Energy Of Nodes Research Articles

Impact of Renewable Energy Resources on the Performance of DTN Networks in the Context of Hierarchical Routing Tree Topology (HRTT)

Delay-Tolerant Networks (DTN) are mobile networks specifically designed to operate in challenging wireless environments where connectivity is not guaranteed, and various issues such as frequent disconnections, long delivery delays, low delivery rates, and high error rates are common. One of the major problems of DTN networks is limited energy resources which impose strict constraints on the operational lifespan of DTN nodes. To solve these problems, we propose the adoption of a long-term strategy for energy management, based on the exploitation of renewable energies and the improvement of the HRTT topological architecture through the direct integration of renewable energy sources within DTN nodes. Indeed, DTN nodes powered by renewable energy resources, such as solar energy, have the potential to recharge themselves, extending their operational lifespan, increasing their storage and transmission capabilities, and reducing dependence on non-renewable energy resources. In this article, we assess the impact of limited energy resources and renewable energy resources like solar panels on the energy performance of DTN networks in the context of the Hierarchical Routing Tree Topology (HRTT), taking into account the average remaining energy as a performance metric. The evaluation of energy performance and the implementation of the HRTT topology are carried out using the ONE (Opportunistic Network Environment) simulator. According to the simulation results, the average remaining energy of nodes using renewable resources is significantly higher compared to the initial energy levels of nodes for all DTN protocols adopted by the nodes in the context of the HRTT topology. However, the average remaining energy of nodes using limited resources is lower compared to their initial energy levels for all DTN protocols adopted by the nodes in the context of the HRTT topology.

Design of an efficient energy based dual level co-operative route protocols in multiple hop WSNs

The design and implementation of an energy efficient dual stage cooperative routing protocol (DSCR) in multiple hop wireless nets is discussed in this paper in brief, with the goal of improving network life-time, boosting dependability, and reducing energy consumption. This contribution’s major goal is to propose an enhanced version of the double stage data transfer system that computes the routing path utilizing cooperative mechanisms and the notion of reduced node energy. The work presented here also includes talks, discussion and graphical representations of the various outcomes achieved for all of the test scenarios, as well as the relevant observations and explanations. The research paper comes to a close with the multiple hop networks’ overall conclusions remarks.

EAKRR : An energy-efficient key management in WSN with ECC-based cryptography

Environmental monitoring, industrial automation, and other applications require Wireless Sensor Networks (WSN). WSN must efficiently manage cryptographic keys for safe communication and energy efficiency. This study introduces “Energy-Aware Key Revocation and Rekeying” (EAKRR) to address energy efficiency and security in WSN using Elliptic Curve Cryptography (ECC). Energy-efficient key management is needed in WSN which have sensor nodes with limited resources and energy. EAKRR is designed to adapt to sensor node energy levels to make crucial key revocation and rekeying decisions while maintaining strong and resilient security. Energy profiling is used by EAKRR to assess sensor node energy consumption during cryptographic and operational operations. Energy conservation is achieved by localized key revocation when a node’s energy drops below a threshold. EAKRR also optimizes security and energy efficiency with ECC-based adaptive key length modifications. EAKRR is compared to LEACH and AES to determine its efficacy. The results show that EAKRR is highly energy efficient, extending the network’s lifespan and maintaining security. Cryptographic operations are careful and wise because they can adjust to energy levels in real time. The balance between security and energy usage shows that ECC-based encryption works. Energy-efficient key management in WSN is crucial. EAKRR solves WSN problems by saving energy, extending network life, and securing the network.

Enhancing data privacy in wireless sensor network using homomorphic encryption

From environmental monitoring to healthcare, WSN are essential. The extensive use of these networks to gather sensitive data requires strong data privacy mechanisms. Cryptographic homomorphic encryption may solve this problem. This study uses CKKS encryption and ciphertext packing to improve Wireless Sensor Network data privacy. CKKS is a fully homomorphic encryption scheme that allows complex data operations. This makes it ideal for data analytics and computations. Ciphertext packing improves computational efficiency, making this combination a good choice for data protection in WSN environments with limited resources. Performance efficiency is crucial to evaluation. This parameter evaluates CKKS encryption with ciphertext packing’s computational resource and execution time efficiency. This study examines how this approach affects WSN node and network performance, revealing its feasibility and practicality in real-world situations. The Data Security parameter highlights CKKS encryption with ciphertext packing’s data security. This comprehensive assessment determines data confidentiality and integrity in the Wireless Sensor Network. The system’s ability to withstand data breaches and unauthorized access is crucial for protecting sensitive data. Any WSN solution must consider energy consumption. This parameter evaluates data encryption and processing energy usage, taking sensor node energy constraints into account. The assessment of CKKS encryption with ciphertext packing in WSN sheds light on its potential to improve data confidentiality while addressing WSN challenges. This research strengthens WSN data protection efforts and lays the groundwork for future advances in this vital field.

