Energy Aware Routing Protocol Research Articles

Software Defined Network Based Enhanced Energy-Aware Load Balancing Routing Protocol

The Software Defined Network (SDN) offers a potential solution toward source development challenges. Several network applications have adopted and used the Energy Aware Routing (EAR) protocol. The main issue with this practice is its excessive energy usage and impact. Subsequently, toward work on the exhibition of directing convention in SDN, the ongoing effort has been planned for a clever Buffalo-based Energy Aware Duty Cycle Routing (BEADCR) convention to reduce energy use. Here, buffalo wellness screens the highly energy-intensive hubs and eliminates them. The information pace of every hub remained noticed, and the zero information rate hubs remained viewed as rest hubs. Moreover, to avoid an impact, the information above of every hub was observed, and the relocation capability was achieved to impart a great burden to the balance hubs through enacting the introduced BEADCR system. It further develops execution by acquiring high observing exactness, less energy utilization, a great Packet-Delivery Ratio (PDR), a great throughput proportion, and great energy effectiveness. At last, the outcome acquired is contrasted with other present systems to decide the viability of the suggested model. The proposed strategy’s checking precision beat the methods by 98.22% for the twentieth hub. 98.59% for the 40th, 98.74% for the 60th, 98.9% for the 80th, and 99.1% for the 100th hub.

A Hybrid Multi-Hop Clustering and Energy-Aware Routing Protocol for Efficient Resource Management in Renewable Energy Harvesting Wireless Sensor Networks

A Hybrid Multi-Hop Clustering and Energy-Aware Routing Protocol for Efficient Resource Management in Renewable Energy Harvesting Wireless Sensor Networks

Trust And Energy-Aware Routing Protocol for Wireless Sensor Networks Based on Secure Routing

Wireless Sensor Network (WSN) is a network area that includes a large number of nodes and the ability of wireless transmission. WSNs are frequently employed for vital applications in which security and dependability are of utmost concern. The main objective of the proposed method is to design a WSN to maximize network longevity while minimizing power usage. In a WSN, trust management is employed to encourage node collaboration, which is crucial for achieving dependable transmission. In this research, a novel Trust and Energy Aware Routing Protocol (TEARP) in wireless sensors networks is proposed, which use blockchain technology to maintain the identity of the Sensor Nodes (SNs) and Aggregator Nodes (ANs). The proposed TEARP technique provides a thorough trust value for nodes based on their direct trust values and the filtering mechanisms generate the indirect trust values. Further, an enhanced threshold technique is employed to identify the most appropriate clustering heads based on dynamic changes in the extensive trust values and residual energy of the networks. Lastly, cluster heads should be routed in a secure manner using a Sand Cat Swarm Optimization Algorithm (SCSOA). The proposed method has been evaluated using specific parameters such as Network Lifetime, Residual Energy, Throughpu,t Packet Delivery Ratio, and Detection Accuracy respectively. The proposed TEARP method improves the network lifetime by 39.64%, 33.05%, and 27.16%, compared with Energy-efficient and Secure Routing (ESR), Multi-Objective nature-inspired algorithm based on Shuffled frog-leaping algorithm and Firefly Algorithm (MOSFA) , and Optimal Support Vector Machine (OSVM).

REERP: A Region-Based Energy-Efficient Routing Protocol for IoT Wireless Sensor Networks

An essential component of the Internet of Things (IoT) is wireless sensor networks (WSNs). Since individual sensor nodes are strongly power-constrained, several techniques are adopted to save power. By grouping nodes into clusters—thus reducing the transmission distance between sensor nodes and the base station (BS)—a clustering protocol can ensure energy preservation and increase the lifetime of the network. However, current clustering techniques have problems with the clustering structure that negatively impact their performance. Whenever routing protocols were implemented for a longer period of time, it was observed that they had a higher rate of energy consumption, a shorter period of stability, and fewer data transfers to the BS. In this paper, an improved region-based routing protocol (REERP) is developed for wireless sensor networks in the IoT is developed. It is based on (i) the addition of new nodes to the already formed clusters, (ii) the selection of the new head node based on the amount of residual energy, (iii) the setup of the multi-hop communication in all the regions of network, and (iv) the utilization of the energy hole reduction method. All of these tactics increase the useful life of the network. Performance has been evaluated against (1) a stable election protocol, (2) a gateway energy-aware routing protocol, and (3) a heterogeneous gateway energy-aware routing protocol, and using the metrics lifetime, energy consumption, number of dead nodes, and number of packets sent to the base station vs. number of packets acquired by the base station. The results of the proposed routing protocol have been found to outperform the state-of-the-art approaches considered.

