The International Journal of Wireless & Mobile Networks (IJWMN) is a bi monthly open access peer-reviewed journal that publishes
articles which contribute new results in all areas of Wireless & Mobile Networks. The journal focuses on all
technical and practical aspects of Wireless & Mobile Networks. The goal of this journal is to bring together
researchers and practitioners from academia and industry to focus on advanced wireless & mobile networking concepts
and establishing new collaborations in these areas.
Top 10 Read Article - International Journal of Wireless & Mobile Networks (IJWMN)
1. Top Read Articles in Wireless&
Mobile Networks
International Journal of Wireless & Mobile
Networks (IJWMN)
ISSN: 0975-3834 [Online]; 0975-4679 [Print]
http://airccse.org/journal/ijwmn.html
2. CUTTING DOWN ENERGY USAGE IN WIRELESS SENSOR
NETWORKS USING DUTY CYCLE TECHNIQUE AND MULTI-HOP
ROUTING
Ali Sedighimanesh1, Mohammad Sedighimanesh2 and Javad Baqeri3
1,2,3 Department of Electrical, Computer and It Engineering, Islamic Azad University of
Qazvin
ABSTRACT
A wireless sensor network is composed of many sensor nodes, that have beengiven out in a
specific zoneandeach of them hadanability of collecting information from the environment
and sending collected data to the sink. The most significant issues in wireless sensor
networks, despite the recent progress is the trouble of the severe limitations of energy
resources.Since that in different applications of sensor nets, we could throw a static or mobile
sink, then all aspects of such networks should be planned with an awareness of energy.One of
the most significant topics related to these networks, is routing. One of the most widely used
and efficient methods of routing isa hierarchy (based on clustering) method. In The present
study with the objective of cutting down energy consumption and persistence of network
coverage, we have offered a novel algorithm based on clustering algorithms and multihop
routing.To achieve this goal, first, we layer the network environment based on the size of the
network.We will identify the optimal number of cluster heads and every cluster head based
on the mechanism of topology control will start to accept members.Likewise, we set the first
layer as gate layer and subsequently identifying the gate’s nodes, we’d turn away half of the
sensors and then stop using energy and the remaining nodes in this layer will join the gate’s
nodes because they hold a critical part in bettering the functioning of the system. Cluster
heads off following layers send the information to cluster heads in the above layer until sent
data will be sent to gate’s nodes and finally will be sent to sink. We have tested the proposed
algorithm in two situations 1) when the sink is off and 2)when a sink is on and simulation
data shows that proposed algorithm has better performance in terms of the life span of a
network than LEACH and ELEACH protocols.
KEYWORDS
Wireless sensor networks, Lifetime, Hierarchical clustering, Hierarchical Routing, Cluster
Topology.
Full Text: https://aircconline.com/ijwmn/V8N4/8416ijwmn02.pdf
Volume Link: https://airccse.org/journal/jwmn_current16.html
3. REFERENCES
[1] L. Borges, F. Velez, and A. Lebres, “Survey on the Characterization and Classification of
Wireless Sensor Networks Applications,” IEEE Communications Surveys & Tutorials, vol.
XX, no. X. pp. 1–1, 2014.
[2] H. Asharioun, H. Asadollahi, T.-C. Wan, and N. Gharaei, “A Survey on Analytical
Modeling and Mitigation Techniques for the Energy Hole Problem in Corona-Based Wireless
Sensor Network,” Wirel. Pers. Commun., vol. 81, no. 1, pp. 161–187, 2015.
[3] F. Shaukat, “A Survey on Testing Network Applications and Protocols,” vol. 2, no. 02.
pp. 316–325, 2015.
[4] G. Sara and D. Sridharan, “Routing in mobile wireless sensor network: a survey,”
Telecommun. Syst., vol. 57, no. 1, pp. 51–79, 2014.
[5] R. C. Carrano, D. Passos, L. C. S. Magalhaes, and C. V. N. Albuquerque, “Survey and
Taxonomy of Duty Cycling Mechanisms in Wireless Sensor Networks,” Communications
Surveys & Tutorials, IEEE, vol. 16, no. 1. pp. 181–194, 2014.
[6] “Energy-efficient routing protocols in wireless sensor networks A survey 2013.”.
[7] G. Han, J. Jiang, L. Shu, J. Niu, and H.-C. Chao, “Management and applications of trust
in Wireless Sensor Networks: A survey,” Journal of Computer and System Sciences, vol. 1.
pp. 1–16, 2013.
[8] S. A. Sert, H. Bagci, and A. Yazici, “MOFCA: Multi-objective fuzzy clustering algorithm
for wireless sensor networks,” Appl. Soft Comput., vol. 30, no. 0, pp. 151–165, May 2015.
[9] D. V Jose and G. Sadashivappa, “Mobile Sink Assisted Energy Efficient Routing
Algorithm for Wireless Sensor Networks,” The World of Computer Science and Information
Technology, vol. 5, no. 2. pp. 16–22, 2015.
[10] A. Ali Ahmed, “An enhanced real-time routing protocol with load distribution for
mobile wireless sensor networks,” Comput. Networks, vol. 57, no. 6, pp. 1459–1473, Apr.
2013.
[11] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient
communication protocol for wireless microsensor networks,” System Sciences, 2000.
Proceedings of the 33rd Annual Hawaii International Conference on. p. 10 pp. vol.2, 2000.
[12] S. Lindsey and C. S. Raghavendra, “PEGASIS: Power-efficient gathering in sensor
information systems,” in IEEE Aerospace Conference Proceedings, 2002, vol. 3, pp. 1125–
1130.
[13] M. F. K. Abad and M. A. J. Jamali, “Modify LEACH Algorithm for Wireless Sensor
Network,” Int. J. Comput. Sci. Issues, vol. 8, no. 5, pp. 219–224, 2011.
[14] D. Mahmood, N. Javaid, S. Mahmood, S. Qureshi, A. M. Memon, and T. Zaman,
“MODLEACH: A variant of LEACH for WSNs,” in Proceedings - 2013 8th International
Conference on Broadband, Wireless Computing, Communication and Applications, BWCCA
2013, 2013, pp. 158–163.
4. [15] M. Ghiasabadi, M. Sharifi, N. Osati, S. Beheshti, and M. Sharifnejad, “TEEN: a routing
protocol for enhanced efficiency in wireless sensor networks,” 2008 Second Int. Conf. Futur.
Gener. Commun. Netw., vol. 1, no. C, pp. 2009–2015, 2001.
[16] A. Manjeshwar and D. P. Agrawal, “APTEEN: a hybrid protocol for efficient routing
and comprehensive information retrieval in wireless,” in Proceedings 16th International
Parallel and Distributed Processing Symposium, 2002, p. 8 pp.
[17] O. Younis and S. Fahmy, “HEED: a hybrid, energy-efficient, distributed clustering
approach for ad hoc sensor networks,” IEEE Trans. Mob. Comput., vol. 3, no. 4, pp. 366–
379, 2004.
[18] S. Chand, S. Singh, and B. Kumar, “Heterogeneous HEED Protocol for Wireless Sensor
Networks,” Wirel. Pers. Commun., vol. 77, no. 3, pp. 2117–2139, 2014.
[19] O. Boyinbode, H. Le, and M. Takizawa, “A survey on clustering algorithms for wireless
sensor networks,” Int. J. Space-Based Situated Comput., vol. 1, no. 2–3, pp. 130–136, 2011.
[20] N. A. Pantazis, S. A. Nikolidakis, and D. D. Vergados, “Energy-Efficient Routing
Protocols in Wireless Sensor Networks: A Survey,” IEEE Commun. Surv. Tutorials, vol. 15,
no. 2, pp. 551–591, 2013.
[21] K. Chen, “Unequal Cluster-Based Routing Protocol in Wireless Sensor Networks,”
Journal of Networks, vol. 8, no. 11. 2013.
[22] Y. Jiang, W. Shi, X. Wang, and H. Li, “A distributed routing for wireless sensor
networks with mobile sink based on the greedy embedding,” Ad Hoc Networks, vol. 20. pp.
150–162, 2014.
