Delay and Throughput Aware Cross-Layer TDMA Approach in WSN-based IoT Networks

International Journal of Computer Networks & Communications (IJCNC) Vol.17, No.2, March 2025
DOI: 10.5121/ijcnc.2025.17205 71
DELAY AND THROUGHPUT AWARE
CROSS LAYER TDMA APPROACH IN
WSN BASED IOT NETWORKS
BOUDI Raid 1
, GHERBI Chirihane 2
and ALIOUAT Zibouda 2
1
LIRE Laboratory of Constantine 2, Abdelhafid Boussouf University
Center of Mila, 43000, Mila, Algeria.
2
LRSD Laboratory, Ferhat Abbas University of Setif, Setif, Algeria
ABSTRACT
The Internet of Things (IoT) encompasses a wide various of heterogeneous devices that leverage their
capabilities in environmental sensing, data processing, and wireless communication. Among these,
wireless sensors are one of the most widely used technologies in such networks. However, Wireless Sensor
Networks (WSNs) face significant challenges in Medium Access Control (MAC), particularly in power
management and network lifetime. To address these issues and enhance network efficiency and reliability,
we propose a MAC approach for WSNs based on routing data. This approach, termed TDMA-CADH
(TDMA Cross-Layer Approach Aware Delay/Throughput in Heterogeneous WSN), employs a cross-layer
strategy to optimize resource utilization by minimizing transmission delay, maximizing channel throughput,
and ensuring energy efficiency and extended network lifetime. The primary goal of this work is to design an
effective MAC approach for WSNs that adhere to energy consumption and network lifetime constraints
while reducing delay and improving channel throughput. To evaluate the performance of TDMA-CADH,
we conducted simulations using the Network Simulator (NS-3) and compared it with existing approaches,
including Random Leaves Ordering (RAND-LO), Depth Leaves Ordering (DEPTH-LO), Depth Remaining
Leaves Ordering (DEPTH-RELO), and our initial version, Close Remaining Leaves Ordering (CLOSE-
RELO). By including CLOSE-RELO in the comparison, we aimed to assess the advancements achieved in
our new approach. The results demonstrate that TDMA-CADH significantly improves channel throughput
and reduces transmission delay while maintaining energy efficiency and network lifetime. These findings
suggest that our proposed method can effectively enhance the performance of Wireless Sensor Networks in
IoT applications.
KEYWORDS
WSN, Cross-layer, TDMA, Routing, Transmission delay, Throughput, Latency, Energy.
1. INTRODUCTION
Technological advancements are primarily driven by the need to address societal requirements.
Wireless sensor networks (WSNs), a transformative generation of networks, have become a
reality and are significantly impact everyday life [1]. The integration of WSN-based solutions is
rapidly expanding across various fields [2][3]. For instance, WSNs are critical in survival
applications, such as the rapid detection of disasters like gas leaks in factories or forest fires
through distributed sensors [4]. Today, we are witnessing the rise of numerous applications based
on innovative concepts such as smart grids, smart homes, and intelligent transportation systems.
These systems are interconnected infrastructure solutions that are transforming our world in
unprecedented ways. These concepts are integral to the Internet of Things (IoT), where sensors

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bridge the gap between physical infrastructure and information and communication technologies,
enabling intelligent and secure monitoring and management through networked devices [5].
WSNs are considered a revolutionary approach to data collection, enhancing the reliability and
efficiency of infrastructure systems. Compared to wired solutions [6], WSNs offer simpler
deployment and greater device flexibility. Energy conservation in WSNs has been a primary
focus for researchers [7], but this constraint has not overshadowed other critical objectives such
as scalability, architecture, delay, routing, and throughput [8]. Several WSN applications are
designed to manage critical scenarios where data recovery time is crucial [9–11].
This paper presents a study of key mechanisms to minimize delay in WSNs, particularly in
single-hop and multi-hop configurations. Real-time applications often cannot tolerate data delays,
making this a critical area of research. Our work begins with the construction of a hierarchical
network structure following the random deployment of sensors in a defined area. The network
area is subdivided into virtual zones, each managed by a cluster head selected using a proposed
formula. Once clusters are formed, we focus on the method of allocating communication
channels for intra-cluster communication. This channel access method, implemented through
time slots allocated to cluster member nodes, is designed to optimize communication efficiency.
The primary objective of this work is to propose a novel slot allocation approach based on
routing information (routing tree). This approach introduces the concept of independence
between a node’s packets and those of its children in the routing tree. The significance of this
technique lies in its ability to address delays and ensure fair access to the communication channel
among cluster members. The proposed approach is evaluated using key performance metrics such
as transmission delay, communication latency, TDMA (time division multiple access) length,
throughput, and energy consumption. These evaluations are conducted using the widely
recognized Network Simulator 3 (NS3).The main contributions of this paper are as follows:
 A new formula is proposed to establish a hierarchical network structure, enabling efficient
cluster formation and cluster head selection.
 A novel TDMA scheduling method is introduced, leveraging routing tree information to
optimize slot allocation and intra-cluster communication.
 The approach introduces the notion of independence between a node’s packets and those of its
children in the routing tree, enhancing delay management and fairness in channel access.
 The proposed approach is evaluated using five key metrics: Transmission delay,
Communication latency, TDMA length, Throughput and Energy consumption.
 The performance of the proposed approach is validated using the Network Simulator 3 (NS3),
ensuring practical applicability and reliability.
The remainder of the paper is structured as follows: Section 2 reviews related work, Section 3
introduces the network structure, the proposed TDMA-CADH approach, and its modeling,
Section 4 discusses the evaluation results, and Section 5 concludes the work with suggestions for
future research.
2. LITERATURE REVIEW
Wireless sensor networks (WSNs) are an innovative technology that has permeated various
fields, including environmental monitoring, healthcare, and military applications [12, 13]. These
networks consist of small devices, known as sensors, equipped with data transmission and
processing capabilities. The nodes in a WSN are deployed in a specific environment to collect
essential information and collaborate to transmit it to a central sink. Communication within a

