Lifetime Of Wireless Sensor Networks Research Articles

NM-LEACH: A Novel Modified LEACH Protocol to Improve Performance in WSN

Saving energy and improving the lifetime of wireless sensor networks (WSNs) has remained as a key research challenge for some time. Low-energy adaptive clustering hierarchy (LEACH), a classical protocol is designed originally for the purpose of reducing and balancing the network’s energy consumption. However, as the distances between the cluster head (CH) and the member nodes are not taken into consideration, it results in the uneven distribution of the clusters and uneven consumption of the energy in the network. Choosing the CHs with no distinction is an issue as well. Based on the original algorithm, a novel modified LEACH (NM-LEACH) has been proposed, considering critical problems that exist in the network. NM-LEACH protocol is capable of reasonably solving the number of the CHs in each round and takes the energy as a factor of weight under consideration in selecting the CH. The proposed protocol enhances performance by extending the WSN lifecycle, which results in increasing the balance of the energy consumption in the network, and improving the efficiency of the network.

Hybridization of Pigeon inspired with glowworm' swarm optimization based clustering technique in wireless sensor networks

In wireless sensor networks (WSN), clustering is treated as an energy efficient technique employed to achieve maximum network lifetime. But, the process of cluster head (CH) selection for stabilized network operation and prolonged network lifetime remains a challenging issue in WSN. To resolve this issue, this paper presents a new hybridization of pigeon inspired with glowworm swarm optimization (HPIGSO) algorithm based clustering technique in WSN. The proposed HPIGSO algorithm integrates the good characteristics of pigeon inspired optimization (PIO) algorithm and glowworm swarm optimization (GSO) algorithm. The proposed algorithm operates on three major stages namely initialization, CH selection and cluster construction. Once the nodes are deployed, initialization process takes place. Followed by, base station (BS) executes the HPIGSO algorithm and selects the CHs effectively. Subsequently, nearby nodes joins the CH and becomes cluster members (CMs), thereby cluster construction takes place. Finally, the CMs send the data to CHs which is then forwarded to BS via inter-cluster communication. The proficient performance of the HPIGSO method has been evaluated and the results portrayed that the HPIGSO algorithm prolonged the lifetime of WSN over the existing clustering techniques.

Game Theory based Resource Identification Scheme for Wireless Sensor Networks

In modern world of sensing and distributive systems, traditional Wireless Sensor Networks (WSN) has to deal with new challenges, such as multiple application requirements, dynamic and heterogeneous networks. Senor nodes in WSN are resource constrained in terms of energy, communication range, bandwidth, processing delay and memory. Numerous solutions are proposed to optimize the performance and to increase the lifetime of WSN by introducing new resource management principles. Effective and intelligent resource management in WSN involves in resource identification, resource scheduling, and resource utilization. This paper proposes a Bayesian Game Model (BGM) approach to efficiently identify the best node with the maximum resource in WSN for data transmission, considering energy, bandwidth, and computational delay. The scheme operates as follows: (1) Sensor nodes information such as residual energy, available bandwidth, and node ID, etc., is gathered (2) Energy and bandwidth of each node are used to generate the payoff matrix (3) Implementation of node identification scheme is based on payoff matrix, utilities assigned, strategies and reputation of each node (4) Find Bayesian Nash Equilibrium condition using Starring algorithm (5) Solving the Bayesian Nash Equilibrium using Law of Total Probability and identifying the best node with maximum resources (6) Adding/Subtracting reward (reputation factor) to winner/looser node. Simulation results show that the performance of the proposed Bayesian game model approach for resource identification in WSN is better as compared with the Efficient Neighbour Discovery Scheme for Mobile WSN (ENDWSN). The results indicate that the proposed scheme has up to 12% more resource identification accuracy rate, 10% increase in the average number of efficient resources discovered and 8% less computational delay as compared to ENDWSN.

