Dimensions of Innovative Sensor Technologies

• Resolving task complexity: The complexity may be due to the distributed nature of the tasks and/or the diversity of the tasks in terms of different requirements. For example, intelligent transportation systems, by their very nature, are physically distributed over a wide area. Monitoring such complex systems is behind the capability of a single sensor.
• Increasing the performance: task completion time can be dramatically decreased if many sensors cooperate to perform the tasks in parallel. Moreover, the use of multiple sensors allows extended spatial and/or temporal coverage.
• Increasing reliability of measurements: increasing the reliability through redundancy because having only a single sensor may work as a bottleneck for the whole system especially in critical times. But when having multiple sensor doing a task and one fails, others could still do the job. This means that from reliability point of view, ensemble measurements on multiple sensors are preferable instead of utilizing a single sensor.
• Reducing data imperfection aspects: data provided by sensors is always affected by different imperfection aspects such as uncertainty and imprecision including vagueness, ambiguity and incompleteness. Other imperfection aspects include inconsistency, out-of-sequence and correlation [1]. Combining data from different sensors help in reducing these imperfection aspects.

Multimodality: The multimodal systems exploit the complementary features of different modalities to lead to superior performance. For example, in an advanced driver assistance system (ADAS), a pedestrian can be tracked in distinct fields of view and across a sensory blind region using multimodal sensors such as cameras and laser scanner. There will always be an error/limitation in the readings provided by each of these sensors, and therefore, the notion of multimodal data fusion is commonly used to tackle various imperfection aspects of data and yield a more accurate estimate. Another example in ADAS is driver emotional state recognition. The emotional state of the driver can be better and more accurately estimated by combining visual information form facial expressions for example and audio information from voice emotions.

Mobility: Technological advances in communication systems and the growing ease in making small, low-power and inexpensive mobile sensors now make it feasible to deploy a group of networked sensors in a number of environments. Sensors mounted on autonomous or teleoperated ground/aerial/submarine vehicles overcome the limitations of stationary sensors as they can sample the environment at different locations, exchange the information with other sensing/acting agents, and collaboratively achieve the required tasks.

Miniaturization: Advances in sensor technology, low-power electronics, and low-power radio frequency (RF) design have enabled the development of small, relatively inexpensive and low-power sensors, called microsensors that can be connected via a wireless network. Moreover, the recent advances in nanotechnology have made it possible to realize even smaller sensors at the nanoscale called nanosensors. These advances emerge to form a new sensing paradigm for extracting data from the environment and enable the reliable monitoring of a variety of environments in many pertinent areas of industrial and commercial importance such as security and surveillance, environment monitoring, industrial process control, pipeline monitoring, biological monitoring, health care and home intelligence.