Conference PaperPDF Available

Secure Data Aggregation Model (SDAM) in Wireless Sensor Networks

Authors:

Abstract and Figures

Nowadays, Wireless Sensor Networks (WSN’s) are becoming more and more promising and applicable to a variety of fields: military, environmental, medical, wild life habitat, and transportation as well wearable devices, target-tracking. WSN are expected to be a main player in the Internet of Things technology. Power management is very important factor in considering WSN’s and it has been demonstrated that communication cost is higher than the computational as nodes consume most of the energy in communication. Added to the fact that sensors could be closely deployed and report the same reading, the data aggregation concept was introduced to resolve those issues and for the sake of better performance at a reduced cost. Nonetheless, sensing devices are prone to failure due to several aspects such as node failure or low batteries as well as being compromised. In this paper, we are introducing a novel method Secure Data Aggregation Model (SDAM) aiming at assuring a secure aggregate communication at a low cost (in terms of resources). Our simulation results showed that implementing SDAM resulted into an increase in the energy efficiency as well as a considerable reduction in cross-layering overhead.
Content may be subject to copyright.
Secure Data Aggregation Model (SDAM) in Wireless
Sensor Networks
Mohamed Ben Haj Frej
Computer Science and Engineering
University of Bridgeport
Bridgeport, CT 06604
mbenhaj@bridgeport.edu
Khaled M. Elleithy
Computer Science and Engineering
University of Bridgeport
Bridgeport, CT 06604
elleithy@bridgeport.edu
Abstract Nowadays, Wireless Sensor Networks (WSN’s) are
becoming more and more promising and applicable to a variety
of fields: military, environmental, medical, wild life habitat, and
transportation as well wearable devices, target-tracking. WSN
are expected to be a main player in the Internet of Things
technology. Power management is very important factor in
considering WSN’s and it has been demonstrated that
communication cost is higher than the computational as nodes
consume most of the energy in communication. Added to the fact
that sensors could be closely deployed and report the same
reading, the data aggregation concept was introduced to resolve
those issues and for the sake of better performance at a reduced
cost. Nonetheless, sensing devices are prone to failure due to
several aspects such as node failure or low batteries as well as
being compromised. In this paper, we are introducing a novel
method Secure Data Aggregation Model (SDAM) aiming at
assuring a secure aggregate communication at a low cost (in
terms of resources). Our simulation results showed that
implementing SDAM resulted into an increase in the energy
efficiency as well as a considerable reduction in cross-layering
overhead.
Keywords availability, base station (BS), cluster head (CH),
secure Data Aggregation model (SDAM), sensor node (SN),
wireless sensor network (WSN).
I. INTRODUCTION
Following to the digital modernization, there has been a
tremendous growth in Wireless Sensor Networks (WSN) field;
with many applications such as in the military, environmental
monitoring, agriculture production and medical field. WSN
consist on a considerable number of motes (sensor nodes)
placed in a known topology or on ad hoc basis in the desired
location in order to collect information [1]. Each mote is a low
end device assuring processing, sensing and communication
abilities. Below are the motes’ main characteristics:
Limited battery life with no ability to recharge it in
certain situations or because it is not worth it as they
are meant to be disposable
Low processing capabilities, as they should be
designed based on required resources only, in order
to keep them at a low price.
Limited data storage capacity
Since the sensors are generally deployed close to each other,
they could be reporting the same values, there have been a
need to summarize the data then forward it to upper nodes for
the sake of energy consumption optimization.
Wireless sensor networks data aggregation consists on
reducing the flow of communication and optimizing the
energy consumption.
In data aggregation, designated motes will collect and
process the data packets from their surrounding nodes then
forward them to the base station making a huge impact on the
traffic as well as on the communication time. Thus it does its
job in increasing the efficiency. However it comes with its
inconvenience as it affects other constraints such as security
and delay. The aggregated sensors are vulnerable to intruders
which can inject false data in WSN.
Secure communication requires that the encryption will take
place before transmitting the data. While for aggregation
protocols, it would be preferable to process the data and
forward it to the next hop prior to encryption. So it is a
challenging task to combine both taking in consideration that
we could not apply the computing encryption protocols to
wireless sensor networks due to their limitation in resources.
Although there could be some compromise while adopting
aggregation and encryption, both are deemed essential for a
stable and accurately performing network [2].