Energy sink-holes avoidance method based on fuzzy system in wireless sensor networks

The existence of a mobile sink for gathering data significantly extends wireless sensor networks (WSNs) lifetime. In recent years, a variety of efficient rendezvous points-based sink mobility approaches has been proposed for avoiding the energy sink-holes problem nearby the sink, diminishing buffer overflow of sensors, and reducing the data latency. Nevertheless, lots of research has been carried out to sort out the energy holes problem using controllable-based sink mobility methods. However, further developments can be demonstrated and achieved on such type of mobility management system. In this paper, a well-rounded strategy involving an energy-efficient routing protocol along with a controllable-based sink mobility method is proposed to extirpate the energy sink-holes problem. This paper fused the fuzzy A-star as a routing protocol for mitigating the energy consumption during data forwarding along with a novel sink mobility method which adopted a grid partitioning system and fuzzy system that takes account of the average residual energy, sensors density, average traffic load, and sources angles to detect the optimal next location of the mobile sink. By utilizing diverse performance metrics, the empirical analysis of our proposed work showed an outstanding result as compared with fuzzy A-star protocol in the case of a static sink.

Energy Harvesting Rectenna Design for Enhanced Node Lifetime In WSNs

With the rising popularity and advent of many services on Wireless Sensor Networks (WSNs), there is a compelling need to have energy-efficient solutions. The sensors and devices are powered by batteries whose life is limited, and at times it is impossible to replace the batteries, especially in remote applications. In such a scenario, Energy Harvesting (EH) stands as an undisputed candidate for enhancing the network lifetime. Radio Frequency (RF) energy is the most commonly available, ubiquitous, and reliable energy source among all the available energy sources. While RF signal carries both information and energy, EH is possible for long-distance and mobile environments. This work discusses initial research in the domain of EH in wireless networks via Radio Frequency (RF) signals. The paper presents an EH rectenna for energy harvesting over 2.45 GHz (Wi-Fi band). The receiving antenna is designed to pick up the radio signal in the RF range (2.45 GHz) from the free space. The four patch elements design has an antenna substrate made with RT with a dielectric constant of 2.2. The paper presents the simulation results of the basic parameters of the antenna, such as return loss, input impedance, bandwidth, gain, directivity, and efficiency. H-shaped slot antenna and modified H-shaped antenna (with circular slot) are designed with a gain of 8.24 dB and 8.32 dB, return loss of -10 dB and -16 dB, and bandwidth of 64.8 MHz and 868 MHz. The high gain, large bandwidth, properly matched impedance for minimum return loss, and high efficiency of the modified H-shaped patch antenna makes it eligible for energy harvestin.

Utilizing Genetic Algorithm and Artificial Bee Colony Algorithm to Extend the WSN Lifetime

Extending the lifetime of Wireless Sensor Networks (WSN) is an important issue due to the mission assigned to these networks. The sensors collect data relevant to a specific field. Then, the sensors send the collected data to a base station where it is analyzed, and a suitable reaction can be taken. Sensors in WSN depend on a battery with limited energy to do their work. Data transmission and receiving consume energy, which may lead to the loss of the whole network or some of the essential nodes. For this reason, energy must be preserved as long as possible to prolong the network lifetime. Several types of research were presented with different approaches to minimize power consumption. In this paper, we present a hybrid technique that includes two population-based algorithms: genetic algorithm (GA) and artificial bee colony (ABC) with clustering approaches. This proposed novel technique aims to reduce the dissipation of power consumption per sensor node in the WSN, and as a consequence, the lifetime of the WSN is extended. The ABC algorithm was used to improve an initial population, which was used in the GA. Also, we used two approaches of clustering; clustering based on genetic algorithm and K-means clustering beside LEACH protocol. The experimental results show that the proposed approach approved its efficiency in lifetime extending through an increasing number of the operational nodes per round and transmission.

