Average Transmission Delay Research Articles

EBH-IoT: Energy-efficient secured data collection and distribution of electronics health record for cloud assisted blockchain enabled IoT based healthcare system

The integration of Health IoT (H-IoT) and blockchain technologies are being heavily exploited and used in many domains, especially for e-healthcare to collect the data i.e electronic health record (EHR) from the patient. The H-IoT devices have the ability to provide real-time sensory data from patients to be processed and analyzed, and distributed. Blockchain is providing decentralized computation, distribution and storage for EHR data. Therefore, the integration of H-IoT and Blockchain technologies can become a reasonable choice for the design of decentralized H-IoT-based e-healthcare systems. But the H-IoT network has some intrinsic challenges like low computation, energy constraint, security, energy optimization, data storage, and real-time data analytic. Also, conventional EHR-based systems suffer from issues such as the potential loss of data, inadequate security and consensus on the unchangeable nature of health records, fragmented connections between different institutions, and ineffective clinical data retrieval methods, among other challenges. In this article, first, we study the performance of blockchain technology in the healthcare system. Second, we propose an improved Harris Hawk Optimization algorithm (HHO) based clustering mechanism for the collection and sharing of EHR. The proposed system was tested and validated using the Hyperledger-fabric based electronic healthcare record (EHR) sharing system along with Matlab. The proposed system achieves 12%, and 7% incremental improvement in terms of latency, throughput for the Blockchain networks. While the proposed clustering technique achieves 10%, 12%, 14%, and 16% improvements in alive node, energy consumption, throughput and average transmission delay compared to existing state of the art.

Research on Secure Community Opportunity Network Based on Trust Model

With the innovation of wireless communication technology and the surge of data in mobile networks, traditional routing strategies need to be improved. Given the shortcomings of existing opportunistic routing strategies in transmission performance and security, this paper proposes a community opportunistic routing decision-making method based on the trust model. This algorithm calculates the node’s trust value through the node’s historical forwarding behavior and then calculates the node’s trust value based on the trust model. Thresholds and trust attenuation divide dynamic security communities. For message forwarding, nodes in the security community are prioritized as next-hop relay nodes, thus ensuring that message delivery is always in a safe and reliable environment. On this basis, better relay nodes are further selected for message forwarding based on the node centrality, remaining cache space, and remaining energy, effectively improving the message forwarding efficiency. Through node trust value and community cooperation, safe and efficient data transmission is achieved, thereby improving the transmission performance and security of the network. Through comparison of simulation and opportunistic network routing algorithms, compared with traditional methods, this strategy has the highest transmission success rate of 81% with slightly increased routing overhead, and this algorithm has the lowest average transmission delay.

Signal transmission diversity based successive interference cancellation-slotted aloha

Efficient medium access protocols are crucial in leveraging the advancement in the physical layer techniques that have been proposed for the next-generation wireless networks. However, collisions among data packets in random access protocols reduce channel efficiency and hence the throughput. In this paper, we propose a MAC protocol based on both the signal transmission diversity and Successive interference cancellation (SIC) to improve the throughput. In the proposed Medium Access Control (MAC) protocol, the receiver receives two sets of signals from the transmitters over two uncorrelated time slots. Inter slot SIC is first carried out at the receiver over two independent sets of received interfered signals. Signals are decoded by canceling those signals that have already been decoded in one set from the other set. Closed-form expressions for the packet decoding probability, system throughput, average packet transmission delay, and energy efficiency have been derived by considering order statistics of received signal power. Analytical results are validated against simulation. Maximum achievable throughput of the proposed scheme is shown to be four times that of slotted Aloha and twice that of a slotted Aloha with SIC.

Distance Energy-Efficient Soft Computing Model for Data Forwarding in Healthcare Sensor Network

Data forwarding is a crucial process in computer networking and telecommunications. It involves the transmission of data packets from a source to a destination within a network. The forwarding process is fundamental for enabling communication between devices on different segments of a network and plays a vital role in maintaining the overall performance and reliability of the network infrastructure. This paper presents the development and evaluation of the Threshold Congestion Data Forwarding Soft Computing (TCDF-SC) algorithm, designed for healthcare sensor networks. The algorithm combines threshold-based congestion management with soft computing techniques to enhance data forwarding efficiency in dynamic healthcare environments. Through extensive simulations, TCDF-SC demonstrates scalability, achieving high total packets forwarded and maintaining a reliable packet delivery ratio as the network scales. The algorithm minimizes average transmission delay and optimizes energy consumption, ensuring timely and energy-efficient data transmission. Comparative analysis against existing protocols, LEACH and BCP, highlights TCDF-SC’s superior performance across key metrics, positioning it as a promising solution for healthcare sensor networks. This research contributes to the advancement of data transmission optimization in healthcare, addressing critical requirements of reliability, energy efficiency, and adaptability in dynamic healthcare settings. Further validation and real-world experimentation will enhance the algorithm’s applicability in diverse healthcare scenarios, fostering advancements in patient monitoring and healthcare applications.