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WSN is facilitated through a layered model, inspired by the OSI (Open Systems Interconnection)
model, where each layer operates independently and is responsible for specific functionalities and
optimizations [14–16].
This work focuses on applications that deploy sensor networks for environmental monitoring
purposes. In such scenarios, sensors periodically collect data, which is then routed to the sink.
Many WSN applications involve reactive assistance through actuators, where sensors and
actuators work together, requiring varying degrees of transmission delay. For instance, forest fire
monitoring and river water level monitoring are critical applications that demand rapid data
delivery to the base station to prevent damage. While we do not address real-time issues directly,
we aim to reduce transmission delays in multi-hop communications between sensors.
Specifically, we focus on two layers crucial to communication decisions: the Network layer,
which determines the path between transmitter and receiver using routing protocols, and the Data
Link layer, which organizes access to the communication channel through MAC (Medium
Access Control) protocols.
Cross-layering exploits dependencies between protocols across different layers to enhance
performance [17]. This approach allows a protocol to utilize information from another protocol to
achieve its objectives. Our work emphasizes cross-layer approaches between routing and MAC
protocols, aiming to optimize transmission delay and throughput without compromising energy
efficiency. At the network level, we consider multi-hop routing, which enables data relaying from
a transmitter node to the control node. At the data link level, we focus on contention-free MAC
protocols, such as TDMA (Time Division Multiple Access) [18]. In this context, the MAC
protocol relies on information from the routing protocol to establish a communication schedule
for network nodes.
Cross-layer MAC approaches can be categorized into two types: contention-based [19-
20]and contention-free [19]. Contention-based approaches, such as MAC-CROSS [21], R-MAC
[22], CL-MAC [23], and AreaCast [24], operate in a distributed manner. For example, MAC-
CROSS reduces energy consumption by minimizing the number of active nodes during data
transmission. It modifies RTS (Request To Send) and CTS (Clear To Send) packets by adding
fields like "Final Destination Address" and "Next Address" to optimize routing [21]. Similarly,
R-MAC uses a single control packet, called a "Pion," to minimize end-to-end latency [22]. CL-
MAC improves traffic flow management and reduces delays using a Flow Setup Packet (FSP)
[23], while AreaCast enhances routing reliability and energy efficiency by automatically
replacing failed nodes [24].
In contrast, contention-free cross-layer MAC approaches, such as CoLaNet [25], Rand-LO,
Depth-LO, Depth-ReLO [26–29], and Close-ReLO [30], adopt centralized techniques. CoLaNet,
for instance, constructs a routing tree called MinDegree and uses a coloring algorithm to establish
a TDMA schedule for each node, optimizing energy use and reducing interference [25].
However, CoLaNet's initial node coloring process, which starts with the node of the highest
degree, may not optimize latency. To address this, Rand-LO randomly selects leaves of the
routing tree for time slot allocation [26], while Depth-LO sorts leaves based on their depth in the
tree, prioritizing those farthest from the sink [27, 28]. Close-ReLO introduces a different
approach by prioritizing nodes closer to the root, regardless of their depth, to optimize resource
allocation and improve network performance [30].
Despite their advantages, contention-based MAC protocols using a distributed approach with
network layer coordination have limitations [28]. For instance, the routing protocol is not
explicitly specified, and sleep/wake periods are only defined for direct neighbors, leaving other
nodes unaccounted for. Additionally, simultaneous transmissions are not managed effectively.

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Centralized approaches, such as TDMA without contention, also face challenges. For example, in
CoLaNet, Rand-LO, Depth-LO, and Depth-ReLO, the initial node selection for coloring may
impact latency, and coloring all neighbors of an already colored node may not align with
communication directions. Nodes in the middle of the routing tree must wait for their
predecessors to complete transmissions before sending their own data, which can introduce
delays.
3. MODELING AND METHODOLOGY
3.1. Hierarchical Structure
Routing protocols in wireless sensor networks (WSNs) can be categorized based on network
structure into flat routing protocols and hierarchical routing protocols. In flat routing protocols,
all nodes generally assume identical roles and functionalities. Conversely, hierarchical routing
protocols assign specific roles to certain sensors, granting them privileged functions compared to
others. A critical factor in enhancing the lifetime and efficiency of WSNs is the design of the
network. In this section, we outline the proposed methodology for constructing an adaptive
hierarchical structure, specifically focusing on clustering-based network design.
Zone-Based partitioning. The network is initially partitioned into square zones of equal size,
determined by the number of deployed nodes. Following this subdivision, each zone is assigned a
unique identifier, referred to as the Zone ID, and is characterized by its specific coordinates
(x_Zone, y_Zone). This spatial partitioning is illustrated in Fig. 1, which provides a visual
representation of the network divided into zones.
Figure 1. Network area partitioning into zones.
Establishing network topology. The initialization phase of wireless sensor networks begins with
neighbor discovery, where each node broadcasts a HELLO message containing its details. This
process collects essential information about neighboring nodes, enabling the selection of optimal
neighbors and forwarders. The phase concludes with the creation of a neighborhood table, which
supports efficient communication and routing decisions.
Cluster-Head Selection. In clustering networks, selecting cluster heads (CHs) is vital for network
efficiency. Clustering organizes nodes into local groups, each comprising a CH and member
nodes [31]. CHs manage communication between their members and the base station (BS), while
member nodes rely on CHs for data transmission. Since CHs consume more energy, their election
is based on metrics like residual energy (ResEnerg(Nodeu)), distance from the zone center
(Distn(CenterZone, Nodeu)), and node weight, Deg(Nodeu), representing the number of
neighbors. Each node calculates its cost using Equation (2), where NbrNode is the total number
of nodes in the network, and α, β, and γ are weighting parameters for the metrics. The node with