A reinforcement learning-based sleep scheduling algorithm for cooperative computing in event-driven wireless sensor networks

Emergency event monitoring is an important application of wireless sensor networks (WSNs). In the traditional cloud-assisted WSNs, the monitored data needs to be sent back to the cloud for processing. The round-trip of huge and burst data causes long delay and high energy consumption. With the increase of the computing capability of sensor nodes, local processing of events can be accomplished through cooperative computing between sensor nodes. However, if a node continues to undertake cooperative computing tasks, the energy consumption of nodes will be unbalanced and the network lifetime will be shortened. Sleep scheduling is an effective method to achieve energy balance, which can activate idle nodes alternately to participate in cooperative computing. In this paper, in order to make WSN more efficient to complete the local processing of events, we build a new system model based on node cooperative computing, and propose a multi-node Q learning-based cooperative computing node selection algorithm to obtain the sleep scheduling strategy. Simulation results show that compared with the classical algorithms, the proposed algorithm can complete more event processing, improve the reliability, and prolong the lifetime of WSNs.

SCADA: scalable cluster-based data aggregation technique for improving network lifetime of wireless sensor networks

SCADA: scalable cluster-based data aggregation technique for improving network lifetime of wireless sensor networks

An improved routing protocol for raw data collection in multihop wireless sensor networks

Wireless sensor networks (WSNs) are an effective and efficient method for collecting data from a target area, and prolonging the lifetime of WSNs has been a focus of scientific research due to the limited energy of sensor nodes. However, traditional studies of WSNs are based on the assumption that WSNs collect aggregated data with redundant sensor nodes in an ideal radio environment. These assumptions may not be acceptable in practical applications. To address this problem, this paper introduces a novel application scenario for WSNs in which raw data are collected by a multihop network without redundant sensor nodes, and a new hybrid tree-based and cluster-based routing protocol for raw data collection (HTC-RDC) is proposed to prolong the lifetime of WSNs that can work in novel application scenarios. The experimental results demonstrate that the proposed HTC-RDC enables the WSNs to achieve their expected functions and prolongs the network lifetime by an average of 11.4% compared to the existing protocols in the explored application scenario.

End-to-end data collection strategy using mobile sink in wireless sensor networks

Several data collection algorithms, which are based on the combination of using mobile sinks and multiple-hop forwarding, have been proposed to prolong the network lifetime of wireless sensor networks. However, most approaches treat the collection point selection and touring path planning as two independent problems, which leads to a sub-optimal solution for data collection. This article proposed an ant colony optimization based end-to-end data collection strategy to perform the collection point selection and the touring path planning simultaneously. The proposed algorithm first constructs a data-forwarding tree, and then heuristically selects collection points and plans a touring path at the same time. The performance evaluation shows that the end-to-end strategy can improve the network lifetime of wireless sensor network compared to other approaches, especially in the unbalanced distribution scenario of sensors. The end-to-end strategy is also capable of being integrated with other methods.

Collection tree-oriented mesh routing optimization for extending the lifetime of wireless sensor networks

Routing optimization in wireless sensor networks facilitates to reduce the overhead of the maintaining of wireless sensor networks and extend the lifetime of wireless sensor networks. Collection tree-based routing protocol, which does not require route discovery, has been widely used for low overheads of calculation and storage. However, with collection tree-based routing protocol, some nodes easily become the bottleneck points and quickly run out of the energy. To deal with this drawback, this article proposes a collection tree-oriented mesh routing strategy with cooperatively consuming the residual energy among the neighboring sensor nodes. The collection tree-oriented mesh routing is formulated into a linear programming problem with the purpose to maximize the network lifetime. By solving the optimization problem, the optimal mesh routing and data forwarding scheme is derived. Experimental simulations show that the proposed collection tree-oriented mesh routing optimization strategy can extend the network lifetime by more than 20%.

Survey on WSN Network Lifetime Through Leach Clustering Schemes

Sensor nodes deployed in wireless sensor networks (WSN) are resource constrained, battery operated and have a restricted energy resource. The significant number of wireless sensor network applications aim to extend the longevity of the network by using various strategies. Clustering algorithms were proven to be among the best efficient ways for increasing wireless sensor network reliability. In a wireless sensor network, clustering-based solutions control the network operations to manage the restricted energy in the optimal effective way to extend the network lifetime. Reviews in related subject could assist in gathering thorough and timely information about wireless sensor networks lifetime study through different clustering protocols. This research review study paper presents an extensive study of existing low energy adaptive clustering hierarchy (LEACH) homogeneous clustering procedures and analysis.

