This technical report surveys cross-layer design in wireless sensor networks (WSNs), highlighting solutions for issues such as energy efficiency and network longevity. It presents various protocols, including CAODV and GTBS, which aim to optimize routing and resource management across layers. The findings conclude that cross-layer techniques can enhance WSN performance by addressing challenges inherent to the layered architecture.

1. See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/311951735
Cross Layer Design in Wireless Sensor Networks: Issues and Solutions
Technical Report · December 2016
DOI: 10.13140/RG.2.2.11103.87203
CITATIONS
0
READS
283
1 author:
Some of the authors of this publication are also working on these related projects:
Cross Layer Design in Wireless Sensor Networks: Issues and solutions View project
Tejaswi Patel
Carleton University
1 PUBLICATION 0 CITATIONS
SEE PROFILE
All content following this page was uploaded by Tejaswi Patel on 29 December 2016.
The user has requested enhancement of the downloaded file.

2. Abstract—In this paper, the main focus is on the research work
that has been proposed in the field of cross layer design for
Wireless Sensor Networks (WSN). A taxonomy of the reviewed
work is presented in this paper. The works done to improve the
routing schemes, making WSN more energy efficient and
maximizing the network lifetime are described in this paper. This
paper is a survey of the issues in the wireless sensor networks and
how it can be solved using the cross layer design in order to
overcome those challenges. Based on the study we conclude that
the cross layer techniques can provide efficient solutions.
I. INTRODUCTION
Sensor network is constrained computational and have less
storage capacity having an ad hoc network operational
environment [MEACSRA]. Different Sensors can
communicate over different range. When the nodes are in
coverage range they connect through an automatic
configuration.
The Layered architecture can be defined as a stack protocol
layers where each layer performs operation within its fixed
boundary and also allows changes to the underlying
technology. This approach provides modularity,
transparency and standardization. But it is not good for
wireless networks domain. It creates new problems like
hidden and exposed terminal problem, weakening of signal,
fading, spatial contention and reuse. In order to manage the
resources, cross layer architecture improves ability, highlight
power and computation and for effective decision to utilize
storage, processing and energy. Cross layer design in
wireless networks is recommend where the wireless channel
pervade the functions of all layers in the old protocol stack.
Sensor nodes can be static, it can change the location and
corresponding parameters by the environmental effects or
mobile entities. Upward flow of information notifies about
the lower layer’s network conditions to the upper layers and
vice versa. The interaction between two layers at runtime can
be done by the iteration loop between the identical and design
may include the merging of various layers.
Wireless Communication has been identified as high power
consuming operation in wireless sensor networks, it makes
energy efficiency a major need in wireless networks. WSN
research need to study challenges which includes their wide
applications, unique network topology, unique traffic
patterns and energy resource constraints.
Also, there arise several problems with the cross layer
designs for wireless sensor networks. Problems such as lack
of modularity, decreased robustness, risk of instability and
problems in system enhancements [1]. AODV (Ad hoc
demand vector) can be used for WSN but in case of high node
density, it can lead to overhead. To overcome this problem
CAODV routing protocol is used. [2]. CAODV helps make
wireless sensor network energy-efficient. In this paper, we
have identified the issues
Energy consumption increases exponentially with the
communication distance according to the energy
consumption model [3]. For that we can use multi hop
communication for data gathering to save energy. Clustering
is widely used in WSNs. LEACH (Low Energy Adaptive
clustering hierarchy algorithm) is developed cluster routing
protocol for WSNs. The protocol uses randomization to
distribute uniformly the energy load to the sensors.
gTBS (Green Task Based Sensing) scheme was proposed in
[4] for energy efficient cross layer design for WSN. It utilizes
techniques for reliable and energy efficient WSN. In [5] they
propose cross layer technique that increases the network
lifetime. The goal is to create a flexible cross layer design for
WSN that addresses the criticality of energy conservation.
Apart from that in [6] they proposed a multi hop virtual
MIMO communication protocol based on cross layer design
in order to increase the energy efficiency, reliability and
provide end-to-end QoS guarantee. In this protocol the
network irregularities, multi-hop routing, hop by hop routing
and end to end QoS providing are together considered with
the virtual MIMO scheme. Multiple clusters are formed and
the Cluster head (CH) form a multi-hop backbone. The
MIMO scheme is included in the single hop transmission
between every pair of CHs and CH selects the Cooperative
node (CN) depending on the channel quality based on the
novel strategy. The average energy consumption for every
successful packet transmitted by the protocol is modeled and
an optimal set of transmission parameters is found to reduce
the energy consumption.
