Lifetime Maximization Research Articles

EELTM: An Energy Efficient LifeTime Maximization Approach for WSN by PSO and Fuzzy-Based Unequal Clustering

The Wireless Sensor Network (WSN) personifies vital and active functions in multi-disciplinary research sectors, as it can deploy in harsh and antagonistic atmospheres where the deployment of a wired system is not possible. However, designing an energy efficient and durable WSN is still a key challenge. Though the contribution of the clustering mechanism attempts to augment the network LifeTime, the energy consumption in the Cluster Heads (CHs) is rapidly high. This led to the frequent change of CHs and minimized the network’s lifetime. To diminish these issues, we propose an Energy Efficient LifeTime Maximization (EELTM) approach which utilizes the intelligent techniques Particle Swarm Optimization (PSO) and Fuzzy Inference System (FIS). Further, we propose an optimal CH–CR selection algorithm in our approach which exploits the fitness values calculated by the PSO technique to determine two optimal nodes in each cluster to act as CH and Cluster Router (CR). The selected CH exclusively gathers the information from its cluster members, whereas the CR is liable for receiving the gathered information from its CH and transferring it to the BS. Thus, the overhead of CH is reduced. Another intelligent technique is that FIS figures out the radius for each CH, and thereby it partitions the network into unequal clusters. The performance of our proposed EELTM approach is analyzed, and evaluations are elaborated with well-known existing clustering algorithms. To assess the proficiency of EELTM and to evaluate the endurance of the network, efficiency parameters such as total-remaining-energy, first-node-expires and fifty-percent-expires are exploited. The experimental outcomes justify that the EELTM approach surpasses the existing mechanisms by 14%.

Electro-Thermal and Aging Lithium-Ion Cell Modelling with Application to Optimal Battery Charging

This paper deals with optimal charging versus aging minimization for lithium-ion batteries. The optimal charging strategy proposed involves charging controllers whose design relies on a battery model. The model, especially designed for automotive battery management systems applications, is recalled in this paper. It provides the voltage response of a cell to an input current. It also models side reactions that produce degradation mechanisms and thus decrease battery performance. Side reaction modelling involves taking into account the temperature cell variations, which are thus also modelled. The association of the three above-mentioned sub-models leads to an electro-thermal battery aging model used to design an optimal charging strategy that simultaneously takes into account the minimization of charging time and maximization of battery lifetime. Thus, to achieve a charging controller that manages battery health, an appropriate charging trajectory was computed by solving an optimization problem minimizing aging. Then, a charge control loop was designed. The nonlinear behavior of the battery was taken into account through the linearization of the electro-thermal aging model in different operating conditions. To take into account the resulting linear model family, the CRONE design methodology was used. The principles of this methodology are recapped and the design of the charging control loop is explained. The efficiency of the resulting charge controller is illustrated by several simulations.

RETRACTED ARTICLE: Energy efficient momento based dynamic scheduling for lifetime maximization in WSN

RETRACTED ARTICLE: Energy efficient momento based dynamic scheduling for lifetime maximization in WSN

Game-theory-based lifetime maximization of multi-channel cooperative spectrum sensing in wireless sensor networks

Accurate and efficient detection of the radio-frequency spectrum is a challenging issue in wireless sensor networks (WSNs), which are used for multi-channel cooperative spectrum sensing (MCSS). Due to the limited battery power of sensors, lifetime maximization of a WSN is an important issue further sensing quality requirements. The issue is more complex if the low-cost sensors cannot sense more than one channel simultaneously, because they do not have high-speed Analogue-to-Digital-Convertors which need high-power batteries. This paper proposes a novel game-theoretic sensor selection algorithm for MCSS that extends the network lifetime assuming the quality of sensing and the limited ability of sensors. To this end, an optimization problem is formulated using the “max–min” method, in which the minimum remaining energy of sensors is maximized to keep energy balancing in the WSN. This paper proposes a coalition game to solve the problem, in which sensors act as game players and decide to make disjoint coalitions for MCSS. Each coalition senses one of the channels. Other nodes, that decide to sense none of the channels, turn off their sensing module to reserve energy. First, a novel utility function for the coalitions is proposed based on the remaining energy and consumption energy of sensors besides their detection quality. Then, an algorithm is designed to reach a Nash-Equilibrium (NE) coalition structure. The existence of at least one NE, converging toward one of the NEs, and the computational complexity of the proposed algorithm are discussed. Finally, simulations are presented to demonstrate the ability of the proposed algorithm, assuming the systems using IEEE802.15.4/Zigbee and IEEE802.11af.

