This document provides a summary of a student's master's thesis that compares different medium access control (MAC) techniques for energy harvesting wireless sensor networks. The student models the performance of a sensor network using random access and time-division multiple access (TDMA) MAC protocols. The modeling accounts for factors like the intermittent nature of energy harvesting, limited battery capacity, and packet arrival patterns. The random access protocol allows nodes to transmit randomly, while the TDMA protocol assigns dedicated time slots to nodes. The thesis presents the system models and implementation details of simulating the two MAC protocols using MATLAB.

1. 1
Energy Harvesting Wireless Sensor Networks
A comparative study of different MAC techniques
Devendra Kumar Lavaniya
Masters of Science, Networked Systems
Donald Bren School of Information and Comp Science
University of California, Irvine
dlavaniy@uci.edu
Abstract—‘Energy Harvesting’ devices have been gaining
popularity in recent years due to their capability of autonomously
collecting or ‘harvesting’ energy from the environmental sources
to facilitate long-term, even ‘perpetual’ operation, especially in a
wireless sensor network deployment. With the advent of energy
harvesting module in sensor networks, many aspects of the
networks has been rethought, specially the protocols which have
been redesigned to better estimate the energy generation and
availability. This paper provides some of the important
comparisons and tradeoffs of implementing two different medium
access control (MAC) protocols i.e. Random Access and
Deterministic (TDMA) using a simple Markov Model. [3][4]
Keywords—energy; harvesting; wireless; sensor; MAC, random
access, protocols.
I. INTRODUCTION
Sensor networks[1] are formed from a collection of sensing
nodes which communicate with one another, typically through
wireless channels, in order to collect spatially distributed data
about their environment. Due to their design and usage
limitation, a typical wirless sensor node is battery-operated
which provides limited power source. This warrants the need for
an alternative approach to fulfill the energy needs of the sensor
nodes, potentially using the ambient energy sources. This
approach needs an extra module in the sensor nodes called
energy harvesting (EH) which dynamically generates, stores and
manages the node’s power needs for potentially a very long
(perpetual) time.
However, in contrast to a battery-operated sensor, where
energy efficiency and conservation are crucial to prolong the
device lifetime, in an EH Sensor (EHS) the energy supply is
potentially unlimited, but its availability is random and
intermittent over time; the focus thus shifts to the management
of the harvested energy, so as to ensure a stable operation of the
EHS and to minimize the deleterious impact of energy depletion.
In this paper we will focus on discussing the performance of
a typical energy harvesting wireless sensor network when
employing different MAC techniques. We will start with small
introduction to wireless sensor networks, its limitations and why
energy harvesting is a necessity followed by the system model
and implementation.
II. ENERGY HARVESTING
A. WSN : Limitations and Challenges
Wireless Sensor Networks (WSNs) [2] have played a major
role in the research field of multi-hop wireless networks as
enablers of applications ranging from environmental and
structural monitoring to border security and human health
control. However, a typical sensor network operations are
constrained to just monitoring a physical space or object and
collecting data due to limitations in available power, memory
and processing capabilities. These limitations pose some
challenges including how to manage energy consumption, task
allocation, data collection, security and reliability in a WSN
while constrained by the limited resources.
Research on WSNs has been driven (and somewhat limited)
by a common focus: Energy efficiency. Batteries typically
power nodes of a WSN, which are static source of power. Once
their energy is depleted, the node is “dead.” Only in very
particular applications batteries can be replaced or recharged.
However, even when this is possible, the
replacement/recharging operation is slow and expensive, and
decreases network performance. Additionally battery leakage
depletes batteries within a few years even if they are seldom
used. For these reasons recent research on long-lasting WSNs is
taking a different approach, proposing energy harvesters
combined with the use of rechargeable batteries and super
capacitors (for energy storage) as the key enabler to “perpetual”
WSN operations.
B. Energy Harvesting WSN
Energy Harvesting-based WSNs (EHWSNs) are the result of
endowing WSN nodes with the capability of extracting energy
from the surrounding environment. Energy harvesting can
exploit different sources of energy, such as solar power, wind,
mechanical vibrations, temperature variations, magnetic fields,
etc. Continuously providing energy, and storing it for future use,
energy-harvesting subsystems enable WSN nodes to last
potentially for ever.