A Population-Based Iterated Greedy Algorithm for Maximizing Sensor Network Lifetime.

Finding dominating sets in graphs is very important in the context of numerous real-world applications, especially in the area of wireless sensor networks. This is because network lifetime in wireless sensor networks can be prolonged by assigning sensors to disjoint dominating node sets. The nodes of these sets are then used by a sleep–wake cycling mechanism in a sequential way; that is, at any moment in time, only the nodes from exactly one of these sets are switched on while the others are switched off. This paper presents a population-based iterated greedy algorithm for solving a weighted version of the maximum disjoint dominating sets problem for energy conservation purposes in wireless sensor networks. Our approach is compared to the ILP solver, CPLEX, which is an existing local search technique, and to our earlier greedy algorithm. This is performed through its application to 640 random graphs from the literature and to 300 newly generated random geometric graphs. The results show that our algorithm significantly outperforms the competitors.

Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks.

The seamless operation of inter-connected smart devices in Internet of Things (IoT) wireless sensor networks (WSNs) requires consistently available end-to-end routes. However, the sensor nodes that rely on a very limited power source tend to cause disconnection in multi-hop routes due to power shortages in the WSNs, which eventually results in the inefficiency of the overall IoT network. In addition, the density of the available sensor nodes affects the existence of feasible routes and the level of path multiplicity in the WSNs. Therefore, an efficient routing mechanism is expected to extend the lifetime of the WSNs by adaptively selecting the best routes for the data transfer between interconnected IoT devices. In this work, we propose a novel routing mechanism to balance the energy consumption among all the nodes and elongate the WSN lifetime, which introduces a score value assigned to each node along a path as the combination of evaluation metrics. Specifically, the scoring scheme considers the information of the node density at a certain area and the node energy levels in order to represent the importance of individual nodes in the routes. Furthermore, our routing mechanism allows for incorporating non-cooperative nodes. The simulation results show that the proposed work gives comparatively better results than some other experimented protocols.

Charging strategy and scheduling algorithm for directional wireless power transfer in WRSNs

Wireless Power Transfer (WPT) technology plays a significant role to prolong the lifetime of wireless rechargeable sensor networks (WRSNs). To achieve stable and reliable energy supplements via wireless charging, the optimization of the trajectory of mobile chargers is crucial. In this paper, a charging strategy and scheduling algorithm for directional wireless power transfer in WRSNs is proposed. Firstly, the charging demand degree is defined to determine the priority of charging requests. Then, to avoid the occurrence of node's energy exhausted, the charger's orientation angle selection algorithm based on charging priority is designed. Finally, we formulate the problem of directional charger's deployment into discrete unit disk cover problem and propose a moving path planning scheme based on improved Genetic Algorithm to optimize the energy charging efficiency. Simulation results illustrate the benefit of our proposed scheme over the benchmark.

SCSAP: Spiral Clustering Based on Selective Activation Protocol for industrial tailored WSNs

Wireless Sensor Networks (WSNs) are generally deployed with low-cost devices that have low power consumption and small-size sensor nodes. They can be used in several monitoring and control applications such as; health, agriculture, and environment. With the advent of Industry 4.0 and Artificial Intelligence (AI), WSNs attracted many industrial applications and specific implementation scenarios. In general, the sensor nodes compromising of WSNs consume vast energy during tracking and monitoring. This poses a challenge especially since most of WSNs are battery-operated and have very limited energy sources. Therefore, it is important to optimize power consumption and prolong the lifetime of WSNs. Many protocols have been proposed with emphasis on data forwarding and routing. These protocols, however, have not been designed to address interference and transmissions issues, such as communication link instability and packet drops. In addition, a quiet substantial amount of energy would be consumed during transmission, which leads to a degradation in network performance. In this research, an effective solution is proposed based on Spiral Clustering Based on Selective Activation Protocol (SCSAP). The objective is to enhance energy consumption and improve network performance, which makes it a good candidate for several Industrial tailored WSNs. In this solution, the network nodes are classified into two types; super and normal. Super nodes have a significant power source and are utilized to construct clusters and act as Cluster Heads (CHs). They have pre-defined locations with an optimized number of nodes selected for the best coverage that is determined in the Stable Election Protocol (SEP). As a result, the power consumption is reduced in the dynamic formatting of clusters. Normal nodes, on the other hand, are static. They have limited power and are connected to the super nodes. Normal nodes operate in two modes; active and sleep. In the active mode, they send data to the CHs and the Time Division Multiple Access (TDMA) is used to guarantee that the transmitted data are correctly received by the other nodes and that the power consumption of re-transmission is largely reduced. The simulation results of SCSAP have shown a 40% enhancement of network lifetime over the Threshold-sensitive Stable Election Protocol (TSEP). They have also shown that SCASP’s remaining power has descended slower than the Low-energy Adaptive Clustering Hierarchy (LEACH) protocol.

