Smart Security Implementation for Wireless Sensor Network Nodes

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Journal of Wireless Sensor Networks

ISSN: 2001-6417

Smart Security Implementation for Wireless Sensor Network


Celestine Iwendi


, Alastair Allen


and Kennedy Offor

2,* 1. Communications & Optical Engineering Research Group, University of Aberdeen, Fraser

Noble. Building, AB24 3UE Aberdeen, Scotland; E-Mail: and

2. Department of Electrical/Electronic Engineering, Anambra State University, Nigeria E–mail

* Author to whom correspondence should be addressed; E-Mail: Tel.: +2347088997382.

Received: 15 June 2013 / Accepted: 03 July 2013 / Published: 08 July 2013


In the territory of concurrent systems such as wireless sensor networks (WSN), the computational nodes being used in wireless sensor networks faces challenges with security applications. Many different security protocols have been proposed that allow some form of security enhancement but not implemented. This article investigates and implements a number of smart security techniques appropriate for WSN nodes with various trade-off such as power consumption and scalability. We provide a brief survey of the major approaches to security prerogative and methods that could reduce if not eliminate algorithmic complexity and denial of service attacks to sensor nodes.


Blom; AES; Pairwise key; WSN; preallocation; pre-distribution; Contiki; Cooja; uIP; smart performance

1.0 Introduction

Concurrent systems such as Wireless Sensor Networks (WSN) are becoming very useful in various day-to-day communication systems [1]. However, with the surge in the usage; WSN are still hampered by limited communication range, low memory capacity and limited energy. These constraints create a very big challenge for current security relevance.


This article investigates new security implementation for WSN, including various trade-off such as implementation complexity, power dissipation [2], security flexibility and scalability. This smart security implementation is designed to have efficient and flexible key distribution system secured enough to prevent denial-of-service attacks while conserving energy [3]. A reassessment of Blom’s key generation was implemented [4]. Our goal was to provide a secured pre-distribution and distribution of keys by every single node in WSN with a piece of Advanced encrypted (AES) secured data that would be generating a pairwise key with every other node in the network, thereby the resultant pairwise key will now transmit to every other node in the network. This system was implemented in Contiki operating system in place of TinyOS because of the advantage of multithreading. We can summarise the contribution of this research as follows: An intelligent key allocation and Key Pre-distribution that secures the Base station and the sensor nodes before deployment. An intelligent routine mechanism that set-up the stage for encryption in the network, with trade-off in power consumption: a unique combination of advanced encryption standard (AES) with key management technique and alternative security mechanism with uIP-WSN configuration.

2 A Survey of Security Techniques – Literature Review

Security encompasses the characteristics of authentication, integrity, privacy, non-repudiation, and anti-play back [6]. The greater the dependency of information sent or received in the network, the greater the security risk. Various security issues that have received consideration in ad hoc networks [7, 8] are not applicable to WSN due to architectural disparity between these two types of networks [5]. Also, quite a number of other security schemes are already being proposed. If the cluster formation design by [9] currently being investigated is found to be effective in saving energy, there is need to look at the security between the individual nodes, neighbouring nodes and the base station. 2.1 Denial-of-Service (DoS) Threats to WSN

Many WSN deployments are security sensitive and attacks against them may lead to damage to health and safety of people. Denial of service creates conditions for hardware failures, resource exhaustion, bugs, malicious attacks and environmental conditions that could reduce the functionality or totally eliminating a networks ability to perform as expected [10]. In this section, we have specified in a tabular format (Table 1) and later described the DoS vulnerabilities to the networks as studied by many proposed authors in the physical layer, media access control (MAC) layer, network layer, transport layer and application layer.


Table 1. Different Protocol layers: Attacks and Defences [11]

Protocol Layer Attacks Defences

Physical Jamming [11, and 12] Detect and sleep

Route around jammed regions Node destruction [13] Hide or camouflage nodes.

Tamper-proof packaging MAC Layer Denial of sleep [10, 11, and


Authentication and anti-replay protection.