The remainder of the paper is organized as follows. Section
II presents the CAODV for wireless sensor networks In
Section III, the cross layer protocols for wireless sensor
networks. Then, the cross layer design for correlated data
gathering in Section IV. Section V presents the orthogonal
modulation based cross layer design for WSNs. VI has the
energy efficient cross layer approaches thereafter we have
cross layer scheme for video transmission and lastly the cross
Tejasvi Patel, Carleton University, Canada
Cross Layer Design in Wireless Sensor
Networks: Issues and solutions

3. layer approach for energy efficient Underwater WSNs in
section VIII. The conclusion is section IX.
II. CAODV FOR WIRELESS SENSOR NETWORKS [2]
A new protocol called CAODV was proposed by Shuqiang
Zhang, Wei Guo, Kai Wen in 2008. With a motive to avoid the
problem of overhead in wireless sensor network with high node
density. The previously proposed Ad hoc on demand vector
(AODV) protocol was only good for MANETs but inefficient
for wireless sensor networks.
Two main mechanisms used in CAODV are: Delaying
Transmission (DT) and Efficient Broadcasting (EB).
Delaying Transmission (DT): This mechanism reduces the
probability of collision during broadcasting the RREQs and
thus performs Efficient broadcasting (EB). DT uses the
distance information provided by the module of DE which is in
the MAC layer. The distance estimation can be done using a
GPS and the methods based on signal power.
Efficient Broadcasting (EB): This operation reduces the
redundancy of flooding. A node should have a judgement on if
all of its one hop neighbors have already received a RREQ.
From the results obtained they derived that the overhead and
efficiency shows an improvement. Also there was a reduction
in the latency of the route discovery.
III. CROSS LAYER PROTOCOLS FOR WIRELESS SENSOR
NETWORKS
In paper [7] the authors Jaehyun kim, Lee and Seoggyu Kim
proposed an Enhanced Cross Layer Protocol (ECLP) for
energy efficiency in wireless sensor networks by integrating
MAC and routing protocol. It delivers data to the sink node with
the adaptive duty cycling and tree based energy aware routing
algorithm. It reduces the overhead cost and latency.
From the results they found ECLP to be performing better than
other protocols in the terms of energy efficiency and also
latency.
In 2010 [8] the authors came up with novel concept i.e.
initiative determination and shows how some traditional
networking functionalities can be used to design a cross layer
operation of medium access, distributed routing and local
congestion controlling functions. So the design principle of
XLP is a unique cross layering in a way that the information
and function of three fundamental communication paradigm
considered in a single protocol operation. They first studied the
effects of XLP parameters on the whole network performance.
Then they compared XLP with other five other layered
protocols suites consisting the state of art protocols and a cross
layer protocol. And lastly they discuss the overall
communication complexity of those solutions. Parameters
affecting the XLP operation are angle based routing, SNR
threshold and duty cycle. Results derived that the failure rate
increases with the decrease in duty cycle parameter. The
network throughput increases with the increase in the duty
cycle. The end to end latency performance increases with the
increase in the SNR threshold. When compared with five other
protocols, the throughput achieved by XLP is 55 percent higher
than that of the layered protocol suites. In the layered protocol
suites the cross layer information is not efficiently used for each
function. XLP employs a hop by hop congestion control
mechanism to choose the nodes which has lower queue
occupancy in a less time. In terms of energy, XLP consumes
28-66 percent less energy per packet as compared to the energy
efficient protocol like PRR-SMAC. XLP employs an adaptive
routing technique that provides an energy efficient path in terms
of link quality and energy consumption distribution [8].
The major advantage of cross layer design for communication
protocols is the implementation efficiency. In layered structure
there is a computation delay due to sequential handling of a
packet. The implementation issues are also significant for a
complete comparison. The final goal in the cross layer design
technique is to make a single communication module that is
accountable to all functionalities of each networking layer.