Amongst every operation that might be performed by a
sensor node, gathering and transmitting data are the ones that
expend considerably higher vitality when contrasted with
other operations [3]. Data accumulation serves to lessen
correspondence by joining Data parcels. WSNs are as a rule
conveyed in threatening and unattended situations, so the need
for secure Data conglomeration is further emphasized.
II. RELATED WORK
There are numerous Secure Data Aggregation plans
proposed in writing. The base station (BS) takes wise choices
in view of the Data that is assembled amid Data collection
stage. Identifying anomaly values in the data frames is a vital
errand in choice making for different applications like
observing, issue determination and interruption location in
WSNs. Exceptions are characterized as "estimations that
altogether go a miss from the ordinary example of detected
data". The plausible wellsprings of anomalies in WSN
incorporate clamor and blunders, occasions, and vindictive
assaults [3]. Off base data will antagonistically influence the
estimation of the collected data which is utilized by the BS for
deciding. For instance, if the sensor nodes are conveyed in
backwoods to sense temperature esteem, then an exception in
temperature quality can show a flame and the BS will need to
call the flame station [4]. Henceforth, anomaly identification
is essential for distinguishing crisis in the system. Along these
lines, the exception recognition instrument ought to be
consolidated with data conglomeration plans [5]. In this paper,
they have proposed a secure data conglomeration conspire that
recognizes exception values. Their plan distinguishes
exception values and in the meantime gives data realness and
data uprightness. They have utilized multivariate information
investigation method to handle anomaly in associated
variables. It permits the BS to identify the false data infusion
amid the data conglomeration and to recognize the
compromised node.
A broad study of anomaly identification plans has been
conducted. The authors have considered a bunch based
topology with hubs sorted as: base station (BS), cluster head
(CH) and basic sensor nodes (SN). They have utilized
multivariate information investigation method to handle
anomaly in associated variables. They have utilized the PCA
model for flaw discovery [3]. The CH recognizes the arriving
information as ordinary or unusual. They have utilized factual
methods to discover sensor information to enhance the
exception discovery precision. They have utilized a
conglomeration tree as a system model. They have planned
anomaly recognition plan taking into account commit
disseminate system. The authors have presented a model that
executes inquiries, distinguishes and sends clients an
arrangement of readings that are expected to be exceptions [4].
They have utilized to distinguish a hub as anomaly hub. The
authors have proposed a SDA plan which utilizes a bunch
based system model. The group head runs the peculiarity
recognition calculation and sends the IDs and check of the
anomaly values to the guardian group head. Their
methodology is taking into account hearty vital segment
investigation [4]. To uncover the abnormalities the authors
have utilized the relationship among the detected information.
III. CLUSTERING DATA AGGREGATION
A. Wireless Sensor networks Limitations
Since their elaboration, there have been continuous efforts to
design efficient and simple WSN’s. Hence, there are some
constraints to be considered while designing a wireless sensor
network with a simple topology [6].
Energy considerations: nodes can do limited
computations as they have less memory and are limited in
power supply. Nodes completely depend on their batteries
for data transmissions as well as all the other tasks.
Reliability and Fault Tolerance: the ability to keep
transmitting the information even when some of the nodes
become unavailable by taking another path.
Scalability: when the number of nodes increases, it
becomes complex to manage and organize the nodes
which could affect the overall communication.
Data Delivery Models: There are different models such as
event driven, query driven, and hybrid. These modes
control the flow of data from sensors to sink nodes. The
type of model used is based upon the applications.
Quality of Service: there is a set of protocols to measure
the energy utilized, time taken for routing the information
and to prioritize the data transfer from sensor to sink
node.
Network Dynamics: it is not always the case that all the
components in the WSN such as nodes, sink nodes,
sensors are static. There is a need to also support
mobility.
B. Clustering process:
Clustering process steps consists on the following:
Get ready information: institutionalizing property and
characterizing measurement.
Choosing property: picking viable property from essential
properties and putting them away into a vector.
Collecting property: changing the picked properties to
new properties.
Clustering: picking some sort of separation capacity that
has the fitting properties as the estimation of closeness
and after that grouping.
Evaluating on the after effect of grouping: performing
assessment of three sorts: outside legitimacy assessment,
inside legitimacy assessment and relativity test
assessment [7].