Figure 1 shows the system architecture of a wireless sensor
node with energy harvesting capabilities. The components
involved are :

2. 2
Fig 1: Schematic diagram of EH wireless node architecture
1) The energy harvester(s), in charge of converting external
ambient or human-generated energy to electricity; 2) a power
management module, that collects electrical energy from the
harvester and either stores it or delivers it to the other system
components for immediate usage; 3) energy storage, for
conserving the harvested energy for future usage; 4) a
microcontroller; 5) a radio transceiver, for transmitting and
receiving information; 6) sensory equipment; 7) an A/D
converter to digitize the analog signal generated by the sensors
and makes it available to the microcontroller for further
processing, and 8) memory to store sensed information,
application- related data, and code.
III. SYSTEM MODEL
In order to model the EH process[3][4][6], we consider a
slotted-time system, where slot k is the time interval [k, k + 1),
k ∈ Z+. At each time instant k, the EHS has a new data packet
to send to the reciever. We assume that a strict delay constraint
is enforced at the EHS: the packet is either sent to the reciever
over the interval [k, k + δ), where δ ∈ (0, 1] is the duty cycle,1
or it is dropped.
The EHS battery is modeled by a buffer. As in some previous
related works, we assume that each position in the buffer can
hold one energy quantum and that the transmission of one data
packet requires the expenditure of one energy quantum.The
maximum number of quanta that can be stored, i.e., the battery
capacity, is emax and the set of possible energy levels is denoted
by E = {0,1,...,emax}. At time k + 1, k ∈ Z+[4][5], the amount
of energy in the buffer is
Ek+1 =min{Ek −Qk +Bk,emax}, (1)
where {Bk} is the energy arrival process and {Qk} is the ac-
tion process. Qk = 1 if the current data packet is transmitted,
which results in the expenditure of one energy quantum, and Qk
= 0 otherwise. Bk models the randomness in the energy
harvested in slot k. We assume that Bk ∈ {0, 1}, i.e., either one
energy quantum is harvested, or no energy is harvested at all.
Moreover, the energy harvested in time-slot k can be used only
in a later time-slot. As a consequence, if the battery is depleted,
i.e., Ek = 0, then Qk = 0. We model the underlying EH process
{Ak} as a two-state Markov chain, with state space {G,B},
where G and B denote the GOOD and BAD harvesting states,
respectively. We also have considered the events of energy
outage and overflow i.e Ek =0 in slot k or (Ek=emax) ∩ (Bk=1)
∩ (Qk=0) in slot k respectively. Under energy outage, no
transmission is performed due to insufficient energy while with
the overflow, the surplus energy arrived cannot be stored due to
fully charged battery.
Fig 2 shows how the two-state Markov chain is used to model
the harvesting states. In order to more accurately simulate the
energy generation and arrival process (Bk) , we used two
variables for the steady state transitions :
Fig 2 : Two-state markov chain for EH process
Here, ‘e’ depicts how much energy is generated on average or
steady state probability of being in state G , and ‘b’ is the average
length of sequences of states G or bustiness of the energy
arrival.[5][6] If the energy arrival probablity is greater than the
transition probablities mentioned above the EH process will
change states , otherwise it will stay in the same state.
Additionally , we have considered the same model to
simulate the packet arrival process in our implementation based
on average packet arrival and bustiness of the traffic. In order to
model the different access protocols few additional system
variables are introduced :
• Buffer Capacity : The model implements a packet buffer
with fixed capacity for a more realistic capture of network
behaviour. Buffer overflow is also considered as a factor for
packet drop/failure.
• Battery Capacity : Limited size battery is implemented using
the logic described above.
• Maxiumum retransmission window : Fixed size window is
used to allow retransmission of failed packet due to
insufficient energy in the battery upto a limit,W.
• No of nodes/users : This parameter is used to enable multiple
users/nodes trying to transmit packets over a single
communication channel.