RSSI-adaptive local dual-mode communication for the Internet of Things in power distribution

Abstract The Internet of Things (IoT)’s rapid development is placing new demands on communication technology for power distribution. This paper proposes the application scheme of RSSI adaptive and dual-mode communication technology in the power distribution area, realizes the dual-mode networking technology adopted to improve communication quality, and verifies the reliability and superiority of the dual-mode communication technology scheme through analysis and application. The RSSI adaptive dual-mode communication module is designed, the RSSI model algorithm is modified, and the logical topology of the communication network and the data processing technology based on compressed sensing are analyzed. We focus on analyzing the performance of dual-mode communication networks for power consumption information collection and the information reception rate and real-time performance of MQTT protocol mapping for power distribution IoT. The average transmission delay was tested to be 198.5ms for QoS=0, 478.0ms for QoS=1, and 786.3ms for QoS=2 without encryption. When using SASL/TLS for transmission encryption, the average transmission delay is 992.7ms for QoS=0, 1203.6ms for QoS=1, and 1461.1ms for QoS=2.

Hybrid backscatter communication for IoT devices in remote areas

The IoT devices placed in remote locations require a battery replacement very often, which is not a convenient option. Backscatter communication can resolve this problem, as backscatter communication is a data transmission in which an RF signal incident from the gateway is used for energy harvesting, and this energy will be employed for data transmission. In this paper, a hybrid contention-based TDMA scheme is proposed, which provides slots to devices by dividing them into groups, and then contention is employed in groups to acquire a slot; if a device is not able to transmit during harvest, then transmit (HTT) period, then it can transmit in variable sub frame and the devices which are not able to completely transmit during HTT period can reserve subframes. The proposed hybrid scheme is compared with the TDMA scheme for average transmission delay.The proposed scheme provides scalability. The difference between the average transmission delay of TDMA and the proposed hybrid scheme is from 6 to 20 s, depending on the number of devices added and when traffic is generated.

Redundant node identification using Siamese neural network to enhance the availability in visual sensor network

SummaryMany monitoring applications related to surveillance, tracking and multipurpose visual monitoring have taken into consideration the use of wireless visual sensor networks. When sensors are deployed over a monitored field that could potentially damage the monitoring capability and availability in the visual sensor network. In order to overcome these issues, faulty nodes are identified and replaced by redundant node based on Siamese network in this research. Initially, camera nodes are randomly deployed in the visual sensor network, and the data are received from the network through gateway. To identify the redundant nodes, initially, the frames are divided into equivalent time slot, and then, Siamese network is utilized to identify the redundant nodes in a network. Siamese neural network is type of convolutional neural network that is utilized to recognize the similar images in the network. After that, faulty nodes are identified based on some parameters such as entropy, energy, transmission delay and network coverage. If the average energy, entropy, transmission delay and network coverage are below the threshold value, then the node is identified as faulty node. Finally, replace the faulty node with redundant node to enhance the availability in the visual sensor network for critical monitoring applications. The simulation analysis shows that the developed approach takes 772 s to identify redundant node and take 27 s to identify fake nodes and the developed method is executed at 1308 s. Thus, this prediction model helps to improve the coverage quality of target‐based monitoring in order to achieve availability.

A Distributed Caching Approach for Minimizing Average Transmission Delay in Ultra-Dense Networks

Caching at the network edge is a promising approach to alleviate the backhaul congestion pressure and reduce transmission delay in ultra-dense networks (UDNs). However, the existing content caching methods do not fully explore and exploit potential caching and computation capabilities. In this paper, we propose a distributed caching approach as well as the corresponding content addressing method. Furthermore, a problem of caching optimization with the target to minimize the average transmission delay is investigated. We transform this nondifferentiable nonlinear programming problem into a linear programming problem. Then we utilize an Alternating Direction Method of Multipliers (ADMM) algorithm to solve this problem. Simulation results reveal that the proposed algorithm can converge to the global optimum and our proposed distributed caching approach reduce the average transmission delay significantly. In popular caching strategy, the average transmission delay in our proposed method decreases over 30% and 50% when compared with caching maximum distance separable (MDS) packets and caching entire files, respectively.

