Article

Optimal Number of Cluster Head Selection for Efficient Distribution of Sources in WSNs

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Abstract

In this paper, we compare problems of cluster formation and cluster-head selection between different protocols for data aggregation and transmission. We focus on two aspects of the problem: (i) how to guess number of clusters required to proficiently consume available sources for a sensor network, and (ii) how to select number of cluster-heads to cover up sensor networks more proficiently. A sensor in Wireless Sensor Networks (WSNs) can communicate directly only with other sensors that are within a radio range in a cluster. However, in order to enable communication between sensors not within communication range, they must form new clusters in distributed sensors. Several clustering algorithms such as LEACH, DEEC, and SEP have been proposed with the objectives of energy minimization, route-path selection, increased connectivity and network longevity. LEACH protocol and the similar ones assume an energy homogeneous system where a node is not likely to fail due to failure in connectivity and packet dropping. More recent protocols like SEP and TEEN considered the reverse that is energy heterogeneity which is more applicable to case of WSNs. We developed a bi-dimensional chain model to select average number of for DEEC. Simulation results are used to compare performance of different protocols to found optimal solutions of above mentioned problems.

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... Controlling the size of the cluster is also important for balancing the load. In the proposed mechanism, we use the Markov chain model [21] to select an optimal number of CHs in the network according to the number of available sensor nodes for optimal resource utilization. In this designed framework, we also limit the number of C MNs in a cluster to divide the energy overhead and other CH-related responsibilities. ...
... The current clustering protocols [7][8][9][10][11][12]utilize the distributed algorithm for CH selection that increase the computational overhead and also causes the resources to drain very quickly. We use Markov Bi-directional Chain Model (MBCM) [21,26] to equally divide the sensing area into clusters, and to examine the behavior of the cluster formation process. We also used this formulation [21,26] for optimal CH selection and to analyze the stochastic characteristics of the MOCHs in the sense of the mean value, the probability mass function (PMF), the standard deviation (SD), and the coefficient of variation (COV) for the optimal selection of CHs. ...
... We use Markov Bi-directional Chain Model (MBCM) [21,26] to equally divide the sensing area into clusters, and to examine the behavior of the cluster formation process. We also used this formulation [21,26] for optimal CH selection and to analyze the stochastic characteristics of the MOCHs in the sense of the mean value, the probability mass function (PMF), the standard deviation (SD), and the coefficient of variation (COV) for the optimal selection of CHs. Through this formulation [21,26], the obtained CHs are optimal in number and well-distributed all across the network, which leads to higher energy efficiency, better fairness among nodes, and prolonged the network lifetime. ...
Article
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The longer network lifetime of Wireless Sensor Networks (WSNs) is a goal which is directly related to energy consumption. This energy consumption issue becomes more challenging when the energy load is not properly distributed in the sensing area. The hierarchal clustering architecture is the best choice for these kind of issues. In this paper, we introduce a novel clustering protocol called Markov chain model-based optimal cluster heads (MOCHs) selection for WSNs. In our proposed model, we introduce a simple strategy for the optimal number of cluster heads selection to overcome the problem of uneven energy distribution in the network. The attractiveness of our model is that the BS controls the number of cluster heads while the cluster heads control the cluster members in each cluster in such a restricted manner that a uniform and even load is ensured in each cluster. We perform an extensive range of simulation using five quality measures, namely: the lifetime of the network, stable and unstable region in the lifetime of the network, throughput of the network, the number of cluster heads in the network, and the transmission time of the network to analyze the proposed model. We compare MOCHs against Sleep-awake Energy Efficient Distributed (SEED) clustering, Artificial Bee Colony (ABC), Zone Based Routing (ZBR), and Centralized Energy Efficient Clustering (CEEC) using the above-discussed quality metrics and found that the lifetime of the proposed model is almost 1095, 2630, 3599, and 2045 rounds (time steps) greater than SEED, ABC, ZBR, and CEEC, respectively. The obtained results demonstrate that the MOCHs is better than SEED, ABC, ZBR, and CEEC in terms of energy efficiency and the network throughput.
... Routing protocols improve the lifetime of a network and specifically the stability period of a network. Protocols [14], [15], [16], [18], [19], [27], [28], [29], [30], [31], [35] and [40] are proposed to achieve these goals. ...