A Spatial Clustering and Matching Game–Based Multihop Routing Algorithm for Heterogeneous UAVs

Unmanned aerial vehicle (UAV) clusters are increasingly deployed in military and civilian applications, necessitating efficient and reliable communication networks for cooperative operations. However, heterogeneous UAVs pose significant challenges for data transmission within and outside the cluster due to their high mobility, rapid topology changes, and limited node energy. For the routing planning problem of transmitting data from autonomous, heterogeneous UAVs to the cloud, existing methods struggle to accommodate the heterogeneity of UAVs and the communication bandwidth requirements, given the NP‐hard nature of the problem. To address this issue, this paper first constructs an exact model based on mixed‐integer linear programming to describe the heterogeneous UAVs, comprehensively searches the solution space, and generates a reasonable routing scheme for the heterogeneous UAVs, guiding the cluster communication network system design. Secondly, a heuristic algorithm is proposed that leverages density–based spatial clustering and matching game theory. The algorithm calculates relay nodes based on local density and relative distance. It generates an approximate optimal data transmission path for each UAV through a multilevel matching process, effectively reducing communication delay within the cluster. Finally, the paper conducts simulation experiments in randomly generated regional scenarios to validate the efficiency and effectiveness of the proposed method.

Enhanced ACO in clustering algorithm for QoS in WSN enabled IoT

Various sensor nodes whose deployment is done in a random way are included in Wireless sensor networks (WSN) within a region for gathering the information out of the respective atmospheres as well as forwards it towards the Base Station (BS). Constrained sources of energy are allotted to the WSN nodes. The consumption of energy of nodes must be reduced for obtaining the enhanced lifetime of the network. Clustering is referred as a technique that reduced the consumption of energy in WSNs. By transmitting the gathered data to the sink through the Cluster Head (CH) nodes located within the clustered networks, the energy of the node can be saved. In WSNs, fault tolerance is a significant issue to consider. The entire data communication system can be rendered inoperable by the failure of just one cluster head. Within the scope of this research, a cluster-based routing method with fault tolerance is provided. The purpose of this study is to present an innovative technique for increasing tolerance of failure and data accumulation in clustered WSN by making use of backup CHs and improving the relay node selection mechanism by modifying ACO. The selection of the backup cluster head is what allows for fault tolerance in this case (BKCH). The method can be broken down into two distinct stages. In the first step, the network is categorized into clusters; in the second step, CHs and BCHs are chosen from the pool of candidates. Communication within the cluster takes place between the member nodes and the CH nodes. Aggregator nodes (AG) are utilised for the purpose of inter-cluster communication, and the modified ACO is utilised to determine which node serves as the most effective relay between CH and AG. When compared with other energy-conserving protocols, this technique uses less energy while increasing fault resilience and PDR.

Analysis of Energy Efficiency Routing Protocols for Wireless Sensor Network

Abstract: A Wireless Sensor Network (WSN)isacollectionoftinypower,multifunctional,andcommunications nodes that observe and records one or more parameters at distinct places. Then,it converts recorded data to signals that can be processed. Such nodes are randomly implementedon a large or small scale, this becomes a significant field for study because these networks areusedtodayinnumerousconsumerandindustrialapplications,forinstanceinhealthcare,industry, transport system, government security, military systems, agriculture, and underwatersensor systems [1, 2]. The deployment of a huge amount of sensor nodes may enable greatermonitoring with high accuracy, but it can be very costly or even impossible to charge or replacebatteries because of the challenging deployed environment. These scattered sensor nodes arecapable of collecting and transferring data back to an internal base station (BS) or other sensors,sending and receiving information drain node's energy, Therefore, the best way to improve thelife of WSN is by selecting information transfer paths to minimize the complete drainage ofenergy along the route and to balance the load between the nodes [3]. The BS can be either amobileora fixed node that connects thesensor network t oacurrent infrastructure for communication or the internet. Since the WSNs have become an important element of the modern infrastructure of communication. To transmit information to t heir destination effectively, the power consumption and maximize network lifetime have become the criticalparameterin routing protocols [4]