An Outage-Probability-Aware MAC Protocol for UAV-Assisted Networks

Unmanned aerial vehicles (UAVs) are widely used in wireless communication, either base stations (BS) or relays. In some occasions of UAV-assisted communication such as low-altitude communication in an urban environment, the channel conditions can be poor due to obstacles. Thus, they will experience an outage and cannot receive the signal transmitted by the UAVs. In this paper, we propose an outage-probability-aware medium access control (MAC) protocol (OPM), based on the study of outage probabilities in different UAV-assisted environments. Specifically, we first analyze different outage scenarios in information transmission. Then, the physical-layer outage probability with channel conditions and ground users’ mobility taken into consideration is discussed, while an outage detection method is proposed, and different measures are taken according to the detection results. Thirdly, a three-dimensional (3D) Markov chain model is used to investigate per-user behavior for the proposed OPM. Finally, the simulation results on packet discard probability, average transmission delay, and system throughput are analyzed. The results show the effectiveness of the proposed protocol, which leads to a 30%-50% decrease in both the probability of packet discard and average transmission delay, along with an increase in throughput of more than 40%.

A Network Scheduling Method Based on Segmented Constraints for Convergence of Time-Sensitive Networking and Industrial Wireless Networks

In industrial applications, it is necessary to select different types of networks according to different communication requirements. To meet this requirement, a converged network of wired and wireless networks is frequently employed. Notably, fulfilling the end-to-end transmission requirements of converged networks is challenging. As a solution, converged-network scheduling methods have proved valuable. In this paper, a network scheduling method for the convergence of industrial wireless networks and time-sensitive networks is proposed. Additionally, the proposed method is tested and verified. The results show that the end-to-end average transmission delay is reduced and the jitter is acceptable.

Construction of FANETs for user coverage and information transmission in disaster rescue scenarios

In this paper, we consider a flying ad hoc network (FANET) construction problem in a post-disaster scenario where ground users (GUs) lose contact with the outside world. The main objective of the FANET is to provide communication coverage for as many as GUs, and to transmit disaster information back to the rescue center as soon as possible. To solve this FANET construction problem, we first propose a FANET construction algorithm based on particle swarm optimization (PSO) algorithm. To improve the performance of the PSO-based FANET construction algorithm, we further model the FANET construction problem as an ordinary potential game (OPG), and propose a better-response-based FANET construction algorithm. Several simulation experiments are presented to evaluate the effectiveness of these proposed two algorithms. Simulation results demonstrate that both the PSO-based FANET construction algorithm and the better-response-based FANET construction algorithm can be used to maximize the coverage rate of GUs and minimize the average transmission delay from rescue UAVs to the gateway UAV. Moreover, the better-response-based FANET construction algorithm has a fast convergence rate, and performs better than the PSO-based FANET construction algorithm and other baselines in terms of coverage rate and average transmission delay.

MiFi: Bounded Update to Optimize Network Performance in Software-Defined Data Centers

A controller needs to solve the multi-commodity flow problem and globally update the network under tight time constraints to maintain optimal network configurations. This centralized optimization in data centers involves many variables and constraints, which has a slow convergence speed and little scalability. In this paper, we propose MiFi, which aims to Minimize Flow cost or intuitively average transmission delay (delay or latency of flows), under reconfiguration budget constraints in data centers. Thus, we formulate this optimization problem as a constrained Markov Decision Process and propose a set of algorithms to solve it in a scalable manner. We first develop a propagation algorithm to identify the flows mostly affected in terms of latency and configuration in the next update. Then, we set a limitation range (the subset of switches requiring network updates) for updating them to improve adaptability and scalability by updating a less number of flows each time to achieve fast operations. Further, based on the Drift-Plus-Penalty method in Lyapunov theory, we propose a heuristic policy without prior information of flow demand and a renewal policy with a performance guarantee to minimize the additive optimality gap. To the best of our knowledge, MiFi is the first paper that studies the range and frequency of flow reconfigurations, which has both theoretical and practical significance in the area. Emulations and numerical simulations, which are much better than the estimated theoretical bound, show that MiFi outperforms the state of the art algorithms in terms of latency by over 45% while making improvements in adaptability and scalability.