C. Data aggregation process:
As previously mentioned, wireless sensor networks consist
on tiny sensors deployed in large number in the field. Each
node is a low end device that integrates data processing,
wireless communication and sensing abilities. Setting a base
node and data aggregation has to be done towards other nodes
in the network hierarchy and leads into saving energy. In case
of Multi hops network the data aggregation will consist on
getting the min, max and sum and sending it to the upper node
[8]. The data aggregation process totally depends on
mathematical models. It will decide the following:
How to locate a bunching inclination in the
information.
How to locate a superior approach to seek the bunch
and in addition the gathering hubs.
How to discover a test system in order to demonstrate
that the segments are right and secure [9].
IV. SECURE WIRELESS SENSOR NETWORKS
A wireless sensor network is considered to be secured if it
offers all the following services:
Authentication and Data Confidentiality: nodes can
carry confidential information of an organization. In
order to prevent any hacking attacks, nodes should
communicate within secured channels after the
authentication. Before giving access, WSN should
confirm the identity of all its nodes. The best way to
protect the data is to send it in an encrypted form by
using encoding and decoding techniques.
Data Integrity: providing exact data sent by the
sensors without any alterations.
Data Freshness: Data transferred should be fresh and
new. Nodes get busy unnecessarily if same message
is sent repeatedly, this could reduce the battery life of
the node and reduce the efficiency of the system.
Availability: A node is completely dependent on it
battery life. A node should be able to send a message
if its battery life is going down and let the other
nodes take another path to communicate.
Self-Organization: all the dynamic nodes should
organize themselves to form a multi path
communication system making the fault tolerance of
the wireless network more efficient.
Secure Localization: Location of all the sensors in a
wireless sensor network should be traced accurately.
All these features make an ideal wireless sensor network.
But in reality, it is a bit difficult to design a WSN which could
strictly follows all the protocols. Formally a WSN should
authenticate all the nodes, detect and prevent any malicious
attacks, and have a data recovery solution in case one or more
nodes have been compromised
V. PROPOSED SOLUTION SECURE DATA
AGGREGATION MODEL
In this paper we are proposing a new secure data
aggregation consisting of a sub layer between the MAC and
the network layers. SDAM’s primary objective is to ensure
secure communication by eliminating the dilemna of having
the data encrypted before aggregation while the aggregation
protocols perform better on unencrypted data.
A. SDAM Components:
The SDAM consists of three modules: Operations sub-layer,
Data aggregation module and Communications sub-layer
(please refer to figure 1).
Operations Sub-layer responsible on checking based
on the MAC address of the device to determine if it is
coming from a legitimate or illegitimate node. If the
node is deemed illegitimate, the packets will be
dropped.
Data Aggregation Module: responsible for removing
the repetition by applying aggregation algorithms as
well as deciding on the size of the packets.
The Communications sub-layer is responsible to
decide on the number of packets that are needed to
aggregate and then forward them to the MAC layer.
Both incoming and outgoing traffic are sent by the
MAC layer and then forwarded to the SDAM
module.
Below is the diagram of the proposed solution:
Network Layer
MAC Layer
Operations
Sub-layer
Communications
Sub-layer
Data Aggregation
Figure 1: Secure Data Aggregation Model
VI. SIMULATION RESULTS
This section shows the experimental performance of our
proposed SDAM. The performance of the proposed model
was measured by using Network Simulator-2 (NS2) on
Ubuntu 14.2 operating system. The measurements are done on
a network size of 700 m x 700 m. We have assumed that there
are two types of nodes in the network:
Homogenous
Heterogeneous.
The homogenous nodes are distributed in the network that
sense and relay the data. The heterogeneous nodes are head
nodes in each cluster with more power and bandwidth. Each
homogeneous node initially uses 4 joules energy, whereas
each heterogeneous node uses 16 joules energy.
We have implemented our proposed model on each cluster
head node and assumed that the topology is static. Our goal in
this simulation is to apply the secure aggregation. The base
station is located at point (0, 850). The size of the packets is
128 bytes. The remaining parameters are given in Table 1.
