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Optimized Node Selection Process for quality of service provisioning over wireless multimedia sensor networks


Abstract and Figures

Several quality of service (QoS) routing strategies focus on the improvement of throughput and end-to-end delays in wireless sensor networks (WSNs). With emergence of wireless multimedia sensor networks (WMSNs), data traffic can be poised into reliability-demanding data packets and time-sensitive data packets. In such situations, node optimization and load balancing can improve QoS provisioning. Thus, the trade-off between network lifetime and ensuring the QoS provisioning has been of paramount importance. This paper introduces the Optimized Node Selection Process (ONSP) approach for robust multipath QoS routing for WMSNs. This approach is based on determining the optimized node that helps resilient route discovery for improving the QoS parameters. The selection of optimized nodes make the solid chain for route selection using residual energy and received signal strength indicator (RSSI). The second goal of this approach is to prolong the network lifetime by introducing the load-balancing algorithm, which determines the optimized and braided paths. These paths avoid bottlenecks and improves throughput, end-to-end delay, on-time packet delivery and prolongs the network lifetime.
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Optimized Node Selection Process for Quality of
Service Provisioning over Wireless Multimedia
Sensor Networks
Adwan Alanazi and Khaled Elleithy
Department of Computer Science and Engineering
University of Bridgeport, CT-06604, USA;
Abstract—Several quality of service (QoS) routing strategies
focus on the improvement of throughput and end-to-end delays in
wireless sensor networks (WSNs). With emergence of wireless
multimedia sensor networks (WMSNs), data traffic can be poised
into reliability-demanding data packets and time-sensitive data
packets. In such situations, node optimization and load balancing
can improve QoS provisioning. Thus, the trade-off between
network lifetime and ensuring the QoS provisioning has been of
paramount importance. This paper introduces the Optimized
Node Selection Process (ONSP) approach for robust multipath
QoS routing for WMSNs. This approach is based on determining
the optimized node that helps resilient route discovery for
improving the QoS parameters. The selection of optimized nodes
make the solid chain for route selection using residual energy and
received signal strength indicator (RSSI). The second goal of this
approach is to prolong the network lifetime by introducing the
load-balancing algorithm, which determines the optimized and
braided paths. These paths avoid bottlenecks and improves
throughput, end-to-end delay, on-time packet delivery and
prolongs the network lifetime.
Keywords— Wireless sensor networks, Quality of service, Routing,
optimized path, braided paths.
Wireless sensor networks (WSNs) comprise of the
promising technology mounted for resolving several solutions,
covering military, health, civilian, commercial and
environmental applications [1] ,[2],[3],[4],[5]. WSNs involve
a large number of small and low-cost sensors, which are
equipped with computation capabilities and wireless
communication [6]. However, despite the benefits that the
exploitation of a WSN brings, their deployment is limited due
to energy limitations posed by the sensor nodes. The energy
expenditure of wireless sensor networks depends on the data
processing, environmental sensing and wireless
communication. Hence, most of the QoS routing protocols aim
mostly at the accomplishment of the energy preservation.
Since some of the routing protocols designed for WSNs follow
the attainment of energy efficiency, they are practically
unsuitable for QoS provision in WSNs[7].
The latest advances in complementary metal-oxide
semiconductor (CMOS) cameras and small microphones have
led to the development of Wireless Multimedia Sensor
Networks (WMSN) as a class of wireless sensor networks.
WMSN is a network of wirelessly interconnected sensor nodes
that are able to extract multimedia content such as images,
audio and video about the ambience and send that to the sink.
Routing protocols in WMSNs should be designed with
minimum communication overhead and low-processing
convolution. The sensor nodes generally function in pervasive
locations without user involvement. Thus, the routing should
be done by using a load-balancing scheme to take adaptive
decisions for balancing the load for each route with respect to
the external environment. Furthermore, the routing protocols
must be performance-efficient and scalable [8]. In wireless
multimedia sensor networks, it is important to deploy the
powerful load-balancing routing approaches to support
applications such as security monitoring, battlefield
intelligence, environmental tracking and emergency response
[9]. These applications require multipath QoS routing
protocols to create the tradeoff between energy consumption
and QoS parameters prior to delivering the data to the sink
node [10],[11]. The multi-path QoS routing protocols establish
multiple paths to balance the network traffic between source-
node to destination-node. In literature, there are many
approaches available for conventional networks. However,
these approaches are too complicated to be considered
WMSNs. In Addition, WMSNs differ in nature from wired
network because nodes in WMSN hold a single queue that is
connected with a single transmitter [9],[12]. The main purpose
of introducing the multi-path routing protocol is fault
tolerance, bandwidth aggregation, reducing delay and load
balancing [13]. We focus on the multi-path QoS routing
protocol for improving the network lifetime and improving the
throughput, reducing the end-to-end delay as well as on-time
packet delivery.
