Conference PaperPDF Available

Efficient key distribution protocol for wireless sensor networks

Efficient Key Distribution Protocol for Wireless
Sensor Networks
Majid R Alshammari and Khaled M Elleithy
School of Computer Science and Engineering
University of Bridgeport
Bridgeport, Connecticut 06604
Abstract—Key distribution is a challenging issue for Wireless
Sensor Networks (WSNs) because sensor nodes are built from
resource-constrained devices that carry limited-power batteries.
Thus, a key distribution scheme for WSNs must be efficient -
at least in terms of energy consumption and storage. However,
most proposed key distribution schemes in the literature ignore
energy consumption and do not consider efficiency. Therefore, we
propose an efficient key distribution protocol that is designed to
suit resource-constrained devices such as WSNs. In this research,
we utilized OPNET Modeler to create and to model a wireless
sensor node and then developed a wireless sensor network. Our
sensor model not only calculate the energy consumption of a node
transceiver but also it computes the energy consumption that is
caused by wireless channel effects. Furthermore, we utilized an
automatic cryptographic protocol verifier, ProVerif, to verify the
security properties of the proposed protocol. The findings show
that the proposed protocol is secure and more efficient compared
to key distribution schemes in the literature.
Index Terms—efficient key distribution protocol, Security of
wireless sensors networks, Key distribution protocol for resource-
constrained wireless devices.
The Wireless Sensor Networks (WSNs) is a distributed and
interconnected collection of sensor nodes that link a spatial
space or an object to computing systems for the purpose of
monitoring, controlling, or targeting. The concept of WSNs
was developed by the U.S. military. Then academic institutions
began to improve upon this technology. Recent advances in
technology allow wireless sensor nodes to be cost-effective
and small in size. Today, WSNs enable Cyber-Physical Sys-
tem (CPS) applications [1–4], and they have become a core
technology of Internet of things (IoT) [5–8]. Thus, WSNs have
become rapidly involved in a variety of modern applications
such as in the military, health, agriculture, environment, home
and commercial automation, and transportation [9–19].
The security of these applications depends on securing the
exchanged packets among sensor nodes. However, wireless
sensor nodes are resource-constrained devices that require
efficient implementation. In the literature, there are a variety
of schemes that have been proposed to address key distribu-
tion. Some schemes applied asymmetric encryption without
a proper adjustment for these resource-constrained devices.
These type of schemes may be secure against some attacks,
but it is not efficient in terms of energy consumption. Some
other schemes employed symmetric encryption and relied on
energy-consuming techniques such as storing many keys in
each sensor node, engaging intermediary nodes, or exchanging
many packets for finding a shared key between two sensor
nodes. Additional schemes adapted quantum cryptography to
address the key distribution issue, although quantum cryptog-
raphy is not yet practical in resource-constrained devices.
Therefore, we proposed efficient key distribution protocol
for resource-constrained wireless devices. The motivation was
to design efficient key distribution protocol that is secure,
practical, and feasible to implement in resource-constrained
devices. The proposed protocol achieves security key distri-
bution attacks and consumes less energy compared to other
schemes in the literature.
The remaining paper is organized as follows: Section II
presents the related work. Section III describes in detail
the proposed protocol. Section IV describes the efficiency
analysis. Section V presents security formal verification for
the proposed protocol. Section VI presents the conclusion.
Key distribution schemes in WSNs have been
comprehensively examined in the literature [20–23]. However,
in this research, we present a general view of the existing key
distribution schemes in WSNs. We classify key distribution
schemes in WSNs into two domains: private key-based978-1-5386-4649-6/18/$31.00 c
2018 IEEE
and public key-based schemes. The private key-based
schemes include many types such as intermediaries-based
and probabilistic-based key schemes. The public key-based
can be categorized into integer factorization problem (IFP)
and discrete logarithm problem (DLP) [24, 25]. The following
table presents the definition of our evaluation metrics.
TABLE I: Evaluation metrics
Efficiency metric Definition
Energy consumption The amount of energy that is consumed
during the key distribution/key estab-
lishment process.
