Increasing network lifetime in an energy-constrained wireless sensor network

Abstract

Energy in Wireless Sensor Networks is a scarce resource, therefore an energyefficient management is required to increase the network lifetime. In this paper, we study the problem of optimal power allocation, taking into account the estimation of total signal-to-noise ratio (SNR) at the Fusion Center (FC). We consider that nodes transmit their data to the Fusion Center over quasi-static Rayleigh fading channels (QSRC). In order to analyze our approach, we will investigate first the orthogonal channels, and secondly the non-orthogonal ones introducing a virtual MISO in the communication. We consider in both cases that the nodes have Channel State Information (CSI). Simulations that have been conducted using these two channel configurations show that, thanks to our new algorithm, the network lifetime is extended by an average that can reach 82,80% compared to the network lifetime in the other methods.

Energy Efficiency of MIMO Cooperative Networks with Energy Harvesting Sensor Nodes

“…In [16][17] optimal solutions are presented for maximizing a static network lifetime through a graph theoretic approach using a static (multicast/broadcast) tree. In [18] [25], the total energy consumption is minimized using an optimal water-filling solution.…”

“…Virtual MIMO with a Single Relay In order to show the viability and the performance of the novel algorithms, we compare it to the equal power method (EP) [25]. We fixed the transmission power corresponding to the source node for10dB and we vary the modulation of the transmit information.…”

Using neural networks to reduce sensor cluster interferences and power consumption in smart cities

“…In general, a high interference level degrades the overall system capacity, the system efficiency, and the system reliability, i.e., the IoT-sensors quality of service (QoS) parameters (Lynggaard and Skouby, 2015;Chincoli and Liotta, 2018). These QoS parameters call for new and better optimisation techniques and protocols at all levels in the OSI model (Abdellaoui et al, 2013;Iqbal et al, 2015). Most of today's IoT-sensor networks use a greedy approach to deal with these challenges where the individual IoT-sensor nodes optimise their own QoS parameters such as energy consumption, bit-rate, and transmit power.…”

A Load-Balanced Clustering Algorithm Using Fuzzy Logic for Maximizing Lifetime of Wireless Sensor Networks

“…Consequently, energy-efficiency is a vital concern in WSN design. Moreover, sensors are usually deployed randomly, so two sensors may be closely adjacent, sensing the same data, raising the need for data aggregation [9].…”