A Link Cost Prediction Method Based on Node Energy Computation for Dynamic Route Switching in MANET

A Link Cost Prediction Method Based on Node Energy Computation for Dynamic Route Switching in MANET

Improvement of Energy Consumption in Fog Computing Via Task Offloading

The role of fog computing is to lessen the burden of Cloud and device computing. The purpose of this project is to solve the energy consumption and latency issues in the IoT 6G device as well as propose an approach to match the fog nodes with the IoT devices in task-offloading. This project implemented the Gale Shapley Stable Marriage Matching Game with a task-offloading energy efficiency technique. One-to-one matching is applied, in four fog nodes and four IoT devices according to the size of the task in IoT devices and energy in fog nodes. Simulation results prove that the proposed approach improves energy consumption and latency by 36.15% and 37.5% respectively.

Frozen Base Gateway Discovery Algorithm for Mobile Ad-Hock Network with Internet Connectivity Using AODV + Routing Algorithm

A mobile ad-hoc network (MANET) is a self-organized, auto-configured network and the mobile nodes have been free to move. To enable the MANET to connect to the internet it uses an intermediate bridge called a gateway. The MANET and the internet gateway is a heterogeneous network, which needs a gateway discovery. There are three types of gateway discovery mechanisms: proactive, reactive, and hybrid gateway discovery mechanism. The main challenge in the integration of the MANET and the Internet is, that there are high link disconnection results from node mobility and limited energy. In addition link disconnection, packet drop, and end-to-end delay. This study examines the impact of node mobility and energy in the frozen path from the source node to the destination node of MANET In this study, the Ad-hoc on-demand distance vector routing (AODV) routing protocol is used and modified to examine the gateway selection for MANET and the Internet. Moreover, the AODV+ control message RREQ and RREP and the routing table are improved to store the energy factor, path queue length, and frozen factor of the path. The proposed gateway discovery algorithm is known as frozen-based gateway AODV+ (F_AODV+), The stability factors of the path from the source node to the gateway, are calculated using a simple additive weighting method of the energy factor, average path queue node, and the path with the Maximum energy factor and minimum queue length is the more Frozen to select Frozen gateway from the source node to distension node.A mobile ad-hoc network (MANET) is a self-organized, auto-configured network and the mobile nodes have been free to move. To enable the MANET to connect to the internet it uses an intermediate bridge called a gateway. The MANET and the internet gateway is a heterogeneous network, which needs a gateway discovery. There are three types of gateway discovery mechanisms: proactive, reactive, and hybrid gateway discovery mechanism. The main challenge in the integration of the MANET and the Internet is, that there are high link disconnection results from node mobility and limited energy. In addition link disconnection, packet drop, and end-to-end delay. This study examines the impact of node mobility and energy in the frozen path from the source node to the destination node of MANET In this study, the Ad-hoc on-demand distance vector routing (AODV) routing protocol is used and modified to examine the gateway selection for MANET and the Internet. Moreover, the AODV+ control message RREQ and RREP and the routing table are improved to store the energy factor, path queue length, and frozen factor of the path. The proposed gateway discovery algorithm is known as frozen-based gateway AODV+ (F_AODV+), The stability factors of the path from the source node to the gateway, are calculated using a simple additive weighting method of the energy factor, average path queue node, and the path with the Maximum energy factor and minimum queue length is the more Frozen to select Frozen gateway from the source node to distension node.

