E²-MACH: Energy efficient multi-attribute based clustering scheme for energy harvesting wireless sensor networks

Abstract

Internet of things have emerged enough due to its applications in a wide range of fields such as governance, industry, healthcare, and smart environments (home, smart, cities, and so on). Internet of things–based networks connect smart devices ubiquitously. In such scenario, the role of wireless sensor networks becomes vital in order to enhance the ubiquity of the Internet of things devices with lower cost and easy deployment. The sensor nodes are limited in terms of energy storage, processing, and data storage capabilities, while their radio frequencies are very sensitive to noise and interference. These factors consequently threaten the energy consumption, lifetime, and throughput of network. One way to cope with energy consumption issue is energy harvesting techniques used in wireless sensor network–based Internet of things. However, some recent studies addressed the problems of clustering and routing in energy harvesting wireless sensor networks which either concentrate on energy efficiency or quality of service. There is a need of an adequate approach that can perform efficiently in terms of energy utilization as well as to ensure the quality of service. In this article, a novel protocol named energy-efficient multi-attribute-based clustering scheme (E2-MACH) is proposed which addresses the energy efficiency and communication reliability. It uses selection criteria of reliable cluster head based on a weighted function defined by multiple attributes such as link statistics, neighborhood density, current residual energy, and the rate of energy harvesting of nodes. The consideration of such parameters in cluster head selection helps to preserve the node’s energy and reduce its consumption by sending data over links possessing better signal-to-noise ratio and hence ensure minimum packet loss. The minimized packet loss ratio contributes toward enhanced network throughput, energy consumption, and lifetime with better service availability for Internet of things applications. A set of experiments using network simulator 2 revealed that our proposed approach outperforms the state-of-the-art low-energy adaptive clustering hierarchy and other recent protocols in terms of first-node death, overall energy consumption, and network throughput.

Notice of Removal: An Improved Equal Hierarchical Cluster-Based Routing Protocol for EH-WSNs to Enhance Balanced Utilization of Harvested Energy

“…In data transmission phase, CH nodes use CSMA and fixed spreading code to send the collected data to the sink. E 2 -MACH in [8] is an energy efficient clustering scheme selecting CH nodes based on multiple attributes, such as current residual energy, energy harvesting rate, number of neighbors, and quality of the link. This scheme did not design the routing tree construction phase for data transmission.…”

“…There are several solutions to tackle the dimensions of different factors such as min-max normalization in [33] and the method in [34], where normalization method by maximum value is used. Note that only in (8) ) in radio range R/2 to exchange local information with its neighbors for CH selection. And the low level CH is selected by using formula (9) in the same way just as in the first level.…”

“…As CREW [27] uses the similar waiting time scheme for CH election to the one of CPMHE, we also compare IEHCR with it. E 2 -MACH [8] is also chosen for comparison, as it adopts the similar clustering method to the one of IEHCR. E 2 -MACH, as a pure clustering protocol, did not introduce any data transmission schemes.…”

Enhancing Harvested Energy Utilization for Energy Harvesting Wireless Sensor Networks by an Improved Uneven Clustering Protocol

“…The DEARER protocol selects nodes with high energy arrival rates or being close to the sink to serve as the CH nodes. Recently, Haq et al [21] proposed a cluster-based algorithm called E 2 -MACH for EH-WSNs. It selects CHs based on a weighted function defined by multiple attributes, such as link statistics, neighborhood density, current residual energy, and the rate of energy harvesting of nodes.…”

“…It selects CHs based on a weighted function defined by multiple attributes, such as link statistics, neighborhood density, current residual energy, and the rate of energy harvesting of nodes. However, the data transmission scheme was not introduced in [21]. For all the above cluster-based algorithms for EH-WSNs, similar to LEACH, the competition scheme was adopted to select the CHs.…”