Detect and sleep method Broadcast attack protection

Interrogation [10, 11, and 13] Authentication and anti-replay protection

Network Spoofing, replaying or altering clustering messages [10, and 13]

Authentication and replay protection. Secure cluster formation

Hello Floods [10, 13, and 16] Pairwise authentication Geographical routing Homing [13] Header encryption

Dummy packets

Sybil [10, and 17] Radio resource testing, key validation, position authentication

Wormhole [15] Location based routing protocols Transport Synchronise flood [10, and


Synchronised cookies Desynchronised attack [11,

and 13]

Packet authentication Application Overwhelming Sensors [11] Sensor tuning

Data aggregation

Path-based DoS [18] Authentication and anti-replay protection

Re-programming attack [11, 19 and 20]

Authentication and anti-replay protection.

Authentication streams

3 Enhanced Security Technique for Wireless Sensor Networks

This section explains the proposed smart security technique for Wireless Sensor Network nodes. The generation of the network parameters using, Blom’s proposed scheme for pre-distribution that tends to allow any of the sensors in WSN to find one common pairwise key as long as a network contains more than λ- sensors, the network is then considered to be perfectly secured. Our scheme also solves the issues arising from the distribution of keys with a more smart method for securing the data in the network called the Enhanced Security technique. This method involves the integration of advanced encryption standard (AES) to the key management that allows the network to have extra security in the transmission and reception of data. This chapter also analyses the matrices compromised in pairwise key generation as well as detailed description of each matrices. Each of the units is designed for both performance and confidentiality view point to allow the network to achieve scalability.


3.1 Key Generation Technique

The Blom’s pairwise key pre-distribution idea was proposed to be used in WSS base station to construct (λ+1)×Nmatrix G over finite fieldGF q( ). Although, Blom’s scheme does not propose how G matrix should be generated, the only requirement is that its columns must be linearly independent in order to achieve the λ-secure property. In our implementation, we decided to use the Vandermonde [21, 22, and 23] matrix since it is easy to implement and meets the requirement. A random symmetric (λ+1) x (λ+1) matrix D over finite field GF q( )should also be constructed. The matrix D is considered

to be private information and none of the nodes in the WSS is supposed to know the D matrix while G can be known. In order to calculate the final K matrix from which we can extract the pairwise keys, the base station should first multiply the D and G matrix, then transpose and multiply the result with the G matrix. This section therefore explains how the various parameters are generated.

3.1.1 Generation of G matrix

Matrix G is a (λ+1)×N matrix over a finite field GF q( ) where N is number of nodes in the network, generated by the base station in the pre-allocation phase. Blom’s pairwise Key-Distribution Scheme does not propose how G matrix should be generated; the only requirement is that its columns must be linearly independent in order to achieve the λ-secure property. In the matrix generation implementation; Vandermonde matrix was used because it is not complex like the other methods and meets the requirement.

( )

( )



( )

( )



2 3 2 2 2 2 3 2 2 3 1 1 1 1 N N N s s s s s s s G s s s s s λ λ λ λ         =           (1)

Let q be the smallest prime number that is larger than length of the pairwise key, s be the primitive element of a finite field GF q( ) and Nq . Then, if ( , 2,

s s 2,....,

s z)

s ( , 2,... ,....,2 N)

s s s s are

distinct, the columns of GF q( )are linearly independent. In the implementation there is no need to generate the entire G matrix all at once. In fact, as more nodes connect, the G matrix has to be extended. Therefore, the base station keeps track only of the matrix seed n

s where n is the number of currently connected nodes and generates column GC for the node as follows:





2 1 n n c n S S G S λ         =         (2)


3.1.2 Generation of D matrix

Along with G matrix the base station also generates random symmetric (λ+1) (× λ+1)D matrix over a finite field GF q( )but unlike G matrix, the D matrix is kept secret and must not be disclosed to any sensor node or adversary in the wireless sensor network at any time.

(3) The D matrix must be generated all at once and it has to be stored in the base station the entire lifespan of the network.

3.1.3 Generation of A matrix

A is N×(λ+1) matrix that is the result of computation ( )T

A= DG where ( )T

DG is transpose of (DG). Each row of a matrix is disclosed the one sensor in the wireless sensor network in order to allow the node to generate its pairwise key with other sensors.

