Impairments-aware time slot allocation model for energy-constrained multi-hop clustered IoT nodes considering TDMA and DSSS MAC protocols

The energy resources available to nodes in wireless sensor networks are limited, so they must be wisely used. Clustering is a useful technique for reducing energy consumption and extending the life of a network. In this study, we presented an Energy-Saving Clustering Algorithm (ESCA) to reduce energy consumption and increase the network's lifetime. The clustering phase is based on cluster construction that is centralized and cluster heads that are distributed. The clustering is stationary and determined using a centralized K-means method, with the created clusters remaining static throughout the process. Subsequently, according to the varying amounts of energy in the nodes, it chooses and rotates the cluster heads (CHs) within those clusters to reduce energy expenditure before the data transmission phase to the base station (BS). The suggested ESCA is compared to the current two MOFCA, and IGHND clustering methods using a Python-based custom simulator. As a consequence, the suggested ESCA efficiently tackles the energy use issue while also greatly extending the network's lifetime.

An Improved Energy Efficient Clustering Protocol for Wireless Sensor Networks

The energy efficiency and stability of wireless sensor networks (WSNs) have always been a hot issue in the research. Clustering is a typical architecture for WSNs, and cluster heads (CHs) play a vital role. Unreasonable CH selection causes a lot of energy consumption. In this paper, we propose a competition-based unequal clustering multihop approach (CUCMA). CHs are selected by competition. First, the cluster radius (CR) of a node is calculated according to the distance to base station (BS). Then, CR is resized based on the number of around nodes. Only the nodes with high residual energy and appropriate distances to the selected CHs maybe become CHs, which are usually closer to the surrounding nodes. CUCMA and four related approaches are simulated in different scenarios. The results are analyzed, and it is proved that CUCMA balances the energy consumption of the CHs and reduces the energy consumption of the whole networks, thus leading to prolong the lifetime of WSNs.

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A Competition-Based Unequal Clustering Multihop Approach for Wireless Sensor Networks

The increased traffic on the roads is a direct outcome of growing urban populations. Congestion on the roads is a result of the road infrastructure’s incapacity to manage traffic effectively. The government is also affected by traffic congestion, in addition to the general public. Government expenditures are increased by congestion, which also makes residents more stressed and harms the environment. In order to boost system efficiency and cut down on travel time, alternate routes that steer clear of busy road segments can be suggested to drivers. Therefore, this paper discusses about the methods for analyzing real-time traffic data, the algorithms that provide a real-time, energy-efficient, and optimal path from the source to the destination depending on the traffic. This paper also discusses the benefits and limitations of these techniques and algorithms in a Smart Urban Traffic Management System (SUTMS) with the results of contemporary study on the same topic.

Smart Urban Traffic Management System using Energy Efficient Optimized Path Discovery

Field-clustering with sleep awake mechanism with fuzzy in wireless sensor network

A collection of sensor nodes are available in wireless sensor network for gathering the distinguish data from environment. This sensing process consumes more energy of the network which effects the whole network life time. So energy usage in efficient manner is the main issue to maintaining the network. Clustering is the process used for reducing the energy consumption. K-means is the post popular clustering algorithm to form the clusters. In this paper, propose energy efficient clustering i.e quad clustering based on K-means algorithm. This approach improves the performance of wireless sensor network in terms of network lifetime. As simulation shows the proposed work is better than single cluster in case of distance coverage as well as energy consumption.

Energy Efficient Quad Clustering based on K-means Algorithm for Wireless Sensor Network

As a discipline, Machine Learning has been adopted and leveraged widely by researchers from several domains. There is a huge range of classifiers already available in machine learning and it has kept on growing with the advancement of this field. However, it is very hard to pick the best classifier among the several similar classifiers suitable for any problem. Recent advancement in this field for solving this issue is the Multiple Classifier System (MCS). It comes under the umbrella of ensemble learning and gives comparatively a better and definite result than a single classifier. MCS has two layers—(i) Base layer—contains a number of ML Classifiers appropriate for any specific task—and (ii) Meta Learner Layer—which aggregates the results from base layer classifiers by using techniques, such as Voting and Stacking. However, the job of selecting the appropriate classifiers from various classifiers or from a family of classifiers for a specific classification or prediction task on any dataset is still unraveling. This work emphasizes determining the characteristics of the selection method of base classifiers in the MCS. Moreover, which Meta Learner layer from Stacking and Voting aggregates the better result according to the different sizes of the base classifiers?

A Selection Method for Computing the Ensemble Size of Base Classifier in Multiple Classifier System

Wireless sensor network (WSN) is a vast field for the research and development in many types of applications. In WSN, multiples nodes deployed into the environment; each node has energy level. Optimized energy consumption is main concern for any kind of applications like as military/battlefield, smart farming, medical science, vehicular ad hoc networks (VANET). Thousands of nodes deployment in sensor area become a typical task and later maintain the energy consumption as well. The level of consumption of energy consumption of network needs to be focused to prolong the life time. This research work increases the energy level of sensor network using distance-based technique for WSN. It elects the cluster head on the bases of distances between sensor nodes and from base station also considered. The implementation shows the graphical representation of sensor nodes and calculates the energy consumption of each node along with cluster head and also gives the comparison between clustering and quad clustering. This technique represents total energy which is transmission energy, receiving energy, and data aggregation energy through a graph. This proposed work examines that enhanced distance-based technique increases the life time of sensor network for the advancements in the WSN applications. 

Bhawnesh Kumar

Papers

Energy Efficient Quad Clustering based on K-means Algorithm for Wireless Sensor Network

A Selection Method for Computing the Ensemble Size of Base Classifier in Multiple Classifier System

Distance-based Energy-Efficient Clustering Approach for Wireless Sensor Networks