An energy-aware self-organizing clustering algorithm for UWB wireless sensor networks

The clustering algorithms for wireless sensor networks might differ depending on the application and network architecture. Not all clustering algorithms are suitable for wireless indoor monitoring system. In this article, we summarize the features of wireless indoor monitoring system, analyze the clustering algorithms for wireless sensor networks, and provide a clustering algorithm based on K-means for wireless indoor monitoring system.

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.

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.

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.

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.

Wireless sensor networks, by providing an unprecedented way of interacting with the physical environment, have become a hot topic for research over the last few years. As with any new technology, results from real experimentations using these networks are still scarce, as real deployments are either costly, or still unfeasible in the current state of technology. There is therefore an increasing need for simulation tools allowing the testing of different architectures, communication protocols or information processing algorithms in sensor networks. In this paper, we investigate a simulation framework for the testing of data processing in wireless sensor network applications. In a first stage, data is generated using partial differential equations, allowing the modeling of a large panel of physical phenomena. In a second stage, sensing unit operating system and network constraints are simulated using an instance of a versatile simulator to account for the platform characteristics. Insights provided by the proposed simulation frame are illustrated by a set of experiments on a heat source detection task.

Appropriate clustering and routing algorithms in multi-hop wireless sensor networks considerably affect the energy consumption and prolong the network lifetime. Besides, the required inter-cluster communications in these networks may lead to imbalanced energy consumption among cluster-heads that in turn decreases the network lifetime. In this paper, a gradient-based clustering algorithm for energy-efficient routing (GCER) in wireless sensor networks is proposed. The main idea is based on partitioning the sensing region in such a way that the total energy consumption of the network is minimized. The algorithm uses unequal clustering structure which balances the energy consumption among the cluster-heads and lessens the effect of hotspot problem. Furthermore, a distributed protocol for choosing cluster-heads and routing is deployed to balance the energy among all sensor nodes. Simulation results show that the proposed scheme balances the energy consumption among the cluster-heads and increases the network lifetime compared to some recent reported clustering schemes in the literature.

Data dissemination strategies and communication protocols that minimize the use of energy can significantly prolong the lifetime of a sensor network. Sensor network is a distributed event-based system that differs from traditional communication network. Transporting information in an energy efficient manner is critical in operating this network for a long period of time. Hierarchical routing protocols are actively studied in terms of the energy usage, packet latency, and security in the presence of node compromise attacks. In this paper, we focus on hierarchical routing protocols and networks with homogeneous nodes are used to design an energy efficient wireless sensor network.

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.

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.

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.

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.