Optimal Data Transmission Research Articles

A modified whale‐dragonfly algorithm and self‐adaptive cuckoo search‐based clustering strategy for augmenting network lifetime in wireless sensor networks

SummaryDesigning an energy efficient and durable wireless sensor networks (WSNs) is a key challenge as it personifies potential and reactive functionalities in harsh antagonistic environment at which wired system deployment is completely infeasible. Majority of the clustering mechanisms contributed to the literature concentrated on augmenting network lifetime and energy stability. However, energy consumption incurred by cluster heads (CHs) are high and thereby results in minimized network lifetime and frequent CHs selection. In this paper, a modified whale‐dragonfly optimization algorithm and self‐adaptive cuckoo search‐based clustering strategy (MWIDOA‐SACS) is proposed for sustaining energy stability and augment network lifetime. In specific, MWIDOA‐SACS is included for exploiting the fitness values that aids in determining two optimal nodes that are selected as optimal CH and cluster router (CR) nodes in the network. In MWIDOA, the search conduct of dragon flies is completely updated through whale optimization algorithm (WOA) for preventing load balancing at CHs. It minimized the overhead of CH by adopting CHs and CR for collecting information from cluster members and transmitting the aggregated data from CHs to the base station (BS). It included self‐adaptive cuckoo search (SACS) for achieving sink mobility using radius, energy stability, received signal strength, and throughput for achieving optimal data transmission process after partitioning the network into unequal clusters. Simulation experiments of the proposed MWIDOA‐SACS confirmed better performance in terms of total residual energy by 21.28% and network lifetime by 26.32%, compared to the competitive CH selection strategies.

A Certain Investigations on Energy Efficient Techniques on Wireless Sensor Networks over Smart Grid

Energy efficiency plays biggest role in the wireless sensor network as the sensors are smaller and restricted in their resource capacity. Due to their nature of restricted resource capacity, data transmission would become more complex. So, it is required to concentrate more on data transmission strategies, to ensure the interruption avoided data transmission. There are different examination strategies has been presented before for performing data transmission. Among them most of research methodologies focused on attaining energy consumption reduced optimal data transmission. The working principlee and processing flow of previous research methodologies has been discussed here in details. This survey article is to discuss the research techniques which attempts to perform energy consumption reduced data handling, so that network lifetime of various sensor nodes can be utilized effectively along with increased data transmission rate. And this research work discussed the merits and demerits analysed over each research techniques discussed here. Finally, this research work is concluded with the performance analysis over varying number of nodes. The examination of the analysis work is done in the matlab. The mathematical qualities have been examined to predict the exhibition level of various examination procedures as far as their packet transmission rate, delay and energy utilization.

A Novel Gradient Boosted Energy Optimization Model (GBEOM) for MANET

Mobile Ad Hoc Network (MANET) is an infrastructure-less network that is comprised of a set of nodes that move randomly. In MANET, the overall performance is improved through multipath multicast routing to achieve the quality of service (quality of service). In this, different nodes are involved in the information data collection and transmission to the destination nodes in the network. The different nodes are combined and presented to achieve energy-efficient data transmission and classification of the nodes. The route identification and routing are established based on the data broadcast by the network nodes. In transmitting the data packet, evaluating the data delivery ratio is necessary to achieve optimal data transmission in the network. Furthermore, energy consumption and overhead are considered essential factors for the effective data transmission rate and better data delivery rate. In this paper, a Gradient-Based Energy Optimization model (GBEOM) for the route in MANET is proposed to achieve an improved data delivery rate. Initially, the Weighted Multi-objective Cluster-based Spider Monkey Load Balancing (WMC-SMLB) technique is utilized for obtaining energy efficiency and load balancing routing. The WMC algorithm is applied to perform an efficient node clustering process from the considered mobile nodes in MANET. Load balancing efficiency is improved with a higher data delivery ratio and minimum routing overhead based on the residual energy and bandwidth estimation. Next, the Gradient Boosted Multinomial ID3 Classification algorithm is applied to improve the performance of multipath multicast routing in MANET with minimal energy consumption and higher load balancing efficiency. The proposed GBEOM exhibits ∼4% improved performance in MANET routing.

