IEEE Computational Intelligence Magazine - February 2020 - 39

can provide enough resources, and the goal of this learning is to
'guide' nodes as to how (or when) to wake up.
Poisson Process-Based Request Estimation

Due to the limited storage and computational resources of energy-harvesting IoT devices, it is impossible to create a complicated schedule for each of them. Here, we design a traffic model
based on an interrupted Poisson process (IPP) to describe the
traffic requirement at each node. The goal is to search for a selfgenerating message transmission requirement threshold N i such
that the transmission module would be active. The IPP has been
proven to be influential in two-state traffic modeling, and it is
widely used in various wireless traffic management technologies,
such as 3GPP and LAN. We use the IPP model to perform a
preliminary analysis of node behavior and, most importantly, to
guarantee the delay performance and energy savings.
In a 24-h service period, the periods of activity and inactivity
are both exponentially distributed on the timeline, with the average lengths of r -1 1 and r -2 1 [34], respectively. The active period
begins when the node generates a packet and ends when the
node finishes the packet's transmission. At each specific node, the
transmission requirement rate m follows the Poisson process during the inactive period, and the average rate for one node is
mt = mr2 /(r1 + r2). At each time, we assume that the node generates a packet of a usual size, and the transmission module remains
inactive until N packets are ready.
Our optimization goal is to make the sleep mode efficient;
with an easily derived sleep probability p s based on the above
m , we obtain the expected total power consumption
P = p s P in + (1 - p s) (Pon + Pt ) + Ps F.

(4)

According to the above equation, we can explore three
energy-efficient approaches: increase the probability of the
inactive mode with energy consumption P in, reduce the

transmission power Pt with the slope d that indicates the
power consumption depending on the load, or decrease the
mode switching frequency F associated with the switching
cost Ps . Based on the IPP model, it is easy to prove that the
packet threshold N monotonically increases with r 2 . Thus, a
longer idle period corresponds to a larger packet redundancy
threshold if energy consumption is optimized.
To determine the optimal set of packet redundancy N, we
use the brute force search in small-scale data or directly deduce
it using the approaches of previous studies. As shown in Fig. 6,
each threshold corresponds to a different waking probability,
which also refers to a different expected power consumption.
The new results for the packet redundancy threshold N and
requirement rate m are indexed at the control module in BSs
and sent to each corresponding node. When the service environment changes or an incident occurs, the algorithm uses the
monotony of the optimal setting to calculate the packet redundancy efficiently. For example, in most of the cases, if the packet
generating rate decreases, which leads to a larger probability of
being in the inactive state, then we only need to search for a
smaller redundancy threshold to minimize the power consumption (that is monotonically increasing with the active ratio).
AI-Controlled Power Switching System

Using the data forwarding process, the BSs can always collect
the activation records of each node, which could update the
sleeping schedule based on such lagging data. The hysteretic
data analysis is acceptable for timetable-based services (e.g.,
temperature sensors and signal lamps) but unsuitable for other
human activity-oriented services (e.g., smart parking) and is
even less appropriate in case of an emergency [35]. To overcome the considerable unreliability of analysis results obtained
using historical data, we apply a new AI-controlled switching
system to predict the future trend of the service request.

×10-4
Node = 50
Node = 100
Node = 150
Node = 200
Node = 250

3.0
2.5
2.0
1.5
1.0
0.5
0.0

5

10
15
Guardfactor for Interference
(a)

30
Optimal Number of Relays f

Maximum Throughput U

3.5

Range = r
Range = 2r
Range = 3r
Range = 4r
Range = 5r

25
20
15
10
5
0

200

400
600
Number of Nodes
(b)

800

1,000

FIGURE 5 Automatic throughput optimization. (a) Throughput changes by the predefined guard factor. (b) Change in the optimal number of
relays with network size.

FEBRUARY 2020 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE

39



IEEE Computational Intelligence Magazine - February 2020

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