Highly Optimized Energy Saving Protocol for Flying ad-hoc Network

bstract: FANET has opened up a wide arena for the study and implementation of drone and UAV (unmanned aerial vehicle) efficiency in a variety of military and rescue applications. In this paper, we propose a hybrid energy-aware routing protocol. The approaches are based on two swarm intelligence methods: ant colony optimization (ACO) and particle swarm optimization (PSO). The performances of these approaches are compared with other bio-inspired feature selection methods based on ant colony optimization, particle swarm optimization, and grey wolf optimization. we propose a hybrid energy-aware clustering technique using a genetic algorithm for cluster formation and management. The proposed scheme shows better results as compared to other bioinspired clustering algorithms on the basis of evaluation benchmarks such as end-to-end latency, packet delivery ratio, energy consumption, time complexity, and throughput, and as a result, hybrid ACOPSO is used to enhance ACOPSO efficiency. The results indicate that the proposed scheme has improved throughput by 60% and 38% with respect to ant colony optimization and, grey wolf optimization, respectively. In terms of average cluster building time while average energy consumption has improved by 23% and 33% when compared to the ant colony optimization and grey wolf optimization, respectively. From the comparison graph, it is concluded that our proposed method has achievable throughput which is significantly increasing compared to other methods.

An Energy-Efficient Protocol based on Recursive Geographic Forwarding Mechanisms for Improving Routing Performance in WSN

In situations where a regular network may not be appropriate or is unable to function correctly, data collection and real-time transmission are commonly done using Wireless Sensor Networks (WSNs). In order to accomplish this type of operation, the network's durability and performance are extremely vital. One of the key elements that directly affect a network's lifespan and performance is the routing protocol. This study proposes a Geographic Forwarding Energy Efficient Routing Protocol (GF-EERP), an enhanced version of the Geographic Energy Aware Routing (GEAR) protocol that helps to extend the network's lifespan and performance. The introduction of node categorization, a unique technique for choosing the region head, use of multi-hop communication method and removal of dead nodes serve as the foundation of the GF-EERP. In this study, the proposed protocol's performance is also compared to other protocols already in use, like Geographic Adaptive Fidelity (GAF), Geographic Energy Aware Routing (GEAR), Greedy Perimeter Stateless Routing (GPSR) and Multihop-Geographic Energy Aware Routing (M-GEAR), using a variety of performance metrics including Network Delay, Data Delivery Ratio and Network Throughput. The simulation outcomes prove that the performance of GF-EERP, with its distinct node categorization and region head selection mechanism is superior to the existing protocols.

Multi-Objective Cluster Head-Based Energy-Aware Routing Protocol using Hybrid Woodpecker and Flamingo Search Optimization Algorithm for Internet of Things Environment