[23] Z. Han, J. Wu, J. Zhang, L. Liu, and K. Tian, “A general self-organized tree-based
energy-balance routing protocol for wireless sensor network,” IEEE Transactions on Nuclear
Science, vol. 61, no. 2. pp. 732–740, 2014.
[24] J. Zhu, C.-H. Lung, and V. Srivastava, “A hybrid clustering technique using quantitative
and qualitative data for wireless sensor networks,” Ad Hoc Networks, vol. 25, Part A, no. 0,
pp. 38–53, Feb. 2015.
5. MOBILITY LOAD BALANCING BASED ADAPTIVE HANDOVER IN
DOWNLINK LTE SELF-ORGANIZING NETWORKS
Hana Jouini1, Mohamed Escheikh1,Kamel Barkaoui2 and Tahar Ezzedine1
1University of Tunis El Manar, Enit, Sys’Com , 1002 Tunis, Tunisia
2Cedric-Cnam : 2 Rue Conté 75003 Paris, France
ABSTRACT
This article investigates mobility load balancing (MLB) algorithm implementation through
network simulator (ns-3) in long term evolution (LTE) systems employing orthogonal
frequency division multiple access (OFDMA) for downlink (DL) data transmission. MLB is
introduced by the third generation partnership project (3GPP) as a key target of LTE self-
organizing networks (SONs) [1]. Our contribution is twofold. First, we implemented
elementary procedures (EPs) related to load management (LM) function of the X2-
application protocol (X2AP) as specified in TS 136.423 [2]. We particularly focused on EPs
’Resource Status Reporting Initiation Procedure’ and 'Resource Status Reporting Procedure’.
Second, we implemented a MLB based adaptive handover (HO) algorithm enabling to
configure adaptively HO hysteresis threshold for each neighbouring cell, of an overloaded
cell, according to its current load information. Numerical results show how, through suitable
simulation scenarios, MLB enables enhancing network performance in terms of overall
throughput, packet loss ratio (PLR) and fairness without incurring HO overhead.
KEYWORDS
LTE, load management, X2AP, elementary procedure, mobility load balancing
Full Text: https://aircconline.com/ijwmn/V8N4/8416ijwmn06.pdf
Volume Link: https://airccse.org/journal/jwmn_current16.html
6. REFERENCES
[1] European Telecommunications Standards Institute. LTE; Evolved Universal Terrestrial
Radio Access Network (E-UTRAN); Self-configuring and self-optimizing network (SON)
use cases and solutions, 2011.
[2] European Telecommunications Standards Institute. LTE; Evolved universal terrestrial
radio access network (E-UTRAN); X2 application protocol (X2AP), 2013.
[3] Kyuho Son, Song Chong, and G. Veciana. Dynamic association for load balancing and
interference avoidance in multi-cell networks. Wireless Communications, IEEE Transactions
on, 8(7):3566–3576, July 2009.
[4] P Mūnoz, R Barco, and I de la Bandera. Load balancing and handover joint optimization
in lte networks using fuzzy logic and reinforcement learning. Computer Networks, 76:112–
125, 2015.
[5] Wen-Yu Li, Xiang Zhang, Shu-Cong Jia, Xin-Yu Gu, Lin Zhang, Xiao-Yu Duan, and
JiaRu Lin. A novel dynamic adjusting algorithm for load balancing and handover co-
optimization in lte son. Journal of Computer Science and Technology, 28(3):437–444, 2013.
[6] Ridha Nasri and Zwi Altman. Handover adaptation for dynamic load balancing in 3gpp
long term evolution systems. arXiv preprint arXiv:1307.1212, 2013.
[7] Zhang Peng Huang, Jing Liu, Qiang Shen, Jin Wu, and Xiaoying Gan. A threshold-based
multi-traffic load balance mechanism in lte-a networks. In Wireless Communications and
Networking Conference (WCNC), 2015 IEEE, pages 1273–1278. IEEE, 2015.
[8] Qi-Ping Yang, Jae-Woo Kim, and Tae-Hyong Kim. Mobility prediction and load
balancing based adaptive handovers for lte systems. International Journal on Computer
Science and Engineering, 4(4):638, 2012.
[9] European Telecommunications Standards Institute. LTE ; Evolved Universal Terrestrial
Radio Access (E-UTRA); Radio Resource Control (RRC); Protocol specification, Nov. 2012.
[10] Lte protocols and specifications. http://lteworld.org/lte-protocols-specifications.
Accessed: Dec. 2015.
[11] X2 interface function in lte a connection between two enodebs.
http://www.teletopix.org/4g-lte/x2-
interface-function-in-lte-a-connection-between-two-enodebs/. Accessed: Dec. 2015.
[12] Christopher Cox. An introduction to LTE: LTE, LTE-advanced, SAE and 4G mobile
communications. John Wiley & Sons, 2012.
[13] Alcatel-lucent. The LTE Network Architecture: A comprehensive tutorial, 2013.
[14] Self-organizing networks. http://www.3gpp.org/technologies/keywords-acronyms/105-
son. Accessed: Dec. 2015.
[15] Panagiotis Fotiadis, Michele Polignano, Daniela Laselva, Benny Vejlgaard, Preben Mo-
gensen, Ralf Irmer, and Neil Scully. Multi-layer mobility load balancing in a heterogeneous
7. lte network. In Vehicular Technology Conference (VTC Fall), 2012 IEEE, pages 1–5. IEEE,
2012.
[16] Dah-Ming Chiu and Raj Jain. Analysis of the increase and decrease algorithms for
congestion avoidance in computer networks. Computer Networks and ISDN systems,
17(1):1–14, 1989.
[17] Network simulator 3. url: https://www.nsnam.org/. Accessed: Mai 2015.
[18] Nicola Baldo, Manuel Requena-Esteso, Marco Miozzo, and Raymond Kwan. An open
source model for the simulation of LTE handover scenarios and algorithms in ns-3.In
Proceedings of the 16th ACM international conference on Modelling, analysis & simulation
of wireless and mobile systems, pages 289–298. ACM, 2013.
[19] Tracy Camp, Jeff Boleng, and Vanessa Davies. A survey of mobility models for ad
hocnetwork research. Wireless communications and mobile computing, 2(5):483–502, 2002.
[20] Fan Bai and Ahmed Helmy. A survey of mobility models. Wireless Ad hoc
Networks.University of Southern California, USA, 206, 2004.
[21] European Telecommunications Standards Institute. Self-Organizing Networks (SON)
Policy Network Resource Model (NRM) Integration Reference Point (IRP); Information
Service (IS), 2012.
8. MULTI-STAGES CO-OPERATIVE/NONCOOPERATIVE SCHEMES
OF SPECTRUM SENSING FOR COGNITIVE RADIO SYSTEMS
Anwar Mousa1 and Tara Javidi2
University of California, San Diego (UCSD)- Jacobs School of Engineering
9500 Gilman Dr., La Jolla, CA 92093-0407
ABSTRACT
Searching for spectrum holes in practical wireless channels where primary users experience
multipath fading and shadowing, with noise uncertainty, limits the detection performance
significantly. Moreover, the detection challenge will be tougher when different band types
have to be sensed, with different signal and spectral characteristics, and probably overlapping
spectra. Besides, primary user waveforms can be known (completely or partially) or unknown
to allow or forbid cognitive radios to use specific kinds of detection schemes! Hidden
primary user’s problem, and doubly selective channel oblige the use of cooperative sensing to
exploit the spatial diversity in the observations of spatially located cognitive radio users.
Incorporated all the aforementioned practical challenges as a whole, this paper developed a
new multistage detection scheme that intelligently decides the detection algorithm based on
power, noise, bandwidth and knowledge of the signal of interest. The proposed scheme
switches between individual and cooperative sensing and among featured based sensing
techniques (cyclo-stationary detection and matched filter) and sub-band energy detection
according to the characteristics of signal and band of interest.Compared to the existing
schemes, performance evaluations show reliable results in terms of probabilities of detection
and mean sensing times under the aforementioned conditions.