Cyber-Attack Detection in Autonomous Vehicle Networks by Energy Aware Optimal Data Transmission with Game Fuzzy Q-Learning based Heuristic Routing Protocol

The automotive sector has seen a dramatic transition due to rapid technological advancement. Network connection has improved, enabling the transfer of the cars' technologies from being fully machine- to software-controlled. Controller area network (CAN) bus protocol manages network for autonomous vehicles. However, due to the intricacy of data and traffic patterns that facilitate unauthorised access to a can bus and many sorts of assaults, the autonomous vehicle network still has security flaws as well as vulnerabilities. This research proposes novel technique in cyber attack detection in autonomous vehicle networks enhanced data transmission based optimization and routing technique. Here the autonomous vehicle network optimal data transmission has been carried out using energy aware lagrangian multipliers based optimal data transmission. The cyber attack detection has been carried out using fuzzy q-learning based heuristic routing protocol. The experimental results has been carried out based on optimal data transmission and attack detection in terms of throughput of 95%, PDR of 94%, End-end delay of 46%, energy efficiency of 96%, network lifetime of 95%, attack detection rate of 88%.

RETRACTED: Data location integration with stable routing: Stable and optimal data transmission in wireless sensor networks

AbstractWhile transmitting data over wireless sensor networks (WSN), energy conservation and stable routing are two major problems while transmitting data over the Internet. The main problem with data transmission is that it consumes high energy consumption and network overheads. Therefore, an effective routing protocol should ensure transmission stability in the dynamic sensor environment. Data location integration with stable routing (DLI‐SR) has been introduced to achieve this goal. The DLI‐SR approach first determines the cluster head according to the node location in which node energy consumption, node degree, and distance have been considered. Then the cluster members are selected, and information is transmitted to the sink node by ensuring the data transmission stability. Network stability is determined with the help of link quality, the number of hops, and residual energy. These characteristics are used to maintain the network's stability and minimize its high energy consumption. Here, the stable centrality concept is introduced for choosing the first stable nodes, and the stable path is selected from the source node. The hot spot is maintained to reduce the high energy consumption due to the stable nodes' changing. The effective utilization of node and network characteristics helps to improve the overall network performance. This system is implemented via the NS‐2 simulation tool, and its excellence is compared with existing methods.

Optimal Data Transmission and Improve Efficiency through Machine Learning in Wireless Sensor Networks

Each sensor node in WSN is typically equipped with a limited capacity small battery. Energy-efficient communication is therefore considered a key component of network life extension. In addition, as the utilization of the sensor network increases, duplicate data and abnormal data is also collected to reduce the accuracy of the data in various environments. AI is used to recognize data anomaly values and increase packets' accuracy by removing out-of-range data. This can improve performance through optimal data transmission, resulting in increased network life, energy efficiency, and reliability. This paper proposes a protocol called MLQ-MAC that reflects the above. MLQ-MAC uses AI techniques to consider different types of data packets. The data collected by the sensor removes the measurement anomaly and duplicate data and stores it in a different transmission queue by priority. Efficient data transfer is possible by using an AI Discriminator for accurate classification before being stored on a transmission queue. The AI-Discriminator classifies a variety of factors, including the collection environment, characteristics of network applications, and so on. It also uses two new technologies: self-adaptation and scheduling for efficient transmission. In the protocol, the receiver adjusts the duty cycle according to to transmit urgency to improve network QoS. Finally, the simulation results show that the MLQ-MAC protocol reduces energy consumption at the receiver by up to 3.4% and per bit by up to 2.3% and improves packet delivery accuracy by up to 3%.