In this paper, we propose Optimized Node Selection
Process: ONSP is an energy efficient and quality of service
based multi-path routing protocol for wireless multimedia
sensor networks that selects optimized disjoint and braided
paths to achieve load balancing though splitting the network
traffic on the primary path (optimized path) and braided paths
(other alternative paths). Optimized node selection process
improves the delivery of data reliability using a received
signal strength indicator and residual energy models. In order
to transmit the data over optimized and braided paths, the
load-balancing algorithm is used to guarantee load-balancing
over the network traffic to avoid the congestion and improves
the throughput while reducing latency.
The remnants of the paper are organized as follow: In Section
2, we present the Optimized Node Selection Process. Section
3, describes the load-balancing algorithm. Section 4, presents
simulation-setup and performance evaluation. Finally, section
5 concludes the paper.
The optimized node selection process involves the use of
mathematical formulation to determine the node having
enough resources to forward the data to next node based on
residual energy, optimal path and received signal strength
indicator (RSSI).
To determining the optimum node discovery, each path
between source node and destination node is defined as
ǡ ǤǤǤǡܲ
ሻ. Where, P: set of paths, ܲ : the source
node and ܲ : the base station, which spans over ܲെʹ
(intermediate nodes between source and destination). Thus,
residual energy of each intermediate node can be determined
after creating the corresponding path and finishing the one
event-detection cycle obtained as follows:
ூୀଵ ܲǡܲ௜ାଵሺͳሻ
where ‘ܴԢ is the residual energy of each intermediate node on
the path. The residual energy of each node could be different
and depending on the participation of the communication node
and how much node involves in the communication and
ܧܲǡܲ௜ା is the required energy for routing the message
between two intermediate nodesܲܽ݊݀ܲ௜ା. Let us assume
‘X’ is the set of possible paths ܺൌݔ
ǡݔǡݔǡǤǤǤǡݔ between
source node and destination. Therefore, optimum path
between two nodes can be determined as
ܴǣݔא ܺሺʹሻ
where ԢݔԢ is the optimum path between two nodes.
The signal strength is translated into distance. As a result, the
existing techniques experience the problem due to noise
interference, multi-path fading, and irregular signal
propagation that highly affect the correctness of ranging
estimate. To overcome these problems, we apply an improved
approach of determining the RSSI for optimized routing path.
The localization accuracy can be endorsed to fulfill the
requirements for optimization. We apply localization
refinement, region partition and regular node placement. In
RSSI, the distance between transmitter ԢܶԢ and receiver ԢܴԢ
can be obtained by using log-normal shadowing approach
described as:
ݎ ͳͲ݈݊݋݃ݎ
ݎ ܩιሺ͵ሻ
where ܴݎ: the received power, ܴݎ: received power of
point,ݎ: the distance between receiver and transmitter,ݎ:
reference distance, ݊ : exponent factor for power loss, and
ܩι:Gaussian random variable that is used for the change of the
power when setting the distance. In practices, basic shadowing
model is used for determining the distance based on RSSI.
ݎ ͳͲ݈݊݋݃ݎ
We assume that reference distance is 1 meter, so we can obtain
resilient RSSI as follows:
ܴܵܵܫ ൌ ܴݎൌ ο׊ െ ͳͲ݈݊݋݃ݎሺͷሻ
where ο׊: received signal strength. This RSSI-based
localization covers mentioned limitations and also helps
determine an optimized route.
To balance the load over the network, the traffic is routed
through multiple routes. We use the dynamic load-balancing
approach for all paths from source to destination. The
bandwidth is distributed over these paths according to the
traffic load. The paths consist of optimized and braided paths.