PARAMETERS
VALUES
Size of network
1600 × 1600 square
meters
Number of nodes
180
Number of aggregation generated
1050
Energy of homogenous node
4 joules
Energy of heterogeneous node
16 Joules
Packet size
128 bytes
Data Rate
280
kilobytes/second
Sensing Range of node
35 meters
Simulation time
450 Seconds
Average Simulation Run
05
Base station location
(0,850)
Transmitter Power
13.2 mW
Receiver Power
11.4 mW
Table 1: Simulation Parameters
Based on the initial simulation results, we will be focusing
on evaluating the following metrics:
Energy Efficiency
Cross-Layering Overhead
A. Energy Consumption
Our generated scenario consists of 180 nodes that are
randomly distributed. 10% of the nodes are malicious
constitute a hurdle on the way of aggregation. Our proposed
SDAM is implemented on each cluster head node. Once, the
nodes collect the data from the sensing event that are
forwarded to the cluster head node, which is responsible to
apply aggregation and balance the communication between
MAC sub-layer and network layer.
Based on the results on Figure 2. Based on the results, we
observed that 1.34 joules energy is consumed when using
proposed model whereas, during the simulation period, while
1.53 joules energy is consumed without using SDAM.
Applying SDAM made the network more efficient in terms
of energy consumption and more secure as it detects the
malicious nodes and avoids data loss. Without SDAM, the
packets are captured by the malicious nodes because the
sensor nodes do not have the capability to identify the possible
malicious activities so that the captured packets are lost and
retransmitted.
As a result, the additional energy is consumed for
retransmission the packets. We have proved that our proposed
model saved 0.19 joules energy that is approximately 9.8%
saving in the entire network and that our proposed model
could be used to extend the network lifetime.
Figure2: Energy consumption using SDAM and without SDAM approaches
during the entire simulation time
B. Cross-Layering Overhead
Using the same simulation setup we have measured the
overhead cross-layering for 1800 aggregations (Figure 3-a)
and for 3600 aggregations (Figure 3-b).
Figure 3a: Cross-layering Overhead With and Without SDAM
Figure 3b: Cross-layering Overhead With and Without SDAM- Doubling the
number of Aggregations
Our simulation results shows the following: the overhead
with SDAM is: 0.7 milliseconds for 1800 aggregations and 0.8
for 3600 aggregations Without the SDAM, it is 1.64
milliseconds for 1.92 milliseconds for 3600 aggregations.
The simulation results shows 43 % improvement for 1600
aggregations and around the same improvement (42 %) with
3600 aggregations.
These results indicate that our proposed SDAM solution
shows considerable improvement in the cross-layering
overhead along with its energy efficiency optimization.
VII. CONLUSION
Considering the limited resources of the wireless sensor
networks and the impact of the use of encryption on
aggregated data; In this paper we have proposed a simplistic
and efficient method. The approach consisted 0n adding
modules able to authenticate non-legitimate nodes in the
network as well as facilitate the communications, in an energy
efficient way as shown by our simulation results. Our
simulation results also showed an around 40% improvement in
the cross-layering overhead.
ACKNOWLEDGMENT
Special thanks to Dr. Abdul Razaque, from the Cleveland
State University, who has been with a help especially in the
simulation section.
REFERENCES
[1] Razaque, Abdul, and Khaled Elleithy. "Modular Energy-Efficient and
Robust Paradigms for a Disaster-Recovery Process over Wireless Sensor
Networks." Sensors 15, no. 7 (2015): 16162-16195.
[2] Karthikeyan, B., Velumani, M., Kumar, R., and Inabathini, S.R.:
‘Analysis of data aggregation in wireless sensor network’, in Editor
(Ed.)^(Eds.): ‘Book Analysis of data aggregation in wireless sensor
network’ (IEEE, 2015, edn.), pp. 1435-1439.
[3] Rezvani, M., Ignjatovic, A., Bertino, E., and Jha, S.: ‘Secure data
aggregation technique for wireless sensor networks in the presence of
collusion attacks’, Dependable and Secure Computing, IEEE
Transactions on, 2015, 12, (1), pp. 98-110.
[4] Bharuka, K., and Jinwala, D.C.: ‘A secure data aggregation protocol for
outlier detection in wireless sensor networks using aggregate Message
Authentication Code’, in Editor (Ed.)^(Eds.): ‘Book A secure data
aggregation protocol for outlier detection in wireless sensor networks
using aggregate Message Authentication Code’ (2014, edn.), pp. 1-6.
[5] Sahana, S., and Amutha, R.: ‘Data aggregation in wireless sensor
networks’, in Editor (Ed.)^(Eds.): ‘Book Data aggregation in wireless
sensor networks’ (2014, edn.), pp. 1-6.
