Energy harvesting rates of sensors in renewable (e.g., solar energy) wireless sensor networks are not only lower than their energy consumption rates but also temporally varying. Existing studies exploited spatial data correlations among sensors to reduce their energy consumptions, where the data correlations mean that the sensing data of nearby sensors have high similarities. They assumed that the sensing data of nearby sensors are very likely to highly correlated. They adopted a coarse-grained spatial-correlation model, in which sensors are partitioned into different clusters such that the sensors in the same cluster have high data similarities with each other. Then, only the sensor with the maximum residual energy in each cluster sends its sensing data, while the other sensors do not. We, however, notice that the data similarities among nearby sensors in real sensor networks may vary significantly, i.e., ranging from very similar to not similar at all. Since the existing algorithms require that the sensors in the same cluster have high data similarities with each other, the sensors in a network may be partitioned into many clusters and each cluster consists of only a few sensors, where two nearby sensors belong to two different clusters if the sensing data of the two sensors are not highly correlated. Therefore, in the existing studies, many sensors have to send all their data as there are many clusters. Unlike the existing studies, in this article, we first propose a fine-grained spatial correlation model, in which sensors are partitioned into only a few clusters and each cluster consists of many sensors. Then, each cluster master sensor sends all its data to the sink, while the majority of other sensors in the cluster transmit only their nonredundant data, thereby significantly saving sensor energy consumptions. We formulate a novel sensor clustering problem under the proposed model, which is to partition sensors into different clusters and choose a representative sensor for each cluster such that the amount of suppressed redundant data transmissions is maximized. We propose a randomized (0.5-ε)-approximation algorithm for the clustering problem, where E is a given constant with 0 <; ε ≤ 0.5. To further reduce sensor energy consumption, we consider temporal data correlations, where the sensing data by a sensor in a short period are likely to be highly correlated. We investigate a data utility maximization problem that allocates sensor data rates and routing so that the accumulative utility of both spatially and temporally correlated data received by the sink is maximized. We devise a near-optimal algorithm for the problem. We finally evaluate the performance of the proposed algorithms through experiments. the experimental results show that the proposed algorithms are very promising.

Proposed in this paper is an uplink multipaths propagation channel model. The varying distribution of the direction of arrival (DOA) is derived, for a maneuvering mobile station (MS) approaching the static base station (BS). In the landmobile communication system, the MS frequently changes its location, and travel between the neighboring micro cells. The conventional geometric models usually assume the static positions of the BS and the MS. Its not clear how the uplink DOA distribution would be affected by a moving MS. This work aims to fill this literature gap, by considering the different scenarios that the MS is: far away from the BS and its micro cell, in transition between the neighboring micro cells, and near the BS. The channel model is characterized by the geometry between the MS, the BS and the spatial distribution of the scatterers. Furthermore, the antenna at either the BS or the MS is assumed to be effective in some certain region due to the receiving sensitivity and the limited emission power. The numerical simulations show the variation on the uplink DOA distribution depending on the movement of the MS.

For a directional (a.k.a. directive) transceiver misaligned towards an omni-directional transceiver, the multipaths' time-of-arrival (TOA) distribution is herein derived in closed form explicitly in terms of the "geometric modeling" parameters. This derivation allows the directional beamwidth to mis-point to any arbitrary azimuth-direction, even if entirely away from the intended omni-directional transceiver.

This paper analytically derives closed-form expressions of the uplink received-signal's polarization-parameterized spatial-correlation-coefficient functions across the basestation antenna-array's spatial aperture, based on a geometrical model of idealized spatial relationships among the transmitter, the scatterers, and the receiving antennas. The derived formulas fit well with some empirical data.