1 ,1 1 , 2 1 , ( 1 ) 1 ,1 1 , 2 1 , 2 ,1 2 , 2 2 , ( 1 ) 2 ,1 2 , 2 2 , ( 1 ) ,1 ( 1 ) , 2 ( 1 ), ( 1 ) ( 1 ),1 ( 1 ) , 2 ( 1 ) , T N N N D D D G G G D D D G G G A D D D G G G λ λ λ λ λ λ λ λ λ + + + + + + + + +             =             (4)

In practice, since the base station does not preserve the entire G matrix, a row of A matrix Ar is generated from the column of G matrix Gc at a time when a sensor is connecting to the network as follows: ( 1) ( 1) ( 1) 1, 2, ( 1), 1 1 1 ( ), ( ),... ( ) r k k k k k k k k k A D G D G D G λ λ λ λ + + + + = = =   = ∗ ∗ ∗

 (5)

is then disclosed to the node where it is used for computation of pairwise keys. 3.1.4 Generation of K matrix

K matrix is the result of computation K = AG.

1,1 1,2 1,( 1) 1,1 1,2 1,( 1) 2,1 2,2 2,( 1) 2,1 2,2 2,( 1) ,1 ,2 ,( 1) ( 1),1 ( 1),2 ( 1), N N N N




















λ λ λ λ λ λ λ λ + + + + + + + +










Since D is a symmetric matrix we can see that:

( )T T T T ( )T

K = AG= DG G=G D G=G DG= AG (7)

K is therefore a symmetric matrix. This means that where is an element of K on i-th row and j-th column. Kj i, is used as a pairwise key between nodes i and j .

1 ,1 1 , 2 1 , ( 1 ) 2 ,1 2 , 2 2 , ( 1 ) ( 1 ) ,1 ( 1 ), 2 ( 1 ) , ( 1 ) D D D D D D D D D D λ λ λ λ λ λ + + + + + +       =      


1,1 1, 2 1, 1,1 2 ,1 ,1 2 ,1 2 , 2 2 , 1, 2 2 , 2 , 2 ,1 , 2 , 1, 2 , , N N N N N N N N N N N N K K K K K K K K K K K K K K K K K K          =                   (8)

In practice when node i wants to communicate with node j, it sends the seed i

s . Node j is then able to generate the column Gc of matrix G and using the row Ar of matrix A it received from the base station compute pairwise keyKi j, as follows:

( 1) , , , 1 ( ) i j k c r k k K G A λ + = =

∗ (9)

Because matrix A is symmetric, when node i receives from the node seed j

s and is able to generate the exact same pairwise key as node .

3.2 Key Allocation and Pre-Distribution of Sensor Nodes

One of the biggest issues in WSN is the ability of the sensor nodes to have a secure communication. In order to achieve a complete security in the whole network, it is essential to look at few assumptions that were taking into consideration: a) The security in the base station is reliable. b) The base station has unlimited power supply and high computational ability to support the network. c) That existing node and incoming node must maintain a unique master key. d) That every node has the ability to send data to the base station once it is in the communication range. e) That every node has the ability to function as a cluster head.

The type of key agreement that is currently been used is the pre-distribution scheme, where key information is allocated to all sensor nodes before deployment. This method becomes the approved method to solving the key agreement issues as explained by [3]. This is the first step of the proposal for security concern and schemes in maintaining energy efficient data gathering. Blom’s scheme is therefore the initiator to the ideas considered in this article because it has a means of generating keys and also the ability to secure the channel [4]. In addition, the research was reconstructed as the apparent vulnerability aspect of the Blom’s scheme. This key exchange protocol makes use of a very trusted sensor ID pre-allocated in each sensor. The seed will then generate a matrix over a predetermined field.

