Energy-effiecient DSPs for wireless sensor networks - IEEE Signal Processing Magazine

Only in recent years, the challenge of ubiquitous sensing environments has become a technical possibility with growing research in wireless sensing networks. As the field of mobile computing and communication advances, so does the idea of a distributed, ad-hoc wireless network of hundreds to thousands of microsensors, which can be randomly scattered in the area of interest. A wireless communication network between sensors facilitates sensor collaboration, and a low-power digital signal processor (DSP) can do the analysis of sensor data locally. Networked microsensors enable a variety of new applications such as warehouse inventory tracking, location sensing, machine-mounted sensing, patient monitoring, and building climate control [1]-[3]. One prime example of a microsensor application is the use of acoustic sensors for environmental monitoring. Acoustic sensors are highly versatile and can be used in a variety of applications, such as speech recognition, traffic monitoring, and medical diagnosis. The sensor application that will be investigated in this article is source tracking and localization. Multiple sensors can be used to pinpoint the location of an acoustic source (e.g., moving vehicle, speaker) by using a line of bearing estimation technique. Source localization can be useful for traffic monitoring, speech applications, and military exercises. Large sensor arrays have long been used in civil and military applications to extend the field of view of the user. However, most current sensing systems consist of a few large macrosensors, which can be highly sensitive and expensive. Macrosensor systems are not fault tolerant; one faulty sensor can cause the entire system to fail. Networks of wireless microsensor nodes are becoming more popular for reasons such as lower cost, ease of deployment, and fault tolerance. Fig. 1 shows examples of various microsensor networks. There are many new challenges to be faced in implementing signal processing algorithms and designing energy-efficient DSPs for microsensor networks. The first challenge is that all nodes are energy constrained. As the number of sensors increases, it becomes infeasible to recharge all of the batteries of the individual sensors. To prolong the lifetimes of the wireless sensors, all aspects of