Abstract
Ad hoc and wireless sensor networks are characterized by their ability to monitor phenomena in the most adverse scenarios. However, to perform well, these networks need to be self-adjusting and save energy. In general, these networks operate without human interference and require strategies to provide longer operating life. This paper investigates the energy consumption in a random multihop ad hoc network, comparing the slotted Aloha with the CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) implemented in the IEEE 802.11 Distributed Coordination Function (DCF) as medium access control (MAC) protocols. We obtain the optimal transmission power as a function of physical and link layers parameters which results the optimized energy consumption per successfully transmitted bit. In this paper, we find that there are values of these parameters that can be used to extend the battery life of wireless communication devices comparing the Aloha and CSMA/CA performance.
Highlights
In wireless ad hoc and sensor networks, one of the big challenges is the energy resources management
We propose in this paper a comparison between two approaches that use as medium access control (MAC) protocols the slotted Aloha [11] and the CSMA/CA employed in IEEE 802.11 Distributed Coordination Function (DCF) [10]
We present the numerical results obtained using Eqs. (26) and (27) in order to explore the behavior of the average energy consumption per bit (in units of dBmJoules per bit)
Summary
In wireless ad hoc and sensor networks, one of the big challenges is the energy resources management. The devices in these networks perform tasks, in general, without human interference, using batteries as a source of energy [2]. The device becomes inoperable and may decrease the network performance. For this reason, optimization techniques has been developed to improve the network lifetime [3]. The concern with preserving the energy resources of these devices can be seen in Internet of Things (IoT) networks, whose application can be, for example, in a smart home [4], or in a more remote context, as is the case of a static sensor network to monitor volcanic activity [5]
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