An assessment of natural and human disturbance effects on Mexican ecosystems: current trends and research gaps

Mexico harbors more than 10% of the planet’s endemic species. However, the integrity and biodiversity of many ecosystems is experiencing rapid transformation under the influence of a wide array of human and natural disturbances. In order to disentangle the effects of human and natural disturbance regimes at different spatial and temporal scales, we selected six terrestrial (temperate montane forests, montane cloud forests, tropical rain forests, tropical semi-deciduous forests, tropical dry forests, and deserts) and four aquatic (coral reefs, mangrove forests, kelp forests and saline lakes) ecosystems. We used semiquantitative statistical methods to assess (1) the most important agents of disturbance affecting the ecosystems, (2) the vulnerability of each ecosystem to anthropogenic and natural disturbance, and (3) the differences in ecosystem disturbance regimes and their resilience. Our analysis indicates a significant variation in ecological responses, recovery capacity, and resilience among ecosystems. The constant and widespread presence of human impacts on both terrestrial and aquatic ecosystems is reflected either in reduced area coverage for most systems, or reduced productivity and biodiversity, particularly in the case of fragile ecosystems (e.g., rain forests, coral reefs). In all cases, the interaction between historical human impacts and episodic high intensity natural disturbance (e.g., hurricanes, fires) has triggered a reduction in species diversity and induced significant changes in habitat distribution or species dominance. The lack of monitoring programs assessing before/after effects of major disturbances in Mexico is one of the major limitations to quantifying the commonalities and differences of disturbance effects on ecosystem properties.

Wireless Sensor Network Deployment

Wireless Sensor Networks (WSNs) are widely used for various civilian and military applications, and thus have attracted significant interest in recent years. This work investigates the important problem of optimal deployment of WSNs in terms of coverage and energy consumption. Five deployment algorithms are developed for maximal sensing range and minimal energy consumption in order to provide optimal sensing coverage and maximum lifetime. Also, all developed algorithms include self-healing capabilities in order to restore the operation of WSNs after a number of nodes have become inoperative. Two centralized optimization algorithms are developed, one based on Genetic Algorithms (GAs) and one based on Particle Swarm Optimization (PSO). Both optimization algorithms use powerful central nodes to calculate and obtain the global optimum outcomes. The GA is used to determine the optimal tradeoff between network coverage and overall distance travelled by fixed range sensors. The PSO algorithm is used to ensure 100% network coverage and minimize the energy consumed by mobile and range-adjustable sensors. Up to 30% - 90% energy savings can be provided in different scenarios by using the developed optimization algorithms thereby extending the lifetime of the sensor by 1.4 to 10 times. Three distributed optimization algorithms are also developed to relocate the sensors and optimize the coverage of networks with more stringent design and cost constraints. Each algorithm is cooperatively executed by all sensors to achieve better coverage. Two of our algorithms use the relative positions between sensors to optimize the coverage and energy savings. They provide 20% to 25% more energy savings than existing solutions. Our third algorithm is developed for networks without self-localization capabilities and supports the optimal deployment of such networks without requiring the use of expensive geolocation hardware or energy consuming localization algorithms. This is important for indoor monitoring applications since current localization algorithms cannot provide good accuracy for sensor relocation algorithms in such indoor environments. Also, no sensor redeployment algorithms, which can operate without self-localization systems, developed before our work.

Extended Coverage for Public Safety and Critical Communications Using Multi-hop and D2D Communications

In this thesis, we proposed the use of device-to-device (D2D) communications for extending the coverage area of active base stations, for public safety communications with partial coverage. A 3GPP standard compliant D2D system level simulator is developed for HetNets and public safety scenarios and used to evaluate the performance of D2D discovery and communications underlying cellular networks. For D2D discovery, the benefits of time-domain inter-cell interference coordi- nation (ICIC) approaches by using almost blank subframes were evaluated. Also, the use of multi-hop is proposed to improve, even further, the performance of the D2D discovery process. Finally, the possibility of using multi-hop D2D communications for extending the coverage area of active base stations was evaluated. Improvements in energy and spectral efficiency, when compared with the case of direct UE-eNB communi- cations, were demonstrated. Moreover, UE power control techniques were applied to reduce the effects of interference from neighboring D2D links.