Energy Balanced Routing Research Articles

Energy-Balanced Routing Protocol With Nonuniform Clustering for Underwater Acoustic Sensors Networks

Energy-Balanced Routing Protocol With Nonuniform Clustering for Underwater Acoustic Sensors Networks

Retraction Note: Soft computing and trust-based self-organized hierarchical energy balance routing protocol (TSHEB) in wireless sensor networks

Retraction Note: Soft computing and trust-based self-organized hierarchical energy balance routing protocol (TSHEB) in wireless sensor networks

Application of EEUC-based inter aircraft ultraviolet communication network algorithm in energy consumption optimization of drone swarm

With the widespread application of drone technology, energy management of drone swarms has become a key challenge in ensuring sustained and efficient operations. To optimize the energy consumption of drone swarms, researchers have proposed the energy balance plane routing algorithm. This algorithm uses ultraviolet light for low visibility communication and has shown promising results in managing the energy of drone swarms. To address the issues of uneven cluster head distribution and reduced energy consumption in other algorithms, an improved non-uniform clustering energy balancing routing algorithm is proposed. Compared to existing algorithms, the improved non-uniform clustering energy balancing routing algorithm achieved the lowest average communication consumption among 5 nodes and prolonged node failure time. The performance of the research method has been verified through simulation experiments, which is of great significance in maintaining energy consumption balance and can improve the energy efficiency and sustainability of the network. This study can provide more effective solutions for the development and application of drone technology, promoting its widespread application and promotion in various fields.

Multiobjective-energy centric honey badger optimization based routing for wireless body area network

Wireless Body Area Network (WBAN) is an interconnection of tiny biosensors that are organized in/on several parts of the body. The developed WBAN is used to sense and transmit health-related data over the wireless medium. Energy efficiency is the primary challenges for increasing the life expectancy of the network. To address the issue of energy efficiency, one of the essential approaches i.e., the selection of an appropriate relay node is modelled as an optimization problem. In this paper, energy efficient routing optimization using Multiobjective-Energy Centric Honey Badger Optimization (M-ECHBA) is proposed to improve life expectancy. The proposed M-ECHBA is optimized by using the energy, distance, delay and node degree. Moreover, the Time Division Multiple Access (TDMA) is used to perform the node scheduling at transmission. Therefore, the M-ECHBA method is used to discover the optimal routing path for enhancing energy efficiency while minimizing the transmission delay of WBAN. The performances of the M-ECHBA are analyzed using life expectancy, dead nodes, residual energy, delay, packets received by the Base Station (BS), Packet Loss Ratio (PLR) and routing overhead. The M-ECHBA is evaluated with some classical approaches namely SIMPLE, ATTEMPT and RE-ATTEMPT. Further, this M-ECHBA is compared with the existing routing approach Novel Energy Efficient hybrid Meta-heuristic Approach (NEEMA), hybrid Particle Swarm Optimization-Simulated Annealing (hPSO-SA), Energy Balanced Routing (EBR), Threshold-based Energy-Efficient Routing Protocol for physiological Critical Data Transmission (T-EERPDCT), Clustering and Cooperative Routing Protocol (CCRP), Intelligent-Routing Algorithm for WBANs namely I-RAW, distributed energy-efficient two-hop-based clustering and routing namely DECR and Modified Power Line System (M-POLC). The dead nodes of M-ECHBA for scenario 3 at 8000 rounds are 4 which is less when compared to the dead nodes of EBR.

