The spatial distribution of surface air temperatures is essential for understanding and modelling high‐relief environments. Good estimations of the surface temperature lapse rate (STLR) and the 0°C isotherm height (H0) are fundamental for hydrological modelling in mountainous basins. Although STLR changes in space and time, it is typically assumed to be constant leading to errors in the estimation of direct‐runoff volumes and flash‐floods risk assessment. This paper characterizes daily and seasonal temporal variations of the in‐situ STLR and H0 over the western slope of the subtropical Andes (central Chile). We use temperature data collected during 2 years every 10 min by a 16 sensors network in a small catchment with elevations ranging between 700 and 3,250 m. The catchment drains directly into Santiago, the Chilean capital with more than seven million inhabitants. Resulting values are compared against those obtained using off‐site, operational data sets. Significant intra‐ and inter‐day variations of the in‐situ STLR were found, likely reflecting changes in the low‐level temperature inversion during dry conditions. The annual average in‐situ STLR is −5.9°C/km during wet‐weather conditions. Furthermore, STLR and H0 estimations using off‐site gauges are extremely sensitive to the existence of gauging stations at high elevations.

Moving data across communication networks is often subject to deadline requirements. An example is early warning of disasters of natural origin, where sensor measurements at the disaster location must be communicated across a network within a predefined maximum delay in order for a consequent warning to be timely. In this work, we present a probabilistic model that allows for characterizing the delay experienced by sensor measurements in a wireless sensor network from source to sink depending upon the routing metric used for forwarding the data through the network. Using link delay probability distributions and the probabilities of following different paths to the sink, source-to-sink delay distributions are found for routing policies based on minimum hop-count, minimum mean delay and the Joint Latency (JLAT) protocol. An algorithm for calculating the end-to-end source to sink delay probability density function (PDF) is presented for the general case of networks that use routing tables whose input for routing decisions is the remaining time-to-deadline. The work provides a general tool for routing delay analysis, allowing for comparison of the deadline miss probability between different routing policies. An improved form of JLAT is proposed. Its deadline miss probability is found using the presented algorithm and compared to the ones determined for minimum hop-count, minimum mean delay and JLAT by means of an example.

We consider the problem of meeting deadline constraints in wireless communication networks. Fulfilling deadlines depends heavily on the routing algorithm used. We study this dependence generically for a broad class of routing algorithms. For analyzing the impact of routing decisions on deadline fulfillment, we adopt a stochastic model from operations research to capture the source-to-destination delay distribution and the corresponding probability of successfully delivering data before a given deadline. Based on this model, we propose a decentralized algorithm that operates locally at each node and exchanges information solely with direct neighbors in order to determine the probabilities of achieving deadlines. A modified version of the algorithm also improves routing tables iteratively to progressively increase the deadline achievement probabilities. This modified algorithm is shown to deliver routing tables that maximize the deadline achievement probabilities for all nodes in a given network. We tested the approach by simulation and compared it with routing strategies based on established metrics, specifically the average delay, minimum hop count, and expected transmission count. Our evaluations encompass different channel quality and small-scale fading conditions, as well as various traffic load scenarios. Notably, our solution consistently outperforms the other approaches in all tested scenarios.

Base station cooperation is envisioned as a key technology for future cellular networks, as it has the potential to eliminate intercell interference and to enhance spectral efficiency. To date, there is still lack of understanding of how imperfect carrier and sampling frequency synchronization between transmitters and receivers limit the potential gains and what the actual system requirements are. In this paper, OFDM signal model is established for multiuser multicellular networks, describing the joint effect of multiple carrier and sampling frequency offsets. It is shown that the impact of sampling offsets is much smaller than the impact of carrier frequency offsets. The model is extended to the downlink of base-coordinated networks and closed-form expressions are derived for the mean power of users’ self-signal, interuser, and intercarrier interference, whereas it is shown that interuser interference is the main source of degradation. The SIR is inverse to the base stations’ carrier frequency variance and to the square of time since the last precoder update, whereas it grows with the number of base stations and drops with the number of users. Through user selection, the derived SIR upper bound can be approached. Finally, system design recommendations for meeting synchronization requirements are provided.

Singular value decomposition (SVD) beamforming is an attractive tool for reducing the energy consumption of data transmissions in wireless sensor networks whose nodes are equipped with multiple antennas. However, this method is often not practical due to two important shortcomings: it requires channel state information at the transmitter and the computation of the SVD of the channel matrix is generally too complex. To deal with these issues, we propose a method for establishing an SVD beamforming link without requiring feedback of actual channel or SVD coefficients to the transmitter. Concretely, our method takes advantage of channel reciprocity and a power iteration algorithm (PIA) for determining the precoding and decoding singular vectors from received preamble sequences. A low-complexity version that performs no iterations is proposed and shown to have a signal-to-noise-ratio (SNR) loss within 1 dB of the bit error rate of SVD beamforming with least squares channel estimates. The low-complexity method significantly outperforms maximum ratio combining diversity and Alamouti coding. We also show that the computational cost of the proposed PIA-based method is less than the one of using the Golub–Reinsch algorithm for obtaining the SVD. The number of computations of the low-complexity version is an order of magnitude smaller than with Golub–Reinsch. This difference grows further with antenna array size.

The delay distribution observed between the data link layer of a pair of neighbouring nodes in wireless mesh networks is studied. Specifically, we model the delay for unslotted carrier sense multiple access with collision avoidance with acknowledgments and multiple transmission retries under given channel access and block-fading statistics. This medium access control mechanism is used, for instance, by the IEEE 802.15.4 Standard. It has three different possible outcomes: frame transfer success (acknowledgement received), channel access failure and frame transfer failure (no acknowledgement received, all transmission attempts used). We model these outcomes in terms of the maximum allowed number of channel access attempts, the maximum number of transmission attempts, the probability of busy channel and the frame-loss probability. All these parameters are local to each node and hence our model allows for modeling link delays from the perspective of each individual node. This lies in contrast with prior work, based mostly on Markov chain approaches, which tend to be computationally intensive, often mathematically intractable and require taking a global view over the network. We validate our results with Monte-Carlo simulations and show that the proposed model for frame transfer success and frame transfer failure are accurate in terms of the delay distribution and mean value. In the case of channel access failure, the model is less precise but can be used as an upper bound for the expected delay.