KEYWORDS
spectrum sensing,local and cooperative,cognitive radio, sub-band energy detection,
probability of detection, mean detection time
Full Text: http://aircconline.com/ijwmn/V8N4/8416ijwmn01.pdf
Volume Link: https://airccse.org/journal/jwmn_current16.html
9. REFERENCES
[1] J. Kantiand G. Singh Tomar “Various Sensing Techniques in Cognitive Radio Networks:
A Review” International Journal of Grid and Distributed Computing Vol. 9, No. 1 (2016),
pp.145-154.
[2] SenerDikmese, Paschalis C. Sofotasios, TeroIhalainen, Markku Renfors and
MikkoValkama, “Efficient Energy Detection Methods for Spectrum Sensing under Non-Flat
Spectral Characteristics,” IEEE Journal on Selected Areas in Communications (JSAC),
DOI:10.1109/JSAC.2014.236-1074, Oct. 2014.
[3] W. Yue, B. Zheng, and Q. Meng, “Cyclostationary property based spectrum sensing
algorithms for primary detection in cognitive radio systems,” Journal of Shanghai Jiaotong
University (Science), vol. 14, no. 6, pp. 676–680, Dec. 2009
[4] H. Sun et al., “Wideband spectrum sensing for cognitive radio networks- a survey” IEEE
Wireless Communications • April 2013, pp 74-81
[5] Ejaz et al. " SNR-based adaptive spectrum sensing for cognitive radio networks".
International Journal of Innovative Computing, Information and Control Vol. 8, No: 9, 6095–
6106 (2012)
[6] Ejaz et al. " I3S: Intelligent spectrum sensing scheme for cognitive radio networks"
EURASIP Journal onWireless Communications and Networking 2013, 2013:26
[7] Amardip Kumar et al. "An Adaptive and Efficient Local Spectrum Sensing Scheme in
Cognitive Radio Networks" International Journal of Computer Applications (0975 – 8887)
Volume72– No.23, June 2013
[8] W Yue, B Zheng, Q Meng, W Yue, Combined energy detection one-order cyclostationary
feature detection techniques in cognitive radio systems. J China Univ. Posts Telecommun.
17(4), 18–25 (2010)
[9] K. Srisomboo et al., “Two-stage Spectrum Sensing for Cognitive Radio under Noise
Uncertainty” Proceeding of the Eighth International Conference on Mobile Computing, 2015,
pp 19-24.
[10] WEjaz, NU Hasan, MA Azam, HS Kim, Improved local spectrum sensing for cognitive
radio networks. EURASIP J. Adv. Signal Process(2012).http://
asp.eurasipjournals.com/content/2012/1/242
[11] S Geethu, GL Narayanan, A novel high speed two stage detector for spectrum sensing.
Elsevier Procedia Technol.6,682–689(2012)
[12] PR Nair, APVinod, KGSmitha, AKKrishna, Fast two-stage spectrum detector for
cognitive radios in uncertain noise channels. IET Commun. 6(11),1341–1348(2012)
[13] Akyildiz, Ian F. et al.,"Cooperative spectrum sensing in cognitive radio networks: A
survey", Physical Communication 4 (2011) 40–62.
[14] Lamiaa Khalid and AlaganAnpalagan, “Adaptive Assignment of Heterogeneous Users
for GroupBased Cooperative Spectrum Sensing” IEEE TRANSACTIONS ON WIRELESS
COMMUNICATIONS, VOL. 15, NO. 1, pp 232-246, 2016.
10. [15] J. So, T. Kwon “Limited reporting-based cooperative spectrum sensing for multiband
cognitive radio networks” Int. J. Electron. Commun. (AEÜ) 70 (2016) 386–397
[16] W.-Y. Lee, I.F. Akyildiz, Optimal spectrum sensing framework for cognitive radio
networks, IEEE Transactions on Wireless Communications 7 (10) (2008) 3845–3857.
[17] E. Peh, Y.-C. Liang, Y.L. Guan, Y. Zeng, Optimization of cooperative sensing in
cognitive radio networks: a sensing-throughput tradeoff view, IEEE Transactions on
Vehicular Technology 58 (9) (2009) 5294–5299.
[18] A. Ghasemi, E.S. Sousa, Spectrum sensing in cognitive radio networks: the cooperation-
processing tradeoff, Wireless Communicationsand Mobile Computing 7 (9) (2007) 1049–
1060.
[19] X. Zhou, J. Ma, G. Li, Y. Kwon, A. Soong, Probability-based combination for
cooperative spectrum sensing, IEEE Transactions on Communications 58 (2) (2010) 463–
466.
[20] Tandra, R., et al.,"SNR walls for signal detection", IEEE J. Sel. Topics Signal Process.,
2008, 2, (1), pp. 4–17.
[21] Amardip Kumar et al. "An Adaptive and Efficient Local Spectrum Sensing Scheme in
Cognitive Radio Networks" International Journal of Computer Applications (0975 – 8887)
Volume72– No.23, June 2013
[22] W. Yue, B. Zheng, Q. Meng and W. Yue, Combined energy detection and one-order
cyclostationary feature detection techniques in cognitive radio systems, The Journal of China
Universities of Posts and Telecommunications, vol.17, no.4, pp.18-25, 2010.
[23] IEEE Computer Society, IEEE Std 802.22–2011 Part 22: Cognitive Wireless RAN
Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Policies and
Procedures for Operation in the TV Bands. IEEE Standard for Information Technology, 1–
672 (2011)
11. COMPARING VARIOUS CHANNEL ESTIMATION TECHNIQUES
FOR OFDM SYSTEMS USING MATLAB
Raghad K. Mohammed
Department of Basic Sciences, College of Dentistry
University of Baghdad, Baghdad, Iraq
ABSTRACT
This paper compares the performance of various channel estimation techniques for OFDM
systems over quasi-static channels using MATLab. It compares the performance of five
channel estimation techniques, these are: decision directed (DD), linear interpolation, second-
order interpolation, discrete Fourier transform (DFT) interpolation, minimum mean square
error (MMSE) interpolation. The performance is evaluated in terms of two widely-used
performance measures, namely, bit-error rate (BER) and the mean square error (MSE) for
different levels of signal-to-noise ratio (SNR). The OFDM model is explained and
implemented using MATLab to run different simulations. The simulation results demonstrate
that the DD channel estimation provides the lowest BER and MSE as compared to
interpolation techniques, at the cost of extra processing delay and comparatively sensitive to
channel variations between OFDM symbols. Also, the MMSE interpolation outperforms all
other interpolation techniques.
KEYWORDS
OFDM, pilot-based channel estimation, pilot allocation, direct decision, interpolation channel
estimation, LS, MMSE, MATLab
Full Text: https://aircconline.com/ijwmn/V11N3/11319ijwmn02.pdf
Volume Link: https://airccse.org/journal/jwmn_current19.html
12. REFERENCES
[1] Henrik Schulze and Christian Luders. Theory and Applications of OFDM and CDMA:
Wideband Wireless Communications. John Wiley & Sons, 2006.
[2] Yong Soo Cho, Jaekwon Kim, Won Young Yang, Chung G. Kang. MIMO-OFDM
Wireless Communications with MATLAB, John Wiley & Sons, August 2010.
[3] Mathuranathan Viswanathan. Digital Modulations using MATLab: Build Simulation
Models from Scratch. E-book, June, 2017.
[4] Srishtansh Pathak and Himanshu Sharma. Channel Estimation in OFDM Systems.
International Journal of Advanced Research in Computer Science and Software Engineering
(IJARCSSE), Vol.3, No.3, pp. 312-327, 2013.
[5] Elizabeth A. Thompson, Charles McIntosh, James Isaacs, Eric Harmison, Ross Sneary.
Robot Communication Link Using 802.11n or 900 MHz OFDM. Journal of Network and
Computer Applications (JNCA), Vol. 52, Issue 6, pp. 37-51, June 2015.
[6] Jeffrey G. Andrews, Arunabha Ghosh, and Rias Muhamed. Fundamentals of WiMAX-
Understanding Broadband Wireless Networking. Prentice Hall, Second Edition, 2007.
[7] Christopher Cox. An Introduction to LTE: LTE, LTE-Advanced, SAE and 4G Mobile
Communications. John-Wiley & Sons, March 2012.