Key dependency When two sensor nodes cannot find
a common key, they search for a
third/intermediary node that shares a
common key with each one of the two-
sensor nodes.
Key connectivity The probability of two nodes sharing a
common key.
Storage overhead The memory required to store encryp-
tion keys or parameters that are re-
quired to produce encryption keys.
In [26] the authors proposed a private key-based scheme
called Peer Intermediaries for Key Establishment in Sensor
Networks (PIKE). PIKE is an intermediaries-based scheme,
and it represents a sensor network nby n.nmatrix. The
scheme employs some sensor nodes as trusted intermediaries
during the key distribution process. Each sensor has an ID
in the form of (x, y)based on its position in the matrix.
Moreover, each node is loaded with a pairwise secret key that
is shared only with each node in the two sets: (i, y)i
{1,2,3, ..., n1}and (x, j)i∈ {1,2,3, ..., n1}.
Keys are deployed such that in any pair of Aand B, there
exists at least one node Cthat shares a pairwise key with both
Aand B. However, this approach suffers from key dependency
because when two sensor nodes cannot find a common key
between them, they broadcast many packets to other nodes
searching for an intermediary node that shares a key with each
one of them. Also, the searching process for an intermediary
node consumes high energy. Moreover, this approach requires
each node to store 2n1keys.
Another private key-based scheme is the probabilistic-based
key distribution scheme. This kind of scheme depends on the
probability of two sensor nodes sharing a common key. In [27]
the authors proposed a probabilistic-based scheme that consists
of three phases: key pre-distribution, shared-key discovery, and
path-key establishment. In the key pre-distribution phase, a
large pool of keys p and their identifiers are generated. Then
randomly drawing keys k out of the pool p to constitute a key
ring for each sensor node based on the following formula:
Pkey = 1 ((Pk)!)2
where Pkey is the probability of two nodes sharing a
common key. Next, the key rings are loaded into each of the
sensors memory and the key identifiers of the key rings are
saved with sensor identifiers on a trusted controller node. The
i-th controller node is loaded with a shared key for each node.
In the shared-key discovery phase, two sensor nodes discover a
shared key by broadcasting a list of their key rings identifiers.
Also, the two sensor nodes could hide the key sharing patterns
by broadcasting a list, li ={α||Eki(α)||i= 1, ..., k}, for
every key on the key ring, where is a challenge and i is an
index. The ability to decrypt Eki(α)by the receiver will reveal
the challenge αand then establish a shared key with the sender.
In the path-key establishment phase, a path-key is assigned to
each pair of sensor nodes that do not share a key but are
connected to other sensor nodes at the end of the shared-
key discovery phase. However, this approach is also energy-
consuming because finding a common key between two sensor
nodes required the broadcasting of too many packets. Also, it
requires a large memory to store the key ring especially when
the probability of sensor nodes share a common key is close
to (one). As a result, key connectivity in this approach is not
In practice, the public key-based schemes in WSNs depend
on two major families. One is based on an integer factoriza-
tion problem (IFP) such as RSA cryptosystem. The other is
based on discrete logarithm problem (DLP) such as Diffie-
Hellman key exchange (DHKE) and Elliptic curve Diffie-
Hellman (ECDH). However, in context of resource-constrained
devices such as wireless sensor nodes, implementing IFP or
DLP without a proper adjustment is inefficient in terms of
energy consumption and storage overhead.
In [28] the author discussed using public infrastructure such
as RSA to improve the security of wireless sensor networks.
The study considered the topology of the WSN as a set of
sensor nodes that wirelessly connected and reported collected
data to the base station. This approach, requires large memory
as each node must store a number of keys that is equal to the
number of nodes.
In [29], the authors proposed a public key-based key
distribution scheme using ECDH. The scheme consists of two
phases - before deployment and after deployment phase. In
the first phase, all nodes are configured with the elliptic curve
(EC) parameters, and basepoint G. Then αnis generated to
calculate Pn=αnGfor all nnodes. After that, αnis stored
in each corresponding node and all Pnare stored in the sink
node. The sink in the second phase creates a new secret key
band calculates its public key Q=bG. Then it broadcasts
the public key to all nodes. Each node calculates the new
key kn=αnQ, whereas the sink calculates the new key
kn=bPn. The downside of this scheme is that each node
has to store all EC parameters such as the field that curve is
defined over, the αand bvalues that define the curve, and
the generator point G.