IV. IMPLEMENTATION
In this paper, we tried to model the behavior of a multi user
single channel system based on two different medium access
control paradigm i.e.
Random Access: All nodes with packets to deliver try to
transmit it in the next time slot. Backoff time dependent of
maximum retransmission window.
Deterministic (TDMA): Based on no of nodes, different time
slots are pre-assigned and incoming packet is transmitted
accordingly.
In order to implement the access protocols we used a simple
model using MATLAB with following details:

3. 3
A. Random Access
Figure 3 is the flow diagram to better understand the
implementation of the random access MAC technique. The
diagram only explains the central logic involved and not the
whole process and validations.
Fig 3 : flow diagram for random access MAC implementation
In order to implement the random access protocol we
introduced two more system variables :
• Collision Flag : It is a boolean flag variable which is used
to identify if two or more nodes are ready to transmit
packets simultaneously.
• Backoff : Randomised value calculated every time collision
flag is set i.e two or more nodes try to transmit packets in
same slot. We chose to implement binary exponential
backoff i.e. the random value is between 0 and (2W
-1)
where ‘W’ is current retransmission window. Its value is
decreased if no collision occurs till it reaches ‘0’ and then
the collision flag is reset to allow packets to transmit
successfully.
B. Deterministic (TDMA)
To model the deterministic MAC techniques, we chose to
implement the basic concept of time-division multiple access.
Figure 4 provides a flow diagram which depicts the central logic
behind its implementation.
Fig 4 : flow diagram for TDMA MAC implementation
The logic is much simpler and straight-forward in case of
TDMA as we just have to divide the time slots, ‘k’ equally
among the number of nodes/users transmitting the packets over
the channel. We used an additional system variable ‘Time-slot’
to determine the time slot for transmission for each node. As we
already know, as each channel is provides its own separate slot
for transmission, there are no collisions.
V. RESULTS AND CONCLUSIONS
Based on the implementation described in the previous section
we were able to plot the performance statistics, especially the
throughput that is calculated as successful transfer per time slot.
Plot I shows show the throughput varies for the two access
techniques when increasing number of simultaneous users over
a single channel. As u can easily deduce, random access proves
to be on par with TDMA for 1-2 simultaneous connections but
then the performance degrades.
Plot I: Throughput w.r.t No of users
Additionally, we compared the performance of the two
techniques by varying one of the system variables while
keeping everything else constant. Plot II, III and IV depicts the
throughput when varying the battery capacity (E-max), Buffer
capacity and maximum retransmission window (W)
respectively.
Plot II : Throughput w.r.t Battery Capacity

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Plot III: Throughput w.r.t Buffer Capacity
Plot IV: Throughput w.r.t Max retx window
One can easily deduce from the plots that in case of TDMA,
throughput climbs close to 1 even for small values of battery
and buffer capacity as well as the maximum retransmission
window as it’s a deterministic access technique for a after
sufficiently large value for these parameters, successful
transmission can be achieved in almost each time slot.
In case of random access technique, the transitions are a bit
more interesting. We can notice that throughput is lower for
smaller values of the variables and increase steadily to a point
after which either the plot flattens or declines slightly. This is
because for specific set of conditions, there is a threshold value
for each of these state variables where we achieve maximum
throughput.
Plot V demonstrates one more interesting but rather expected
result w.r.t random access technique. It plots how the reason of
packet dropped varies when we change the maximum
retransmission window (W). Plot shows that for small W value,
more packets are dropped due to exhaustion of its value while
for larger W values, packets are dropped primarily due to buffer
overflow.
Plot V : Dropped packets for Random Access
Overall, we can easily conclude that for a multi-user single
channel access with a sufficient large battery and buffer capacity
as well as maximum retransmission window employed TDMA
performance much better than random access technique.
However we cannot claim from the results above which access
technique is better overall as there are many more aspects ,
especially related to the energy harvesting (EH) which are not
taken into account in this implementation.
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ENERGY HARVESTING , International Journal of Automation and
Computing , 05(4), October 2008, 133-139
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University of Hull, “Comparison of Energy Harvesting Systems for
Wireless Sensor Networks .”, International Journal of Automation and
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