An event-aware cluster-head rotation algorithm for extending lifetime of wireless sensor Network with smart nodes

Smart sensor nodes can process data collected from sensors, make decisions, and recognize relevant events based on the sensed information before sharing it with other nodes. In wireless sensor networks, the smart sensor nodes are usually grouped in clusters for effective cooperation. One sensor node in each cluster must act as a cluster head. The cluster head depletes its energy resources faster than the other nodes. Thus, the cluster-head role must be periodically reassigned (rotated) to different sensor nodes to achieve a long lifetime of wireless sensor network. This paper introduces a method for extending the lifetime of the wireless sensor networks with smart nodes. The proposed method combines a new algorithm for rotating the cluster-head role among sensor nodes with suppression of unnecessary data transmissions. It enables effective control of the cluster-head rotation based on expected energy consumption of sensor nodes. The energy consumption is estimated using a lightweight model, which takes into account transmission probabilities. This method was implemented in a prototype of wireless sensor network. During experimental evaluation of the new method, detailed measurements of lifetime and energy consumption were conducted for a real wireless sensor network. Results of these realistic experiments have revealed that the lifetime of the sensor network is extended when using the proposed method in comparison with state-of-the-art cluster-head rotation algorithms

Gaussian functional shapes-based type-II fuzzy membership-based cluster protocol for energy harvesting IoT networks

With the advancements in Internet of Things (IoT) technologies, energy harvesting IoT devices are becoming significantly important. These tiny IoT devices can harvest bounded energy, thus need an efficient protocol to conserve the energy in more efficient manner. From the review, it is found that the development of an efficient energy efficient protocol for energy harvesting IoT is still an open area of research. It is found that fuzzy based energy harvesting IoTs has shown significant improvement over the existing protocols. However, the fuzzy logic suffers from the data uncertainty issue. Therefore, in this paper, Gaussian functional shapes-based type-II fuzzy membership function is used to elect the cluster heads among the IoT devices to reduce the energy consumption of energy harvest IoTs. Thereafter, inter-cluster data aggregation is used. Finally, the communication between the elected cluster heads and the cloud servers or sink. Extensive experiments are drawn by considering the existing and the proposed protocols for energy harvesting IoTs. Comparative analysis reveals that the proposed type-II fuzzy membership function-based protocol outperforms the existing protocols in terms of bandwidth analysis, throughput, conserve energy, network lifetime, and average consumed energy

Enabling Hardware Green Internet of Things: A review of Substantial Issues

Between now and the near future, the Internet of Things (IoT) will redesign the socio-ecological morphology of the human terrain. The IoT ecosystem deploys diverse sensor platforms connecting millions of heterogeneous objects through the Internet. Irrespective of sensor functionality, most sensors are low energy consumption devices and are designed to transmit sporadically or continuously. However, when we consider the millions of connected sensors powering various user applications, their energy efficiency (EE) becomes a critical issue. Therefore, the importance of EE in IoT technology, as well as the development of EE solutions for sustainable IoT technology, cannot be overemphasised. Propelled by this need, EE proposals are expected to address the EE issues in the IoT context. Consequently, many developments continue to emerge, and the need to highlight them to provide clear insights to researchers on eco-sustainable and green IoT technologies becomes a crucial task. To pursue a clear vision of green IoT, this study aims to present the current state-of-the art insights into energy saving practices and strategies on green IoT. The major contribution of this study includes reviews and discussions of substantial issues in the enabling of hardware green IoT, such as green machine to machine, green wireless sensor networks, green radio frequency identification, green microcontroller units, integrated circuits and processors. This review will contribute significantly towards the future implementation of green and eco-sustainable IoT