Enhancing energy efficiency of wireless sensor network for mining industry applications

Recent advances in sensing modules and radio technology will enable small but smart sensors to be deployed for a wide range of environmental monitoring applications. They collect data from different environment or infrastructures in order to send them to the cloud using different communications platforms. These data can be used to provide smarter services. However, they are various issues and challenges related to the ubiquitous sensors that should be solved. In this paper we interest on analysis of wireless sensor network from an energy management perspective. The idea behind the energy-efficiency wireless sensor networks is that each node can only transmit to a limited number of other nodes directly. The limited resources of nodes imply that the transmission range is limited. In order to transfer the data to the final destination, the traffic must be relayed using intermediate nodes, creating a multi-hop route. The total energy consumption associated with an end-to-end transmission over such a route can be significantly reduced if the nodes are correctly configured. In this paper, underground mine monitoring system is presented with an overview of the related issues and challenges such as reliability, cost, and scalability

Energy Efficiency Adaptation for Multihop Routing in Wireless Sensor Networks

Wireless sensor networks (WSNs) are considered as a suitable solution for long-time and large-scale outdoor environmental monitoring. However, an important feature that distinguishes the WSNs from traditional monitoring systems is their energy constraints. In fact, these nodes have often a limited and usually nonreplenishable power source. Therefore managing these limited resources is a key challenge. In this paper we use an optimization scheme based on adaptive modulation and power control for a green routing protocol. The optimization mechanism is subject to certain QoS requirements in terms of total end-to-end delay time and bit error rate. The simulation results show that the proposed algorithm can, theoretically, reduce the consumed energy of the sensor nodes almost to half