Residual Energy Of Nodes Research Articles

Particle swarm optimization-based energy efficient clustering protocol in wireless sensor network

The nodes in the wireless sensor network are furnished with restricted and irreplaceable battery power. The continuous sensing, computation, and communication drain out the energy of sensors very quickly. The optimal utilization of the sensor energy has always been a key issue for all the applications in the wireless sensor network. To manage the energy issue of nodes, various approaches were proposed in the past which focused on designing the proper energy management methods. The clustering of sensors is one of the most popular techniques used to manage the energy-related concerns of networks. In this paper, a particle swarm optimization-based energy efficient clustering protocol (PSO-EEC) is proposed to enhance the network lifetime and performance. The proposed protocol uses the particle swarm optimization technique to select the cluster head and relay nodes for the network. The cluster head is selected by employing the particle swarm optimization based fitness function which considers the energy ratio (initial energy and residual energy) of nodes, distance between nodes and cluster head, and node degree to appoint the most optimal node for the cluster head job. For the data transfer to base station, the proposed scheme uses the fitness value based on residual energy of cluster head and distance to base station parameters to nominate the relay nodes for the multi-hop data transfer to the base station. The performance of the proposed protocol is compared with the various existing approaches in terms of different performance parameters such as energy expenditure, network lifetime, and throughput to evaluate its effectiveness. The proposed scheme has improved the lifetime of the network by 238%, 136%, 106%, and 71% as compared to the existing MDCH-PSO, MCHEOR, MOPSO, and HSA-PSO techniques used in the simulation results for the comparison purpose. The stability period of the network in proposed scheme is approximately 396%, 321%, 246%, and 126% more than the existing MDCH-PSO, MCHEOR, MOPSO, and HSA-PSO protocol .

Robust joint resource allocation algorithm for hierarchical underwater acoustic communication networks based on outage probability minimization

AbstractThis study investigates a joint resource allocation (channel selection (CS) and power allocation (PA)) problem with multiple channels, users, constraints, and uncertainties in the underwater acoustic communication networks (UACNs). A hierarchical joint resource allocation model is formulated considering different user requirements. This model aims to minimize the maximum outage probability of lower users while satisfying the outage requirements of upper users. This study presents a robust distributed power allocation (RDPA) algorithm to solve complex PA problems with nonconvex mixed integers and uncertainties in UACNs. The residual energy of nodes is considered in the proposed RDPA algorithm to balance their energy, thereby extending the working time of the entire network. The proposed robust CS algorithm considers the actual and potential interferences of all neighboring nodes to decrease the interferences of nodes and the entire network, thereby reducing the outage probability of the network. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

An Energy-Constrained Forwarding Scheme Based on Group Trustworthiness in Mobile Internet of Things

Residual energy of devices is a key factor to determine whether Internet of Things (IoT) applications can proceed smoothly. Partnership cheating behavior of malicious nodes easily distorts the trust evaluation results only based on the one-to-one relationship between nodes, which greatly reduces the security of data transmission. To balance the excessive energy loss of mobile devices and improve the reliability of data transmission, this article proposes an energy-constrained forwarding scheme based on group trustworthiness between communities (ECF-GT) for mobile IoT, which does everything possible to increase the reliability of data forwarding by improving the accuracy of relay selection. First, it leverages nodal residual energy ratio and two common social attributes between nodes to measure the unidirectional trustworthiness between devices, and constructs the community structure by using our published <i>Dynamic Discovery of Trustworthiness Overlapping Community</i> method. Then, based on the one-to-many, many-to-one, many-to-many trustworthiness between nodes and/or communities, a group dynamic trustworthiness assessment method is proposed to enhance the reliability of evaluating relays. Finally, ECF-GT combines nodal residual energy and group trustworthiness to design forwarding utilities and rules. The experimental results show the effectiveness of ECF-GT. For example, when the number of malicious nodes accounts for 25&#x0025; of the total number of nodes, ECF-GT can save about 17.3&#x0025; energy on average comparing to other three well-known forwarding models on <i>Reality</i> dataset.