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the highest score in its zone becomes the CH. This process ensures energy efficiency and extends
thenetwork's lifetime, with Emin and Emax defining the minimum and maximum energy values
within the energy interval. Fig. 2 illustrates this phase.
𝐶𝑜𝑠𝑡𝐸𝑛𝑒𝑟𝑔 = 𝛼 ∗ 𝑅𝑒𝑠𝐸𝑛𝑒𝑟𝑔(𝑁𝑜𝑑𝑒𝑢)(1.1)
𝐶𝑜𝑠𝑡𝐷𝑒𝑔 = 𝛽 ∗ (𝐸𝑚𝑖𝑛 + (𝐸𝑚𝑎𝑥 − 𝐸𝑚𝑖𝑛) ∗ 𝐷𝑒𝑔(𝑁𝑜𝑑𝑒𝑢) 𝑁𝑏𝑟𝑁𝑜𝑑𝑒
⁄ ) (1.2)
𝐶𝑜𝑠𝑡𝐷𝑖𝑠𝑡 = 𝛾 ∗ (𝐸𝑚𝑖𝑛 + (𝐸𝑚𝑎𝑥 − 𝐸𝑚𝑖𝑛)
∗ 1 (𝐷𝑖𝑠𝑡𝑛(𝐶𝑒𝑛𝑡𝑒𝑟𝑍𝑜𝑛𝑒, 𝑁𝑜𝑑𝑒𝑢) + 1)
⁄ ) (1.3)
𝐶𝑜𝑠𝑡(𝑁𝑜𝑑𝑒𝑢) = 1 − 1 (𝐶𝑜𝑠𝑡𝐸𝑛𝑒𝑟𝑔 + 𝐶𝑜𝑠𝑡𝐷𝑒𝑔 + 𝐶𝑜𝑠𝑡𝐷𝑖𝑠𝑡)
⁄ (2)
Figure 2. Diagram illustrating the process of selecting a cluster head.
Cluster Creation. In this phase, cluster heads (CHs) broadcast a message to announce available
clusters and invite nodes to join. Non-CH nodes receive this message and select the nearest CH
based on proximity. They then send a join request to their chosen CH. This process, illustrated in
Fig. 3, establishes the cluster structure within the network.
3.2. Proposed Approach
In this section, we will present our proposed approach that is dedicated not only to intra-cluster
communications but also to inter-cluster communications. For this purpose, a multi-hop cluster
tree must be considered in order to apply this TDMA scheduling approach.

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Figure 3. Diagram illustrating the process of cluster formation.
Functioning of TDMA-CADH approach. The principle and objective of the TDMA-CADH
approach, not only the minimization of transmission delays but also the fairness in the sharing of
the channel between the network nodes is ensured. For each cluster of the network, the
scheduling principle is summarized in 4 steps:
1. Select an unvisited node in the cluster, giving the priority to a node with the least depth.
2. If there is more than one node selected in step 1, a node is designated randomly.
3. Find a compatible slot for each selected node by following these steps:
 Define this node as a visited node.
 Find a compatible slot for this node by starting the search from the beginning of TDMA.
 For all successive parents (non-root) of this node, find a slot by applying the bottom-up
slot allocation method.
4. Repeat from step 1 until all nodes in the cluster are visited. The algorithm 1 (See Fig.4) below
shows the TDMA scheduling process for the TDMA-CADH approach.
The following functions are used in Algorithm 1:
 AddNode(u, N): Adds node u to the vector N.
 ClosestNode(N): Retrieves a node from vector N with the smallest depth in the cluster’s
routing tree.
 PathToRootOfNode(u): Returns the path toward the root in the routing tree of node u.
 firstNodeOf(PathToRoot): Retrieves the first node in the path toward the root of the tree.
 compatibleSlotForNode(v, i): Checks if slot i is compatible with node v.
 deleteFirstNodeOf(vector): Removes the first node from the specified vector.
 addNodeInTransmitterVector(v, i): Adds node v to its compatible slot i as a sender.
 addNodeToReceiverVector(w, i): Adds node w to its compatible slot i as a receiver.
 getParentId(A, v): Retrieves the parent node of v.
 lengthVector(vector): Returns the size of the specified vector.
 addNewSlotInTransmitterVector() and addNewSlotInReceiverVector(): Adds a new slot
at the end of the TDMA schedule for transmitters and receivers, respectively.
 DeleteNode(u, N): Deletes node u from vector N.

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Figure 4. Proposed TDMA-CADH Approach (Algorithm 1)
3.3. Notations and Modeling
Network Connectivity.We model a wireless sensor network (WSN) as an undirected
graph G=(V,E), where V represents the set of vertices (network nodes) and E represents the set of
edges (connections between sensors). In a network of N sensors, each node is identified by an
integer i∈[0,N], with node 0 representing the sink (SB). The sink is always positioned at the
center of the upper side of the deployment area. Nodes are deployed randomly and remain
stationary within the area of interest.