3.2.1 Pre-Distribution Technique: Base Station Effect

Easy deployment of security key algorithm: The article implements the deployment of the generated k pairwise key from the Base station. When base station receives a unicast request, it generates key and sends it to the node. When node receives the key, it stops to unicast to the base station and start to unicast to its entire neighbours. The first thing that happens is that it will request a unicast seed. When the node receives a seed request: it unicast its seed to the sender. When sender receives the seed, it says something like "Sending message to node x.y using pairwise key k". When the node receives a message it says something like "Received message from x.y, using pairwise key k". This system is an efficient security key algorithm method for pre-distribution.


During implementation, it was discovered that the scheme will not be effective with the original idea due to the following reasons: We would have to store information for every single node. We would have to know the exact number nodes in the network. When adding a new node we would have to update information on every single node. Therefore instead of storing nth row of the K matrix we should store nth of the A = (DG)T matrix. That gives us (λ+1) columns on every single node. When

two nodes want to communicate, they will first exchange their seeds N

S where S a primitive element of GF q( )and n is the number of node in the network, therefore the K generated becomes:

( 1 ) . , , 1 ( ) n m n k k m k K A G λ+ = =


That gives us sufficient scalability so we can add new nodes without breaking down the running network. The base station keeps a counter of number of nodes in the network and when new node n wants to join the network, the base station increments the counter, generates a column of G for the node n

( 1, 2,..., 16)A A A (11) Therefore, the base station keeps track only of the matrix seed n

s where n is the number of currently connecting node and generates column Gc for the node. The base station also sends the seed n

s and the n th: row of A to the node n. The node n is now ready for distribution.

3.3 Key Distribution

Key distribution is an aspect of key pre-distribution that involves distribution of keys onto nodes before they are deployed. The sensor nodes therefore build up the network with the help of their secret k-keys after deployment. During the key distribution phase, pairwise keys are generated, placed in sensor nodes and each nodes searches the area in its sensing range to find another node to communicate with. This section describes steps employed to actualise the enhanced security technique. 3.3.1 Mesh Distribution

We proposed and implemented an intelligent Mesh pre-allocation routine mechanism. The code implements pairwise key pre-allocation, routing, and when communication is initialized, first the sender sends it’s pairwise key seed to the receiver. The receiver responds with its pairwise key seed and then the sender sends the message. The seeds are used for initiating encryption using AES. The mesh module sends packets using multi-hop routing to a specified receiver in the network using the Rime communication stack. This second phase in ensuring the routing is shorter in transmission so as to reduce the energy consumption and also the risk of attack from an intruder. We did channel the security concern to the multi hop routing with each node maintaining a routing table to select the next hop. The attributes of the table are node_id, distance, residual_ energy, hop_support and rank. For each node_id there is a record in the table. Node_id is unique for every node. Distance represents the distance from the neighbour cluster-head containing node_id. Residual_energy indicates how much energy the neighbouring cluster-head has. A cluster-head may relay more than one neighbouring cluster-heads’ data. So hop_support acts like a counter that informs about how many cluster-heads are being served by this cluster-head. The rank contains an integer value that represents how far the neighbour node is from BS. At the outset of network setup, every node has to get its rank. The nodes use predefined transmission range represented as r to calculate their ranks. Nodes in a particular cluster communicate with their CH directly. Nodes periodically inform CH about their residual energy levels


so that node with highest residual energy could be selected as next CH. The strength of this energy proposal is that it should minimize hop count and thus minimize energy consumption as applicable to security protocol. It also makes the routine dynamic, thereby giving hope of a security protocol in mobile wireless sensor network nodes. Therefore, to allow communication within the wireless sensor network, we needed to implement a routing mechanism that would: a) Allow node to communicate with base station. b) Allow node to communicate with other nodes. c) Allow node to communicate even if they are not neighbours. d) Allow node to find the shortest path to the destination. e) Be easy to implement. f) Be energy efficient

We decided to use our modified mesh routine mechanism from Contiki OS standard library, because it provided all the features stated above.

The smart mesh routine mechanism sends packets using multi-hop routine to a specified receiver somewhere in the network and uses three channels, one for multi-hop forwarding and two for the route discovery. As part of route discovery, it floods the entire network with route discovery request packets until the packet reaches its destination. When the packet reaches its destination, the destination node floods the network with route discovery response packet until it reaches the original route discovery request packet sender then knows that the shortest path to the receiver goes through the neighbour it received the response from first. It then chooses the neighbour as the next hop and using the multi-hop routing sends the data to the receiver.

