Energy Level Of Sensor Nodes Research Articles

Enhanced Fuzzy Logic Zone Stable Election Protocol for Cluster Head Election (E-FLZSEPFCH) and Multipath Routing in wireless sensor networks

A Wireless Sensor Network (WSN) is a system of sensor nodes that communicate with one another and with their surroundings. Wireless sensor nodes are now used as the foundation for the higher-level industrial internet of things applications. Any type of real-time Internet of Things (IoT) application, as long as the data is collected utilizing the device's sensors. WSN are severely limited in terms of energy. Routing is one of the most challenging components of WSNs. Unbalanced energy utilization during data packet routing operations is one of the most serious difficulties with WSNs. This issue must be considered because the energy levels of sensor nodes are limited. We used WSNs multipath routing to save energy, improve scalability, and balance load. Many investigations are being undertaken to increase sensor node energy efficiency and network lifetime. More attention must be devoted to the clustering and routing components of communication in order to conserve energy. Clustering, also known as node clustering is a network organizing approach which helps with energy efficiency, workload distribution, data collection, and a connected hierarchy. The network is made up of numerous sensor nodes that must connect with one another in order for data to reach its goal, and when messages are not sent to intermediary nodes, problems arise. With multipath routing, numerous paths between the source and destination can be created, improving the possibility of data reaching the target and providing additional copies via alternate paths. The proposed routing solution uses less energy and extends the life of the network while compared with well-known approaches i.e., LEACH, ECPF, CHEF, UCR, DFLC, ACAWT, and Gupta. According to the simulation results and overall analysis, the proposed technique is much better than all the previous approaches under different number of nodes and simulation time in terms of end-to-end time reduced by 17.09% to 68.13%, energy consumption reduced by 13.23% to 29.12%, packet loss rate declined by 16.33% to 82.38% and drop rate of alive sensor nodes are much better shows high network resilience.

A classy energy efficient spider monkey optimization based clustering and data aggregation models for wireless sensor network

SUMMARYEstablishment of energy efficient and reliable data routing in wireless sensor network (WSN) is one of the most critical and challenging task in the recent days. Also, the overall performance and lifetime of WSN is highly depends on the energy level of sensor nodes, hence it is most essential to save the energy of network. For this purpose, the different types of clustering and data aggregation mechanisms are developed in the conventional works, which are focusing on improving both the energy conservation and lifetime of network. Yet, it facing the challenges of increased computational complexity, inefficient routing of data, high controlling overhead, and reduced reliability. Thus, the proposed work objects to develop a novel energy efficient mechanism by integrating the functionalities of advanced clustering, path selection, and data aggregation methodologies. Here, the spider monkey optimization based energy efficient routing protocol is developed for optimally selecting the cluster head (CH) based on certain parameters of energy, distance, and weight value. In this framework, the data transmission is performed between the source to destination nodes through the relay nodes and CHs, which helps to minimize the energy consumption of network. Then, the Classy Bellman‐Ford algorithm is deployed for identifying the best paths having shortest distance with the sink nodes. Consequently, an anticipated data aggregation mechanism is utilized for ensuring the security and reliability of data transmission in WSN. For evaluation assessment, various performance metrics have been utilized to validate the results of proposed methodology, and also the obtained values are compared with some other recent state‐of‐the‐art models for proving the betterment of proposed mechanism.

Energy‐aware intelligent hybrid routing protocol for wireless sensor networks

AbstractDesigning a wireless sensor network (WSN) energy‐aware routing protocol is a thought‐provoking mission. Thus, this article presents an energy‐saving routing protocol for WSNs. The proposed protocol considers the energy level of sensor nodes and the distance to the base station to optimally determine the best route. It also takes advantage of the inherent complementarity of clustering techniques. The proposed routing scheme also exploits data aggregation to improve energy utilization and reduce communication costs. To choose the flawless route between a source node and the base station, ant colony optimization and A* algorithms are integrated. Finally, the performance of the proposed routing technique is compared with those of other existing up‐to‐date protocols. The results show that the proposed routing protocol outperforms them in terms of total energy consumption, network lifetime, stability period, and goodput.

