Candidate Cluster Head Research Articles

An Area Coverage and Energy Consumption Optimization Approach Based on Improved Adaptive Particle Swarm Optimization for Directional Sensor Networks.

Coverage is a vital indicator which reflects the performance of directional sensor networks (DSNs). The random deployment of directional sensor nodes will lead to many covergae blind areas and overlapping areas. Besides, the premature death of nodes will also directly affect the service quality of network due to limited energy. To address these problems, this paper proposes a new area coverage and energy consumption optimization approach based on improved adaptive particle swarm optimization (IAPSO). For area coverage problem, we set up a multi-objective optimization model in order to improve coverage ratio and reduce redundancy ratio by sensing direction rotation. For energy consumption optimization, we make energy consumption evenly distribute on each sensor node by clustering network. We set up a cluster head selection optimization model which considers the total residual energy ratio and energy consumption balance degree of cluster head candidates. We also propose a cluster formation algorithm in which member nodes choose their cluster heads by weight function. We next utilize an IAPSO to solve two optimization models to achieve high coverage ratio, low redundancy ratio and energy consumption balance. Extensive simulation results demonstrate the our proposed approach performs better than other ones.

Research on Uneven Clustering APTEEN in CWSN Based on Ant Colony Algorithm

In this paper, APTEEN is applied to cognitive wireless sensor networks, and an ant colony-based uneven clustering APTEEN algorithm (ACUCAPTEEN) is proposed. The algorithm combines routing and spectrum allocation with cross-layer design method, improves energy efficient uneven clustering protocol and introduces it to APTEEN, and uses ant colony algorithm to complete inter-cluster path search, which reduces the task of cluster head. In the candidate cluster head selection process, the residual energy is not considered and the competition radius is fixed, so that the nodes with many idle channels but low residual energy become cluster heads, and the cluster heads far away from the base station still have more intra-cluster tasks with less residual energy. For these problems, an optimized ACUCAPTEEN algorithm(OACUCAPTEEN) is proposed to optimize the candidate cluster heads, the residual energy is taken as a factor of the candidate cluster head probability, and the competition radius is optimized. The simulation results show that the ACUCAPTEEN algorithm and optimization algorithm can reduce the node death rate and extend the network life cycle.

Efficient Deployment of Key Nodes for Optimal Coverage of Industrial Mobile Wireless Networks.

In recent years, industrial wireless networks (IWNs) have been transformed by the introduction of mobile nodes, and they now offer increased extensibility, mobility, and flexibility. Nevertheless, mobile nodes pose efficiency and reliability challenges. Efficient node deployment and management of channel interference directly affect network system performance, particularly for key node placement in clustered wireless networks. This study analyzes this system model, considering both industrial properties of wireless networks and their mobility. Then, static and mobile node coverage problems are unified and simplified to target coverage problems. We propose a novel strategy for the deployment of clustered heads in grouped industrial mobile wireless networks (IMWNs) based on the improved maximal clique model and the iterative computation of new candidate cluster head positions. The maximal cliques are obtained via a double-layer Tabu search. Each cluster head updates its new position via an improved virtual force while moving with full coverage to find the minimal inter-cluster interference. Finally, we develop a simulation environment. The simulation results, based on a performance comparison, show the efficacy of the proposed strategies and their superiority over current approaches.

A Distributed Multi-competitive Clustering Approach for Wireless Sensor Networks

Wireless sensor networks (WSNs) need simple and effective approaches to reduce energy consumption because of limited energy. Clustering nodes is an effective approach to make WSNs energy-efficient. In this paper we propose a distributed multi-competitive clustering approach (DMCC) for WSNs. First, the nodes with high residual energy are selected to act as cluster head candidates (CHCs). Second, cluster heads (CHs) are selected from the CHCs based on a hybrid of competition. If the distances to the selected CHs are suitable, a CHC with more neighbor nodes and smaller average distance to its neighbor nodes is more likely to become a CH. If the number of CHs selected from the CHCs is insufficient, more CHs are selected from non-CHCs continually according to residual energy until the CHs number is suitable. DMCC makes the CHs number stable and distribute the CHs evenly. Simulation experiments were performed on to compare DMCC with some related clustering approaches. The experimental results suggest that DMCC balances the load among different clusters and reduces the energy consumption, which improves the network lifetime.

