Increase Network Lifetime Research Articles

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.

Autonomous underwater vehicles support for enhanced performance in the Internet of underwater things

AbstractThe Internet of underwater things (IoUT) has become a promising and vibrant paradigm which mainly relies on the underwater acoustic wireless sensor networks (UASN). IoUT was first proposed to facilitate offshore real‐time monitoring and exploration of underwater environments. It is now becoming a key component for building and connecting the futuristic underwater smart cities. However, Acoustic waves used in underwater communication suffer from long propagation delays and high transmission energy requirements. Therefore, approaches for autonomous underwater vehicle (AUV) assisted data collection and cluster‐based routing were proposed to overcome these drawbacks. In this article, we present a smart genetic‐based AUV path planning algorithm for data collection to enhance the performance of UASN. In addition, we combine our smart data collection technique with a dynamic location‐unaware clustering algorithm to further reduce energy consumption with mobility consideration. Our simulation results showed that our genetic‐based technique achieves more than 145% increase in network lifetime and almost a 75% reduction in energy expenditure compared with traditional diagonal path selection technique. More simulations comparing our combined technique with a novel clustering technique showed that the network lifetime is twice as better, total energy consumption is nearly 30% less, and the delivery ratio is 15% more at low densities.

Genetic algorithm based sink mobility for energy efficient data routing in wireless sensor networks

In a sink mobility based Wireless Sensor Network, the sink node moves on a trajectory in the network region and collects the data of the sensor nodes in the vicinity. Sink mobility reduces the distance from average source node to sink and saves network transmission energy. This paper proposes a Genetic Algorithm (GA) based sink mobility technique for WSN. Network region is divided into the optimal number of clusters and a sink movement trajectory is built over there. The GA process determines the optimal sink locations on the trajectory for each cluster. The moving sink stops at the optimal sink locations and gathers data from the nodes of the related clusters. An optimal sink location consumes minimum node energy in data transmission. For determining the optimal sink location for a cluster, the GA initializes a population of chromosomes. Further, a network energy consumption model is proposed that implements the fitness evaluation operator of the GA process. The developed GA model converges into a set of optimal sink locations on the trajectory for each cluster. The results depict that the GA based sink mobility provides increase in network lifetime than other protocols.

Energy-Efficient Intelligent Routing Scheme for IoT-Enabled WSNs

Recently, the Internet of Things (IoT) has attracted much interest in its wide applications, such as smart healthcare, home automation, transportation, and smart city. In these IoT-based systems, wireless sensor networks (WSNs) are highly used to gather information needed by smart environments. However, due to huge heterogeneous data coming from different sensing devices, IoT-enabled WSNs face different challenges, such as high communication delay, low throughput, and poor network lifetime. In this article, a deep-reinforcement-learning (DRL)-based intelligent routing scheme is proposed for IoT-enabled WSNs that significantly reduce delay and increase network lifetime. The proposed algorithm divides the whole network into different unequal clusters depending on the current data load present in the sensor node that significantly prevents immature death of the network. An extensive experiment on the proposed algorithm is performed using ns3. The experimental results are compared with the state-of-the-art algorithms to demonstrate the efficiency of the proposed scheme in terms of the number of alive nodes, packet delivery, energy efficiency, and communication delay in the network.

A Novel Hybrid HNN and Firefly Algorithm to Overcome Denial of Sleep Attack on Wireless Sensor Nodes

A typical Wireless Sensor Network (WSN) comprises of multiple nodes that are used to control as well as monitor the environment and perform pre-described actions. Based on the network, the sensor nodes are distributed and their energy consumption proves to be challenging. When the nodes are located near the sink, they serve as the interface for data transfer between the sink and the node. Because of this, there is a decrease in the networks lifetime and further the energy consumption of the nodes increases significantly. Denial-of-sleep attack is a threat that sensor nodes face in WSNs. DoSA is the condition when there is much loss of energy at the nodes by preventing them from entering into sleep mode and power save mode. We propose a hybrid methodology of Hopfield neural network and firefly algorithm using leach to tackle this issue such that there is a significant increase in network lifetime and energy consumption patterns.

