Unbalanced Energy Consumption Research Articles

Energy-efficient Communication in an Underwater Acoustic Sensor Network

Energy efficiency is one of the major concerns in UWSNs due to limited energy budget of the underwater sensor nodes. In this paper, the solution of avoiding energy holes in UWSNs is discussed while taking in to consideration of the unique characteristics of the underwater channel.so here proposed Even Data Distribution scheme to tackle the energy holes created due to unbalanced energy consumption in UWSNs. Using this scheme, to balance the energy usage through the network provided that sensors can use multiple transmission range when they send or forward the periodically generated data. In this proposed scheme the sensor node can able to adjust their transmission range up to three possible levels. And also determine the set of possible next hops with the associated load weights that lead to a fair energy consumption among all under water sensor. Hence energy holes can be avoided and consequently the network lifetime is highly increased.

A Genetic-Algorithm-Based Dynamic Transmission of Data for Communicable Disease in IoMT Environment

Recent advancements in the field of the Internet of medical things (IoMT) have enabled the real-time monitoring and treatment of patients with communicable infectious diseases while minimizing human intervention. However, IoMT devices face challenges such as unbalanced energy consumption, memory constraints, computation power, and low latency, which can deter the efficient transfer of patient monitoring data. Thus, there is an urgent need to establish an energy-efficient infrastructure for IoMT devices to remotely monitor and collect data on communicable diseases. For this, a genetic algorithm (GA) based dynamic transmission of data for communicable diseases in the IoMT environment is proposed in this paper. The energy utilization of the IoMT is enhanced by considering the GA evolutionary processing based on the dynamic sensor range. The proposed work incorporates a periphery of the fixed area for deploying the IoMT devices to settle the energy hole problem. Multiple sinks and direct information collection concepts are also introduced which further improve the performance and reduce the movement of data packets. The proposed protocols not only optimize energy usage but also provide a robust approach for massive data collection and communication.

HP-ECD: Heuristic Prophet protocol based on energy balance,cache optimization, and asynchronous dormancy

In opportunistic networks, opportunistic routing protocols are used for nodes to forward messages, which are the core technology to realize various opportunistic network applications. However, the existing opportunistic routing protocols generally have three problems: Key nodes forwarded too many messages, resulting in unbalanced energy consumption; Delivered message still occupies the cache space of nodes, resulting in the loss of undelivered message; Nodes constantly sense their surroundings and forward messages, resulting in rapid energy depletion. To better solve these problems, this paper proposes HP-ECD: heuristic Prophet routing protocol based on energy balance, cache optimization, and asynchronous dormancy. Based on Prophet, First, the HP-ECD defines the message forwarding benefits based on multiple node attributes and message importance, to realize the energy balance mechanism of nodes. Then, the HP-ECD designs the message delivery list based on the node history information, to realize the cache optimization mechanism of nodes. Finally, the HP-ECD determines whether a node is in dormancy based on the node running status, to realize the asynchronous dormancy mechanism of nodes. Simulation results show that, compared with Epidemic, Prophet, and EC-CW, HP-ECD greatly improves the message delivery rate and average remaining energy, and reduces the routing overhead rate and average storage time.

Dynamic and Energy Efficient Cache Scheduling Framework for IoMT over ICN

The Internet of Medical Things (IoMT) is the network of medical devices, hardware infrastructure, and software applications used to connect the healthcare information technology. Massive traffic growth and user expectations cause challenges in the current exhausting models of IoMT data. To reduce the IoMT traffic, Information Centric Network (ICN) is a suitable technique. ICN uses persistent naming multicast communication that reduces the response time. ICN in IoMT provides a promising feature to reduce the overhead due to the distribution of commonly accessed contents. Some parameters such as energy consumption, communication cost, etc., influence the performance of sensors in the IoMT network. Excessive and unbalanced energy consumption degrades the network performance and lifetime. This article presents a framework called Dynamic Cache Scheme (DCS) that implements energy-efficient cache scheduling in IoMT over ICN to reduce network traffic. The proposed framework establishes a balance between the multi-hop traffic and data item freshness. The technique improves the freshness of data; thus, updated data are provided to the end-users via the effective utilization of caching in IoMT. The proposed framework is tested on important parameters, i.e., cache-hit-ratio, stretch, and content retrieval latency. The results obtained are compared with the state-of-the-art models. Results’ analysis shows that the proposed framework outperforms the compared models in terms of cache-hit-ratio, stretch, and content retrieval latency by 59.42%, 32.66%, and 18.8%, respectively. In the future, it is intended to explore the applicability of DCS in more scenarios and optimize further.