A Wi-Fi dynamic routing algorithm based on energy prediction for wildlife monitoring wireless network

The routing protocol of wildlife monitoring Wi-Fi (Wireless Fidelity) networks cannot balance node energy consumption, leading to early node death. Therefore, the research on energy balance in wildlife monitoring Wi-Fi networks is a hot topic. In order to balance the energy consumption of Wi-Fi networks and extend the lifespan of wireless networks, we designed the low energy dynamic routing protocol LEACH-EP (Low Energy Adaptive Clustering Hierarchy- Energy Prediction) based on energy prediction by analyzing the long-range dependent characteristics of the remaining energy time series (RETS) of wireless network nodes. This protocol uses the LSTM (Long Short-Term Memory) model to predict the remaining energy of network nodes, and then dynamically plans routes using future remaining energy. We conducted a networking experiment in the Anzihe Nature Reserve in Chengdu, China, and the Energy Balance Factor index of the wireless network significantly improved. The Mean Absolute Error value of network nodes is less than 60 mW, which is less than 10% of the average daily energy consumption of nodes. Half of the surviving network nodes have achieved an increase to 55.2%, and the network death time has been extended by 38.6%. The experimental results show that the energy prediction routing protocol LEACH-EP can significantly extend the node survival life and balance network energy consumption.

Trilateration method based node localization and energy efficient routing using rsa for under water wireless sensor network

UWSN refers to a collection of numerous underwater wireless sensor-nodes dispersed throughout the marine environment. Proposed work develops node-localization and optimal relay node selection based routing approach for UWSN. Target nodes initially listen to the beacon for a certain amount of time whenever they are within range of an anchor node before retrieving an anchor-node with RSS data. Next, the Euclidean distance between a target node and an anchor node is determined. After that, another two anchor nodes were placed within the transmission range. The suggested approach then uses modified COOT-optimization to determine the target sensor node's coordinates in an effort to reduce localization-error. After that, the process of uneven clustering and selecting the cluster heads was done. For identifying the ideal relay node, the proposed technique employs a parameter-based approach to CH discovery, particularly RSO. Finally, using an information fusion technique, the sensing information is sent through relay nodes. Proposed localization and routing algorithm is validated using some of the parameter which achieves better performance like 94% localization accuracy, 6% localization error, 2.4% throughput value and 81% packet delivery ratio. It performs better when compared to other existing approaches such as DEEC, SEP, ELEACH and LEACH whose localization accuracy are 93%, 84%, 80% and 78%. Thus the techniques utilized in this proposed approach are the best choice for node localization and routing in UWSN.

Self‐attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

SummaryWSNs have a wide range of applications, and the effective Wireless Sensor Network (WSN) design includes the best energy optimization techniques. The nodes in wireless sensor networks run on batteries. The existing cluster head selection methods do not take into account the latency and rate of wireless network traffic when optimizing the node's energy constraints. To overcome these issues, a self‐attention based generative adversarial network (SabGAN) with Aquila Optimization Algorithm (AqOA) is proposed for Multi‐Objective Cluster Head Selection and Energy Aware Routing (SabGAN‐AqOA‐EgAwR‐WSN) for secured data transmission in wireless sensor network. The proposed method implements the routing process through cluster head. SabGAN classifiers are utilized to select the CH based on firm fitness functions, including delay, detachment, energy, cluster density, and traffic rate. After the selection of the cluster head, the malicious node gains access to the cluster. Therefore, the ideal path selection is carried out by three parameters: trust, connectivity, and degree of amenity. These three parameters are optimized under proposed AqOA. The data are transferred to the base station with the support of optimum trust path. The proposed SabGAN‐AqOA‐EgAwR‐WSN method is activated in NS2 simulator. Finally, the proposed SabGAN‐AqOA‐EgAwR‐WSN method attains 12.5%, 32.5%, 59.5%, and 32.65% higher alive nodes; 85.71%, 81.25%, 82.63%, and 71.96% lower delay; and 52.25%, 61.65%, 37.83%, and 20.63% higher normalized network energy compared with the existing methods.

Three-Dimensional Iterative Enhancement for Coverage Hole Recovery in Underwater Wireless Sensor Networks