The proposed protocol comprises four phases:pre-
deployment phase,key distribution phase,Post-key distribution
phase, and key refreshment phase. The following shows steps
of our proposed protocol.
Pre-Deployment Phase:
{KP, KR} ←RSAgen
=AKsink and KR
Sink node := AKsink and Sensor node := AKnodes
Key Distribution Phase:
Sink node:
− {0,1}128 and timestamp T
CEAKsink (Ksession || T)
==CEAKsink (Ksession || T)
Sensor nodes:
== CEAKsink (Ksession || T)
PDAKnodesCEAKsink (Ksession || T)
fverif (T) = (accept, if T time threhsold
reject, if T > time threhsold
Post-Key Distribution Phase:
Sensor nodes:
dataD, and timestamp T
Ksession (dataD || T)
Ksession (dataD || T)
Sink node:
== CE
Ksession (dataD || T)
Ksession CE
Ksession (dataD || T)
fverif (T) = (accept, if T time threhsold
reject, if T > time threhsold
Key Refreshment Phase:
Sink node:
− {0,1}128 and timestamp T
CEAKsink (Knewsession || T)
==CEAKsink (Knewsession || T)
Sensor nodes:
== CEAKsink (Knewsession || T)
PDAKnodesCEAKsink (Knewsession || T)
fverif (T) = (accept, if T time threhsold
reject, if T > time threhsold
A. Pre-deployment Phase
In the pre-deployment phase, the protocol consists of three
off-line steps. Utilizing the RSA key generation algorithm to
generate a pair of asymmetric keys {KP, KR} ←RSAgen.
KPis defined as the sink node key, AKsink , and loaded into
the sink node, whereas KRis defined as the sensor nodes
key, AKnodes, and loaded into the sensor nodes.
B. Key Distribution Phase
After deploying the sensor nodes, the sink node gen-
erates a random session key, Ksession
− {0,1}128, and
a timestamp T, it then encrypts them using its asymmet-
ric key AKsink, and then it sends the cipher to the sen-
sor nodes send
==CEAKsink (Ksession || T). Since
each sensor node posesses the asymmetric key AKnodes,
that have already been loaded to its memory, a sensor
node can decrypt the cipher PDAKnodesC
EAKsink (Ksession || T), and verifies the timestamp T,
fverif (T) = (accept, if T time threhsold
rej ect, if T > time threhsold . If Tis less
than or equal to a predefined threshold, the sensor node accepts
the session key Ksession. Otherwise, the session key Ksession,
is discarded.
C. Post- Key Distribution Phase
After key distribution phase occurs, each sensor node
possesses the session key Ksession. When a sensor node
wants to send dataD to the sink node, it generates a
timestamp Tfor preventing any potential replay attack,
concatenates it with the dataD, and then encrypts
them by Ksession using a probabilistic encryption
algorithm , and sends the cipher to the sink node
Ksession (dataD || T).The sink node decrypts
the cipher PD
Ksession CE
Ksession (dataD || T),
verifies the timestamp T, and then accepts the dataD.
D. Key Refreshment Phase
In the key refreshment phase, the sink node generates
a new random session key, Knewsession
− {0,1}128, and
a timestamp T. Then encrypts them using its asymmetric
key AKsink and sends the cipher to the sensor nodes
==CEAKsink (Knewsession || T). Since each sensor
node already possesses the asymmetric key AKnodes, a
sensor node can decrypt the cipher, PDAKnodes C
EAKsink (Knewsession || T), and can verify the timestamp
T,fverif (T) = (accept, if T time threhsold
rej ect, if T > time threhsold . If the
Tis less than or equal to a predefined threshold, the sensor
node accepts the new session key Knewsession. Otherwise, the
new session key Knewsession is rejected.