[6] Jose, J., Manoj Kumar, S., and Jose, J.: ‘Energy efficient recoverable
concealed data aggregation in wireless sensor networks’, in Editor
(Ed.)^(Eds.): ‘Book Energy efficient recoverable concealed data
aggregation in wireless sensor networks’ (2013, edn.), pp. 322-329.
[7] Durrani, N.M., Kafi, N., Shamsi, J., Haider, W., and Abbsi, A.M.:
‘Secure multi-hop routing protocols in Wireless Sensor Networks:
Requirements, challenges and solutions’, in Editor (Ed.)^(Eds.): Book
Secure multi-hop routing protocols in Wireless Sensor Networks:
Requirements, challenges and solutions’ (IEEE, 2013, edn.), pp. 41-48.
[8] Mingxin, Y., Jingsha, H., and Xuguang, S.: ‘Research on Secure Data
Aggregation in Wireless Sensor Networks Based on Clustering Method’,
in Editor (Ed.)^(Eds.): ‘Book Research on Secure Data Aggregation in
Wireless Sensor Networks Based on Clustering Method’ (2011, edn.),
pp. 1-4.
[9] Ben Othman, S., Trad, A., Youssef, H., and Alzaid, H.: ‘Secure data
aggregation in wireless sensor networks’, in Editor (Ed.)^(Eds.): ‘Book
Secure data aggregation in wireless sensor networks’ (IEEE, 2013,
edn.), pp. 55-58.
[10] CAO, X.-m., LI, W.-l., and YANG, G.: ‘Research on Secure Data
Aggregation in Wireless Sensor Network’, Computer Technology and
Development, 2012, 11, pp. 060.
... Data aggregation is a technique in which redundant information is removed before processing by the server. Data aggregation is treated as a regression problem and is solved by SVM [92] and reinforcement [93] respectively. Table 5 shows the summary of the literature survey of ML techniques for WSN-IoT. ...
... Pandemic medical treatment (e.g., COVID-19 or Corona Virus) Data Aggrigation SVM [92] and Reinforcement [93] Data Aggregation is treated as a Regression task in the ML context. ...
Article
Full-text available
Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications.
... ML have the adaptability by learning from the previous experiences which does not need to program explicitly. ML mainly focus on the program that can retrieve data which is valuable for their learning [5]. Recently advanced ML approach has increased in WSN. ...
Conference Paper
Full-text available
Abstract:To form a WSN properly is very important with the desired throughput and lifetime. Properly forming of WSN with desired output is the key of successful operation of WSN. To main factor (throughput and lifetime) prediction based on various inputs can help the management to design the network. This paper introduced a new approach to predict class based on throughput and lifetime of LEACH based clustering protocol. The various supervised machine learning algorithm is used to predict classification and compare with them. The simulation results show that logistic regression can predict with 80% accuracy.
... In this paper, data mule are classified and multi hop fashion to sink node. The intermediate nodes are calculated and simulate it [6]. We used OMNET++ simulator for simulating WSN and evaluate the performance. ...
Article
Wireless sensor network is useful for many applications such as monitoring environment, assets, source location and protection. The data mule is mobile data collector and it is used to collect sensed data from sensor node. The data can be moved from one node to another node. The main problem is reliable and energy efficient. Wireless sensor network has reliable and trustful data aggregation services. In this paper, we propose data privacy protection method for data mule in WSN environment. Existing methods are more computation difficulties and communication over heads. In our method, we use encrypted data and share to other nodes without stealing the data. This method is called as Random slicing method. The each data mule values are collected from sensor node and forward to next layer. The data reaches to next hop from base station. The sink service enabled for reliable communication. The experiments are conducted to evaluated the performance and compare the results.
... IoT gateways provide connectivity and usually include remote control and monitoring applications. Edge intelligence enables a so-called model updating and aggregation process [17], where IoT gateways build local machine learning models respectively, share local model parameters dynamically in real time and build a global model collectively. The updated parameters of the global model are then feed to the IoT gateways respectively. ...
Article
Full-text available
A detection model of Internet of Things encrypted traffic based on edge intelligence is proposed in the paper, which can reduce the communication times of distributed Internet of Things gateways in the process of edge intelligence as well as the encrypted traffic detection model establishment time, in order to solve the problems that it is difficult to carry out efficient classification and accurate identification of the encrypted traffic of Internet of Things. In this paper, four new classification and identification methods for encrypted traffic are put forward, namely time-sequence behavior analysis, dynamic behavior analysis, key behavior analysis and two-round filtering analysis. The experimental results show that when the sample size is 1600, the encrypted traffic detection model establishment time is less than 100 seconds, and the accuracy of all the four new traffic classification methods is more than 92% and the recall rates of them are more than 83%.