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Connection Tree. The network topology is organized as a hierarchical tree divided into levels.
The highest level contains the sink (or cluster head), which connects to nodes at lower levels.
These nodes, in turn, may connect to other nodes at even lower levels, forming a routing tree. The
purpose of this tree is to identify a relay node (parent node) for each transmitting node, which
forwards data toward the sink or cluster head. In our research, the geographic distance is the
metric used to construct the tree, resulting in a geographic tree. The sink (or cluster head) serves
as the root, and each node selects its nearest neighbor to the sink as its parent. The routing tree is
defined by a vector A=[P1,…,Pi,…,Pn], where each element Pi represents the parent identifier of
node i in the tree.
Pathway to the Root. In a sensor network, the path P(n,r) represents the route data takes from
node n to the root r. Specifically, P(n,r) denotes the sequence of nodes a data packet traverses to
reach the root from node n.
Conflicting Nodes List. In wireless networks, including WSNs, shared communication media
expose transmissions to interference. In our simplified interference model, each node v maintains
a list of nodes that cannot transmit simultaneously with it. Interference is defined by a
vector C=[(v1,L1),…,(vi,Li),…,(vn,Ln)], where each element Ci is a pair (vi,Li). Here, vi is the
node identifier, and Li is the list of nodes conflicting with vi. A conflict exists between nodes vi
and vj if:
1. Both nodes are a child and its parent (∀(i,j)∈E).
2. An intermediate node connects them, where the intermediate node is the parent of at least
one of the two nodes (∀(i,k),(k,j)∈E such that k=parent(i) or k=parent(j)).
TDMA Modeling. A TDMA schedule consists of numbered slots of varying lengths. During a
slot, a node can transmit its own data or data from its children, while other nodes enter sleep
mode to conserve energy, except for the parent of the transmitting node, which remains active to
receive data. Conflicting nodes cannot share the same time slot. We model TDMA in a sensor
network using an allocation matrix Schedulen×L, where n is the number of nodes and L is the
TDMA length (number of slots). Each element Frame[i,j] of the matrix is defined as follows:
 Frame[i,j]=node identifier: Node i is in transmission mode during slotj.
 Frame[i,j]=node identifier: Node i is in reception mode, receiving data from its child
during slot j.
 Frame[i,j]=0: Node i is in sleep mode during slot j, neither transmitting nor receiving.
3.4. Evaluation Metrics
Numerous MAC approaches leveraging routing information exist in the literature. To evaluate
their performance, we define five key metrics: delay, latency, throughput, TDMA length,
and energy consumption, which are critical in WSNs. In periodic communication mode, where
each sensor transmits a packet per frame, latency is measured from a reference time t0t0 (frame
initiation), while transmission delay is measured from the actual time the node transmits its
packet. These metrics ensure a comprehensive assessment of performance.
Average transmission delay in the cluster.The transmission delay in a wireless sensor network
(WSN) refers to the time between a sensor node sending a data packet and its reception at the
destination. It is measured in time units (UT), where each UT represents the duration of
transmitting and receiving a single packet. In a hierarchical network, the cluster head (CH)
establishes the TDMA table and collects data from its cluster members.

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For a member node i sending a packet to the CH (nCH) via the path ni→n1→n2→⋯→nk→nCH, the
transmission delay for node i is calculated as:
𝑇𝑟𝑎𝑛𝑠𝐷𝑒𝑙𝑎𝑦𝑖 = ∑ 𝑠𝑙𝑜𝑡𝐷𝑢𝑟𝑎𝑡𝑖𝑜𝑛(𝑗)
𝑛𝑠𝑙𝑜𝑡𝑘
𝑗=𝑠𝑙𝑜𝑡𝑖
(3)
 sloti: Transmission slot allocated to node i.
 slotk: Transmission slot allocated to node k (a child of the CH).
 SlotDuration(j): Duration of slot j in time units.
The average transmission delay for a cluster is:
𝑇𝑟𝑎𝑛𝑠𝐷𝑒𝑙𝑎𝑦𝑐𝑙𝑢𝑠𝑡𝑒𝑟 = ( ∑ 𝑇𝑟𝑎𝑛𝑠𝐷𝑒𝑙𝑎𝑦𝑚
𝑚 ∈ 𝑀𝑒𝑚𝑏𝑟𝑒𝑠𝐶𝑙𝑢𝑠𝑡𝑒𝑟
)/𝑛𝑏𝑟𝑀𝑒𝑚𝑏𝑟𝑒 (4)
 m: Identifier of a member node.
 nbrMember: Number of nodes in the cluster.
The average transmission delay for the entire network is:
𝑇𝑟𝑎𝑛𝑠𝐷𝑒𝑙𝑎𝑦𝑀𝑜𝑦 = ( ∑ 𝑇𝑟𝑎𝑛𝑠𝐷𝑒𝑙𝑎𝑦𝑐)/𝑛𝑏𝑟𝐶𝑙𝑢𝑠𝑡𝑒𝑟
𝑐 ∈ 𝐿𝑖𝑠𝑡𝐶𝑙𝑢𝑠𝑡𝑒𝑟
(5)
 c: Cluster identifier.
 nbrCluster: Number of clusters in the network.
Average Latency in a Cluster. Latency refers to the time between a sensor requesting to transmit
a data packet and its reception at the destination. Unlike transmission delay, latency uses the first
slot of the TDMA frame as the reference time. The latency for node i is:
𝐿𝑎𝑡𝑒𝑛𝑐𝑦𝑖 = ∑ 𝑠𝑙𝑜𝑡𝐷𝑢𝑟𝑎𝑡𝑖𝑜𝑛(𝑗)
𝑛𝑠𝑙𝑜𝑡𝑘
𝑗=1
(6)
The average latency for a cluster is:
𝐿𝑎𝑡𝑒𝑛𝑐𝑦𝑐𝑙𝑢𝑠𝑡𝑒𝑟 = ( ∑ 𝐿𝑎𝑡𝑒𝑛𝑐𝑦𝑚
𝑚 ∈ 𝑀𝑒𝑚𝑏𝑟𝑒𝑠𝐶𝑙𝑢𝑠𝑡𝑒𝑟
)/𝑛𝑏𝑟𝑀𝑒𝑚𝑏𝑟𝑒 (7)
The average latency for the entire network is:
𝐿𝑎𝑡𝑒𝑛𝑐𝑦𝑀𝑜𝑦 = ( ∑ 𝐿𝑎𝑡𝑒𝑛𝑐𝑦𝑐)/𝑛𝑏𝑟𝐶𝑙𝑢𝑠𝑡𝑒𝑟
(8)
Average TDMA Length Intra-Cluster. Since slots vary in size, the TDMA length is expressed in
time units (TU). The length depends on the allocation method and cluster size. The TDMA length
for a single cluster is:

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𝐿𝑒𝑛𝑔𝑡ℎ𝑇𝐷𝑀𝐴𝐶𝑙𝑢𝑠𝑡𝑒𝑟 = ∑ 𝑠𝑙𝑜𝑡𝐷𝑢𝑟𝑎𝑡𝑖𝑜𝑛(𝑗)
𝑛𝑏𝑟𝑆𝑙𝑜𝑡
𝑗=1
(9)
 nbrSlot: Number of slots in the TDMA table.
The average TDMA length for the network is:
𝐿𝑒𝑛𝑔𝑡ℎ𝑇𝐷𝑀𝐴𝑚𝑜𝑦 = ( ∑ 𝐿𝑒𝑛𝑔𝑡ℎ𝑇𝑑𝑚𝑎𝑐
)/𝑛𝑏𝑟𝐶𝑙𝑢𝑠𝑡𝑒𝑟 (10)
Average Throughput in a Cluster. Throughput balances fairness in channel sharing and the
channel operation rate. The throughput for a cluster is:
𝑇ℎ𝑟𝑜𝑢𝑔ℎ𝑝𝑢𝑡𝐶𝑙𝑢𝑠𝑡𝑒𝑟
= ( ∑ 𝑛𝑏𝑟𝑃𝑎𝑞𝑇𝑟𝑎𝑛𝑠(𝑚)
𝑛𝑏𝑟𝑆𝑙𝑜𝑡
𝑚 ∈ 𝑚𝑒𝑚𝑏𝑒𝑟𝑠𝐶𝑙𝑢𝑠𝑡𝑒𝑟
)/𝐿𝑒𝑛𝑔𝑡ℎ𝑇𝐷𝑀𝐴𝑐𝑙𝑢𝑠𝑡𝑒𝑟 (11)
 nbrPaqTrans(m): Number of packets transmitted by node m.
The average throughput for the network is:
𝑇ℎ𝑟𝑜𝑢𝑔ℎ𝑝𝑢𝑡𝑚𝑜𝑦 = ( ∑ 𝑇ℎ𝑟𝑜𝑢𝑔ℎ𝑝𝑢𝑡𝑐
)/𝑛𝑏𝑟𝐶𝑙𝑢𝑠𝑡𝑒𝑟 (12)
Energy Consumption of the Network. While minimizing transmission delay and latency, energy
consumption must not significantly increase, as it impacts network lifetime. We use the energy
consumption model from [32], which considers the distance d between the transmitter and
receiver and the packet size k-bits.
The energy consumed for transmission is:
𝐸𝑇𝑥 (𝑘, 𝑑) = 𝐸𝑒𝑙𝑒𝑐 ∗ 𝑘 + 𝐸𝑎𝑚𝑝 ∗ 𝑘 ∗ 𝑑2
(13)
 𝐸𝑒𝑙𝑒𝑐: Energy to activate the transmitter or receiver circuit.
 𝐸𝑎𝑚𝑝: Energy for the transmission amplifier.
The energy consumed for reception is:
𝐸𝑅𝑥 (𝑘) = 𝐸𝑒𝑙𝑒𝑐 ∗ 𝑘 (14)
These equations ensure that energy efficiency is maintained while optimizing delay and latency.
3.5. Demonstrative Instance
In this section, we provide an illustrative example to demonstrate our concept (see Fig. 5). The
left side of the figure depicts a graph modeling a cluster with 11 nodes, where node 1 serves as
the cluster head (CH). The right side of the figure shows the corresponding routing tree for this
cluster, with node 1 as the root and the remaining nodes as members. These member nodes
transmit captured data to the root in a multi-hop manner. To evaluate the performance of different

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approaches, we compare them using the metrics outlined earlier. Table 1 summarizes, for each
member node, the number of packets transmitted in one round and the list of conflicting nodes,
determined by applying the two conditions described in Section 3.3.
Figure 5. Examples of a graph representing the topology of a cluster (left) and its routing tree (right).
Table 1. Transmitted Packets vs. Conflict Nodes within the clusterrepresented in Fig.5.
Nodes Number of Packets Nodes with conflicts
Node2 1 7, 9, 10
Node3 2 5, 6, 8, 10, 11
Node4 1 6, 9
Node5 1 3, 6, 8, 10
Node6 5 3, 4, 5, 8, 9, 11
Node7 1 2, 9, 10
Node8 1 3, 5, 6, 11
Node9 4 2, 4, 6, 7, 10
Node10 1 2, 3, 5, 7, 9
Node11 1 3, 6, 8
Total 18 /
a) Depth-Relo Approach Intra-Cluster
Table 2 represents the TDMA scheduling, delay, and latency by applying the Depth-ReLO
approach on the cluster shown in Fig.5.
Table 2. Scheduling, delay, and latency according to the Depth-ReLO approach.
Nodes
Scheduling TDMA Delay and latency
 Si : Slot i
 U.T : Time
unit
S1 S2 S3 S4 S5
Transmission
delay
Communication
latency
Node 05 5 0 0 0 0 13 13
Node 11 11 0 0 0 0 13 13
Node 07 7 0 0 0 0 8 8
Node 03 5 3 0 0 0 12 13
Node 08 0 0 8 0 0 10 13
Node 10 0 0 10 0 0 5 8
Node 02 0 2 0 0 0 7 8
Node 09 7 2 10 9 0 4 8
Node 06 11 3 8 0 6 5 13
Node 04 4 0 0 0 0 1 1
Slot
Duration
1 2 1 4 5 78 98 Total (U.T)

