Balance Energy Consumption Of Nodes Research Articles

A Wi-Fi dynamic routing algorithm based on energy prediction for wildlife monitoring wireless network

The routing protocol of wildlife monitoring Wi-Fi (Wireless Fidelity) networks cannot balance node energy consumption, leading to early node death. Therefore, the research on energy balance in wildlife monitoring Wi-Fi networks is a hot topic. In order to balance the energy consumption of Wi-Fi networks and extend the lifespan of wireless networks, we designed the low energy dynamic routing protocol LEACH-EP (Low Energy Adaptive Clustering Hierarchy- Energy Prediction) based on energy prediction by analyzing the long-range dependent characteristics of the remaining energy time series (RETS) of wireless network nodes. This protocol uses the LSTM (Long Short-Term Memory) model to predict the remaining energy of network nodes, and then dynamically plans routes using future remaining energy. We conducted a networking experiment in the Anzihe Nature Reserve in Chengdu, China, and the Energy Balance Factor index of the wireless network significantly improved. The Mean Absolute Error value of network nodes is less than 60 mW, which is less than 10% of the average daily energy consumption of nodes. Half of the surviving network nodes have achieved an increase to 55.2%, and the network death time has been extended by 38.6%. The experimental results show that the energy prediction routing protocol LEACH-EP can significantly extend the node survival life and balance network energy consumption.

Cluster-based fusion detection of soft and hard decisions for underwater non-cooperative targets

Underwater target detection performs the essential contribution in marine exploration. Unfortunately, the little energy available in the underwater environment makes detection difficult. To address the aforementioned obstacles, this paper proposed a detection scheme based on soft and hard decision fusion. The detection scheme consists of two stages: network topology control and the fusion of local and global detection. The first stage, an improved low energy adaptive clustering hierarchy (LEACH) algorithm is introduced into the underwater sensor network to choose the most suitable cluster heads and to introduce relay nodes to establish multi-hop routes between clusters, effectively balancing node energy consumption and extending network lifetime. With the network topology, the generalized likelihood ratio rule (GLRT) is employed by each sensor to detect the underwater non-cooperative target and make the corresponding decision during the second phase. Specifically, the member nodes make the multi-bit local soft decision, and then the cluster head node fuses the decision information from each member nodes to make the one-bit hard decision and transmits it to the fusion center, and finally the fusion center makes the global decision, such that the tradeoff between detection accuracy and energy consumption may be balanced by generating the soft and hard decision fusion technique. Simulations show that the strategy maintains detection performance extremely comparable to that of centralized and conventional multi-bit soft decisions while eliminating communication overhead.

A topology control algorithm for fusion networks based on link quality

To extend the network life, an ordinal potential game is introduced into network topology control, and a network topology control algorithm based on the Theil entropy measure is designed by improving the revenue function. The revenue function is based on the Theil entropy measure, whose factors are in the residual energy of the nodes and their neighbors. By using a primary payoff function that considers all initial network factors and a secondary one that addresses connectivity in case of reduced residual energy, a segmentation function can calculate the model's payoff. Simulation experiments show that compared to the existing game algorithms of 3DK-RNG, DEBA, and EFPC, the proposed algorithm can effectively eliminate redundant links, reduce node degree and network link length, balance node energy consumption, enhance network load, and extend the network life cycle.•A Network Topology Control Algorithm Based on the Fusion of Thiel Entropy and Ordinal Potential Games.•A network topology control method for extending the network lifecycle is successfully established.•It proposes that the performance of the fusion topology control algorithm is significantly superior to other single algorithms in the paper.