The efficient coverage of underwater wireless sensor networks (UWSNs) has become increasingly important because of the scarcity of underwater node resources. Complex underwater environments, water flow forces, and undulating seabed reduce the coverage effect of underwater nodes, even leading to coverage holes in UWSNs. To solve the problems of uneven coverage distribution and coverage holes, a three-dimensional iterative enhancement algorithm is proposed for UWSN coverage hole recovery using intelligent search followed by virtual force. Benefiting from biological heuristic search algorithms, improved particle swarm optimization is applied for node pre-coverage. With the change in iteration times, the adaptive inertia weight, acceleration factor, and node position are constantly updated. To avoid excessive coverage holes caused by search falling into local optimum, underwater nodes are considered as particles in the potential field whose virtual forces are calculated to guide nodes towards higher coverage positions. In addition, based on the optimal node location obtained by the proposed algorithm, the monitoring area is divided based on the clustering idea. The underwater routing protocol DBR based on depth information is subsequently used to optimize node residual energy, and its average is calculated comprehensively and compared with the other three coverage algorithms using the DBR routing protocol. Based on the experimental data, after 100 iterations, the coverage rates for BES, 3D-IVFA, DABVF, and the proposed algorithm are 83.28%, 88.85%, 89.31%, and 91.36%, respectively. Moreover, the proposed algorithm is further verified from the aspects of different node numbers, coverage efficiency, node movement trajectory, coverage hole, and average residual energy of nodes, which provides conditions for resource development and scientific research in marine environments.

Machine learning based centralized dynamic clustering algorithm for energy efficient routing in vehicular ad hoc networks

AbstractVehicular ad hoc network (VANET) is a dynamic and self‐configure wireless network that connects vehicles to provide in‐vehicle infotainment services and comfort to both driver and passengers while on the move. Due to the highly dynamic environment, patchy connectivity, roadside barriers, and a scarcity of road side units (RSUs); existing routing protocols of VANET consume high energy during route construction. As a consequence, the VANET lifespan might be shortened. However, saving energy is a critical factor where VANET is projected to play a key role in the building of sustainable green infrastructure. Clustering is an imperative process where vehicle nodes join to make stable clusters based on common mobility features. In VANET, clustering‐based routing algorithms significantly improve network effectiveness and reduce infrastructure costs. This article proposes fuzzy c‐means (FM) machine learning‐based dynamic clustering algorithm (DCA) to identify trustworthy vehicles for energy‐efficient multi‐hop routing by considering different mobility parameters such as position, direction, speed, and remaining energy of each vehicle node. This article also proposes distance and power‐based variation of FM called fuzzy c‐means using distance and residual power (FMDP) to find stable cluster heads (CHs) for performing better data dissemination to the destination. The proposed DCA outperforms the existing k‐means based clustering algorithms as well as topology‐based DSR and AODV routing protocols in terms of packet delivery ratio, application throughput, end‐to‐end delay, network energy consumption, node energy consumption, and RSU energy consumption. Additionally, clustering methods FM and FMDP of suggested DCA improve end‐to‐end delay by 20%, application throughput by 5%, and reduce network energy consumption by 68% compared with the k‐means based clustering model.

Towards an error indicator-based [formula omitted]-adaptive refinement scheme in kinematic upper-bound limit analysis with the presence of seepage forces

This paper presents a new computational strategy for kinematic upper bound limit analysis in the presence of seepage forces with an improved mesh refinement scheme. In particular, the original adaptive refinement scheme is enhanced with a simple but efficient error-indicator of the nodal plastic dissipation for high-order elements. The proposed refinement criteria facilitate a precise quantitative evaluation of errors in the nodal plastic energy dissipation across each element and ensure that the rate of total refined element number gradually decreases with adaptive step. Adhering to two-dimensional steady state seepage condition, numerical details regarding the calculation of total water head distributions for the seepage field are provided. In a similar manner as treating the unit weight of the soil, the effects of seepage forces are incorporated as body forces in the upper bound formulation. Detailed numerical procedure of the proposed error indicator-based h-adaptive refinement scheme incorporating the inclusion of seepage forces is addressed and implemented in the in-house code. Several benchmark problems, including both homogeneous and non-homogeneous soils, are analyzed to evaluate the excellent performance of the proposed error indicator-based h-adaptive refinement scheme in upper-bound limit analysis and assess the influence of seepage forces on the stability of geostructures through comparison with that for dry condition.

An Energy Efficient Cluster Head Selection in WSN Based on Enhanced Chicken Swarm Optimization

Clustering of data are ab effective energy saving method for wireless sensor networks (WSN). However, they incur additional costs in data collections from sensor nodes and sending them to base station (BS) after aggregations. Cluster heads (CHs) in hierarchical cluster-based WSNs use more energy. Therefore, choosing a suitable CHs is essential to conserve energy for sensor nodes and prolong the life of the WSN. Provide energy-saving cluster head selection techniques in this work. where enhanced fuzzy means clustering is used to perform the first cluster construction. Enhanced Chick Swarm optimization (ECSO) selects CHs which is evaluated strictly for varying WSN scenarios and counts of CHs and nodes for values of remaining energies of sensor nodes, sink distances, and distances within clusters. To demonstrate that the proposed technique is superior, the results are compared with those of several other currently used algorithms.

