Energy-efficient data acquisition for accurate signal estimation in wireless sensor networks

Long-term monitoring of an environment is a fundamental requirement for most wireless sensor networks. Owing to the fact that the sensor nodes have limited energy budget, prolonging their lifetime is essential in order to permit long-term monitoring. Furthermore, many applications require sensor nodes to obtain an accurate estimation of a point-source signal (for example, an animal call or seismic activity). Commonly, multiple sensor nodes simultaneously sample and then cooperate to estimate the event signal. The selection of cooperation nodes is important to reduce the estimation error while conserving the network’s energy. In this paper, we present a novel method for sensor data acquisition and signal estimation, which considers estimation accuracy, energy conservation, and energy balance. The method, using a concept of ‘virtual clusters,’ forms groups of sensor nodes with the same spatial and temporal properties. Two algorithms are used to provide functionality. The ‘distributed formation’ algorithm automatically forms and classifies the virtual clusters. The ‘round robin sample scheme’ schedules the virtual clusters to sample the event signals in turn. The estimation error and the energy consumption of the method, when used with a generalized sensing model, are evaluated through analysis and simulation. The results show that this method can achieve an improved signal estimation while reducing and balancing energy consumption

A Highly Reliable, Low Power Consumption, Low-Cost Multisensory Based System For Autonomous Navigational Mobile Robot

There has been remarkable growth in most real-time systems in the area of autonomous mobile robots. Collision-free path planning is one of the critical requirements in designing mobile robot systems since they all featured some obstacle detection techniques. This work focuses on the collaborations of low cost multi-sensor system to produce a complementary collision-free path for mobile robots. The proposed algorithm is used with a new model to produce the shortest, and most energy-efficient path from a given initial point to a goal point. Multiple sensors are utilized together, so the benefits of one compensate for the limitations of the other. The experimental results demonstrate that the robot is capable of measuring different distances to obstacles in unknown environments. Moreover, this work aims to minimize the energy consumption of a wheeled mobile robot in dynamic environments. The total energy consumption is evaluated in multiple directions, where both motional energy and operational energy are considered, while the robot is moving in dynamic environments and avoiding collisions. A time complexity analysis and a comparison of the proposed model, and states-of-arts methods are presented by using required resources and the overall performance of the proposed model. The proposed model is characterized by its low cost, low power consumption, and its efficiencies to follow the shortest path while avoiding collisions