... i. SCALABILITY ALD of AODV is less in lower scalabilities because when nodes move with same speed as shown in eq. (18), AODV repairs the link quickly but due to less number of hops it takes more time to repair the links. In higher scalabilities, ALD is greater and due to more number of hops, link is maintained quickly as depicted in fig. ...
... 16(a), ALD of FSR and OLSR is high in low speeds as shown in eq. (18) and eq. (20). ...
Article
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Mobility constraints and speed cause the radio link to break frequently, the main issue in Mobile Ad-hoc Networks (MANETs) is how to select the path which is more reliable. In this paper, we propose a model to calculate the reliable link between the nodes and reliable path for the purpose of communication. This paper also evaluates and compares the performance of routing protocols with different number of nodes, mobilities and speeds in MANETs and VANETs using Packet Delivery Ratio (PDR), Normalized Routing Overhead (NRO), End-to-End Delay (E2ED), Average Link Duration (ALD) and Average Path Duration (APD). We select three routing protocols namely Ad-hoc On-demand Distance Vector (AODV), Fish-eye State Routing (FSR) and Optimized Link State Routing (OLSR). We perform these simulations with NS-2 using Two Ray Ground propagation model. The Vanet MobiSim simulator is used to generate a random mobility pattern for VANETs. From the extensive simulations, we observe that AODV is more efficient than both FSR and OLSR at the cost of delay but the ALD and APD of FSR and OLSR are greater as compared to AODV. Moreover these protocols perform better in MANETs as compared to VANETs.
... Routing protocols improve the lifetime of a network and specifically the stability period of a network. Protocols [14], [15], [16], [18], [19], [27], [28], [29], [30], [31], [35] and [40] are proposed to achieve these goals. ...
... i. SCALABILITY ALD of AODV is less in lower scalabilities because when nodes move with same speed as shown in eq. (18), AODV repairs the link quickly but due to less number of hops it takes more time to repair the links. In higher scalabilities, ALD is greater and due to more number of hops, link is maintained quickly as depicted in fig. ...
... 16(a), ALD of FSR and OLSR is high in low speeds as shown in eq. (18) and eq. (20). ...
... TEEN is a hierarchical clustering protocol [6], which groups different sensor nodes into clusters with each having a clusterhead( CH).The job of the sensors within a cluster is to send their sensed data to their respective CH. The CH now sends the aggregated data to higher level CH until the data reaches the sink. ...
... Within our protocol the areas of clusters are decided in advanced and cluster heads are selected based on residual energy and estimated energy cost. O. Younis et al. [6] suggested new vigor powerful process for bunching modems in unplanned alarm systems. Fixated with this specific technique, some form of typical meeting are shown, HEED (Hybrid Energy-Efficient Spread bunching), that often pick class minds in knowledge to half and 1 / 2 of their blend outstanding vigor moreover was extra parameter, such as for instance for instance regarding situation center place to their buddies or even center level. ...
Article
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Wireless sensor networks (WSNs) are becoming popluar day by day due to its wide range of applications. But, sensors have battery constraints i.e. batteries are not rechargable nor replaceble. Therefore, it become significant issue to save the energy of sensor nodes, in such a way that the overall lifetime can be increased. Many clustering and tree-based protocols have been proposed so far to improve the network lifetime of WSNs. This paper has presented a detail review of some well-known energy efficient protocols for WSNs.It has been observed that the Game theory based energy balanced (GTEB) protocol is more efficient than other protocols in terms of network lifetime by balancing energy consumption in large network area using geographical routing protocols. It also compared some well-known protocols based upon certain features.
... Each node has a little amount of energy and that is not rechargeable, so, energy should be used efficiently for the sake of network lifetime. But Previous works on WSNs such as LEACH [1], LEACH-C [16], TEEN [13], SEP [14] and DEEC [15] have shown that the coverage holes may be created during lifetime of the network and that can not be accepted. Clustering technique of LEACH [1] does not assure a fix number of CHs in each round therefore, its behavior is not so appreciable in case of network lifetime. ...
... Nodes=200 Nodes=300 Nodes=400 Nodes=500 Nodes=600 Nodes=700 Nodes=800 Nodes=900 Nodes=1000 We are working on some more clustering and routing techniques to make the network much better and more efficient than DREEM-ME. In future we would like to reduce deficiencies which are expected in this paper and implementation of DREEM-ME in other clustering protocols like Threshold sensitive energy efficient sensor network protocol [13], stable election protocol [14], distributed energy efficient clustering [15], etc. In future, we aim to introduce multiple QoS path parameters [27], energy efficient MAC protocols [37], sink mobility [34] and heterogeneity [32] in our work. ...