Reliable Data Transmission with Sub-Packet Generation and Fusion in Wireless Sensor Networks

When transmitting data in a large number, the number of conflicts that arise is high. This is especially the case when there is dense distribution of marginal wireless sensor network (WSN). Some of the major conflicts that affect the overall operation of a system include heavy transmission delay and large data loss. In this proposed work, a multipath reliable transmission is used for wireless sensor network. In order to determine the reliability of the system, the WSN implements redundancy methodology. As the first step, data is sub-divided into packets of information along with data redundancy. These packets are then transmitted via multi-paths to their corresponding destination nodes. Experimental observation shows that the proposed work indicates a significant increase in network lifetime, reduction in transmission delay and data packet loss rate.

Clustering Based Two Dimensional Motion of Sink Node in Wireless Sensor Networks

The wireless sensor network (WSN) is always known for its limited-energy issues and finding a good solution for energy minimization in WSNs is still a concern for researchers. Implementing mobility to the sink node is used widely for energy conservation or minimization in WSNs which reduces the distance between sink and communicating nodes. In this paper, with the intention to conserve energy from the sensor nodes, we designed a clustering based routing protocol implementing a mobile sink called ‘two dimensional motion of sink node (TDMS)’. In TDMS, each normal sensor node collects data and send it to their respective leader node called cluster head (CH). The sink moves in the two dimensional direction to collect final data from all CH nodes, particularly it moves in the direction to that CH which has the minimum remaining energy. The proposed protocol is validated through rigorous simulation using MATLAB and comparisons have been made with WSN’s existing static sink and mobile sink routing protocols over two different geographical square dimensions of the network. Here, we found that TDMS model gives the optimal result on energy dissipation per round and increased network lifetime.

Automata Based Hybrid PSO–GWO Algorithm for Secured Energy Efficient Optimal Routing in Wireless Sensor Network

The main objective in wireless sensor networks is to exploit efficiently the sensor nodes and to prolong the lifetime of the network. The discussion of energy is a significant concern to extend the lifetime of the network. Moreover, a nature inspired hybrid optimization approach called hybrid Particle Swarm Optimization–Grey Wolf Optimizer (PSO–GWO) is used in this work to efficiently utilize the energy and to transmit the data securely in an augmented path. A Learning Dynamic Deterministic Finite Automata (LD2FA) has been innovated and initiated to learn the dynamic role of the environment. LD2FA is mainly used to provide the learned and accepted string to hybrid PSO–GGWO so that the routes are optimized. Hybrid PSO–GWO is used to choose the optimal next node for each path to obtain the optimal route. The simulation results are obtained in MATLAB for 100–700 sensor nodes in a region of 500 × 500 m2 which demonstrate that the proposed LD2FA based Hybrid PSO–GWO algorithm obtains better results when compared with existing algorithms. It is observed that LD2FA based Hybrid PSO–GWO has an increase of 18% and 48% betterment in lifetime of the network than PSO and GLBCA, nearly 57% and 75% increase in network lifetime when compared with GA and LDC respectively. It also shows an improvement of 24% increase compared to cluster-based IDS, nearly a rise of 90% throughput when compared with lightweight IDS. The consumption of energy is reduced by 13% and 15% than PSO and GA and an increase of 15% utilization of energy than LDC. Therefore, LD2FA based Hybrid PSO–GWO is been considered to efficiently utilize energy in an optimal route.

Coverage Aware Scheduling Strategies for 3D Wireless Video Sensor Nodes to Enhance Network Lifetime

High-density wireless video sensor nodes (VSNs) having limited battery power are deployed randomly in the disaster-hit area for capturing visual data, but its local processing and transmission consume high energy. High deployment density of those VSNs results in a larger overlap in the coverage area across VSNs that can be utilized to cover the sensing region of some VSNs and shut off such VSNs to decrease energy consumption and increase network lifetime without losing much area coverage. Two advanced approaches (APP_5 and APP_6) with realistic 3D rectangular pyramid camera coverage of VSN monitoring 2D target area is proposed in this paper. These approaches reduce the number of active VSNs in the target area and energy consumption maintaining the overall coverage area above some threshold value ensuring network connectivity. The approaches are compared with the three state-of-the-art approaches EX_1, EX_2 and EX_3 in the same simulation setup. Observed that for 150 deployed VSNs over the target area of size $75\times 75$ square meters, APP_5 and APP_6 reduce energy consumption by 6.98% and 18.6% respectively from the existing approach EX_3 (producing a better result among three existing approaches in terms of energy consumption). Reducing the number of active VSNs helps decrease energy consumption at the expense of reduced area coverage. For the same node density, both APP_5 and APP_6 lose a little amount of area coverage (i.e. 0.93% and 0.95%) than the existing approach EX_2 (producing a better result among three existing approaches in terms of percentage of area coverage). Additionally, both the proposed approaches (having the same communication overhead) establish superiority by 3.19%/7.83%/4.25% from EX_1/EX_2/(EX_3) in terms of communication overhead for 100 deployed VSNs on the same target area. Finally, APP_6 substantiates superiority in terms of reduced energy consumption (11.97%) than APP_5 losing a very little percentage (0.02%) of area coverage for 150 deployed VSNs.