In this section we examine the efficiency of our proposed
protocol compared to the following key distribution schemes
[26], [27], and [28] (we recall the efficiency evaluation
metrics that are given in Table I). In this analysis, we utilized
OPNET Modeler to design and to create a model for a
wireless sensor node, and then we used this model to develop
a network of one hundred (100) wireless sensor nodes as
shown in figure 2. The power parameters of our sensor node
model are based on Arduino UNO microcontroller [30] and
XBee transceiver S1 [31]. The findings analysis is based on
capturing the key distribution/establishment process between
two nodes.
Fig. 1: The sensor node model in the form of a WSN.
The following table shows the efficiency analysis for our
proposed protocol and the key distribution schemes. The Tx.F
represents the assumed number of packets the transmitter
needs to send for the key distribution/establishment process.
Tx.A.F, shows the actual number of packets the transmitter
sends during the modeling of the wireless channel effects.
Rx.F shows the number of packets the receiver receives
for the key distribution/establishment process. A.F shows
the number of additional packets that are required for the
key distribution/establishment process (assuming the first
intermediate node has a shared key). T.TRX shows the
total time that the transceiver takes to send and receive
the required packets. E.TRX&E.Tx.P shows the energy
consumed by the transceiver for sending and receiving the
required packets as well as the energy consumed by the
transmitter for output power. The next three rows show the
operations that are involved in key distribution/establishment
process. T.M.K shows the time that the microcontroller
takes to find a common key. T.M.E shows the time that the
microcontroller takes for encryption. T.M.D shows the time
that the microcontroller takes for decryption. E.M shows the
energy consumed by the microcontroller. T.E.C shows the
total energy consumption.
TABLE II: Efficiency analysis for the proposed protocol and the key distri-
bution scheme
Schemes Our
Protocol Scheme[26]aScheme[27] Scheme[28]
Tx.F 1 20 24 2
Tx.A.F 1 28 33 2
Rx.F 1 20 24 2
A.F NA 2+28 NA NA
T.TRX 8.19 ms 442.37 ms 233.47 ms 16.38 ms
E.TRX&E.Tx.P 1.41 mJ 75.68 mJ 39.79 mJ 2.81 mJ
T.M.K NA 10.08 ms 3.51 ms 189.08 ms
T.ME 982.00 ms tbNA 982.00 ms
T.M.D 1502.90 ms tbNA 1502.90 ms
E.M 2484.90 mJ 10.08 mJ 3.51 mJ 2573.98 mJ
T.E.C 37.19 mJ 75.82 mJ 39.84 mJ 39.88 mJ
aWith a probability of 0.99999 that two sensor nodes share a common key.
bTime cannot be calculated because the scheme used an encryption and decryption algorithm during the key
distribution/establishment process and it did not declare its type.
As shown in the above table, the total energy consumption
of our proposed protocol was the lowest compared to the
other key distribution schemes. Figure 2, visualizes the energy
consumption. In an ideal case, performing a key distribution
or key establishment by wireless sensor nodes requires the
sensor nodes to send and receive a specific number of packets.
However, a wireless channel can introduce many effects that
harm some of these packets and make them un-decodable by a
sensor node receiver. After modeling wireless channel effects,
our proposed protocol was only slightly affected compared to
the other key distribution schemes. Also, the storage overhead
of our protocol is the lowest compare to the other schemes
because each node is required to store just one key before the
deployment and one more key after the key distribution phase.
Fig. 2: Energy consumption of the key distribution schemes.
We utilized ProVerif, the automatic cryptographic protocol
verifier to prove the security of our proposed protocol against
key distribution attacks. ProVerif can prove reachability and
secrecy, correspondence assertions (Authentication), and
observational equivalences in a formal model.
We assume the adversary model is based on Dolev-Yao model
[32], where the adversary can eavesdrop, modify, replay, and
delete packets, but cannot capture the sensor nodes physically.