Thesis
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Wireless distributed sensor network consists of randomly deployed wireless sensors having low energy assets. These networks can be used for monitoring a variety of environments. Major problem of these networks is energy constraint and lifetime, so, to overcome these problems different routing protocols and clustering techniques have been introduced. We propose Distributed Regional Energy Efficient Multi-hop Routing Protocol based on Maximum Energy in WSNs (DREEM-ME), which uses a unique technique for clustering to cover these two problems efficiently. Clustering technique of LEACH does not assure a fix number of Cluster Heads (CHs) in each round. Therefore, its behavior is not so appreciable in case of network lifetime. DREEM-ME elects fix number of CHs in each round instead of probabilistic selection of CHs. We also implement the Packet Drop technique in our protocol which makes it more comprehensive and practical. Our simulations and results show that DREEM-ME competes all of its identical protocols.
... In this way, the CHs will also be optimum in every round. As shown by the research that 8 cluster heads are optimum in the network [12]. And other problems with previous protocols are their stability period and network lifetime, which are not good enough. ...
Article
Wireless distributed sensor network consists of randomly deployed sensors having low energy assets. These networks can be used for monitoring a variety of environments. Major problems of these networks are energy constraints and their finite lifetimes. To overcome these problems different routing protocols and clustering techniques are introduced. We propose DREEM-ME which uses a unique technique for clustering to overcome these two problems efficiently. DREEM-ME elects a fix number of cluster heads (CHs) in each round instead of probabilistic selection of CHs. Packet Drop Technique is also implemented in our protocol to make it more comprehensive and practical. In DREEM-ME confidence interval is also shown in each graph which helps in visualising the maximum deviation from original course. Our simulations and results show that DREEM-ME is much better than existing protocols of the same nature.
... JID: CAEE [m3Gsc;December 14, 2015;15:53] of minimum transmission energy, when the node senses data to transmit, it may find the shortest possible route consisting of several hops to the base station. Optimal routes help to save the energy of the network [12]. However, the nodes closer to the base station forward more packets as compared to the distant nodes. ...
Article
Full-text available
Hierarchical clustering technique is an effectual topology control methodology in Wireless Sensor Networks (WSNs). This technique is used to increase the life time of the network by reducing the number of transmissions towards the base station. We propose and validate a new routing protocol termed as Sleep-awake Energy Efficient Distributed (SEED) clustering algorithm. We divide the network sensing field into three energy regions because in SEED cluster heads are communicating directly with the base station. The cluster heads of the high energy region are communicating with the base station through a longer distance and paying extra energy cost as compared to the cluster heads of the low energy region. Same application base sensor nodes form sub-clusters to decrease the number of transmissions towards the base station. In every round, one node from these sub-clusters nodes awake and transmit the data and the rest of them sleep to save available resources. We select six criteria to check the performance of our algorithm. The simulation results show that SEED achieves longer network life time and high throughput as compared to the existing clustering protocols.
... Clustering is one of the methods to improve network lifetime as it can balance the energy dissipation of nodes. Several works have appeared in literature [7], which discuss the clustering techniques. One of the main ideas available in the clustering algorithms is the use of LEACH [8] algorithm. ...
Conference Paper
Wireless sensor network (WSN) is one of the key enablers for Internet of Things (IoT) applications such as smart homes, intelligent manufacturing, agriculture, healthcare monitoring among others. Small sensors are deployed in a specific environment to sense and acquire the vital data and transmit to Base Station (BS). Due to resource constraints of the sensors and the need for long lifetime, energy consumption is a challenging issue that directly affects the network lifetime and performance of the IoT applications. In this paper, we present a novel intelligent clustering technique utilizing a computational intelligence technique, namely fuzzy logic, to efficiently improve the network lifetime and performance. In particular, we propose a load balance clustering algorithm (LBCA) that performs load balancing on the selection of cluster head (CH) among all sensors, based on a priority queue, using a fuzzy inference system, to minimize and distribute the energy consumption. In addition, we propose a scheduling algorithm based on TDMA for reducing unnecessary intra-cluster communication that leads to a prolonged lifetime and enhanced performance. Simulations are conducted to evaluate the performance of the proposed fuzzy logic based clustering technique, taking into account the network lifetime in terms of First Node Dead, Half Nodes Dead and End Node Dead, and the network performance in terms of packets sent to BS. Based on the simulation results, the proposed clustering technique has shown significant benefits compared to other conventional solutions, revealing the proficient network lifetime and performance provided by the proposed fuzzy logic based clustering technique.
