Optimal Energy Aware Clustering in Sensor Networks

Sensor networks is among the fastest growing technologies that have the potential of changing our lives drastically. These collaborative, dynamic and distributed computing and communicating systems will be self organizing. They will have capabilities of distributing a task among themselves for efficient computation. There are many challenges in implementation of such systems: energy dissipation and clustering being one of them. In order to maintain a certain degree of service quality and a reasonable system lifetime, energy needs to be optimized at every stage of system operation. Sensor node clustering is another very important optimization problem. Nodes that are clustered together will easily be able to communicate with each other. Considering energy as an optimization parameter while clustering is imperative. In this paper we study the theoretical aspects of the clustering problem in sensor networks with application to energy optimization. We illustrate an optimal algorithm for clustering the sensor nodes such that each cluster (which has a master) is balanced and the total distance between sensor nodes and master nodes is minimized. Balancing the clusters is needed for evenly distributing the load on all master nodes. Minimizing the total distance helps in reducing the communication overhead and hence the energy dissipation. This problem (which we call balanced k-clustering) is modeled as a mincost flow problem which can be solved optimally using existing techniques

Optimal Reconfiguration Sequence Management

In this paper, we present an efficient optimal algorithm for minimizing runtime reconfiguration (context switching) delay of executing an application on a reconfigurable system. We assume that the basic operations of the application are already scheduled and each of them has to be realized on the reconfigurable fabric in order to be executed. The modeling and algorithm are both applicable to partially reconfigurable platforms as well as MultiFPGA systems. The algorithm can be directly applied to minimize the application runtime for many typical classes of applications, where the actual execution delay of basic operations is negligible compared to reconfiguration delay. We prove the optimality and efficiency of our algorithm and report experimental results, which demonstrate 40% to 2.5% improvement in total runtime reconfiguration delay