... As there are different topologies so there are many ways of the data processing which can be done to reduce the amount of data to be transferred in the network. The data fusion algorithm FTDA [8] is based on the time prediction model which provides the scalability of the prediction model and the ability to proactively identify data redundancy. Data aggregation [11] is applied to collected data from the sensor nodes. ...
Article
Wireless Sensor network comprises many sensor nodes which are used to sense and send data to the nearer node. Likewise, the data will reach the destination or base station. Sensor network faces resource constraints & they are susceptible to environmental conditions which decrease the lifespan of the node. Our objective is to enhance the battery life of the sensor node based on data analyses technique, which is based on a comparative method. This technique will reduce the number of data transfer between sensor nodes, which in turn will enhance battery lifetime.
... Machine learning is a branch of artificial intelligence (AI) which provides the capacity to automatically learn and refine from experience without being specifically programmed. Machine learning concentrates on the creation of computer programs which can access data and use to learn for themselves [10]. It has an important role in several applications of WSN due to the following reasons: ...
Article
Full-text available
Wireless sensor network (WSN) is one of the most promising technologies for some real-time applications because of its size, cost-effective and easily deployable nature. Data clustering and data representation are used for statistical analysis with more efficient. Machine learning algorithms such as linear regression, SVM models, regularization methods, reinforcement learning, and neural networks significantly reduce energy consumption. It provides a comparative analysis of the performance of different methods to help the designers for designing appropriate machine learning based solutions for clustering and data aggregation applications. In this paper we analyzed different algorithms for data representation and clustering in sensor networks. Improved performance can be observed in unsupervised learning comparing to individual component analysis.
... Unlike the common forms of aggregation in [3] and [4] which aggregate the data directly, we use a scheme that aggregates the signatures instead of the data itself. This process involves signing of the data using a hash function and a secret key and then aggregates the signature before sending it to the medical server. ...
Article
Full-text available
Remote healthcare monitoring systems have been successful and are present at the vanguard of the technological revolution of the 21st Century. These systems make use of wireless body area network (WBAN) as one of their major components that is responsible for recording the medical parameters of a patient's health and transmit them to the server where they can be diagnosed. These networks, sometimes also make use of local processing units (LPUs) such as your mobile devices for transmission of the recorded data. Unfortunately, this wireless transmission of data increases the number of risks involved with the sensitive recorded data. This can lead to the violation of user's privacy rights and also causes a decline in the system's integrity and authenticity. This paper presents a novel security and signature aggregation scheme implemented using the Arduino Uno microcontroller, the MAX30100 SPO2 and Pulse Oximetry sensor, and the AD8232 sensor. The Privacy Preservation and Signature Aggregation Scheme (PPSA) for Medical Data that we present in this paper helps provide security against cyber attacks, thus strengthening the integrity and authenticity of the medical data. The Privacy Preservation part of the overall scheme has been implemented using the Blowfish and the Elgamal Encryption schemes, that helps the system to maintain the security of the system, whereas the Signature aggregation scheme involves the use of a co-Decisional Group Diffie-Hellman Signature scheme that helps the system to verify the authenticity of the medical data that is being transmitted within the system. The PPSA scheme, when implemented, was found to give correct results and provide the system with reliable security.
Article
Full-text available
Robust paradigms are a necessity, particularly for emerging wireless sensor network (WSN) applications. The lack of robust and efficient paradigms causes a reduction in the quality of service (QoS) provisioning and additional energy consumption. In this paper, we introduce modular energy-efficient and robust paradigms that involve two archetypes: 1) the operational medium access control (O-MAC) hybrid protocol and 2) the pheromone termite (PT) model. The O-MAC protocol controls overhearing and congestion and increases the throughput, reduces the latency and extends the network lifetime. O-MAC uses an optimized data frame format that reduces the channel access time and provides faster data delivery over the medium. Furthermore, O-MAC uses a novel randomization function that avoids channel collisions. The PT model provides robust routing for single and multiple links and includes two new significant features: 1) determining the packet generation rate to avoid congestion and 2) pheromone sensitivity to determine the link capacity prior to sending the packets on each link. The state-of-the-art research in this work is based on improving both the QoS and energy efficiency. To determine the strength of O-MAC with the PT model, we have generated and simulated a disaster recovery scenario using a network simulator (ns-3.10) that monitors the activities of disaster recovery staff, hospital staff and disaster victims brought into the hospital. Moreover, the proposed paradigm can be used for general purpose applications. Finally, the QoS metrics of the O-MAC and PT paradigms are evaluated and compared with other known hybrid protocols involving the MAC and routing features. The simulation results indicate that O-MAC with PT produced better outcomes.