Internet of Things (IoT) plays a vital role in the world of Internet and integrates computing devices. The digital machines provide unique identifiers. The capacity to send data through the network using a better path is one of the capabilities of digital machines. IoT generates huge data based on fast aggregation data and processes these data efficiently. In this paper, a multi-objective hybrid woodpecker and flamingo search optimization algorithm is proposed for finding optimum Cluster Head (CH)-based energy-aware routing protocol in IoTs environment (Hyb-WFSOA-CHS-IoT). Here, the proposed Hyb-WFSOA-CHS-IoT model executes routing method through CH. The woodpecker and flamingo search optimization algorithm is used as the intelligent CH selection that consumes same energy as sensors. Hybrid woodpecker and flamingo search optimization algorithm (Hyb-WFSOA) is examined by using fitness functions, like distance, delay, energy consumption, throughput. The proposed Hyb-WFSOA-CHS-IoT method is activated in MATLAB software. Then, the performance of the proposed system is examined with different metrics, like delay, packet delivery ratio, throughput, network lifetime, energy consumption. Therefore, the proposed method attains lower delay 28.38%, 32.34% and 47.45%, higher delivery ratio 19.34%, 23.12% and 18.96% and lower energy consumption 11.25%, 7.90% and 12.88% compared with existing methods, like multi-objective fractional gravitational search algorithm for energy-efficient routing on IoT (FGSA-CHS-IoT), multi-objective sunflower-based gray wolf optimization algorithm for multiple path routing on IoT network (SFG-CHS-IoT) and energy-aware routing in the IoT with improved grasshopper metaheuristic algorithm with chaos theory and fuzzy Logic (FLGOA-CHS-IoT).

TEARP: New Temperature and Energy Aware Routing Protocol for Wireless Body Area Network (WBAN)

In the near future, the biomedical research will be one of the major applications for sensor networks. Wireless Body Area Networks (WBANs) consist of a set of bio-sensors that are implanted inside the human body to sense several health conditions, the communication between these sensors is made wirelessly. These sensors are charged by a battery. The nodes will transmit the sensed data through other nodes in the order it to the base station which is located outside the sensing environment which in this case represents the human body. The communication field consumed a significant amount of node’s energy and minimizes the lifetime of the node. Furthermore, the sensor’s operation generates heat which raises the tissue temperature around the implanted sensors. Therefore, the research majorly addresses these issues of communication, a new Temperature and Energy-Aware Routing Protocol (TEARP) has been presented for WBANs implementing a new route-choice system that seeks to equalize the use of energy of sensors and minimize their overheating. This method is depending on a formula that takes into account the remaining energy of sensor nodes, the distance between nodes as well as between nodes to sink, in addition to their temperature when selecting the following relay node. The simulation results show that the TEARP protocol has improved network lifetime efficiency by about 300%and 90%, stability by about 82% and 11%, and residual energy by about 71% and 74% compared to the M-ATTEMPT and Simple protocols respectively.

Novel reliable and dynamic energy-aware routing protocol for large scale wireless sensor networks

<p>Wireless sensor networks (WSN) are made up of an important number of sensors, called nodes, distributed in random way in a concerned monitoring area. All sensor nodes in the network are mounted with limited energy sources, which makes energy harvesting on top of the list of issues in WSN. A poor communication architecture can result in excessive consumption, reducing the network lifetime and throughput. Centralizing data collection and the introduction of gateways (GTs), to help cluster heads (CHs), improved WSN life time significantly. However, in vast regions, misplacement and poor distribution of GTs wastes a huge amount of energy and decreases network’s performances. In this work, we describe a reliable and dynamic with energy-awareness routing (RDEAR) protocol that provides a new GT’s election approach taking into consideration CHs density, transmission distance and energy. Applied on 20 different networks, RDEAR reduced the overall energy consumption, increased stability zone and network life time as well as other compared metrics. Our proposed approach increased network’s throughput up to 75.92% , 67.7% and 9.78% compared to the low energy adaptive clustering hierarchy (LEACH), distributed energy efficient clustering (DEEC) and static multihop routing (SMR), protocols, respectively.</p>

Software-Defined Network-Based Energy-Aware Routing Method for Wireless Sensor Networks in Industry 4.0