[8] Mehdi Alasti, Behnam Neekzad, Jie Hui, and Rath Vannithamby. Quality of Service in
WiMAX and LTE Networks. IEEE Communications Magazine, Vol. 48, Issue 5, May 2010.
[9] Deepak Sharma and Praveen Srivastava. OFDM Simulator Using MATLAB.
International Journal of Emerging Technology and Advanced Engineering, Vol. 3, Issue 9,
pp. 493-496, September 2013.
[10] S. S. Ghorpade and S. V. Sankpal. Behavior of OFDM System Using MATLAB
Simulation. International Journal of Innovative Technology and Research (IJITR), Vol., No.
1, Issue No. 3, pp. 249 – 252, April - May 2013.
[11] S. Sadinov, P. Daneva, and P. Kogias. Description and Simulation of OFDM Reception
Process Journal of Engineering Science and Technology Review, Vol. 7, No. 4, pp. 18-22,
2014.
[12] Orlandos Grigoriadis and H. Srikanth Kamath. BER Calculation Using MATLAB
Simulation for OGDM Transmission. Proceedings of the International Multi-Conference of
Engineers and Computer Scientists (IMECS), Vol II, Hong Kong, 19-21 March 2008.
[13] Kala Praveen Bagadi and Susmita Das. MIMO-OFDM Channel Estimation Using Pilot
Carries. International Journal of Computer Applications (0975 – 888 (IJCA), Vol. 2, No. 3,
May 2010.
[14] H. Sinha, R. Meshram, and G.R. Sinha. BER Performance Analysis of MIMO-OFDM
over Wireless Channel. International Journal of Pure and Applied Mathematics (IJPAM),
Vol. 118, No. 5, pp. 195- 206, 2018.
13. [15] Pratima Manhas and M.K Soni. OFDM Performance Evaluation under Different Fading
Channels using Matlab Simulink. Indonesian Journal of Electrical Engineering and Computer
Science, Vol. 5, No. 2, pp. 260-266, 2017.
[16] A. Z. M. Touhidul Islam. A Comparative Performance Study of OFDM System with the
Implementation of Comb Pilot-Based MMSE Channel Estimation. International Journal on
Computational Sciences & Applications (IJCSA), Vol.3, No.6, pp. 45-53, December 2013.
[17] D. Khosla, S. Singh, R. Singh, and S. Goyal. OFDM Modulation Technique & its
Applications: A Review. Proceedings of the International Conference on Innovations in
Computing (ICIC 2017), pp. 101-105, 2017.
[18] Fateme Salehi, Mohammad‐Hassan Majidi, and Naaser Neda. Channel Estimation Based
on Learning Automata for OFDM Systems. International Journal of Communication Systems,
Vol. 321, Issue 12, August, 2018.
[19] Navjot Kaur and Neetu Gupta. Simulation and Analysis of OFDM and SC-FDMA with
STBC using Different Modulation Techniques. International Journal of Advanced Research
in Computer Engineering & Technology (IJARCET), Vol. 4, Issue 11, pp. 4184-4189,
November 2015.
[20] Himanshi Jain and Vikas Nandal. A Comparison of Various Channel Estimation
Techniques to Improve Fading Effects in MIMO over Different Fading Channels.
International Journal of Current Engineering and Technology (IJCET), Vol. 6, No. 4, pp.
1382-1386, 2016.
[21] Kussum Bhagat and Jyoteesh Malhotra. Performance Evaluation of Channel Estimation
Techniques in OFDM-based Mobile Wireless System. International Journal of Future
Generation Communication and Networking (IJFGCN), Vol. 8, No. 3, pp. 53-60, 2015.
[22] Vishal Sharma and Harleen Kaur. On BER Evaluation of MIMO-OFDM Incorporated
Wireless System. International Journal for Light and Electron Optics, Vol. 127, Issue 1, pp.
203-205, January 2016.
[23] N. Kumar and Anuradha. BER Analysis of Conventional and Wavelet Based OFDM in
LTE using Different Modulation Techniques. IEEE Engineering and Computational
Sciences, March 2014.
[24] M Divya. Bit Error Rate Performance of BPSK Modulation and OFDM-BPSK with
Rayleigh Multiple Channel. International Journal of Engineering and Advanced Technology
(IJEAT), Vol. 2, Issue 4, April 2013.
[25] Song Wang, Jinli Cao, Jiankun Hu. A Frequency Domain Subspace Blind Channel
Estimation Method for Trailing Zero OFDM Systems. Journal of Network and Computer
Applications (JNCA), Vol. 34, Issue 1, pp. 116-120, January 2011.
[26] Li Li. Advanced Channel Estimation and Detection Techniques for MIMO and OFDM
Systems. PhD Thesis, University of York, UK, 2013.
[27] S. Patil and A. N. Jadhav. Channel Estimation Using LS and MMSE Estimators. KIET
International Journal of Communications & Electronics, Vol. 2, No.1, pp. 51-55, April 2014.
[28] Anwar Yousef Al-Tarawneh. An Improved Performance OFDM Channel Estimation
Using PilotSymbol-Aided Technique. MSc Thesis, Mutah University, Jordan, 2015.
14. SEMANTIC TECHNIQUES FOR IOT DATA AND SERVICE
MANAGEMENT: ONTOSMART SYSTEM
L. Nachabe 3, M. Girod-Genet 1 and B. ElHassan 2
1Department of Réseaux et Services Multimédia Mobiles, Telecom SudParis University,
Evry, France
2 Department of Electricity and Electronics, Faculty of Engineering, Branch 1, Lebanese
University, Tripoli, Lebanon.
3 American University of Culture & Education, Faculty of Science, Beirut, Lebanon.
ABSTRACT
In 2020 more than50 billions devices will be connected over the Internet. Every device will
be connected to anything, anyone, anytime and anywhere in the world of Internet of Thing or
IoT. This network will generate tremendous unstructured or semi structured data that should
be shared between different devices/machines for advanced and automated service delivery in
the benefits of the user’s daily life. Thus, mechanisms for data interoperability and automatic
service discovery and delivery should be offered. Although many approaches have been
suggested in the state of art, none of these researches provide a fully interoperable, light,
flexible and modular Sensing/Actuating as service architecture. Therefore, this paper
introduces a new Semantic Multi Agent architecture named OntoSmart for IoT data and
service management through service oriented paradigm. It proposes sensors/actuators and
scenarios independent flexible context aware and distributed architecture for IoT systems, in
particular smart home systems.
KEYWORDS
Smart-Home, IoT, Multi-Agent, distributed systems, WSN, semantic, Ontology, sensing as a
service, interoperability, semantic interoperability.
Full Text: https://aircconline.com/ijwmn/V8N4/8416ijwmn03.pdf
Volume Link: https://airccse.org/journal/jwmn_current16.html
15. REFERENCES
[1] C. Levy and D. Wong, “Towards a smart society,” no. June, 2014.
[2] C. Devices and F. D. Rates, “5G : The Internet for Everyone and Everything.”
[3] Huawei, “5G : A Technology Vision,” Huawei, White paer, pp. 1–16, 2014.
[4] “Smart Society project,” 2016. [Online]. Available: http://www.smart-society-
project.eu/about/.
[5] A. Zaslavsky, C. Perera, and D. Georgakopoulos, “Sensing as a Service and Big Data,”
Proc. Int. Conf. Adv. Cloud Comput., no. July, pp. 21–29, 2012.
[6] X. Sheng, X. Xiao, J. Tang, and G. Xue, “Electrical Engineering and Computer Science
Sensing as a service : A cloud computing system for mobile phone sensing Sensing as a
Service : A Cloud Computing System for Mobile Phone Sensing,” 2012.
[7] Matt Turck, “Internet of Things: Are We There Yet? (The 2016 IoT Landscape),” 2016.
[Online]. Available: http://mattturck.com/2016/03/28/2016-iot-landscape/.
[8] S. C. Workshop, “IERC - IoT European Research Cluster -Role Bring together the EU-
funded projects and policy activities with the aim of : Sustaining Europe ’ s leading position
in the future Internet of Things within a global context,” no. September 2014, 2017.
