scholarly journals An Enhanced Clustering Method for Extending Sensing Lifetime of Wireless Sensor Network

Author(s):  
Yakubu Abdul-Wahab Nawusu ◽  
Alhassan Abdul-Barik ◽  
Salifu Abdul-Mumin

Extending the lifetime of a wireless sensor network is vital in ensuring continuous monitoring functions in a target environment. Many techniques have appeared that seek to achieve such prolonged sensing gains. Clustering and improved selection of cluster heads play essential roles in the performance of sensor network functions. Cluster head in a hierarchical arrangement is responsible for transmitting aggregated data from member nodes to a base station for further user-specific data processing and analysis. Minimising the quick dissipation of cluster heads energy requires a careful choice of network factors when selecting a cluster head to prolong the lifetime of a wireless sensor network. In this work, we propose a multi-criteria cluster head selection technique to extend the sensing lifetime of a heterogeneous wireless sensor network. The proposed protocol incorporates residual energy, distance, and node density in selecting a cluster head. Each factor is assigned a weight using the Rank Order Centroid based on its relative importance. Several simulation tests using MATLAB 7.5.0 (R2007b) reveal improved network lifetime and other network performance indicators, including stability and throughput, compared with popular protocols such as LEACH and the SEP. The proposed scheme will be beneficial in applications requiring reliable and stable data sensing and transmission functions.

In a distributed Wireless Communication Technology, the Wireless Sensor Network (WSN) is a technology developing for sensing and performing different monitoring operations. The proposed algorithm dynamically partitions the Heterogeneous Wireless Sensor Network (HWSN) in to clusters. On the basis of initial energy, the cluster head (CH) is selected in the first round and residual energy with low draining rate protocol (RELDR) is used in the next round for selecting CH. The CH senses and aggregates the data, these summarized data is processed between the clusters and the link is maintained with the base station. Cluster Authority (CA) is a member node that acts as a supervising node which contains remove list and maintains the attacker information. The Technology Multiple Input and Multiple Output(MIMO) is used in the proposed system which reduces the noise in the signal and improves the network performance. During transmission, the unauthenticated nodes which are responsible for data leakage or any malicious activities are detected by the algorithm and information of these nodes are updated in the remove list of CA. The listed unauthenticated nodes or the black hole attack nodes in CA are removed from the network. The proposed algorithm removes the malicious nodes which are affecting the network performance and reconstructs the network by considering only the legitimate nodes. Experimental results will be analyzed for the network parameters like throughput, delay, energy and Packet delivery ratio and compared with the existing systems.


2013 ◽  
Vol 765-767 ◽  
pp. 980-984
Author(s):  
Xi Rong Bao ◽  
Jia Hua Xie ◽  
Shuang Long Li

This article focused on the energy limit property of Wireless Sensor Network, and proposed a residual energy based algorithm WN-LEACH, with the classic network mode of LEACH routing algorithm. The algorithm combines the proportion of residual energy in the total energy with the cumulative number of the normal nodes supported by the cluster heads as a cluster selection reference. In order to balance the energy consumption of each cluster-head, the algorithm took both the different positions of the base station and the initial energy of the network into consideration, and weighted the two factors to balance the energy consumption between transmitting the signals and data fusion. Simulation results show that the algorithm can promote the lifetime of the uneven energy network and does not impair the effects of the LEACH algorithm.


Author(s):  
Peng Xiong ◽  
Qinggang Su

Due to the resource constraint, in wireless sensor network, the node processing ability, wireless bandwidth and battery capacity and other resources are scarcer. For improving the energy efficient and extend the lifetime of the network, this paper proposes a novel algorithm with the distributed and energy-efficient for collecting and aggregating data of wireless sensor network. In the proposed protocol, nodes can autonomously compete for the cluster head based on its own residual energy and the signal strength of its neighbouring nodes. To reduce the energy overhead of cluster head nodes, with a multi-hop way among cluster heads, the collected data from cluster heads is sent to a designated cluster head so as to further send these data to a base station. For improving the performance of the proposed protocol, a new cluster coverage method is proposed to fit the proposed protocol so that when the node density increases, network lifetime can be increased linearly as the number of nodes is increased. Simulations experiments show that network lifetime adopting the proposed protocol is sharply increased. And, the proposed protocol makes all the nodes die (network lifetime is defined as the death of last one node) in the last 40 rounds so that networks adopting the proposed protocol have higher reliability than networks adopting compared protocols.


2017 ◽  
Vol 16 (7) ◽  
pp. 7031-7039
Author(s):  
Chamanpreet Kaur ◽  
Vikramjit Singh

Wireless sensor network has revolutionized the way computing and software services are delivered to the clients on demand. Our research work proposed a new method for cluster head selection having less computational complexity. It was also found that the modified approach has improved performance to that of the other clustering approaches. The cluster head election mechanism will include various parameters like maximum residual energy of a node, minimum separation distance and minimum distance to the mobile node. Each CH will create a TDMA schedule for the member nodes to transmit the data. Nodes will have various level of power for signal amplification. The three levels of power are used for amplifying the signal. As the member node will send only its own data to the cluster head, the power level of the member node is set to low. The cluster head will send the data of the whole cluster to the mobile node, therefore the power level of the cluster head is set to medium. High power level is used for mobile node which will send the data of the complete sector to the base station. Using low energy level for intra cluster transmissions (within the cluster) with respect to cluster head to mobile node transmission leads in saving much amount of energy. Moreover, multi-power levels also reduce the packet drop ratio, collisions and/ or interference for other signals. It was found that the proposed algorithm gives a much improved network lifetime as compared to existing work. Based on our model, multiple experiments have been conducted using different values of initial energy.