Conference Paper
Full-text available
Wireless Sensor Networks (WSNs) have been a subject of extensive research and have undergone explosive growth in the last few years. WSNs utilize collaborative measures such as data gathering, aggregation, processing, and management of sensing activities for enhanced performance. In order to communicate with the sink node, node having low power may have to traverse multi-hops. This requires neighbors' nodes to be used as relays. However, if the relay nodes are compromised or malicious, they may leak confidential information to unauthorized nodes in the WSN. Moreover, in many WSN applications, the deployment of sensor nodes is carried out in an ad-hoc fashion without careful examination. In such networks it is desirable to ensure the source to sink privacy and maximize the lifetime of the network, by finding secure energy-efficient route discovery and forwarding mechanisms. Careful management is also necessary, as processing required for secure routing is distributed over multiple nodes. An important consideration in this regard is energy-aware secure routing, which is significant in ensuring smooth operation of WSNs. As, these networks deal in sensitive data and are vulnerable to attack, it is important to make them secure against various types of threats. However, resource constraints could make the design, deployment and management of large WSNs a challenging proposition. The purpose of this paper is to highlight routing based security threats, provide a detailed assessment of existing solutions and present a Trust-based Energy Efficient Secure Routing Protocol (TEESR). The paper also highlights future research directions in of secure routing in multi-hop WSNs.
Article
Public Key Cryptography was measured too expensive for WSN but it all changed due to the developments in software and hardware prototypes. This paper present and analyze the secure hierarchical data aggregation algorithm which uses an effective Public key cryptography to attain end to end security. Many data aggregation systems which are followed before uses either hop by hop decryption or uses symmetric key cryptography for end to end security but it is not energy efficient. This secure hierarchical data aggregation algorithm does not necessitate additional phase for data integrity verification and also it eludes additional transmissions and computational overhead on the sensor nodes to reduce the amount of energy used up by the network. This Paper measures the execution time and energy consumption of various cryptographic functions of the secure data aggregation algorithm on TelosB sensor network platform, programmed in nesC language and also analyses the performance of the algorithm in the Contiki Os simulator Cooja.
Article
Data aggregation is the most commonly used approach for extending the lifetime of the wireless sensor networks (WSNs). WSNs are exposed to events, errors and malicious activities which can cause unreliable and improper readings sent to the base station, often called as outlier values. These outlier values can indicate an emergency, for example, a rise in temperature value can indicate a fire. Hence, if this outlier value is not handled appropriately, then it can cause serious consequences. The data aggregation schemes do not take such outlier values into consideration and aggregates them with the normal values. Thus, we need to incorporate the outlier detection with the Secure Data Aggregation (SDA) scheme. The adversary can corrupt these outlier values, so the integrity of the outlier values needs to be taken care of. Integrity is classically achieved using a Message Authentication Code (MAC) in many to one communication. Transmission channel capacity of WSN is often small, so MAC represents a significant overhead. This fact introduced scope to find methods to compute the aggregate MAC (AMAC). We have proposed a novel SDA protocol with outlier detection mechanism that uses AMAC.
Article
Recent advancements in wireless communication and electronics have led to the development of low-cost wireless sensor networks (WSNs). A wireless sensor network consists of a large deployment of sensor nodes across a geographical area. When radio applications are considered, the sensor nodes sense the data and broadcast the sensed data to the destination or access point. In most applications, the sensor nodes are powered using batteries which are not rechargeable. In remote applications, it is even impossible to replace the batteries. Hence it is important to extend the network lifetime by minimizing the energy consumption of sensor nodes. It is essential to develop energy-efficient strategies to minimize the usage of battery power. Cooperative Multiple-Input-Multiple-Output (CMIMO) is a technique which is adopted in cluster-based WSN to bring in cooperation among the sensor nodes in a particular cluster. This technique exploits the multiple antennas present and cooperatively transmits the data to access point, thereby reducing the transmission energy. Data Aggregation is a technique which is combined with CMEVIO to reduce the energy cost further by reducing the amount of data in transit. It is believed that closely spaced sensor nodes sense data that are spatially correlated. Thus redundancy exists in the data, which can be removed by data aggregation. The amount of data transmitted to the access point is thus reduced and the transmission cost is also reduced.