Distributed on-demand clustering algorithm for lifetime optimization in wireless sensor networks

Wireless Sensor Networks (WSNs) play a significant role in Internet of Things (IoT) to provide cost effective solutions for various IoT applications, e.g., wildlife habitat monitoring, but are often highly resource constrained. Hence, preserving energy (or, battery power) of sensor nodes and maximizing the lifetime of WSNs is extremely important. To maximize the lifetime of WSNs, clustering is commonly considered as one of the efficient technique. In a cluster, the role of individual sensor nodes changes to minimize energy consumption, thereby prolonging network lifetime. This paper addresses the problem of lifetime maximization in WSNs by devising a novel clustering algorithm where clusters are formed dynamically. Specifically, we first analyze the network lifetime maximization problem by balancing the energy consumption among cluster heads. Based on the analysis, we provide an optimal clustering technique, in which the cluster radius is computed using alternating direction method of multiplier. Next, we propose a novel On-demand, oPTImal Clustering (OPTIC) algorithm for WSNs. Our cluster head election procedure is not periodic, but adaptive based on the dynamism of the occurrence of events. This on-demand execution of OPTIC aims to significantly reduce computation and message overheads. Experimental results demonstrate that OPTIC improves the energy balance by more than 18% and network lifetime by more than 19% compared to a non-clustering and two clustering solutions in the state-of-the-art.

Energy Efficiency in Ad-Hoc On Demand Distance Vector and Dynamic Source Routing Protocols Using High Energy First Algorithm for Mobile Ad-Hoc Networks

The main issue that occurs in battery powered applications in Mobile Ad Hoc networks (MANETs) is energy efficiency. Routing protocols are mainly based on energy related parameters which are used to extend the lifetime of networks. In most of the situations Shortest path routing protocols are used in MANETs which do not consider the energy awareness. Hence power failure of the nodes affect the life time of the network. In this paper we focus on Energy efficiency in Ad-hoc On Demand Distance Vector (AODV) and Dynamic Source Routing (DSR) protocol by using High Energy First (HEF) algorithm to overcome the shortcomings that occur in MANETs due to increase in node density. Simulations are performed using NS-2 and we have compared the performance of AODV and DSR using HEF with the performance of existing DSR and AODV to prove the maximization of lifetime of the network. Simulation results show that the proposed protocols with HEF are efficient than the existing protocols.

Maximizing Lifetime of Range-Adjustable Wireless Sensor Networks: A Neighborhood-Based Estimation of Distribution Algorithm.

Sensor activity scheduling is critical for prolonging the lifetime of wireless sensor networks (WSNs). However, most existing methods assume sensors to have one fixed sensing range. Prevalence of sensors with adjustable sensing ranges posts two new challenges to the topic: 1) expanded search space, due to the rise in the number of possible activation modes and 2) more complex energy allocation, as the sensors differ in the energy consumption rate when using different sensing ranges. These two challenges make it hard to directly solve the lifetime maximization problem of WSNs with range-adjustable sensors (LM-RASs). This article proposes a neighborhood-based estimation of distribution algorithm (NEDA) to address it in a recursive manner. In NEDA, each individual represents a coverage scheme in which the sensors are selectively activated to monitor all the targets. A linear programming (LP) model is built to assign activation time to the schemes in the population so that their sum, the network lifetime, can be maximized conditioned on the current population. Using the activation time derived from LP as individual fitness, the NEDA is driven to seek coverage schemes promising for prolonging the network lifetime. The network lifetime is thus optimized by repeating the steps of the coverage scheme evolution and LP model solving. To encourage the search for diverse coverage schemes, a neighborhood sampling strategy is introduced. Besides, a heuristic repair strategy is designed to fine-tune the existing schemes for further improving the search efficiency. Experimental results on WSNs of different scales show that NEDA outperforms state-of-the-art approaches. It is also expected that NEDA can serve as a potential framework for solving other flexible LP problems that share the same structure with LM-RAS.

W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks.