Optimal data transmission and pathfinding for WSN and decentralized IoT systems using I-GWO and Ex-GWO algorithms

Efficient resource use is a very important issue in wireless sensor networks and decentralized IoT-based systems. In this context, a smooth pathfinding mechanism can achieve this goal. However, since this problem is a Non-deterministic Polynomial-time (NP-hard) problem type, metaheuristic algorithms can be used. This article proposes two new energy-efficient routing methods based on Incremental Grey Wolf Optimization (I-GWO) and Expanded Grey Wolf Optimization (Ex-GWO) algorithms to find optimal paths. Moreover, in this study, a general architecture has been proposed, making it possible for many different metaheuristic algorithms to work in an adaptive manner as well as these algorithms. In the proposed methods, a new fitness function is defined to determine the next hop based on some parameters such as residual energy, traffic, distance, buffer size and hop size. These parameters are important measurements in subsequent node selections. The main purpose of these methods is to minimize traffic, improve fault tolerance in related systems, and increase reliability and lifetime. The two metaheuristic algorithms mentioned above are used to find the best values ​​for these parameters. The suggested methods find the best path of any length for the path between any source and destination node. In this study, no ready dataset was used, and the established network and system were run in the simulation environment. As a result, the optimal path has been discovered in terms of the minimum cost of the best paths obtained by the proposed methods. These methods can be very useful in decentralized peer-to-peer and distributed systems. The metrics for performance evaluation and comparisons are i) network lifetime, ii) the alive node ratio in the network, iii) the packet delivery ratio and lost data packets, iv) routing overhead, v) throughput, and vi) convergence behavior. According to the results, the proposed methods generally choose the most suitable and efficient ways with minimum cost. These methods are compared with Genetic Algorithm Based Routing (GAR), Artificial Bee Colony Based routing (ABCbased), Multi-Agent Protocol based on Ant Colony Optimization (MAP-ACO), and Wireless Sensor Networks based on Grey Wolf optimizer. (GWO-WSN) algorithms. The simulation results show that the proposed methods outperform the others.

Positiveness and Finite-Time Control of Dual-Switching Poisson Jump Networked Control Systems With Time-Varying Delays and Packet Drops

This article studies the stochastically finite-time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2}$</tex-math></inline-formula> control problem for the time-varying delay positive dual-switching Poisson jump networked control systems (TVD-PDSPJ-NCSs) with packet drops. A key issue is that the TVD-PDSPJ-NCSs do not specify the jumping sequence of working processing modes. First, the TVD-PDSPJ-NCSs are modeled for the first time by considering the non-negative total amount of data, work processing modes jumping, and data transmission channel switching of the NCSs. Then, a proper finite-time state feedback controller is designed for the TVD-PDSPJ-NCSs so that the closed-loop dual-switching Poisson jump NCSs are positive, stochastically finite-time bounded, and fulfill the specified <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2}$</tex-math></inline-formula> disturbance attenuation performance simultaneously. By means of stochastic Lyapunov–Krasovskii functional methods, some sufficient conditions in the form of linear matrix inequalities are derived to obtain the finite-time state feedback controller gain. Furthermore, the optimal solution of the switching law is obtained such that the regulator can select the optimal data transmission channel according to the degree of packet drops. Finally, an application example is given to show the feasibility and validity of the proposed results.

Optimum Data Transmission Allocation in Multimodal Communication of Underwater Sensor Networks

Multimodal communication (MC) plays a crucial role in the Underwater Sensor Networks (UWSNs). The appropriate resource allocation of MC can significantly optimize the UWSNs performance. This letter investigates the optimum data transmission allocation in the underwater MC. Considering the fundamental MC tradeoff, the MC energy efficiency model is established and formulated as a mixed integer nonlinear programming problem with the provable convex optimization properties. Then this letter designs two schemes for the various data amounts to optimize the optical-acoustic transmission ratio and an optimized data collection algorithm to save the MC energy. Simulation results validate the effectiveness of this letter.