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The slot duration is expressed in U.T time units (for example in microseconds) where each U.T
represents the duration to send a single packet. From the table 2 we notice that the duration of
Slot 4 is equal to 4 U.T where we find only one node (node 9) which is concerned with the
transmission of data. So, using the routing tree illustrated in Fig.4 we can see that this node will
transmit four packets (it needs four U.T); consequently, 4 U.T is the duration of this slot.
b) Depth-LO Approach Intra-Cluster
The table 3 represents the TDMA scheduling, delay, and latency by applying the Depth-LO
Table 3. Scheduling, delay and latency according to the Depth-LO approach.
Nodes
 Si : Slot i
 U.T : Time
unit
S1 S2 S3 S4 S5
Transmission
delay
Communication
latency
Node 05 5 0 0 0 0 9 9
Node 08 0 8 0 0 0 8 9
Node 11 11 0 0 0 0 9 9
Node 10 0 10 0 0 0 12 13
Node 07 7 0 0 0 0 13 13
Node 02 0 0 2 0 0 11 13
Node 04 4 0 0 0 0 1 1
Node 03 5 0 3 0 0 7 9
Node 06 11 8 3 6 0 5 9
Node 09 7 10 2 0 9 4 13
Slot
Duration
1 1 2 5 4 79 98 Total (U.T)
c) Rand-LO Approach Intra-Cluster
The table 4 represents the TDMA scheduling, delay and latency by applying the Rand-LO
Table 4. Scheduling, delay and latency according to the Rand-LO approach.
Nodes
 Si : Slot i
 U.T : Time
unit
S1 S2 S3 S4 S5
Transmission
delay
Communication
latency
Node 04 4 0 0 0 0 1 11
Node 02 2 0 0 0 0 7 7
Node 11 11 0 0 0 0 12 12
Node 08 0 8 0 0 0 11 12
Node 07 0 7 0 0 0 6 7
Node 10 0 0 10 0 0 5 7
Node 05 5 0 0 0 0 12 12
Node 09 2 7 10 9 0 4 7
Node 03 5 0 0 3 0 9 12
Node 06 11 8 0 3 6 5 12
Slot
Duration
1 1 1 4 5 72 89 Total (U.T)

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d) Close-ReLO Approach Intra-Cluster
The table 5 represents the TDMA scheduling, delay and latency by applying the Close-ReLO
Table 5. Scheduling, delay and latency according to the Close-ReLO approach.
Nodes
 Si : Slot i
 U.T : Time
unit
S1 S2 S3 S4 S5
Transmission
delay
Communication
latency
Node 04 4 0 0 0 0 1 1
Node 02 2 0 0 0 0 7 7
Node 07 0 7 0 0 0 6 7
Node 08 8 0 0 0 0 12 12
Node 10 0 0 10 0 0 5 7
Node 09 2 7 10 9 0 4 7
Node 11 0 11 0 0 0 11 12
Node 05 0 5 0 0 0 11 12
Node 03 0 5 0 3 0 9 12
Node 06 8 11 0 3 6 5 12
Slot
Duration
1 1 1 4 5 71 89 Total (U.T)
e) TDMA-CADH Approach Intra-Cluster
The table 6 represents the TDMA scheduling, delay and latency by applying the TDMA-CADH
Table 6. Scheduling, delay and latency according to theTDMA-CADH approach.
Nodes
 Si :
Slot i
 U.T :
Time
unit
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10
Transmission
delay
Communication
latency
Node 04 4 0 0 0 0 0 0 0 0 0 13 13
Node 09 2 9 7 9 10 0 9 9 0 0 13 13
Node 06 8 3 6 11 6 6 3 0 6 6 8 8
Node 02 2 0 0 0 0 0 0 0 0 0 12 13
Node 08 8 0 0 0 0 0 0 0 0 0 10 13
Node 03 0 3 0 5 0 0 3 0 0 0 5 8
Node 07 0 0 7 0 0 0 0 0 0 0 7 8
Node 10 0 0 0 0 10 0 0 0 0 0 4 8
Node 11 0 0 0 11 0 0 0 0 0 0 5 13
Node 05 0 0 0 5 0 0 0 0 0 0 1 1
Slot
Duration
1 1 1 1 1 1 1 1 1 1 39 55
Total
(U.T)
The following table 7 summarizes the results found in each approach.