Energy Optimized Task Mapping for Reliable and Real-Time Networked Systems

Energy efficiency, real-time response, and data transmission reliability are important objectives during networked systems design. This paper aims to develop an efficient task mapping scheme to balance these important but conflicting objectives. To achieve this goal, tasks are triplicated to enhance reliability and mapped on the wireless nodes of the networked systems with Dynamic Voltage and Frequency Scaling (DVFS) capabilities to reduce energy consumption while still meeting real-time constraints. Our contributions include the mathematical formulation of this task mapping problem as mixed-integer programming that balances node energy consumption, enhancing data reliability, under real-time and energy constraints. Compared with the State-of-the-Art (SoA) , a joint-design problem is considered in this paper, where DVFS, task triplication, task allocation, and task scheduling are optimized concurrently. To find the optimal solution, the original problem is linearized, and a decomposition-based method is proposed. The optimality of the proposed method is proved rigorously. Furthermore, a heuristic based on the greedy algorithm is designed to reduce the computation time. The proposed methods are evaluated and compared through a series of simulations. The results show that the proposed triplication-based task mapping method on average achieves 24.84% runtime reduction and 28.62% energy saving compared to the SoA methods.

An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks

Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the surroundings. Designing energy-efficient data gathering methods in large-scale Wireless Sensor Networks (WSN) is one of the most difficult areas of study. As every sensor node has a finite amount of energy. Battery power is the most significant source in the WSN. Clustering is a well-known technique for enhancing the power feature in WSN. In the proposed method multi-Swarm optimization based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are used for enhancing the network’s lifespan and routing optimization. By using distributed data transmission modification, an adaptive hierarchical clustering-based routing algorithm for power consumption is presented to ensure continuous coverage of the entire area. To begin, a hierarchical clustering-based routing protocol is presented in terms of balancing node energy consumption. The Multi-Swarm optimization (MSO) based Genetic Algorithms are proposed to select an efficient Cluster Head (CH). It also improves the network’s longevity and optimizes the routing. As a result of the study’s findings, the proposed MSO-Genetic Algorithm with Hill climbing (GAHC) is effective, as it increases the number of clusters created, average energy expended, lifespan computation reduces average packet loss, and end-to-end delay.

Data transmitted encryption for clustering protocol in heterogeneous wireless sensor networks

In this paper, we propose a novel encryption schema (ECDH – RSA) based on Elliptic Curves Diffie – Hellman algorithm (ECDH) and RSA algorithm to secure transmitted data in Heterogeneous Wireless sensor networks (HWSNs). The proposed encryption is built under Cheesboard Clustering Routing Method (CCRM). The CCRM used to balancing node energy consumption. To achieve good scalability and performance by deploying a small number of powerful high-end sensors in addition to a large number of lowend sensors. ECDH is used to exchange public and private keys due to its ability to provide high security with small key size. The proposed encryption key is 176-bit and is produced by combining it’s key, node identification number, and distance to its cluster head (CH). To reduce energy consumption of CH, RSA encryption is used to allow CH to aggregate the encrypted data without having to decrypt them. We demonstrated that the proposed method is capable to work with different sensing environments that need to capture text data as well as images. The Simulation results demonstrated that our proposed method greatly improve the network performance in terms of energy consumption and highest paths.

Research of Multidimensional Optimization of LEACH Protocol Based on Reducing Network Energy Consumption

In order to equalize network energy consumption, and extend the life cycle, the optimized protocol based on low-power adaptive cluster stratification (LEACH) is proposed. Firstly, considering residual energy and distance of nodes, the threshold function of the cluster head is modified, and the network region division strategy is optimized, to reasonably adjust the size of clusters. For intracluster transmission, a sleep mechanism is added to balance node energy consumption. And a new barycenter node is added to assist cluster head (CH) to complete the transmission task, thereby extending the service lifetime. In the multihop communication between clusters, in order to calculate the fitting factor of the next hop, we derived a new formula, which takes into account the angle, remaining energy, and distance. Simulation results show that the total remaining energy of the optimized LEACH protocol is reduced by 31.4%, and the network life cycle is increased by about 52%.