Energy efficient data correlation aware opportunistic routing protocol for wireless sensor networks

In this paper, we study the design of data correlation aware opportunistic routing protocol for wireless sensor networks (WSNs) constituent of lossy links for improved network performance. We focus on a scenario where only M < N sensor nodes are required to report their readings, where N is the number of sensor nodes in the network. We formulate the problem of selecting M such reporting sensors among the N nodes as a mutual information maximization problem such that minimal information loss is resulted. Due to the NP-hardness of this issue, we present a greedy informative sensor selection algorithm. To reduce the transmission redundancy and improve the network lifetime performance, we design an energy efficient correlation aware hop-by-hop opportunistic routing protocol EECAR, which takes the following factors into account: spatial correlation characteristics among sensor readings, broadcast nature of wireless channels, and residual battery energy of sensor nodes. EECAR requires very limited routing information to be kept at nodes in the network. Extensive simulation results demonstrate the effectiveness of our greedy informative sensor selection algorithm and also show that EECAR outperforms existing work in terms of network lifetime and data gathering.

Clustering Algorithm for Energy- Efficient Wireless Sensor Network

Wireless networks data aggregation allows in-network processing, reduces packet transmission and data redundancy, and thus helps extend wireless sensor systems to the full duration of their lives. There have been many ways of dividing the network into clusters, collecting information from nodes and adding it to the base station, to extend wireless sensor network life. Certain cluster algorithms consider the residual energy of the nodes when selecting clusterheads and others regularly rotate the selection head of the cluster. However, we seldom investigate the network density or local distance. In this report we present an energy-efficient clustering algorithm that selects the best cluster heads of the system after dividing the network into clusters. The cluster head selection depends on the distance between the base station nodes and the remaining power of this approach.Each node's residual energy is compared to the node count. Our results show that the solution proposed more efficiently extends the life of the wireless sensor network. The algorithm prolongs the life and effectiveness of the Wireless Sensor Network.

Maximizing network throughput by cooperative reinforcement learning in clustered solar-powered wireless sensor networks

Power management in wireless sensor networks is very important due to the limited energy of batteries. Sensor nodes with harvesters can extract energy from environmental sources as supplemental energy to break this limitation. In a clustered solar-powered sensor network where nodes in the network are grouped into clusters, data collected by cluster members are sent to their cluster head and finally transmitted to the base station. The goal of the whole network is to maintain an energy neutrality state and to maximize the effective data throughput of the network. This article proposes an adaptive power manager based on cooperative reinforcement learning methods for the solar-powered wireless sensor networks to keep harvested energy more balanced among the whole clustered network. The cooperative strategy of Q-learning and SARSA( λ) is applied in this multi-agent environment based on the node residual energy, the predicted harvested energy for the next time slot, and cluster head energy information. The node takes action accordingly to adjust its operating duty cycle. Experiments show that cooperative reinforcement learning methods can achieve the overall goal of maximizing network throughput and cooperative approaches outperform tuned static and non-cooperative approaches in clustered wireless sensor network applications. Experiments also show that the approach is effective in response to changes in the environment, changes in its parameters, and application-level quality of service requirements.

Improved energy efficient hierarchical cluster tree based routing to prolong network lifetime

Wireless sensor networks (WSN) play a key role in enabling wireless communication technology among several nodes to remotely communicate and exchange information. WSN consists of tiny sensor nodes equipped with battery, scattered in an area to gather information around an environment and send to data collection node known as sink or base station (BS). WSN have been widely used in various applications like agriculture, fire detection, health care and military and has become imperative necessity for future revolutionary area like UAV (unmanned aerial vehicles), IoT (Internet of things) and smart cities which employs large scale sensor nodes. However sensor nodes are limited to battery, memory, low computational power, resource and bandwidth. Continues sensing of events, makes node to drain its battery faster and goes dead fast. For resource constrained WSN, hierarchical cluster based approaches are considered as energy efficient and improves network performance for large scale WSN. Minimizing energy consumption and extending network lifetime are major challenging issues of WSN, clustering methods with optimized routing have offered solution to optimize energy utilization. To balance energy consumption and improve network lifetime many existing hierarchical clustering approaches have been proposed, however existing method does not consider rotation of cluster head (CH) and considers cluster head selection based on residual energy and distance parameter. In this paper we propose an improved energy efficient cluster tree (IEECT) based routing to improve energy efficiency of hierarchical cluster. IEECT considers modification of existing LEACH (Low energy adaptive clustering hierarchy) protocol to improved energy efficient LEACH by considering energy parameters like residual node energy and average network energy. IEECT accounts optimal number of cluster head (CH) and selection of CH is done using threshold value among sensor nodes. Proposed IEECT combines tree based routing and data aggregation scheme to maintain desirable quality of service. Simulation experiments are carried out by using network simulator. Performance of IEECT is evaluated in terms of PDR, delay, energy consumption, network lifetime and overhead.

