A Survey on Cluster Based Routing Protocols in Wireless Sensor Networks

There is a growing interest in using wireless sensor technologies in various Internet of things scenarios. Considering the huge growth of smart objects and their applications, the need to collect and analyze their product data are becoming one of the main challenges. Sensor nodes are powered by batteries, efficient operations in term of energy are critical. Toward that end, it is desirable for a sensor node to eliminate redundancies in the received data from the neighboring nodes before transferring the final data to the central station. Data aggregation is one of the influential techniques in elimination of data redundancy and improvement of energy efficiency; also it increases the lifespan of Wireless Sensor Networks. In addition, the efficient data aggregation protocol can reduce network traffic. When a specific objective takes place in a specific area, it might be detected by more than one sensor. Considering the main challenges and aspects of data aggregation in wireless sensor networks, a review on different types of data aggregation techniques and protocols are presented in this paper. The ultimate objective of this study is to make the basic foundations to develop new advanced designs based on data integration techniques and clustering that have been proposed so far. Major techniques of data integration in wireless sensor networks covering ground, underground and underwater sensor networks are presented in this paper and the applications, advantages and disadvantages of using each technique are described.

A survey on data aggregation techniques in IoT sensor networks

In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques.

The sensor nodes have limited sensing, computation, communication capabilities and are mostly operated by batteries in a harsh environment with non-replenishable power sources. These restrictions make the sensor network prone to failures because most of the energy is spent on data transmission, sensing, and computing. Many applications such as habitat monitoring, military surveillance and forest fire detection expect the sensor nodes to last for a long time because they operate human unattended. Therefore, the major challenges in designing a wireless sensor network (WSN) are energy conservation, reducing data transmission delay and improving the network lifetime. In this context, data aggregation is an intelligent technique used in WSN, wherein the data from disparate sources are accumulated at intermediate nodes, thereby reducing the number of packets to be sent to the sink. Literature study shows that various routing algorithms are used to perform data aggregation based on the network topology. In order to provide an improved performance amongst the existing, a routing algorithm called cluster-chain mobile agent routing (CCMAR) is proposed in this work. It makes full use of the advantages of both low energy adaptive clustering hierarchy (LEACH) and power-efficient gathering in sensor information systems (PEGASIS). CCMAR divides the WSN into a few clusters and runs in two phases.The proposed system is simulated and evaluated for the performance metrics such as energy consumption, transmission delay and network lifetime. The results demonstrate that the proposed CCMAR outperforms LEACH, PEGASIS and other similar routing algorithm, energy efficient cluster-chain based protocol.

Cluster-chain mobile agent routing algorithm for efficient data aggregation in wireless sensor network

Wireless sensor network is a wireless network consisting of independent sensor, communicating with each other in distributed fashion to monitor the environment. Sensors are usually attached to microcontroller and are powered by battery. The goal of Wireless sensor network is to have long life time and high reliability with maximum coverage. Routing techniques are the most important issue for networks where resources are limited. LEACH is one of the first hierarchical routing approaches for sensor networks. Most of the clustering algorithms are derived from this algorithm. In this paper we propose an improvement on the LEACH Protocol. In our proposed algorithm, every cluster divided into 7 subsections that are called cells. Also every cell has a cell-head. Cell-heads communicate with cluster-heads directly. They aggregate their cell information and therefore they prevent sensors from communicating. In addition, we have made some changes in computation of the threshold value for a cluster-head selection formula. Something that was mentioned, cause efficiency reduce energy consumption and extend the network lifetime. We evaluate LEACH, LEACH-C and Cell-LEACH through extensive simulations using JSIM simulator which shows that Cell-LEACH performs better than LEACH and LEACH-C protocols.

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An improvement on LEACH protocol (Cell-LEACH)

Wireless sensor networks (WSNs) are an emerging technology for monitoring physical world. Different from the traditional wireless networks and ad hoc networks, the energy constraint of WSNs makes energy saving become the most important goal of various routing algorithms. For this purpose, a cluster based routing algorithm LEACH (low energy adaptive clustering hierarchy) has been proposed to organize a sensor network into a set of clusters so that the energy consumption can be evenly distributed among all the sensor nodes. Periodical cluster head voting in LEACH, however, consumes non-negligible energy and other resources. While another chain-based algorithm PEGASIS (power- efficient gathering in sensor information systems) can reduce such energy consumption, it causes a longer delay for data transmission. In this paper, we propose a routing algorithm called CCM (Chain-Cluster based Mixed routing), which makes full use of the advantages of LEACH and PEGASIS, and provide improved performance. It divides a WSN into a few chains and runs in two stages. In the first stage, sensor nodes in each chain transmit data to their own chain head node in parallel, using an improved chain routing protocol. In the second stage, all chain head nodes group as a cluster in a self- organized manner, where they transmit fused data to a voted cluster head using the cluster based routing. Experimental results demonstrate that our CCM algorithm outperforms both LEACH and PEGASIS in terms of the product of consumed energy and delay, weighting the overall performance of both energy consumption and transmission delay.

https://www.cs.sjtu.edu.cn/~guo-my/PDF/Journals/J79.pdf

Yonggong Ma

Papers

A chain-cluster based routing algorithm for wireless sensor networks