Survey on Energy-Efficient and Energy-Balanced WSN Routing Protocols

Wireless sensor network (WSN) is a network comprised of tiny sensor nodes. These sensors are characterized by limited memory, low processing capacity, limited power, and transmission bandwidth. The limited energy constraints inherent in WSNs have triggered an extensive research effort to develop Energy-efficient (EE) and Energy-balanced (EB) routing protocols to prolong the lifetime of the network. This study aims to investigate the overall research productivity and trends of the state-of-the-art research in energy-efficient and energy-balanced routing protocol designed for WSNs. The classification of energy-efficient and energy-balanced routing protocols is also a vital aim of this study. As a result, we conducted a selected relevant study with strict adherence to the pre-defined systematic mapping methodology to ensure unbiased inclusion of the relevant studies. This research provides great insight into the focus of the research community, tools being used, and taxonomies of research conducted in EE and EB WSN protocols for the past decade. In the end, this article also provides a comprehensive discussion of the challenges and future directions.

Energy balanced routing protocol based on improved particle swarm optimisation and ant colony algorithm for museum environmental monitoring of cultural relics

AbstractThe environmental monitoring of cultural relics based on wireless sensor networks in museums demands for the transmission and processing of massive data, which in turn leads to problems, such as heavy network traffic, high time delay, and unbalanced node energy consumption. To solve these problems, an energy balanced routing protocol which can minimise the network energy consumption is proposed. The improved swarm intelligence optimisation algorithm which combined particle swarm optimisation with ant colony optimisation is adopted to construct an environmental monitoring system for cultural relics preservation. Simulation results show that the decrease of the path length for data transmission under the proposed algorithm is 2.5% higher than that of other classical algorithms, indicating that the proposed algorithm can effectively reduce the length of network path and improve the transmission performance of network system. In view of optimisation rounds and simulation time, the energy consumption of the network system under our algorithm is 57.5% less compared with other classical algorithms, and the average residual energy variance is 57.1% less. The cultural relics environmental monitoring system based on the proposed routing protocol has better network load balancing performance.

Energy-balanced routing in wireless sensor networks with reinforcement learning using greedy action chains

Energy-balanced routing in wireless sensor networks with reinforcement learning using greedy action chains

An energy cooperation method of wireless sensor networks based on partially observable Markov decision processes

The performance of routing protocols in wireless sensor networks, such as energy conservation, energy and load balancing. These indicators have a very important impact on the network lifetime. Energy cooperation technology refers to the purposeful transfer of energy from nodes with sufficient energy to nodes with insufficient energy. Firstly, according to POMDP (Partially Observable Markov Decision Process) model, related theories and solving algorithms, this paper proposes an energy cooperation method based on POMDP, establishes an interference decision model based on POMDP, and solves the model. This paper proposes an energy balance routing algorithm based on multi-attribute decision making (EBR_MDM). This algorithm is used to solve the application problem of wireless sensor networks with stable data flow. Including the uneven distribution of network energy, hot spots and energy holes, packet loss caused by node overload and retransmission energy consumption. According to the relative entropy, the attribute weight is calculated, and the optimal forward data is selected to the neighboring nodes. The simulation results show that the network running time of EBR_MDM is increased by 22.1 %, 48.7 % and 101.3 % respectively compared with other comparison protocols. Finally, because the sensor nodes are randomly arranged in the predetermined monitoring area, and the distribution of each node is chaotic, this paper establishes TMA (Trilateral Measurement Algorithm) algorithm to locate the target. The simulation results show that the positioning error of TMA may be about 1.2 m due to trilateration, but it is still within an acceptable range.

Energy and load balancing routing protocol for IoT

SummaryDue to the limitation of node energy resources, the management of energy consumption is one of the most important problems of the internet of things (IoT). Therefore, many studies have tried to optimize and manage energy consumption by focusing on different techniques. Although each of these studies has improved and optimized energy consumption, there are many important problems, including maintaining traffic balance and energy consumption of network nodes. Therefore, a new method is necessary to maintain the load and energy balancing of network nodes. Therefore, this paper introduces energy and load balancing routing protocol for IoT (ELBRP) based on the development of the RPL routing protocol and the efficiency of data distribution technique. The ELBRP performance has three steps. In the first step, along with the process of sending DODAG information object (DIO) messages, the status of network nodes is evaluated. In the second step, the DODAG communication graph is formed according to the ELBRP. In the third step, data transmission is done according to the distribution technique with the goal of balancing traffic and energy. The simulation results using cooja simulator showed the superiority of ELBRP in improving energy consumption and successful delivery ratio, reducing delay and increasing the network lifetime compared to the similar methods.

