A Clustering Algorithm Based on K-means for Wireless Indoor Monitoring System

Automatic localization is one of the major issues in Wireless Sensor Networks (WSN). DV-hop algorithm is a well-known localization algorithm in WSN but with limited localization accuracy. In this paper, an improved DV-hop localization algorithm in hybrid optical wireless sensor networks is proposed based on the optimization of the parameters in WSN. Various factors that affect the localization accuracy of the DV-hop algorithm in WSN are investigated, including the communication radius of the node, the number of beacon nodes and the number of the total nodes. As the DV-hop algorithm is applied into hybrid optical sensor and WSNs (O-WSN) with rectangular topology, different parameters have to be optimized accordingly. Simulation results show that the square topology outperforms the rectangle topology more than 45 % under the same network parameters using the improved DV-hop algorithm. Therefore another improved DV-hop called Sub-Square Weighted DV-hop (SSW DV-hop) is proposed for the rectangle topology. Both simulation and experiment results demonstrate that applying the SSW DV-hop algorithmin O-WSNs could significantly improve the localization accuracy.

A large number of small sensors in wireless sensor network are battery-powered; one of the most important design criteria for this type of network is energy efficiency. Clustering provides an effective way for extending the lifetime of a sensor network. In this paper, we propose a maximum-votes and load-balance clustering algorithm (VLCA) for wireless sensor network Each sensor collects votes from their neighbors and calculates the total vote received. The more votes a sensor accumulates, the more important it is in the whole network During the clustering phase, sensors compete with each other based on the total votes each has received. The algorithm is completely distributed, locating-unaware and independent of network size and topology. Simulation results show that our VLCA can reduce the number of clusters by 20-50% and prolong the lifetime of a sensor network.

Clustering is an efficient technique to improve scalability and life time of wireless sensor networks (WSNs). However, energy efficiency of the sensor nodes and load balancing of the cluster heads (CHs) are the two most important issues that must be addressed in designing clustering algorithm for WSNs. In this paper, we first present a clustering algorithm called energy efficient load-balanced clustering algorithm (EELBCA) that addresses energy efficiency of the sensor nodes and load balancing of the CHs in terms of cardinality of the clusters. EELBCA is a min-heap-based clustering algorithm which is shown to run in O(n log m) time for n sensor nodes and m CHs. We next propose a parameter-based clustering algorithm (PBCA) that considers the energy efficiency of the sensor nodes and also the load balancing of the CHs in terms of overall energy consumption by incorporating communication load of the CHs with respect to the base station. The algorithm requires O(n log n) time. The experimental results show the efficiency of the proposed algorithms in terms of execution time, load balancing, energy consumption, number of live sensor nodes and the number of live CHs.

Limited battery power directly affects the performance and survival time of sensor networks. In this study, solar energy was used to provide additional energy for sensor nodes, and a clustering algorithm for heterogeneous wireless sensor networks (WSNs) based on a solar energy supply is proposed. A cluster head is selected on the basis of the self-replenishment state and the remaining energy of the nodes. A cluster-head selection mechanism and network layer multi-hop routing are adopted to balance the energy consumption of the network. A set of experiments showed that the proposed algorithm can effectively prolong the network lifetime, improve the stability and efficiency of the network, and balance the energy consumption compared with other algorithms under the same environmental energy supply.

Sensor networks are gaining more and more interest in the research community, due to their unique characteristics. The most important problem, though, is energy consumption, which limits the lifetime of the network. Clustering has been proposed as a technique for organizing sensors in order to prolong their lifespan. Although many clustering algorithms have been proposed, very few of them are especially designed for the ultra-wideband (UWB) technology. UWB is a key solution for wireless connectivity characterized by ultralow power consumption and a good degree of robustness to interference. Based on these principles, we propose a novel energy-aware clustering algorithm for UWB sensor networks. Simulation based studies would demonstrate the superior performance that it is achieved by the proposed algorithm.

Radio transmission and reception consumes a lot of energy in a wireless sensor network (WSN), which are made of low-cost, low-power, small in size, and multifunctional sensor nodes. Thus, one of the important issues in wireless sensor network is the inherent limited battery power within the sensor nodes. Therefore, battery power is crucial parameter in the algorithm design in maximizing the lifespan of sensor nodes. It is also preferable to distribute the energy dissipated throughout the wireless sensor network in order to maximize overall network performance. Much research has been done in recent years in the area of low power routing protocol, but, there are still many design options open for improvement, and for further research targeted to the specific applications, need to be done. In this paper, we propose a new approach of an energy-efficient homogeneous clustering algorithm for wireless sensor networks in which the lifespan of the network is increased by ensuring a homogeneous distribution of nodes in the clusters. In this clustering algorithm, energy efficiency is distributed and network performance is improved by selecting cluster heads on the basis of (i) the residual energy of existing cluster heads, (ii) holdback value, and (iii) nearest hop distance of the node. In the proposed clustering algorithm, the cluster members are uniformly distributed and the life of the network is further extended.