[9] T. Instruments, “The Internet of Things : Opportunities & Challenges,” p. 17.
[10] J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): A
vision, architectural elements, and future directions,” Futur. Gener. Comput. Syst., vol. 29,
no. 7, pp. 1645– 1660, 2013.
[11] S. De, P. Barnaghi, M. Bauer, and S. Meissner, “Service modelling for the Internet of
Things,” in Computer Science and Information Systems (FedCSIS), 2011 Federated
Conference on, 2011, pp. 949–955.
[12] T. George and B. George, “OWL-S : Semantic Markup for Web Services.”
[13] S. N. Nambi, C. Sarkar, R. V. Prasad, and A. Rahim, “A unified semantic knowledge
base for IoT,” in Internet of Things (WF-IoT), 2014 IEEE World Forum on, 2014, pp. 575–
580.
[14] M. Compton, P. Barnaghi, L. Bermudez, R. GarcíA-Castro, O. Corcho, S. Cox, J.
Graybeal, M. Hauswirth, C. Henson, A. Herzog, and others, “The SSN ontology of the W3C
semantic sensor network incubator group,” Web Semant. Sci. Serv. Agents World Wide
Web, vol. 17, pp. 25–32, 2012.
[15] V. B. M. Wick, “No Title,” 2016. [Online]. Available:
http://www.geonames.org/ontology/documentation.html.
[16] W. Wang, S. De, R. Toenjes, E. Reetz, and K. Moessner, “A comprehensive ontology
for knowledge representation in the internet of things,” in Trust, Security and Privacy in
Computing and Communications (TrustCom), 2012 IEEE 11th International Conference on,
2012, pp. 1793–1798.
16. [17] L. Nachabe, M. Girod-Genet, and B. ElHassan, “Unified Data Model for Wireless
Sensor Network MyOntoSens Ontology,” IEEE Sens. J., 2015.
[18] L. Nachabe, B. Elhassan, J. Khawaja, and H. Salloum, “Semantic Smart Home System :
OntoSmart to monitor and Assist habitant,” vol. 10, pp. 78–86, 2016.
[19] B. Parsia and E. Sirin, “Pellet: An owl dl reasoner,” in Third International Semantic Web
ConferencePoster, 2004, vol. 18.
[20] J. Ferber, Multi-agent systems: an introduction to distributed artificial intelligence, vol.
1. AddisonWesley Reading, 1999.
[21] B. Technologies, “Bluetooth ® low energy technology.”
[22] M. Lanthaler and C. Gütl, “On using JSON-LD to create evolvable RESTful services,”
in Proceedings of the Third International Workshop on RESTful Design, 2012, pp. 25–32.
[23] Z. Shelby, K. Hartke, and C. Bormann, “RFC 7252: The Constrained Application
Protocol (CoAP).” p. 112, 2014.
17. DEVICE-TO-DEVICE (D2D) COMMUNICATION UNDER LTE-
ADVANCED NETWORKS
Magri Hicham1, Noreddine Abghour2 and Mohammed Ouzzif1
1 RITM Research Lab,ESTC , Hassan II University ,Casablanca, Morocco
2 FSAC, Hassan II University,Casablanca, Morocco
ABSTRACT
Device-to-Device (D2D) communication is a new technology that offer many advantages for
the LTEadvanced network such us wireless peer-to-peer services and higher spectral
efficiency. It is also considered as one of promising techniques for the 5G wireless
communications system and used in so many different fields such as network traffic
offloading, public safety, social services and applications such as gaming and military
applications . The goal of this paper is to present advances on the current 3GPP LTE-
advanced system related to Device-to-Device (D2D). In this paper, we provide an overview
of the D2D types based on the communication spectrum of D2D transmission, namely Inband
D2D communication and Outband D2D communication. Then we present the advantages and
disadvantages of each D2D mode. Moreover, architecture and protocol enhancements for
D2D communications under LTE-A network are described.
KEYWORDS
D2D;LTE-advanced;Inband D2D;Outband D2D;3GPP;5G.
Full Text: https://aircconline.com/ijwmn/V8N1/8116ijwmn02.pdf
Volume Link: https://airccse.org/journal/jwmn_current16.html
19. [15] W. Xu, L. Liang, H. Zhang, S. Jin, J. C. Li, and M. Lei, “Performance enhanced
transmission in device-to-device communications: Beamforming or interference
cancellation?” in Proceedings of IEEE GLOBECOM, 2012, pp. 4296–4301.
[16] B. Zhou, H. Hu, S.-Q. Huang, and H.-H. Chen, “Intracluster deviceto- device relay
algorithm with optimal resource utilization,” IEEE Transactions on Vehicular Technology,
vol. 62, no. 5, pp. 2315– 2326, Jun. 2013.
[17] X. Bao, U. Lee, I. Rimac, and R. R. Choudhury, “DataSpotting: offloading cellular
traffic via managed device-to-device data transfer at data spots,” ACM SIGMOBILE Mobile
Computing and Communications Review, vol. 14, no. 3, pp. 37–39, 2010.
[18] X. Chen, L. Chen, M. Zeng, X. Zhang, and D. Yang, “Downlink resource allocation for
device-todevice communication underlaying cellular networks,” in Proceedings of IEEE
PIMRC, 2012, pp. 232–237
[19] M. Ji, G. Caire, and A. F. Molisch, “Wireless device-to-device Caching networks: Basic
principles and system performance,”arXiv preprintarXiv:1305.5216, 2013.
[20] H. Min, J. Lee, S. Park, and D. Hong, “Capacity enhancement of an interference limited
area for device-to-device uplink underlaing cellular networks,” IEEE Transactions on Wireles
Communications, vol. 10, no. 12, pp. 3995–4000, December 2011.
[21] G. Fodor, E. Dahlman, G. Mildh, S. Parkvall, N. Reider, G. Mikls,and Z. Turnyi,
“Design aspects of network assisted device-to- device communications,” IEEE
Communications Magazine, vol. 50, no. 3, pp.170–177, 2012.
[22] R. Zhang, X. Cheng, L. Yang, and B. Jiao, “Interference-aware Graph based resource
sharing for device-to-device communications underlaying cellular networks,” in Proceedings
of IEEE WCNC, 2013, pp. 140–145.
[23] J. C. Li, M. Lei, and F. Gao, “Device-to-device (D2D) Communication in MU-MIMO
cellular networks,” in Proceedings of IEEE GLOBE COM,2012, pp. 3583–3587.
[24] N. Golrezaei, A. F. Molisch, and A. G. Dimakis, “Base-station assisted device-to-device
communications for high-throughput wire less video networks,” in Proceedings of IEEE ICC,
2012, pp. 7077–7081.
[25] Bin Guo, Shaohui Sun, Qiubin Gao “Interference Management for D2D
Communications Underlying Cellular Networks at Cell Edge” ICWMC 2014 .
[26] S. Shalmashi, G. Miao, and S. Ben Slimane, “Interference management for multiple
device-to-device communications underlaying cellular networks,” IEEE International
Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), 2013, pp. 223-
227.
[27] J. Shin, J. P. Choi, and J.-W. Choi , “An autonomous interference avoidance scheme for
D2D communications through frequency overhearing,” International Conference on ICT
Convergence (ICTC), 2013, pp. 1074-1075.
[28] H. Min, and J. Lee, “Capacity Enhancement Using an Interference Limited Area for
Device-toDevice Uplink Underlaying Cellular Networks,” IEEE Transactions on Wireless
Communications, vol. 10, no. 12, Dec. 2011, pp. 3995-4000.
20. [29] K. Doppler, M Rinne, C. Wijiting, C. B. Ribeiro, and K. Hugl, “Device-to-Device
Communication as an Underlay to LTE-Advanced Networks,” Communications Magazine,
IEEE, vol. 47, no. 12, Dec. 2009, pp 42-49. Copyright
[30] Ji Lianghai Klein, A. ; Kuruvatti, N. ; Schotten, H.D “System Capacity Optimization
Algorithm for D2D Underlay Operation “Communications Workshops (ICC), 2014 IEEE.
