Abstract
Adaptive transmission power control schemes have been introduced in wireless sensor networks to adjust energy consumption under different network conditions. This is a crucial goal, given the constraints under which sensor communications operate. Power reduction may however have counterproductive effects to network performance. Yet, indiscriminate power boosting may detrimentally affect interference. We are interested in understanding the conditions under which coordinated power reduction may lead to better spectrum efficiency and interference mitigation and, thus, have beneficial effects on network performance. Through simulations, we analyze the performance of sensor nodes in an environment with variable interference. Then we study the relation between transmission power and communication efficiency, particularly in the context of Adaptive and Robust Topology (ART) control, showing how appropriate power reduction can benefit both energy and spectrum efficiency. We also identify critical limitations in ART, discussing the potential of more cooperative power control approaches.
Highlights
Wireless sensor networks (WSNs) are gaining significant attention from institutions, entrepreneurs, and researchers alike, due to the promising and innovative applications in the context of Internet of Things (IoT) and due to the lowering cost of the components, which enables a widespread deployment
In adaptive transmission power control (ATPC) the authors use a linear regression model to obtain the coefficients of the linear relationship and predict the appropriate transmission power, based on the desired Received Signal Strength Indicator (RSSI) threshold [17]
We compare the performance of the reference couple for the scenarios LowDense, MidDense, and HighDense, described above, in the three cases of different transmission power MinPow, InterPow, and default transmission power (DefPow), while varying Δx and Δy
Summary
Wireless sensor networks (WSNs) are gaining significant attention from institutions, entrepreneurs, and researchers alike, due to the promising and innovative applications in the context of Internet of Things (IoT) and due to the lowering cost of the components, which enables a widespread deployment. Last category comprehends indoor scenarios and embraces wireless network installations in buildings (e.g., home, office, and shopping mall) [1]. Such areas may be smaller than in the previous cases, but, on the other hand, the density of the sensor nodes may be much higher. Sensor nodes are constrained devices, having low transmission power, battery, memory, and computational capacity [5, 6]. Indoor environments contain many WiFi networks, whose power is much higher than the power used by 802.15.4 devices and Bluetooth communications. The transceivers are overused, which causes further battery depletion, packet delay, and packet losses It is the area where power control can play a crucial role. Because of the large number of nodes and the complexity of WSNs, a flexible, distributed, and collaborative approach should be preferable, as we previously hinted in [15, 16]
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