Stochastic Analysis of Energy Consumption in Wireless Sensor Networks

Abstract

Abstract-Limited energy resources in increasingly sophisticated wireless sensor networks (WSNs) call for a comprehensive crosslayer analysis of energy consumption in a multi-hop network. For reliability analysis in such networks, the statistical information about energy consumption and lifetime is required. However, traditional energy analysis approaches only focus on the average energy consumed. In this paper, instead, we provide a stochastic analysis of the energy consumption in a random network environment. Accordingly, a comprehensive cross-layer analysis framework, which employs a stochastic queueing model, is developed. Using this framework, the distribution of energy consumption for nodes in WSNs during a given time period is found. We show that when the time duration is long, the energy consumption asymptotically approaches the Normal distribution. This distribution of energy consumption is then utilized to investigate the distribution of node lifetime and network lifetime. The developed analysis framework is generic and is parameterized for many WSN protocols, including an anycast protocol as a case study. Comprehensive simulations and testbed experiments are provided to validate the developed model. The cross-layer framework is also used to identify relationships between the distribution of energy consumption and network parameters, such as network density, duty cycle, and traffic rate. To the best of our knowledge, this is the first comprehensive work to investigate probabilistic distribution of energy consumption in WSNs.

Joint Energy Harvesting and Communication Analysis for Perpetual Wireless Nanosensor Networks in the Terahertz Band

“…Classical energy models cannot be used for nanosensor motes mainly because they are focused on analyzing and minimizing the energy consumption of wireless devices whose total energy decreases until their batteries are depleted [31], [33]. Recently, a few models for energy harvesting sensors have been proposed in [16], [18], [21], and [30].…”

“…In contrast to the classical battery-powered devices, the energy of the self-powered devices does not just decrease until the battery is empty, but it has both positive and negative fluctuations. These variations are not captured in classical energy models [31], [33]. Even in several recent models for energy harvesting networks [16], [18], [21], [30], the correlation between the energy harvesting and the energy consumption processes are not fully captured.…”

CRCM: A New Combined Data Gathering and Energy Charging Model for WRSN

“…Figure 1 shows the energy consumption of a sensor in the whole process. We can see the transport and receiving modules consume the most of the energy, the idle listening module also belongs to the communication part, and the energy consumption of communication follows the formula E = kd n (2 < n < 4), the parameter n is influenced by the communication distance, i.e., when the communication distance is long, the parameter n will be also larger, and, meanwhile, the communicating consumption of the sensor node will increase [32,33]. monitoring and prediction is also very important by energy-aware support.…”

A review on stochastic approach for dynamic power management in wireless sensor networks

“…Generally, network lifetime analysis models in the literature assume average node power consumption. A discrete time Markov model for a node with a Bernoulli distribution of arrival process and phase-type distribution of service time has been validated for single and multi-hop network [23]. Observations tell that, these schemes have limitations in terms of architectural modification.…”