Recent technological developments have led to the emergence of the next generation of industry—Industry 4.0. The Industrial Internet of Things (IIoT) is a key enabler of this new manufacturing paradigm where millions of interconnected smart devices, such as sensors and robots, manage massive amounts of data. Wireless sensor networks (WSNs), which allow the integration, flexibility, and scalability of the production line, thus avoiding the need for complex and expensive wired networks, are essential for IIoT. Nevertheless, the nonstop improvements of the smart industry have increased the amount of data transmitted by WSNs, making their nodes, which rely on small batteries, prone to exhaustion. In this scenario, where the transmission could be abruptly interrupted, losing time, information, and money, the development of energy-based management strategies for reducing the energy consumption of WSNs is urgent. In this paper, a software-defined network (SDN)-based energy-aware routing protocol is proposed to optimize the power consumption of WSNs within the framework of IIoT to support Industry 4.0. The SDN controller estimates the energy level of critical nodes in the WSN and decides the best routing path based on their energy consumption rather than on the widely used shortest-path criterion. Experimental results, obtained via a Mininet-Wifi simulation, show that the proposed approach prevents WSNs’ nodes from draining their batteries and abruptly interrupting the data transmission. Hence, valuable retransmission time is saved, potential information loss is prevented, the need for replacing the node’s battery is avoided, and the transmission lifetime is prolonged. In addition, the baseline shortest-path routing method is outperformed in terms of energy consumption and node failure, doubling its transmission time.

Cross-Layer and Energy-Aware AODV Routing Protocol for Flying Ad-Hoc Networks

In recent years, unmanned aerial vehicles (UAVs) have become the trend for different types of research and applications. UAVs can accomplish some technical and risky tasks while still being safe, mobile, and inexpensive to operate. However, UAVs need flying ad-hoc networks (FANET) to operate in inaccessible or infrastructure-less areas. Subsequently, in many military and civil applications, the UAVs are connected ad hoc. FANET-based UAV systems have been developed for search and rescue, wildlife surveys, real-time monitoring, and delivery services. Maintaining the reliability and connectivity among UAV nodes in FANET becomes challenging because of the UAV movement, environmental conditions, energy efficiency, etc. Energy-aware routing protocols have become essential for developing advanced and effective FANETs. This paper presents a proposed Cross-Layer and Energy-Aware Ad-hoc On-demand Distance Vector (CLEA-AODV) routing protocol for improving FANET performance. The CLEA-AODV protocol is mainly divided into three sections: routing with AODV protocol, Glow Swarm Optimization (GSO)-based Cluster Head Selection, and Cooperative Medium Access Control (MAC). The cross-layer approach is implemented on the network layer and the data layer. The major parameters considered to evaluate the performance of the FANET are Packet Success Rate (PSR), Throughput (TP), End-to-End (E2E) delay, and packet drop ratio (PDR). The Network Simulator version 2 (NS2) is used to implement the CLEA-AODV protocol and evaluate the network performance. The results are compared with the standard AODV, Self-Organization Clustering-GSO (SOC-GSO), and Energy Efficient Neuro-Fuzzy Cluster-based Topology Construction with Meta-Heuristic Route Planning (EENFC-MRP) protocols. The results show that the CLEA-AODV surpasses these protocols in terms of PSR, TP, E2E delay, and PDR.

Secure energy aware routing protocol for trust management using enhanced Dempster Shafer evidence model in multi-hop UWAN

Secure energy aware routing protocol for trust management using enhanced Dempster Shafer evidence model in multi-hop UWAN

Reliable network lifetime and energy-aware routing protocol for wireless sensor network using hybrid particle swarm-flower pollination search algorithm

Reliable network lifetime and energy-aware routing protocol for wireless sensor network using hybrid particle swarm-flower pollination search algorithm

Security Control of Denial-of-Service Attacks in Cyber-Physical Systems Based on Dynamic Feedback.