In [9] they propose they develop a routing protocol based on the
cross layer approach to dissipate network state information
adequately with the intention of reducing the consumed energy;
thus increases the network lifetime. In this paper a cross layer
routing algorithm that makes use of the information form
different layers to help the routing protocol make a decision
about the next hop. The algorithm makes use of the fuzzy logic
based approach for the next hop selection. Routing protocols
can be categorized in three parts which are Data centric flat
routing, hierarchical routing and location based routing. Data
centric flat routing protocols perform in network aggregation of
data arriving from various sources to obtain energy efficient
dissemination. Data aggregation is mixing of data coming from
various sources to remove the redundancy, reduce the number
of transactions and therefore save the energy consumption. In
this approach the sink node sends queries to particular regions
with attribute based naming and thereafter waits for desired
types of data from the sensors that are located in the chosen
region. In hierarchical routing the sensor nodes are organized in
the clusters. The local data fusion and aggregation functions are
performed by the cluster heads. This approach provides better
network scalability as it allows multi hop communication
among the cluster. LEACH is one example. The third category
is location based protocol. In this nodes are addressed by their
location where the distance of the neighbor nodes can be found
from the signal strength or by the GPS receivers. To estimate
the energy in efficient manner, location information is needed
to find the distance between the two nodes.
Cross layer routing protocol (XLRP) for WSN support different
transmitting power levels depending on the amount of data.
This protocol saves energy by turning OFF unwanted receivers
based on the received radio signal. In the proposed technique
they make use of the self-adaptive scheme based on fuzzy logic
algorithm that adapts to the changing parameters. It considers
scalability, self-learning and network longevity. Algorithm uses
fuzzy controller and cross layer module for collecting

4. parameters from other layers. These two are used in all nodes.
Nodes have two roles which are sensing and data forwarding.
Figure 1: Cross Layer information Exchange
The Four basic elements of typical fuzzy logic controller are the
fuzzifier, the inference engine, the Fuzzy Rule Base and the
defuzzifier. Simulations show that the proposed cross layer
fuzzy based algorithm performs better than the AODV in terms
of energy consumption.
In 2014, authors in [10] developed a cross layer searching
routing algorithm which is both mobility and energy aware. It
takes care of channel assessing mechanisms, mobility
handling provides route to the nodes in a single hop or multiple
hop. The work contributes: Connectivity among the nodes in
order to find the shortest path from source to the destination. A
mobility and an energy aware cross layer searching routing
algorithm to access and schedule the CAP and CFP duration of
MAC. And lastly the analysis based on the Markov model is
done for stationery and steady state probabilities. Proposed
algorithm has high network performance as successful delivery
is more than flexiTP. In random way point simulation, the
energy consumption is more due to the mobility but increasing
the traffic loads it works efficiently.
In a paper by Ali and Aliha, contributes by developing Optrix-
A cross layer energy efficient network maximization routing
protocol for WSN [11]. They propose a formula using
bandwidth constraint, Optrix-BW to avoid the exceeding
maximum bandwidth of wireless sensor networks and
maintains the congestion control. The equal distribution of
traffic results in an even energy consumption across all nodes
in WSN and ensures that the node is alive all the time. The
maintenance costs are reduced with the increase in the network
lifetime. Optrix is able to converge to within 9% for a large
network topology of nearly 40 nodes. They made a comparison
of optrix and CTP to an optimal routing solution that increases
the network lifetime. And optrix outperformed thn CTP in load
balancing.
IV. CROSS-LAYER DESIGN FOR CORRELATED DATA
GATHERING IN WSNs
To increase the network lifetime, in 2012 [12] a paper was
proposed by collecting the nodes of similar properties into a
cluster. The nodes of the wireless sensor networks are collected
into a single source node. The focus here is to maximize the
network lifetime. It works on the three layers that are physical
layer, MAC layer and routing protocols together. JRPA and a
heuristic algorithm, JRPRA are proposed which increases the
lifetime of the network. They also show how the similar co
related data are being sorted using general parameters in the
wireless sensor networks. Results show that JRPRA is bit better
when compared to the JRPA in some parameters like routing
variables, power variables and access probabilities. PCP and
RSP are used to solve the problem to increase the network
lifetime.
In [13] they studied the performance of RMC protocol in terms
of energy consumption and network lifetime. They enhanced
the basic RMC and named as E-RMC. They believe that the
optimization of one or two issues is not enough for prolonging
the network lifetime of resource poor WSNs. Joint optimization
of multiple issues can yield better performance.
There are three components of RMC protocol:
a) Clustering
b) Routing Scheme
c) MAC strategy
Clustering includes two sub process i.e. Grid identification and
cluster head (CH) selection. RMC only deals with the efficient
transmission of data to sink to improve the network lifetime.
The data gathering process is started with intra cluster
communication in which the cluster head collects the
information from all its cluster members. And then the CH
aggregates all the information. The aggregated information will
be forwarded o the sink in a multi hop manner. The inter cluster
routing process is done in two phases: Horizontal data gathering
and vertical data gathering. The third component we have is the
MAC strategy. It uses channel 25 and channel 26, both are non-
overlapping to avoid the conflicts of the schedule between the
interfered nodes during the cluster communications.