Fragmentation of data packets in wireless sensor network with variable temperature and channel conditions

This study formalizes and solves the problem of minimizing the energy dissipated by a node in order to successfully transmit the initial data amount during one round of communication over a wireless channel affected by Rice fading under varying temperature conditions. In this work, the models for the temperature dependence of the parameters in an incoherent frequency shift key (FSK) demodulator are presented and investigated for the first time, taking into account both the frequency separation and the synchronization type of the clock generator. The studied orthogonality coefficients of FSK signals represent internal parameters in the proposed model of the incoherent Rice wireless channel. This model allows the analytical relationship between the bit error probability (BEP) and node heating temperature, signal fading depth in the channel (Rician K-factor), and signal-to-noise ratio (SNR) to be established.Since it is necessary to retransmit the entire data packet when an erroneous bit is detected in a communication system that uses an automatic repeat request, at high BEP values, it is recommended to fragment packets in order to improve the energy efficiency of the nodes. By using the developed models for calculating energy losses during the communication round, it was determined that optimal fragmentation options (OFO) for data packets can be identified within specific ranges of temperature and Rician K-factor, which includes the overhead costs for packeting and retransmission. As a result, OFO matrices of packets in the "temperature – K-factor" (t0–K) coordinate system were obtained for minimizing the energy loss of nodes at different SNR values. In our example of using TI CC2500 node transceivers for 90-day monitoring of corn vegetation, the t0K-Frag algorithm reduced node energy losses by a factor of K = 3.3 compared to the case of transmitting an unfragmented packet of initial length.

Combined Osprey-Chimp Optimization for Cluster Based Routing in Wireless Sensor Networks: Improved DeepMaxout for Node Energy Prediction

The significant advances in Wireless Sensor Networks (WSNs) facilitate many latest applications, such as intelligent battlefield, home automation, traffic control, and more. WSNs comprise small autonomously organized sensor nodes that are powered by batteries. The processes of collecting information and data storage, processing, and transmission deplete the energy of these small devices. Energy efficiency is still a major issue to address in WSN routing. Clustering is the best method that has been developed to reduce node energy consumption. However, current clustering methods are unable to effectively distribute the energy requirements of the nodes without considering energy characteristics, number of nodes, and flexibility. This study proposed a new cluster-based routing model for WSNs and emphasized the need for an improved clustering process with new optimization techniques. In particular, the improved DeepMaxout model was adopted to predict the energy of the nodes. Cluster Head (CH) selection is performed considering the nodes' energy as a prime factor. After choosing the CH, the CIOO algorithm incorporates new link quality and trust evaluations while determining the routing process. Finally, a comparison of energy utilization factors was performed between the suggested and traditional approaches.

Performance Analysis of MIMO Heterogeneous Wireless Sensor Networks

Wireless Sensor Networks (WSN) are widely used in remote applications related to defence and healthcare. A network with nodes having different capabilities like sensing, various computational capabilities, power-efficient communication, and a varied sensing range is called a heterogeneous wireless sensor network. Heterogeneous wireless sensor networks using MIMO wireless channels are more useful for energy-efficient multi-channel communication. MIMO applications in wireless sensor networks have the potential to enhance throughput, reduce End-to-End Delay, improve packet delivery ratios, and conserve energy in wireless sensor networks. Its implementation needs to be carefully considered in light of the specific deployment conditions and resource constraints of the network, considering proper antenna design, synchronisation mechanisms, and energy-efficient algorithms. This paper presents a comparative performance analysis of MIMO wireless sensor networks and traditional wireless sensor networks without MIMO for various Quality of Service parameters like Packet Delivery Ratio, End to End Delay, Throughput and Residual energy. The research work shows that the application of MIMO in Wireless Sensor Networks enables sensor nodes to collaborate effectively, leading to improved reliability and coverage, and also increases the network's lifetime by conserving energy in resource-constrained sensor nodes through the preservation of Residual Energy.