A less computationally complex clustering algorithm based on dynamic K-means for increasing lifetime of wireless sensor networks

Clustering in wireless sensor networks is a critical issue based on network lifetime, energy efficiency, connectivity and scalability. Sensor nodes are capable to collect data from any geographical region using routing protocol. This research endeavours to design a less computationally time complex clustering algorithm for hierarchical homogeneous wireless sensor network to extend network lifetime. It forms optimal number of clusters and reduces data communication span of sensor nodes using dynamic K-means algorithm. Selection of suitable cluster head is based on ratio of remaining energy of sensor node to its distance from centre of cluster. The simulation results prove that algorithm that has been presented achieves better energy efficiency when compared to other hierarchical homogeneous cluster based algorithms. It increases network lifetime, number of alive nodes per round, data delivered to base station, time of first node, middle node and last node to die for scalable situations in terms of node density and size of sensing region.

Harvested Energy Scavenging and Transfer capabilities in Opportunistic Ring Routing

Energy conservation has always been a prominent design goal for hierarchical routing protocols supporting sink mobility. Advertising the current position of mobile sink introduces control packet overhead which ultimately results in an increase in energy consumption and shorter network lifetime. Energy harvesting through ambient sources have enabled the utilization of rechargeable devices for Wireless Sensor Networks to perpetually remain operational. The modifications in the hierarchical structure of wireless sensor networks along with energy scavenging approaches could possibly minimize the control packet overhead and also provide a significant improvement in energy conservation. In this paper, we propose a novel Harvested Energy Scavenging and Transfer capabilities in Opportunistic Ring Routing protocol which uses a distinguishing approach of hybrid (ring + cluster) topology in which the network architecture is initially supported by the formation of a virtual ring structure and then a two-tier routing topology is used in the virtual ring as an overlay by grouping nodes into clusters. The rate of energy gain from solar harvesting and radio frequency transfer is the criterion for selecting cluster heads. The role of cluster heads is exploited to advertise the mobile sink current position as well as forward the aggregated data towards mobile sink using energy transfer based opportunistic routing. The simulation results reveal that our scheme considerably outperforms the existing benchmarks in terms of control packet overhead, energy conservation, network lifetime, packet delivery ratio and average end-end delay.

T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things

In recent years, the Internet of Things (IoT) has evolved as a research field that transforms human lifestyle from traditional to sophisticated. In IoT, the network plays a crucial role in collecting data from sensors and moving to the sink. Increasing the network lifetime is a challenging task in IoT, which is connected to devices that are limited by resource. Clustering is one of the effective methods of increasing the network lifetime. However, improper cluster head (CH) selection easily drains the energy early in network nodes. With the aim to overcome the issue, this paper proposes the Type-2 Fuzzy Logic-based Particle Swarm Optimization (T2FL-PSO) algorithm to select the optimal CH to extend the network lifetime. The T2FL is highly useful in providing the accurate solution in uncertain network environments. Hence, T2FL is applied on the network parameters, residual energy, and the distance between sensor node and base station to determine the fitness value. Later, virtual clusters are formed on the basis of distance between sensor node and CH and between node and base station. To validate the performance of the proposed T2FL-PSO algorithm, extensive simulations are carried out using MATLAB 2019a. Furthermore, the proposed T2FL-PSO algorithm is compared with Particle Swarm Optimization Clustering (PSO-C) and Particle Swarm Optimization Wang Zhang (PSO-WZ). The result confirms that the proposed T2FL-PSO increases the network lifetime by 10%–15% and the packet transmission ratio by 10%. Compared with similar algorithms, the proposed T2FL-PSO also causes a higher increase of network lifetime.