Trust management-based and energy efficient hierarchical routing protocol in wireless sensor networks

The single planar routing protocol has a slow convergence rate in the large-scale Wireless Sensor Network (WSN). Although the hierarchical routing protocol can effectively cope with large-scale application scenarios, how to elect a secure cluster head and balance the network load becomes an enormous challenge. In this paper, a Trust Management-based and ​Low Energy Adaptive Clustering Hierarchy protocol (LEACH-TM) is proposed. In LEACH-TM, by using the number of dynamic decision cluster head nodes, residual energy and density of neighbor nodes, the size of the cluster can be better constrained to improve energy efficiency, and avoid excessive energy consumption of a node. Simultaneously, the trust management scheme is introduced into LEACH-TM to defend against internal attacks. The simulation results show that, compared with LEACH-SWDN protocol and LEACH protocol, LEACH-TM outperforms in prolonging the network lifetime and balancing the energy consumption, and can effectively mitigate the influence of malicious nodes on cluster head selection, which can greatly guarantee the security of the overall network.

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

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

Residual-Energy Aware Modeling and Analysis of Time-Varying Wireless Sensor Networks

In this letter, the residual-energy aware feature of a sensor node in time-varying wireless sensor networks (WSNs) is analyzed and modeled as a Markov chain, upon which the state-transition probability (STP) about the energy level of any node with undetermined and deterministic residual energy can be evaluated. Based on Markov chain and energy-efficient relay search region models, an energy-efficient routing algorithm is proposed to further analyze the impact of STP with known residual energy on extending network lifetime of time-varying WSNs. Simulation results show that the proposed algorithm can effectively extend network lifetime even more than twice while holding a better energy efficiency as compared with the algorithm without considering node-residual energy changes.

Fuzzy rule-based system for energy efficiency in wireless sensor networks

The sensor nodes consume a large amount of energy to transfer the sensed information directly to the base station (BS). To reduce the energy consumption from the direct transmission, clustering and routing techniques are used. In this paper, we propose clustering and routing algorithms called energy-efficient two-phase approach using fuzzy logic (EETPF). Here, the rule-based fuzzy logic is used to associate the input values of clustering and routing algorithm. The fuzzy system makes decisions according to the residual energy of sensor nodes, distance of sensor nodes from the BS and the number of nodes in the communication range. The crisp values of the input variables are converted into different fuzzy values. The fuzzy output values are converted to crisp values using centroid defuzzification method. The cluster heads (CHs) and routers are selected with respect to the output values. The sensor nodes get allocated to respective CHs according to the load handling capacity of CHs. The routing path is generated according to the capacity of routers. The simulations are conducted on evaluation factors such as energy consumption, active sensor nodes per round and sustainability period of the network. It is observed that EETPF outperforms state-of-the-art algorithms under these evaluation factors.

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%.

Cluster Head Selection in Heterogeneous Wireless Sensor Network Using a New Evolutionary Algorithm

In wireless sensor network (WSN), limited energy resources with the nodes is a complex challenge as far as data routing, collecting and aggregating the data is concerned as all these processes are energy demanding. Network lifetime, stability period, and potential of the WSN are some of the parameters which are to be maximized subject to the constraints. The cluster head selection in the heterogeneous wireless sensor network has not been explored much and needs to be improved further to discover the potential of WSN in this area. In this study, optimal cluster head selection in heterogeneous wireless sensor network through Diversity-Driven Multi-Parent Evolutionary Algorithm with Adaptive Non-Uniform Mutation has been suggested. The efficacy of the proposed technique is tested on Classical Benchmark Functions, and obtained results are compared with the state of the art of algorithms. This algorithm is also validated on a heterogeneous wireless sensor network with cluster head selection as a multi-objective optimization problem. The residual energy of sensor node and distance travelled are to be optimized in order to minimize the fitness function. Simulation suggested that the proposed algorithm is found to be reliable and outperforms in terms of remaining energy of nodes, alive nodes versus round, dead nodes versus rounds, the lifespan of network, throughput, and stability period.