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.

Adaptive multimode transmission in wireless sensor networks with energy harvesting

Limited energy has always been an important factor restricting the development of wireless sensor networks. The unbalanced energy consumption of nodes will accelerate the death of some nodes. To solve the above problems, an adaptive routing algorithm for energy collection sensor networks based on distributed energy saving clustering (DEEC) is proposed. In each hop of data transmission, the optimal mode is adaptively selected from four transmission modes: single-hop cooperative, multi-hop cooperative, single-hop non-cooperative and multi-hop non-cooperative, so as to reduce and balance the energy consumption of nodes. The performance of the proposed adaptive multi-mode transmission method and several benchmark schemes are evaluated and compared by computer simulation, where a few performance metrics such as the network lifetime and throughput are adopted. The results show that, the proposed method can effectively reduce the energy consumption of the network and prolong the network lifetime; it is superior to various benchmark schemes.

Energy-Efficient Controller Placement in Software-Defined Satellite-Terrestrial Integrated Network

The satellite-terrestrial integrated network (STIN), as an integration of the satellite network and terrestrial, has become a promising architecture to support global coverage and ubiquitous connection. The architecture of software-defined networking (SDN) is utilized to intelligently coordinate the global STIN, in which the placement schemes of SDN controllers, including the locations, number, and roles, would produce various performances. However, the uneven distribution of global users leads to the unbalanced energy consumption of satellite resources, which brings a heavy burden for satellites to maintain the control flows for network management. To provide green communication for international economic trade in the countries along the Belt and Road, in this paper, we focus on the energy-efficient controller placement (EECP) problem in the software-defined STIN. The satellite gateways are located in the countries along the Belt and Road, which accounts for a large number of traffic demands and a dense population. The controllers are deployed on the LEO satellites, where each LEO satellite is a candidate controller. The energy consumption for the control paths and the user data links is modeled and then formulated as the flow processing-oriented optimization problem. A modified simulated annealing placement (MSAP) algorithm is developed to solve the EECP problem, in which we use the greedy way to obtain the initial set of controllers, and then the final optimal controller placement result is obtained by the simulated annealing algorithm. Extensive simulations are conducted on the simulated Iridium satellite network topology and statistics data. Compared with other algorithms, the results show that MSAP reduces network energy consumption by 20% and average latency by 25%.

LEACH Clustering Routing Protocol Based on Balanced Energy Consumption

<p>Faced with the problems of unbalanced energy consumption (EC) and short lifetime of nodes in Wireless Sensor Networks (WSN), a Low Energy Adaptive Clustering Hierarchy (LEACH) clustering routing protocol based on energy balance, namely LEACH-EB (LEACH Based on Energy Balance) protocol was proposed. At the initial selection stage, the nodes which are close to the base station (BS) with great remaining energy and many neighbor nodes are selected as the cluster heads (CHs); then, the non-CH nodes enter the clusters which have the least costs based on the strength and remaining energy of the communication signals between themselves and different CHs. At the data transmission stage, if the CH which sends the information is one hop away from BS, the CH needs to select a neighbor CH with the largest forwarding probability as the next hop relay node based on the remaining energy of each neighbor CH, the number of nodes in the cluster, and the distance from BS. The selected neighbor CH continues to determine the next hop in the above manner until the data is successfully sent to BS. Simulation tests show that LEACH-EB protocol can receive more data and extend the network life cycle by 60%, 43.1%, and 13.36% compared with LEACH, LEACH-C, and FIGWO, respectively. </p> <p> </p>

A Non-Dominated Sorting Genetic Algorithm Based on Voronoi Diagram for Deployment of Wireless Sensor Networks on 3-D Terrains

The deployment strategy for wireless sensor networks (WSNs) affects the quality of service (QoS). Adopting a reasonable deployment strategy can improve the QoS of WSNs. In this paper, the problem regarding sensor node deployment for WSNs on three-dimensional (3D) terrain is modeled as a multi-objective optimization problem. The coverage rate of the WSNs, their unbalanced energy consumption, and the number of sensor nodes are used as fitness functions for the optimization problem. We propose a non-dominated sorting genetic algorithm based on a Voronoi diagram (VNSGA) for solving the wireless sensor network deployment issue and improving the QoS of WSNs on 3D terrain. The proposed algorithm applies the Voronoi diagram to obtain the node-sensing radius and communication radius, which are suitable for 3D terrain with respect to calculating the fitness function of the optimization problem. The Pareto optimal solution is obtained by retaining the solution close to the reference point. The experiments compare the proposed algorithm with the Multi-Objective Particle Swarm Algorithm (MOPSO) and the Non-Dominated Sorting Genetic Algorithm III (NSGA-III) on two terrains with different ranges. The experimental results show that the proposed algorithm outperforms the comparison algorithm on both terrains with different range sizes. The proposed algorithm can improve the coverage to 97.95% and reduce the imbalance in energy consumption to 9.13% on large range terrain.