Figure 2: a) Horizontal Data gathering b) Vertical Data gathering
The two phase data gathering process is extended to a four
phase by accounting the nodes in the two diagonals of the
network grid. So they 4 phase we have now are horizontal,
vertical, Diagonal I and Diagonal II. And this is called
Enhanced RMC or E-RMC. They compared the performance of

5. Figure 3
Figure 4: Energy consumption comparison [14]
RMC and E-RMC in terms of energy consumption and network
lifetime. The definition of network lifetime is the duration of
time until 50% of the nodes die. The node is called dead when
its energy goes below 10%.
This protocol is successful in conserving the energy by almost
35 to 40% as compared to the RMC protocol. The proposed
protocol eliminates the need for revision by defining a relation
between the cluster location and its transmission schedule [13].
In a paper by Volkan, Sylvie and Alex addresses the energy
minimization issue for correlated data gathering small scale
WSNs [14]. They formulate the energy consumption reduction
problem as a non-linear convex optimization problem and find
the optimum transmission structure in terms of source coding
rate, transmit power and time slot allocation for an interference
free TDMA based MAC scheme [14]. From the results they
found that the compelling energy savings can be obtained by
using this optimization framework for data gathering. WSNs.
This framework takes into account the factors of the realistic
system. Figure illustrates the Energy Consumption comparison.
V. ORTHOGONAL MODULATION BASED
CROSSLAYER DESIGN FOR ENERGY EFFICIENCY IN
WSNS
In [15] the authors propose the multi-layer energy efficiency
optimal joint design approach for wireless sensor network
which is based on two orthogonal modulation schemes –
frequency shift keying (FSK) and pulse position modulation
(PPM).
From the result they found that the PPM based design is much
more energy efficient than the FSK based design in all
scenarios. There can be a saving of 75% for PPM and 51% for
FSK based design. FSK and PPM are orthogonal modulation
schemes and both can be used with WSN for the energy
efficiency. FSK is generally used in the wireless
communications. PPM is recently being much used in the ultra
wideband (UWB) wireless technology. Setting a unified
variable through the hardware, link, routing layers and MAC it
integrates battery discharge nonlinearity, node circuit operation
mode, signal transmit scheme, a variable-length TDMA scheme
[679] for reducing the network energy consumption. The
optimization and the analysis are done in three node
deployment scenarios:
 Dense WSN
 General WSN
 Sparse WSN
Experimental results show that the PPM based design is more
efficient in terms of energy than the FSK in all the above
mentioned scenarios.
VI. ENERGY EFFICIENT CROSS LAYER DESIGN FOR
WSN
1) gTBS: A green task based sensing[4]
Authors propose an energy efficient cross layer design for WSN
named as gTBS. It is a task based sensing scheme that does not
waste power in unwanted signaling and achieves reliable and
energy efficient WSN. It uses two state sleep and wakeup that
makes the inactive nodes go to sleep. And it uses a gradient
based unicast for overcoming the synchronization problem and
reduces the power consumption to a great extent. The design
starts with merging power adaption with sleep and wakeup.
Here instead of address routing, they make use of the tasks to
increase the probability of having nodes go to sleep when there
is not task request for that node. Only the intended cluster will
be asked to wakeup while the other inactive nodes will keep
sleeping. It is a less complex and an efficient energy aware
scheme. It performs well in delay, packet dropping
They performed a pilot test using TelosB sensors for a proof of
the proposed concept. The protocol design is divided in steps:
1) ID assignment Phase: two level based ID assignment
is done with a hierarchy set to the proximity of the
sink. IDs are assigned by using the 14 bit random
number generation.
2) Task request phase: The sink starts to broadcast the
tasks in the senor network after receiving all new IDs
3) Forwarding Sensed data: The sensors forward the data
to the sink though a gradient after they sense it.
Thereafter we have the implementation phase where we have
challenges like defining the gradient transmission, working

6. with the adaptive powering and efining the sleep and wakeup
periods.
The sleep and the wakeup is allows the nodes to sleep
periodically during the time they are not active. It happens when
waiting for a new task or during execution of certain task.
Two type of sleep states of a node are:
 Periodic Sleep: The nodes are kept into periodic sleep
when the nodes changes between sleep and wakeup
states. It takes upto miliseconds for the receiving any
task.
