Throughput In Wireless Sensor Networks Research Articles

WSN-IoT Forecast: Wireless Sensor Network Throughput Prediction Framework in Multimedia Internet of Things

Accurate throughput predictions can significantly improve the quality of experience (QoE), where QoE denotes a network’s capacity to provide satisfactory service. By increasing the results of good throughput predictions, the best strategy can be planned for managing data transmission networks with the aim of better and faster data transmission, thereby increasing QoE. Consequently, this paper investigates how to predict the throughput of wireless sensor networks utilizing multimedia data. First, we conducted a comparative analysis of relevant prior research on the topic of throughput prediction in Multimedia Internet of Things (Multimedia IoT). We developed a throughput prediction framework for wireless sensor networks based on what we learned from these studies using machine learning. The Throughput Prediction Framework identifies historical throughput data and employs these traits to predict throughput. In the final phase, multiple camera nodes and local servers are utilized to test a framework for throughput prediction. Our analysis demonstrates that WSN-IoT predictions are quite precise. For a 1-second time breakdown, the average absolute percentage error for all investigated scenarios ranges from 1 to 8 percent.

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

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

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

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

High-throughput and energy-efficient data gathering in heterogeneous multi-channel wireless sensor networks using genetic algorithm

The need for high throughput in Wireless Sensor Networks (WSNs) necessitates the use of the Multi-Channel (MC) scheme. Existing multi-channel approaches used single or multi-radio sensors to exploit channel resources. The former scheme yields low throughput as well as considerable overhead for channel switching. On the other hand, the latter is costly due to installing multiple radios per node. This study applies the MC scheme to heterogeneous WSNs to bridge this gap. It assumes some super nodes that are equipped with multiple radios. These nodes are used as Cluster Heads (CHs) and are responsible for delivering the gathered data from normal sensors to the Base Station (BS). A high-throughput and energy-efficient algorithm is proposed to perform clustering, routing, and channel assignment in the heterogeneous setting. The proposed method decouples the original problem into two phases and solves them using genetic algorithm. The first phase is devoted to constructing a spanning tree over super nodes and assigning proper channels to their radios. A novel multi-objective cost function is proposed which prolongs network lifetime considerably compared to existing tree construction approaches. It also increases throughput by balancing the observed interference throughout the network. In the second phase of the proposed algorithm, the proper CH and channel per normal node are determined. The extensive simulations demonstrate that the proposed algorithm obtains high throughput using multiple channels. Additionally, it improves total energy consumption and energy exhaustion per delivered bit to the BS by 21.6% and 48.3% compared to the previous schemes.

Crypto Hash Signature Adopted Blockchain to Detect Spiteful Node to Enhance the Throughput in WSN

Secured routing is the definite maneuvers in a wireless sensor network (WSN) for distributing data packets to the base station. However, attacker node outbreaks will occur throughout the routing process, exacerbating the wireless sensor network's functioning. As a result, a secure routing protocol is essential to ensure route fortification and the effectiveness of wireless sensor networks. For this reason, many studies have shown an interest in improving routing protocol security by adopting cryptographic structures and trust regulation methods. However, most secured routing protocols are dynamically awful in real-time scenarios, making it difficult to distinguish between unsecured routing node performances. In the same way, solutions for detecting and mitigating node attacks are still challenging. In this, Crypto Hash signature (CHS) token are generated for the flow accesses with a secret key belonging to each routing sensor node. The assessment metrics corresponding to average energy consumption, and throughput of token transactions is measured. Then the simulation performance of the proposed SDOR-CHS-SD method provide 18.46%, 8.37% and 26.77% higher token transactions throughput, when compared with existing method like SDOR-SSOA, SDOR-DSM and SDOR-RIL respectively.