Underwater sensor networks (UWSNs) have witnessed significant R&D attention in both academia and industry due to their growing application domains, such as border security, freight via sea or river, natural petroleum production and the fishing industry. Considering the deep underwater-oriented access constraints, energy-centric communication for the lifetime maximization of tiny sensor nodes in UWSNs is one of the key research themes in this domain. Existing literature on green UWSNs are majorly adapted from the existing techniques in traditional wireless sensor network relying on geolocation and the quality of service-centric underwater relay node selection, without paying much attention to the dynamic underwater network environments. To this end, this paper presents an adapted whale and wolf optimization-based energy and delay-centric green underwater networking framework (W-GUN). It focuses on exploiting dynamic underwater network characteristics by effectively utilizing underwater whale-centric optimization in relay node selection. Firstly, an underwater relay node optimization model is mathematically derived, focusing on underwater whale dynamics for incorporating realistic underwater characteristics in networking. Secondly, the optimization model is used to develop an adapted whale and grey wolf optimization algorithm for selecting optimal and stable relay nodes for centric underwater communication paths. Thirdly, a complete workflow of the W-GUN framework is presented with an optimization flowchart. The comparative performance evaluation attests to the benefits of the proposed framework and is compared to state-of-the-art techniques considering various metrics related to underwater network environments.

Lifetime Maximization in Mobile Edge Computing Networks

Mobile edge computing has emerged as a promising technology to augment the computational capabilities of mobile devices. For a multi-user network in which its users periodically compute their tasks with the help of an edge cloud, we investigate the network lifetime maximization problem based on present user task information. We pursue this objective via a minimum energy efficiency maximization (MEEM) strategy that jointly optimizes the fraction of user task computations offloaded to the cloud and the respective allocation of edge computing and network communication resources across the users. We also investigate the network lifetime maximization problem for the case when the user task information is available for all future time slots, as well. This setting represents an upper bound for the MEEM strategy. Optimal solutions for both investigated strategies are formulated via feasibility testing and geometric programming. We show that MEEM can achieve a 70% lifetime improvement over the state-of-the-art and 460% lifetime improvement over the case of local user task computation only. We also show that for a high value of the maximum tolerable delay for completing the computation tasks of the users, MEEM achieves the globally optimal network lifetime performance. Finally, we show that MEEM achieves a significant reduction (3X) in variation of enabled network lifetime over diverse network topologies, relative to the state-of-the-art.

Minimizing the maximum receiver interference in wireless sensor networks using probabilistic interference model

Now in the era of the Internet of Energy (IoE), researchers are more focused on optimal energy utilization. In the wireless sensor network, maximization of battery lifetime or network lifetime is one of the primary research objectives. There are many techniques available to enhance the network lifetime, and interference minimization is one of them. Interference minimization leads to less transmission power consumption in wireless sensor networks and thus enhances the network lifetime. The interference minimization is proven to be NP-Hard problem. In this paper, we have proposed a method to minimize receiver interference using different encoding schemes and genetic algorithm. We have used more realistic probabilistic interference model to calculate receiver interference instead of the graph-based model used in most of the literature. The Genetic Algorithm used to minimize receiver interference uses three different chromosome representation schemes, namely Prüfer code, Edge-set, and Edge-window-decoder. We have used benchmark data sets and special cases like exponential node chain, two exponential node chain, spiral model, one cluster, and two-cluster for experimental simulations. Our proposed algorithm outperforms other algorithms available in the literature like MI-S (Minimizing Interference in Sensor networks), MinMax-RIP (Minimizing Maximum Receiver Interference Problem), and MST (Minimum Spanning Tree: Prim’s algorithm) in terms of minimizing maximum receiver interference in the network.

Mobile wireless sensor network lifetime maximization by using evolutionary computing methods

Due to the continuous development and progress of wireless communication technology and sensor network technology, wireless sensor networks (WSNs) have gradually become an attractive technology that facilitates people's lives. Due to the extensive use of WSNs, maximizing the lifetime of WSNs to obtain real-time and effective information has become a critical concern. This paper studies the life of mobile wireless sensor networks (MWSNs). MWSNs are a special type of WSN in that the sensor nodes are movable within a certain area. A system model is developed to prolong the network lifetime of MWSNs. This paper uses five evolutionary computing (EC) algorithms to develop the MWSN lifetime optimization model. Numerical simulations are performed to study the advantages and disadvantages of the five algorithms for solving the model. The comparison and discussion can provide advice for using EC algorithms to solve MWSN lifetime maximization problems.

Multiattribute‐based routing for lifetime maximization in opportunistic mobile social networks

SummaryWith the advancement in wireless technology and portable devices, smart phones have become one of the belongings of human being. People share their data online, but it has limitations due to transmission range and mobility. Yet opportunistic mobile social network enables users to share data online even if there is no connected path between source and destination. The widespread use of mobile phones equipped with WiFi, Bluetooth, and several other components and contact opportunities among humans bridge the gap between internet available and nonavailable area. In this paper, we have proposed a new routing approach which utilizes both spatial and temporal attributes of user such as probability to meet a particular location and remaining intercontact time between two nodes to select better relay nodes. Generally, users visit different locations such as sports stadium and mall, with varying probability. Users with similar interest form group, and each user has different intercontact time with other users according to their point of interest and visiting pattern. By utilizing multiattributes, different forwarding strategies have been devised for both inter and intragroup routing. The proposed work “point of interest (PoI)‐based routing” is implemented in Opportunistic Network Environment simulator, and the performance is analysed in terms of delivery rate, latency, overhead, goodput, and energy consumed. The simulation results show that PoI diminishes 23% of overhead and yields 24% improvement in goodput over the state‐of‐the‐art protocol. Thus, the simulation results reveal that our proposed work provides the balance between routing performance and resource consumption.