... overall number of vertices (see, e.g. Fareed et al. 2012;Sevgi and Kocyigit 2008). Thus, CluBFS-Fpt1 might result in being truly effective in practice. ...
Article
Full-text available
Given an $n$-vertex non-negatively real-weighted graph $G$, whose vertices are partitioned into a set of $k$ clusters, a \emph{clustered network design problem} on $G$ consists of solving a given network design optimization problem on $G$, subject to some additional constraint on its clusters. In particular, we focus on the classic problem of designing a \emph{single-source shortest-path tree}, and we analyze its computational hardness when in a feasible solution each cluster is required to form a subtree. We first study the \emph{unweighted} case, and prove that the problem is \np-hard. However, on the positive side, we show the existence of an approximation algorithm whose quality essentially depends on few parameters, but which remarkably is an $O(1)$-approximation when the largest out of all the \emph{diameters} of the clusters is either $O(1)$ or $\Theta(n)$. Furthermore, we also show that the problem is \emph{fixed-parameter tractable} with respect to $k$ or to the number of vertices that belong to clusters of size at least 2. Then, we focus on the \emph{weighted} case, and show that the problem can be approximated within a tight factor of $O(n)$, and that it is fixed-parameter tractable as well. Finally, we analyze the unweighted \emph{single-pair shortest path problem}, and we show it is hard to approximate within a (tight) factor of $n^{1-\epsilon}$, for any $\epsilon>0$.
... Authors in [13] compare problems of cluster formation and cluster-head selection between different protocols for data aggregation and transmission. They focused on two aspects of the problem: (i) how to guess number of clusters required to proficiently consume available sources for a sensor network, and (ii) how to select number of cluster-heads to cover up sensor networks more proficiently. ...
... While hundreds of clustering protocols have been proposed, few analytical studies that allow an informed choice for the selection of the most energy-efficient clustering strategies. Authors in [13] determine the best position of the BS when the first node dies (FND) lifetime measure is considered while authors in [14] try to find the optimum number of CHs. This paper compares for the first time widely adopted clustering strategies that are unequal and equal, rotation and non-rotation. ...
Chapter
Wireless Sensor Networks (WSNs) are the essential elements to sense the environment, process and broadcast information into the Internet of Things (IoT) and advanced Unmanned Aerial Vehicles (UAVs) assisted networks. Clustering is an energy-efficient routing technique that has been widely applied to send data to BS. There have been proposed many clustering protocols but it is very hard to decide the most energy-efficient one, to be applied in a particular scenario. In this paper, we analyze the simulation results-based different clustering strategies (e.g., equal, unequal, rotation, and non-rotation). We developed a novel analytical model to support the simulation results and to conclude an informed choice for the selection of the most energy-efficient protocol.
... JID: CAEE [m3Gsc; December 2, 2015;20:0] of minimum transmission energy, when the node senses data to transmit, it may find the shortest possible route consisting of 12 several hops to the base station. Optimal routes help to save the energy of the network [12]. However, the nodes closer to the 13 base station forward more packets as compared to the distant nodes. ...
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A sensor network system consisting of a large number of small sensors with low-power can be an effective tool for collection and integration of data by each sensor in a variety of environments. The collected data by each sensor node is communicated through the network to a single base station that uses all collected data to determine properties of the data. Clustering sensors into groups, yields that sensors communicate information only to cluster heads and then the cluster-heads communicate the aggregated information to the base station. We estimate the optimal number of cluster-heads among randomized sensors in a bounded region. We derive solutions for the values of parameters of our algorithm that minimize the total energy spent in the wireless sensor network when all sensors communicate data from the cluster-heads to the base station. Computer simulation shows that the energy consumption reduce as the optimal number of cluster-heads for the proposed method.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000. Includes bibliographical references (p. 145-154).
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Estimation of the optimal number of cluster-heads in sensor network, in Knowledge-Based Intelligent Information and Engineering Systems
  • H Kim
  • S Kim
  • S Lee
  • B Son