FLAG: fuzzy logic augmented game theoretic hybrid hierarchical clustering algorithm for wireless sensor networks

Stability of the wireless sensor network (WSN) is the most critical factor in real-time and data-sensitive applications like military and surveillance systems. Many energy optimization techniques and algorithms have been proposed to extend the stability of a wireless sensor network. Clustering is a well regarded method in the research communities among them. Hence, this paper presents hybrid hierarchical artificial intelligence based clustering techniques, named FLAG and I-FLAG. The first phase of these algorithms use game-theoretic technique to elect suitable cluster heads (CHs) and later phase of the algorithms use fuzzy inference system to select appropriate super cluster heads (SCHs) among CHs. The I-FLAG is an improved version of FLAG where additional parameters like energy and distance are considered to elect CHs. Simulations are performed to check superiority of the proposed algorithms over the existing protocols like LEACH, CHEF, and CROSS. Simulation results show that the average stability period of WSN is better in FLAG and I-FLAG compared to other protocols, and so is the throughput of WSN during the stability period.

Energy-Efficient Cluster in Wireless Sensor Network Using Life Time Delay Clustering Algorithms

Through Wireless Sensor Networks (WSN) formation, industrial and academic communities have seen remarkable development in recent decades. One of the most common techniques to derive the best out of wireless sensor networks is to upgrade the operating group. The most important problem is the arrangement of optimal number of sensor nodes as clusters to discuss clustering method. In this method, new client nodes and dynamic methods are used to determine the optimal number of clusters and cluster heads which are to be better organized and proposed to classify each round. Parameters of effective energy use and the ability to decide the best method of attachments are included. The Problem coverage find change ability network route due to which traffic and delays keep the performance to be very high. A newer version of Gravity Analysis Algorithm (GAA) is used to solve this problem. This proposed new approach GAA is introduced to improve network lifetime, increase system energy efficiency and end delay performance. Simulation results show that modified GAA performance is better than other networks and it has more advanced Life Time Delay Clustering Algorithms-LTDCA protocols. The proposed method provides a set of data collection and increased throughput in wireless sensor networks.

Traffic and Energy Aware Optimization for Congestion Control in Next Generation Wireless Sensor Networks

Congestion in wireless sensor networks (WSNs) is an unavoidable issue in today’s scenario, where data traffic increased to its aggregated capacity of the channel. The consequence of this turns in to overflowing of the buffer at each receiving sensor nodes which ultimately drops the packets, reduces the packet delivery ratio, and degrades throughput of the network, since retransmission of every unacknowledged packet is not an optimized solution in terms of energy for resource-restricted sensor nodes. Routing is one of the most preferred approaches for minimizing the energy consumption of nodes and enhancing the throughput in WSNs, since the routing problem has been proved to be an NP-hard and it has been realized that a heuristic-based approach provides better performance than their traditional counterparts. To tackle all the mentioned issues, this paper proposes an efficient congestion avoidance approach using Huffman coding algorithm and ant colony optimization (ECA-HA) to improve the network performance. This approach is a combination of traffic-oriented and resource-oriented optimization. Specially, ant colony optimization has been employed to find multiple congestion-free alternate paths. The forward ant constructs multiple congestion-free paths from source to sink node, and backward ant ensures about the successful creation of paths moving from sink to source node, considering energy of the link, packet loss rate, and congestion level. Huffman coding considers the packet loss rate on different alternate paths discovered by ant colony optimization for selection of an optimal path. Finally, the simulation result presents that the proposed approach outperforms the state of the art approaches in terms of average energy consumption, delay, and throughput and packet delivery ratio.

An enhanced energy optimization routing protocol for WSNs

Wireless sensor networks (WSNs) have received increasing attention due to their broad application prospects. However, the nature of sensor nodes, i.e., limited battery life and inefficient protocols, greatly reduces the sensor networks’ lifetime. Therefore, determining how to extend their lifetime has become an important issue for WSNs. This paper focuses on the technique of extending the networks’ lifetime by reducing and balancing energy consumption and proposes an enhanced energy optimization routing protocol (EEORP) for WSNs. EEORP proposes a grid-based cluster head (CH) election algorithm and introduces the energy weight and declaration number order weight factors to reduce the energy consumption in the rotation of CHs. EEORP adopts the dynamic clustering algorithm to reduce the energy consumption in intra-cluster data collection. The hop-count gradient field and grid distance are also introduced in EEORP to minimize the energy consumption in inter-cluster forwarding of the data. Our proposed protocol (EEORP) shows better performance than the existing protocols (LEACH, IEE-LEACH and EAMR) in terms of the networks’ lifetime and data throughput in WSNs, as has been demonstrated experimentally.

