Energy-efficient beaconless geographic routing in energy harvested wireless sensor networks

Oswald Jumira, Riaan Wolhuter, Sherali Zeadally
2012 Concurrency and Computation  
We propose a routing scheme called energy-efficient beaconless geographic routing with energy supply (EBGRES) for wireless sensor networks. EBGRES provides loop-free, fully stateless, energy-efficient source-to-sink routing with minimal communication overhead without the help of prior neighborhood knowledge. It locally determines the duty-cycle of each node, based on an estimated energy budget for each period, which includes the currently available energy, the predicted energy consumption and
more » ... e energy expected from the harvesting device. In EBGRES, each node sends out the data packet first rather than a control message. By sending a data packet first, EBGRES performs the neighbor selection only among those neighbors that successfully received the data packet. EBGRES uses a three-way (DATA/ACK/SELECT) handshake and a timer-assignment function, the Discrete Dynamic Forwarding Delay (DDFD). We investigate the lower and upper bounds on hop count and the upper bound on energy consumption under EBGRES for source-to-sink routing. We further demonstrate the expected total energy consumption along a route toward the sink with the proposed EBGRES approach including a lower bound on energy consumption when the node density increases. a cost effective, ubiquitous, commonly known, and well understood powering technology. However, they present specific challenges that include finite useful life, replacement cost, and disposal concerns. Although they are an ideal solution for many applications, there are many other applications where batteries fail to fit application requirements: for example, the asset is not available to replace the batteries, the cost of battery replacement is too expensive over the life of the product, the device is in a hazardous environment, or the device is embedded and a continuous power supply is required. Applications with these needs provide a good fit for receiving power via ambient energy harvesting [8] . Unlike the microprocessor industry or the communication hardware industry, where the computation capability or the line rate has been continuously improved (almost doubled every 18 months), battery technology has been relatively unchanged for many years [8] . Ambient energy harvesting as a power solution has steadily gained momentum in recent years, especially with significant progress in the functionality of low power embedded electronics such as wireless sensor nodes. We define an energy harvesting node as any system that draws part or all of its energy from the environment such as solar energy, temperature variations, kinetic energy or vibrations. A key distinction of this energy from that stored in the battery is that this energy is potentially infinite, although there may be a limit on the rate at which it can be used. Energy harvesting sensor nodes have either an onboard energy harvesting component as shown in Figure 1 , or a sensor node can be connected to an energy harvesting device/component to form one device. In our research we look at solar-based energy harvesting, which has a diurnal characteristic (available readily during the day and no sunlight at night). By generating power from environmental energy, the dependency on batteries can be reduced or even eliminated [7, 9, 10] . Commercially available energy harvesting devices have been developed by Texas Instruments [11] and Microstrain [12]; however, they are currently expensive but the prices are predicted to fall dramatically in the next 5 years [8] . However, limited attention has been given to routing within a network of sensor nodes running on environmental energy. Once a sensor has been deployed, it must be able to operate as autonomously as possible. Each sensor node in an energy-harvesting WSN must be aware that the amount of environmental energy it can gather depends on the time, location, and the surroundings it operates in. This awareness of surroundings should influence the way it operates, for example, operation of a solar harvesting node will adapt to changes in surrounding weather. Each sensor node must determine the appropriate duty cycle (DC) with which a sensor node can operate perpetually [7] . A duty cycle is the time that a sensor node spends in an active state as a fraction of the total time under consideration to manage energy effectively. Knowledge of energy-harvesting devices characteristics should be incorporated in its routing scheme. Research has been carried out in this field over the years [4, [13] [14] [15] [16] . The energy-aware routing schemes that have been proposed in recent years are not localized, not scalable, rely on the dissemination of route discovery information and routing tables, have limited lifetime, and they rely on beacons for dynamic networks changes. To address these limitations, we need to investigate and propose energy-aware routing schemes that result in increased energy-efficiency, reliability, scalability, and network lifetime. Geographic routing, in which each node forwards packets based only on its location, its direct neighbors, and the destination, is particularly attractive to resource-constrained sensor networks Figure 1 . Key components of an energy harvesting wireless sensor node.
doi:10.1002/cpe.2838 fatcat:o5q5v5ls5zbploarklt4tffda4