Mohammed Rahimi

Studying the feasibility of energy harvesting in a mobile sensor network

We study the feasibility of extending the lifetime of a wireless sensor network by exploiting mobility. In our system, a small percentage of network nodes are autonomously mobile, allowing them to move in search of energy, recharge, and delivery energy to immobile, energy-depleted nodes. We term this approach energy harvesting. We characterize the problem of uneven energy consumption, suggest energy harvesting as a possible solution, and provide a simple analytical framework to evaluate energy consumption and our scheme. Data from initial feasibility experiments using energy harvesting show promising results.

Exploring Tradeoffs in Accuracy, Energy and Latency of Scale Invariant Feature Transform in Wireless Camera Networks

Advances in DSP technology create important avenues of research for embedded vision. One such avenue is the investigation of tradeoffs amongst system parameters which affect the energy, accuracy, and latency of the overall system. This paper reports work on benchmarking the performance and cost of scale invariant feature transform (SIFT) for visual classification on a Blackfin DSP processor. Through measurements and modeling of the camera sensor node, we investigate system performance (classification accuracy, latency, energy consumption) in light of image resolution, arithmetic precision, location of processing (local vs. server-side), and processor speed. A case study on counting eggs during avian nesting season is used to experimentally determine the tradeoffs of different design parameters and discuss implications to other application domains.