Low-energy Clustering Research Articles

Ab initio framework for nuclear scattering and reactions induced by light projectiles

A quantitative and predictive microscopic theoretical framework that can describe reactions induced by α particles (4He nuclei) and heavier projectiles is currently lacking. Such a framework would contribute to reducing uncertainty in the modeling of stellar evolution and nucleosynthesis and provide the basis for achieving a comprehensive understanding of the phenomenon of nuclear clustering (the organization of protons and neutrons into distinct substructures within a nucleus). We have developed an efficient and general configuration-interaction framework for the description of low-energy reactions and clustering in light nuclei. The new formalism takes full advantage of powerful second-quantization techniques, enabling the description of α-α scattering and an exploration of clustering in the exotic 12Be nucleus. We find that the 4He(α,α)4He differential cross section computed with non-locally regulated chiral interactions is in good agreement with experimental data. Our results for 12Be indicate the presence of strongly mixed helium-cluster states consistent with a molecular-like picture surviving far above the 6He+6He threshold, and reveal the strong influence of neutron decay in both the 12Be spectrum and in the 6He(6He,α)8He cross section. We expect that this approach will enable the description of helium burning cross sections and provide insight on how three-nucleon forces influence the emergence of clustering in nuclei.

An energy efficient clustering algorithm based on density and fitness for mobile crowd-sensing network

Mobile crowd-sensing (MCS) is a cutting-edge paradigm that gathers sensory data and generates valuable insights for a multitude of users by utilizing built-in sensors and social applications in mobile devices. This enables a broad spectrum of Internet of Things (IoT) services. We introduce a novel MCS algorithm, Mobile Crowd-sensing Low Energy Clustering (MCLEC), which employs advanced clustering techniques to address issues of data oversampling and energy inefficiency prevalent in MCS networks. MCLEC innovatively adjusts clustering radii based on local node density and the proximity of nodes to the cloud server, thus optimizing data transmission paths and reducing energy consumption. A pivotal enhancement in MCLEC is its cluster head election strategy, which prioritizes leaders based on their energy levels and mobility, thereby enhancing network stability and minimizing the frequency of head re-elections. Our comparisons with established algorithms such as LEACH, LEACH-C, LEACH-M, DEEC, and SEP show that MCLEC significantly improves energy efficiency, reduces server load, and prolongs the lifespan of network nodes, establishing it as an effective solution for IoT applications dependent on MCS. Additionally, MCLEC was compared with other novel clustering algorithms including E-FLZSEPFCH, DFLC, ECPF, ACAWT, UCR, CHEF, and Gupta's algorithm. The results indicate that MCLEC also surpasses most of these algorithms in terms of energy consumption and network lifetime.

Low-Energy Clustering Protocol for Query-Based Wireless Sensor Networks

Nowadays, clustering protocols have been widely used to reduce energy consumption in wireless sensor networks (WSNs). However, when existing clustering protocols are used for query-based WSNs, the energy efficiency becomes limited because in such networks, clusters are formed without considering the positional distribution of query targets, and the procedures of node clustering, cluster head selection and inter-cluster routing are performed in a distributed way, which is energy-consuming. In this paper, a centralized clustering protocol, called Low-Energy Dynamic Clustering (LEDC), is developed to enhance the energy efficiency in query-based WSNs. The main contributions of LEDC include: (1) a novel prediction-based scheme is raised to make the centralized network information maintenance energy-efficient and scalable; (2) centralized optimal dynamic cluster formation algorithms are proposed to cluster the query targets in a centralized way, which not only helps the distribution of clusters match with that of query targets but also saves the high cost incurred in the distributed procedures of node clustering, cluster head selection and inter-cluster routing; (3) an energy-aware forwarding strategy is suggested to improve the energy efficiency and balance the energy load among nodes in inter-cluster communication; (4) a parallel processing algorithm is raised to increase the network throughput and reduce the latency of queries. The performance of LEDC is then compared to the latest clustering protocol. Simulation results demonstrate that LEDC considerably saves the energy of nodes and prolongs the network lifetime.

Clone Chaotic Parallel Evolutionary Algorithm for Low-Energy Clustering in High-Density Wireless Sensor Networks

Because the sensors are constrained in energy capabilities, low-energy clustering has become a challenging problem in high-density wireless sensor networks (HDWSNs). Usually, sensor nodes tend to be tiny devices along with constrained clustering abilities. To have a low communication energy consumption, a low-energy clustering scheme should be designed properly. In this work, a new cloned chaotic parallel evolution algorithm (CCPEA) is proposed, and a low-energy clustering model is established to lower the communication energy consumption of HDWSNs. By introducing CCPEA into the low-energy clustering, an objective function is designed for evaluating the communication energy consumption. For this problem, we define a clone operator to minimize the communication energy consumption of HDWSNs, use the chaotic operator to randomly generate the initial population to expand the search range to avoid local optimization, and find the parallel operator to speed up the convergence speed. In the experiment, the effect of CCPEA is compared to heuristic approaches of particle swarm optimization (PSO) and simulated annealing (SA) for the HDWSNs with different numbers of sensors. Simulation experiments demonstrate that the presented CCPEA method achieves a lower communication energy consumption and faster convergence speed than PSO and SA.