Cuddle death algorithm using ABC for detecting unhealthy nodes in wireless sensor networks

Wireless sensor networks consist of some sensor nodes that have a minimum operational capability, with less memory capacity for storing data and limited energy source. The deployment of these nodes or sensors takes place randomly in a dynamic or static environment. This type of placement of nodes in a hostile environment can be charge by the malicious nodes in a wireless sensor network (WSN). This vulnerability in nodes makes the wireless sensor network unstable and leads to many types of demerits like limited battery lifetime, less computing, and limited memory space. To avoid these attacks and to reduce the impact created by the malicious nodes, we suggest a simple and effective way of detecting and removing unhealthy nodes in the environment. We proposed a simple scheme by using the Artificial Bee Colony algorithm which is known as the cuddle death design. The theme of a scheme is to find the unhealthy or malicious cluster head nodes in the network and removing that particular node without creating any harm to the other nodes in the environment. It also helps to improve the energy efficiency, packet delivery ratio with maximum throughput in WSN.

Link Consistency and Energy Efficiency for Improving Throughput in Wireless Sensor Network

Wireless Sensor Networks (WSNs) necessitate lifelong links as well as energy efficiency. Also the raising commercialization of WSN, supervising applications visits for increased quality of service (QoS). The formation of the energy proficient as well as consistent link is a vital to maintain the enhancement of the WSN performance. In this object, we introduce a Link Consistency and Energy Efficiency (LCEE) for Improving Throughput in Wireless Sensor Network. In this scheme, the link consistency factor (LCF) is used to measure the node link consistency. This LCF is computed by node consistency, Link quality, node remaining energy as well as Link healthiness. The LCEE method primarily enhances the network lifespan durability by minimizing sensor node energy utilization with almost logical throughput as well as the packet obtained rate while the improvement of the QoS parameters in the WSN.

Cluster-tree based energy efficient data gathering protocol for industrial automation using WSNs and IoT

Abstract Integrating Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) is to build a heterogeneous device network for connecting, sharing and storing their information in order to create the application environment as smart. It also helps in industrial automation to predict and build a fault-tolerant environment. The automation of industry can be made possible with the emergence of new developments in sensor manufacturing and communication. In this paper, a Cluster-Tree based Energy Efficient Data Gathering (CTEEDG) protocol is proposed to increase the lifetime and throughput of WSNs. The CTEEDG uses the Fuzzy logic to select the Cluster Head (CH) based on the information collected locally. In the inter-cluster communication phase, the tree topology is established between the clusters towards the Base Station (BS) which ensures the availability of the congestion free shortest path to the BS. From the simulation results, the proposed CTEEDG outperforms the FAMACROW and DL-LEACH by 28.81% and 38.28% in terms of throughput. And also, the proposed method has 29.26% and 49.29% reduction in average energy consumption when compared to the FAMACROW and DL-LEACH.

Data Transmission Direction Based Routing Algorithm for Improving Network Performance of IoT Systems