GDR: A Game Algorithm Based on Deep Reinforcement Learning for Ad Hoc Network Routing Optimization

Ad Hoc networks have been widely used in emergency communication tasks. For dynamic characteristics of Ad Hoc networks, problems of node energy limited and unbalanced energy consumption during deployment, we propose a strategy based on game theory and deep reinforcement learning (GDR) to improve the balance of network capabilities and enhance the autonomy of the network topology. The model uses game theory to generate an adaptive topology, adjusts its power according to the average life of the node, helps the node with the shortest life to decrease the power, and prolongs the survival time of the entire network. When the state of the node changes, reinforcement learning is used to automatically generate routing policies to improve the average end-to-end latency of the network. Experiments show that, under the condition of ensuring connectivity, GDR has smaller residual energy variance, longer network lifetime, and lower network delay. The delay of the GDR model is 10.5% higher than that of existing methods on average.

Performance Analysis of Wireless Sensor Network by Variation in Cluster Head Selection through mLEACH-CS Algorithm

Abstract: The energy depletion at the nodes due to large volume of data transmission and data reception for large scale Wireless Sensor Networks (WSN) is one of the important areas of concern. Unbalanced energy consumption due to data traffic overhead at the nodes near the base station (BS) results in early node death and reduction in network lifetime. To balance the load at the wireless nodes during clustering, a novel hybrid clustering and routing protocol has been proposed based on mLEACH (modified Low-Energy Adaptive Clustering Hierarchy) and Cuckoo Search (CS) algorithm. Initially, mLEACH protocol is utilized to solve a novel equation designed to select the cluster heads by rotation at every round of data transmission based on the maximum permissible cluster heads in the network. In the second phase, CS algorithm is implemented for uniform distribution of the cluster heads in the network for efficient multi-hop data routing in the network based on several weighted factors. The performance of the proposed algorithm is compared with other algorithms in literature in terms of energy consumption and network lifetime for different positions of the base station and varied number of cluster heads in the model application area. The proposed mLEACH-CS algorithm shows a significant improvement by 60% considering 300 nodes in the WSN architecture while it decreased to 40% for denser network architecture. Optimal choice of the number and position of the cluster heads in certain network architecture has proved beneficial for designing an energy efficient wireless network with improved network lifetime.

Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Network

In Wireless Sensor Networks (WSNs), routing algorithms can provide energy efficiency. However, due to unbalanced energy consumption for all nodes, the network lifetime is still prone to degradation. Hence, energy efficient routing was developed in this article by selecting cluster heads (CH) with the help of adaptive whale optimization (AWOA) which was used to reduce time-consumption delays. The multi-objective function was developed for CH selection. The clusters were then created using the distance function. After establishing groupings, the supercluster head (SCH) was selected using the benefit of a fuzzy inference system (FIS) which was used to collect data for all CHs and send them to the base station (BS). Finally, for the data-transfer procedure, hop count routing was used. An Oppositional-based Whale optimization algorithm (OWOA) was developed for multi-constrained QoS routing with the help of AWOA. The performance of the proposed OWOA methodology was analyzed according to the following metrics: delay, delivery ratio, energy, NLT, and throughput and compared with conventional techniques such as particle swarm optimization, genetic algorithm, and Whale optimization algorithm.

Leveraging Energy, Latency, and Robustness for Routing Path Selection in Internet of Battlefield Things

Internet of Battlefield Things (IoBT) connects massive tactical devices to collect battlefield situations and share perceived information. The IoBT can enhance the intelligent battlefield command, collaborative attack, and other applications, such as landmine trigger and post-war clearance. Existing routing path selection methods designed for wireless sensor networks (WSNs) are effective but still face challenges in IoBT scenarios. First, tactical devices follow nonuniform distributions with high density on boundaries in IoBT to prevent the location of devices from being speculated and protect strategic positions, which results in unbalanced energy consumption. Second, increasing latency in IoBT is caused by various data generation probabilities of tactical devices. Third, the military task features, such as landmine explosion, disconnection, and failure of tactical devices, may put forward special requirements on network robustness. To this end, we propose a routing path selection method with joint optimization in IoBT based on nonuniform node distributions and location-related data generation probabilities. Specifically, we first investigate and formulate the distribution and data generation probability of tactical devices. Based on the special features, energy consumption, latency, and network robustness are analyzed during multihop communications in IoBT. Then, a joint optimization problem is formulated to minimize energy consumption and latency, while maximizing the network robustness simultaneously. Furthermore, two path assignment algorithms are developed to solve this optimization problem. Finally, our simulation results show that the proposed routing path selection method can reduce energy consumption and latency with the guaranteed robustness of IoBT.