EERBCR: Energy-efficient regional based cooperative routing protocol for underwater sensor networks with sink mobility

The balance energy consumption of nodes is a key factor to prolong network lifetime. A sensor node in Underwater Wireless Sensor Networks (UWSNs), has very limited power battery, therefore, utilizing the power battery in efficient manner is still a challenging task. To enhance network lifetime, we proposed an Energy-Efficient Regional base Cooperative Routing (EERBCR) protocol with sink mobility for UWSNs. In EERBCR, we divided the network field in 12 regions, three by four vertically and horizontally respectively. Total numbers of four mobile sinks are positioned, having equal distance to each other while 100 sensors nodes are deployed randomly. Each mobile sink travels on pre-defined straight path and covers 3 regions. All the sensor nodes are in sleeping mode, until the sink arrives to their region, upon the arrival of sink node to a region, it broadcasts a hello message. All the nodes in that region receive that message and activate themselves. When the sink is about to leave the region, it broadcasts another packet, informing the nodes about its departure so that the nodes go back into sleeping mode. The simulation results show the validity of EERBCR as it performed better than Depth-Based Routing (DBR), Energy Efficient Depth-Based Routing (EEDBR) and depth and energy-aware dominating set (DEADS) protocols.

Data Reconstructing Algorithm in Unreliable Links Based on Matrix Completion for Heterogeneous Wireless Sensor Networks

In heterogeneous wireless sensor networks, the data collection method based on compressed sensing technology is susceptible to packet loss and noise, which leads to a decrease in data reconstruction accuracy in unreliable links. Combining compressed sensing and matrix completion, we propose a clustering optimization algorithm based on structured noise matrix completion, in which the cluster head transmits the compressed sampling data and compression strategy to the base station. The algorithm we proposed can reduce the energy consumption of the node in the process of data collection, redundant data and transmission delay. The rank-1 matrix completion algorithm constructs an extremely sparse observation matrix, which is adopted by the sink node to complete the reconstruction of the whole network data. Simulation experiments show that the proposed algorithm reduces network transmission data, balances node energy consumption, improves data transmission efficiency and reconstruction accuracy, and extends the network life cycle.

An Improved Threshold-Sensitive Stable Election Routing Energy Protocol for Heterogeneous Wireless Sensor Networks

In the Threshold-Sensitive Stable Election Protocol, sensors are randomly deployed in the region without considering the balanced energy consumption of nodes. If a node that has been selected as a cluster head is located far away from the base station, it will affect the efficiency of the network due to its early death. This paper proposes an improved energy efficient routing protocol named Improved Threshold-Sensitive Stable Election protocol (ITSEP) for heterogeneous wireless sensor networks. Firstly, we use a node state transformation mechanism to control the number of cluster heads in high-density node areas. Secondly, the proposed protocol improves the threshold formula by considering the distance from the node to the base station, the number of neighbor nodes, its residual energy, and the average distance between nodes. In addition, an optimal route with minimum energy consumption for cluster heads has been selected throughout data transmission. Simulation results show that this algorithm has achieved a longer lifetime than the stable election protocol algorithm, modified stable election protocol algorithm, and threshold-sensitive stable election protocol algorithm for the heterogeneous wireless sensor network.

Joint design of clustering and in-cluster data route for heterogeneous wireless sensor networks

A heterogeneous wireless sensor network comprises a number of inexpensive energy constrained wireless sensor nodes which collect data from the sensing environment and transmit them toward the improved cluster head in a coordinated way. Employing clustering techniques in such networks can achieve balanced energy consumption of member nodes and prolong the network lifetimes. In classical clustering techniques, clustering and in-cluster data routes are usually separated into independent operations. Although separate considerations of these two issues simplify the system design, it is often the non-optimal lifetime expectancy for wireless sensor networks. This paper proposes an integral framework that integrates these two correlated items in an interactive entirety. For that, we develop the clustering problems using nonlinear programming. Evolution process of clustering is provided in simulations. Results show that our joint-design proposal reaches the near optimal match between member nodes and cluster heads.