A. Reachability and Secrecy
ProVerif proves the reachability and secrecy properties by
investigating the reachability of a term xto an adversary
A. In the proposed protocol, ProVerif investigated whether
the sensor dataD is available to the adversary Aby
using: query attacker (dataD). When the result is
not attacker(dataD[]) is true, that means the dataD
is not derivable by the adversary. The following figure
shows the verification result of reachability and secrecy in
our protocol. The analysis proved that the sensor dataD
was secured and the adversary was unable to derive an attack .
Fig. 3: Verification of reachability and secrecy.
B. Correspondence Assertions/Authentication
ProVerif proves authentication by using a sequence of
events defined as correspondence assertions. We employed
a sequence of events in the proposed protocol for modeling
authentication. The following figure shows the verification
result of authentication in the proposed protocol. The analysis
confirmed that authentication is achieved in our proposed
C. Observational Equivalence
ProVerif can verify the observational equivalence between
processes. In our proposed protocol, we employed this feature
to check whether the adversary can distinguish between the
session key and a random copy of it. The following figure
shows the verification result of observational equivalence in
our proposed protocol. In process 26, we leaked the two copies
of the session key Ksession to an adversary. The analysis
showed that the adversary could not distinguish between the
two keys. As a result, during the key refreshment process, the
adversary cannot distinguish between the keys.
Many key distribution schemes have been proposed for
WSNs. However, proposing a key distribution scheme without
considering the number of packets that are involved in the
key distribution process is not practical for such resource-
constrained devices. Since, each time the distance double
Fig. 4: Verification of authentication.
Fig. 5: Verification of observational equivalence.
between two wireless sensor nodes, four times the amount of
power is required. Also, ignoring the wireless channel effects
is not realistic because every wireless channel has effects
that contribute to energy consumption. Therefore, when we
designed our protocol, we considered all of those issues, and
we proposed an efficient key distribution protocol. We utilized
OPNET Modeler for developing and creating a model for a
wireless sensor node. We also used an automatic cryptographic
protocol verifier, ProVerif, to prove the soundness and the
security of our proposed protocol. The findings show that
our proposed protocol is secure and consumes less energy
compared to other key distribution schemes. We argue that
our proposed protocol has important applications especially in
those that required security and less energy consumption.
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The increase of the digitalization taking place in various industrial domains is leading developers towards the design and implementation of more and more complex networked control systems (NCS) supported by Wireless Sensor Networks (WSN). This naturally raises new challenges for the current WSN technology, namely in what concerns improved guarantees of technical aspects such as real-time communications together with safe and secure transmissions. Notably, in what concerns security aspects, several cryptographic protocols have been proposed. Since the design of these protocols is usually error-prone, security breaches can still be exposed and maliciously exploited unless they are rigorously analyzed and verified. In this paper we formally verify, using ProVerif, three cryptographic protocols used in WSN, regarding the security properties of secrecy and authenticity. The security analysis performed in this paper is more robust than the ones performed in related work. Our contributions involve analyzing protocols that were modeled considering an unbounded number of participants and actions, and also the use of a hierarchical system to classify the authenticity results. Our verification shows that the three analyzed protocols guarantee secrecy, but can only provide authenticity in specific scenarios.
... We utilized the existing cryptographic primitives to design a protocol that is simple, practical and feasible to implement on resource-constrained devices such as wireless sensor nodes. This work extends our preliminary work introduced in [17] by improving its efficiency and security. The contributions of our work can be summarized as follows. ...
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Modern wireless sensor networks have adopted the IEEE 802.15.4 standard. This standard defines the first two layers, the physical and medium access control layers; determines the radio wave used for communication; and defines the 128-bit advanced encryption standard (AES-128) for encrypting and validating the transmitted data. However, the standard does not specify how to manage, store, or distribute the encryption keys. Many solutions have been proposed to address this problem, but the majority are impractical in resource-constrained devices such as wireless sensor nodes or cause degradation of other metrics. Therefore, we propose an efficient and secure key distribution protocol that is simple, practical, and feasible to implement on resource-constrained wireless sensor nodes. We conduct simulations and hardware implementations to analyze our work and compare it to existing solutions based on different metrics such as energy consumption, storage overhead, key connectivity, replay attack, man-in-the-middle attack, and resiliency to node capture attack. Our findings show that the proposed protocol is secure and more efficient than other solutions.
