We study energy management policies for the compression and transmission of source data collected by an energy-harvesting sensor node with a finite energy buffer (e.g., rechargeable battery) and a finite data buffer (memory) between source encoder and channel encoder. The sensor node can adapt the source and channel coding rates depending on the observation and channel states. In such a system, the absence of precise information about the amount of energy available in the future is a key challenge. We provide analytical bounds and scaling laws for the average distortion that depend on the size of the energy and data buffers. We furthermore design a resource allocation policy that achieves almost optimal distortion scaling. Our results demonstrate that the energy leakage of state of art energy management policies can be avoided by jointly controlling the source and channel coding rates.

In this paper, we develop a partner selection protocol for enhancing the network lifetime in cooperative wireless networks. The case-study is the cooperative relayed transmission from fixed indoor nodes to a common outdoor access point. A stochastic bivariate model for the spatial distribution of the fading parameters that govern the link performance, namely the Rician K-factor and the path-loss, is proposed and validated by means of real channel measurements. The partner selection protocol is based on the real-time estimation of a function of these fading parameters, i.e., the coding gain. To reduce the complexity of the link quality assessment, a Bayesian approach is proposed that uses the site-specific bivariate model as a-priori information for the coding gain estimation. This link quality estimator allows network lifetime gains almost as if all K-factor values were known. Furthermore, it suits IEEE 802.15.4 compliant networks as it efficiently exploits the information acquired from the received signal strength indicator. Extensive numerical results highlight the trade-off between complexity, robustness to model mismatches and network lifetime performance. We show for instance that infrequent updates of the site-specific model through K-factor estimation over a subset of links are sufficient to at least double the network lifetime with respect to existing algorithms based on path loss information only.

In cyber-physical systems where sensors measure the temporal evolution of a given phenomenon of interest and radio communication takes place over short distances, the energy spent for source acquisition and compression may be comparable with that used for transmission. Additionally, in order to avoid limited lifetime issues, sensors may be powered via energy harvesting and thus collect all the energy they need from the environment. This work addresses the problem of energy allocation over source acquisition/compression and transmission for energy-harvesting sensors. At first, focusing on a single-sensor, energy management policies are identified that guarantee a minimum average distortion while at the same time ensuring the stability of the queue connecting source and channel encoders. It is shown that the identified class of policies is optimal in the sense that it stabilizes the queue whenever this is feasible by any other technique that satisfies the same average distortion constraint. Moreover, this class of policies performs an independent resource optimization for the source and channel encoders. Suboptimal strategies that do not use the energy buffer (battery) or use it only for adapting either source or channel encoder energy allocation are also studied for performance comparison. The problem of optimizing the desired trade-off between average distortion and backlog size is then formulated and solved via dynamic programming tools. Finally, a system with multiple sensors is considered and time-division scheduling strategies are derived that are able to maintain the stability of all data queues and to meet the average distortion constraints at all sensors whenever it is feasible.

The radio channels between nodes of an indoor peer-to-peer network show specific fast fading characteristics. Depending on the mobility and on the scattering properties of the environment, different kinds of fading distributions can occur: Ricean fading between static nodes, but also Rayleigh or even double-Rayleigh fading between mobile nodes. We investigate fast fading in indoor peer-to-peer networks based on radio channel measurements. It turns out that the fading statistics change over time. While the predominant fading mechanism is a combination of Rayleigh and double-Rayleigh fading, Ricean fading also occasionally occurs. On top of that, indoors, the statistics of the fast fading change over time even for small-motions of the nodes, since the propagation environment is inhomogeneous. We comprehensively model these effects using a hidden Markov model, parameterized from our measurements. The model is validated, revealing a convincing fit between the model and the measurements.

We experimentally characterize nomadic peer-to peer channels in a multi-wall indoor office environment. In particular, we derived models of path-loss, shadowing and fading statistics at 3.8 GHz in fixed scenarios, and compare them with existing results. Highlights of the paper include a multi-wall path loss model, the separation between obstruction, nomadic, and dynamic shadowing, a dynamic shadowing correlation model, as well as a multi-link analysis of MIMO links.

This work addresses the problem of energy allocation over source compression and transmission for a single energy-harvesting sensor. An optimal class of policies is identified that simultaneously guarantees a maximal average distortion and the stability of the queue connecting source and channel encoders, whenever this is feasible by any other strategy. This class of policies performs an independent resource optimization for the source and channel encoders. Analog transmission techniques as well as suboptimal strategies that do not use the energy buffer (battery) or use it only for adapting either source or channel encoder energy allocation are also studied.

Cooperative transmission techniques for ad hoc and wireless sensor networks are known to increase the network lifetime. Indeed, the improved spatial diversity allows a more efficient energy usage. Under the Rayleigh fading assumption, the selection of cooperative partners is typically based on the knowledge of the average channel power. However, Rayleigh fading is not a suitable model in a large number of practical scenarios, in particular for indoor-to-outdoor applications. In these scenarios additional information of the fading distribution is needed for partner selection. The main focus of this work is to provide an analytical framework to evaluate the impact of the fading statistics on partner selection algorithms. A distributed multi-link channel model is derived from indoor-to-indoor and indoor-to-outdoor channel measurements in order to simulate practical scenarios where the proposed analytical framework is tested. Finally, we introduce a novel partner selection strategy that exploits the distributed knowledge of the effective coding gains provided by the wireless links fading statistics.

In this paper, an empirical model of the indoor distributed channel is presented. In particular, the shadowing and fading statistics are extracted from experimental data at 2.45 GHz in stationary and mobile scenarios. Highlights of the paper include a separate model for static and dynamic shadowing, a model for shadowing correlation, as well as a single analytical distribution of small-scale fading for various types of indoor node mobility.