 Continuous sleep: To sense within a task a node may
go into sleep state between two sensing actions then
wakeup to sense and sleep again [4].
To evaluate the proposed scheme they adopted a network of
TelosB. They setup a test bed in which they collected two types
of data:
 Temperature measurements
 Analysis measurements
These analysis measurements include sink ID number, sensor
node ID number, gradient, number of sensor nodes, number of
requested events, total transmit time, total delay at the sink, total
duty time, sent packets, received packets, supplied voltage, and
finally the sleep time of each sensor node[4].
The evaluation results shows that the power consumption is
reduced to 20%-55%. When compared with the traditional
TBS. The delay is reduced to 54%- 145%. And finally the
delivery ratio by 24%- 73%.
2) Multi-hop LEACH based cross layer design for large
scale wireless sensor networks [3]
It a challenging need for the energy efficient routing and data
gathering protocol in a large scale environment. LEACH is a
low energy adaptive clustering hierarchy algorithm used for
clustering in Wireless sensor networks. But its not really
suitable for large scale networks. The authors in paper [3] came
up with a novel multi-hop clustering cross layer protocol based
on LEACH. Taking into consideration the SNR of different
links and residual energy of sensor nodes.
The main goal of the paper is to balance energy consumption
which leads to the individual lifetime extension of each sensor
node. The proposed new approach considers a Ray ground
propagation model. They assume two types of energy: the
transmission energy and the reception energy. Some
modifications are made to the original LEACH algorithm and
its categorized in two parts: Set up phase and a steady phase.
1) Set up phase: Each node makes a choice of if it wants
to be a CH or not depending on the choice of the
desired percentage of the cluster head and the residual
energy threshold. Except CH all the nodes selects their
CH depending on the relative SNRs to the different
CHs by making use of the RTS and CTS packets.
2) Steady state phase: Communication over long
distances requires high energy. So for large scale
networks direct communication between CH and BS
is not adapted. So what they did is they extended
LEACH to a multi hop version over short distances.
By increasing the hops the lifetime of the sensor
network can be improved.
Data transmission takes place in two stages: Intra Cluster
communication and inter cluster communication.
Figure 5. Intra cluster communication (left) Inter cluster
communication (right) [3]
Intra cluster communication: A source node S chooses to
send the data directly to its cluster head or via a relay sensor
node R in the same cluster. Relay node is considered in the three
conditions. 1) Residual energy of R > Eth 2) the SNR value of
S-R and R-CH are more than a given threshold. 3) The
transmitted energy used by the S-R-CH path is less than that is
used by S-CH path.
Inter cluster communication: In this they considered a source
CH node S that broadcasts RREQ packet to find its routing path
to the BS D.
The Simulation were performed to find the network lifetime,
average energy consumption and successful packet delivery
rate.
VII. CROSS LAYER SCHEME FOR VIDEO
TRANSMISSION OVER WIRELESS SENSOR NETWORKS
The main objective of routing protocols of WMSNs is to
increase the throughput and reduce the end to end delay.
Multipath routing handles multimedia transmission better as it
can load balance the traffic over multiple paths. This is
advantageous because it can increase the bandwidth and
reliability and reduces the delay and the power is distributed
evenly. In a paper by M. Kader and A. Youssif, they present a
energy aware and adaptive cross layer scheme for video
transmission over wireless sensor networks. The scheme
includes 3 major features.
1) Adaptive MPEG-4 video encoder
2) Packet queue scheduling
3) Path scheduling component
It uses the cross layer communication. Application, network,
datalink and physical layers are optimized and can
communicate with each other. At the application layer MPEG4
encoder configures multimedia application encoder using

7. adaptive encoding parameters according to the wireless channel
status which is communicated form the physical layer [7].
It applies the path scheduling to route packets through various
paths based on the type pf packet and QoS guarantees. And
lastly it uses the adaptive priority queue which stores each
packet in a buffer based on the type which is communicated
form the application layer in different queues. In case of
congestion this priority queues save the packet with high
priority and drop the packet with less priority.
The new scheme is called as E-ACWSN. NS2 simulator is used
for the simulation purpose. Peak Signal to noise ratio is used
widely to measure the video quality level based on the original
and processed video sequences. E-ACWSN gives better quality
of video of 30.23 dB. Video quality metric gives a value from a
range of 0-5 for the video quality. Mean opinion score rates the
quality of video sequences from 1 to 5. 5 is best while 1 is worst.