DCGCR: Dynamic Clustering Green Communication Routing for Intelligent Transportation Systems

For the effective green communications amongst the vehicles, the energy-efficient routing protocol for intelligent transportation system (ITS) is essential. Due to the high speed and recurring topological variations of Vehicular sensor Networks, identifying a connected route with a sufficient latency is a difficult task with many constraints and obstacles. Therefore, to overcome this, we developed the statistical approach to theoretically determine the load congestion and consumption of energy during the lifetime of the sensor network for ITS. Hence, dynamic clustering green communication routing (DCGCR) protocol is proposed for vehicular communication. To manage energy consumption and enhance the lifetime of the network deployed on the roadside units (RSU), we analyze the evolution of energy holes and apply our analytical conclusions for ITS with WSN routing. The proposed routing protocol considers various metrics: i) energy consumption of vehicular sensor nodes,ii) network stability iii) reliability and iv) amount of data exchange among vehicles. The efficiency of the proposed computational model in calculating the lifetime of the vehicular network and energy hole evolution process is demonstrated through extensive computation results. DCGCR approach is compared with the various energy-aware routing algorithms namely, Dynamic Energy Balanced Routing (DEBR), Geographic Greedy Routing (GGR), double cost function-based routing (DCFR) and found that proposed approach achieves more accuracy with 7% less failure rate.

A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks

Underwater wireless sensor network attracted massive attention from researchers. In underwater wireless sensor network, many sensor nodes are distributed at different depths in the sea. Due to its complex nature, updating their location or adding new devices is pretty challenging. Due to the constraints on energy storage of underwater wireless sensor network end devices and the complexity of repairing or recharging the device underwater, this is highly significant to strengthen the energy performance of underwater wireless sensor network. An imbalance in power consumption can cause poor performance and a limited network lifetime. To overcome these issues, we propose a depth controlled with energy-balanced routing protocol, which will be able to adjust the depth of lower energy nodes and be able to swap the lower energy nodes with higher energy nodes to ensure consistent energy utilization. The proposed energy-efficient routing protocol is based on an enhanced genetic algorithm and data fusion technique. In the proposed energy-efficient routing protocol, an existing genetic algorithm is enhanced by adding an encoding strategy, a crossover procedure, and an improved mutation operation that helps determine the nodes. The proposed model also utilized an enhanced back propagation neural network for data fusion operation, which is based on multi-hop system and also operates a highly optimized momentum technique, which helps to choose only optimum energy nodes and avoid duplicate selections that help to improve the overall energy and further reduce the quantity of data transmission. In the proposed energy-efficient routing protocol, an enhanced cluster head node is used to select a strategy that can analyze the remaining energy and directions of each participating node. In the simulation, the proposed model achieves 86.7% packet delivery ratio, 12.6% energy consumption, and 10.5% packet drop ratio over existing depth-based routing and energy-efficient depth-based routing methods for underwater wireless sensor network.

An Energy Efficient Routing Protocol based on Reinforcement Learning for WSN

Wireless Sensor Network (WSN) seems to be critical because they are responsible for maintaining network routes, packet forwarding, and higher multi-hop connectivity. Clustering nodes is still a powerful technique for modelling routing protocol for WSNs, as it increases the range of communication services with energy efficiency. This paper focuses on the energy efficiency and improved lifetime of the network based on the reinforcement learning protocols. The system can adapt to network changes, such as energy efficiency, mobility and make better routing decisions attributable to Reinforcement Learning (RL). The legal restrictions on sensor nodes are taken into consideration and an energy-balancing routing model based upon reinforcement learning has been provided. The results show that the enhanced protocol outperforms the state of energy savings and network lifetime when compared to Q-learning and LARCMS energy-efficient routing protocols. The proposed protocol's effectiveness is analysed by end to end delivery and packet delivery.