A mean field game-theoretic cross-layer optimization for multi-hop swarm UAV communications

Unmanned aerial vehicle (UAV) multi-hop communication networks are foreseen to be widely employed in both military and civilian scenarios. However, in ultra-dense scenarios with swarm UAVs, nodes are highly dynamic mobile, ultra-dense deployment and non-centralized distribution. These characteristics make the centralized resource management policy not apply. Meanwhile, existing routing protocols can't meet the performance challenges of high dynamic, topology and link frequency changes of ultra-dense scenarios with swarm UAVs. To solve the above challenges of resource management and routing protocol, a cross-layer optimization method is presented with a novel mean field game (MFG) in this paper. It is based on the cross-layer design method of the MFG theory and jointly considers the power resources in the physical layer, frequency resources in the medium access control (MAC) layer, and routing resources in the network layer. By dividing into subproblems, the original problem is solved. Meanwhile, the optimal data transmission path can be selected through the management and allocation of frequency resources and power resources. A cross-layer resource management dynamic source routing (CLRM-DSR) protocol is designed based on that which adds link quality measurement. The simulation results show that the presented CLRM-DSR with the proposed resource management scheme can improve the data packet transmission rate, reduce end-to-end delay, and lower routing overhead for the multi-hop swarm UAV communication network.

Implementation of Low Resource Parking Information System Prototype Based on Wireless Sensor Network

In everyday life, humans need vehicles to support various activities. One thing that must be considered is the issue of parking availability in public places such as office buildings, campuses, shopping centers. In the previous technology, a parking system using automatic hydraulics was made. However, it requires a complex construction. In this study, a wireless parking availability information system was developed. This system is built using a ZigBee communication network. The installed low-cost light sensor will detect the presence or absence of a car occupying a parking slot. Then every driver who enters the parking area can find out information on parking availability. Based on the test results, the system can automatically provide information related to the status of the parking lot (filled or empty) with a sensor threshold (light intensity) of 153 cd. The maximum delay generated by the system during testing is 2.518462ms. The optimal data transmission distance is 40 meters.

Research of functioning processes of wireless sensor networks

The article is devoted to the research of problems of optimal control of data flow routing in heterogeneous sensor networks of variable topology under conditions of constraints and uncertainties. To solve the problem of synthesis of optimal data transmission routes in sensor networks, it is necessary to synthesize a graph model, formally define the optimization problem, investigate the processes of functioning of network elements and obtain formalized descriptions of the dependence of network elements. The mathematical model of functioning of the distributed system of intelligent data flow control in sensor networks of variable topology is considered based on the formal model of the functioning of the distributed system of intelligent network management, the properties of the processes of optimal load management of nodes and the data network as a whole are investigated. The results of the research allowed to obtain formal descriptions of the dependence of the load of the computing system of the sensor network on the time of the data transmission process, waiting time, service time on the load of the computing system of the sensor network, to determine quantitative indicators of delay and loss of data packets depending on the functioning modes.

High bandwidth data and image transmission using a scalable link model with integrated real-time data compression

Our society increasingly relies on the transmission and reception of vast amounts of data using serial connections featuring ever-increasing bit rates. In imaging systems, for example, the frame rate achievable is often limited by the serial link between a camera and host even when modern serial buses with the highest bit rates are used. This paper reports a scalable link system with a bandwidth and interface standard that can easily be adapted to suit a particular application. The scalable serial link approach has been extended with lossless data compression with the aim of further increasing dataflow at a given bit rate. The compression method is integrated into the scalable transceivers providing a comprehensive solution for optimal data transmission over a variety of different interfaces. The system is fully implemented on an FPGA using a fully hardware based system. A Terasic DE4 board was used for implementing and testing the system using Quartus-II software and tools for design and debugging. The impact of compressing the image and carrying the compressed data through parallel lines is similar to the impact of compressed the same image inside a single core with a higher compression ratio, in this system between 7.5 and 126.8.