Low-energy dynamic clustering scheme for multi-layer wireless sensor networks

It is inconvenient to replace the sensor nodes with limited energy in wireless sensor networks (WSNs) when they are deployed in remote or dangerous monitoring area, that makes energy consumption become the main issue to limit wide application of WSNs. Existing clustering methods only consider heterogeneity of node energy for head selection, irrespective of differences in network structure and node density. Fuzzy logic scheme is applied in this paper to consider multiple factors clustering with the purpose of prolonging network lifetime. In addition to the initial energy, the relative density of nodes and the relative distance between nodes and base station are considered to select the cluster head dynamically. Experimental results have demonstrated that the proposed dynamic clustering scheme can balance the energy consumption of the nodes in both homogeneous and heterogeneous networks, effectively prolong the lifetime of the network, maintain the accuracy of data aggregation.

Improved design of low energy clustering algorithm for mobile sensor network

LEACH class clustering algorithms have been widely used in the topology control of mobile sensor networks because of their simplicity, practicality and good energy efficiency. Aiming at the common problems of LEACH clustering algorithm, such as large energy consumption of nodes, short network life cycle, easy loss of data transmission, etc., according to the set mobile sensor network model, based on LEACH-Mobile algorithm, a low-energy clustering algorithm LEACH-MII is designed. LEACH-MII clustering algorithm assigns tasks according to the working conditions of sensor nodes, combines the activity, location and energy consumption of nodes to select and rotate cluster heads; and uses dynamic time slot allocation mechanism to avoid time slot reallocation caused by frequent join-cluster and leave-cluster of sensor nodes, so as to ensure the stability and connectivity of the network structure, reduce the energy consumption of nodes, and extend the life cycle of the network. In order to verify the feasibility and effectiveness of LEACH-MII clustering algorithm, the simulation experiment of LEACH-MII clustering algorithm is carried out. The simulation results show that The data transmission efficiency, average node energy consumption and network life cycle of LEACH-MII network are 30%, 20% and 15% better than LEACH-Mobile network, respectively. In addition, the performance of LEACH-MII network is better than that of LEACH-Mobile network, such as the load balance degree, the scalability of network monitoring range, and the speed effect of network life cycle.

A Biologically Inspired Algorithm for Low Energy Clustering Problem in Body Area Network

The growing application of body area networks (BANs) in different fields makes the low energy clustering a paramount issue. A clustering optimization algorithm in BANs is a fundamental scheme to guarantee that the essential collected data can be forwarded in a reliable path and improve the lifetime of BANs. Low energy clustering is a technique, which provides a method that shows how to reduce network communication costs in BANs. A careful low energy clustering scheme is one of the most critical means in the research of BANs, which has attracted considerable attention, comprising monitoring capability constraints. However, the classical clustering method leads to high cost when constraints such as large overall energy consumption are undertaken. Hence, a binary immune hybrid artificial bee colony algorithm (BIHABCA), a randomized swarm intelligent scheme applied in BANs, motivated by immune theory and hybrid scheme is introduced. Furthermore, we designed the formulation that considers both distances between two nodes and the length of bits. Finally, we have compared the energy cost optimized by BIHABCA with a shuffled frog leaping algorithm, ant colony optimization, and simulated annealing in the simulation with different quantity of nodes in terms of energy cost. Results show that the energy cost of the network optimized by the proposed BIHABCA method decreased compared to those by the other three methods which mean that the proposed BIHABCA finds the global optima and reduces the energy cost of transmitting and receiving data in BANs.