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Table 7. Table summarizing approach results.
Approaches Transmission
Delay (U.T)
Communication
Latency (U.T)
TDMA Length
(U.T)
Throughput
(Packets/U.T)
Depth-ReLO 7.8 9.8 13 1.38
Depth-LO 7.9 9.8 13 1.38
Rand-LO 7.2 8.9 12 1.50
Close-ReLO 7.1 8.9 12 1.50
TDMA-CADH 3.9 5.5 10 1.80
4. RESULTS AND DISCUSSION
NS-3, a discrete event network simulator for research and teaching, is free software under GNU
GPLv2. We used version 27 on Ubuntu 16.04 for our simulations. Table 8 lists the simulation
parameters. To highlight the contribution of our proposed approach, we provide comparisons
based on the evaluation metrics in Section 3.4.
Table 8. Simulation Parameters.
Parameter Name Description
Area of the Network 200 x 200 (m²)
Number of Nodes 100, 200, 300, 400, 500
Number of Clusters
100 Nodes: 4 clusters
Simulation Time One round
Position of the Nodes
All nodes are deployed randomly
except the sink (Xsink = 100, Ysink =
0)
Transmission Range 20 meters
Initial Energy Variable between 2 and 4 Joules
Eelec 50 nJoule/bit
Eamp 100 pJoule/bit/m²
Clustering Parameters α = 0.8, β = 0.2, γ = 0.2
4.1. Comparison by Transmission Delay
The graph in Fig. 6 illustrates the average transmission delay per cluster relative to the number of
nodes, revealing that TDMA-CADH achieves the lowest delay by decoupling a node’s packets
from those of its children, enabling immediate data transmission without waiting. In contrast,
Close-ReLO, Rand-LO, and Depth-LO exhibit stabilizing delays due to their shared "leaf
privilege" scheduling principle, which prioritizes leaf nodes. Depth-ReLO, however, incurs the
highest delay as it prioritizes nodes farthest from the root, delaying transmissions for nodes with
fewer packets. Notably, the average delay decreases as the number of nodes increases across all
approaches, attributed to the consistent depth of nodes in the routing tree and the dynamic nature
of cluster sizes and network cluster counts.

85
Figure 6. Average transmission delay per cluster.
4.2. Comparison by Communication Latency
This graph depicts the average communication latency as a function of the number of nodes, with
the results in Fig. 7 aligning closely with those in Fig. 7, as transmission delay directly impacts
communication latency. The observed consistency between the two metrics stems from the same
underlying factors discussed earlier. Specifically, TDMA-CADH demonstrates the lowest latency
due to its efficient packet separation mechanism, while Depth-ReLO exhibits the highest latency,
as its prioritization of distant nodes delays transmissions for nodes with fewer packets. These
findings reinforce the relationship between transmission delay and communication latency across
the evaluated approaches.
Figure 7. Average communication latency per cluster.
4.3. Comparison by TDMA Length
Fig. 8 illustrates the average intra-cluster TDMA length relative to the number of nodes for the
discussed approaches. The results demonstrate that the TDMA-CADH approach achieves the
shortest TDMA length, outperforming all other methods, while Depth-ReLO records the longest
TDMA length. This highlights the significant improvement offered by TDMA-CADH in
minimizing the average TDMA length per cluster. The efficiency of TDMA-CADH stems from
its ability to decouple the packets of a node from those of its children, eliminating unnecessary
delays. For instance, in traditional approaches, if two nodes share the same slot—one transmitting
two packets (2 U.T) and the other four packets (4 U.T)—the slot size expands to 4 U.T, causing

86
the first node to experience a 2 U.T delay. In contrast, TDMA-CADH avoids such inefficiencies,
ensuring optimal slot utilization and minimal TDMA length.
Figure 8. Average length TDMA per cluster.
4.4. Comparison by Throughput
Fig. 9 depicts the average throughput in relation to the number of nodes, comparing the
performance of different approaches. In this periodic communication mode, where all approaches
start with the same number of packets and routing tree, the variation in throughput is directly
influenced by the differences in TDMA length. A higher throughput corresponds to a shorter
TDMA length, as it allows for more efficient data transmission within the same timeframe. The
results from Fig. 8 and Fig. 9 confirm that the TDMA-CADH approach achieves the highest
throughput, attributed to its minimal TDMA length. This efficiency underscores the superiority of
TDMA-CADH in optimizing throughput by reducing unnecessary delays and maximizing slot
utilization.
Figure 9. Average energy consumption per cluster.
4.5. Comparison by Total Energy Consumed
The graph illustrates the average energy consumption relative to the number of nodes, comparing
the energy efficiency of different approaches during both the setup phase and data transmission
for a single frame (See Fig.10). As the primary focus is on modifying TDMA scheduling, the
evaluation of average energy consumption per cluster ensures that TDMA-CADH maintains
energy efficiency without compromise. The results reveal that all approaches, including TDMA-

87
CADH, exhibit similar energy consumption levels with slight variations, confirming that the
proposed modifications do not negatively impact energy usage. This consistency highlights that
TDMA-CADH achieves its scheduling improvements without introducing additional energy
overhead.
Figure 10. Average energy consumption per cluster.
In the following analysis, we evaluate the performance of the approaches by scaling the network
under fixed conditions. Specifically, the number of cluster heads is set to 9, while the number of
nodes is varied from 100 to 300. This setup allows us to assess the scalability and efficiency of
each approach as the network size increases. The evaluation is conducted based on the following
performance metrics. The motivation behind this analysis is to understand how the approaches
perform in larger networks while maintaining a consistent cluster structure, ensuring that the
solutions remain effective and energy-efficient even as the number of nodes grows. This is
critical for real-world applications where network scalability and resource optimization are key
concerns.
4.6. Comparison by Transmission Delay (Scalability)
The results in Fig. 11 illustrate the relationship between average transmission delay and the
number of nodes, confirming earlier findings and demonstrating the scalability of the approaches.
As the network density increases with more nodes, clusters become denser, leading to higher
transmission delays. Among the evaluated methods, Close-ReLO and TDMA-CADH consistently
achieve the shortest delays, proving their effectiveness in handling higher network density.
Conversely, Depth-ReLO exhibits the longest delays due to its prioritization mechanism, as
previously discussed. These findings, supported by Fig. 11, highlight the influence of network
density on transmission delay and underscore the superior performance of Close-ReLO and
TDMA-CADH in maintaining efficiency as the network scales.