With the continuous development of wireless communication technology, the Internet of Things (IoT) is being used in a wide range of fields. The IoT collects and exchanges large amounts of data with objects, either tangible or intangible, such as sensors or physical devices, connected to the Internet. Wireless sensor networks (WSNs) are components of IoT systems. WSNs are used in various IoT systems, such as monitoring, tracking, and detection systems, to extract relevant information and deliver it to users. WSNs consist of sensor nodes with low power, low cost, and multiple functions. Because sensor nodes have limited resources, such as power and memory, a reduction in the energy efficiency of the sensor nodes in WSNs will lead to a decrease in wireless network performance and an increase in packet loss, which affects IoT system performance. Therefore, this study aimed to find an energy-efficient routing method that extends the lifetime of WSNs by minimizing the battery use of sensor nodes to improve the network performance of IoT systems. Conserving energy from sensor nodes and increasing network throughput in WSNs involves having protocols. The low-energy adaptive clustering hierarchy (LEACH ) protocol is a well-known hierarchical routing protocol in WSNs that constructs clusters and transmits data. LEACH increases energy efficiency by transmitting data from sensor nodes to the base station (BS) through the cluster head. It is widely adopted in the WSN network field, and many protocols are being studied to improve cluster header selection and data transmission to increase the energy efficiency of sensor nodes. In this study, we attempted to improve energy efficiency by removing unnecessary energy from LEACH. In LEACH, when the sensor node is located between the BS and the cluster head, the sensor node transmits data to the cluster head in the opposite direction of the BS. The data sent to the cluster head are transmitted in the direction of the BS. Thus, transmission in the opposite direction consumes unnecessary energy and affects the WSN performance of IoT systems. In this study, we propose a D-LEACH (direction-based LEACH) protocol based on the received signal strength indicator (RSSI) that improves the efficiency of transmission energy considering the data transmission direction of sensor nodes. D-LEACH aims to balance the energy of the sensor nodes and improve the performance of WSNs in the IoT system by reducing unnecessary energy consumption caused by reverse transmission considering the data transmission direction of the sensor nodes. In the course of the paper, we refer to the routing protocol of WSNs to improve network performance and describe LEACH. We also explain the D-LEACH protocol proposed in this paper and confirm the performance improvement of WSNs in an IoT system through simulation.

Prediction-based energy efficient opportunistic routing protocol in wireless sensor networks

Opportunistic routing is a most promising routing method for improving the network lifetime and throughput of wireless sensor networks (WSN). It utilises the broadcast nature of WSN, that allows to select and prioritise a node from the potential forwarder set as next-hop forwarder. The design of energy efficient routing protocol becomes one of the major concern in WSN, due to limited battery power of a sensor node. The common energy loss reasons of existing routing protocols are: 1) packets may travel through a specific set of sensor nodes will drain out a battery energy very soon; 2) packets discard will create a packet retransmission. Opportunistic routing broadcast nature will avoid packet retransmission. The proposed energy efficient Markov prediction-based opportunistic routing (EEMPOR) protocol uses the following metrics to select and prioritise the energy efficient nodes namely, number of transactions of a node and residual energy of a node. Then proposed protocol shows that Markov prediction-based opportunistic routing protocol provides better network performance than non-prediction-based method. Simulation results show, that the proposed protocol outperforms the existing OR in terms of consumed energy, residual energy, network lifetime, and throughput.

PREDICTION BASED ENERGY EFFICIENT OPPORTUNISTIC ROUTING PROTOCOL IN WIRELESS SENSOR NETWORKS

Opportunistic routing is a most promising routing method for improving the network lifetime and throughput of wireless sensor networks (WSN). It utilises the broadcast nature of WSN, that allows to select and prioritise a node from the potential forwarder set as next-hop forwarder. The design of energy efficient routing protocol becomes one of the major concern in WSN, due to limited battery power of a sensor node. The common energy loss reasons of existing routing protocols are: 1) packets may travel through a specific set of sensor nodes will drain out a battery energy very soon; 2) packets discard will create a packet retransmission. Opportunistic routing broadcast nature will avoid packet retransmission. The proposed energy efficient Markov prediction-based opportunistic routing (EEMPOR) protocol uses the following metrics to select and prioritise the energy efficient nodes namely, number of transactions of a node and residual energy of a node. Then proposed protocol shows that Markov prediction-based opportunistic routing protocol provides better network performance than non-prediction-based method. Simulation results show, that the proposed protocol outperforms the existing OR in terms of consumed energy, residual energy, network lifetime, and throughput.