For E-ACWSN the recorded number is of 3. 81. End to end
Delay for E-ACWSN is 36 milliseconds. Delivery ratio is
calculated as the ratio of total video frames received to the total
video frames set. For E-ACWSN it is 98%. And lastly the
energy recorded is 42, 26 and 12 joules at 5000, 10000 and
20000 seconds.
The new scheme E-ACWSN shows better video as compared
to QPS, QPS+ AOMDV and ACWSN. And also it selects the
path with the minimum power usage.
VIII. CROSS LAYER APPROACH FOR ENERGY
EFFICIENT UNDERWATER WSNS [16]
The underwater wireless sensor networks has got specific
features which are quiet challenging to use. It is challenging to
maintain the power supply, change batteries and even the node
can’t be changed easily. The Underwater WSN are spars and
therefore eats more power. In the paper [16] they decided to
focus on this issue of energy efficiency. They came up with a
cross layer approach CL-VBF in which the routing layer is able
to take care of power management with the MAC layer. The
proposed CL-VBF achieves higher efficiency in energy
consumption but at the cost of packet delivery ratio.
 Power levels in VBF
Nodes in the transmission process have the maximum
power so the transmission can be done efficiently. The
transmitting nodes are within the routing pipe width w
and power level P1. Other nodes are having power level
P2. All nodes does not have the same power levels.
To evaluate the performance of the proposed cross
layer approach, they make the use of Aqua Sim. Which
is a NS2 base simulator for underwater wireless sensor
networks. They calculated the overall energy
consumption, per node energy consumption, Average
per node energy consumption, total end to end delay and
packet delivery ratio against parameter like no. of nodes,
transmission range and packet length.
Figure 6: VBF with power level [16]
The energy consumption is same for both CL-VBF and VBF if
there are 4 nodes and keeps increasing for both the protocols
and when the nodes are 8, we find maximum difference in
energy consumption. For CL –VBF it is 230.65 J and for VBF
it is 250J. So with increasing number of nodes the energy
efficiency increases as compared with VBF. The end to end
delay reduced upto 96%, packet delivery ratio achieved was
nearly 14%. With different packet seizes the energy
consumption decreased by 76%, end to end delay decreased by
94%. The packet delivery ratio was about 24% for the highest
packet size.
IX. CONCLUSION
In this paper we presented the literature on the cross layer
protocol, designs, and improvements for the wireless sensor
networks in different domains. We discussed various different
cross layer techniques to make the Wireless sensor networks
more energy efficient. We also studied how the video
transmission can be done effectively using the cross layer
approach. Furthermore we studied the cross layer approach for
underwater WSNs. The survey results shows that the cross layer
design methodology for energy constrained based wireless
sensor network is an efficient approach.
REFERENCES
[1] N.P. Mahalik. "Introduction" Sensor networks and Configuration, 2007
[2] Khatri, Urvashi, and Shilpa Mahajan. "Cross-layer design for wireless
sensor networks: A survey." Computing for Sustainable Global
Development (INDIACom), 2015 2nd International Conference on. IEEE,
2015.
[3] Ammar, Amira Ben, et al. "Multi-hop LEACH based cross-layer design
for large scale wireless sensor networks." Wireless Communications and
Mobile Computing Conference (IWCMC), 2016 International. IEEE,
2016.
[4] A. Alhalafi, L. Sboui, R. Naous, B. Shihada, "gTBS: A green task-
based sensing for energy efficient wireless sensor networks", IEEE
Conf. Comput. Commun. Workshops (INFOCOM'16/MiSeNet'16),
Apr. 2016.
[5] Kusumamba, S., and SM Dilip Kumar. "A reliable cross layer routing
scheme (CL-RS) for wireless sensor networks to prolong network

8. lifetime." Advance Computing Conference (IACC), 2015 IEEE
International. IEEE, 2015.
[6] Yuan, Yong, Zhihai He, and Min Chen. "Virtual MIMO-based cross-layer
design for wireless sensor networks." IEEE Transactions on Vehicular
Technology 55.3 (2006): 856-864.
[7] Kim, Jaehyun, Jaiyong Lee, and Seoggyu Kim. "An enhanced cross-layer
protocol for energy efficiency in wireless sensor networks." Sensor
Technologies and Applications, 2009. SENSORCOMM'09. Third
International Conference on. IEEE, 2009.
[8] Vuran, Mehmet C., and Ian F. Akyildiz. "XLP: A cross-layer protocol for
efficient communication in wireless sensor networks." IEEE Transactions
on Mobile Computing 9.11 (2010): 1578-1591.