The Optimized path is the primary path that is allotted more
Bandwidth and braided paths are alternate paths to balance the
traffic depicted in Figure 1. The bandwidth is reserved for
each route based on optimized load balancing (OLB)
algorithm. Let us assume that expected load ԢԢ on optimized
and braided paths need to be updated. This is the reason that
original ԢԢ is distributed on the all candidate paths and their
respective values are updated as follows
ൌെ׊ߴǡ߸ߔ ൅ ሺܭ െ ሻ׊א ܴఃଵ
ൌെ׊ߴǡ߸ߔ׊ב ሺܴ׫ܴఃଵሻሺ͸ሻ
where Ԣߴ: source, and Ԣ߸: distination. We deduct the
bandwidth-demand value Ԣ׊ߴǡ߸ߔ that is passed through
each link. Each link creates optimized Ԣܴ
and braided paths
over the network. Optimized path Ԣܴ
is the primary
route. The tangible reservation isԢߔ. In case of reserved
bandwidth for optimized load balancing, ߔൌߔ for all the
links ܮאܴ. From another perspective, in case of reserved
bandwidth-delay for OLB, we divide an end-to-end delay into
different link delay limitations. As a result, each link along the
optimized and braided paths has the different reserved
Thus, ߔ൒ߔ. For the links along a braided path ԢܴఃଵԢ, the
in both cases ranges between 1 and ԢߔԢ based on the
shared bandwidth on the links ԢܮԢ. ܭǣ Initial energy of link
and  : residual energy of link, which are calculated before and
after reserving the bandwidth for paths of the network. The
expected load ԢԢ for each path is updated over each link. In the
end, having setup the all possible routes, the most utilized
links will get highest value.
Algorithm 1: Determining the optimized and braided path for
end-to-end bound delay ԢՆԢand bandwidth of ߔ.
1. Input: Optimized specification ߴǡ߸ǡߔǡՆ
2. Expected load of each link , residual energy of link ,
and total energy Ȉ
3. Set ߴ of T candidate pair of braided pairs (ܯ,ܯ)
4. Output: optimized path ܴ and braided path ܴʣଵ
5. While all links ܮ do
6. ݐο
7. ݁݊݀ݓ݄݈݅݁
8. ܱ௠௜௡ ൌ ݂݅݊݅݊݅ݐݕ ; ܱ௠௜௡: ( Rest of links except
optimized and braided links)
9. while each braided pairs (M,N) א ߴ that meets the
requirements of (Նǡߔ do
10. Divide Ն individually along the braided pairs ܯand
11. Recalculate the residual energy of link
12. Recalculate the link costs ݐο
13. Recalculate the network metric ܰ
14. If ܰ ൏ܱ௠௜௡ then
15. ܱ௠௜௡ ൌܰǢܴ =ܯ ;ܴఃଵ ൌܯ
16. ݂݁݊݀݅
17. End while
18. If ܱ௠௜௡ ൐ߜ ;ߜǣ value of braided link
19. Reject ܱ௠௜௡
20. else
21. Choose ܴʣǡ ܴఃଵ as optimized and braided links for
22. end if
In order to analyze the performance of the optimized node
selection process for path discovery. We observed a wireless
multimedia sensor network that was constituted the size 400 m
x 400 m. The performance of ONSP is compared with other
QoS routing protocols: Multi-Path and Multi-SPEED
(MMSPEED) Protocol, Multimedia Geographic Routing
(MGR) and Sequential Assignment Routing (SAR). The
rationale behind selecting these protocols is that they are
probabilistic protocols and focusing on improving the QoS.
Our proposed protocol is having same features. Thus,
comparison would help determine effectiveness of our
protocol. Thus, the network topology considered the following
Figure 1: Optimized route discovery process using load-balancing approach
A static sink is set farther from the sensing field.
Each node is initially allotted uniform energy.
Each node senses the field at the variable rates and is
responsible for forwarding the data to sink node.
The sensor nodes are 50% mobile.
Each sensor node possesses homogenous capabilities
involving the same communication capacity and
computing resources.
The location of sensor nodes is determined in
The aforesaid network topology is suitable for several
WMSNs applications, such as home monitoring, and
reconnaissance, airport surveillance, biomedical applications,
home automation, fire detection, agriculture and machine
failure diagnosis. In the actual application of a proposed
approach, it may be used for airport surveillance where the
sensor nodes are static and mobile, which are used for
monitoring the travelers and staff members. The simulation
was conducted by using network simulator-2[14]. The
scenario consists of 500 homogenous sensor nodes with initial
energy of 4 joules. The base station is located at point (0, 500).
