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## Age minimization in energy harvesting communications

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Latency assessment in communication systems is commonly approached through measuring throughput (the amount of data that could be transmitted in a certain amount of time), or transmission delay (the amount of time it takes to transmit a certain amount of data). In this chapter, we introduce an alternative perspective on assessing latency in energy harvesting communication systems, namely, through the notion of the age-of-information (AoI) metric. Different from throughput and transmission delay, AoI measures the amount of time elapsed, since the latest amount of data has reached its destination. Therefore, it provides a mathematical measure of data freshness and timeliness at the destinations, and hence, is very suitable to assess latency for applications in which a fresh stream of data is continuously required over a period of time, such as in surveillance videos, remote sensing systems, and vehicular networks. Minimizing AoI, however, leads to relatively different characteristics for optimal policies when compared to those maximizing throughput or minimizing transmission delay. This chapter discusses and characterizes AoI-optimal policies in the context of energy harvesting communications, in which transmitters do not have enough energy to transmit data all the time and maintain its freshness at the receivers. The notion of AoI is introduced first, along with some related works. Then, the focus shifts to single transmitter-receiver pair systems. For these, the effects of having different battery sizes on the optimal policies are shown for perfect (zeroerror) channels and erasure channels. This chapter is concluded by some takeaways and future directions for this active line of research.

Chapter Contents:

• 8.1 Introduction: the age-of-information (AoI)
• 8.1.1 Status updating under energy harvesting constraints
• 8.1.1.1 Summary of related works
• 8.1.1.2 Categorization
• 8.1.2 Chapter outline and focus
• 8.2 Status updating over perfect channels
• 8.2.1 The case B=∞
• 8.2.2 The case B=1
• 8.2.3 The case B < ∞
• 8.2.3.1 Renewal state analysis
• 8.2.3.2 Multi threshold policy
• 8.3 Status updating over erasure channels
• 8.3.1 The case B=∞
• 8.3.1.1 Updating without feedback
• 8.3.1.2 Updating with perfect feedback
• 8.3.2 The case B=1
• 8.3.2.1 Updating without feedback
• 8.3.2.2 Updating with perfect feedback
• 8.4 Conclusion and outlook
• References

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