[9] Jaradat, Toleen, et al. "Energy efficient cross-layer routing protocol in
wireless sensor networks based on fuzzy logic." 2013 9th International
Wireless Communications and Mobile Computing Conference
(IWCMC). IEEE, 2013.
[10] Paulus, Rajeev, Arvind Kumar Jaiswal, and Mukesh Kumar.
"MEACSRA: Mobility and energy-aware cross-layer searching routing
algorithm for wireless sensor network." Reliability, Infocom
Technologies and Optimization (Trends and Future Directions)(ICRITO),
2016 5th International Conference on. IEEE, 2016.
[11] Shareef, Ali, Aliha Shareef, and Yifeng Zhu. "Optrix: Energy aware cross
layer routing using convex optimization in wireless sensor networks."
Networking, Architecture and Storage (NAS), 2015 IEEE International
Conference on. IEEE, 2015.
[12] He, Shibo, et al. "Cross-layer optimization of correlated data gathering in
wireless sensor networks." IEEE Transactions on Mobile Computing
11.11 (2012): 1678-1691.
[13] Neela, Narsimha, and O. B. V. Ramanaiah. "A Comprehensive Cross-
Layer Framework for Optimization of Correlated Data Gathering in
Wireless Sensor Networks." Advanced Computing (IACC), 2016 IEEE
6th International Conference on. IEEE, 2016.
[14] Dedeoglu, Volkan, Sylvie Perreau, and Alex Grant. "Cross-layer energy
minimization in correlated data gathering wireless sensor networks." 2012
IEEE 13th International Workshop on Signal Processing Advances in
Wireless Communications (SPAWC). IEEE, 2012.
[15] Tang, Qiuling, et al. "Orthogonal Modulation Based Cross-Layer
Approach for Energy Efficiency in Wireless Sensor Networks." Cyber-
Enabled Distributed Computing and Knowledge Discovery (CyberC),
2010 International Conference on. IEEE, 2010.
[16] Parmar, Jekishan K., and Mrudang Mehta. "A cross layered approach to
improve energy efficiency of underwater wireless sensor network."
Computational Intelligence and Computing Research (ICCIC), 2014
IEEE International Conference on. IEEE, 2014.
[17] Melodia, Tommaso, Mehmet C. Vuran, and Dario Pompili. "The state of
the art in cross-layer design for wireless sensor networks." International
Workshop of the EuroNGI Network of Excellence. Springer Berlin
Heidelberg, 2005.
[18] Karaca, Ozlem, and Radosveta Sokullu. "Comparative study of cross
layer frameworks for wireless sensor networks." Wireless
Communication, Vehicular Technology, Information Theory and
Aerospace & Electronic Systems Technology, 2009. Wireless VITAE
2009. 1st International Conference on. IEEE, 2009.
[19] Wang, Chonggang, et al. "Upstream congestion control in wireless sensor
networks through cross-layer optimization." IEEE journal on selected
areas in communications 25.4 (2007): 786-795.
[20] Al-Jemeli, Marwan, and Fawnizu A. Hussin. "An energy efficient cross-
layer network operation model for IEEE 802.15. 4-based mobile wireless
sensor networks." IEEE Sensors Journal 15.2 (2015): 684-692.
[21] Jun, Tu, and Ye Zhi-wei. "An adaptive HARQ system based on cross-
layer design in wireless sensor networks." Intelligent Systems and
Applications (ISA), 2010 2nd International Workshop on. IEEE, 2010.
[22] Hurni, Philipp, et al. "Multi-hop cross-layer design in wireless sensor
networks: A case study." 2008 IEEE International Conference on
Wireless and Mobile Computing, Networking and Communications.
IEEE, 2008.
[23] Das, Sankar Narayan, and Sudip Misra. "Correlation-aware cross-layer
design for network management of wireless sensor networks." IET
Wireless Sensor Systems 5.6 (2015): 263-270.
[24] Chudasama, Shaktisinh R., and Sameer D. Trapasiya. "Packet size
optimization in wireless sensor network using cross-layer design
approach." Advances in Computing, Communications and Informatics
(ICACCI, 2014 International Conference on. IEEE, 2014.
[25] Xiong, Qingxu, and Xiang Li. "Cross-layer design of MAC and
application semantics in wireless sensor networks." Communication
Systems and Network Technologies (CSNT), 2014 Fourth International
Conference on. IEEE, 2014.