Collaborative Caching in Edge Computing via Federated Learning and Deep Reinforcement Learning

By deploying resources in the vicinity of users, edge caching can substantially reduce the latency for users to retrieve content and relieve the pressure on the backbone network. Due to the capacity limitation of caching and the dynamic nature of user requests, how to allocate caching resources reasonably must be considered. Some edge caching studies improve network performance by predicting content popularity and actively caching the most popular content, thereby ignoring the privacy and security issues caused by the need to collect user information at the central unit. To this end, a collaborative caching strategy based on federated learning is proposed. First, federated learning is used to make distributed predictions of the preferences of users in the nodes to develop an effective content caching policy. Then, the problem of allocating caching resources to optimize the cost of video providers is formulated as a Markov decision process, and a reinforcement learning method is used to optimize the caching decisions. Compared with several basic caching strategies in terms of cache hit rate, transmission delay, and cost, the simulation results show that the proposed content caching strategy reduces the cost of video providers, and has higher cache hit rate and lower average transmission delay.

Performance and Improvement Analysis of the Underwater WSN Using a Diverse Routing Protocol Approach

The planet Earth is the most water-rich place because oceans cover more than 75% of its land area. Because of the extraordinary activities that occur in the depths, we know very little about oceans. Underwater wireless sensors are tools that can continuously transmit data to one of the source sensors while also monitoring and recording the physical and environmental parameters of their surroundings. An underwater wireless sensor network (UWSN) is the name given to the network created by the collection of these underwater wireless sensors. This particular technology is the most efficient way to analyse performance parameters. A network path is chosen to send traffic by using the routing method, a process that is also known as a protocol. The routing protocols ad-hoc on-demand distance vector (AODV), dynamic source routing (DSR), dynamic manet on demand routing protocol (DYMO), location-aided routing 1 (LAR 1), optimized link state routing (OLSR), source-tree adaptive routing optimum routing approach (STAR-ORA), zone routing protocol (ZRP), and STAR-least overhead routing approach (STAR-LORA) are a few models of routing techniques. By changing the number of nodes in the model and the maximum speed of each node, performance parameters such as average transmission delay, average jitter, percentage of utilisation, and power used in transmit and receive modes are explored. The results obtained using QualNet 7.1 simulator suggest the suitability of routing protocols in the UWSN.

Performance Analysis of Grid-Oriented Underwater Wireless Sensor Network by Deploying Constant Bit Rate Application

Underwater Wireless Sensor Network, often known as UWSN, is an appealing research zone because of the mysterious aspect of the ocean. A network of sensor nodes and vehicles that work together as part of the UWSN to collect information and carry out activities in collaboration. Because of the sensor nodes and the limited battery capacity, it is essential for UWSN to have an efficient network. The significant delay in propagation, network dynamics, and probability of error all influence underwater communication, making it difficult to exchange or update sensor nodes. This article put up the idea of a Grid-oriented underwater wireless sensor network (GO-UWSN) and carried out an analysis based on the criteria of energy consumption, utilization, average transmission delay, average jitter, average path loss and average E- 2- E delay in various modes.

High-speed inter-satellite link construction technology for navigation constellation oriented to engineering practice

Abstract This article presents a study on the design of high-speed inter-satellite links for the global navigation satellite system, which consists of 24 middle earth orbit, 3 inclined geosynchronous orbit, 3 geostationary orbit satellites, and 4 typical ground stations based on the laser link of slowly varying continuous system and high-speed Ka link. Pre-allocation of node connections in inter-satellite link construction is conducted by employing the optimized Dijkstra algorithm using finite-state automata. In addition, topological design and routing planning principles are utilized for high-speed inter-satellite links. Based on full constellation orbit data of a regression period and locations of the ground station nodes, an hourly link construction program is obtained using the scheme described above. The calculation result of the position dilution of precision values of all satellites, the average inter-satellite transmission delay, and the inter-satellite link switching meet the requirements of measurement, data transmission, and management for navigation constellation.

Underwater Wireless Sensor Network Performance Analysis Using Diverse Routing Protocols

The planet is the most water-rich place because the oceans cover more than 75% of its land area. Because of the unique activities that occur in the depths, we know very little about oceans. Underwater wireless sensors are tools that can continuously transmit data to one of the source sensors while monitoring and recording their surroundings’ physical and environmental parameters. An Underwater Wireless Sensor Network (UWSN) is the name given to the network created by collecting these underwater wireless sensors. This particular technology has a random path loss model due to the time-varying nature of channel parameters. Data transmission between underwater wireless sensor nodes requires a careful selection of routing protocols. By changing the number of nodes in the model and the maximum speed of each node, performance parameters, such as average transmission delay, average jitter, percentage of utilization, and power used in transmit and receive modes, are explored. This paper focuses on UWSN performance analysis, comparing various routing protocols. A network path using the source-tree adaptive routing-least overhead routing approach (STAR-LORA) Protocol exhibits 85.3% lower jitter than conventional routing protocols. Interestingly, the fisheye routing protocol achieves a 91.4% higher utilization percentage than its counterparts. The results obtained using the QualNet 7.1 simulator suggest the suitability of routing protocols in UWSN.