In this scheme as shown in Figure 1, node with ID 1 wants to communicate with mode with ID 6, so it floods the network with route discovery request packet and receives multiple responses, but chooses the first and therefore the fastest one.

Figure 1. Enhanced Routine Scheme

3.4 Sensor Node Placement and Localization: Scalability Effect

This article proposes and implements a mechanism whereby new nodes can be added without breaking down the network, giving sufficient scalability. The method involves sending a request to the Base station. When base station receives a unicast request, it generates key and sends it to the node. When node receives the key, it stops to unicast to the base station and start to unicast to all its neighbours. That gives us sufficient scalability so we can add new nodes without breaking down the running network. The base station keeps a counter of number of nodes in the network and when new node n wants to join the network, the base station increments the counter, generates a column of G for the node n. The base station also helps with the scalability by only keeping track of the matrix seed Sn where n is the number of currently connecting node and generates column Gc for the node. The base station then sends the seed n


4 Results and Discussion

This section detailed the experimentation of the proposed implementation for enhanced security technique for Wireless Sensor Network nodes in Rime and IP scenario. The Enhanced pre-distribution scheme allows any of the sensors in WSN to communicate to one another using uIP common pairwise key as long as the network contains more than λ- sensors, the network is then considered to be perfectly secured. Our scheme also solves the issues arising from the node congestion due to data distribution of keys with a more smart method for securing the data in the network called the Alternatives. This method involves the integration of uIP stack to the key management that allows the network to have extra security in the transmission and reception of data.

4.1 Network Implementation Essentials

We implemented Enhanced pairwise key pre-distribution scheme on two networking protocols, uIP and Rime to compare performance, energy efficiency and efficiency in general of the two methods. Contiki’s Rime networking stacks provides an energetically inexpensive way of distributing small amounts of data amongst a sensor neighbours. It comes with various functions to send data using unicast or multicast by singlehop or multihop. It does not provide any functionality to communicate on Internet Protocol network.

The uIP is an open source TCP/IP stack capable of being used with tiny 8-bit and 16-bit microcontrollers [25]. It implements RFC-complaint IPv4, IPv6, TCP and UDP (the latter two compatible with IPv4 and IPv6). uIP is very optimized, only the required features are implemented. For Instance there is a single buffer for the whole stack, used for received packets as well as for those packets to send. Moreover, Contiki's uIP networking stack comes with initialisation of various modules required for correct communication in wireless sensor network such as uIP-fw (uIP forwarding) and uip-over-mesh modules.

We have therefore, used the Enhanced pairwise key pre-distribution scheme module running under Rime’s mesh but adds ability to communicate on Internet Protocol network. It is expected to have the same or even increased power consumption and decreased performance over the example using Rime protocol. Below, we provide comparison of the two methods in terms of energy efficiency and performance.

4.2 Test Methodology

We tested our two applications using Cooja that comes preinstalled on the Instant Contiki distribution. It provides various tools to debug ContikiOS applications without having physical sensors by emulating them. Firstly we have chosen strategic position of one base station and three nodes. The programs were designed to communicate to the node of ID 3, so we placed node 3 next to the base station, node with ID 2 on a place so it can reach node with ID 3 to test single-hop communication and the last node with ID 4 so it can reach node with ID 2 but cannot reach node with ID 3 to test the multi-hop communication. Table 2 shows the positions of nodes 1, 2, 3, 4


Table 2. Sensor node positioning Sensor ID X Y Z 1 2 3 4 104.8 66.33 83.55 34.25 53.3 66.37 31.93 82.17 0 0 0 0

Also, the following screenshots as shown in Figure 2 illustrates the visibility of each node in the wireless sensor network.

Figure 2. Node Placement Simulations

4.3 Performance

The table below shows speeds of single-hop and multi-hop communication for key pre-distribution and communication phase for both uIP and Rime protocols.