Increasing the Lifetime of Underwater Acoustic Sensor Networks: Difference Convex Approach

The aim of this article is to develop and implement an optimal method for relay node placement (RNP) to increase lifetime of underwater acoustic sensor networks. Employing a relay node to extend the lifetime of critical node is an important problem in network lifetime maximization, whereas energy consumption of relay node is also a limiting factor. Consequently, maximizing the lifetime of critical node and minimizing consumption of relay node are the two main conflicting objectives that are met by applying the multiobjective optimization. To transform the above objectives into a single objective, weighted sum method is applied, which uses weights. Moreover, the RNP problem investigated in this article is a nonconvex problem, which difference convex algorithm (DCA), is developed in order to compute the optimal solution. Application of DCA offers the advantage of finding a global optimal solution. Simulation results backed by theoretical analysis confirm that the proposed approach is superior to the relay node adjustment method in terms of extending the network lifetime and efficiency, whereas their computational complexity is the same.

Empirical Electrical and Degradation Model for Electric Vehicle Batteries

Battery degradation is one of the key barriers to the correct deployment of electric vehicle technology. Therefore, it is necessary to model, with sufficient precision, the State of Health (SoH) of batteries at every moment to know if they are useful as well as to develop operating strategies aimed at lifetime maximization. This paper presents a commercial electric vehicle with a nickel-cobalt-manganese (NCM) battery cell model that is composed of electrical and degradation submodels given by cycling aging. The studied cell is an LG Chem E63 cell, which is used in Renault Zoe electric vehicles. This degradation model is based on experimental results that are interpolated in the Hermite Cubic Interpolation Polynomial (PCHIP), with the exception of the number of cycles, whose impact is determined by a potential law. Temperature and C-rate are found to be the most influential factors in the aging of these batteries. The degradation model developed presents an RMSE of 1.12% in capacity fade and 2.63% in power fade. Furthermore, an application of the model is presented, in which high demanding (highway), average demanding (mixed), and low demanding (urban) driving environments are analyzed in terms of degradation.

Type 2 Fuzzy Logic based Unequal Clustering algorithm for multi-hop wireless sensor networks

Wireless sensor network (WSN) is an integral part of IoT and Maximizing the network lifetime is a challenging task. Clustering is the most popular energy efficient technique which leads to increased lifetime stability and reduced energy consumption. Though clustering offers several advantages, it eventually raises the burden of CHs located in proximity to the Base Station (BS) in multi-hop data transmission which makes the CHs near BS die earlier than other CHs. This issue is termed as hot spot problem and unequal clustering protocols were introduced to handle it. Presently, some of the clustering protocols are developed using Type-2 Fuzzy Logic (T2FL) but none of them addresses hot spot problem. This paper presents a Type-2 Fuzzy Logic based Unequal Clustering Algorithm (T2FLUCA) for the elimination of hot spot problem and also for lifetime maximization of WSN. The proposed algorithm uses residual energy, distance to BS and node degree as input to T2FL to determine the probability of becoming CHs (PCH) and cluster size. For experimentation, T2FLUCA is tested on three different scenarios and the obtained results are compared with LEACH, TEEN, DEEC and EAUCF in terms of network lifetime, throughput and average energy consumption. The experimental results ensure that T2FLUCA outperforms state of art methods in a significant way.

A hyper-heuristic framework for lifetime maximization in wireless sensor networks with a mobile sink

Maximizing the lifetime of wireless sensor networks &#x0028 WSNs &#x0029 is an important and challenging research problem. Properly scheduling the movements of mobile sinks to balance the energy consumption of wireless sensor network is one of the most effective approaches to prolong the lifetime of wireless sensor networks. However, the existing mobile sink scheduling methods either require a great amount of computational time or lack effectiveness in finding high-quality scheduling solutions. To address the above issues, this paper proposes a novel hyperheuristic framework, which can automatically construct high-level heuristics to schedule the sink movements and prolong the network lifetime. In the proposed framework, a set of low-level heuristics are defined as building blocks to construct high-level heuristics and a set of random networks with different features are designed for training. Further, a genetic programming algorithm is adopted to automatically evolve promising high-level heuristics based on the building blocks and the training networks. By using the genetic programming to evolve more effective heuristics and applying these heuristics in a greedy scheme, our proposed hyper-heuristic framework can prolong the network lifetime competitively with other methods, with small time consumption. A series of comprehensive experiments, including both static and dynamic networks, are designed. The simulation results have demonstrated that the proposed method can offer a very promising performance in terms of network lifetime and response time.