A Data Collection Method for Mobile Wireless Sensor Networks Based on Improved Dragonfly Algorithm

For the sensing layer of the Internet of Things, the mobile wireless sensor network has problems such as limited energy of the sensor nodes, unbalanced energy consumption, unreliability, and long transmission delay in the data collection process. It is proved by mathematical derivation and theory that this is a typical multiobjective optimization problem. In this paper, the optimization goal is to minimize the energy consumption and improve the reliability under time-delay constraints and propose a path optimization mechanism to optimize the mobile Sink of mobile wireless sensor networks based on the improved dragonfly optimization algorithm. The algorithm takes full advantage of the abundant storage space, sufficient energy, and strong computing power of the mobile Sink to ensure network connectivity and improve network communication efficiency. Through simulation comparison and analysis, compared with random movement method, artificial bee colony algorithm, and basic dragonfly optimization algorithm, the energy consumption of the network is reduced, the lifespan of the network is increased, and the connectivity and transmission delay of the network are improved. The proposed algorithm balances the energy consumption of the sensors nodes to meet the network service quality and improve the reliability of the network.

Splitting–merging-based automatic scheduling scheme for balancing energy consumption and task allocation in WSANs

In wireless sensor and actuator networks (WSANs), sensing region partition, task allocation, and collector schedule greatly affect the performance of WSANs. Specifically, nonuniform data aggregation causes unbalanced energy consumption, thereby reducing the network lifetime. Additionally, unbalanced allocation of data collection tasks of collectors increases the gathering delay. Thus, it is critical to balance the energy consumption of sensor nodes, as well as task allocation and scheduling with the limited number of collectors. This study proposes a novel splitting–merging-based automatic scheduling scheme to balance energy consumption and task allocation for WSANs. First, a sensing region splitting–merging scheme is proposed to make the data load uniform, in which the sensing region is self-organized into many subregions taking into account the maximum number of sensor nodes and the maximum distance between sensor nodes in each subregion. Second, a task allocation strategy based on genetic algorithm is proposed to optimize the number of collectors and uniformly assign tasks to collectors. Finally, the trajectory of each collector is optimized by applying ant colony optimization algorithm. Numerical experiments show that the proposed method outperforms well in terms of prolonging the network lifetime and reducing the gathering delay when compared with several related methods.

Heating rate effect during sintering on the technological properties of Brazilian red ceramics

Unbalanced energy consumption in the production of ceramic artifacts is responsible for considerable undesirable impacts, such as increased emissions of polluting gases, excessive consumption of fuel materials, land degradation, and unpredictable financial costs. By contrast, the practice of optimizing the ceramic sintering, which in associated with firing of high temperature, can result in increased productivity and reduced production costs preserving an environmentally friendly production system. Moreover, it allows further improvements in the quality of the final product. This work compares the effect of different sintering cycles, with heating rates of 2, 15, and 30 °C/min, on the technological properties of a Brazilian industrial clay ceramic body. Initially the clay ceramic specimens were characterized in terms of mineralogical, chemical, and physical properties. Specimens were prepared by extrusion and fired at temperatures of 800, 900, and 1000 °C. The evaluated properties by standard tests were water absorption, post-firing linear shrinkage, and flexural strength. It was found that owing to sintering carried out at higher heating rates, red ceramic products with superior technological properties were obtained. This is an unprecedented conclusion for common clay ceramics produced in Brazil. Based on the promising obtained results, in a clear and detailed way, the benefits of rapid sintering cycle application for conventional brick production are evidenced.

Optimal Deployment of Heterogeneous Nodes to Enhance Network Invulnerability

Wireless sensor networks (WSN) can be used in many fields. In wireless sensor networks, sensor nodes transmit data in multi hop mode. The large number of hops required by data transmission will lead to unbalanced energy consumption and large data transmission delay of the whole network, which greatly affects the invulnerability of the network. Therefore, an optimal deployment of heterogeneous nodes (ODHN) algorithm is proposed to enhance the invulnerability of the wireless sensor networks. The algorithm combines the advantages of DEEC (design of distributed energy efficient clustering) clustering algorithm and BAS (beetle antenna search) optimization algorithm to find the globally optimal deployment locations of heterogeneous nodes. Then, establish a shortcut to communicate with sink nodes through heterogeneous nodes. Besides, considering the practical deployment operation, we set the threshold of the mobile location of heterogeneous nodes, which greatly simplifies the deployment difficulty. Simulation results show that compared with traditional routing protocols, the proposed algorithm can make the network load more evenly, and effectively improve energy-utilization and the fault tolerance of the whole network, which can greatly improve the invulnerability of the wireless sensor networks.