LQEAR: Link Quality and Energy-Aware Routing for Wireless Multimedia Sensor Networks

Wireless Multimedia Sensor Networks (WMSNs) consist of smart sensor nodes that retrieve multimedia data and scalar sensor data. Currently, because of their popularity, WMSNs are used in a variety of applications. Handling and routing large amount of multimedia data in resource constrained network to guarantee Quality of Service (QoS) is a challenge. In WMSNs, communication links are typically error-prone and unreliable. The presence of bad quality links on a path may cause higher packet error rate and results in low throughput. This paper presents Link Quality and Energy-Aware Routing (LQEAR) scheme, which finds energy-aware high-quality links along the path. Next forwarding node is selected based on the link quality, remaining energy and the distance between next forwarding node and the sink. The adaptive cost function can be customized for reliable and high throughput transmission, high network lifetime, and for balanced optimal QoS performance. LQEAR finds multiple node-disjoint paths between source and sink node. Simulation results show that LQEAR selects excellent quality links to offer better reliability, high throughput, and efficiently balance energy consumption of nodes to extend network lifetime.

Energy Conservation Protocol using Sleep Scheduling in WSN

Wireless Sensor Network is made-up of large number of sensor node and one or more sink node. Sensor nodes are small in size and low in cost. Sensor network are equipped with sensor devices and powered by small size batteries. Since each sensor node has low energy, these nodes are usually put to sleep mode to conserve energy, and this helps to increase the network lifetime. Sleep scheduling algorithm should take care that data continuously routed form sensor node to sink node. In this paper we are going discuss sleep scheduling for sensor network using energy conservation protocol and tree integrated with proposed sleep scheduling algorithm. The root node in tree is sink node and other sensor nodes are branch nodes and leaf nodes. The leaf nodes are always put to sleep mode and branch nodes are remain awake. This ensures that path must exist from any sensor node to sink node. The tree is periodically reconstructed considering the remaining energy of each node by checking balance energy consumption of nodes, and removes the failed nodes from the tree. This proposed approach greatly minimize average energy consumption rate of all sensor node as we are putting more number of nodes in sleep mode. In this paper, we are proposing a novel Efficient Energy-Aware Routing Protocol (EEAR) to increase lifetime of sensor network by minimizing energy consumption and balancing load between all the sensor nodes. We are also going to discuss simple but efficient approach named intra cluster coverage to handle area coverage problem.

Energy Balance Routing Algorithm Based on Energy Heterogeneous WSN

Balancing energy load is a key problem in wireless sensor network (WSN) research. For balancing node energy consumption and prolong the network lifetime, this paper proposes an improved routing algorithm EBRA (Energy Balancing Routing Algorithm) based on energy heterogeneous WSN. To maximize the energy efficiency of network nodes, the EBRA weights the probability of cluster head election. According to the estimate value of the network average remaining energy and the residual energy of network nodes, we can calculate the new cluster head election threshold. The simulation results show that the utilization of energy balance of EBRA algorithm is improved 74%, 30% and 23%, compare with LEACH, SEP and DCHS, respectively.

Energy Optimized Routing Algorithm in Multi-sink Wireless Sensor Networks

Learning from the concept of potential field in physics, the hybrid virtual potential field of the wireless sensor network was constructed based on the hop and the residual energy of nodes. Aimed at maximizing the network lifetime, we proposed an energy optimized routing algorithm for multi-sink wireless sensor networks, using virtual force of the virtual potential field as the routing decision criteria. Avoid strategy for low-energy nodes and load balancing strategy for multiple sinks were applied to dynamically adjust the potential value and achieve balanced energy consumption of nodes. Simulation results show that the routing algorithm balances the energy consumption of nodes effectively and extends the network lifetime, compared with similar algorithms.