Development of a monitoring system for disassembled towers with internal suspension poles

The traditional construction monitoring methods of suspended pole-mounted decomposed towers are mostly manual monitoring. The monitoring personnel has multiple blind spots, and the possibility of misjudgment based on personal experience is relatively large. It is difficult to ensure the construction safety of the suspended pole decomposing tower. For this reason, combined with the current power Internet of Things technology, this paper develops an intelligent monitoring system for suspended pole-mounted decomposing towers. According to the construction technology and its safety requirements of inner suspension derrick for transmission tower erection in sections, this system is classified into intellisense layer, wireless transport layer and information integration layer. According to the physical characteristics of the seven major risk points of the inner suspension pole group tower, the intellisense layer developed corresponding sensing equipment to obtain risk information. In the wireless transport layer, the ZigBee and 4G communication technologies are selected to interconnect self-constituted LAN and 4G wide area networks, to complete on-site data interaction and long-distance transmission. In the information integration layer, the force of cable, the inclination and height of derrick, and the distance between derrick and tower are determined. The system has been verified by the 500 kV delivery project of Fujian Zhouning Pumped Storage Power Station. The average error of critical monitoring point data is 4.14%, and the average data transmission delays in the far and near fields of the system are 18 ms and 176 ms.

Cooperative self-scheduling routing protocol based IOT communication for improving life time duty cycled energy efficient protocol in SDN controlled embedded network

Internet of Things (IoT) is a developing interconnecting medium in a wireless network through internet of communication in network devices like sensors, smart devices and controllers. This interconnect the devices via the internet to make communication. Due to large sectors IoT devices and Software-Defined Networking (SDN) controllers contains energy constraints in improper routing strategies. Specifically in health care industries, the patient monitoring data's consumes higher energy to reduce the lifetime of network communication leads data losses during the IoT devices collecting the information become loss.The embedded communication are relatively controls through the Software defined network to maintain the energy consumption to improve the lifetime of energy efficient network. But leastpropagation energy losses arise communication losses because of traffic, packet losses, latency beyond with throughput limits. The main problem is Passive Infrared Sensor (PHR) sensor nodes and Relay nodes attain more energy because of non-uniform node displacement in routing in uneven energy drainage. So resolving these problems, in this system propose a cooperative self-scheduling routing protocol based IoT communication for improving energy efficient SDN controlled embedded network.Initially this construct the Neighbor Distance Discovery Protocol (NDDP) to identify the minimum depletion path by identifying Sensor closest route to the relay node. Then construct the Multipath Cooperative Self-Scheduling Protocol (MCSSP) to formalize the non-traffic route. By estimating the energy consumption on each sensor node on routing medium, this considers the prior routing communication rate by calculating average transmission delay, sensor absorption rate, power consumption, node response rate, and packet loss rate. UsingLife Time Duty Cycled Energy Efficient Protocol (LTDCEEP) to calculate the delimit of the feature limits along the network threshold latency energy limits. Based on the transition limit the weights of the nodes are estimated by energy consumption from each duty cycle by consuming more transmission energy, the route be scheduled to optimize the communication delay to reduce the power consumption in the sense of responsive nodes. This improves the power consumption rate, latency, power quality in transmission, throughput ratio in SDN network communication.

Performance Analysis of Underwater Wireless Sensor Network by Deploying FTP, CBR, and VBR as Applications

Oceans cover more than 75% of the planet’s land surface, making it the most water-rich place on the Earth. We know very little about oceans because of the extraordinary activities that take place in the depths. Underwater wireless sensors are devices that are able to monitor and record the physical and environmental parameters of their surroundings, as well as transmit these data in a continuous manner to one of the source sensors. The network that is formed by the collection of these underwater wireless sensors is referred to as an underwater wireless sensor network (UWSN). The analysis of performance parameters is thought to be most effectively done with this particular technology. In this paper, we will investigate various performance parameters in a random waypoint mobility model by shifting the maximum speed of a node and altering the number of nodes in the model. These parameters include average transmission delay, average jitter, average pathloss, percentage of utilization, and energy consumed in transmit, receive, and idle modes. The QualNet 7.1 simulator is utilized in order to conduct analyses and performance studies.