Energy Efficiency In Wireless Sensor Networks Research Articles

Increasing life-time of wireless sensor network using energy-efficient and fault tolerance algorithms

Energy-efficiency ofwireless sensor networks (WSN) becomes an essential issue in the research area. This is because of the energy constraints in WSN that depend on a battery, which is difficult to replace or recharge; therefore, multiple clustering algorithms were proposed to achieve efficiency in using the available energy as much as possible. This paper proposed energy-efficient and fault-tolerance algorithms that enhance thelow energy adaptive clustering hierarchy (LEACH) protocol by three algorithms. The first focuses on selecting the best cluster head and the second focuses on minimizing the required nodes within the same cluster. Simultaneously, the third fault tolerance algorithm uses software engineering techniques like sleep schedules to increase network lifetime as much as possible. The testing results of the proposed algorithms prove the claim of enhancing the lifetime of WSN. In order to check improvement of lifetime of WSN we have compered the results of the proposed algorithms with standered algorthim. The results show prove the claim of enhancing the life-time of WSN, where the total lifetime of WSN rise from about 550 rounds to reach 4100 when utilized self-checking process and rised up to 5200 after enhance minimum distans.

Enhanced Double Cluster Head Selection using Ant-Colony Optimization for Energy-Efficient Routing in Wireless Sensor Network

The energy-efficient routing strategies regulate the network lifetime for wireless sensor networks (WSNs). The researchers have continuously improved the WSN lifetime using their algorithms and strategies since the sensor world. Clustering significantly improves the network lifetime and energy efficiency in WSN. In this paper, the concept of double cluster heads (CHs) is introduced in a single cluster of the WSN and Ant Colony Optimization as the nature-inspired algorithm is applied for a CH selection between double CHs. The Ant Colony Optimization is based on the natural behavior of the ant. The simulation result presents that the proposed algorithm enhances the network lifetime and residual energy because a double cluster head significantly reduces the workload of a single cluster head. The other nature-inspired algorithms can be applied for the energy efficiency of WSN in the future.

Weights-Based Energy-Efficient Wireless Sensor Network Protocol with Firefly Synchronization for Illegal Logging

Weights-Based Energy-Efficient Wireless Sensor Network Protocol with Firefly Synchronization for Illegal Logging

LDCA: Lightweight Dynamic Clustering Algorithm for IoT-Connected Wide-Area WSN and Mobile Data Sink Using LoRa

Wide-area monitoring applications of the Internet-of-Things (IoT) connected wireless sensor network (WSN) consists of sensor nodes (SNs) with limited hardware and energy sources. The distributed nature of such a network and the difficulty of remote access make it more demanding to design an energy-efficient WSN. Moreover, long-range and low-power wireless connectivity is a challenge in IoT-connected applications. Present WSN topologies deal mainly with fixed SNs, SN distribution, and fixed data sink (DS). The majority of the control layer is implemented in the lower hierarchical layer or in a virtual middle layer, which reduces the network lifetime due to excessive processing and data transmission activities. This article proposes a real-time lightweight dynamic clustering algorithm (LDCA) for a WSN that supports the following two scenarios with limited processing resources: 1) with mobile DSs and static SNs (such as DS and SNs mounted on unmanned aerial vehicles, autonomous vehicles) and 2) with mobile SNs and static DSs (such as livestock monitoring or autonomous robots in smart farming, and urban monitoring). The proposed algorithm is based on the received signal strength indicator and signal-to-noise ratio of a long-range (LoRa) interface and its residual energy. Mathematical models were derived for real-time clustering using LoRa. Memory requirement and clustering efficiency of the constrained SN and DS for various mobility scenario were evaluated. The proposed LDCA reduces the energy requirement to 33% compared to static clustering, by reducing the number of concurrent clusters and hops. In addition, a hardware-based approach was used to validate the LDCA algorithm, and evaluate its performance in terms of energy efficiency, packet delivery rate, and network lifetime.

