The growing popularity of mobile devices in our daily life demands higher throughput of wireless networks. The new communication standard 802.11n has significantly improved throughput because of the use of advanced technologies such as the multiple-input multiple-output communication technique. Because mobile devices are usually battery-operated, power efficiency is critical; on the other hand, delay performance can be improved by transmitting at high power. To address the conflicting requirement of power saving and small delay, power scheduling is needed. In the past, many approaches to power scheduling have been proposed for real-time applications, but few of them have considered complicated modes of channel state information(CSI) in multiple-input multiple-output. In this paper, we study this and classify the CSI into four types, namely, constant, slow fading, fast fading, and unknown. For known CSI, we propose an optimal algorithm for power scheduling. For unknown CSI, we propose an approximate algorithm based on some heuristics. To improve resource utilization, a stochastic delay-bound method is proposed for fast-fading condition. Simulation results demonstrate that the performance achieved by the optimal and heuristic algorithms agrees well with the analysis.

Sensor networks are deployed in many fields for its availability and timeliness to capture significant information. It is very important to provide a guaranteed delay for some timesensitive applications. Unfortunately, many previous work focus on routing strategy or energy management, and these related results bring longer delay to sensor networks. Actually, the theoretical bound is significant for sensor network deployment. Some articles have concerned the delay bound of aggregating flows, but all of these methods only provide some deterministic results, which is not consistent with the reality that the sensor node generates signal randomly and wireless links could undergo stochastic changes. In this paper, an aggregation output property in sensor networks with sink-tree structure is derived from stochastic network calculus theory. Based on this property, a stochastic delay bound of aggregation flows is obtained. Compared with the result in deterministic network calculus, the new bound is very tight and more applicable. A brief conclusion and the future work are also introduced in the last section.

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Wireless sensor networks (WSNs) have been widely investigated in the past decades because of its applicability in various extreme environments. As sensors use battery, most works on WSNs focus on energy efficiency issues (e.g., local energy balancing problems) in statically deployed WSNs. Few works have paid attention to the global energy balancing problem for the scenario that mobile sensor nodes can move freely. In this paper, we propose a new routing protocol called global energy balancing routing protocol (GEBRP) based on an active network framework and node relocation in mobile sensor networks. This protocol achieves global energy efficiency by repairing coverage holes and replacing invalid nodes dynamically. Simulation and experiment results demonstrate that the proposed GEBRP achieves superior performance over the existing scheme. In addition, we analyze the delay performance of GEBRP and study how the delay performance is affected by various system parameters.Copyright © 2013 John Wiley & Sons, Ltd.

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