness of the proposed scheme. The simulation results are compared with state-of-the-art algorithms in terms of stability period, network lifetime, throughput, average energy consumption, fairness index, data transmission latency, and message overhead.

Cluster-Based Transmission Diversity Optimization in Ultra Reliable Low Latency Communication

Intra-vehicular communication is an emerging technology explored spontaneously due to higher wireless sensor-based application demands. To meet the upcoming market demands, the current intra-vehicular communication transmission reliability and latency should be improved significantly to fit with the existing 5G and upcoming 6G communication domains. Ultra-Reliable Low-Latency Communication (URLLC) can be widely used to enhance the quality of communication and services of 5G and beyond. The 5G URLLC service is highly dependable for transmission reliability and minimizing data transmission latency. In this paper, a multiple-access scheme named Cluster-based Orthogonal Frequency Subcarrier-based Multiple Access (C-OFSMA) is proposed with 5G URLLC’s high requirement adaptation for intra-vehicular data transmission. The URLLC demanded high reliability of approximately 99.999% of the data transmission within the extremely short latency of less than 1 ms. C-OFSMA enhanced the transmission diversity, which secured more successful data transmission to fulfill these high requirements and adapt to such a network environment. In C-OFSMA, the available sensors transmit data over specific frequency channels where frequency selection is random and special sensors (audio and video) transmit data over dedicated frequency channels. The minimum number of subcarrier channels was evaluated for different arrival rates and different packet duplication conditions in order to achieve 99.999% reliability within an air-interface latency of 0.2 ms. For the fixed frequency channel condition, C-OFSMA and OFSMA were compared in terms of reliability response and other packet duplication. Moreover, the optimal number of clusters was also evaluated in the aspects of the reliability response for the C-OFSMA system.

High-Speed, Low-Latency In-Vehicle Network Based on the Bus Topology for Autonomous Vehicles: Automotive Networking and Applications

High-speed and low-latency data transmission is a crucial requirement of in-vehicle networks (IVNs), primarily to support connected and autonomous vehicles (CAVs), which require many sensors, cameras, and critical/safety data transmissions. To improve network performance for next-generation IVNs and reduce the wire harnesses complexity, bus topology-based IVNs, e.g., 10BASE-T1S and the controller area network with extended length (CAN-XL), have been proposed. However, their performance is insufficient with respect to data rates and latency to support next-generation IVN requirements. In this article, we propose technical methods to improve the performance of bus topology-based IVNs for next-generation IVNs, providing high-speed and low-latency data transmission with wideband and high-order modulated signaling by employing equalizers and ultralow latency for critical data transmission via a preemption method within round-robin transmission.

Distributed Authentication Model for Secure Network Connectivity in Network Separation Technology.

Considering the increasing scale and severity of damage from recent cybersecurity incidents, the need for fundamental solutions to external security threats has increased. Hence, network separation technology has been designed to stop the leakage of information by separating business computing networks from the Internet. However, security accidents have been continuously occurring, owing to the degradation of data transmission latency performance between the networks, decreasing the convenience and usability of the work environment. In a conventional centralized network connection concept, a problem occurs because if either usability or security is strengthened, the other is weakened. In this study, we proposed a distributed authentication mechanism for secure network connectivity (DAM4SNC) technology in a distributed network environment that requires security and latency performance simultaneously to overcome the trade-off limitations of existing technology. By communicating with separated networks based on the authentication between distributed nodes, the inefficiency of conventional centralized network connection solutions is overcome. Moreover, the security is enhanced through periodic authentication of the distributed nodes and differentiation of the certification levels. As a result of the experiment, the relative efficiency of the proposed scheme (REP) was about 420% or more in all cases.

Machine-type communications in NOMA-based terahertz wireless networks

Terahertz (THz) band is one of the most promising aspects of wireless communication systems because of its potential to meet the growing demands for the envisioned next-generation of cellular communications. THz band connectivity can alleviate bandwidth shortages and transmit power constraints using multiple-input multiple-output (MIMO) systems. To obtain better throughput and enhanced spectral efficiency, non-orthogonal multiple access (NOMA) configuration can be incorporated into MIMO systems, as NOMA uses non-orthogonal resource allocation by assigning the same carrier frequency to multiple devices in the power domain. In this paper, we focus on 2 × 1 MIMO-NOMA and cooperative 2 × 1 MIMO-NOMA systems for THz spectrum in the downlink transmission. In order to evaluate the effectiveness of the proposed system architecture, we derive accurate data rate, transmission latency, and reliability expressions for 2 × 1 MIMO-NOMA system consists of critical and non-critical devices by leveraging finite block length theory. Moreover, we derive the closed-form expressions for power splitting coefficients, data rate, transmission latency, and reliability for the considered cooperative 2 × 1 MIMO-NOMA system. Extensive numerical results are presented to validate the feasibility of the proposed 2 × 1 MIMO-NOMA as well as the cooperative 2 × 1 MIMO-NOMA systems in the THz band.