Energy-balanced data collection with path-constrained mobile sink in wireless sensor networks

Data collection through the mobile sink in wireless sensor networks (WSNs) is an effective solution to the hot-spot or sink-hole problem caused by multi-hop routings using the static sink. However, most of the existing research focuses on energy balance of the global network, but ignores the impact of local energy imbalance on the network lifetime. Therefore, this paper proposes an energy-efficient data collection algorithm to extend the network lifetime by balancing inter-cluster and inner-cluster energy (BIIE). In the proposed BIIE, we design an improved hierarchical clustering algorithm to reduce communication costs. To balance the energy between clusters, we design an efficient mechanism to select the optimal rendezvous node (RN) for each cluster and construct the traveling path of the mobile sink to access all RNs by particle swarm optimization (PSO). To balance the energy of the inner cluster, a local re-clustering mechanism is designed according to the residual energy level of sensor nodes. We also conducted simulation tests which confirm that the proposed BIIE can increase the network lifetime by approximately 46% and shorten the path length of the mobile sink by approximately 7% in comparison with other commonly-used algorithms (i.e., WRP and EAPC).

Multipath routing through the firefly algorithm and fuzzy logic in wireless sensor networks

Routing is one of the major challenges in wireless sensor networks (WSNs). Unbalanced energy consumption in the routing process of data packets is one of the main issues in WSNs. The issue needs consideration, because the energy level of sensor nodes is limited. Multipath routing methods reduce energy consumption, improve scalability and provide load balancing in WSNs. In this study, we suggested a multipath routing method for homogeneous WSNs. The proposed method includes 3 phases: clustering the network nodes, discovering the paths between CHs, and maintaining the paths. In the first phase, wireless sensor network is clustered through the firefly algorithm. In the second phase, routing is performed between CHs based on the fuzzy logic. Routing between CHs results in creating 2 paths: primary path and backup path. CHs transmit data packets to the base station through the primary paths; however, failures in primary paths cause CHs to employ backup paths. In the third phase, the paths are maintained so that path breakages cause to restart route discovery. The results of the simulation reveal that the proposed multipath routing outperforms other routing methods in end-to-end delay, energy consumption, packet loss rate, and network lifetime.

Monitoring for Rare Events in a Wireless Powered Communication mmWave Sensor Network.

The use of a wireless sensor network to monitor an area of interest for possible hazardous events has become a common practice. The difficulty of replacing or recharging sensor batteries dictates the use of energy harvesting as a means to extend the network’s lifetime. To this end, energy beamforming is used in a millimeter wave wireless power sensor network with randomly deployed nodes. A simple protocol is proposed that allows nodes to report their charging conditions in an effort to select efficient energy-beamforming strategies. Analytical expressions for the probability of successful information reception and successful reporting are provided for two benchmark schemes: the random and the circular energy-beamforming scheme. A Markov chain is used for the former to model the energy level of sensor nodes. Simple sector selection strategies are presented and their performance, in terms of delay and failure information delivery, is assessed through simulations.

Energy Aware Cluster Based Multi-hop Energy Efficient Routing Protocol using Multiple Mobile Nodes (MEACBM) in Wireless Sensor Networks

Routing in Wireless Sensor Networks (WSNs) is the most significant and the challenging issue for the researchers in terms of enhancing its performance in terms of network lifetime, energy efficiency, scalability, connectivity, throughput, etc. They have the incredible capability to interact and gather data from any physical environment with help of routing protocols. Many routing protocols based solutions have been proposed in the recent years for accomplishing the preferred level of performance in WSNs for these issues. The hierarchical heterogeneous cluster based energy efficient routing protocols are more efficient as compared to flat and location based routing protocols due to the presence of nodes heterogeneity in terms of energy level of sensor nodes which enhances the lifetime of the network. The most recent trend that extensively enhances the functionality and the performance of WSNs is the use of mobile sensor nodes. In this paper, the authors proposed a novel concept regarding mobile sensor nodes is proposed called Mobile Energy Aware Cluster Based Multi-hop (MEACBM) routing protocol for hierarchical heterogeneous WSNs which selects CHs on the basis of newly proposed probability equation which selects only that sensor node as Cluster Head (CH) which has the highest energy among other sensor nodes by introducing a new term S(i).E in the equation. It considers hierarchical heterogeneous clustering considering three levels of sensor nodes; multi-hoping for inter-cluster communication and connectivity of sensor nodes within the whole network area. In MEACBM, after the deployment of sensor nodes and formation of clusters, the whole network area is divided into sectors and inside each sector a mobile sensor node is placed which act as Mobile Data Collector (MDC) for collecting data from CHs. This technique helps in significantly reducing the energy consumption of sensor nodes for transferring information to the Base Station (BS). Simulation performance based results indicates the effectiveness of MEACBM routing protocols by comparing it with other contemporary cluster based routing protocols in terms of network lifetime, stability, throughput, number of CHs and number of dead nodes.