[31] B. Zhou, H. Hu, S.-Q. Huang, and H.-H. Chen, “Intracluster deviceto-device relay
algorithm with optimal resource utilization,” IEEE Transactions on Vehicular Technology,
vol. 62, no. 5, pp. 2315– 2326, Jun. 2013.
[32] J. C. Li, M. Lei, and F. Gao, “Device-to-device (D2D) communication in MU-MIMO
cellular networks,” in Proceedings of IEEE GLOBECOM,2012, pp. 3583–3587.
[33] G. Fodor, E. Dahlman, G. Mildh, S. Parkvall, N. Reider, G. Mikls, and Z. Turnyi,
“Design aspects of network assisted device-to-device communications,” IEEE
Communications Magazine, vol. 50, no. 3, pp. 170–177, 2012.
[34] W. ALLIANCE, “Wi-Fi Peer-to-Peer (P2P) Specification v1. 1,” WI-FI ALLIANCE
SPECIFICATION, vol. 1, pp. 1–159, 2010
[35] Z. Alliance, “Zigbee specification,” Document 053474r06, Version, vol. 1, 2006.
[36] S. Bluetooth, “Bluetooth specification version 1.1,” Available HTTP: http://www.
bluetooth. com, 2001
[37] A. Asadi and V. Mancuso, “Energy efficient opportunistic uplink Packet forwarding in
hybrid wireless networks,” in Proceedings of the fourth international conference on Future
energy systems, 2013.
[38] On the compound impact of opportunistic scheduling and D2D communications in
cellular networks, Accepted for publication in ACM MSWIM, 2013.
[39] C. Xu, L. Song, Z. Han, D. Li, and B. Jiao, “Resource allocation using a reverse iterative
combinatorial auction for device-to-device underay cellular networks,” in Proceedings of
IEEE GLOBECOM, 2012, pp. 4542–4547.
[40] A. Asad , V.Mancuso” On the compound impact of opportunistic scheduling and D2D
communications in cellular networks”, Accepted for publication in ACM MSWIM, 2013.
[41] A. Asad , V.Mancuso” “WiFi Direct and LTE D2D in action,” Accepted for publication
in IEEE Wireless Days, 2013.
[42] N. Golrezaei, A. G. Dimakis, and A. F. Molisch, “Device-to-device collaboration
through distributed storage,” in Proceedings of IEEE GLOBECOM, 2012, pp. 2397–2402.
[43] A. Asadi and V. Mancuso, “Energy efficient opportunistic uplink packet forwarding in
hybrid wireless networks,” in Proceedings of the fourth international conference on Future
energy systems, 2013, pp. 261–262.
[44] R1-132861. Final report of 3GPP TSG RAN WG1 #73 v1.0.0, August 2013.
[45] 3GPP TR 22.803 v12.1.0, “Feasibility study for proximity services (ProSe)” 2013
[46] R1-132115, “Discussions on LTE device to device communication”, ZTE, May 2013
21. [47] 3GPP TS 29.343 V12.1.0 (2014-12), “Proximity-services (ProSe) function to ProSe
application server aspects (PC2)”; Stage 3 (Release 12).
[48] B.Raghothaman, E. Deng, R. Pragada, G. Sternberg, T. Deng, and K.Vanganuru,
“Architecture and protocols for LTE-based device to device communication,” in Proc. IEEE
Int. Conf. Computing, Networking Communications, San Diego, CA, Jan. 2013, pp. 895–
899.
[49] 3GPP TS 23.303 version 12.2.0 Release 12, Proximity-based services (ProSe); Stage.2.
ETSI TS 123 303 V12.2.0. 2014-09.
[50] Athul Prasad, Andreas Kunz, Genadi Velev, Konstantinos Samdanis, and JaeSeung Song
, Energy Efficient D2D Discovery for Proximity Services in 3GPP LTE-Advanced Networks.
IEEE vehicular technology magazine ,December 2014.
[51] 3GPP TS 24.334: "Proximity-services (Prose) User Equipment (UE) to Proximity-
services (ProSe) Function aspects (PC3); Stage 3".June2014
[52] 3GPP TS 29.344 V12.3.0 (2015-06), Proximity-services (ProSe) Function to Home
Subscriber Server (HSS) aspects; Stage 3 (Release 12), June 2015.
[53] S. Mumtaz and J. Rodriguez (eds.), Smart Device to Smart Device Communication,
DOI: 10.1007/978-3-319-04963-2_1,Springer International Publishing Switzerland 2014.
22. INCREASING WIRELESS SENSOR NETWORKS LIFETIME WITH
NEW METHOD
Mohammad Sedighimanesh1, Javad Baqeri2 and Ali Sedighimanesh3
1,2,3Department of Electrical, Computer and It Engineering, Islamic Azad University of
Qazvin, Qom, Iran
ABSTRACT
One of the most important issues in Wireless Sensor Networks (WSNs) is severe energy
restrictions. As the performance of Sensor Networks is strongly dependence to the network
lifetime, researchers seek a way to use node energy supply effectively and increasing network
lifetime. As a consequence, it is crucial to use routing algorithms result in decrease energy
consumption and better bandwidth utilization. The purpose of this paper is to increase
Wireless Sensor Networks lifetime using LEACH-algorithm. So before clustering Network
environment, it is divided into two virtual layers (using distance between sensor nodes and
base station) and then regarding to sensors position in each of two layers, residual energy of
sensor and distance from base station is used in clustering. In this article, we compare
proposed algorithm with wellknown LEACH and ELEACH algorithms in homogenous
environment (with equal energy for all sensors) and heterogeneous one (energy of half of
sensors get doubled), also for static and dynamic situation of base station. Results show that
our proposed algorithm delivers improved performance.
KEYWORDS
Wireless Sensor Networks (WSNs), Routing protocols, Clustering in Wireless Sensor
Networks
Full Text: https://aircconline.com/ijwmn/V8N4/8416ijwmn04.pdf
Volume Link: https://airccse.org/journal/jwmn_current16.html
23. REFERENCES
[1] N. Pantazis, S. A. Nikolidakis, and D. D. Vergados, "Energy-efficient routing protocols in
wireless sensor networks: A survey," Communications Surveys & Tutorials, IEEE, vol. 15,
pp. 551-591, 2013.
[2] S. A. Nikolidakis, D. Kandris, D. D. Vergados, and C. Douligeris, "Energy efficient
routing in wireless sensor networks through balanced clustering," Algorithms, vol. 6, pp. 29-
42, 2013.
[3] V. Choudhary and M. K. Mahajan, "Energy-Efficient Protocols in Wireless Sensor
Networks: A Survey," Energy, vol .4 , . 2014
]4[ A. Nayyar and A. Gupta, "A Comprehensive Review of Cluster-Based Energy Efficient
Routing Protocols in Wireless Sensor Networks," IJRCCT, vol. 3, pp. 104-110, 2014.
http://ijrcct.org/index.php/ojs/article/view/539
[5] Y. Sheng, Z. Baoxian, L. Cheng, and H. Mouftah, "Routing protocols for wireless sensor
networks with mobile sinks: a survey," Communications Magazine, IEEE, vol. 52, pp. 150-
157, 2014.
[6] C. Tunca, S. Isik, M. Y. Donmez, and C. Ersoy, "Distributed Mobile Sink Routing for
Wireless Sensor Networks: A Survey," Communications Surveys & Tutorials, IEEE, vol. 16,
pp. 877-897, 2014.
[7] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, "Energy-efficient
communication protocol for wireless microsensor networks," in System Sciences, 2000.
Proceedings of the 33rd Annual Hawaii International Conference on, 2000, p. 10 pp. vol.2.
[8] W. Akkari, B. Bouhdid, and A. Belghith, "LEATCH: Low Energy Adaptive Tier
Clustering Hierarchy," Procedia Computer Science, vol. 52, pp. 365-372, 2015. Article
(CrossRef Link)
[9] S. Mottaghi and M. R. Zahabi, "Optimizing LEACH clustering algorithm with mobile
sink and rendezvous nodes," AEU-International Journal of Electronics and Communications,
vol. 69, pp. 507- 514, 2015.