Collaborative Three-Tier Architecture Noncontact Respiratory Rate Monitoring Using Target Tracking and False Peaks Eliminating Algorithms

Monitoring respiratory rate is crucial for helping identify respiratory disorders. Devices for conventional respiratory monitoring are inconvenient and scarcely available. Recent research has demonstrated the ability of non-contact technologies, such as photoplethysmography and infrared thermography, to gather respiratory signals from face and monitor breathing. However, current non-contact respiratory monitoring techniques have poor accuracy because they are sensitive to environmental influences like lighting and motion artifacts. Furthermore, frequent contact between users and the cloud in real-world medical application settings might cause service request delays and potentially the loss of personal data. We proposed a non-contact respiratory rate monitoring system with a cooperative three-layer design to increase the precision of respiratory monitoring and decrease data transmission latency. To reduce data transmission and network latency, our three-tier architecture layer-by-layer decomposes the computing tasks of respiration monitoring. Moreover, we improved the accuracy of respiratory monitoring by designing a target tracking algorithm and an algorithm for eliminating false peaks to extract high-quality respiratory signals. By gathering the data and choosing several regions of interest on face, we were able to extract the respiration signal and investigate how different regions affected the monitoring of respiration. The results of the experiment indicate that when the nasal region is used to extract the respiratory signal, it performs experimentally best. Our approach performs better than baseline approaches while transferring fewer data.

QoS Violation Probability Minimization in Federating Vehicular-Fogs With Cloud and Edge Systems

Due to the fact that most user equipments (UEs) do not have enough computation power, time-sensitive tasks should be offloaded to other computation resources. In such cases, a cloud is an ideal destination to which computation tasks can be offloaded because of its huge data processing power. However, the distance between a UE and the cloud may increase the latency of data transmission. At the same time, edges and vehicular- fogs (consisting of electric vehicles with computing resources) are closer to UEs and mostly remain under-utilized. To address this issue, a federated architecture with UEs, vehicular-fogs (parking lot fog and traffic intersection fog), edges, and cloud is proposed along with a probabilistic offloading strategy. The quality of service (QoS) violation probability can be minimized by finding the optimal offloading probabilities while satisfying the delay constraint through an optimal offloading probability estimation algorithm based on the subgradient method proposed in this pa- per. Our results show that the QoS violation probability and the average waiting time can be decreased by 10%12% and 45%, respectively, for a federated architecture with two vehicular-fogs as compared to an architecture without any fogs. As offloading to edges and cloud has longer communication delays than offloading to vehicular-fogs, the offloading probabilities to the edges in the architecture with two vehicular-fogs in the case of heavy traffic are reduced by nearly 35% as compared to the architecture without any fogs.

VCMaker: Content-aware configuration adaptation for video streaming and analysis in live augmented reality

The emergence of edge computing has enabled mobile Augmented Reality (AR) on edge servers. We notice that the video configurations, i.e., frames per second (fps) and resolution, significantly affect the key metrics such as detection accuracy, data transmission latency and energy consumption in real AR application. Besides the time-varying bandwidth, we observe that the video contents, such as moving velocities of target objects, have remarkable impacts on the configuration selection. In addition, we take the energy consumption on data transmission into consideration. In this paper, we propose VCMaker, a system that generates video configuration decisions using reinforcement learning (RL). VCMaker trains a neural network model that selects configuration for future video chunks based on the collected observations. Rather than rely on any pre-programmed models, VCMaker learns to make configuration decisions solely through empirical observations of the resulting performances of historical decisions. In addition, we leverage the dynamic Region of Interest (RoI) encoding and motion vector-based object detection mechanisms to advance VCMaker. We implemented VCMaker and conducted extensive evaluations. The results show that VCMaker achieves a 20.5%–32.8% higher detection accuracy, and 25.2%–45.7% lower energy consumption than several state-of-the-art schemes.

Low-Latency QC-LDPC Encoder Design for 5G NR.