In order to gather information more efficiently, wireless sensor networks (WSNs) are partitioned into clusters. The most of the proposed clustering algorithms do not consider the location of the base station. This situation causes hot spots problem in multi-hop WSNs. Unequal clustering mechanisms, which are designed by considering the base station location, solve this problem. In this paper, we introduce a fuzzy unequal clustering algorithm (EAUCF) which aims to prolong the lifetime of WSNs. EAUCF adjusts the cluster-head radius considering the residual energy and the distance to the base station parameters of the sensor nodes. This helps decreasing the intra-cluster work of the sensor nodes which are closer to the base station or have lower battery level. We utilize fuzzy logic for handling the uncertainties in cluster-head radius estimation. We compare our algorithm with some popular algorithms in literature, namely LEACH, CHEF and EEUC, according to First Node Dies (FND), Half of the Nodes Alive (HNA) and energy-efficiency metrics. Our simulation results show that EAUCF performs better than the other algorithms in most of the cases. Therefore, EAUCF is a stable and energy-efficient clustering algorithm to be utilized in any real time WSN application.

The wireless sensor networks (WSNs) are usually deployed among widely area for environmental monitoring, data collection and other applications. In order to reduce the energy consumption and extend the lifetime of WSNs, as well as receive the data quickly, a source-based data aggregation algorithm (SBDA) for clustering wireless sensor networks is proposed. The method aggregated the data from sensor nodes and marked different transmission priority for the data. Simulation results show that, compared to existing protocols, the proposed method achieves a longer life time, and a lower packet drop ratio.

In wireless sensor networks (WSNs), sensor nodes are usually powered by battery and thus have very limited energy. Saving energy is an important goal in designing a WSN. It is known that clustering is an effective method to prolong network lifetime. However, how to cluster sensor nodes cooperatively and achieve an optimal number of clusters in a WSN still remains an open issue. In this paper, we first propose an analytical model to determine the optimal number of clusters in a wireless sensor network. We then propose a centralized cluster algorithm based on the spectral partitioning method. The advantage of the method is that the partitioned subgraphs have an approximately equal number of vertices while minimizing the number of edges between the two subgraphs. Then, we present a distributed clustering algorithm based on fuzzy C-means method and the selection strategy of cooperative nodes and cluster heads based on fuzzy logic. Finally, simulation results show that the proposed algorithms outperform the hybrid energy-efficient distributed clustering algorithm in terms of energy cost and network lifetime.

In order to improve the location accuracy of unknown nodes in APIT algorithm in mobile wireless sensor networks, an improved APIT algorithm is proposed. A new test method called gravity center combination has been proposed for the InToOut and OutToIn errors in APIT. The algorithm further builds an anchor box by combining one-hop and two-hop information of nodes to limit the range and finally determine the location of unknown nodes to reduce the number of nodes that cannot be located. By improving the coverage and node availability, the positioning accuracy is eventually improved. The complexity is lowered by the lack of requirement of additional hardware, thus the improved algorithm is relatively easy to implement. The simulation results of the verification demonstrate that the updated algorithm improves the coverage ratio and the node usage efficiency compared with the other algorithm, thus makes the mobile node location more accurate.

Due to the characteristics of energy-limited, how to reduce the energy consumption of the wireless sensors network has become the crucial point. Research shows that it can reduce energy consumption to organize the nodes in the network into a hierarchical cluster structure. This paper synthetically analyzes several existing cluster algorithms, and then proposes an energy-efficient and variation-sensitive clustering algorithm. This algorithm makes some modification on the basis of some existing classical clustering algorithms. In order to reduce energy consumption, we adopt some special measures to form cluster, elect cluster head and change the way in which the sense data is constantly sent to cluster head by non-cluster head node.

An on-demand weighed clustering algorithm is proposed against the limitation of wireless sensor network nodes with limited energy. This algorithm obtains more local network information by means of information interaction between nodes, and by comprehensively considering such factors as the current energy value, degree and transmission power, etc. of nodes, it makes different clustering decisions as per different application backgrounds of networks. Simulation results show that this algorithm has a more rational clustering by reducing energy consumption and lengthening network lifetime, compared to Low Energy Adaptive Clustering Hierarchy, a representative clustering algorithm.

To prolong network lifetime, lifetime maximization routing based on genetic algorithm (GALMR) for wireless sensor networks is proposed. Energy consumption model and node transmission probability are used to calculate the total energy consumption of nodes in a data gathering cycle. Then, lifetime maximization routing is formulated as maximization optimization problem. The select, crosss, and mutation operations in genetic algorithm are used to find the optimal network lifetime and node transmission probability. Simulation results show that GALMR algorithm are convergence and can prolong network lifetime. Under certain conditions, GALMR outperforms PEDAP-PA, LET, Sum-w and Ratio-w algorithms.

Providing route stability isconsidered to be crucial task in Wireless Sensor Networks (WSNs). Hierarchical topologyis considered to preserve the energy of the sensor networks in which the nodes with the higher residual energy is used to gather the data. However, most of the existingmethods on clustering have adopted a two-layer hierarchy. This paper proposes an Enhanced Three Layer Energy Efficient Clustering (ETLEEC) algorithm for WSNs. The proposed method uses an extra layer called the lattice head where the cluster head collects the information form sensor nodes and sends it to the lattice head which in turn send to the base station. Simulations are performed in network simulator and the results show that the proposed system is more efficient in terms of energy and quality of service compared to all other existing distributed algorithms.

Wireless sensor networks are principally categorized by insufficient energy resource. Naturally, communication between the nodes is the utmost energy consuming act that they perform. Hence, development of a well-organized clustering algorithm can play a vital part in enhancing the lifetime of network. Currently, nature inspired methodologies are very common in dealing with it. This work presents a centralized approach that deals with energy-awareness of wireless sensor networks using the Krill Herd algorithm. The performance of the suggested algorithm is assessed with famous clustering protocols. The simulation results show that suggested approach can maximize sensor network lifetime over other algorithms of the same category.