Conference Paper
In-network data aggregation is an effective method to reduce the amount of data transmitted and therefore saves energy consumption in sensor networks. However, an adversary may compromise some sensor nodes, and use them to forge false values as the aggregation result. Previous secure data aggregation schemes have tackled this problem from different angles. The goal of those algorithms is to ensure that the Base Station (BS) does not accept any forged aggregation results. Based on our survey of existing research efforts for ensuring secure data aggregation, a novel approach that uses homomorphic encryption and Message Authentication Codes (MAC) to achieve confidentiality, authentication and integrity for secure data aggregation in wireless sensor networks is proposed. Our experiments show that our proposed secure aggregation method significantly reduces computation and communication overhead.
Article
Due to limited computational power and energy resources, aggregation of data from multiple sensor nodes done at the aggregating node is usually accomplished by simple methods such as averaging. However such aggregation is known to be highly vulnerable to node compromising attacks. Since WSN are usually unattended and without tamper resistant hardware, they are highly susceptible to such attacks. Thus, ascertaining trustworthiness of data and reputation of sensor nodes is crucial for WSN. As the performance of very low power processors dramatically improves, future aggregator nodes will be capable of performing more sophisticated data aggregation algorithms, thus making WSN less vulnerable. Iterative filtering algorithms hold great promise for such a purpose. Such algorithms simultaneously aggregate data from multiple sources and provide trust assessment of these sources, usually in a form of corresponding weight factors assigned to data provided by each source. In this paper we demonstrate that several existing iterative filtering algorithms, while significantly more robust against collusion attacks than the simple averaging methods, are nevertheless susceptive to a novel sophisticated collusion attack we introduce. To address this security issue, we propose an improvement for iterative filtering techniques by providing an initial approximation for such algorithms which makes them not only collusion robust, but also more accurate and faster converging.
Conference Paper
Concealed data aggregation (CDA) is important in wireless sensor networks, because it provides an energy efficient secure communication by allowing in network data aggregation on encrypted data. Privacy homomorphism (PH) based algorithms are the basis of CDA. However, it supports only a limited number of aggregation functions. Recoverability of individual sensor readings from the concealed data aggregation result at the BS overcomes the limitation of PH based algorithms on aggregation function. Thus, it allows authentication and integrity checking. But this technique reduces the node energy due to the computation overhead of encryption, signature operation as well as the transfer of both. So a mechanism that saves the energy of sensor nodes is required. The proposed technique overcomes this by transmitting the difference data rather than raw data from sensor node to cluster head. This differential data transfer achieves more energy and bandwidth efficiency than the existing recoverable concealed data aggregation scheme. The proposed differential data based recoverable data aggregation scheme increases the network lifetime by avoiding the redundant data transfer from each sensor node. Thus, it reduces the transmission overhead in the secure communication.
Article
Continuous aggregation is usually required in many sensor applications to obtain the temporal variation information of aggregates. However, in a hostile environment, the adversary could fabricate false temporal variation patterns of the aggregates by manipulating a series of aggregation results through compromised nodes. Existing secure aggregation schemes conduct one individual verification for each aggregation result, which could incur great accumulative communication cost and negative impact on transmission scheduling for continuous aggregation. In this paper, we identify distinct design issues for protecting continuous in-network aggregation and propose a novel scheme to detect false temporal variation patterns. Compared with the existing schemes, our scheme greatly reduces the verification cost by checking only a small part of aggregation results to verify the correctness of the temporal variation patterns in a time window. A sampling-based approach is used to check the aggregation results, which enables our scheme independent of any particular in-network aggregation protocols as opposed to existing schemes. We also propose a series of security mechanisms to protect the sampling process. Both theoretical analysis and simulations show the effectiveness and efficiency of our scheme.
Article
We discuss the technology detail of data aggregation in wireless sensor networks (WSNs) in this paper, which consists of three parts. First, we apply the classical K-average clustering algorithm to the phase of cluster division which is the first step of the aggregate tree. Second, the cluster head node and the cluster group nodes are constructed based on the entropy formula H(Sg|x)