A Novel Approach for Cluster Head Selection using Trust Function in WSN

The enhancement of new technology in the sensor network shows a significant result in every aspect of life such as military surveillance, hospitals, mining and hospitals etc. The nodes are scattered randomly in RoI (Region of Interest) and data is transmitted to Base Station (BS) using the multi-hop technique. The Wireless Sensor Network (WSN) become an important research field for challenging problems as energy consumption, efficient cluster head selection process, routing algorithm, network strength, packet loss, energy loss and so forth. The agenda in the paper is to enhance Residual Energy (RE) of nodes and network lifetime. The problem is solved by using an efficient clustering and Cluster Head (CH) selection process.The cluster head selection is based on the maximum node residual energy and the minimum distance from the base station. The Proposed protocol worked in two stages. The new Threshold value T(H) is calculated for the cluster head selection process in the first stage. The data fusion method based on the trust function is used to get accurate data in the second stage. The energy model is utilized to reduce the excessive energy transmission inside the network. The Proposed protocol is compared with Stable Election Protocol and achieves 44% lifetime improvement, 59\% stability improvement and 15% in survival rate respectively.

Game algorithm based on link quality: Wireless sensor network routing game algorithm based on link quality

Aiming at the problems of low data transmission efficiency and uneven energy consumption caused by unreliable link communication in the routing process of wireless sensor networks, this article designs a routing game algorithm based on link quality. In this article, the index for evaluating link quality is defined first. Then, the link quality, node residual energy, and minimum jump transmission strategy are integrated into the utility function to establish a game model to determine the best next hop transmission node. Finally, the routing optimal transmission path is obtained according to the analysis of the existence of Nash equilibrium in the game. In the simulation experiment, the influence of the change of link quality parameters on the performance of the algorithm is analyzed, and the proposed algorithm is compared with non-linear weight particle swarm optimization (NWPSO) algorithm and Low Energy Adaptive Clustering Hierarchy-Improvement (LEACH-IMPT) algorithm in three aspects: the number of surviving nodes, network lifetime, and network energy consumption. The results show that the network lifetime of this method is 16.8% longer than that of LEACH-IMPT algorithm and 7.5% longer than that of NWPSO algorithm. This shows that the algorithm can effectively balance the network energy consumption and prolong the network life cycle. In addition, according to the routing path obtained in the simulation experiment, the optimality of its link quality is verified in the real experimental environment, and the experimental results prove the feasibility of the method in this article in practice.

A new energy aware cluster head selection for LEACH in wireless sensor networks

Internet of Things (IoTs) as a new network pattern for the intelligent world usually uses wireless sensor networks (WSNs) as a perception layer which consisted of numerous number of sensor nodes scattered in the environment to gather intended information. The selected information then is sent to a base station (BS) to be sent to cloud server for further processing. Since the energy of sensor nodes is limited, the most significant challenge in these networks is reducing the energy consumption of the network. It is proved that dividing the network to clusters can significantly reduce the energy consumption. One of the most popular clustering protocols in WSNs is the Low-Energy Adaptive Clustering Hierarchy (LEACH). In this protocol, cluster heads (CHs) are selected randomly which results in poor performance in real scenarios. In this article, a new energy aware CH selection algorithm is proposed which selects CHs based on the residual energy, the position and centrality of nodes. It uses a variable range upon which the centrality and the number of neighbours of each node are calculated. Simulation results show that the proposed algorithm outperforms LEACH, Multi-hope Routing with LEACH (MR-LEACH) and Enhanced Multi-hop LEACH (EM-LEACH) in terms of reducing energy consumption, increasing network lifetime and improving network reliability.