[26] Gao, Fei, et al. "Design and optimization of a cross-layer routing protocol
for multi-hop wireless sensor networks." Sensor Network Security
Technology and Privacy Communication System (SNS & PCS), 2013
International Conference on. IEEE, 2013.
[27] Choe, Hyun Jung, Preetam Ghosh, and Sajal K. Das. "Cross-layer design
for adaptive data reporting in wireless sensor networks." Pervasive
Computing and Communications, 2009. PerCom 2009. IEEE
International Conference on. IEEE, 2009.
[28] Saini, Rakesh Kumar, and Sandip Vijay. "Cross-Layer Design in Wireless
Sensor Network: A Review." IUP Journal of Telecommunications 8.2
(2016): 35.
[29] Gajjar, Sachin, Mohanchur Sarkar, and Kankar Dasgupta.
"FAMACROW: Fuzzy and ant colony optimization based combined mac,
routing, and unequal clustering cross-layer protocol for wireless sensor
networks." Applied Soft Computing 43 (2016): 235-247.
[30] Ranjan, Rajeev, and Shirshu Varma. "Challenges and implementation on
cross layer design for wireless sensor networks." Wireless Personal
Communications 86.2 (2016): 1037-1060.
[31] R. Xie, F. Richard Yu, H. Ji, and Y. Li, “Energy-Efficient Resource
Allocation for Heterogeneous Cognitive Radio Networks with
Femtocells,” IEEE Trans. Wireless Comm., vol. 11, no. 11, pp. 3910-
3920, Nov. 2012.
[32] C. Liang and F. Richard Yu, “Wireless Network Virtualization: A Survey,
Some Research Issues and Challenges,” IEEE Comm. Surveys and
Tutorials, vol. 17, no. 1, pp. 358-380, Firstquarter 2015.E. E. Reber, R. L.
Michell, and C. J. Carter, “Oxygen absorption in the earth’s atmosphere,”
Aerospace Corp., Los Angeles, CA, Tech. Rep. TR-0200 (4230-46)-3,
Nov. 1988.
[33] J. Zuo, C. Dong, S. X. Ng, L. L. Yang and L. Hanzo, “Cross-Layer Aided
Energy-Efficient Routing Design for Ad Hoc Networks,” IEEE
Communications Surveys & Tutorials, vol. 17, no. 3, pp. 1214-1238,
thirdquarter 2015.
[34] L. Zhu, F. Richard Yu, B. Ning, and T. Tang, “Cross Layer Design for
Video Transmissions in Metro Passenger Information Systems,” IEEE
Trans. Vehicular Technology, vol. 60, no. 3, pp. 1171-1181, Mar. 2011

9. B. Fu, Y. Xiao, H. J. Deng and H. Zeng, “A Survey of Cross-Layer Designs in
Wireless Networks,” IEEE Communications Surveys & Tutorials, vol. 16, no.
1, pp. 110-126, First Quarter 2014.
[35] R. Xie, F. Richard Yu, and H. Ji, “Dynamic Resource Allocation for
Heterogeneous Services in Cognitive Radio Networks with Imperfect
Channel Sensing,” IEEE Trans.Veh. Tech., vol. 61, no. 2, pp. 770-780,
Feb. 2012.
[36] F. Yu, V. Krishnamurthy, and V.C.M. Leung, “Cross-Layer Optimal
Connection Admission Control for Variable Bit Rate Multimedia Traffic
in Packet Wireless CDMA Networks,” IEEE Trans. Signal Processing,
vol. 54, no. 2, pp. 542-555, Feb. 2006.
[37] s-Layer Handoff
Design in MIMO Enabled WLANs for Communication-Based Train
Control (CBTC) Systems,” IEEE J. Sel. Areas in Comm. (JSAC), vol. 30,
no. 4, pp. 719-728, Mar. 2012.
[38] Chaturvedi, Anshu, D. N. Goswami, and Shivjay Singh. "Energy efficient
cluster head selection for cross layer design over wireless sensor
network." International Journal of Communication Networks and
Distributed Systems 16.4 (2016): 335-351.
[39] Shang, Fengjun, Xiaolin Deng, and Yongkui Zhou. "A Key Management
and Cross-layer Routing Scheme for Wireless Sensor Networks."
International Journal of Security and Its Applications 10.7 (2016): 119-
134.
[40] Louail, Lemia, and Violeta Felea. "Routing and TDMA Joint Cross-Layer
Design for Wireless Sensor Networks." International Conference on Ad-
Hoc Networks and Wireless. Springer International Publishing, 2016.
View publication statsView publication stats