Design and Research of Building Indoor Environment Monitoring System Relying on 3D Modeling and Image Processing

In order to improve the monitoring effect of building indoor environment, this paper carries out indoor environment simulation through 3D modeling, uses image processing technology to collect environmental monitoring data, and proposes a routing algorithm applied in this environment. Moreover, this paper introduces the energy consumption model of sensor nodes and the multi-attribute decision-making method and uses the multi-attribute decision-making to propose an adaptive energy balance routing algorithm based on the indoor environment of the building. In addition, this paper establishes a simulation analysis, compares it with several other routing algorithms, and verifies that the proposed algorithm has better balance in network energy consumption and longer network life cycle. Finally, this paper combines three-dimensional modeling and image processing technology to construct the building indoor environment monitoring system. It can be seen from the simulation experiments that the proposed building indoor environment monitoring system based on 3D modeling and image processing can meet the actual needs of intelligent building indoor monitoring.

Energy Balance Routing Protocol for Wireless Sensor Networks Based on Fuzzy Control Strategy

The existing routing protocols for wireless sensor networks were not reasonable in design, which limited their application. Most of the existing studies did not take into account the energy consumption of the network and the balanced use of the energy of sensor nodes, which led to the unsatisfactory application effect of wireless sensor networks in some fields. Therefore, from the perspective of energy balance in wireless sensor networks, this paper proposed a construction method of an energy balance routing protocol in wireless sensor networks based on a fuzzy control strategy. Firstly, based on the analysis of the basic composition of wireless sensor networks and the structure of sensor nodes, this paper expounded the basic process of wireless data transmission and summarized the classification and characteristics of routing protocols in wireless sensor networks from different angles. Secondly, according to the node data transmission characteristics of wireless sensor networks, the energy balance use model of sensor nodes was established, and the design method of the energy balance routing protocol based on fuzzy control strategy was proposed, and the data transmission link was optimized. Finally, through experimental comparative analysis, the results showed that the energy balanced routing protocol proposed in this paper can effectively realize the energy balanced use of the network data transmission process. Compared with other common routing protocols, the wireless sensor network routing protocol proposed in this paper can not only improve the data transmission efficiency and reduce the data redundancy but also save energy consumption and prolong the network running time. The design method of routing protocol proposed in this paper will be conducive to the optimization and application of routing protocol in wireless sensor networks and provide a theoretical basis for the related research of wireless sensor networks.

A Distributed Unequal Clustering Routing Protocol Based on the Improved Sine Cosine Algorithm for WSN

In order to balance the overall energy consumption and improve the energy efficiency of wireless sensor network (WSN), a distributed energy-balanced unequal clustering routing protocol based on the improved sine cosine algorithm (DUCISCA) is proposed. Firstly, DUCISCA adopts a time-based cluster head competition algorithm. In this algorithm, the broadcast time depends on the residual energy of the candidate cluster head, the distance to the base station, and the number of neighbour nodes. Secondly, a competition radius considering the distance from node to base station and the residual energy of node is proposed. It can balance energy consumption of nodes in different locations to avoid the “hot spot” problem. At the same time, it adopts a time-based broadcast mechanism. The waiting time depends on the residual energy of CCHs, the distance to the BS, and the number of neighbour nodes, which can effectively reduce the overhead of nodes. Thirdly, the energy of cluster head, the number of neighbour nodes, and the distance from the ordinary node to the cluster heads need to be taken into account to get a better clustering result. Finally, in order to speed up convergence and improve the ability to jump out of local optimum, the improved sine-cosine algorithm (ISCA) based on Latin hypercube sampling and adaptive mutation is proposed. The improvement strategies adopted by ISCA are expressed as follows: Firstly, the diversity of the population is enhanced through LHS population initialization. Secondly, the adaptive weight strategy is introduced to accelerate the convergence speed of the algorithm. Finally, the population is disturbed by Gaussian mutation or Levy flight to jump out of the local optimum. The standard deviation of cluster heads’ residual energy in intercluster communication is taken as the objective function to search the energy-balanced intercluster data forwarding path based on ISCA. Compared with EEUC, DEBUC, I-EEUC, and M-DEBUC, the simulation results prove that DUCISCA can effectively balance the overall network energy consumption and prolong the network lifetime.