A Power Controlled Energy-efficient Clustering Scheme for Heterogeneous Wireless Sensor Networks

Topology control is one of the most important technologies in wireless sensor networks. Many applications, combined with the methods of power control and hierarchical topology control strategy, can ultimately achieve the purpose of energy saving and topology optimization. Taken into consideration both power control and average energy of adjacent region, the paper proposes an energy efficient clustering algorithm for heterogeneous WSNs, called Power Controlled Clustering algorithm with Average Energy of Adjacent Region (PCCAEAR). In PCCAEAR, the cluster heads election process is divided into two stages. Firstly, the candidate cluster heads are elected by a probability based on the ratio between residual energy of each node and average energy of adjacent region. Secondly, a regional cluster head can be identified as the node with the highest residual energy within a certain range, while a candidate cluster head is communicating with other reachable candidate cluster heads in a certain transmission power. The cluster heads are elected in a stable number per round and evenly distributed in network, which make the network energy consumption more balanced. Simulation results show that PCCAEAR algorithm has a longer network lifetime, with the first node dead round number increasing by 6%, 14% and 22% respectively compared to DEEC, TCAC and REAC algorithms.

An Uneven Clustering Routing Algorithm Based on Energy Load Balancing

In order to improve the network lifetime and energy efficiency, an uneven clustering routing protocol based on energy load balancing (ELBUC) is proposed. In the cluster head selection phase, sensor nodes are selected as candidate cluster heads by turns in ELBUC; in the data transmission phase, it proposes a least energy consumption routing strategy, which comprehensively considers the candidate routing node’s energy and the number of its members, so that the cluster head which has fewer nodes in the cluster become the rely node. Simulation results show that ELBUC protocol not only significantly prolongs the lifetime of the network, but also effectively balance the energy consumption between nodes, and improve the network energy efficiency.

Evidence-Efficient Multihop Clustering Routing Scheme for Large-Scale Wireless Sensor Networks

Energy consumption and transmission reliability are the most common issues in wireless sensor networks (WSNs). By studying the broadcast nature of data transmission in WSNs, the mechanism of guaranteeing reliable transmission is abstracted as propagation of responsibility and availability. The responsibility and availability represent the accumulated evidence of nodes to support reliable transmission. Based on the developed mechanism, an evidence-efficient cluster head rotation strategy and algorithm are presented. Furthermore, backbone construction algorithm is studied to generate the minimum aggregation tree inside the candidate cluster heads. A minimum aggregation tree-based multihop routing scheme is also investigated, which allows the elected cluster heads to choose the optimally main path to forward data locally and dynamically. As a hybridization of the above, an evidence-efficient multihop clustering routing (EEMCR) method is proposed. The EEMCR method is simulated, validated, and compared with some previous algorithms. The experimental results show that EEMCR outperforms them in terms of prolonging network lifetime, improving transmission reliability, postponing emergence of death nodes, enhancing coverage preservation, and degrading energy consumption.

Study of Wireless Sensor Network Location Algorithm and Data Collection Protocol

It is deduced that the network life time is inversely proportional to the zone area of the collection data that each sink is responsible for. That is to say, when the zone area in the charge of a sink is smaller, the average load of nodes distributed in this area and the energy consumption are smaller, while the network life time is longer. MSGP protocol is proposed, with several round cluster head candidate zones, fixed member zones and public zones being delimited to make approximately uniform clustering. The cluster head is responsible for collecting a meaningful event in the cluster, making data fusion and then forwarding it to sink. Multi-sinks move according to the zone division rules and the rule of free movement. Specific analysis is made from three aspects of data direction, single round data collection of sink, and network life time.

Adaptive Weighted Clustering Algorithm for Mobile Ad-hoc Networks

In this paper we present a new algorithm for clustering MANET by considering several parameters. This is a new adaptive load balancing technique for clustering out Mobile Ad-hoc Networks (MANET). MANET is special kind of wireless networks where no central management exits and the nodes in the network cooperatively manage itself and maintains connectivity. The algorithm takes into account the local capabilities of each node, the remaining battery power, degree of connectivity and finally the power consumption based on the average distance between nodes and candidate cluster head. The proposed algorithm efficiently decreases the overhead in the network that enhances the overall MANET performance. Reducing the maintenance time of broken routes makes the network more stable, reliable. Saving the power of the nodes also guarantee consistent and reliable network.

R-bUCRP: A Novel Reputation-Based Uneven Clustering Routing Protocol for Cognitive Wireless Sensor Networks

Energy of nodes is an important factor that affects the performance of Wireless Sensor Networks (WSNs), especially in the case of existing selfish nodes, which attracted many researchers’ attention recently. In this paper, we present a reputation-based uneven clustering routing protocol (R-bUCRP) considering both energy saving and reputation assessment. In the cluster establishment phase, we adopt an uneven clustering mechanism which controls the competitive scope of cluster head candidates to save the energy of WSNs. In the cluster heads election phase, the residual energy and reputation value are used as the indexes to select the optimal cluster head, where the reputation mechanism is introduced to support reputation assessment. Simulation results show that the proposed R-bUCRP can save node energy consumption, balance network energy distribution, and prolong network lifetime.