With the integration and development of multiple disciplines, as well as a large amount of research on multilevel interconnection networks, wireless sensor networks have become a new access point and have attracted widespread attention worldwide. However, limited by the characteristics of the wireless sensor network itself, the energy problem has repeatedly restricted its function, which needs to be solved urgently. This paper mainly improves the Geographical and Energy-Aware Routing (GEAR) based on cyber-physical systems and energy-aware routing protocols, which reduces the resource consumption of network nodes, achieves network power balance, and further extends the system life cycle. Although the open sharing of the network layer makes CPS more efficient in actual use, it also increases the risks faced by CPS. Among these potential risks, cyberattacks have become the main security problem that CPS must solve with the greatest destructive power. Among them, Denial of Service (DoS) is the simplest and most destructive type of network attack. We can allow the system to reject normal requests for sending and receiving data using the network resources of the communication channel, thereby performing security control. From the perspective of control, this paper studies the security control of CPS in DOS attacks. Firstly, considering the diversity of CPS packet loss factors, assuming that there is an upper limit on the probability characteristics of the total number of packet loss development, the Markov process of inherent packet loss and the Markov process of DoS attack satisfying this constraint are analyzed, and the above two independent packet loss factors are described on this basis. Two independent packet loss factor descriptions are given in the following. In this paper, through the research of wireless sensor networks, it is applied to the cyber-physical system and the cyber-physical system is further improved.

Provide energy-aware routing protocol in wireless sensor networks using bacterial foraging optimization algorithm and mobile sink.

Wireless sensor networks (WSNs) include small sensor nodes with battery and processing power and limited memory units then improving power consumption is a major design challenge for any sensor network. In this paper, a new algorithm for routing in the wireless sensor network is proposed using the ultra-innovative algorithm for bacterial Foraging and mobile sink, which leads to energy efficiency. In the proposed method, the number of sensor nodes is determined according to two criteria: the amount of energy on the battery surface and the distance to the sink ahead, which leads to the formation of regular clusters in the network. Nodes adopt a multi-step routing scheme within the network to communicate with the sink. also, the mobile sink is used to balance the load and help consume uniform energy throughout the network. The simulation results show better performance of the proposed method in terms of energy consumption by 17.99%, throughput rate by 30.04%, end-to-end delay by 46.04%, signal-to-noise ratio by 32.81%, delivery rate successfully Data to the sink is 0.80 times higher than the AFSRP (Artificial Fish Swarm Routing Protocol).

Energy-Aware Routing Protocol with Fuzzy Logic in Industrial Internet of Things with Blockchain Technology

It is the expansion and use of the Industrial Internet of Things (IIoT) in various industrial sectors and applications that are referred to as the Industrial Internet of Things (IIoT). The Industrial Internet of Things includes industrial applications such as robots, medical devices, and software-defined manufacturing processes. In terms of energy conservation, routing is extremely essential. The creation of an energy effectual steering procedure leads to a substantial rise in energy consumption. To minimize network traffic and increase network life, the article presented an Industrial IoT Fuzzy Logic Energy-Aware Routing Protocol (FLEA-RPL), which decreases network traffic as well as improves network life. The most suitable parent for data transfer is selected based on, among other things, the routing parameters charge, residual energy, and expected transmission count. Since the load routing metric is taken into consideration during the construction of the route, the data traffic is spread across the network. This increases network’s lifetime while maintaining a high packet delivery ratio. The proposed work proposes a Multilayer Energy-Aware Aware RPL (MCEA-RPL) cluster for the Internet of Things to decrease network data traffic while increasing the lifetime of the network. It is split into three phases, each including the creation of network rings, intraring divisions, and intercluster routing. First and foremost, the virtual ring is created in the network. Secondly, each ring forms an identical cluster and chooses the CH node. Finally, it is responsible for the maintenance and performance of the DODAG. Data transfer from the lesser sheet to the DODAG root is known as data transfer. By using Blockchain technology, the lifetime of a network may be extended by reducing the number of identical data package transfers. This article offers Enhanced Mobility Support RPL (EM-RPL) in Industrial IoT which enhances mobility support with blockchain and spreads system generation. It comprises two processes: a collection of the parental static node and selection of the parent moving node. The static parent selection method uses routing metrics load and residual energy to identify the parent that is most suited for data transfer. Two phases of mobile parent selection must be distinguished: data transmission and route rediscovery. The mobile node utilizes furious logic to compute the hand-off value of the metric packet errors ratio and the signal strength indication received from the base station. If the hand-off value exceeds the threshold limit, the DODAG route has to be changed to work correctly. The EM-RPL thus increases the package delivery rate by reducing the amount of route interruption caused by mobility, while offering an efficient handling mechanism.