Multi Objective Concerned Network Coding Technique for Optimal Data Transmission

The Internet of Things (IoT) seems to developed in the real-world scenario due to increased utilization of sensor driven technologies. There are various research works has been proposed earlier for the network coding to ensure the reliable data transmission. However, existing research techniques doesn’t focus on the reliable route path selection which might affect the rate of data transmission. These issues are focused in the proposed research method by introducing the method namely Multi-Objective concerned Network Coding Technique (MO-NCT). In this work initially multicast tree construction is performed using Particle Swarm Optimization method. The main goal of this research work is ensuring the reliable and successful data transmission. Here the optimal network nodes will be chosen for constructing the multi cast tree. The multiple objectives considered in this work for the selection of the nodes are residual energy, remaining bandwidth level and throughput of nodes. To ensure the reliable data transmission even with the presence of larger coverage area, this work attempted to select the backup forwarder nodes which will act as intermediate hop relay nodes. The overall analysis of the research work is done in the NS2 simulation environment from which it is proved that the proposed method MO-MRP tends to achieve the optimal data transmission rate.

Fairness-oriented computation offloading for cloud-assisted edge computing

Mobile edge computing, which enhances the computing power of mobile devices via computation offloading technology, has been proposed as a promising paradigm to alleviate the mismatch between limited computational resources and ever-increasing computational requirements. An appropriate offloading strategy must be identified to optimize the computation offloading performance. Moreover, a mobile user is generally a selfish individual; therefore, we should consider the fairness problem when determining the offloading strategy. In this paper, we focus on computation offloading in a cloud-assisted edge computing system with multiple users, an edge server and a cloud server and aim to optimize the response time for each user. A fairness-oriented approach, including the determination of the application offloading strategy, the data transmission strategy and the cloud–edge cooperation strategy, is proposed. Initially, the computation offloading problem is formulated as a noncooperative game. Then, we find the optimal cloud–edge cooperation strategy and the optimal data transmission strategy based on a theoretical analysis and propose an iterative algorithm to identify the optimal offloading strategy for each user. Finally, we evaluate the proposed approach by means of extensive experiments, and the experimental results show that the proposed approach can significantly reduce the response time of mobile devices.

Optimal Data Transmission for WSNs with Data-Location Integration

Wireless sensor networks (WSNs) have good performance for data transmission, and the data transmission of sensor nodes has the function of symmetry. However, the wireless sensor nodes are facing great pressure in data transmission due to the increasing amount and types of data that easily cause premature energy consumption of some nodes and, thus, affects data transmission. Clustering algorithm is a common method to balance energy consumption, but the existing algorithms fail to balance the network oad effectively for big data transmission. Therefore, an optimal data transmission with data-location integration (ODTD-LI) is proposed for WSNs in this paper. For optimal data transmission, we update the network topology once for one round. In the proposed algorithm, we perform calculations of the optimal cluster heads, clustering and data transmission routing through three steps. We first deploy N homogeneous and symmetry nodes in a square area randomly and calculate the optimal number of cluster heads according to the node ocations. then, the optimal number of cluster heads, energy consumption, the distances and degrees of the nodes are taken into consideration during the clustering phase. Direct communication is carried out within a cluster, and the member nodes of the cluster pass the information directly to the cluster head. Lastly, an optimal hybrid routing from each cluster node to Sink is constructed for data transmission after clustering. The simulations verify the good performance of the proposed algorithm in view of the ifetime, average delay, coverage rate (CR) and oad balance of the network compared with the existing algorithms. Through the research conducted in this paper, we find that our work has good performance for selecting the hybrid routing in the network with the nodes randomly arranged.

Designing GA based effective transmission strategies for intra-WBAN communication

Wireless Body Area Networks enable a new trend of proactive health care exploiting battery-powered wearable and/or implantable sensor nodes. The transmission of biomedical signals consumes most of the scarce energy resources in a WBAN. State-of-the-art transmission mechanisms either focus on energy efficiency or timely data delivery. However, an optimal balance between the two subject to various ambient conditions is a necessity. Accordingly, in this paper, an optimal data transmission policy is formulated at the node level, considering node lifetime, node heating, and reliable data delivery for various ambient conditions. The proposed model is divided into two phases. In the pre-deployment phase, each node is stochastically modeled following the Markov Decision Process. The formulation involves a reward function that attempts a trade-off between energy consumption and reliable data delivery. Then, a genetic algorithm is applied to obtain a set of optimal transmission decisions for each node based on the reward function for a range of ambient conditions. Finally, in the deployment phase, the results of the previous phase are mapped to an algorithm to be executed at WBAN nodes. The work is experimentally evaluated via simulation and compared with the state-of-the-art approaches. The performance is also observed based on the real-life dataset. Results demonstrate that around 85–90% of data packets could be delivered to the sink with minimal energy so that the nodes hardly get heated.

