Passively Controllable Smart Antennas

We recently introduced passively controllable smart (PCS) antenna systems for efficient wireless transmission, with direct applications in wireless sensor networks. A PCS antenna system is accompanied by a tunable passive controller whose adjustment at every signal transmission generates a specific radiation pattern. To reduce co-channel interference and optimize the transmitted power, this antenna can be programmed to transmit data in a desired direction in such a way that no signal is transmitted (to the far field) at pre-specified undesired directions. The controller of a PCS antenna was assumed to be centralized in our previous work, which was an impediment to its implementation. In this work, we study the design of PCS antenna systems under decentralized controllers, which are both practically implementable and cost efficient. The PCS antenna proposed here is made of one active element and its programming needs solving second-order-cone optimizations. These properties differentiate a PCS antenna from the existing smart antennas, and make it possible to implement a PCS antenna on a small-sized, low-power silicon chip

Localized minimum-energy broadcasting for wireless multihop networks with directional antennas

Abstract—There are a number of proposals to achieve energy-efficient broadcasting in wireless multihop networks using directional antennas. However, these proposals are based on centralized algorithms, which require global knowledge of the topology of the network. Such global protocols are not suitable for ad hoc networks because of the high number of control messages required to gather such global information. We propose several localized algorithms, where each node needs to know only geographic position of itself and its neighbors. We also introduce a new energy consumption model for directional antennas, which generalizes models commonly used. Our first protocol is called DRBOP and it follows the one-to-one communication model to reach to all nodes following the relative neighborhood graph (RNG). RNG preserves connectivity and can be locally computed by each node without any message exchange. Each node that receives a message for the first time from one of its RNG neighbors will rebroadcast it to each of its remaining RNG neighbors separately. The transmission power is adjusted for each transmission to the minimal necessary for reaching the particular neighbor. Since the average degree of RNG is about 2.5, approximately 1.5 rebroadcasts are done by each node to its neighbors. Next, we describe DLBOP, where RNG is replaced by the local minimum spanning tree (LMST) graph, which is a localized topology resembling the minimum spanning tree. We then observe that, for very dense networks, it is more energy-efficient to reach more than one neighbor at a time. A one-to-many protocol efficient for dense networks is proposed. We then describe an efficient localized protocol, which adaptively switches (without any threshold) between one-to-one and one-to-many communication models and is efficient for both sparse and dense networks. Our simulation results show that for different energy models, the adaptive protocol is able to achieve a competitive performance compared to centralized algorithms while having a fully localized operation. Index Terms—Distributed networks, power management, directional antenna, broadcasting.