Joint Nodes and Sink Mobility Based Immune Routing-Clustering Protocol for Wireless Sensor Networks

Recently, mobile wireless sensor network has drawn attention widely. In this paper, Joint Nodes and Sink Mobility based Immune routing-Clustering protocol (JNSMIC) is proposed to support the mobility of the sink and the sensor nodes together. It depends on using the mobile sink for solving the hot spot problem and the Multi-Objective Immune Algorithm (MOIA) for clustering the network and finding the visiting locations of the mobile sink. The JNSMIC protocol considers different objectives during the clustering process, namely the consumption energy, network coverage, link connection time (LCT), residual energy and mobility. Also, it reduces the computational time of finding cluster heads (CHs) by dividing it into two phases. In the first phase, the candidate CHs set is formed based on residual energy, mobility factor and LCT of sensor nodes. While in the second phase, the MOIA algorithm is utilized to determine the final CHs subject to reducing the communication cost, improving the packet delivery ratio and ensuring network stability. JNSMIC performs the clustering process only if the remaining energy is below a threshold value thus the computational time and overhead control packets are reduced. In JNSMIC, the deputy CH concept is considered to perform the task of CH during CH failure. Furthermore, the proposed protocol performs a fault-tolerance process after transmitting each frame to maintain the link stability among CHs and their members which improves the throughput. Simulation results show that the JNSMIC protocol can effectively ameliorate the throughput while simultaneously giving lower energy expenditure and end-to-end delay.

Load aware multipath data forwarding for enhanced lifetime of WSN

Modern Wireless sensor network (WSN) requires strict constraints on delay and energy consumption. These networks are internet of things (IoT), multimedia sensor network, and body area network. It requires huge amount of bandwidth to deliver the data. Since, time critical data must be reached at sink node without loss because sensor network has energy limitations and hardware constraints. In this paper, an efficient multipath routing algorithm is proposed to enhanced the lifetime of the network. The proposed scheme, rather than one route, discovered independent routes from source to destination. The Lagrangian relaxation method is used to model hop to hop distance for prolonging the network lifetime. The proposed routing technique selects the multiple paths by focusing on hop count and the residual energy of sensor nodes. Simulation results show that the proposed routing protocol improves the network performance in terms of packet delivery ratio, energy consumption, first dead node and average end to end delay.

A novel protocol for stable period and lifetime enhancement in WSN

Energy optimization is one of the open issues in wireless sensor networks. Most of the work focuses on clustering techniques to reduce the energy consumption and enhancement of stability period. In this paper we have proposed a novel region based routing protocol for the rise in stability duration and lifetime improvement. The whole sensing area is divided among nine regions and dedicated to different variety of nodes. Dynamically changing cluster head election probability has been exploited for heterogeneous wireless sensor networks. In this paper, cluster head selection is based on residual energy of nodes, region in which the node is residing and minimum number of clusters per round. The proposed protocol is simulated using MATLAB and the results establish that it performs better than the ETSSEP in terms of stability period, throughput and lifetime. Proposed routing protocol frames more stable routing environment than the ETSSEP. It is also evident from the results that stability is increased by 35.03% in comparison to ETSSEP. Lifetime and throughput have also been improved by 23.16% (for 50% live node) and 25.58% respectively.

An Efficient and Secure Data Forwarding Mechanism for Opportunistic IoT

Internet of Things (IoT) is a heterogeneous network of interconnected things where users, smart devices and wireless technologies, collude for providing services. It is expected that a great deal of devices will get connected to the Internet in the near future. Opportunistic networks(OppNet) are a class of disruption tolerant networks characterized by uncertain topology and intermittent connectivity between the nodes. Opportunistic Internet of Things(OppIoT) is an amalgamation of the OppNet and IoT exploiting the communication between the IoT devices and the communities formed by humans. The data is exposed to a wide unfamiliar audience and the message delivery is dependent on the residual battery of the node, as most of the energy is spent on node discovery and message transmission. In such a scenario where a huge number of devices are accommodated, a scalable, adaptable, inter-operable, energy-efficient and secure network architecture is required. This paper proposes a novel defense mechanism against black hole and packet fabrication attacks for OppIoT, GFRSA, A Green Forwarding ratio and RSA (Rivest, Shamir and Adleman) based secure routing protocol. The selection of the next hop is based on node’s forwarding behavior, current energy level and its predicted message delivery probability. For further enhancing the security provided by the protocol, the messages are encrypted using asymmetric cryptography before transmission. Simulations performed using opportunistic network environment (ONE) simulator convey that GFRSA provides message security, saves energy and outperforms the existing protocols, LPRF-MC (Location Prediction-based Forwarding for Routing using Markov Chain) and RSASec (Asymmetric RSA-based security approach) in terms of correct packet delivery by 27.37%, message delivery probability is higher by 34.51%, number of messages dropped are reduced by 15.17% and the residual node energy is higher by 14.08%.