Equalization of Finite-Alphabet MMSE for All-Digital Massive MU-MIMO mm-Wave Communication

For more than twenty years, growing the performance and efficiency of wireless communications systems using antenna arrays has been an active field of study. Wireless networks with multiple-input multiple-output are also part of the current norms and are implemented around the world. Access points or BSs with comparatively few antennas are used for standard MIMO systems, and the resulting increase in spectral efficiency was relatively modest. A Multiple-Input Multiple-Output platform’s capacity is researched where the transmitter outputs are processed and quantified by a set of limit quantizes through an analogue linear combining network. The linear mixing weights and cutoff levels are chosen from with a collection of possible combinations as a function of the transmitted signal. Millimetre-wave networking requires optimum data transmission to various computers on same moment network in combination with large multi-user actually massive. In order to guarantee efficient data transmission, the heavy insertion loss of wave propagation at su ch a faster speed needs proper channel estimation. A new channel estimation algorithm called Beam space Channel Estimation is suggested. From a set of possible configurations, the capacity of a massive stream from which antennas signals are handled by an analog channel as a part of the channel matrix, linear mixture weights and frequency modulation levels are selected. Probable implementations of specific analogue receiver designs for the combined network model, such as smart antenna selection, sign antennas output thresholding or linear output processing. To demonstrate the effectiveness of BEACHES in service and have FPGA implementation results, we are developing VLSI architecture. Our results show that for large MU-MIMOs, mm-wave communications with hundreds of antennas, specially made denoising can be done at maximum bandwidth and in an equipment format.

Data Aggregation Mechanisms on the Internet of Things: A Systematic Literature Review

Nowadays, the Internet of Things (IoT) has gained considerable attention in different academic and real-world research domains. Due to the data-driven nature of IoT, the data aggregation process in such systems is significantly challenging. The main goal of data aggregation mechanisms is to achieve high Quality of Services (QoS), including optimal data transmission delay, reliability, and energy consumption. Despite various types of available review papers on this particular topic, we feel that the existing literature does not satisfy the requirements of being up to date and comprehensive. This paper presents a systematic literature review for data aggregation mechanisms on IoT that illustrates the critical challenges in the context design issues. Further, the data aggregation mechanisms are divided into two main categories: client-server-based and mobile agent-based ones. The client-server-based data aggregation mechanisms are also presented in three groups: cluster-based, tree-based, and centralized methods. Finally, we outline possible future research trends in data aggregation on IoT.

Network Lifetime Improved Optimal Routing in Wireless Sensor Network Environment

In the field of Wireless Sensor Networks, major problem arises due to the unbalanced consumption of energy. The unbalanced consumption will reduce the lifetime of a network performance. In the existing work, replacement of node is a difficult process and conservation of network parameters is not concentrated. It leads to the failure of network in different situations. These issues are resolved by introducing a density aware optimal clustering which is used to perform clustering to ensure the data transmission in a reliable manner. In the proposed work, Energy Balancing and Optimal Routing Based Secured Data Transmission based on clustering is introduced. It enhances the lifetime uniformly deployed data-gathering sensor networks using a balanced energy consumption technique. Ant Colony Optimization method is used to select the optimum head of the cluster to enhance lifetime of a network. Different techniques have been proposed for optimal edge disjoint routing. The data packets are forwarded in an optimum way by these techniques. Hybrid Genetic Particle Swarm Optimization algorithm is used to select the optimum edge disjoint route paths. The parameters of Quality of Service like reliability, capacity of processing, bandwidth and energy are considered to make an optimum selection. Based on the similarity values, sensor data’s are grouped and these data are aggregated to ensure the consumption energy which makes an optimum data transmission.