Wireless Sensor Network Energy Model and Its Use in the Optimization of Routing Protocols

Abstract

In this study, a Wireless Sensor Network (WSN) energy model is proposed by defining the energy consumption at each node. Such a model calculates the energy at each node by estimating the energy of the main functions developed at sensing and transmitting data when running the routing protocol. These functions are related to wireless communications and measured and compared to the most relevant impact on an energy standpoint and performance metrics. The energy model is validated using a Texas Instruments CC2530 system-on-chip (SoC), as a proof-of-concept. The proposed energy model is then used to calculate the energy consumption of a Multi-Parent Hierarchical (MPH) routing protocol and five widely known network sensors routing protocols: Ad-hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR), ZigBee Tree Routing (ZTR), Low Energy Adaptive Clustering Hierarchy (LEACH), and Power Efficient Gathering in Sensor Information Systems (PEGASIS). Experimental test-bed simulations were performed on a random layout topology with two collector nodes. Each node was running under different wireless technologies: Zigbee, Bluetooth Low Energy, and LoRa by WiFi. The objective of this work is to analyze the performance of the proposed energy model in routing protocols of diverse nature: reactive, proactive, hybrid and energy-aware. Experimental results show that the MPH routing protocol consumes 16%, 13%, and 5% less energy when compared to AODV, DSR, and ZTR, respectively; and it presents only 2% and 3% of greater energy consumption with respect to the energy-aware PEGASIS and LEACH protocols, respectively. The proposed model achieves a 97% accuracy compared to the actual performance of a network. Tests are performed to analyze the consumption of the main tasks of a node in a network.

Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing

“…This transition opens up a new set of challenges. At a local level, we need to reduce energy [ 9 ] and computation costs by means of data-fusion [ 10 ] or smart routing [ 11 ]. At a global level, the BSS needs to adopt new models in order to deal with the issues this extended complexity imposes.…”

Energy-Aware Activity Control for Wireless Sensing Infrastructure Using Periodic Communication and Mixed-Integer Programming

“…Furthermore, energy-aware routing protocols can be em-ployed to reduce transmission time and energy. To summarize, the following techniques can facilitate energy saving in WSNs: (1) transmission power control [13][14][15]adjustment of the transmit power of the radio module to maintain a transmission range; (2) clustering and activity control [4,10,16,17]-clustering of nodes into clusters and temporal activation and deactivation of sensory nodes; (3) energy-aware routing [18,19]-calculating energy-aware routing tables, multi-path routing sink mobility; (4) energy-efficient data reduction [2,3,20]-aggregation and routing of data over a multi-hop network, processing data in intermediate nodes to reduce redundant data before transmission, adaptive sampling.…”

Development of Sensors-Based Agri-Food Traceability System Remotely Managed by a Software Platform for Optimized Farm Management

“…The optimization of energy consumption is crucial in WSN applications for agriculture since usually the nodes are distributed in remote areas where the maintenance for changing or recharging the batteries is difficult. Del Valle-Soto et al developed a WSN energy model to estimate the energy consumption of each node during the different operations (data acquisition and transmission), when the routing protocol is running [ 39 ]. The model validation is performed by employing TI CC2530-based boards, which run a multiparent hierarchical (MPH) routing protocol.…”