Hybrid fuzzy logic and artificial Flora optimization algorithm-based two tier cluster head selection for improving energy efficiency in WSNs

Wireless Sensor Network (WSN) is the one of the hot area of research in which energy stability and network lifetime are considered to be the twin challenges during its application. Clustering is the optimum energy efficiency strategy that organizes the sensor nodes into potential groups for the objective of attaining energy stability and network lifetime. In this energy potent clustering process, cluster head selection is determined to be highly significant in order to balance energy among the nodes sensor nodes. Moreover, two-tier cluster head selection that includes temporary and final cluster head is identified to be challenging in WSNs. In this paper, Hybrid Fuzzy Logic and Artificial Flora Optimization Algorithm (FL-AFA)-based Two Tier Cluster Head Selection is proposed for improving energy efficiency and prolog network lifetime. This FL-AFA scheme achieved the cluster head selection in two stages, such as, i) Temporary Cluster Head (TCH) selection using FL and, ii) Final Cluster Head (FCH) selection using AFA. In the first stage, the concept of fuzzy logic applied over the input parameters of residual energy (RE), distance to BS (DTBS), and node degree (NDE). In the second stage, the benefits of AFA is employed for computing the fitness function through distance to nearby nodes (DNN), cluster compactness estimation factor (CCEF), and position estimation (PE). Simulation experiments of the proposed FL-AFA scheme and the benchmarked schemes are conducted based on the evaluation metrics of energy efficiency, network lifetime, average delay, and packet delivery ratio (PDR) under the impact of different sensor nodes. .

Energy efficient fuzzy clustering and routing using BAT algorithm

Clustering of sensor nodes is one of the prominent methods applied to Wireless Sensor Networks (WSN). In the cluster-based WSN scenario, the sensor nodes are assembled to generate clusters. The sensor nodes are composed of limited battery power. Therefore, energy efficiency in WSN is crucial. A load of sensor node and its distance from base station (BS) are the significant factors of energy consumption. Therefore, load balancing according to the transmission distance is necessary for WSN. In this paper, we propose a load-balanced clustering algorithm using Fuzzy C means (FCM) algorithm and an energy-efficient routing approach using BAT-algorithm (FC-RBAT). The cluster heads (CHs) are selected according to the score of the sensor node from each cluster. After selection of the CHs, the BAT-inspired routing algorithm is applied on the CHs. The best routing path from each CH to the BS is obtained from the proposed approach. The simulations are conducted on evaluation factors such as energy consumption, active sensor nodes per round, the sustainability of the network and the standard deviation of a load of the sensor node. It is observed that FC-RBAT outperforms compared algorithms, namely EAUCF, DUCF and SGA, under the evaluation factors.

Routing Protocols for Energy Efficiency in WSNs: A review

Wireless sensor networks (WSNs) is a significant part of networking area. They are cheap, efficient, and small, they are become more and more important for several applications. However, with the widespread use of applications that rely on wireless sensor networks, they continue to suffer from limitations such as data routing and security. WSNs can be used for several applications. Nodes of Sensor work on batteries. It is impossible to charge or replace the battery in some applications. For that purpose, optimal use of node’s energy is a key issue in the network. Routing is a significant challenge to extend the lifespan of network. Several protocols are implemented in WSNs. In this study, a survey on clustering techniques, present a characteristic and requirements routing design of WSNs. Result of this study shows how to solve some of these problems. WSN routing protocols have to ensure shortest paths, minimum energy consumption, less delay and maximum lifespan of network.

An energy-efficient wireless sensor network construction algorithm for air quality condition detection system

In India, the Air Quality Index (AQI) is calculated according to the air pollutants' values of PM2.5, PM10, NO2, NH3 SO2, CO, and O3. Firstly, to collect these values from an area, this paper proposes an energy-efficient Wireless Sensor Network layout, constructed by the proposed Almost Equally Dividing Region Clustering algorithm. These values are to be stored at the Internet Website through that network. Secondly, to detect exact air quality conditions, this paper simultaneously proposes the Air Quality Condition Detection System that takes these pollutants’ values as the inputs and after the classification calculations displays the air quality condition, AQI range, health impact information for that range. The system classifier model, the Random Forest model provides 93% accuracy, 90% precision, 90% recall, and 89% F1 score. With the same inputs, the system produces the exact result as given in the website ‘National Air Quality Index’ available at ‘https://app.cpcbccr.com/AQI_India/’.