[10] F. Xiangning and S. Yulin, "Improvement on LEACH protocol of wireless sensor
network," in Sensor Technologies and Applications, 2007. SensorComm 2007. International
Conference on, 2007, pp. 260-264 .
[11] N. Sharma and A. Nayyar, "A Comprehensive Review of Cluster Based Energy Efficient
Routing Protocols for Wireless Sensor Networks," International Journal of Application or
Innovation in Engineering & Management (IJAIEM), vol. 3, 2014.
[12] D. Goyal and M. R. Tripathy, "Routing protocols in wireless sensor networks: A
survey," in Advanced Computing & Communication Technologies (ACCT), 2012 Second
International Conference on, 2012, pp. 474-480.
[13] N. A. Latiff, N. A. Latiff, and R. Ahmad, "Enhancement of Wireless Sensor Network
Lifetime with Mobile Base Station Using Particle Swarm Optimization," International
Review on Computers and Software (IRECOS ( , vol. 10, pp. 189-199, 2015.
24. [14] S. Mahajan, J. Malhotra, and S. Sharma, "An energy balanced QoS based cluster head
selection strategy for WSN," Egyptian Informatics Journal, vol. 15, pp. 189-199, 2014.
[15] S. A. Sert, H. Bagci, and A. Yazici, "MOFCA :Multi-objective fuzzy clustering
algorithm for wireless sensor networks," Applied Soft Computing, vol. 30, pp. 151-165,
2015.
25. SHARED INFORMATION BASED SECURITY SOLUTION FOR
MOBILE AD HOC NETWORKS
Shailender Gupta1 and Chander Kumar2
1Department of Electronics Engineering, YMCA Institute of Engineering, Faridabad
2Department of Computer Engineering, YMCA Institute of Engineering, Faridabad
ABSTRACT
The mobile ad hoc networks get subjected to security threats like other wireless networks.
But due to their peer to peer approach and absence of infrastructural resources the mobile ad
hoc networks can not use strong cryptographic mechanisms as used by their other wireless
counterparts. This led to the development of trust based methods as security solutions
wherein a trusted node is relaxed from security checks when the trust value reaches to a
particular limit. The trust methods are prone to security risks but have found their acceptance
due to efficiency over computationally expensive and time consuming cryptographic
methods. The major problem with the trust methods is the period during which trust is
growing and is yet to reach the requisite threshold. This paper proposes security mechanism
dependent upon Random Electronic Code Book (RECB) combined with permutation
functions. The proposed mechanism has low time complexity, is easier to implement,
computationally inexpensive and has very high brute force search value. It can be used as the
temporary security guard during the trust growth phase. The impetus behind the proposed
design is the reliance upon shared information between the peers in the ad hoc networks.
KEYWORDS
Ad hoc Networks, Security, Trust, Cryptography
Full Text: http://airccse.org/journal/jwmn/0210s13.pdf
Volume Link: https://airccse.org/journal/jwmn_current10.html
26. REFERENCES
1. Wireless LAN media access control (MAC) and physical layer(PHY) specifications, IEEE
Standard 802.11, First edition, 1999
2. Rafael Timoteo de Sousa Jr., Robson de Oliveira Albuquerque, Maíra Hanashiro, Yamar
Aires da Silva and Paulo Roberto de, Lira Gondim, “Towards Establishing Trust in MANET:
an Integrated Approach for Auto-configuration, Authentication and certification”
International Journal of Forensic Computer Science, IJoFCS(2006) I, 33-40
3. Brian B. Luu, Barry J. O’Brien, David G. Baran, and Rommie L. Hardy” A Soldier-Robot
Ad Hoc Network” Proceedings of the Fifth Annual IEEE International Conference on
Pervasive Computing and Communications Workshops(PerComW'07)
4. C.E. Perkins and E.M. Royer. Ad hoc on demand Distance Vector routing, mobile
computing systems and applications, 1999. Proceedings. WMCSA ’99. Second IEEE
Workshop on, 1999, p90 - p100.
5. Murthy, S. and J.J. Garcia-Luna-Aceves, An Efficient Routing Protocol for Wireless
Networks, ACM Mobile Networks and App. J., Special Issue on Routing in Mobile
Communication Networks, Oct. 1996, pp. 183-97.
6. L. Zhou and Z. J. Haas, “Securing Ad Hoc Networks,” IEEE Network Magazine, Nov.
1999.
7. S. Marti and T. Giuli and K. Lai and M. Baker, “Mitigating Routing Misbehavior in
Mobile ad hoc networks,” in The Sixth Annual ACM/IEEE International Conference on
Mobile Computing and Networking, Boston, MA, USA, Aug. 2000.
8. F. Wang, B. Vetter, and S. Wu. Secure routing protocols: Theory and practice. Technical
report, North Carolina State University, May 1997.
9. C. Perkins, E. Belding-Royer, and S. Das. RFC3561: ad hoc on-demand distance vector
(AODV) routing. Internet RFCs, 2003.
10. D.B. Johnson, D.A. Maltz, J. Broch, et al. DSR: The dynamic source routing protocol for
multihop wireless ad hoc networks. Ad hoc networking, 5:139–172, 2001.
11. Z.J. Haas. A New Routing Protocol for the Reconfigurable Wireless Networks. In
Proceedings of the IEEE International Conference on Universal Personal Communications
(ICUPC, pages 562–566, 1997.
12. C.E. Perkins and P. Bhagwat. Highly dynamic destination-sequenced distance-vector
routing (DSDV) for mobile computers. ACM SIGCOMM Computer Communication
Review, 24(4):234–244, 1994.
13. Y.C. Hu, D.B. Johnson and A. Perrig, “SEAD: Secure efficient distance vector routing for
mobile wireless ad hoc networks,” IEEE, Proceedings of the Fourth IEEE Workshop on
Mobile Computing Systems and Applications (WMCSA’02), 0-7695-1647-5, 2002.
14. Y. Hu, A. Perrig and D. Johnson, Ariadne: A Secure On-demand Routing Protocol for Ad
Hoc Networks, in Proceedings of ACM MOBICOM’02, 2002.
27. 15. P. Papadimitratos and Z.J. Haas. “Secure routing for mobile ad hoc networks,” SCS
Communication Networks and Distributed Systems Modeling and Simulation Conference
(CNDS 2002), Jan 2002.
16. B. Dahill, B. N. Levine, E. Royer, and C. Shields, “A secure routing protocol for ad hoc
networks,” in Proceedings of the International Conference on Network Protocols (ICNP), pp.
78- 87, 2002.
17. ftp://MANET.itd.nrl.navy.mil/pub/MANET/2001-10.mail, October 8, 2001.M. Zapata, N.
Asokan, “Securing ad hoc routing protocols”, WiSe’02, ACM 1-5813-585-8, September 28,
2002, pp.1-10.
18. S. Marti, T.J. Giuli, K. Lai, and M. Baker. “Mitigating routing misbehavior in mobile ad
hoc networks,” in Proc. 6th Annual Int. Conf. on Mobile Computing and Networking
(MobiCom'00), Boston, MA, August 2000, pp.255-265.
19. S. Buchegger and J. Le Boudec, “Performance analysis of the CONFIDANT protocol:
(Cooperation of nodes - fairness in dynamic adhoc networks),” in Proc. IEEE / ACM
Workshop on Mobile Ad Hoc Networking and Computing (MobiHoc'02), Lausanne,
Switzerland, June 2002, pp.226-336.
20. P. Michiardi and R. Molva, “Core: a collaborative reputation mechanism to enforce node
cooperation in mobile ad hoc networks,” Communication and Multimedia Security
Conference (CMS'02), September 2002.
21. Q. He, D. Wu and P. Khosla, "SORI: A Secure and Objective Reputation-based Incentive
Scheme for Ad-hoc Networks", in Proc. IEEE WCNC2004, Mar. ’04.
22. S. Bansal and M. Baker, “Observation-based cooperation enforcement in ad hoc
networks,” Research Report cs.NI/0307012, Stanford University, 2003. (ocean 22)
23. A. A. Pirzada, C. McDonald and A. Datta, “ Performance Comparison of Trust Based
Reactive Routing Protocols” IEEE transaction on mobile computing, Vol. 5, No. 6, June
2006, pp 695- 710.