Table 3. Key Pre-distribution and Communication Phase Network Stack Single-hop Communication Multi-hop Pre-Allocation Communicatio n Pre-Allocation Communicatio n Rime uIP 46ms 91ms 7ms 7ms *NA *A 1845ms 15ms

* Pre-allocation in Rime's example is made using unicast that correctly does not allow pre-allocation on multi-hop connections. Because uIP communication uses Rime's mesh connection by default, it


automatically allows pre-allocation phase on multi-hop and further steps are needed to forbid this behaviour.

4.4 Power Consumption

We have measured power consumption using energest in our code by capturing energest values before and after the transmission and computing the actual consumption. Energest is use for obtaining per-component power consumption in Contiki. Thus, using the formula below if we do not divide by runtime, calculates the energy consumption during runtime.





Power pW = txend txstart * 20mA * 3V / 32768−

The 20mA are pre-measured (or from data sheet), 3V is the operational voltage (approximated) and 32768 is ticks per second (as of Contiki 2.5). By measuring and computation of the final values we got following power consumption as shown in Figure 3 but the value of the Multi-hop Communication (sender) of the Rime stack had to be multiplied by 0.1 because the original value distorts other values displayed in the graph.

Figure 3. Power Consumption Analysis of uIP and Rime using Enhanced scheme 5 Conclusions

The fear of possible interference, corrupting a message transmitted by sensor nodes to and from the Base station was highly considered in the novelty of this article. Treating issues like denial-of –service (DOS) and algorithmic complexity attacks is not an easy task and it can detour the effective usage or benefit of WSN to the field of healthcare, Oil and Gas and military formations. This article has presented the smart security technique for wireless sensor network nodes with key management system as the direct approach to sensor nodes communication and security. It highlights a coordinated and careful usage of advanced encryption standard (AES), generation of the key parameters that makes the sensors communicate more freely, confidentially and with integrity. The technique also makes a very strong case in making sure that scalability was achieved during implementation.

The uIP and Rime resources have exposed what was expected with our model. As we’ve shown from the figures, there are a quite number of solutions that makes the Enhanced Key management scheme


suitable for uIP-WSN communication. The actual implementation of the modification of uIP open source stack to accommodate the sensor nodes code using the key distribution method as an interface with sensor nodes within an IP domain therefore provides communication faster than the traditional Rime communication but with decreased performance without creating problems of overhead, and memory capacity. Although the power consumption tends to be higher than the Rime, but it all depends on trade-off of different performance control and what the designer is prone to secure.


1. Tavares, J.; Velez, F. J.; Ferro, J.M. Application of Wireless Sensor Networks to Automobiles. Measurement Science Review, vol. 8, No. 3, 2008, pp. 65-70. DOI:10.2478/v10048-008-0017-8 2. Iwendi, C.O. Power consumption trade-off analysis in an OFDM communication chain. IEEE

AFRICON, 2011, vol., no., pp.1-6, 13-15 Sept. 2011 DOI: 10.1109/AFRCON.2011.6071957

3. Iwendi, C.O. & Allen, A.R. Wireless Sensor Network Nodes: Security and Deployment in the Niger-Delta Oil and Gas Sector. International Journal of Network Security & Its Applications, vol 3, no. 1, pp. 68–79 Jan. 2011

DOI: 10.5121/ijnsa.2011.3105

4. Blom, R. An optimal class of symmetric key generation systems. Proceedings of EUROSRYPT’84 pp. 335-338 1984

5. Pathan, A.S.K.; Lee, H.; Hong, C.S. Security in wireless sensor networks: Issues and

challenges. The 8th International Conference on Advanced Communication Technology, ICACT 2006. Volume 2, 20-22 Feb. 2006. Page(s):6 pp.