The size of the packets is 256 bytes. The residual energy of
each node after 7 cycles is calculated based on the residual
energy model described in section 2. The rest of parameters
are explained in Table 1.
Based on simulation, we are interested in the following
Average delivery rate
Average energy consumption
End-to-end delay
A. Average delivery rate
Table 1: Simulation parameters and its corresponding values
Size of network 400 × 400 square meters
Number of nodes 500
Queue-Capacity 25 Packets
Mobility Model Random way mobility model
Maximum number of
retransmissions allowed
Initial energy of node 4 joules
Size of Packets 256 bytes
Data Rate 250 kilobytes/second
Sensing Range of node 40 meters
Simulation time 9 minutes
Average Simulation Run 08
Base station location (0,500)
Transmitter Power 12 mW
Receiver Power 13 mW
Buffer threshold 1024 Bytes
One of the important metrics in investigating the routing
protocols is an average delivery ratio. In Figure 2, node failure
probability and an average delivery ratio are depicted. ONSP
outperforms other routing protocols: MMSPEED, MGR and
SAR. The average delivery ratio decreases by node failure.
However, node failure highly affects other participant routing
protocols as compared with ONSP. The reason of the better
performance of ONSP is the inclusion the load-balancing
algorithm and optimized node processing approach based on
residual energy and RSSI. The performance of ONSP reduces
maximum to 18% by node failure, but other MMSPEED, SAR
and MGR reduce the performance maximum up to 40%.
Figure 2. Average delivery rate on variable node failure probability
B. Average energy consumption
Figure 3 shows the result of energy consumption based on
node failure probability. We note that ONSP outperforms
MMSPEED, SAR and MGR. The energy consumption is also
not highly affected due to QoS provisioning (throughput and
delay). Hence, trade-off between reducing the energy
consumption and improving QoS provisioning is proved that
reduce the expenditure. The maximum average energy
consumption for ONSP on 0.027 node failure probability is
0.037 joule/packet as compared with other protocols that range
from 0.052 to 0.063 Joule/packet. The result demonstrates that
ONSP consumes almost half of energy as compared to
MMSPEED, SAR, and MGR due to node failure probability.
Figure 3.Average energy consumption VS node failure probability
C. End-to-end delay
End-to-end delay is another significant parameter for
investigating the QoS based routing protocols. The packet
end-to-end delay increases as time interval increases as
depicted in Figure 4. In this experiment, we use variable size
of packet arrival rate at the sender side. We measure an end-
to-end delay for both non-real time and real time data traffic.
Based on the results, we validate that ONSP outperforms other
participating routing protocols. The maximum end-to-end
delay at the end of simulation for ONSP is 0.047 second that is
almost 50% less than other routing protocols.
Figure 4. End-to-end delay at different time interval
D. Lifetime
The main goal is to improve the lifetime of WMSN that is
trade-off between energy consumption and network lifetime.
We use variable network topology size to determine the
lifetime of the network illustrated in Figure 5. In the
experiment, we have proved that the lifetime of the network is
improved using ONSP. In addition, we have also determined
that increase in network size also improves the lifetime of
network. The overall performance of ONSP is better than all
competing routing protocols at variable network size. ONSP
improves the network lifetime approximately by 37.5% which
is a much better outcome.
Figure 5. Lifetime of network at varying network topologies
In this paper, we have introduced the Optimized Node
Selection Process (ONSP) for improving the quality of service
provisioning based on multi-path routing for wireless
multimedia sensor networks. This approach is designed
particularly for real-time and non-real time traffic. Our
approach uses the multi-path paradigm based on optimized
and braided paths for improving the network life. This
approach uses the optimized node process model for
determining the improved node that helps route discovery. Our
ONSP uses the residual energy, and received signal strength
indicator to determine the next optimized node for the paths
building phase.
This paper also introduces the load-balancing algorithm that
helps balance the loads over all the paths in order to improve
the network lifetime and guarantee the QoS provisioning. To
demonstrate the strength of the proposed approach, we have
used ns2. Based on simulation result, we have studied and
evaluated the QoS metrics; end-to-end delay, energy
consumption, network lifetime and data delivery rate. The
results validate that our approach outperforms to MMSPEED,
SAR and MGR routing protocols. It is also validated that our
approach can be better choice for airport surveillance system
because of the extended network lifetime. In the future, we
will enhance our approach by incorporating other models to
obtain more outcomes for several wireless multimedia sensor
network applications.