IoT with BlockChain: A Futuristic Approach in Agriculture and Food Supply Chain

Agricultural food production is projected to be 70% higher by 2050 than it is today, with the world population rising to more than 9 billion, 34% higher than it is now. The farmers have been forced to produce more with the same resources. This pressure means that optimizing productivity is one of the main objectives of the producers but also in a sustainable way. Not only does agriculture face a decline in production, but it has also had to face limitations in data collection, storing, securing, and sharing, climate change, increases in input prices, traditional food supply chain systems where there is no direct connection between the farmer and the buyer, and limitations on energy use. Existing IoT‐based agriculture systems have a centralized format and operate in isolation, leaving room for unresolved issues and major concerns, including data security, manipulation, and single failure points. This paper proposes a futuristic IoT with a blockchain model to meet these challenges. Further, this paper also proposes and novel energy‐efficient clustering IoT‐based agriculture protocol for lower energy consumption and network stability and compares its results with its counterpart low‐energy adoptive clustering hierarchy (LEACH) protocol. The simulation results show that the proposed protocol network stability is 23% higher as compared to LEACH as first node of LEACH dies at 168 rounds while IoT‐based agriculture first node dies after 463 rounds. Similarly, IoT‐based agriculture protocol energy consumption is 68% lower than that of LEACH. The proposed protocol also extends the network life to more rounds and demonstrates an increase of 112%.

Multi-feature fusion energy-saving routing in internet of things based on hybrid ant colony algorithm

This paper analyses the research status of sensor networks and several improved LEACH protocols. It is known that there are some shortcomings in current low-energy clustering protocols: the problem of uneven network cluster and unequal energy consumption of each node in the cluster group leads to excessive energy consumption of some nodes, the whole network life cycle is also greatly shortened. This paper proposes a multi-feature fusion energy-saving routing algorithm based on hybrid ant colony algorithm to optimise and upgrade LEACH energy-saving routing model for the internet of things on the basis of LEACH (NPCHS-Leach) to improve the problems of short lifetime and low energy utilisation caused by existing clustering routing protocols, it improves and pro-longs the network life cycle. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments.

Biologically Inspired Low Energy Clustering for Large Scale Wireless Sensor Networks

The recent technological advances in data gathering, embedded micro-devices and mobile networking has significantly advanced the applications of small-size and numerous tiny nodes. Large scale wireless sensor networks (LSWSNs) are intensively studied and used in the fields of traffic avoidance, intelligent family, medical diagnostic, environmental, multimedia surveillance, military affairs and so on. The recent success of emerging LSWSNs technology has encouraged researchers to develop new low energy clustering algorithm in this field. In LSWSNs, reducing communication energy consumption of sensor will not lead to maximize network lifetime for the total system. The low energy clustering is a typical NP-hard combinatorial optimization problem. In this paper, an immune adaptive cuckoo search algorithm (IACSA) is given to reduce total energy consumption. We first design a fitness function to evaluate energy consumption of system. The IACSA is designed to improve the energy efficiency for LSWSNs. It has the advantages of immune generator that takes into account different benefits and adaptive operator to enhance the convergence rate. Simulations are conducted to show a comparison of IACSA with the shuffled frog leaping algorithm (SFLA), particle swarm optimization (PSO) and artificial fish swarm algorithm (AFSA). Results show that the proposed IACSA has lower energy consumption compared to the SFLA, AFSA and PSO, which means that the proposed method reduces the energy consumption.

Implementation of IMHT LEACH Protocol for WSN

Wireless sensor network (WSN) consists of sensor nodes, which is divided into multiple levels for efficient communication. In WSN there are lots of protocols developed with different goals, but LEACH (Low Energy Clustering Hierarchy) is considered one of the most important among all other protocols. Lots of protocols are proposed as successors of LEACH but Multi-Hop Technique LEACH (MHT-LEACH) is considered effective one in terms of energy saving. MHT divides the whole network into two levels with reference to the threshold distance. But MHT suffers from area deployment problem when monitored area is considerably large. Improvement for this limitation of the MHT-LEACH, a new protocol is proposed named as Improved MultiHop Technique LEACH (IMHT-LEACH). In which the whole network is divided into multiple levels based on the distance between each other, which is proved to be more effective in terms of power saving. This paper illustrates practical implementation of IMHT-LEACH algorithm and parameters related to communication links like multiple hopping of data, response time and connection drop in half an hour in the implemented algorithm.

An Improved Niche Chaotic Genetic Algorithm for Low-Energy Clustering Problem in Large-Scale Wireless Sensor Networks

Large-scale wireless sensor networks consist of a large number of tiny sensors that have sensing, computation, wireless communication, and free-infrastructure abilities. The low-energy clustering scheme is usually designed for large-scale wireless sensor networks to improve the communication energy efficiency. However, the low-energy clustering problem can be formulated as a nonlinear mixed integer combinatorial optimization problem. In this paper, we propose a low-energy clustering approach based on improved niche chaotic genetic algorithm (INCGA) for minimizing the communication energy consumption. We formulate our objective function to minimize the communication energy consumption under multiple constraints. Although suboptimal for LSWSN systems, simulation results show that the proposed INCGA algorithm allows to reduce the communication energy consumption with lower complexity compared to the QEA (quantum evolutionary algorithm) and PSO (particle swarm optimization) approaches.