Effect on Packet Delivery Ratio (PDR) & Throughput in Wireless Sensor Networks Due to Black Hole Attack

Wireless Sensor Networks are in rapid advance occupying every field of our lives. They are in great demand and are widely used in transmission of data like temperature, pressure, humidity, speed etc. As these networks are wireless and are easily prone to intrusion by the attackers. Hence the basic concern is security of data. The nodes in the network will be sending information between the nodes, and in between the nodes intrusion takes place with attack like wormhole attack, black hole attack, sybil attack, hello flood attack etc. which corrupts data. These attacks effect the efficiency of the network and the parameters like packet delivery ratio and throughput of the network is affected. Black hole is a severe attack in network which alters most of the data before it is received at the sink, hence has to be detected and prevented. In this paper, Adhoc on demand distance vector (AODV) protocol is used to detect and prevent the black hole attack using Network Simulator (NS-2.3)

Improved BEST-MAC protocol for WSN using optimal cluster head selection

Use of wireless technology increases rapidly in present days because of convenient use for its users. Wireless sensor network is one of the widely used wireless technologies. Although wireless sensor network has many advantages over other communication technologies, but still anguish from some limitations like limited battery power, control overheads, scalability, data aggregation, throughput etc. MAC layer is considered as most power consuming part over communication. Numerous MAC protocols had been designed to enhance the lifetime of wireless sensor network. In this paper, we have chosen a TDMA based MAC protocol for our proposed work. In our proposed work we try to improve existing work i.e. BEST-MAC protocol using artificial intelligence. We considered two performance parameters that are energy efficiency and throughput of wireless sensor network to improve the life span of WSN using optimal cluster head selection process. Our proposed work is implemented with MATLAB version R2012a and results are included in this paper. The results shows the energy is saved by 3.085% and throughput is improved by 51.26% in the proposed work as compared to BEST-MAC protocol.

An energy‐efficient and balanced clustering approach for improving throughput of wireless sensor networks

SummaryMaximizing the lifespan of wireless sensor networks is currently drawing a lot of attention in the research community. In order to reduce energy consumption, sensor nodes that are far from the base station avoid sending data directly. As a result, several disjoint clusters are formed, and nodes within a cluster send their data through the cluster head to avoid long transmissions. However, several parameters related to transmission cost need to be considered when selecting a cluster head. While most of the existing research work considers energy and distance as the most stringent parameters to reduce energy consumption, these approaches fail to create a fair and balanced cluster. Consequently, unbalanced clusters are formed, resulting in the degradation of overall performance. In this research work, a cluster head selection algorithm is proposed that covers all parts of the sensing area in a balanced manner, saving a significant amount of energy. Furthermore, a capture effect–based intracluster communication mechanism is proposed that efficiently utilizes the time slot under various traffic conditions. A Näive Bayes classifier is used to adapt the window size dynamically according to the traffic pattern. Finally, a simulation model using OMNeT++ is developed to compare the proposed approach with the pioneer clustering approach, LEACH, and the contemporary LEACH‐MAC protocol in terms of performance. The results of the simulation indicate that the proposed approach improves the overall performance in terms of network lifetime, energy efficiency, and throughput.

Dual Parametric Stabilization of Interference and Throughput in Wireless Sensor Network- Optical Communication

Dual Parametric Stabilization of Interference and Throughput in Wireless Sensor Network- Optical Communication

EasyLB: Adaptive Load Balancing Based on Flowlet Switching for Wireless Sensor Networks.

Load balancing is effective in reducing network congestion and improving network throughput in wireless sensor networks (WSNs). Due to the fluctuation of wireless channels, traditional schemes achieving load balancing in WSNs need to maintain global or local congestion information, which turn out to be complicated to implement. In this paper, we design a flowlet switching based load balancing scheme, called EasyLB, by extending OpenFlow protocol. Flowlet switching is efficient to achieve adaptive load balancing in WSNs. Nevertheless, one tricky problem lies in determining the flowlet timeout value, . Setting it too small would risk reordering issue, while setting it too large would reduce flowlet opportunities. By formulating the timeout setting problem with a stationary distribution of Markov chain, we give a theoretical reference for setting an appropriate timeout value in flowlet switching based load balancing scheme. Moreover, non-equal probability path selection and multiple parallel load balancing paths are considered in timeout setting problem. Experimental results show that, by setting timeout value following the preceding theoretical reference, EasyLB is adaptive to wireless channel condition change and achieves fast convergence of load balancing after link failures.