In this paper, we study distributed scheduling in multihop multiple-input-multiple-output (MIMO) networks. We first develop a "MIMO-pipe" model that provides the upper layers a set of rates and signal-to-interference-plus-noise ratio (SINR) requirements that capture the rate-reliability tradeoff in MIMO communications. The main thrust of this paper is then dedicated to developing distributed carrier sense multiple access (CSMA) algorithms for MIMO-pipe scheduling under the SINR interference model. We choose the SINR model over the extensively studied protocol-based interference models because it more naturally captures the impact of interference in wireless networks. The coupling among the links caused by the interference under the SINR model makes the problem of devising distributed scheduling algorithms very challenging. To that end, we explore the CSMA algorithms for MIMO-pipe scheduling from two perspectives. We start with an idealized continuous-time CSMA network, where control messages can be exchanged in a collision-freemanner, and devise a CSMA-based link scheduling algorithm that can achieve throughput optimality under the SINR model. Next, we consider a discrete-time CSMA network, where the message exchanges suffer from collisions. For this more challenging case, we develop a "conservative" scheduling algorithm by imposing a more stringent SINR constraint on the MIMO-pipe model. We show that the proposed conservative scheduling achieves an efficiency ratio bounded from below.

Smart meters, designed for information collection and system monitoring in smart grid, report fine-grained power consumption to utility providers. With these highly accurate profiles of energy usage, however, it is possible to identify consumers' specific activities or behavior patterns, thereby giving rise to serious privacy concerns. This paper addresses these concerns by designing a cost-effective and privacy-preserving energy management technique that uses a rechargeable battery. From a holistic perspective, a dynamic programming framework is designed for consumers to strike a tradeoff between smart meter data privacy and the cost of electricity. In general, a major challenge in solving dynamic programming problems lies in the need for the knowledge of future electricity consumption events. By exploring the underlying structure of the original problem, an equivalent problem is derived, which can be solved by using only the current observations. An online control algorithm is then developed to solve the equivalent problem based on the Lyapunov optimization technique. It is shown that without the knowledge of the statistics of the time-varying load requirements and the electricity price processes, the proposed online control algorithm, parametrized by a positive value V, is within O (1/{V) of the optimal solution to the original problem, where the maximum value of V is limited by the battery capacity. The efficacy of the proposed algorithm is demonstrated through extensive numerical analysis using real data.