2020 ◽  
Author(s):  
Hamid Reza Farahzadi ◽  
Mostafa Langarizadeh ◽  
Mohammad Mirhosseini ◽  
Seyed Ali Fatemi Aghda

AbstractWireless sensor network has special features and many applications, which have attracted attention of many scientists. High energy consumption of these networks, as a drawback, can be reduced by a hierarchical routing algorithm. The proposed algorithm is based on the Low Energy Adaptive Clustering Hierarchy (LEACH) and Quadrant Cluster based LEACH (Q-LEACH) protocols. To reduce energy consumption and provide a more appropriate coverage, the network was divided into several regions and clusters were formed within each region. In selecting the cluster head (CH) in each round, the amount of residual energy and the distance from the center of each node were calculated by the base station (including the location and residual energy of each node) for all living nodes in each region. In this regard, the node with the largest value had the highest priority to be selected as the CH in each network region. The base station calculates the CH due to the lack of energy constraints and is also responsible for informing it throughout the network, which reduces the load consumption and tasks of nodes in the network. The information transfer steps in this protocol are similar to the LEACH protocol stages. To better evaluate the results, the proposed method was implemented with LEACH LEACH-SWDN, and Q-LEACH protocols using MATLAB software. The results showed better performance of the proposed method in network lifetime, first node death time, and the last node death time.


2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Zuo Chen ◽  
Min He ◽  
Wei Liang ◽  
Kai Chen

Wireless sensor network (WSN) is a kind of distributed and self-organizing networks, in which the sensor nodes have limited communication bandwidth, memory, and limited energy. The topology construction of this network is usually vulnerable when attacked by malicious nodes. Besides, excessive energy consumption is a problem that can not be ignored. Therefore, this paper proposes a secure topology protocol of WSN which is trust-aware and of low energy consumption, called TLES. The TLES considers the trust value as an important factor affecting the behavior of node. In detail, the TLES would take trust value, residual energy of the nodes, and node density into consideration when selecting cluster head nodes. Then, TLES constructs these cluster head nodes by choosing the next hop node according to distance to base station (BS), nodes’ degrees, and residual energy, so as to establish a safe, reliable, and energy saving network. Experimental results show that the algorithm can effectively isolate the malicious node in the network and reduce the consumption of energy of the whole network.


2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Asis Kumar Tripathy ◽  
Suchismita Chinara

Wireless sensor network swears an exceptional fine-grained interface between the virtual and physical worlds. The clustering algorithm is a kind of key technique used to reduce energy consumption. Many clustering, power management, and data dissemination protocols have been specifically designed for wireless sensor network (WSN) where energy awareness is an essential design issue. Each clustering algorithm is composed of three phases cluster head (CH) selection, the setup phase, and steady state phase. The hot point in these algorithms is the cluster head selection. The focus, however, has been given to the residual energy-based clustering protocols which might differ depending on the application and network architecture. In this paper, a survey of the state-of-the-art clustering techniques in WSNs has been compared to find the merits and demerits among themselves. It has been assumed that the sensor nodes are randomly distributed and are not mobile, the coordinates of the base station (BS) and the dimensions of the sensor field are known.


Author(s):  
Ekaterina Andreevna Evstifeeva ◽  
Valeriy Dmitrievich Semeykin

Clustering, as one of the energy-efficient approaches, is widely used in wireless sensor networks. This method is based on creating clusters and selecting cluster head nodes in a wireless sensor network. Clustering saves network energy because data transfer is restricted between multiple nodes. Thus, clustering is provided between several nodes, and the service life of the wireless sensor network can be extended. Since the parent cluster node interacts with other nodes of the network, a node with a high level of residual energy must be selected to perform this role. When the energy level of the selected cluster head node becomes lower than the threshold value, then the re-election of this node takes place. It should be noted that multiple patterns of choosing cluster head nodes built using various parameters (residual node energy, distance from the base station to a node, distance between the head node and a cluster member, the number and proximity of neighboring nodes, etc.) lacked for a factor of energy consumption, i.e. how many times nodes communicated to each other. To cope with the problem, this paper presents a prognostic algorithm for selecting a cluster head node using fuzzy logic. This algorithm suggests using a number of input parameters, such as the residual energy of the node, the proximity of neighboring nodes, and the centralization of the node in the cluster. The proposed algorithm has been implemented using the software package MATLAB Fuzzy Logic Toolbox. The simulation results prove the advantages of the proposed technique; application of the input parameters mentioned above helps select optimal cluster head nodes in a wireless sensor network, which increases power efficiency of a wireless sensor network.


2019 ◽  
Vol 14 (2) ◽  
pp. 183-198 ◽  
Author(s):  
Jothi Kumar C ◽  
Revathi Venkataraman

Wireless Sensor Network comprises of a number of small wireless nodes whose role is to sense, gather, process and communicate. One of the primary concerns of the network is to optimize the energy consumption and extend the network lifespan. Sensor nodes can be clustered to increase the network lifespan. This is done by selecting the cluster head for every cluster and by performing data fusion on the cluster head. The proposed system is using an energy efficient hierarchical routing protocol named Energy Optimized Dynamic Clustering (EODC) for clustering large ad-hoc WSN and route the data towards the sink. The sink receives the data collected from the set of cluster heads after every round. The cluster head was selected using Particle Swarm Optimization (PSO) approach and the cluster members are allocated based on Manhattan distance. The metrics used to find the fitness function are location, link quality, energy of active node and energy of inactive node. The system employs shortest path approach to communicate between the cluster heads till it reaches the base station. By this, we have increased the energy efficiency and lifetime of the network. The analysis and outcomes show that the EODC was found to outperform the existing protocol which compares with this algorithm.


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