A modified distance-based energy-aware (mDBEA) routing protocol in wireless sensor networks (WSNs)

A modified distance-based energy-aware (mDBEA) routing protocol in wireless sensor networks (WSNs)

Energy Aware Routing Protocol with Data Fusion and Machine Learning

The current wireless and communication system may be attributed to the contributions made by the Sensor Network in a significant measure. During the last decade, several efforts have been performed to examine and propose answers to challenges about the energy efficiency of wireless sensor network communications. Several different researchers has done these efforts. The challenge of constructing economical energy-use paths has not yet been overcome. Because sensors have limited computational capabilities, which are frequently coupled with energy limitations, it is rather difficult to guarantee that a sensor’s lifespan will be longer. This is because of the energy constraints often associated with these limitations. The results of this research have led to the development of a one-of-a-kind communication system for sensor networks that is not only environmentally friendly but also supported by three distinct revolutionary frameworks. The framework that has been recommended, which goes by the name Potential Energy Efficient Data Fusion (PEE-DF), is the one that is in charge of the optimization of energy. It achieves this with the aid of probabilistic approaches and clustering. The K-SOM (Korhonen self-organizing map) framework was designed using a globular topology, which aids load balancing during data fusion. K-SOM stands for Korhonen self-organizing map. This was done to ensure we got the most out of our resources. A novel method to routing is presented by the technique, which has the potential to be used to assist in the operation of energy-efficient routing in large-scale wireless sensor networks. The framework for the Tree-Based Fusion Technique (TBFT), which has been offered, comes up with a new way for dynamic reconfiguration. This is accomplished via the introduction of the concept of routing agents. The strategy enables the system to recognise which sensor has a greater energy dissipation rate and then instantly moves data fusion work to a more energy-efficient node. This allows the system to save energy. This approach, based on thresholds, enable a sensor to act as a cluster head up until it reaches its threshold remnant energy and then as a member node once it exceeds threshold residual energy. In other words, it may play both roles simultaneously. It is possible to fulfil both of these responsibilities at the same time. The findings have been mathematically modelled using a standard radio-energy model, which has enhanced the robustness of the findings, which is highly positive. The results were encouraging because of the increased robustness of the findings. Compared to the benchmark previously established for energy-efficient strategies, the proposed system demonstrates higher performance in terms of its ability to communicate while using less energy. In contrast to LEACH, the recommended system's findings reveal an almost fifty percent decrease in energy consumption, and at the same time, a reduction in the amount of time required to carry out the operation..

CFEP-mobile: competitive fruit fly optimiser-based energy-aware routing protocol under mobile sink-based WSN

Wireless sensor networks (WSNs) are comprised of numerous promising applications from where the sensed data are collected from remote applications. With respect to the promising applications related to data gathering, the desired information from the unreachable or remote locality, WSN poses a major challenge for the energy-efficient routing in data for improving communication. The literature presents several energy-aware routing protocols to maximise the lifespan of the sensor nodes. Consequently, an energy-efficient clustering approach, based on competitive fruit fly optimiser (CFFO) is developed in this paper to enhance the energy and the total lifetime of nodes such that the cluster head is chosen optimally. The hybrid optimisation approach named CFFO, is developed for controlling the convergence rate of the CFFO with the newly designed fitness function by considering the four various objectives like energy, distance, delay, and link lifetime. However, the developed CFFO algorithm is designed by the integration of the competitive swarm optimiser (CSO) and the fruit fly optimisation algorithm (FFOA). This method achieved efficient performance in terms of metrics like PDR, energy, and delay with the maximum PDR of 100%, high energy of 0.367 J, and minimum delay of 0.0925 sec, respectively.

An Energy and Delay Aware Routing Protocol for Wireless Sensor Network assisted IoT to maximize network lifetime

An Energy and Delay Aware Routing Protocol for Wireless Sensor Network assisted IoT to maximize network lifetime