A Cross-Layer Approach to Energy Balancing in Wireless Sensor Networks

Abstract

Summary. We consider a many-to-one real-time sensor network where sensing nodes are to deliver their measurements to a base station under a time constraint and with the overall target of minimizing the energy consumption at the sensing nodes. In wireless sensor networks, the unreliability of the links and the limitations of all resources bring considerable complications to routing. Even in the presence of static nodes, the channel conditions vary because of multipath fading effects due to the motion of people or objects in the environment, which modify the patterns of radio wave reflections. Also, sensing nodes are typically battery-powered, and ongoing maintenance may not be possible: the progressive reduction of the available energy needs to be factored in.The quality of the links and the remaining energy in the nodes are the primary factors that shape the network graph; link quality may be measured directly by most radios, whereas residual energy is related to the node battery voltage, which may be measured and fed into the microcontroller. These quantities may be used to form a cost function for the selection of the most efficient route. Moreover, the presence of a time constraint requires the network to favor routes over a short number of hops (a.k.a. the long-hop approach, in the sense that a small number of long hops is used) in order to minimize delay. Hop number information may be incorporated into the cost function to bias route selection toward minimum-delay routes. Thus, a crosslayer cost function is obtained, which includes raw hardware information (remaining energy), physical layer data (channel quality), and a routing layer metric (number of hops).A route selection scheme based on these principles intrinsically performs node energy control for the extension of the lifetime of the individual nodes and for the achievement of energy balancing in the network; intuitively, the long-hop approach permits the time-sharing of the critical area among more nodes. A novel, practical algorithm based on these principles is proposed with the constraints of the currently available hardware platforms in mind. Its benefits are investigated with the help of computer simulation and are illustrated with an actual hardware implementation

A Cross-Layer Routing Protocol for Balancing Energy Consumption in Wireless Sensor Networks

“…Its aim is congestion avoidance and not energy consumption balancing as it is the case in our protocol. A strategy with energy-balancing guarantees is proposed in [38]. Authors aim to investigate the benefits of a joint metric which considers hop count, link quality, and remaining battery power metric.…”

PSAR: power-source-aware routing in ZigBee networks

“…Piccunelli et al [29] present a strategy to build a routing tree based on a cross-layer cost function incorporating remaining energy, channel quality, and number of hops. Similarly, Boughanmi et al [3] use a cost function in order to satisfy the energy and delay constraints of the paths to be used.…”

“…At the path discovery phase of ZigBee, this modified function is used. Unlike studies in [29] and [3], Peng et al [28] use the two ZigBee routing methods presented in the ZigBee standard as they are, but choose one of them according to the data service requiring routing functionality. In some studies, such as [19] and [32], multi-path routing is exploited in order to prolong the network lifetime.…”

A Discussion of Maximizing System Performance in Wireless Sensor Networks

“…Besides, many works considered energy-efficiency in terms of lifetime of a network. In [2], a cross-layer cost function was constructed and a route selection scheme was established to perform node energy control for the extension of the lifetime of the individual nodes and for the achievement of energy balancing in the network. In [6,7], routing to maximize the network lifetime was considered and heuristics were proposed.…”