Data Freshness and End-to-End Delay in Cross-Layer Two-Tier Linear IoT Networks

Abstract

The operational and technological structures of radio access networks have undergone tremendous changes in recent years. A displacement of priority from capacity-coverage optimization (to ensure data freshness) has emerged. Multiple radio access technology (multi-RAT) is a solution that addresses the exponential growth of traffic demands, providing degrees of freedom in meeting various performance goals, including energy efficiencies in IoT networks. The purpose of the present study was to investigate the possibility of leveraging multi-RAT to reduce each user's transmission delay while preserving the requisite quality of service (QoS) and maintaining the freshness of the received information via the age of information (AoI) metric. First, we investigated the coordination between a multi-hop network and a cellular network. Each IoT device served as an information source that generated packets (transmitting them toward the base station) and a relay (for packets generated upstream). We created a queuing system that included the network and MAC layers. We propose a framework comprised of various models and tools for forecasting network performances in terms of the end-to-end delay of ongoing flows and AoI. Finally, to highlight the benefits of our framework, we performed comprehensive simulations. In discussing these numerical results, insights regarding various aspects and metrics (parameter tuning, expected QoS, and performance) are made apparent.

Keywords: IoT; ad hoc network; age of information; cellular network; delay; multi-RATs integration; queuing theory.

Figure 1

Use cases covered by our model.

Figure 2

A two-tier IoT Network.

Figure 3

Two-tier IoT network packet transmission cycle and cross-layer flow chart.

Figure 4

Expected end-to-end delay over two-tier IoT network.

Figure 5

A sample of the evolution of AoI over time.

Figure 6

Setting 1: The delay experienced at each mobile device when varying the forwarding probability with the bit error rate $\xi \left({\gamma }_i\right)$.

Figure 7

Setting 2: The delay experienced at each mobile device when varying the forwarding probability with the bit error rate $\xi \left({\gamma }_i\right)$.

Figure 8

Setting 3: The delay experienced at each mobile device when varying the forwarding probability with the bit error rate $\xi \left({\gamma }_i\right)$.

Figure 9

Setting 1: The delay experienced at each mobile device when varying the forwarding probability with the arrival rate ${\lambda }_i^Q$.

Figure 10

Setting 2: The delay experienced at each mobile device when varying the forwarding probability with the arrival rate ${\lambda }_i^Q$.

Figure 11

Setting 3: The delay experienced at each mobile device when varying the forwarding probability with the arrival rate ${\lambda }_i^Q$.

Figure 12

Setting 1: The delay experienced at each mobile device when varying the attempt probability with the bit error rate $\xi \left({\gamma }_i\right)$.

Figure 13

Setting 2: The delay experienced at each mobile device when varying the attempt probability with the bit error rate $\xi \left({\gamma }_i\right)$.

Figure 14

Setting 3: The delay experienced at each mobile device when varying the attempt probability with the bit error rate $\xi \left({\gamma }_i\right)$.

Figure 15

The delay experienced at each mobile device when varying the attempt probability with the arrival rate ${\lambda }_i^Q$.

Figure 16

Setting 1: The delay experienced at each mobile device when varying the fraction of cellular traffic with the bit error rate $\xi \left({\gamma }_i\right)$.

Figure 17

Setting 1: The delay experienced at each mobile device when varying the fraction of cellular traffic with the arrival rate ${\lambda }_i^Q$.

Figure 18

The delay experienced at each mobile device when varying the arrival rate ${\lambda }_i^Q$.

Figure 19

Setting 1: The AoI experienced at each mobile device when varying the forwarding probability with the bit error rate $\xi \left({\gamma }_i\right)$.

Figure 20

Setting 2: The AoI experienced at each mobile device when varying the forwarding probability with the bit error rate $\xi \left({\gamma }_i\right)$.

Figure 21

Setting 3: The AoI experienced at each mobile device when varying the forwarding probability with the bit error rate $\xi \left({\gamma }_i\right)$.

Figure 22

Setting 1: The AoI experienced at each mobile device as a function of forwarding probability $f_i$ for various values of the arrival rate ${\lambda }_i^Q$.

Figure 23

Setting 2: The AoI experienced at each mobile device as a function of forwarding probability $f_i$ for various values of the arrival rate ${\lambda }_i^Q$.

Figure 24

Setting 3: The AoI experienced at each mobile device as a function of forwarding probability $f_i$ for various values of the arrival rate ${\lambda }_i^Q$.

Figure 25

Setting 1: The AoI experienced at each mobile device as a function of attempt probability for various bit error rates $\xi \left({\gamma }_i\right)$.

Figure 26

Setting 2: The AoI experienced at each mobile device as a function of attempt probability for various bit error rates $\xi \left({\gamma }_i\right)$.

Figure 27

Scenario 3: The AoI experienced at each mobile device as a function of attempt probability for various bit error rates $\xi \left({\gamma }_i\right)$.

Figure 28

The AoI experienced at each mobile device as a function of attempt probability $q_i$ for various arrival rates ${\lambda }_i^Q$.

Figure 29

Setting 2: The AoI experienced at each mobile device as a function of attempt probability $q_i$ for various arrival rates ${\lambda }_i^Q$.

Figure 30

Setting 1: The AoI experienced at each mobile device when varying the fraction of cellular traffic with the bit error rate $\xi \left({\gamma }_i\right)$.

Figure 31

Setting: The AoI experienced at each mobile device when varying the fraction of cellular traffic with the arrival rate ${\lambda }_i^Q$.

Figure 32

The AoI experienced at each mobile device when varying the arrival rate ${\lambda }_i^Q$.