... These nodes message each other directly or via other nodes and collect data for further monitoring and controlling of parameters in physical world scenarios, for example, Biological system and IT framework. Based on environment area of the physical scenario being monitored, WSNs can be spread in some thousands of nodes for measuring temperature, light, and heat or other physical quantities [1]. Among such huge number of nodes, some are Gateway nodes (Sink) which can communicate user directly or via fixed wired networks as shown in Fig. 01. ...
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Wireless Sensor Networks-most prevalent application-based networks today, are networks using cost-efficient sensing, computing and communication in physical world scenarios like Disaster Management, Environmental observation, Armed forces surveillance to Industrial process control monitoring, Patient remote vitals monitor via bio-instrumentation and emergency situations. Sensor Network literature review suggests that Designing PHY & MAC functionalities for nodes with low duty cycle and optimal transmit power in dense network is major research issue and requires utmost researchers’ attention to find out ways for solving it efficiently. This Paper first highlights about WSN protocols stack and design issues of each layer. An important MAC protocols classification based on four channel access methods namely Contention, Scheduling, Polling and Hybrid is presented. All methods are classified on the basis of Type, Energy Efficiency, synchronization, Adaptiveness and Scaling to give easy reference for applications. Further, performance of Channel access based BMAC protocol with varying transmission power is examined in terms of energy consumption, data packet transmission & reception, data forwarding and preamble transmission & reception. In Last, Present research trends focusing on combined enhancement of different layers are discussed. The ultimate target in MAC protocols research is towards realization of less delay, improved QoS, reduced overheads and efficient power consumption mechanisms
Conference Paper
The Internet of Things is imposing an evolution of the capabilities of wireless sensor networks. The new IP-based 6LoWPAN standard for low power sensor networks allows an almost seamless connection of local sensor networks to the Internet. On the other hand, the connection to the Internet also opens doors for unauthorized nodes to become part of the local network. The most important challenge in preventing this, is the implementation of a key management architecture, keeping in mind that the sensor nodes are constrained in power consumption and data storage capacity. This paper builds on a previously proposed symmetric key management scheme for 6LoWPAN networks presented by Smeets et [1]. The original scheme is based on wired bootstrapping for the enrollment of new nodes, while the paper at hand proposes a wireless method. We analyze the original wired scheme and propose an improved wireless scheme, elaborating on the practical implementation on Zolertia Z1 nodes running Contiki-OS. We show that it is possible to provide end-to-end security using wireless bootstrapping within the constraints of the tiny nodes at hand.
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Owing to their low deployment costs, wireless sensor networks (WSN) may act as a key enabling technology for a variety of spatially distributed cyber-physical system (CPS) applications, ranging from intelligent traffic control to smart grids. However, besides providing tremendous benefits in terms of deployment costs, they also open up new possibilities for malicious attackers, who aim to cause financial losses or physical damage. Since perfectly securing these spatially distributed systems is either impossible or financially unattainable, we need to design them to be resilient to attacks: even if some parts of the system are compromised or unavailable due to the actions of an attacker, the system as a whole must continue to operate with minimal losses. In a CPS, control decisions affecting the physical process depend on the observed data from the sensor network. Any malicious activity in the sensor network can therefore severely impact the physical process, and consequently the overall CPS operations. These factors necessitate a deeper probe into the domain of resilient WSN for CPS. In this chapter, we provide an overview of various dimensions in this field, including objectives of WSN in CPS, attack scenarios and vulnerabilities, the notion of attack resilience in WSN for CPS, and solution approaches toward attaining resilience. We also highlight major challenges, recent developments, and future directions in this area.
