An Intelligent and Knowledge-based Overlapping Clustering Protocol for Wireless Sensor Networks (original) (raw)
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Energy Efficient Clustering Algorithm in Wireless Sensor Networks using Fuzzy Logic Control
2011 IEEE Colloquium on Humanities, Science and Engineering (CHUSER)
In general, environment monitoring cluster based hierarchical routing protocol is among the most common protocol being opted due to the load balancing among each other sensor. Sensors are randomly deployed in a specific area to collect useful information periodically for a few months or even a few years. Therefore, battery power limitation becomes a challenging issue. It is also impractical to maintain the network lifetime by changing the battery frequently. Low energy adaptive cluster hierarchical (LEACH) is one of the common clustering protocols that will elect the cluster head based on the probability model which will possibly lead to a reduce in network lifetime due to election of cluster head with a least desired location in the network. For wireless sensor networks, the distribution of cluster head selection directly influences the network's lifetime. This paper presents factors which will affect the network lifetime and apply fuzzy logic based cluster head selection conducted in base station. The base station considers two selection criteria from sensor nodes which are energy level and distance to the base station to select the suitable cluster head that will prolong the first node die (FND) time, data stream guaranteed for every round and also increase the throughput received by the base station before FND.
Clustering using Fuzzy Logic in Wireless sensor Networks
– Efficient use of energy resources is the one of most challenging research issue in wireless sensor networks, because the battery limits the lifetime of the sensor nodes. To enhance the energy efficiency the clustering hierarchical architecture is most favorable approach. In clustering, the network is divided into sections known as clusters and each cluster selects a leader among the cluster members according to set of rules define by user. Only the leader is allow to communicate with base station. LEACH is the first dynamic and self-organized clustering in wireless sensor networks. It use probabilistic model for cluster head election. After LEACH many techniques has been proposed and inspired from LEACH. These techniques has defects such as large overhead generation and uncertainty in selecting cluster heads. In order to reduce these demerits fuzzy logic based clustering techniques proposed such as CHEF (cluster head election mechanism using fuzzy logic). Fuzzy logic based techniques generate lower overhead and fuzzy logic reduces uncertainties in cluster head selection. In this paper we introduce new clustering technique using fuzzy logic. We use fuzzy logic to calculate the value of timer, which is responsible for forming clusters in the network. We performed simulation in MATLAB and results are compared with LEACH and CHEF clustering techniques. Simulation results shows that the proposed clustering technique is more energy efficient than the LEACH and CHEF in term of network lifetime.
Cluster Head Selection in Wireless Sensor Networks under Fuzzy Environment
ISRN Sensor Networks, 2013
Clustering is one of the important methods for prolonging the network lifetime in wireless sensor networks (WSNs). It involves grouping of sensor nodes into clusters and electing cluster heads (CHs) for all the clusters. CHs collect the data from respective cluster’s nodes and forward the aggregated data to base station. A major challenge in WSNs is to select appropriate cluster heads. In this paper, we present a fuzzy decision-making approach for the selection of cluster heads. Fuzzy multiple attribute decision-making (MADM) approach is used to select CHs using three criteria including residual energy, number of neighbors, and the distance from the base station of the nodes. The simulation results demonstrate that this approach is more effective in prolonging the network lifetime than the distributed hierarchical agglomerative clustering (DHAC) protocol in homogeneous environments.
A Fuzzy Logic-Based Clustering Algorithm for WSN to Extend the Network Lifetime
IEEE sensor journal, 2016
Wireless sensor network (WSN) brings a new paradigm of real-time embedded systems with limited computation , communication, memory, and energy resources that are being used for huge range of applications where the traditional infrastructure-based network is mostly infeasible. The sensor nodes are densely deployed in a hostile environment to monitor, detect, and analyze the physical phenomenon and consume considerable amount of energy while transmitting the information. It is impractical and sometimes impossible to replace the battery and to maintain longer network life time. So, there is a limitation on the lifetime of the battery power and energy conservation is a challenging issue. Appropriate cluster head (CH) election is one such issue, which can reduce the energy consumption dramatically. Low energy adaptive clustering hierarchy (LEACH) is the most famous hierarchical routing protocol, where the CH is elected in rotation basis based on a probabilistic threshold value and only CHs are allowed to send the information to the base station (BS). But in this approach, a super-CH (SCH) is elected among the CHs who can only send the information to the mobile BS by choosing suitable fuzzy descriptors, such as remaining battery power, mobility of BS, and centrality of the clusters. Fuzzy inference engine (Mamdani's rule) is used to elect the chance to be the SCH. The results have been derived from NS-2 simulator and show that the proposed protocol performs better than the LEACH protocol in terms of the first node dies, half node alive, better stability, and better lifetime.
A New Fuzzy Clustering Algorithm to Enhance Lifetime of Wireless Sensor Networks
AECIA 2016: Proceedings of the Third International Afro-European Conference for Industrial Advancement , 2016
Due to limitations of resource in wireless sensor networks (WSNs) enhancing the network lifetime has been of great concern. An efficient routing algorithm is known as clustering algorithm based routing protocol. In which getting optimal cluster heads (CHs) and a number of them has been defiance. In this paper, a new fuzzy clustering algorithm is proposed to maximize the lifetime of WSNs. Network field in this approach, contains two types of sensors: free sensors that communicate directly with sink, and clustered sensors that send the sensed data to the sink through CHs which are preselected. This approach uses fuzzy logic to select free sensor nodes and CHs with four fuzzy parameters. These parameters are energy level of sink and sensor proximity to the sink in terms of free sensors selection, and energy level of sensor node and centrality of sensors in terms of CHs selection. The main goal of our algorithm is to extend the lifetime of WSNs by minimizing distributing the workload on CHs. The simulation results show that our proposed is more efficient than SET protocol.
Institute of Advanced Engineering and Science (IAES), 2024
Wireless sensor networks (WSNs) are of significant importance in many applications; nevertheless, their operational efficiency and longevity might be impeded by energy limitations. The low energy adaptive clustering hierarchy (LEACH) protocol has been specifically developed with the objective of achieving energy consumption equilibrium and regularly rotating cluster heads (CHs). This study presents a novel technique, namely the hierarchical fuzzy logic controller (HFLC), which is integrated with the LEACH protocol to enhance the process of CH selection and effectively prolong the network's operational lifespan. The HFLC system employs fuzzy logic as a means to address the challenges posed by uncertainty and imprecision. It assesses many aspects, including residual energy, node proximity, and network density, in order to make informed decisions. The combination of HFLC with LEACH demonstrates superior performance compared to the conventional LEACH protocol in terms of energy efficiency, stability, and network durability. This study emphasizes the potential of intelligent and adaptive mechanisms in improving the performance of WSNs by improving the survivability of nodes by reducing the energy consumption of the nodes during the communication of network process. It also paves the way for future research that integrates soft computing approaches into network protocols.