LOA-RPL: Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion Optimization Algorithm to Maximize Network Lifetime

Energy conservation is a significant task in the Internet of Things (IoT) because IoT involves highly resource-constrained devices. Clustering is an effective technique for saving energy by reducing duplicate data. In a clustering protocol, the selection of a cluster head (CH) plays a key role in prolonging the lifetime of a network. However, most cluster-based protocols, including routing protocols for low-power and lossy networks (RPLs), have used fuzzy logic and probabilistic approaches to select the CH node. Consequently, early battery depletion is produced near the sink. To overcome this issue, a lion optimization algorithm (LOA) for selecting CH in RPL is proposed in this study. LOA-RPL comprises three processes: cluster formation, CH selection, and route establishment. A cluster is formed using the Euclidean distance. CH selection is performed using LOA. Route establishment is implemented using residual energy information. An extensive simulation is conducted in the network simulator ns-3 on various parameters, such as network lifetime, power consumption, packet delivery ratio (PDR), and throughput. The performance of LOA-RPL is also compared with those of RPL, fuzzy rule-based energy-efficient clustering and immune-inspired routing (FEEC-IIR), and the routing scheme for IoT that uses shuffled frog-leaping optimization algorithm (RISA-RPL). The performance evaluation metrics used in this study are network lifetime, power consumption, PDR, and throughput. The proposed LOA-RPL increases network lifetime by 20% and PDR by 5%–10% compared with RPL, FEEC-IIR, and RISA-RPL. LOA-RPL is also highly energy-efficient compared with other similar routing protocols.

FQ‐MEC: Fuzzy‐Based Q‐Learning Approach for Mobility‐Aware Energy‐Efficient Clustering in MANET

Different schemes have been proposed for increasing network lifetime in mobile ad hoc networks (MANETs) where nodes move uncertainly in any direction. Mobility awareness and energy efficiency are two inescapable optimization problems in such networks. Clustering is an important technique to improve scalability and network lifetime, as it relies on grouping mobile nodes into logical subgroups, called clusters, to facilitate network management. One of the challenging issues in this domain is to design a real‐time routing protocol that efficiently prolongs the network lifetime in MANET. In this paper, a novel fuzzy‐based Q‐learning approach for mobility‐aware energy‐efficient clustering (FQMEC) is proposed that relies on deciding the behavioral pattern of the nodes based on their stability and residual energy. Also, Chebyshev’s inequality principle is applied to get node connectivity for load balancing by taking history from the monitoring phase to increase the learning accuracy. Extensive simulations are performed using the NS‐2 network simulator, and the proposed scheme is compared with reinforcement learning (RL). The obtained results show the effectiveness of the proposed protocol regarding network lifetime, packet delivery ratio, average end‐to‐end delay, and energy consumption.

A New Clustering Routing Protocol for Homogeneous Wireless Sensor Networks Powered by Renewable Energy Sources

The technology of wireless sensor networks (WSNs) is in constant development and it made great progress in many applications. One of the most popular problems in WSNs is the limited energy storage power at every sensor node. This paper aims to propose and develop a new distributed clustering algorithm for energy harvesting wireless sensor networks denoted by DEH-WSN (Energy Harvesting for Distributed Clustering Wireless Sensor Networks Protocol) that relies on matching between clustering and energy harvesting in a distributed topology. DEH-WSN uses initial and residual energy capacity of the nodes to choose cluster heads. Simulation results prove that the proposed method increases network lifetime and the effective throughput.

An extended ACO-based mobile sink path determination in wireless sensor networks

In wireless sensor networks (WSNs), a mobile sink accumulate the data instead of routing directly to the sink to avoid the hotspot problem. In this process, it traverses a predetermined path by visiting a set of nodes called the rendezvous point (RP), and all the non-rendezvous points can transmit their data to the closest RP. Identifying the best collection of RPs and determining the mobile sink traveling path will decrease data loss and improve network performance. However, choosing a set of RPs and the route between them is a challenging task. It is more complicated in the event-driven applications due to the uneven data rate of SNs. In this context, we propose an extended ant colony optimization (ACO)-based mobile sink path construction for event-driven WSNs. In this, the best set of the RPs and the efficient mobile sink traveling path between them is determined. In addition to this, the RPs re-selection mechanism also adopted for balancing the energy between the nodes. After that, the virtual RPs are introduced to minimize the data transmissions between the sensor nodes and RPs. This process will improve WSNs’ performance in terms of reducing data losses while increasing network lifetime. The improved performance of the extended ACO-MSPD over existing is confirmed through simulation tests.

