Achieving the Age-Energy Tradeoff with a Finite-Battery Energy Harvesting Source

We study the problem of minimizing the time-average expected Age of Information for status updates sent by an energy-harvesting source with a finite-capacity battery. In prior literature, optimal policies were observed to have a threshold structure under Poisson energy arrivals, for the special case of a unit-capacity battery. In this paper, we generalize this result to any (integer) battery capacity, and explicitly characterize the threshold structure. We provide the expressions relating the threshold values on the age to the average age. One of these results, that we derive from these expressions, is the unexpected equivalence of the minimum average AoI and the optimal threshold for the highest energy state.

[1]  Roy D. Yates,et al.  Lazy is timely: Status updates by an energy harvesting source , 2015, 2015 IEEE International Symposium on Information Theory (ISIT).

[2]  Roy D. Yates,et al.  Update or wait: How to keep your data fresh , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[3]  Elif Uysal-Biyikoglu,et al.  Scheduling status updates to minimize age of information with an energy harvesting sensor , 2017, 2017 IEEE International Symposium on Information Theory (ISIT).

[4]  Jingxian Wu,et al.  Optimal Status Update for Age of Information Minimization With an Energy Harvesting Source , 2017, IEEE Transactions on Green Communications and Networking.

[5]  Sennur Ulukus,et al.  Age-Minimal Transmission in Energy Harvesting Two-Hop Networks , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[6]  R.A. Nichols,et al.  Blue Force Tracking Network Modeling and Simulation , 2006, MILCOM 2006 - 2006 IEEE Military Communications conference.

[7]  Anthony Ephremides,et al.  Age of information under random updates , 2013, 2013 IEEE International Symposium on Information Theory.

[8]  R. Gallager Stochastic Processes , 2014 .

[9]  Elif Uysal-Biyikoglu,et al.  Age of information under energy replenishment constraints , 2015, 2015 Information Theory and Applications Workshop (ITA).

[10]  Sanjit Krishnan Kaul,et al.  Minimizing age of information in vehicular networks , 2011, 2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[11]  Eytan Modiano,et al.  Optimizing age-of-information in a multi-class queueing system , 2015, 2015 IEEE International Symposium on Information Theory (ISIT).

[12]  Roy D. Yates,et al.  Real-time status: How often should one update? , 2012, 2012 Proceedings IEEE INFOCOM.

[13]  Marian Codreanu,et al.  Age of information with packet management , 2014, 2014 IEEE International Symposium on Information Theory.

[14]  Sennur Ulukus,et al.  Age minimization in energy harvesting communications: Energy-controlled delays , 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers.

[15]  Roy D. Yates,et al.  The Age of Information: Real-Time Status Updating by Multiple Sources , 2016, IEEE Transactions on Information Theory.

[16]  Alʹbert Nikolaevich Shiri︠a︡ev,et al.  Optimal Stopping and Free-Boundary Problems , 2006 .

[17]  Vangelis Angelakis,et al.  Age of information of multiple sources with queue management , 2015, 2015 IEEE International Conference on Communications (ICC).

[18]  Roy D. Yates,et al.  Status updates through M/G/1/1 queues with HARQ , 2017, 2017 IEEE International Symposium on Information Theory (ISIT).

[19]  Girts Strazdins,et al.  LynxNet: Wild Animal Monitoring Using Sensor Networks , 2010, REALWSN.

[20]  Rajai Nasser,et al.  Age of information: The gamma awakening , 2016, 2016 IEEE International Symposium on Information Theory (ISIT).

[21]  Anthony Ephremides,et al.  Effect of Message Transmission Path Diversity on Status Age , 2016, IEEE Transactions on Information Theory.

[22]  A R Al-Ali,et al.  A Mobile GPRS-Sensors Array for Air Pollution Monitoring , 2010, IEEE Sensors Journal.