Wireless sensor networks (WSNs) are expected to play an important role in a wide range of civilian and military applications, including environment monitoring (e.g., soil and air contaminants), seismic-structure analysis, marine micro-organisms research, military surveillance and reconnaissance, etc. In many deployment scenarios, sensors must be powered by batteries, which makes energy consumption a critical factor in the design of a WSN and calls for efficient and distributed energy management solutions that maximize the operational lifetime of the network. In this project, we are developing such solutions through intelligent topology management, adaptive sensing, clustering, cooperative (virtual) MIMO communications, and energy harvesting. Our agenda includes:

• Dynamic Activation of Sensors in Location-Unaware WSNs. To improve their reliability, sensors are typically redundantly deployed to account for unexpected failures and improve the fidelity of the received measurements. Redundancy means that some parts of the monitored area are covered by more than one sensor at the same time. While redundancy achieves better reliability, it does not necessarily improve the coverage time, defined as the time until a certain percentage of the area is no longer monitored by any sensor. To prolong coverage time, the network topology can be controlled by selecting a subset of nodes that actively monitor the field and putting the remaining nodes to sleep. We are developing and implementing distributed algorithms that enable GPS-less sensors to determine their active/sleep patterns (including the duration of the active/sleep period) based only on local information gathered from neighboring sensors or from "anchor" nodes (e.g., fixed access points with GPS capability). Our work is motivated by many practical scenarios in which sensors are required to operate without line-of-site communications to satellites (hence, the inability to acquire GPS coordinates).
• Maximizing Quality of Coverage in Active WSNs with Energy Harvesting Capabilities. As the cost of photovoltaic (solar) panels continues to go down, energy harvesting is becoming an attractive solution for operating/maintaining a WSN and reducing the overhead of replacing the chemical batteries of its sensing nodes. However, proper utilization of energy harvesting can be burdensome and complex. The unpredictable nature of the solar profile (cloud cover, shadows of buildings, etc.) introduces a high degree of uncertainty and difficulty in the management of sensors (adjusting their sensing ranges, sampling intervals, etc.). Consequently, the problem of guaranteeing a minimum coverage for targets, i.e., providing a guaranteed quality of coverage (QoC), becomes more acute. This problem is particularly relevant to active sensor networks (e.g., surveillance applications, radar sensors), where periods of low coverage represent times of vulnerability to outside influences. As such, it is important to eliminate these periods by ensuring that the minimum coverage experienced throughout the operational time is maximized. In this project, we are developing novel approaches for exploiting harvested solar energy, with the goal of maximizing the QoC of an active WSN. More specifically, we consider solar-powered active WSNs in which each sensor's range is controllable. In this context, QoC is defined as the minimum number of targets that can be simultaneously monitored during the period of network operation. Considering an accurately modeled profile for the solar power flux (which can be deterministic or random) and a (re)charge/discharge battery model, we are investigating the joint optimization of sensor radii (adapting the sensing ranges) and the routing matrix (relaying traffic between sensor nodes) in a WSN that is comprised of N solar-powered sensing nodes and M monitored targets. We are also investigating efficient sensor scheduling mechanisms for supporting fault-tolerant WSN communications (e.g., guaranteeing k-coverage, where k is the redundancy factor), at the minimal possible energy overhead. Computationally efficient algorithmic solutions are being developed, along with testbed implementation.
• Clustering Algorithms for WSNs. When sensors are deployed in large numbers, it is extremely inefficient to operate them as a flat (ad hoc) topology, where each sensor acts as a data source and as a relay to other sensors. In such scenarios, clustered designs are known to provide better network manageability and improved network lifetime. In a clustered WSN, sensors are grouped into dynamically formed clusters, and each cluster is assigned one of its members to act as a cluster head (CH). The CH is responsible for collecting data from the members of its own cluster, fusing (e.g., summarizing, aggregating) the collected data, and then transmitting a summary report to a command-and-control center (CCC). The key questions that we seek to answer here is how to allow sensors to self-cluster with no or little intervention from the CCC, how to dynamically elect and re-elect the CHs, and how to design energy-efficient routing protocols in such a clustered architecture.