Protocol Design for Irregular Repetition Slotted ALOHA With Energy Harvesting to Maintain Information Freshness

TL;DR

Numerical results demonstrate that, despite energy-harvesting constraints, IRSA can achieve a level of information freshness comparable to systems with unlimited energy.

Abstract

We investigate an internet-of-things system where energy-harvesting devices send status updates to a common receiver using the irregular repetition slotted ALOHA (IRSA) protocol. Energy shortages in these devices may lead to transmission failures that are unknown to the receiver, disrupting the decoding process. To address this issue, we propose a method for the receiver to perfectly identify such failures. Furthermore, we optimize the degree distribution of the protocol to enhance the freshness of the status updates. Our optimized degree distribution mitigates the adverse effects of potential transmission failures. Numerical results demonstrate that, despite energy-harvesting constraints, IRSA can achieve a level of information freshness comparable to systems with unlimited energy.

Figures (4)

• Figure 1: A frame with $M = 5$ slots and $4$ active devices. The first two devices choose degree $2$ and the last two choose degree $3$.
• Figure 2: Decoding process of IDENTIFY for the frame in Fig. \ref{['fig:IRSA_EH_example']}. The candidate list of each slot is depicted above the corresponding column. In each step, a packet (with the check mark) is decoded and added to the candidate lists in the slots of its intended replicas. The receiver then tries removing each subset of the candidate lists from the corresponding slots. It succeeds if a singleton slot is obtained.
• Figure 3: , throughput, average , and vs. average total number of updates per slot $\alpha U$ for $U = 1000$ devices, $M = 100$ slots, $E = 2$ energy units, and $\eta M = 2$ energy units/frame. For AVOID, we set $\Lambda_b(x) = x^b$, $\forall b \in [0:E]$. For IDENTIFY, we set $\Lambda(x) = x^3$ for all initial battery levels. We also depict the performance achieved with unlimited energy and degree distribution $\Lambda(x) = x^3$.
• Figure 4: The minimized average vs. $\alpha$, $E$, $\eta$, or $M$. Here, except for the varying parameter, we set $U = 1000$ devices, $\alpha U = 1$ updates/slot, $M = 100$ slots, $E = 2$ energy units, and $\eta = 0.02$ energy units/slot.

Theorems & Definitions (7)

• Theorem 1: lower bound
• proof
• Example 1: Impact of unknown packet drops
• Theorem 2: Initial battery level evolution of AVOID
• proof
• Theorem 3: Performance guarantee for IDENTIFY
• proof