In order to meet the low latency and high throughput requirements of data transmission in 5th generation (5G) New Radio (NR), it is necessary to minimize the low power encoding hardware latency on transmitter and achieve lower base station power consumption within a fixed transmission time interval (TTI). This paper investigates parallel design and implementation of 5G quasi-cyclic low-density parity-check (QC-LDPC) codes encoder. The designed QC-LDPC encoder employs a multi-channel parallel structure to obtain multiple parity check bits and thus reduce encoding latency significantly. The proposed encoder maps high parallelism encoding algorithms to a configurable circuit architecture, achieving flexibility and support for all 5G NR code length and code rate. The experimental results show that under the 800 MHz system frequency, the achieved data throughput ranges from 62 to 257.9 Gbps, and the maximum code length encoding time under base graph 1 (BG1) is only 33.75 ns, which is the critical encoding time of our proposed encoder. Finally, our proposed encoder was synthesized on SMIC 28 nm CMOS technology; the result confirmed the effectiveness and feasibility of our design.

Random Forest for Data Aggregation to Monitor and Predict COVID-19 Using Edge Networks

Medical sensors and distributed edge networks hold promise in advanced control and prediction of infectious diseases, such as COVID-19. Their integration can lower communication latency, bandwidth utilization, and energy consumption while achieving high application scalability and reliability. Existing edge-devices-enabled e-healthcare frameworks face two serious challenges that affect their success. First, real-time medical sensors generate similar or redundant readings used for disease prediction, mostly for non-COVID-19 patients, which increases data transmission latency and energy consumption. Second, predicting the risk level of COVID-19 patients using lightweight machine learning requires minimal training time to be useful. To address these challenges, we develop an edge-centric e-healthcare framework for online health data monitoring and analysis to predict the risk level of COVID-19 patients. In the proposed framework, a statistical data aggregation method (MEAN function) is deployed at local gateway devices to remove redundant data to minimize communication latency and energy usage. Geo-distributed edge servers are used to predict with 97 percent accuracy the risk level to each patient with Random Forest (RF) on the aggregated data. Finally, the efficiency of the RF algorithm is demonstrated with standard classification techniques.

On multiplexing data streams using trellis-coded modulation in centralized wireless networks

Problem statement: The proliferation of services and applications requiring ultra-low latency and high reliability of data transmission in communication networks leads to creating new approaches and architectures in order to ensure the simultaneous transmission of Enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low Latency Communication (URLLC) traffic. Providing efficient eMBB and URLLC multiplexing schemes with preset key performance indicators for each stream is the most challenging problem in wireless network development. Purpose: To provide a simultaneous transmission of eMBB and URLLC streams without reducing the user experience of eMBB services by developing a multiplexing scheme and the coherent architecture of physical (PHY) and media access control (MAC) layers in the downlink channel. Results: An eMBB and URLLC multiplexing scheme has been proposed, along with a coherent architecture for PHY and MAC layers, ensuring the given wireless network key performance indicators. The proposed solution performance has been estimated by simulation. The multiplexing scheme outperforms the baseline solution in Bit Error Rate and Frame Error Rate metrics. The coherent PHY and MAC layers architecture provides transmission with an arrival rate of 400 messages per millisecond and 99% message delivery probability in one millisecond. Practical relevance: The obtained results allow communication system developers to deploy centralized wireless networks at industrial objects.

DEQLFER — A Deep Extreme Q-Learning Firefly Energy Efficient and high performance routing protocol for underwater communication

With an advent of Underwater sensor networks, underwater communication has reached its new dimension of research. These networks are characterized by the elongated end to end delay, high energy utility and most importantly dynamic network topologies. By incorporating these characteristics, numerous automated routing algorithms has been proposed to achieve the energy efficient and low latency data transmission. But still, short-comings still exists due to the above mentioned characteristics and the most comprehensive routing algorithms are badly desired. In this article, a novel routing scheme based on Q-learning framework and Deep Extreme Learning Machines aided with Adaptive Firefly Routing algorithm to address the above mentioned research constraints including energy efficiency and network unsteadiness in underwater communication , that practices the hybrid combination of reward function and adaptive fireflies to determine the optimal routing mechanism. In this algorithm, traditional q-learning mechanism has been replaced by the powerful q-deep extreme learning mechanism which uses the adaptive reward function for the varying underwater environment and to boost the packet-delivery ratio (PDR) and throughputs. Also the paper uses the powerful firefly aided routing mechanism to achieve the energy efficient data transmission and to avoid the void dilemma problems. The extensive experimentations has been conducted on the proposed algorithm and compared with other state of art schemes such as Q deep q-Learning energy aware routing protocol (DQLER), DELR Protocols and VBF protocols in which the proposed algorithm has outperformed than the compared existing algorithms in terms of complexity, energy consumption , packet delivery ratio and end to end delay.