EEGW: AN ENERGY-EFFICIENT GREY WOLF ROUTING PROTOCOL FOR FANETS

Unmanned Aerial Vehicles (UAVs) or flying drones are employing to retrieve data from their respective sources and help to accomplish Flying Ad hoc Networks (FANETs). These wireless networks deal with challenges and difficulties such as power consumption, packet losses, and weak links between the nodes. This is due to the high mobility of nodes, frequent network partitioning, and uncertain flying movement of the flying drones. Consequently, reduce the reliability of data delivery. Moreover, unbalanced energy consumption results in an earlier failure of flying drone and accelerate the decrease of network life. The performance of FANETs depends on the capabilities of energy consumption of each flying drone. They are expected to live for a longer period to manage the cost overhead. Energy-efficient routing is an important factor that helps in improving the lifetime of FANETs. In this research, we propose an Energy-Efficient Grey Wolf (EEGW) routing protocol for FANETs. This protocol is comprised of Grey Wolf Optimizer (GWO) inspired by the leadership hierarchy of grey wolves. It helps in minimizing the energy consumption, packet loss ratio, and aerial transmission loss incurred during transmission.

Cache-aware mobile data collection schedule for IoT enabled multi-rate data generator wireless sensor network

The energy hole problem is a major concern in the wireless sensor network (WSN) given the uneven energy depletion of the power-constrained sensor nodes. Recently, several studies have incorporated a mobile data collector (MDC) to collect data from sensor nodes where the MDC reaches a set of data collection points (DCPs) to collect data from nearby nodes. This strategy has resolved the underlying cause of unbalanced energy consumption by avoiding the long chain of multi-hop routing. However, in most of the existing protocols, sensor nodes information is relayed to the Base Station (BS) to nominate MDC sojourn points and travel sequence. Such a centralized approach results in a substantial exchange of messages among sensor nodes causing high energy consumption. Moreover, these approaches perceived a uniform data generation rate at sensor nodes while ignoring the limited buffer cache capacity during the MDC path design. In this context, this paper proposes a distributed MDC sojourn point nomination method that significantly improves the message complexity and energy consumption of the WSN. The selected DCPs information is relayed to the BS where a modified Ant Colony Optimization algorithm is applied to construct the MDC traversal path. The performance of the proposed protocol is extensively analysed with some related existing schemes in terms of network parameters like energy efficiency, network lifetime, packet delivery ratio, and end to end delay. Moreover, the scalability of the proposed scheme is evaluated by varying the number of MDC and sensor nodes in the network.

Particle-Water Wave Optimization for Secure Routing in Wireless Sensor Network Using Cluster Head Selection

Wireless sensor network (WSN) has some great advantages, such as flexible communication, low power consumption, and low cost. In view of the WSN clustering algorithm, the dynamic cluster head selection methods are put forward in order to solve the problem of the unreasonable cluster head selection that may lead to the overlapping coverage and unbalanced energy consumption in the cluster communication. Also, security is an important aspect in the WSN. Various security and routing protocols are developed for increasing the efficiency of packet transmission, but discovering the optimal path without degrading transmission reliability poses a challenging task in the sensor network. Hence, an effective and optimal secure routing algorithm named Particle-Water Wave Optimization (P-WWO) is developed in this research for routing the data packets in secure path. The proposed P-WWO algorithm is designed by integrating the Particle Swam Optimization (PSO) with the Water Wave Optimization (WWO). The secure route needed to broadcast the data packets is determined through the selection of Cluster Head using the PSO-based cellular automata with fitness measure. However, the fitness measure is computed by considering the factors, like energy, delay, trust, consistency factor, and maintainability factor. Accordingly, the routing path with the minimal distance and less delay is accepted as the optimal path using the proposed P-WWO based on the fitness value. The route maintenance process enables the proposed optimization to decide whether the packets can be transmitted in the selected route or need to re-route the data. Moreover, the proposed P-WWO obtained better performance using the metrics, such as energy balancing index, coverage, number of alive-nodes, and average energy left with the values of 0.9246 99.9%, 144, and 0.666 J, respectively.