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

Nowadays, network lifetime is an important factor in the wireless sensor network, and it produces due to a large amount of data communication between the nodes and less battery storage. To avoid this issue, a new paradigm called trust-based self-organized hierarchical energy balance routing protocol is proposed and it helps to solve the most important issues in wireless sensor networks such as security and storage. Here, trust denotes the energy level maintaining in each sensor node and transaction between its neighbor nodes and cluster heads. This trust-based self-organized hierarchical energy balance routing protocol helps to enhance the network lifetime to maintain the energy level of sensor node as well as drastically increase the packet delivery rate by increasing the number of nodes in the wireless networks.

Double warning thresholds for preemptive charging scheduling in Wireless Rechargeable Sensor Networks

Wireless power transfer technique provides new alternatives for solving the limited power capacity problem for ubiquitous mobile wireless devices, and makes wireless rechargeable sensor networks (WRSNs) promising. However, mainly due to the underestimate of unbalanced influences of spatial and temporal constraints posed by charging requests, traditional scheduling strategies achieve rather low charging request throughput and success rate, posing as a major bottleneck for further improvement. In this paper, we propose a Double Warning thresholds with Double Preemption (DWDP) charging scheme, in which double warning thresholds are used when residual energy levels of sensor nodes fall below certain thresholds. By introducing specific comparison rules, warning thresholds can be used to adjust charging priorities of different sensors, warn the upcoming recharge deadlines, as well as support preemptive scheduling. Then DWDP is extended to where multiple Wireless Charging Vehicles (WCVs) are employed, and a Collaborative Charging DWDP, namely CCDWDP is proposed. Finally, we conduct extensive simulations to manifest the advantages of DWDP as well as CCDWDP. Simulation results reveal that DWDP can achieve better performance in guaranteeing the successful scheduling of the high-priority task and improving stability of the system. CCDWDP outperforms in terms of high charging throughput and short charging delay.

Energy and Spectral Efficient Cognitive Radio Sensor Networks for Internet of Things

Energy and spectral efficient solutions are indispensable to the success of Internet of Things (IoT). The design and development of energy and spectral efficient solutions for IoT are very challenging mainly because of the large-scale deployment of a massive number of sensors and devices. Energy harvesting and cognitive radios (CRs) are considered as promising technologies for energy and spectral efficiency, respectively. In this paper, we propose an energy and spectrum efficient scheme for CR sensor networks (CRSNs). We present an architecture of CRSNs for IoT, in which sensor nodes can access the spectrum opportunistically and harvest energy from ambient radio-frequency sources. We then propose an energy management scheme that consists of: 1) energy-aware mode switching strategy which allows sensor nodes to perform dedicated energy harvesting based on their current energy level and 2) cluster head selection algorithm which considers current and average of past energy levels of sensor nodes to achieve a balance between network performance and lifetime. Furthermore, for reliable intracluster reporting, we propose a channel management strategy to assign the best quality channel to the sensor nodes in terms of stability and reliability. Extensive simulation results demonstrate the effectiveness of the proposed energy and spectrum efficient scheme and show superiority over existing schemes.

A Software-Defined Surveillance System With Energy Harvesting: Design and Performance Optimization

Even though energy harvesting is a promising technology for energy-efficient surveillance systems, energy harvesting levels are highly dynamic depending on the time and location. Thus, the deployment of nonenergy-harvesting sensor nodes (NHSs) and sophisticated sleep scheduling of sensor nodes are necessary for performance guaranteed surveillance systems. In this paper, we present a software-defined surveillance system (SDSS) in which a centralized controller determines the sleep schedules of energy harvesting and NHSs on the basis of the collected information such as the spatial distribution of targets and the energy levels of sensor nodes. To derive the optimal sleep schedules minimizing the number of active sensor nodes while providing sufficient surveillance performance, a constraint Markov decision process problem is formulated and the optimal policy on sleep scheduling is obtained by linear programming. The evaluation results demonstrate that the SDSS with the optimal policy can reduce energy consumption by employing fewer active sensor nodes while providing the required level of target monitoring probability.