Energy efficient algorithms for enhancing lifetime in wireless sensor networks

Energy efficient algorithms for enhancing lifetime in wireless sensor networks

Extending lifetime of heterogeneous wireless sensor networks using spider monkey optimization routing protocol

The nodes of wireless sensor networks (WSN) are severely restricted in terms of computing capabilities, limited communications, and limited power supplies, as it is difficult or impossible to replace or recharge the sensor battery. Consequently, the energy of nodes is one of the most important resources to consider when designing of WSNs. So, most of the routing protocols in WSNs are to assure the saving of energy as a significant aim for improvement. Nevertheless, just providing power is not sufficient to extend the lifetime of WSN. Where unbalance energy depletion in WSNs is a challenging issue often leading to splits the network and reduces its lifetime, also retrogression of its performance. This paper, therefore, uses a powerful routing protocol named spider monkey optimization routing protocol (SMORP) to generate an optimal data routing of the pathway for heterogeneous WSNs (HWSNs). SMORP, here, can compute the best way from a sensor to the sink through the cluster head, inside the intra-cluster, and the inter-cluster respectively. For this purpose and the organization of heterogeneous nodes, this paper uses the clustering partition. The simulation results revealed that SMORP significantly improves in terms of data latency reduction, stabilizing depletion of energy, and maximizing the network lifetime for HWSNs.

An energy‐efficiency‐adaptive clustering formation mechanism for the wireless sensor networks

Energy inequality caused by the process of cluster head election has a large influence on energy efficiency and the network lifetime of wireless sensor networks (WSNs). To this end, a novel concept of EIec is proposed to evaluate the equality degree of energy consumption. Related theorems for establishing the candidate set of cluster heads are proposed, with the aim of promoting energy equality in each cluster. Subsequently, a novel energy-efficiency-adaptive cluster formation mechanism based on economic (ECFE) theory is proposed and detailed. Finally, extensive experiments are carried out to assess its energy efficiency and the network performance by comparisons with the existing classic and latest intelligent clustering algorithms. The results indicate that ECFE improves not only the energy efficiency but also the network performance effectively.

Adaptive Sensor Sensing Range to Maximise Lifetime of Wireless Sensor Network

Wireless Sensor Network (WSN) is commonly used to collect information from a remote area and one of the most important challenges associated with WSN is to monitor all targets in a given area while maximizing network lifetime. In wireless communication, energy consumption is proportional to the breadth of sensing range and path loss exponent. Hence, the energy consumption of communication can be minimized by varying the sensing range and decreasing the number of messages being sent. Sensing energy can be optimized by reducing the repeated coverage target. In this paper, an Adaptive Sensor Sensing Range (ASSR) technique is proposed to maximize the WSN Lifetime. This work considers a sensor network with an adaptive sensing range that are randomly deployed in the monitoring area. The sensor is adaptive in nature and can be modified in order to save power while achieving maximum time of monitoring to increase the lifetime of WSN network. The objective of ASSR is to find the best sensing range for each sensor to cover all targets in the network, which yields maximize the time of monitoring of all targets and eliminating double sensing for the same target. Experiments were conducted using an NS3 simulator to verify our proposed technique. Results show that ASSR is capable to improve the network lifetime by 20% as compared to other recent techniques in the case of a small network while achieving an 8% improvement for the case of a large networks.