Spectral Partitioning and Fuzzy C-Means Based Clustering Algorithm for Wireless Sensor Networks

Wireless sensor networks are normally utilized for monitoring and controlling of specific environments. They are made out of a large number of low-cost sensor nodes which are densely separated in distributed environments. The information collected by sensors should be transmitted to a base station. Recent developments indicate that the interest in applications of wireless sensor networks has increased and expanded on a large scale. One of the most important issues in this type of networks is limited energy. Clustering is a suitable method for increasing network lifetime that protects the limited sensor resources by energy saving. Meta-heuristic algorithms have been widely used for clustering of wireless sensor networks. In most complicated problems, it is required to evaluate a multitude of possible modes, to determine an accurate answer. Meta-heuristic algorithms can provide an answer in acceptable time constraints. They play an effective role in solving such problems by optimizing the energy consumption of the networks that has a significant impact on the network lifetime. In this paper, the application of clustering algorithms in wireless sensor networks has been investigated. The selected clustering methods are based on the meta-heuristic algorithms.

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.

In this paper, we propose an efficient centralized clustering algorithm for wireless sensor networks. The algorithm which organizes the sensors into clusters uses memetic algorithm to determine cluster heads and sizes. Memetic algorithms which are similar to genetic algorithms are population-based heuristic search approaches for optimization problems. To assess the efficiency of the proposed clustering technique, we compare the network lifetime with those of other clustering algorithms. The results show that the proposed algorithm considerably increases the network lifetime.

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.

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.

One of the main emphases of the prevalent clustering and routing algorithms in wireless sensor networks is efficient energy utilization aimed towards enhanced network lifetime. In this paper we are proposing a clustering approach where the sensor nodes are clustered together based on a defined spatial correlation and the geographical location of each sensor nodes. The Cluster Head or Cluster Leader along with routing path is decided by our proposed algorithm based on the estimated chance-value during each rounds of data transfer. Thus in our scheme a multi-hop routing is effected wherein either two-level or three-level hops are dynamically chosen based on the spatial correlation existing between the nodes for the data transfer from the sensing node to the sink. It has been observed that our proposed algorithm outperforms the standard algorithms namely Low Energy Adaptive Clustering Hierarchy (LEACH), Stable Election Protocol (SEP) and Distributed Energy Efficient Clustering (DEEC) Protocol with regards to system throughput and Network Lifespan of WSN.

In the research on energy-efficient networking methods for precision agriculture, a hot topic is the energy issue of sensing nodes for individual wireless sensor networks. The sensing nodes of the wireless sensor network should be enabled to provide better services with limited energy to support wide-range and multi-scenario acquisition and transmission of three-dimensional crop information. Further, the life cycle of the sensing nodes should be maximized under limited energy. The transmission direction and node power consumption are considered, and the forward and high-energy nodes are selected as the preferred cluster heads or data-forwarding nodes. Taking the cropland cultivation of ginseng as the background, we put forward a particle swarm optimization-based networking algorithm for wireless sensor networks with excellent performance. This algorithm can be used for precision agriculture and achieve optimal equipment configuration in a network under limited energy, while ensuring reliable communication in the network. The node scale is configured as 50 to 300 nodes in the range of 500 × 500 m2, and simulated testing is conducted with the LEACH, BCDCP, and ECHERP routing protocols. Compared with the existing LEACH, BCDCP, and ECHERP routing protocols, the proposed networking method can achieve the network lifetime prolongation and mitigate the decreased degree and decreasing trend of the distance between the sensing nodes and center nodes of the sensor network, which results in a longer network life cycle and stronger environment suitability. It is an effective method that improves the sensing node lifetime for a wireless sensor network applied to cropland cultivation of ginseng.

Energy conservation is the most significant issue in wireless sensor networks due to limited power sources of sensor nodes that cannot be recharged or replaced. Clustering is considered to be energy-efficient in WSN as it reduces the number of sensor nodes taking part in long range communication. During clustering, each sensor node selects a cluster head (CH) to join a cluster among several candidate CHs. The selection of cluster head uses different parameters such as residual energy of cluster head and distance between node and cluster head. A poor selection of cluster head will lead to increased energy consumption in network that will degrade the network lifetime. Furthermore, sensor nodes are likely to fail due to different factors such as limited energy and environment hazards. So, the fault tolerance is another challenge for long run of the WSNs. In this paper, we propose a distributed fault-tolerant multi-objective clustering algorithm (DFMCA). In this algorithm, each sensor takes its decision using local information only. We also propose a recovery algorithm in case of sudden death of the cluster heads in the network. A simulation result demonstrates that our algorithm outperforms the existing state-of-the-art algorithms, namely FTCA and LBCA, in terms of network lifetime.

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.

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.

A novel differential evolution based clustering algorithm for wireless sensor networks

Clustering in wireless sensor networks is one of the crucial methods for increasing of network lifetime. There are many algorithms for clustering. One of the cluster based algorithm in wireless sensor networks is LEACH algorithm. In this paper we proposed a new clustering method for increasing of network lifetime. In proposed method Clustering is done symmetrically and the best node with respect to remained energy and distance of other nodes in comparing with each that selected as a cluster head. Although in this protocol we didn’t use GPS but we could find geographical position nodes so easily. However, failures in higher level of hierarchy e.g. cluster-head cause more damage to the system because they also limit accessibility to the nodes that are under their supervision. In this paper we propose an efficient mechanism to recover sensors from a failed cluster. In this performance of the proposed algorithm via computer simulation was evaluated and compared with other clustering algorithms. The simulation results show the high performance of the proposed clustering algorithm.

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.