With  the  advent  of  5G  and  M2M  architectures, energy  harvesting  devices  are  expected  to  become  far  more prevalent. Such devices harvest energy from ambient sources such as  solar  energy  or  vibration  energy  (from  machines)  and  use  it for sensing the environmental parameters and further processing them. Given that the rate of energy consumption is more than the rate  of  energy  production,  it  is  necessary  to  frequently  halt  the processor and accumulate energy from the environment. During this period it is mandatory to take a checkpoint to avoid the loss of data. State of the art algorithms use software based methods that  extensively  rely  on  compiler  analyses. In  this  work,   we formally  model such systems,  and  show  that  we  can  arrive  at  an  optimal  check-pointing  schedule  using  a  quadratically  constrained  linear  program  (QCLP)  solver.  Using  this  as  a  baseline,  we  show  that existing algorithms for checkpointing significantly under perform. To  model  more  complex  situations  where the  energy  varies,  we  create  a  novel  checkpointing  algorithm that  adapts  itself  according  to  the  ambient  energy.  We  obtain  a speedup of 2−5× over the nearest competing approach, and we are  within 3−8% of  the  optimal  solution  in  the  general  case where  the  ambient  energy  exhibits  variations.