88
Figure 11. Average transmission delay per cluster (Scalability).
4.7. Comparison by the Latency of Communications (Scalability)
This graph depicts the average communication latency relative to the number of nodes. As shown
in Fig. 12, the evolution of average intra-cluster communication latency is presented, revealing
that Close-ReLO and TDMA-CADH achieve the lowest delays. These approaches outperform
others due to their efficient scheduling mechanisms, which minimize latency. In contrast, the
remaining approaches exhibit higher latencies, attributed to the same factors discussed earlier,
such as prioritization strategies and network density. These results further emphasize the
effectiveness of Close-ReLO and TDMA-CADH in reducing communication latency, even as the
network scales.
Figure 12. Average communication latency per cluster (Scalability).
4.8. Comparison by TDMA length (Scalability)
The graph presents the average TDMA length aboutthe number of nodes, comparing the
performance of different approaches. As shown in Fig. 13, the average TDMA length per cluster
is evaluated for network sizes of 100, 200, and 300 nodes. A clear trend emerges as the number
of nodes increases, with cluster size expanding and influencing the TDMA length. The results
reveal that our approach achieves a shorter TDMA length compared to other methods,
particularly in large-scale networks. This advantage underscores the effectiveness of our
approach in optimizing scheduling and maintaining efficiency, even as the network grows in size
and complexity.

89
Figure 13. Average length TDMA per cluster (Scalability).
4.9. Comparison by Throughput (Scalability)
This graph illustrates the average channel throughput in relation to the number of nodes. As
demonstrated in Fig. 14, the TDMA-CADH approach achieves the highest average channel
operation rate compared to the other methods. This superior performance is directly linked to the
TDMA length, as previously explained. When two approaches transmit the same number of
packets within a frame, the one with the shorter TDMA length achieves a higher operation rate.
The results confirm that TDMA-CADH, with its minimal TDMA length, maximizes throughput,
highlighting its efficiency in optimizing channel utilization and enhancing overall network
performance.
Figure 14. Average throughput per cluster (Scalability).
5. CONCLUSIONS
The Internet of Things (IoT) represents a transformative advancement in connectivity, integrating
billions of heterogeneous devices, with wireless sensor networks (WSNs) playing a pivotal role.
These networks, composed of sensors with varying computational, storage, and energy capacities,
are tasked with collecting and transmitting environmental data to a base station. Addressing the
challenges of minimizing transmission delay and ensuring fair channel utilization, this work
leverages cross-layer optimization between the network and MAC layers. We propose two novel
approaches: Close-ReLO, a TDMA-based MAC protocol that optimizes scheduling using routing

90
tree information to reduce delay, and TDMA-CADH, which extends this by ensuring fairness in
channel access while further minimizing latency. Simulations on the NS3 platform demonstrate
that both approaches significantly outperform existing methods in terms of delay and latency
while maintaining energy efficiency through optimized duty cycles and interference-free
simultaneous communications. Additionally, both methods exhibit strong scalability, adapting
effectively to larger network sizes.
For future work, we propose extending the evaluation of Close-ReLO and TDMA-CADH to
inter-cluster communication scenarios to assess their scalability in more complex topologies.
Integrating these approaches with advanced clustering techniques could further enhance network
efficiency. Additionally, exploring their application in flat network architectures and extending
cross-layer optimization to include the physical layer would provide a more holistic framework
for performance improvement. Finally, incorporating greater heterogeneity in sensor
communication technologies could address the diverse requirements of emerging IoT
applications. These directions aim to build on the strengths of our current contributions and
address the evolving challenges in WSNs and IoT systems.
CONFLICTS OF INTEREST
The authors declare no conflict of interest.
ACKNOWLEDGEMENTS
The authors would like to thank everyone, just everyone!
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AUTHORS
Raid BOUDI achieved his Master’s degree in Network and Distributed Systems from
the University of Ferhat Abbas Setif-1, Algeria, in 2020. Currently, he is pursuing his
Ph.D. in Computer Science at Abdelhafid BOUSSOUF University, Center of Mila,
Algeria. His research interests primarily focus on Network Analysis, Wireless Sensor
Networks (WSN), Internet of Things (IoT), and Software-Defined Networking (SDN),
aiming to advance the performance and adaptability of next-generation networked
systems.
Chirihane Gherbi obtained her PhD in Computer Science from Larbi Ben Mhidi
University, OEB, Algeria, in 2017. She is currently an associate professor at the
College of Science, Computer Science Department, Ferhat Abbas University, and a
member of the Network and Distributed Systems Laboratory (LRSD). Her principal
areas of interest in research are wireless sensor networks (WSN), routing protocols in
wireless communication, fault tolerance, security in the Internet of Things (IoT), and
machine learning.
Zibouda Aliouat obtained her engineer diploma in 1984 and MSc in 1993 from
Constantine University. She received her PhD from Setif 1 University of Algeria. She
was an assistant professor at Constantine University from 1985 to 1994. Currently, she
is a professor in Computer Engineering Department at Setif 1 University of Algeria.
Her research interests are in the areas of computer networks and communication
modeling and simulation, wireless sensor networks (WSN), fault tolerance of
embedded systems and security and privacy in the Internet of Things (IoT), Internet of
vehicles (IoV), and Nanonetworks communication. Specifically, it focused on clustering routing protocols
in wireless communication and MAC layer.

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