Real-Time Collection of Training Distance of Long-Distance Runners Based on Wireless Sensor Network

The real-time and accurate collection of long-distance runners’ training distance can provide real-time scientific basis for long-distance runners’ physical training. Therefore, the real-time collection method of long-distance runners’ training distance based on wireless sensor network is studied in this paper. In this model, the position tracking algorithm of long-distance runners based on wireless sensor network is introduced to collect the training distance of long-distance runners. When collecting the training distance, the energy balanced clustering routing algorithm based on energy and location is used to optimize and balance the energy consumption distribution of nodes and prolong the network life cycle, so that the training distance of long-distance runners can be collected in real time. Through the test, in the straight line and curve training path, this method has significant accuracy in collecting the training distance of single and multiple long-distance runners and can collect the training distance of long-distance runners in real time, which has significant application value.

Distributed energy balance routing algorithm for wireless sensor network based on multi-attribute decision-making

The clustering routing protocol proposed in this paper is based on the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. Stable Election Protocol (SEP) is a clustering routing protocol proposed for secondary heterogeneous networks. Unlike LEACH, it is not a round-robin algorithm based on the number of nodes, but based on energy balance. This article first promotes SEP and adapts it to multi-level heterogeneous networks. On this basis, the Stable Election Protocol Energy Consider (SEP-EC) algorithm based on node residual energy is proposed, and its feasibility is proved theoretically. The research results show that for the Ad Hoc network with high load, high mobility and energy limitation, the load energy balance multi-path routing protocol proposed in this paper can not only effectively use multiple paths with better quality, but also can perceive path stability in real time. It terminates the call of unstable path early, and effectively reduces the packet loss rate, delay and route discovery frequency of Ad Hoc network. The average end-to-end delay of the SEP-EC protocol proposed in this paper is 0.52 s, and the average remaining surviving nodes is 83.5%.

Event Driven Application Specific Prioritized Data Transmission Approach for Underwater Wireless Sensor Networks

The paper presents a novel framework based on prioritized event detection for Underwater Wireless Sensor Network (UWSN). The proposed approach named Event Driven Energy Balanced Routing (EDEBR) takes the routing decision based on oceanic environment parameters (temperature, density, salinity and pressure). The approach of EDEBR considers two routing phase based on annotated parameters of Critical and Non-critical event priority. Here critical data routing phase focuses on fast and reliable delivery of data and Non-critical data routing phase focuses on the network lifetime through energy balancing technique. The proposal is evaluated and compared against the standard state-of-art protocol on the basis of following three evaluation parameters i) end to end delay, ii) Network lifetime and iii) Throughput of the network. Here in case of critical data the end to end delay is reduced this satisfies the condition for fast delivery. In case of non-critical data, the energy is conserved this enhances the overall life time. Lastly the maximum throughput is achieved in cases of critical and non-critical. Thus the proposal describes a novel routing mechanism for different priority data based on annotated critical and non-critical priorities in underwater environment. The viability of proposal for both cases critical and non-critical is validated through the simulation.

An Energy-Balanced Routing Algorithm Based On Forward Aware Factor for Machine-To-Machine Communication Networks

An Energy-Balanced Routing Algorithm Based On Forward Aware Factor for Machine-To-Machine Communication Networks

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.