Maximizing Lifetime of Wireless Sensor Networks with Mobile Sink Nodes

In order to maximize network lifetime and balance energy consumption when sink nodes can move, maximizing lifetime of wireless sensor networks with mobile sink nodes (MLMS) is researched. The movement path selection method of sink nodes is proposed. Modified subtractive clustering method, k-means method, and nearest neighbor interpolation method are used to obtain the movement paths. The lifetime optimization model is established under flow constraint, energy consumption constraint, link transmission constraint, and other constraints. The model is solved from the perspective of static and mobile data gathering of sink nodes. Subgradient method is used to solve the lifetime optimization model when one sink node stays at one anchor location. Geometric method is used to evaluate the amount of gathering data when sink nodes are moving. Finally, all sensor nodes transmit data according to the optimal data transmission scheme. Sink nodes gather the data along the shortest movement paths. Simulation results show that MLMS can prolong network lifetime, balance node energy consumption, and reduce data gathering latency under appropriate parameters. Under certain conditions, it outperforms Ratio_w, TPGF, RCC, and GRND.

A Type of Node Deployment Strategy Based on Variable Acceleration Motion for Wireless Sensor Networks

For the purpose of balancing node energy consumption in Wireless Sensor Networks and in the premise of considering network coverage, a kind of node broadcasting scheme at fixed intervals under variational acceleration straight-line movement model is proposed in this paper. Simulation results illustrate that the approach proposed in this paper has a superior performance on balance of energy consumption compared to IEA method and uniform broadcasting as well as random broadcasting methods. And the energy saving effect is close to that of the theoretical deployment model of data fusion and nondata fusion.

An Energy Balanced Clustering Algorithm Based on LEACH Protocol

The primary objectives of the wireless sensor network routing protocol design are balancing network energy consumption and extending the entire network lifetime. This paper analyses the effectiveness of LEACH protocol in cluster-head selection, and proposes an improved clustering algorithm. This new algorithm takes nodes residual energy and location information into account, optimizes the selection method of the threshold for electing cluster-head, improves optimal cluster-head selection strategy that is normal nodes select the optimal cluster-head based on the cost function. Simulation results show that the improved protocol is better than LEACH in balancing node energy consumption, improving the efficiency of data transmission and prolonging the network life.

Research and Improvement about LEACH Routing Protocol for Wireless Sensor Networks

In recent years, as the development of wireless sensor network, people do some deep researches on cluster-based protocol, most around the prolongation of the lifetime of WSN and decline of energy consumed by the sensors. This paper analyses of classical clustering routing protocol based on LEACH, aiming at the node energy foot presents energy improved clustering routing algorithm, the random cluster head selection algorithm of threshold to be changed, lowering the threshold, in the original threshold increases the node's remaining energy factor, reduces the communication load of cluster nodes, and simulation. The simulation results show that the LEACH-E improved algorithm, energy saving, reducing balance node energy consumption, effectively prolongs the network lifetime.

Mobile Sensor Deployment Optimization for k-Coverage in Wireless Sensor Networks with a Limited Mobility Model

Sensor deployment is one of the key issues in wireless sensor networks (WSNs). An optimal placement of sensors is propitious to the maximum possible utilization of the available sensors, balancing node energy consumption, and prolonging the network lifetime. In this paper, we consider the mobile sensor deployment optimization for k-coverage of sensor networks with limited mobility. Since a mobile sensor’s cost is higher than that of a static sensor, we want to allow partial sensors to move to fill the coverage holes and vacancies. Through the analysis of the node distribution of randomly deployed sensor networks, we present the density of the vacancy for k-coverage of sensor networks, and the number of mobile sensors required to move to fill the coverage holes and vacancies. Moreover, since sensor mobility consumes much more energy than sensing and communication, we restrict sensors to move in a limited mobility model, i.e., the disk-based mobility model, which can reduce the sensor moving distance greatly. In this paper, we attempt to improve k-coverage of the mobile sensor networks using the improved particle swarm optimization algorithm under limited mobility. Simulation results show that few mobile sensors in the disk-based mobility model can realize the k-coverage, which reduces the cost of the sensor networks and moving distance of mobile sensors.