6. Undercoffer, J.; Avancha, S.; Joshi, A.; Pinkston, J. Security for sensor networks, CADIP Research Symposium, 25-26 Oct. 2002

7. Zhou, L.; Haas, Z.J. Securing ad hoc networks. IEEE Network, Volume 13, Issue 6, Nov. – Dec. 1999, pp. 24 – 30.

8. Strulo, B.; Farr, J.; Smith, A. Securing mobile ad hoc networks – A motivational approach. BT Technology Journal Volume 21 Issue 3, 2003, pp. 81 -89

9. Murshed, G. Energy efficient data gathering in wireless sensor network, PhD transfer report, University of Aberdeen, Scotland, February 2009

10. Kaplantzis, S. Security models for wireless sensor networks. PhD Conversion Report, Monash University, Australia, March 2006

11. Raymond, D.R.; Midkiff, S.F. Denial-of-Service in wireless sensor networks: Attacks and Defences. IEEE Pervasive Computing Magazine. Volume 7, Issue 1, Jan. – Mar. 2008. Page(s):74 – 81

12. Xu, W.; Trappe, W.; Zhang, Y.; Wood, T. The feasibility of launching and detecting jamming attacks in wireless networks. Proc. 11th Annual Int’l Conf. Mobile Computing and Networking, ACM Press, 2005, pp. 46 – 57

13. Wood, A.D.; Stankovic, J.A. Denial of Service in Sensor Networks. Computer, vol. 35, issue. 10, Oct. 2002, pp. 54 – 62. DOI: 10.1109/MC.2002.1039518

14. Stajano, F.; Anderson, R. The Resurrecting Duckling: Security Issues for Ad Hoc Wireless Networks. Proc. 7th Int’l Workshop Security Protocols, Springer, 1999, pp. 172 – 194.


15. Karlof, C.; Wagner, D. Secure Routing in Wireless Sensor Networks: Attacks and

Countermeasures. Proc. 1st IEEE Int’l Workshop Sensor Network Protocols and Applications, IEEE Press, 2003, pp. 113 – 127. DOI: 10.1109/SNPA.2003.1203362

16. Yu, Y.; Govindan, R.; Estrin, D. Geographical and Energy Aware Routing: A Recursive Data Dissemination. Protocol for Wireless Sensor Networks, tech. report UCLA/CSD-tr-01-0023, Computer Science Dept., Univ. of California, Los Angeles, 2001.

17. Newsome, J.; Shi, E.; Song, D.; Perrig, A. The Sybil attack in sensor networks: Analysis & Defenses. Third International Symposium on Information Processing in Sensor Networks, IPSN, 26 – 27 April 2004 Page(s): 259 – 268

18. Deng, J.; Han, R.; Mishra, S. Defending against Path-Based DoS Attacks in Wireless Sensor Networks. Proc. 3rd ACM Workshop Security of Ad Hoc and Sensor Networks, ACM Press, 2005, pp. 89 – 96.

19. Hui, J.W.; Culler, D. The Dynamic Behaviour of a Data Dissemination Protocol for Network Programming at Scale. Proc. 2nd ACM Conf. Embedded Networked Sensor Systems, ACM Press, 2004, pp. 81 – 94.

20. Dutta, P.K.; Hui, J.W.; Chu, D.C.; Culler, D.E. 2006. Securing the deluge Network programming system. In Proceedings of the 5th international conference on Information processing in sensor networks (IPSN '06). ACM, New York, NY, USA, 326-333.


21. Neagoe, V.E. Inversion of the Van der Monde Matrix. IEEE Signal Processing Letters, Volume 3, Issue 4 August 2002, Page(s): 119 – 120.

22. Oruk, H. LU Factorization of the Vandermonde Matrix and its Applications. Science Direct: Applied Mathematics Letters 20 (2007) 982-987

23. Merouane, D.; Øyvind, R. Applications and Fundamental Results on Random Vandermond Matrix. Journal for Computing Research Repository. CORR. Vol. Abs/0802.3 July 2008. 24. Heinzelman, W.R.; Chandrakasan, A.; Balakrishnan, H. Energy-efficient communication

protocol for wireless microsensor networks. Proceedings of the 33rd Annual Hawaii International Conference on Systems Sciences Jan 4-7, 2000 Page(s): 10pp. vol 2

25. Dunkels, A. Full TCP/IP for 8-bit architectures. Proc of the 1st international conference on mobile systems, applications and services, MobiSys ’03, New York, NY, USA: ACM, 2003, pp. 85-98.

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