A Hyper-Heuristic Heterogeneous Multisensor Node Scheme for Energy Efficiency in Larger Wireless Sensor Networks Using DEEC-Gaussian Algorithm

A wireless sensor network (WSN) is an intellect-sustainable network that comprises multiple spatially distributed sensor nodes and several sink nodes that collect data from sensors. WSNs remain an active research area in the literature due to challenging factors such as the selection of sensor location according to a given premise, finding optimal routing algorithm, and ensuring energy efficiency and consumption. Minimizing energy and prolonging the network lifetime in the WSNs are the focus of this research work. In the literature, a clustering approach is used in grouping sensor nodes into clusters and is seen as an effective technique used in optimizing energy consumption in WSNs. Hence, in this paper, we put forward a novel clustering-based approach by amalgamating the Gaussian elimination method with the Distributed Energy-Efficient Clustering to produce DEEC_Gaussian (DEEC_Gaus) to stabilize energy efficiency optimization in WSNs. We took the advantages of DEEC and Gaussian elimination algorithms to resolve energy efficiency problems in WSNs. DEEC presents attributes such as increased heterogeneity performance level, clustering stability in operation, and energy efficiency which helps to prolong network lifetime while the Gaussian elimination algorithm added an additional advantage to improve and optimize energy efficiency, to aggregate packets of operations performed in the network lifestyle of energy efficiency in WSNs. The simulations were carried out using MATLAB software with 1000 to 1500 nodes. The performance of the proposed work was compared with state-of-the-art algorithms such as DEEC, DDEEC, and EDEEC_E. The simulated results presented show that the proposed DEEC-Gauss outperformed the three other conventional algorithms in terms of network lifetime, first node dead, tenth node dead, alive nodes, and the overall timing of the packets received at the base station. The results showed that the proposed hyper-heuristic heterogeneous multisensor DEEC-Gauss algorithm presented an average percentage of 3.0% improvement for the tenth node dead (TND) and further improvement of 4.8% for the first node dead (FND). When the performance was compared to the state-of-the-art algorithms in larger networks, the overall delivery was greatly improved and optimized.

Energy Optimization in Multi-Hop Wireless Sensor Networks based on Proposed Harmony Search Routing Algorithm

Spread application of wireless sensor networks (WSNs), has led many researchers aiming at energy optimization in these networks. The Proposed Energy Efficient Harmony Search Routing Algorithm (PEEHSRA) in accordance with a novel energy model is stated in this study to minimize the amount of energy consumption in WSNs. We first proposed an energy model on the basis of modulation scheme parameters for multi-hop transmission. Then, in order to develop a harmony search routing algorithm, pitch adjustment rate in the harmony memory was introduced based on both, the proposed energy model, and the node’s distance. Also, an energy-efficient fitness function was determined, which results in the most energy-efficient route for the network. At the end, four modulations including MQAM, MQPSK, OQPSK and MFSK were applied and the most energy-efficient one with corresponding parameters was specified. By comparing the results of the proposed method to the state-of-the-art studies, the improvements in extending the lifetime of the network was proved. These findings seem quite useful in designing an energy-efficient multi-hop WSN and pave the way for enhancement of the widespread applications in these networks.

Energy-efficient event pattern recognition in wireless sensor networks using multilayer spiking neural networks

Motivated by the energy-efficient computation of the brain, an energy-efficient wireless sensor network infrastructure is built for solving the pattern recognition task. In this work, spatiotemporal patterns originate from the wireless sensor nodes that use pulse-based networking schemes like pulse position-coded packet data unit and spiked time-division multiple access. Pulse-based networking schemes use spatiotemporal patterns to encode information rather than using traditional energy-expensive packets. These spatiotemporal patterns are learned through biologically plausible learning rule of Tempotron which is a neuronal binary classifier that uses a gradient-descent supervised learning rule to adjust the weights of synaptic inputs. A single Tempotron-trained spiking neuron is shown to read out information encoded in the spike timings of synaptic inputs. However, due to the simplicity of the single Tempotron classifier, its performance is inherently limited. In this work, we devise the multilayer spiking neuron training rules for event pattern classification in distributed wireless sensor networks. We also show that the proposed architecture improves classification accuracy by a considerable amount as compared to a single Tempotron model’s performance.

Linear Regression based Power Optimization of Wireless Sensor Network in Smart City

The modern smart cities are highly dependent on the performance of the Internet of Things (IoT) based energy-efficient sensor networks. Energy efficiency is a critical and indispensable issue for wireless sensor networks (WSNs). In the collection of sensor nodes, one node is selected to collect data and forward it to the base station. The modern base stations in smart cities are unmanned aerial vehicle (UAV) based. This paper presents a linear regression based model, where the initial residual energy and its corresponding transmission power are submitted to the proposed system, then it generates a prediction model since transmission power depends on the residual energy of the sensor node. Based on this model the transmission power of the sensor node can be calculated for data transmission as a higher residual node is best suitable for data transmission to the base station. As presented in the simulation result, the regression based model gives better performance for energy efficiency in WSN.