A novel cooperative ant-based clustering approach for wireless multimedia sensor networks

ONTARE. REVISTA DE INVESTIGACIÓN DE LA FACULTAD DE INGENIERÍA
 This Cluster has been well received as one of the most effective solutions to enhance energy efficiency and scalability of large-scale wireless sensor networks. However, it is rather challenging to conforma cohesive group of Clusters having permanent communication among Cluster heads. In this paper, a unique look at the Cluster head election problem is taken into account, specifically concentrating on applications in which the formation and maintenance of a virtual backbone among Cluster heads is the main requirement. Our approach for Cluster-based network organization is based on the use of collective social agents in order to lead the formation of these Clusters. Our algorithm uses ants to choose good Cluster head candidates and to create a virtual backbone among these router nodes.

A Clustering Routing Protocol for WSN Based on Type-2 Fuzzy Logic and Ant Colony Optimization

Aiming at the problem of load balancing and lifetime prolonging for wireless sensor networks (WSNs), and considering complex uncertainties existed in WSNs, this paper proposes a clustering routing protocol CRT2FLACO for WSN based on type-2 fuzzy logic and ant colony optimization (ACO). Specifically, in the cluster set-up phase, a type-2 Mamdnai fuzzy logic system (T2MFLS) is built to handle uncertainties better and balance the network load, in which three important factors--residual energy, the number of neighbor nodes and the distance to the base station (BS) of a node--are considered as inputs, and the probability of the node to be a candidate cluster head (CH) and the CH competition radius as outputs of our T2MFLS, to select the final CHs; in the steady-state phase, in order to reduce the transmission consumption, all the CHs are linked into a chain using ACO algorithm, then each CH send its data packet to the leader along link, which is a CH eventually transmitting packets to the BS. The simulation results show that the proposed routing protocol can effectively balance network load and reduce the transmission energy consumption of CHs, thus greatly prolong the lifetime of WSN.

An Energy Aware Distributed Clustering Algorithm Using Fuzzy Logic for Wireless Sensor Networks with Non-uniform Node Distribution

Clustering routing protocols are more adequate for Wireless Sensor Networks (WSNs) due to the higher energy utilization rate and scalability. However, non-uniform node deployment makes the cluster-based routing protocols less efficient, since the energy consumption of the nodes is more imbalanced. In this paper, an Energy Aware Distributed Clustering using Fuzzy Logic (EADC-FL) routing algorithm is proposed for the network with non-uniform node distribution. EADC-FL selects the most appropriate nodes as the cluster heads to save more energy in data transmission phase. It considers the energy of the sensor nodes as the primary parameter to elect the candidate cluster heads. Afterwards, it takes into consideration the node degree and node centrality of the sensor nodes by using Fuzzy Logic to identify the final cluster heads. EADC-FL constructs the clusters in equal sizes to make the energy consumption of cluster members balanced and uses a cluster-based routing algorithm to balance the energy consumption among the cluster heads. Moreover, the proposed algorithm performs clustering on demand instead of each round in order to reduce the energy dissipation of the network due to the overhead of consecutive clustering phases. The results demonstrate that EADC-FL significantly decreases the energy consumption and extends the network lifetime of network with non-uniform node distribution .

An adaptive competition-based clustering approach for wireless sensor networks

Hierarchical architecture is an effective scheme to make wireless sensor networks (WSNs) scalable and energy-efficient. Clustering the sensor nodes is a well-known two-layered architecture suitable for WSNs and has been extensively explored for different purposes and applications. In this paper a novel clustering approach called the adaptive competition-based clustering approach (ACCA) is proposed for WSNs. Selecting the cluster heads in the proposed ACCA is performed based upon a hybrid of local competition and the distances among the cluster heads. First, by the new proposed competition scheme, the nodes which are with the high residual energy and closer to the centre of the density of the nodes are elected and form an initial set of cluster head candidates. Then the candidates with suitable distances to other neighbor candidates are elected as the cluster heads. The proposed algorithm is fast with a low time and message complexity. It offers a longer lifetime for the networks, and at the same time, a proper level of fault tolerance. The proposed ACCA is simple enough to be implemented in real systems. Different simulation experiments are performed on different states and the algorithm is compared with some well-known and related clustering approaches. The experiments suggest that in terms of longevity and fault-tolerance, ACCA outperforms the well-known and relevant clustering protocols LEACH, HEED, and EECABN.

A Clustering Sleep Scheduling Mechanism Based on Sentinel Nodes Monitor for WSN

This paper proposes a clustering sleep scheduling mechanism based on sentinel nodes monitoring for WSN. The mechanism combines the network clustering strategy with the node dormancy strategy, and improves the method of selecting the candidate cluster heads randomly in Energy-Efficient Unequal Clustering (EEUC) algorithm. The conception of sentinel node is introduced based on EEUC, and the neighbor node set of sentinel node will be dormant when the sentinel node’s data change rate is lower than the setting threshold. Simulation results show that this mechanism can effectively balance the energy consumption of the entire network, and significantly extend the network lifetime.