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The Wireless Sensor Network (WSN) is a service provider for remote sensing, including wireless transmission and infrastructure, or for the central monitoring of network assets. Over the years, many researchers have tested the WSN for many tests, such as area Monitoring, Environmental Monitoring, Disaster Management, and Security Monitoring. This does not apply to suggestions for detection and monitoring frameworks. In the present investigation, a strategy is proposed secure directing protocol which offering a versatility. Choice of security, bunch training and high-level cassette results also compared with the LEACH Protocol Control Protocol as progressive network stability.
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In the recent time, the huge expansion in the application of the real-time applications for smartphones resulted in new difficulties when it is time to develop new protocols for mobile ad-hoc networks (MANETS). Most important within these difficulties is to activate real-time applications for mobile ad-hoc networks which includes quality of service (QoS) support, like bandwidth constraints and stability issues. Adding to this of course the reliability of the nodes as an important factor that has a direct effect on the network performance and data integrity.In this paper we will discussed the QoS main issue that affect several factors in the mobile ad hoc networks, which is the reliability. Because of the fact that the reliability and the availability of the nodes can be interacted made me use both the terms throughout the paper. we will suggest the efficient multi-path Quality of service routing (EMQR), (EMQR) as a QoS guarantee for the stability and reliability issues in the network. Next, we will discuss the availability issues as a QoS guarantee and for this we will suggest the best suitable protocol which is “an efficient warning energy aware Cluster head” WEAC as a solution to the availability issue.The two protocols will guarantee the availability and the reliability in MANETs as it is the new direction for routing algorithms design and it is a way to enhance the availability by controlling the Cluster head based on its power level as a metric for availability as in WEAC protocol and the reliability by increasing the reserved bandwidth for the route as mentioned in EMQR protocol.
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Many routing protocols have been proposed for wireless sensor networks. These routing protocols are almost based on energy efficiency. However, the recent advances in of complementary metal-oxide semiconductor (CMOS) camera and small microphones have led to the development of Wireless Multimedia sensor networks (WMSN) as a class of wireless sensor networks which pose additional challenges. The transmission of imaging and video data needs routing protocol with both energy efficiency and Quality of Service (QoS) characteristics in order to guarantee the efficient use of the sensor nodes and effective access to the collected data. Also, with integration of real time applications in the Wireless Senor Networks (WSNs), the use of QoS routing protocols is not only becoming a significant topic but is also gaining the attention of researches. In designing an efficient QoS routing protocol, the reliability and guarantee of end-to-end delay are critical events while conserving energy. Thus, considerable research has been proposed for designing energy efficient and robust QoS routing protocols. In this paper, we present a state of the art research work based on QoS routing protocols for Wireless Multimedia sensor networks (WMSN) that have already been proposed. This paper categorizes the QoS routing protocols into probabilistic and deterministic protocols. In addition, both categories are classified into soft and hard real time protocols by highlighting the QoS issues including limitations and features of each protocol. Furthermore, we have compared the performance of known routing protocols using network simulator-2 (NS2). This paper also focuses on the design challenges and future research directions as well as highlights the characteristics of each QoS routing protocol.
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Wireless multimedia sensor networks with sensing and processing abilities of multimedia data have recently emerged as one of the most important technologies for high quality monitoring. The routing scheme for multimedia data is an important research issue addressed in wireless multimedia sensor networks. In this paper, we propose a disjointed multipath routing scheme for real-time data transmission in wireless multimedia sensor networks. The proposed scheme uses a hybrid routing protocol based on Bluetooth and Zigbee in order to overcome the limitation of low bandwidth in conventional sensor networks. The proposed scheme also performs disjointed multipath routing based on competition to alleviate the delay of routing path setup. To show the superiority of our proposed scheme, we compare it with the existing scheme through performance evaluation. Our experimental results show that our proposed scheme reduces the end-to-end delay by about 30% and the routing path setup costs by about 22% over the existing scheme. Our scheme also increases data reception rates by about 690% over the existing scheme on average.