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We have tried to develop the guidance system for farmers to cultivate using various phenological indices. As the sensing part of this system, we deployed a new Wireless Sensor Network (WSN). This system uses the 920 MHz radio wave based on the Wireless Smart Utility Network that enables long-range wireless communication. In addition, the data acquired by the WSN were standardized for the advanced web service interoperability. By using these standardized data, we can create a web service that offers various kinds of phenological indices as secondary information to the farmers in the field. We have also established the field management system using thermal image, fluorescent and X-ray fluorescent methods, which enable the nondestructive, chemical-free, simple, and rapid measurement of fruits or trees. We can get the information about the transpiration of plants through a thermal image. The fluorescence sensor gives us information, such as nitrate balance index (NBI), that shows the nitrate balance inside the leaf, chlorophyll content, flavonol content and anthocyanin content. These methods allow one to quickly check the health of trees and find ways to improve the tree vigor of weak ones. Furthermore, the fluorescent x-ray sensor has the possibility to quantify the loss of minerals necessary for fruit growth.
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Water is essential for human survival. Although approximately 71% of the world is covered in water, only 2.5% of this is fresh water; hence, fresh water is a valuable resource that must be carefully monitored and maintained. In developing countries, 80% of people are without access to potable water. Cholera is still reported in more than 50 countries. In Africa, 75% of the drinking water comes from underground sources, which makes water monitoring an issue of key concern, aswatermonitoring can be used to trackwater quality changes over time, identify existing or emerging problems, and design effective intervention programs to remedy water pollution. It is important to have detailed knowledge of potable water quality to enable proper treatment and also prevent contamination. In this article, we review methods for water quality monitoring (WQM) from traditional manual methods to more technologically advanced methods employing wireless sensor networks (WSNs) for in situ WQM. In particular, we highlight recent developments in the sensor devices, data acquisition procedures, communication and network architectures, and power management schemes to maintain a long-lived operational WQM system. Finally, we discuss open issues that need to be addressed to further advance automatic WQM using WSNs.
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In the recent past we have observed many technological revolutions, including the transition from the analog world into its digital counterpart and from centralized wired solutions to distributed and pervasive wireless systems. In particular, the advent of lowcost and low-power transceivers, together with the development of compact-size and open standard stacks, have made possible. Wireless Sensor Networks (WSNs), largely adopted for both home/office and industrial monitoring applications. The nowadays ambitious goal is to sample, collect and analyze every piece of information around us, in order to improve production efficiency and ensure optimal resource consumption. The “Internet of Things” (IoT), i.e. the capability of connecting every possible device to the World Wide Web, is the practical answer to this request. The very large amount of information that is consequently generated could be profitably handled using “cloud” services, i.e. flexible and powerful hardware/software frameworks capable to deliver computing as a service. The aim of this work is to resume pros and cons of well-accepted WSN technologies, suggesting their possible extension towards already available cloud services.
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The construction of a battlefield surveillance system is very important for monitoring the attack of enemy aircrafts and missiles, which integrates various sensors and mobile devices. Then, multiple battlefield surveillance systems can be connected together to form a battlefield surveillance network. The mobile nodes can be deployed in a certain region to monitor enemy aircrafts and missiles. Thus, some important issues have to be solved efficiently, including the cooperation across the administrative domains of a cloud network, the direction-of-arrival (DOA), and a polarization estimation algorithm for a mobile wireless sensor network (MWSN). In this paper, the architecture of a battlefield surveillance system is constructed based on mobile cloud computing and 5G link. The root multiple signal classification (Root-MUSIC)-like algorithm is proposed for estimating the 1-D DOA and a polarization parameter with a uniform linear array. The Root-MUSIC algorithm is replaced by the Fourier transform, the former algorithm that can be extended to an arbitrary topology structure of a MWSN. Then, the proposed algorithm is extended to the 2-D DOA and a polarization estimation in further. Based on the deployment of different MWSNs, the estimation results of DOA and polarization parameters are fused in order to improve the estimation performance. Finally, the parameter information (DOA and polarization parameter) of enemy aircrafts and missiles can be achieved. The computer simulation verifies the effectiveness of the proposed algorithm. The proposed algorithm ensures the parameter estimation accuracy with a low computational complexity.
