No abstract

There is currently tremendous interest in deploying energyharvesting wireless sensor networks. Engineering such systems requires striking a careful balance between sensing performance and energy management. Our work addresses this problem through the design and analysis of a harvestingaware utility-based sensing rate allocation algorithm. Based on a network utility formulation, we show that our algorithm is optimal in terms of assigning rates to individual nodes to maximize overall utility, while ensuring energy-neutral operation. To our knowledge, our work is the first optimal solution that maximizes network utility through rate assignments for tree-structured energy harvesting sensor networks. Our algorithm is fast and efficient with running time O(N 3 ), where N is the number of nodes. We evaluate the performance, scalability, and overhead of our algorithm for various utility functions and network sizes, underlining its significant advantages.

No abstract

No abstract

As Cyber-Physical-Systems (CPSs) evolve they will be increasingly relied on to support time-critical and performance-intensive monitoring and control activities. Further, many CPSs that utilize Wireless Sensor Networking (WSN) technologies will require the use of energy harvesting methods to extend their lifetimes. For this application class, there are currently few algorithmic techniques that combine performance sensitive processing and communication with efficient management techniques for energy harvesting. Our paper addresses this problem. We first propose a general purpose, multihop WSN architecture capable of supporting time-critical CPS systems using energy harvesting. We then present a set of Harvesting Aware Speed Selection (HASS) algorithms. Our technique maximizes the minimum energy reserve for all the nodes in the network, thus ensuring highly resilient performance under emergency or fault-driven situations. We present an optimal centralized solution, along with an efficient, distributed solution. We propose a CPS-specific experimental methodology, enabling us to evaluate our approach. Our experiments show that our algorithms yield significantly higher energy reserves than baseline methods.