A Low Energy Clustering Routing Protocol Based on the Partition

In wireless sensor network (WSN), LEACH protocol is a typical representative of the hierarchical topology control protocol. Compared to plane multiple hops routing protocol, it can significantly prolong the life cycle of the network. But LEACH protocol having the problem of uneven distribution at the cluster head election, this paper proposes a low energy adaptive clustering routing protocol based on the partition. On the basis of the partition, cluster heads are selected by the energy of nodes and their location information. Simulation experiments show that the proposed protocol can be more effective than LEACH to balance the energy and improve the life cycle of the network.

Sleeping Cluster based Medium Access Control Layer Routing Protocol for Wireless Sensor Networks

Wireless sensor networks play a vital role in remote area applications, where human intervention is not possible. In a Wireless Sensor Network (WSN) each and every node is strictly an energy as well as bandwidth constrained one. Problem Statement: In a standard WSN, most of the routing techniques, move data from multiple sources to a single fixed base station. Because of the greater number of computational tasks, the existing routing protocol did not address the energy efficient problem properly. In order to overcome the problem of energy consumption due to more number of computational tasks, a new method is developed. Approach: The proposed algorithm divides the sensing field into three active clusters and one sleeping cluster. The cluster head selection is based on the distance between the base station and the normal nodes. The Time Division Multiple Access (TDMA) mechanism is used to make the cluster remain in the active state as well as the sleeping state. In an active cluster 50% of nodes will be made active and the remaining 50% be in sleep state. A sleeping cluster will be made active after a period of time and periodically changes its functionality. Results: Due to this periodic change of state, energy consumption is minimized. The performance of the Low Energy Adaptive and Clustering Hierarchy (LEACH) algorithm is also analyzed, using a network simulator NS2 based on the number of Cluster Heads (CH), energy consumption, Lifetime and the number of nodes alive. The simulation studies were carried out using a network simulation tool NS2, for the proposed method and this is compared with the performance of the existing protocol. The superiority of the proposed method is highlighted.

An energy efficient clustering protocol for routing in Wireless Sensor Network

In Wireless Sensor Network (WSN), the power supply is generally a non-renewable battery, consequently, energy effectiveness is a crucial factor. To maximise the battery life and therefore the duration of network service, a robust wireless communication protocol providing a best energy efficiency is required. In this paper, we present an Enhanced Low Energy Clustering Protocol for Routing in WSN (ELECP). The ELECP is a decentralised clustering algorithm that can be used in the case where the area sensed data are not perfectly correlated. The technique, used to partition the network, allows the distribution of energy load among any sensor nodes; this extends network lifetime. Simulation results show that the network lifetime is increased largely comparable to existing schemes.

MAPLE: a framework for mobility‐aware pro‐active low energy clustering in ad hoc mobile wireless networks

AbstractWe propose a framework for mobility‐aware pro‐active low energy (MAPLE) clustering in ad hoc mobile wireless networks. Most of the clustering approaches proposed in the literature primarily focus on the algorithmic aspects of clustering without considering the practical implementation issues and these are often reactive in nature. The proposed approach addresses the problem of clustering in a medium‐access control framework and enables pro‐active and energy‐efficient clustering by exploiting the node mobility information. In particular, for pro‐active clustering we introduce a method to exploit the link level information to estimate the mobility pattern of the wireless nodes and the cost of using a wireless link in terms of required transmission power. A channel reservation technique is used to reduce the number of contentions among the nodes while accessing the channel during cluster formation. Simulation results show that the proposed framework results in superior clustering performance in terms of stability, load distribution, and control overhead compared to other clustering approaches proposed in the literature. Copyright © 2006 John Wiley & Sons, Ltd.

Reliable data delivery in wireless sensor networks using distributed cluster monitoring

In a clustering-based Wireless Sensor Network (WSN), cluster heads play an important role by serving as data forwarders amongst other network organisation functions. Thus, malfunctioning and/or compromised sensor nodes that serve as Cluster Head (CH) can lead to unreliable data delivery. In this paper, we propose a scheme (called Secure Low Energy Clustering (SecLEC)) that incorporates secure cluster head selection and distributed cluster head monitoring to achieve reliable data delivery. The SecLEC framework is flexible and can accommodate various tracking and monitoring mechanisms. Our goal in this paper is to understand the performance impact of adding such security mechanisms to the clustering architecture in terms of energy consumed and prevented data losses. Our experiments show that with SecLEC, BS detects such anomalous nodes with the latency of one network operation round and the data loss due to malicious nodes is up to 0.5% with a reasonable communication overhead.