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Wireless sensor networks (WSNs) are required to provide different levels of Quality of Services (QoS) based on the type of applications. Providing QoS support in wireless sensor networks is an emerging area of research. Due to resource constraints like processing power, memory, bandwidth and power sources in sensor networks, QoS support in WSNs is a challenging task. In this paper, we discuss the QoS requirements in WSNs and present a survey of some of the QoS aware routing techniques in WSNs. We also explore the middleware approaches for QoS support in WSNs and finally, highlight some open issues and future direction of research for providing QoS in WSNs.
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The wide utilization of Wireless Sensor Networks (WSNs) is obstructed by the severely limited energy constraints of the individual sensor nodes. This is the reason why a large part of the research in WSNs focuses on the development of energy efficient routing protocols. In this paper, a new protocol called Equalized Cluster Head Election Routing Protocol (ECHERP), which pursues energy conservation through balanced clustering, is proposed. ECHERP models the network as a linear system and, using the Gaussian elimination algorithm, calculates the combinations of nodes that can be chosen as cluster heads in order to extend the network lifetime. The performance evaluation of ECHERP is carried out through simulation tests, which evince the effectiveness of this protocol in terms of network energy efficiency when compared against other well-known protocols.
Introduction to Network Simulator NS2 is a primer providing materials for NS2 beginners, whether students, professors, or researchers for understanding the architecture of Network Simulator 2 (NS2) and for incorporating simulation modules into NS2. The authors discuss the simulation architecture and the key components of NS2 including simulation-related objects, network objects, packet-related objects, and helper objects. The NS2 modules included within are nodes, links, SimpleLink objects, packets, agents, and applications. Further, the book covers three helper modules: timers, random number generators, and error models. Also included are chapters on summary of debugging, variable and packet tracing, result compilation, and examples for extending NS2. Two appendices provide the details of scripting language Tcl, OTcl and AWK, as well object oriented programming used extensively in NS2. © 2012 Springer Science+Business Media, LLC. All rights reserved.
In this paper, a new multi-objective approach for the routing problem in Wireless Multimedia Sensor Networks (WMSNs) is proposed. It takes into account Quality of Service (QoS) requirements such as delay and the Expected Transmission Count (ETX). Classical approximations optimize a single objective or QoS parameter, not taking into account the conflicting nature of these parameters which leads to sub-optimal solutions. The case studies applying the proposed approach show clear improvements on the QoS routing solutions. For example, in terms of delay, the approximate mean improvement ratios obtained for scenarios 1 and 2 were of 15 and 28 times, respectively.
With the increasing demand for real time applications in the Wireless Senor Network (WSN), real time critical events anticipate an efficient quality-of-service (QoS) based routing for data delivery from the network infrastructure. Designing such QoS based routing protocol to meet the reliability and delay guarantee of critical events while preserving the energy efficiency is a challenging task. Considerable research has been focused on developing robust energy efficient QoS based routing protocols. In this paper, we present the state of the research by summarizing the work on QoS based routing protocols that has already been published and by highlighting the QoS issues that are being addressed. The performance comparison of QoS based routing protocols such as SAR, MMSPEED, MCMP, MCBR, and EQSR has also been analyzed using ns-2 for various parameters.
Design and development of power-aware, scalable and performance-efficient routing protocols for wireless sensor networks (WSNs) is an active area of research. In this paper, we show that insect-colonies-based-intelligence – commonly referred to as Swarm Intelligence (SI) – serves as an ideal model for developing routing protocols for WSNs because they consist of minimalist, autonomous individuals that through local interactions self-organize to produce system-level behaviors that show life-long adaptivity to changes and perturbations in an external environment. In this paper, we propose bee-inspired BeeSensor protocol that is energy-aware, scalable and efficient. The important contribution of this work is a three phase protocol design strategy: (1) we first take inspiration from biological systems to develop a distributed, decentralized and simple routing protocol, (2) we formally model important performance metrics of our protocol to get an analytic insight into its behavior, and (3) we improve our protocol on the basis of our analysis in phase 2. We then evaluate its performance in a sensor network simulator. The results of our experiments demonstrate the utility of this three phase protocol engineering, which helped BeeSensor in achieving the best performance with the least communication and processing costs – two main sources of energy consumption in sensor networks – as compared to other SI based WSN routing protocols.