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Sensor nodes deployed outdoors for field surveillance are subject to environmental detriments. In this article, we propose a heterogeneous sensor network composed of sensor nodes with different environmental survivability to make it robust to environmental damage and keep it at a reasonable cost. We, for the first time, study the scheduling problem in such heterogeneous sensor networks for critical location surveillance applications. Our goal is to monitor all the critical points for as long as possible under different environmental conditions. We identify the underlying problem, theoretically prove its NP-complete nature, and propose a novel adaptive greedy scheduling algorithm to solve the problem. The algorithm incorporates several heuristics to schedule the activity of both regular and robust sensors to monitor all the critical points, while at the same time minimizing and balancing the network energy consumption. Simulation results show that our algorithm efficiently solves the problem and outperforms other alternatives.
Cryptography, in particular public-key cryptography, has emerged in the last 20 years as an important discipline that is not only the subject of an enormous amount of research, but provides the foundation for information security in many applications. Standards are emerging to meet the demands for cryptographic protection in most areas of data communications. Public-key cryptographic techniques are now in widespread use, especially in the financial services industry, in the public sector, and by individuals for their personal privacy, such as in electronic mail. This Handbook will serve as a valuable reference for the novice as well as for the expert who needs a wider scope of coverage within the area of cryptography. It is a necessary and timely guide for professionals who practice the art of cryptography. The Handbook of Applied Cryptography provides a treatment that is multifunctional: It serves as an introduction to the more practical aspects of both conventional and public-key cryptography It is a valuable source of the latest techniques and algorithms for the serious practitioner It provides an integrated treatment of the field, while still presenting each major topic as a self-contained unit It provides a mathematical treatment to accompany practical discussions It contains enough abstraction to be a valuable reference for theoreticians while containing enough detail to actually allow implementation of the algorithms discussed Now in its third printing, this is the definitive cryptography reference that the novice as well as experienced developers, designers, researchers, engineers, computer scientists, and mathematicians alike will use.
Cattle health monitoring on the feedlot is a crucial but nontrivial task. The conventional way of monitoring relies heavily on the cowboy's visual surveillance, which makes the animal monitoring quality highly subjective and correlated with the obviousness of the observed traits. In order to achieve early detection of each individual animal's illness, in this paper, a wireless sensor network system is developed to monitor the animal's feeding and drinking behaviors. A directional antenna is used to allow one router to monitor multiple animals simultaneously, and an energy efficient mesh routing strategy is proposed to aggregate the monitoring data. The performance of the proposed system has been evaluated through numerical analysis and simulations. The contributions of this paper lie in the novelty and feasibility of using directional antenna and wireless sensor network technologies for feedlot animal health monitoring.
Security is one of the important and challenging aspects in wireless sensor network owing to their wireless nature combined with limited memory, energy, and computation. We can classify security issue of the wireless sensor network into five broad categories as cryptography techniques, key management, routing protocols, intrusion detection and data aggregation. Since the key management forms an underlying factor for efficient routing protocol and cryptography in wireless sensor network, we focus on key management issue. This paper outlines the constraints, security requirements and attacks, which are related to the key management and routing. Further novel classification of key distribution schemes have been proposed. The proposed novel classification and comparison distinctly brings to the fore gaps in the existing solutions of research which can be put to use by researchers in the area to identify current challenges for designing efficient key distribution scheme. The paper concludes with possible future research directions on key distribution in WSNs.
Cryptographic primitives are fundamental building blocks for designing security protocols to achieve confidentiality, authentication, integrity and non-repudiation. It is not too much to say that the selection and integration of appropriate cryptographic primitives into the security protocols determines the largest part of the efficiency and energy consumption of the wireless sensor network (WSN). There are a number of surveys on security issues on WSNs, which, however, did not focus on public-key cryptographic primitives in WSNs. In this survey, we provide a deeper understanding of public-key cryptographic primitives in WSNs including identity-based cryptography and discuss their main directions and some open research issues that can be further pursued. We investigate state-of-the-art software implementation results of public-key cryptographic primitives in terms of execution time, energy consumption and resource occupation on constrained wireless devices choosing popular IEEE 802.15.4-compliant WSN hardware platforms, used in real-life deployments. This survey provides invaluable insights on public-key cryptographic primitives on WSN platforms, and solutions to find tradeoffs between cost, performance and security for designing security protocols in WSNs.