Self-Adaptive Sampling Rate to Improve Network Lifetime using Watchdog Sensor and Context Recognition in Wireless Body Sensor Networks

Todays, Wireless Body Sensor Networks (WBSNs) are used as a useful way in health monitoring. One of the most important problems regarding Wireless Body Sensor Network (WBSNs) is network lifetime. This factor mainly relies on the energy consumption of sensors. In fact, during capturing vital sign data and also communicating them to the coordinator, the biosensors consume energy. In this article, we are interested to propose an energy efficient Adaptive Sampling (AS) rate specification algorithm to set the amount of sensed data. According to the National Early Warning Score (NEWS), the sensors gather data and detect emergency data. Two scenarios have been used; the first is utilizing context recognition to indicate the active and sleep sensors in different time slices and the second is using watchdog sensors for checking patient situation in critical condition. Simulation results show that the proposed method can save energy and increase network lifetime by up to 4 times more than the previous work. In addition, our methods allow on average 75% improvement in overhead data reduction while maintaining more than 90% data integrity.

A new data aggregation approach for WSNs based on open pits mining

Wireless sensor networks consist of a large number of sensor nodes with limited energy, which is widely used in various Internet of things scenarios in recent years. Regarding the vast use of smart objects and applications, one of the big challenges is to collect and analyse the data. Sensor energy limitations and data redundancy are the primary challenges in these networks and reduce network lifetime as well. Therefore, the nodes try to eliminate redundant data, before transferring it to the central station. Data aggregation in IoT such as wireless sensor network plays an important role because in IoT there are heterogeneous data collected from different sources which need more energy to send data. One of the solutions to reduce energy, in this case, is to process and aggregate data prior to sending it. Data aggregation is an effective technique in reducing the data redundancy as well as improving energy efficiency; It also increases the lifespan of Wireless Sensor Networks. Integrating and combining relevant and identical data prevents sending additional packets, and minimizes the redundancy, saves energy, and increases network lifetime. The main purpose of this paper is to provide a new data aggregation method based on the open-pit mining idea efficiently. In this approach, the wireless sensor network is divided into several clusters, and in each cluster, a central node is specified, around which some hypothetical pits are considered to aggregate and send data.

Data Aggregation using Difference transfer for Load Reduction in Periodic Sensor Networks

One of the basic challenges in wireless sensor networks is energy conservation. Sensor nodes are energy constrained and prudent energy usage is of utmost importance. Data aggregation aims to reduce amount of data communicated across the network without loss in information, thereby reducing the energy costs, and increasing network lifetime. In this paper, we propose a novel, simple and easy to implement method to reduce the amount of periodic data transferred from the sensor nodes to the sink. Instead of sending a set of measures at the end of every time period, we propose sending the first measure, and for every subsequent measure in that time period, we send the difference with respect to first measure. Differences are represented by a group of binary bits. Differences are also chosen in an adaptive manner in order to maintain precision between the data measured at sensor nodes and data reconstructed from binary bit patterns at sink. We evaluated our technique against two real world data-sets with vastly different properties. Results indicate 85–88.5% reduction in amount of data sent and transmission energy.

Increasing fault tolerance ability and network lifetime with clustered pollination in wireless sensor networks

The significance of wireless sensor network (WSN) applications is to examine dangerous and remote fields that are not reachable or complex or expensive to reach human insights. This feature leads to self-managed networking that may face enormous confronts in energy consumption, fault tolerance and network lifetime based restraints owing certain non-renewable energy resources. From this research, an effectual managing strategy has been provided and methodology to tolerate faults in a network has been anticipated. To model these kinds of framework, fault recognition and revitalization approaches for handling diverse faulty levels have to be considered, i.e. communication and network nodes among them are used. The anticipated management strategy and protocol model may improve network-based fault tolerance ability in network nodes and corresponding transmission among them. As well as, network lifetime is increased for about five times higher than existing methods. Subsequently, it is validated that network fault tolerance proficiency is decreased with 2% and improved network lifetime with 10%. It improves data transmission and reduces energy consumption and increases network lifetime. Simulation is carried out in MATLAB 2018a environment. This model provides the finest results in terms of fault tolerance, network lifetime and energy consumption.