DAHDA: Dynamic Adaptive Hierarchical Data Aggregation for Clustered Wireless Sensor Networks

A Wireless Sensor Network (WSN) has gained a tremendous attention of researchers with its dynamic applications. The constant monitoring of critical scenarios has make WSNs an attractive choice for researchers at a large scale. The main objective is to increase the network lifetime for optimal and efficient utilization of resources in WSNs. For optimum functionality, various approaches have been proposed based upon clustering. Network lifetime is related with energy level of sensor nodes deployed in region of interest. As sensor nodes have limited lifetime, so there is a need to develop an algorithm for aggregating the sensors data in WSNs. A novel Dynamic Adaptive Hierarchical Data Aggregation (DAHDA) algorithm has been presented for evolving, uniform and non-uniform networks while maintaining the data accuracy. In addition, the algorithm is able to handle sudden bursts in the underlying data by recording the data in the area of interest for the whole event duration. The experimental evaluation on real and synthetic data shows that the algorithm performs well in terms of extending the lifetime of the network, maintaining the original distribution of the sensors as long as possible and maintaining the accuracy of the sensed data. DAHDA is an adaptive hierarchical aggregation algorithm for WSNs. The proposed algorithm has been simulated and its performance has been compared with the existing approaches in terms of residual energy, number of alive nodes, data accuracy, sudden burst detection, sensor distribution, lifetime of last node, first node and average lifetime of node for uniform, non-uniform and evolving networks.

Energy Efficient Wireless Sensor Networks based on Clustering Techniques

In WSNs, Energy is a scarcest resource of sensor nodes and it determines the lifetime of sensor nodes. These are battery powered sensor nodes. These small batteries have limited power and also may not easily rechargeable or removable. Long communication distance between sensors and a sink can greatly drain the energy of sensors and reduce the lifetime of a network. In WSNs, energy is a big factor to be considered. Various techniques are used to optimize energy level of sensor nodes of WSN. We will make a review of some of these wireless sensor network techniques which are used recently and are helpful in improving energy efficiency of wireless sensor nodes.

Effects of Sensor Nodes Mobility on Routing Energy Consumption Level and Performance of Wireless Sensor Networks

Due to wireless sensor networks (WSN) characteristics, routing in these networks is a challenging task particularly when the wireless nodes, which are energy constrained, are moving. Mobility in WSNs can cause frequent route changes or breaks. In case of route breaks, each routing protocol reacts differently which in turn cause a different pattern in the energy consumption. Therefore, it is important to consider the impact of mobility on energy level of sensor nodes to select a proper routing scheme for a particular WSN. In this paper, an attempt has been made to measure energy consumption level of the sensor nodes while moving through the WSN environment on different locations. The main contribution of this paper is to compare and analyze performance of the mobile nodes using two well-know routing protocols, DSR and AODV, in terms of three performance metrics as energy efficiency, packet delay, and packet lost rate. The comparison has been done using NS2 network simulator.

A Comprehensive Review and Performance Analysis of Data Gathering Algorithms for Wireless Sensor Networks

Wireless sensor networks comprise of vast numbers of sensor nodes deployed to monitor a particular event (fire, intrusion, etc.) or measure a parameter (like temperature, pressure) value representative of the physical condition of the ambient environment. There is a growing need of using energy-efficient data gathering algorithms that can effectively aggregate the monitored/measured data from the individual sensor nodes through a properly constructed communication topology and transmit a single representative data to a control center (sink) that is typically located far away from the network field. In order to maximize node lifetime and be fair to all nodes in the network, such a communication topology has to be dynamically constructed for every round of data gathering by taking into consideration available energy levels of sensor nodes. This paper presents a comprehensive description of two broad categories of data gathering algorithms for wireless sensor networks – the classical algorithms that are not energy-aware and modern energy-aware data gathering algorithms. These algorithms can also be classified based on the communication topology they choose to construct and use for data gathering. The authors also present an extensive simulation study that demonstrates the individual as well as the comparative performance of these data gathering algorithms.

Error Analysis and Kernel Density Approach of Scheduling Sleeping Nodes in Cluster-Based Wireless Sensor Networks

Energy consumption is an important research topic in wireless sensor networks. Putting sensor nodes to sleep is one of the most popular ways to save energy in battery-powered sensor nodes. Many existing research studies on sleeping techniques are based on preknowledge of deployment of sensor nodes, e.g., a known probability distribution of sensor nodes in a target-sensing field. Thus, whether a scheduling-sleeping scheme has good performance mostly depends on preknowledge of the deployment of sensor nodes. In this paper, we first show the discrepancy of system performance metrics, including energy consumption and network lifetime, based on inaccurate preknowledge of the deployment of sensor nodes in a cluster-based sensor network. Through analytical studies, we conclude that the discrepancy is very large and cannot be neglected. We hence propose a distribution-free approach to study energy consumption. In our approach, no assumption of the probability distribution of deployment of sensor nodes is needed. The proposed approach has yielded a good estimation of network energy consumption. Furthermore, previous studies normally assume that battery energy levels of sensor nodes are the same. However, in a real network, battery quality is different, and the energy in each sensor node is a random variable. We provide a mathematical approximation and a standard deviation study for energy consumption, as well as a more in-depth study for network lifetime under random batter energy.