Real time energy efficient data aggregation and scheduling scheme for WSN using ATL

Wireless Sensor Network (WSN) has been used for data collection in different situations. Various methods are described earlier for the problem, but they consider only limited parameters in data collection which introduces poor lifetime, throughput and energy holes. To overcome this issue, a real time energy efficient data aggregation approach is presented here. The method schedules the nodes of the network for their working based on the parameters (Availability-Throughput-Lifetime) ATL. Using all these parameters, the method estimates Data Availability Support, Network Throughput Support and Network Lifetime Support measures. Using all these support measures, the method computes the Data Aggregation Support (DAGS) measure. Using the measure estimated, the nodes are scheduled for their working. Similarly, the nodes are selected for data aggregation based on the above support measures estimated. The ATL approach achieves higher data aggregation performance with higher lifetime maximization.

Interference-aware lifetime maximization with joint routing and charging in wireless sensor networks

Radio Frequency based Wireless power transfer (RF-WPT) has become increasingly popular in recent years. Its ability to harvest Radio Frequency (RF) energy enables a novel approach to charge low-power wireless devices, resulting in benefits to product design, usability, and reliability in wireless sensor networks (WSN). It is however known that RF-WPT also introduces interference to wireless communications, leading to poor data throughput. The joint routing and charging remains a challenging job in RF energy harvesting networks. In this paper, we take initial steps towards understanding both data routing and charger scheduling in WSNs. We propose a smart interference-aware scheduling to maximize network lifetime and avoid potential data loss caused by charging interference. Then, we theoretically prove the optimality of the proposed design, i.e., $$1 - \frac{\phi }{W}$$, where W is an arbitrary positive integer and $$\phi$$ is determined by network properties. The evaluation indicates that the proposed design can guarantee 99% optimality and significantly improve network lifetime in WSNs.

A Temperature-Suppression Charging Strategy for Supercapacitor Stack With Lifetime Maximization

It is expected, the growing demand for supercapacitor energy storage system (ESS) in which thousands of supercapacitor cells are connected as a stack for meeting the desired load power. The lifetime of ESS dramatically depends on the operating thermal effects of individual cells. In this paper, a temperature-suppression charging strategy is proposed for supercapacitor ESS to maximize its lifetime. First, the charging current and equivalent series resistance (ESR) construct the supercapacitor thermal model, and the ESR represents the degradation degree of the supercapacitor. Second, the maximization of system lifetime and equalization of unit degradation degree jointly form a convex optimization problem with the charging time constraint. The optimization problem is solved to obtain the equivalent charging current, which is realized through pulse width modulation (PWM) digital control. Simulation and experimental results show that the proposed method has the advantages of reducing ESS operating temperature and maximizing its lifetime.

Efficient traffic control and lifetime maximization in mobile ad hoc network by using PSO–BAT optimization

Recent developments in dynamic mobile ad-hoc network enhance the network speed and reliability. The nodes in the dynamic ad-hoc network are moving in nature. Due to the increased subscribers in this network, the network traffic has increased to manifold which in turn creating the challenge of maintaining the energy level. In path optimization process in mobile ad-hoc network consumes more energy and the draining of the energy is dependent on network reliability and connectivity. Further, the network also suffers by harmful attacks such as denial of service attack, black hole attack and warm hole attack. The primary focus of this paper is to prevent these attacks with the help of dynamic mobile ad-hoc network on demand protocol and hybrid meta-heuristics methodologies, and also to reduce the energy drain rate. This is achieved by estimating the velocity and fitness value of the nodes. Finally, the empirical simulation results of hybrid particle swarm optimization with bat algorithm (PSO–BAT) shows that the energy drain rate level is reduced 90% as 1 mJ/s than ad-hoc on demand vector. The end-to-end delay minimized to 50% than existing Ad hoc on-demand distance vector routing. The performance metrics routing overhead and execution time has been reduced and throughput is gradually increased in PSO–BAT optimization in dynamic mobile ad hoc network scenario.