Energy Efficient Big Data Infrastructure Management in Geo-Federated Cloud Data Centers

Abstract The hot-tempered development of hassle on big data processing make obligatory an intense load on computation, storage and networking in data centres. We suggested an approach of data centre node clustering for an efficient data placement and data retrieval which is unlike the routine in centralised architecture. The main objective for the proposed system is the shortcomings present in the conventional centralised server which is mainly the assumption that a single head is in the connectivity range of all other nodes. We proposed Hit Rate Geographical Locations Analysis Algorithm (HIRGLAA) for the dynamic election of cluster head based on the periodic hit rate analysis performance. We suggested candidate cluster heads containing redundant routing information to ensure data storage backup. Thus the proposed system assures Quality of Services (QoS) such as increased reliability, robustness, an energy efficient remote access and its efficiency can be validated by extensive simulation based studies.

A Clustering Routing Protocol for Energy Balance of Wireless Sensor Network based on Simulated Annealing and Genetic Algorithm

The LEACH is a popular protocol used in wireless sensor network analysis and simulation. This paper analyses the advantages and disadvantages of LEACH protocol and then puts forward a clustering routing protocol for energy balance of wireless sensor network based on simulated annealing and genetic algorithm. When the sensor nodes are deployed randomly in the area, Firstly, we cluster the sensor nodes by simulated annealing and genetic algorithm and then calculate the cluster center of each cluster. If the energy of the node in the cluster is higher than the average energy of the cluster, it will become the candidate cluster head; at last the candidate cluster head becomes the cluster head according to the distance from the cluster center of the cluster. Simulations show that the new program could improve Energy Hotspot caused by the uneven distribution of cluster head in LEACH protocol, thus it can balance the wireless sensor network load balance and extend the lifecycle of wireless sensor network.

A Density-based Energy-efficient Clustering Heterogeneous Algorithm for Wireless Sensor Networks

Clustering is an efficient method adopted in various routing algorithms for wireless sensor networks. However, most clustering algorithms are not suitable for heterogeneous networks. In this paper, we propose a Density-based Energy-efficient Clustering Heterogeneous Algorithm (DECHA). In DECHA, we define the density of a node and together with its energy condition to adjust the probability for the candidate cluster head selection dynamically. Candidate cluster heads further evaluate the energy level of its neighbors and adjust to find more proper cluster heads. Moreover, we design an intra-cluster algorithm as well as a multi-hop inter-cluster routing algorithm. Simulation results show that cluster heads are properly deployed in a heterogeneous wireless sensor network. Compared with some popular algorithms, in our DECHA, the stability period and network lifetime and prolonged and total energy consumption is prominently reduced.

Proposal for Efficient Routing Protocol for Wireless Sensor Network in Coal Mine Goaf

The existing uneven clustering algorithm have disadvantages of coal mine with long strip structure. This paper proposed a UCEB-CMF protocol to improve the safety of coal mine monitoring system. The new protocol optimize the selection mechanism of the cluster head, avoid the lower residual energy nodes become the cluster head. Improve the calculation method of non-uniform competition radius and the competition way of the candidate cluster head, so the new protocol can ensure the node which close to the Sink node and has the more energy to priority become the cluster head. Propose a multipath routing algorithm according to the characteristics of the node in the coal mine goaf is die easily, it can ensure the persistence of the data transmission. Simulation results show that the routing protocol effectively balances the energy consumption among cluster heads and achieves an obvious improvement on the network life time.

Adaptive Clustering with Dynamic Cluster Radius for Wireless Sensor Network

Topology control in Wireless Sensor Network (WSN) has recently been an area of intense investigation, especially the cluster-based topology control algorithms. In this study, we propose Adaptive Clustering with Dynamic Cluster Radius (ACDCR), a cluster-based topology control algorithm. This protocol proves to have several salient features, such as higher energy efficiency, and better QoS (Quality of Service). The design is accomplished by employing the hop-adaptive cluster radius, cluster head selection criteria with left energy threshold, and several other mechanisms. In ACDCR, only nodes with higher residual energy level than their neighbors are identified as cluster head candidates. Each cluster is assigned with a hop related radius for load balancing. In addition, nodes are allowed to setup direct links with the base station, rather than only their cluster heads. Simulation study shows that ACDCR outperforms HEED (Hybrid Energy-Efficient Distributed Clustering) in terms of network lifetime and QoS with fairly reasonable tradeoffs.