WITHDRAWN: Performance analysis of energy efficient infrastructure-less wireless sensor networks optimize by Bio-inspired optimization

Challenge of ADHOC WSN is to maintain the stability of the network. The stability of network is directly related to energy and quality of service parameters (QoS) like time delay, throughput, etc. In case if the network is not stable then these parameters degrade and reduce the performance of ADHOC WSNs. Network stability depends upon energy as discussed above, and here we have discussed its effect on the performance of the system. In other words, ADHOC WSNs are networks comprising of tiny sensors nodes (wireless in nature) capable of transmitting data within the network via the sink. As the sensing elements are tiny devices hence the power source consists of limited energy which limits the lifetime of the network. Many techniques have been proposed and developed (as discussed in the literature review) by the researchers to efficiently utilize the limited energy available with the sensor nodes to increase the lifetime of the network. Still, the area of optimizing energy efficiency required further exploration. So, this thesis addresses the problem and tries to solve the issue by using the proposed approaches Optimization, improving residual energy and increasing throughput packet wise. In average there is 4–7% improvement in energy and 5–7% increased throughput.

Enhanced Particle Swarm Optimization for Effective Relay Nodes Deployment in Wireless Sensor Networks

One of the critical design problems in Wireless Sensor Networks (WSNs) is the Relay Node Placement (RNP) problem. Inefficient deployment of RNs would have adverse effects on the overall performance and energy efficiency of WSNs. The RNP problem is a typical example of an NP-hard optimization problem which can be addressed using metaheuristics with multi-objective formulation. In this paper, we aimed to provide an efficient optimization approach considering the unconstrained deployment of energy-harvesting RNs into a pre-established stationary WSN. The optimization was carried out for three different objectives: energy consumption, network coverage, and deployment cost. This was approached using a novel optimization approach based on the integration of the Particle Swarm Optimization (PSO) algorithm and a greedy technique. In the optimization process, the greedy algorithm is an essential component to provide effective guidance during PSO convergence. It supports the PSO algorithm with the required information to efficiently alleviate the complexity of the PSO search space and locate RNs in the spots of critical significance. The evaluation of the proposed greedy-based PSO algorithm was carried out with different WSN scenarios of varying complexity levels. A comparison was established with two PSO variants: the classical PSO and a PSO hybridized with the pattern search optimizer. The experimental results demonstrated the significance of the greedy algorithm in enhancing the optimization process for all the considered PSO variants. The results also showed how the solution quality and time efficiency were considerably improved by the proposed optimization approach. Such improvements were achieved using a simple integration technique without adding to the complexity of the system and introducing additional optimization stages. This was more evident in the RNP scenarios of considerably large search spaces, even with highly complex and challenging setups.

Data Redundancy Reduction for Energy-Efficiency in Wireless Sensor Networks: A Comprehensive Review

Data Redundancy Reduction for Energy-Efficiency in Wireless Sensor Networks: A Comprehensive Review

IoT Based Energy-Efficient Data Aggregation Wireless Sensor Network in Agriculture: A Review

Sensor nodes generate Wireless Sensor Networks (WSNs), these networks have considerable application in the areas of habitat safety, disaster management, surveillance in defense, security & many more areas. WSNs are compact in size, with short battery power & additionally their processing capabilities are low. This restriction of battery power makes them vulnerably faulty. In order to save this limited power, redundant data must be stored inside the sensor node during aggregation which will result in a reduction power dissipation associated with the sending of unnecessary data. By aggregating data, we can control energy consumption by reducing redundancy. Data aggregation is a really effective technique for WSN. In this paper we discuss the aggregation of data and their complex energy-efficient approach used for data aggregation in WSN. This paper highlights the latest innovations in WSNs vital for the research in agricultural domain, further we present their classification & did a comparative analysis of the discussed protocols, the nomenclature of energy saving & harvesting strategies used in agricultural monitoring. Further it discuss the difficulties and drawbacks of WSNs in context of agriculture, The presented comparative study will helpful in increasing number of data processing opportunities available through the Internet of Things (IoT).

