A learning automata-based clustering algorithm for sensor networks

Over the recent years, one of the most important problems in wireless sensor network is to develop a routing protocol that has energy efficiency. Since the power of the sensor nodes is limited, conserving energy and network life is a critical issue in wireless sensor network. In this paper, first we propose an ELCH (Extending Lifetime of Cluster Head) routing protocol that has self-configuration and hierarchal routing properties. It reforms the existing routing protocols in several aspects and constructs clusters on the basis of radio radius and the number of cluster members. In this method, the clusters in the network are equally distributed. We also suggest a novel clustering algorithm for sensor networks, which lets sensors vote for their neighbors in order to elect suitable cluster heads.

Study on the Rough-set-based Clustering Algorithm for Sensor Networks

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.

Today the main concern is to improve the lifetime of Wireless Sensor Network (WSN) because WSN is collection of no. of small battery equipped sensor nodes which consume battery to communicate with each other and once battery is dead it is not possible to recharge it. They have the huge responsibility of sensing the environment, collect the data and then transfer it to Base station. For transmitting the data from edge node to parent node, sensor nodes must know the path which may or may not be the shortest that increases the overhead of path formation. To overcome the path formation overhead, clustering is one solution which further leads to the problem of non-coverage area. In this work, we are proposing a Distance based Angular Clustering Algorithm (DACA) on heterogeneous network which will overcome the path formation and non-coverage area problem by combining the data-aggregation algorithm to prolong the lifetime of wireless network.

Wireless networks play a crucial role in the communication systems nowadays. Wireless networks are being increasingly used in the communication among devices of the most varied types and sizes. User mobility, affordability, flexibility and ease of use are few of many reasons for making them very appealing to new applications and more users everyday. A Wireless Sensor Network (WSN) is composed of sensor nodes spread over the field to sense the data. The sensed data must be gathered & transmitted to Base Station (BS) for end user queries. The used sensor nodes being in- expensive having low computation power & limited energy so are not as much reliable as their expensive macro sensor counter parts but their size and cost enable hundred to thousand of micro sensors to achieve high quality fault tolerant system. In an environment where in each round all sensor nodes have to send data to base station; it is required to effectively utilize energy of sensor nodes so as to increase the life- time of the system. The use of data aggregation & fusion as proposed in LEACH increases system lifetime by a factor of 8 as compared to conventional routing protocols. In this work, our main focus is the static sensors are randomly selected and the base stations have their information all a priori. Basically, the sensors are in direct communication range of each other and can transmit to and receive from the base station. The nodes periodically sense the environment and have always data to send in each round of communication. The nodes fuse/ aggregate the data they receive from the others with their own data, and produce only one packet regardless of how many packets they receive. The problem is to find a routing scheme or an efficient protocol to deliver data packets collected from sensor nodes to the base station. It maximizes the lifetime of the sensor network under the system model given above. However, the definition of quality of service of the sensor network provides is not specified. Secondly, where the nodes are densely deployed, the quality of the system is affected as soon as a significant amount of nodes die, since adjacent nodes record identical or related data. In this case, the lifetime of the network is the time elapsed until half of the nodes or some specified portion of the nodes die. In general terms, the time in rounds where the last node depletes all of its energy defines the lifetime of the overall sensor network. Taking these different possible requirements under consideration, our work provides a proper timing of all deaths for all algorithms in detail as well as chooses the shortest possible path for communication with better memory management scheme and leaves the decision which one to choose to system designers.

Sensor devices have been widely applied to variety applications, including internet of things (IoT), cyber physical systems (CPS) and environmental monitoring systems. Because all sensing data in such applications are very important for data analysis and mining in a host machine, sensor devices are equipped with non-volatile memory (NVM) [1] and nonvolatile processor [2], [3] to prevent sensing data in sensor nodes from data loss. As a result, sensing data can be permanently stored in a sensor device without the risk of data loss even if the battery of the sensor device is run out. Based on such observations, the lifetime of a sensor network should be redefined due to non-volatile technologies, such as NVM and NV processor. Conventionally, the lifetime of a sensor network is the time at which the first sensor node uses up its battery due to the consideration of sensor network QoS [4], [5]. However, since sensing data can be permanently stored in a local drive, the lifetime of a sensor network should be redefined as the time at which the first out-of-date data is produced in a sensor node. If the battery of a sensor node is run out but the sensor node does not contain any out-of-date data, the sensor node will be regarded as a moribund node. If all moribund nodes can be rescued by a rechargeable technology, the sensor network will be immortal. Such observations motivate us to design an energy-efficient solution to achieve the goal of immortal sensor network by exploiting the benefits of wireless charging vehicle.

An energy-saving routing architecture with a uniform clustering algorithm for wireless body sensor networks

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.

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.

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.

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.

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.

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.

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.

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.