Energy-Efficient Cluster-Based Wireless Sensor Networks Using Adaptive Modulation: Performance Analysis

Wireless Sensor Networks (WSNs) play a vital role in modern technology since they have recently emerged into enormous essential applications of the Internet of Things (IoT). However, WSNs encounter a shortage in the lifetime due to limitations in the power supply. Accordingly, many solutions are reported in literature to deal with energy saving problem in the WSNs. In this paper, a novel method is presented to minimize energy consumption using adaptive modulation that is jointly integrated with clustering technique . This method is considered as a promising solution for dense and sparse cluster-based WSNs to improve energy efficiency. In the suggested solution, the adaptive modulation is implemented in the communication link between cluster members (CMs) and cluster head (CH). Besides, distance-based adaptive modulation step function is proposed in which the optimum modulation order is selected to achieve the minimum energy consumption between CMs and CH. The proposed method is evaluated extensively in order to investigate the impact of adaptive modulation in cluster-based WSNs using M-ary Quadrature Amplitude Modulation (MQAM) system. The performance evaluation addresses both energy consumption and throughput by using two metrics: cluster density, and cluster size. Regarding simulation results, by varying cluster density and cluster size, the adaptive modulation shows significant saving in energy consumption where it constitutes a lower bound for energy consumption. Also, it shows a great impact on throughput where it constitutes an upper bound for the throughput. Moreover, the adaptive modulation shows a considerable leverage on energy saving for small number of clusters, conversely, the energy saving decreases as the number of clusters increase. Finally, it is concluded that these findings can provide a remarkable guidance for designing an energy efficient WSNs.

Cooperative Game Theory Approach for Energy-Efficient Node Clustering in Wireless Sensor Network

Energy consumption is one of the critical challenges in designing wireless sensor network (WSN) since it is typically composed of resource-constrained devices. Many studies have been proposed clustering to deal with energy conservation in WSN. Due to its predominance in coordinating the behaviors of many players, game theory has been considered for improving energy efficiency in WSN. In this paper, we evaluate the performance of cooperative game theoretic clustering (CGC) algorithm which employs cooperative game theory in a form of 3-agent cost sharing game for energy-efficient clustering in WSN. Furthermore, we compared its performance to a well-known traditional clustering method, low-energy adaptive clustering hierarchy (LEACH), in terms of network lifetime and stability, and total residual energy. The simulation results show that CGC has better performance compared to LEACH due to the cooperation among cluster heads in coalition. CGC has higher alive nodes with stability improvement of first node dies (FND) by 65%, and the improvement by 52.4% for half node dies (HND). However, with the increasing of the number of nodes, the performance of LEACH is getting better compared to CGC.

Energy Efficient and High PDR Wireless Sensor Networks using DCHS and FIS

Wireless Sensor Network (WSN) is forthcoming technology that afford assorted applications in hearth care, military, industrial and object tracking. WSN is a wireless device consisting of dispersed autonomous apparatus using sensors to collectively adviser physical or environmental conditions. This paper is presenting WSN using dynamic cluster head selection (DCHS) and fuzzy inference system (FIS) technique. The DCHS methods for WSN are put leading in procedure to solve the problem of the arbitrary cluster head selection that may lead to the extending description and irregular energy consumption in the cluster communication. FIS is a scheme that uses fuzzy inclination theory to atlas inputs to outputs. FIS is depend on mamdani rules and Fuzzy rules are a compilation of linguistic statements that characterize how the FIS should make a judgement regarding classifying an input or regulating an output. The proposed technique is implemented MATLAB 2012R and calculated average energy consumption and success rate packet delivery ratio.

Energy Efficient Wireless Sensor Network to Enhance Network Security by Detecting Clone Nodes

For replica identification and clone detection a centralized system is proposed in this research, where the network has been separated into inspection node and segments. A clone node can be identified by checking the inspection nodes ID along with the Network security (Cryptographic) key. Detection of clone node done using Chord algorithm, where each and every node in the network have assigned with a unique keygenerated at random. Before the data transmission, it has been given with its key and the assigned keys have been verified for the cloned keys already assigned for some other nodes in the network by the witness node. Also the neighbor node ID and it locations have to be listed to those nodes. In this scheme, Energy-Efficient Clustering Protocol (EECP) has been used to implement different energy saving methods.