24. “Cryptography and Network Security: Principles and Practices”, William Stallings,
Pearson Education, First Indian Reprint, 2003.
25. “Introduction to Automata Theory, Languages and Computation” , J. E. Hopcroft, Rajeev
Motwani, J. D. Ullman, Pearson Education Second impression 2009.
26. S. Capkun, L. Buttyan, and J. P. Hubaux, “ Self Organised Public Key Management for
Mobil Ad hoc Networks” IEEE transaction on Mobile Computing, Vol. 2, No. 1, Jan 2003,
pp 52-64.
27. “Ad hoc Wireless Networks: Architectures and Protocols”, C. S. R. Murthy and B. S.
Manoj Pearson Education Fourth impression 2009.
28. www.random.Org
29. http:www.robertnz.net/true_rng.html
28. SYSTEM LEVEL SIMULATION FOR TWO TIER MACRO-FEMTO
CELLULAR NETWORKS
1Shiqi Xing, 2 Pantha Ghosal, 3 Shouman Barua, 4 Ramprasad Subramanian and 5Kumbesan
Sandrasegaran
Centre for Real-time Information Networks
School of Computing and Communications, Faculty of Engineering and Information
Technology, University of Technology Sydney, Sydney, Australia.
ABSTRACT
LTE is an emerging wireless communication technology to provide high- speed data service
for the mobile phones and data terminals. To improve indoor coverage and capacity
Femtocells are included in 3GPP since Release 8. There is no common simulation platform is
available for performance justification of LTEFemtocells. LTE-Sim is an object-oriented
open source simulator which incorporates a complete protocol stack can be used for
simulating two-tier macro-femto scenarios. To the best of our knowledge no paper provides
the guideline to perform system level simulation of Femtocell networks. Here, in this paper
Femtocells performance is evaluated in multi-Macrocells and multi-Femtocells environment
with interference from Microcells and Macrocell users along with the scripting.
KEYWORDS
Channel quality indicator (CQI), Femto Access Point (FAP), Macro eNodeB (MeNB),
Macrocell User Equepment (MUE), Moblity Management Entity(MME), Signal to
Interference Plus Noise Ratio(SINR), Physical Layer(PHY)
Full Text: https://airccse.org/journal/jwmn/6614ijwmn01.pdf
Volume Link: https://airccse.org/journal/jwmn_current14.html
30. Fault Detection and Recovery in Wireless Sensor Network Using
Clustering
Abolfazl Akbari1 , Arash Dana2 , Ahmad Khademzadeh3 and Neda Beikmahdavi4
1Dept. of Computer Engineering, Islamic Azad University Ayatollah Amoli Branch, Amol,
Iran
2Islamic Azad University, Central Tehran Branch, Tehran, Iran
3Iran Telecom Research Center, Tehran, Iran
4 Islamic Azad University Ayatollah Amoli Branch, Amol, Iran
ABSTRACT
Some WSN by a lot of immobile node and with the limited energy and without further charge
of energy. Whereas extension of many sensor nodes and their operation. Hence it is
normal.unactive nodes miss their communication in network, hence split the network. For
avoidance split of network, we proposed a fault recovery corrupted node and Self Healing is
necessary. In this Thesis, we design techniques to maintain the cluster structure in the event
of failures caused by energy-drained nodes. Initially, node with the maximum residual energy
in a cluster becomes cluster heed and node with the second maximum residual energy
becomes secondary cluster heed. Later on, selection of cluster heed and secondary cluster
heed will be based on available residual energy. We use Matlab software as simulation
platform quantities. like, energy consumption at cluster and number of clusters is computed
in evaluation of proposed algorithm. Eventually we evaluated and compare this proposed
method against previous method and we demonstrate our model is better optimization than
other method such as Venkataraman, in energy consumption rate.
KEY WORDS
Sensor Networks, clustering, fault detection, fault recovery.
Full Text: https://airccse.org/journal/jwmn/0211ijwmn12.pdf
Volume Link: https://airccse.org/journal/jwmn_current11.html
31. REFERENCES
[1] A. Bharathidasas, and V. Anand, “Sensor networks: An overview”, Technical report,
Dept. of Computer Science, University of California at Davis, 2002
[2] D. Estrin, R. Govindan, J. Heidemann, and S. Kumar, "Next century challenges: Scalable
coordination in sensor networks", in Proceedings of ACM Mobicom, Seattle, Washington,
USA, August 1999, pp. 263-- 270, ACM.
[3] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, "A Survey on Sensor
Networks", IEEE Communications Magazine, pp. 102--114, August 2002.
[4] D. Estrin, L. Girod, G. Pottie, M. Srivastava, “Instrumenting the world with wireless
sensor networks”, In Proceedings of the International Conference on Acoustics, Speech and
Signal Processing (ICASSP 2001.
[5] E. S. Biagioni and G. Sasaki, “Wireless sensor placement for reliable and efficient data
collection”, in the 36th International Conference on Systems Sciences, Hawaii, January 2003.
[6] G. Gupta and M. Younis, “Load-Balanced Clustering in Wireless Sensor Networks”, in
the Proceedings of International Conference on Communication (ICC 2003), Anchorage, AK,
May 2003.
[7] J. Chen, S. Kher and A. Somani, “Distributed Fault Detection of Wireless Sensor
Networks”, in DIWANS'06. 2006. Los Angeles, USA: ACM Pres.
[8] F. Koushanfar, M. Potkonjak, A. Sangiovanni- Vincentelli, “Fault Tolerance in Wireless
Ad-hoc Sensor Networks”, Proceedings of IEEE Sensors 2002, June, 2002.
[9] W. L. Lee, A. Datta, and R. Cardell-Oliver, “Network Management in Wireless Sensor
Networks”, to appear in Handbook on Mobile Ad Hoc and Pervasive Communications, edited
by M. K. Denko and L. T. Yang, American Scientific Publishers.
[10] G. Venkataraman, S. Emmanuel and S.Thambipillai, “Energy-efficient cluster-based
scheme for failure management in sensor networks” IET Commun, Volume 2, Issue 4, April
2008 Page(s):528 – 537
[11] L. Paradis and Q. Han, “A Survey of Fault Management in Wireless Sensor Networks”,
Journal of Network and Systems Management, vol. 15, no. 2, pp. 171-190, 2007.
[12] L. M. S. D. Souza, H. Vogt and M. Beigl, “A survey on fault tolerance in wireless sensor
networks”, 2007.
[13] W. L Lee, A.D., R. Cordell-Oliver, WinMS: Wireless Sensor Network-Management
System, An Adaptive Policy-Based Management for Wireless Sensor Networks. 2006.
[14] L. B. Ruiz, I. G.Siqueira, L. B. Oliveira, H. C. Wong, J.M. S. Nigeria, and A. A. F.
Loureiro. “Fault management in event-driven wireless sensor networks”, MSWiM’04,
October 4-6, 2004, Venezia, Italy
[15] G. Gupta and M. Younis; Fault tolerant clustering of wireless sensor networks;
WCNC’03, pp. 1579.1584.
32. [16] M. Ding, D. Chen, K. Xing, and X. Cheng, “Localized fault-tolerant event boundary
detection in sensor networks”, in Proceedings of the 24th Annual Joint Conference of the
IEEE Computer and Communications Societies (INFOCOM '05), vol. 2, pp. 902–913,
Miami, Fla, USA, March 2005
[17] C. Hsin and M.Liu, “Self-monitoring of Wireless Sensor Networks”, Computer
Communications, 2005. 29: p. 462-478
[18] S. Chessa and P. Santi, “Crash faults identification in wireless sensor networks”,
Comput. Commun., 2002, 25, (14), pp. 1273-1282.
[19] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, "Energy-Efficient
Communication Protocol for Wireless Microsensor Networks," Proc. Hawaii Int'l Conf.
System Sciences 2000.
[20] GUPTA G., YOUNIS M.: ‘Fault-tolerant clustering of wireless sensor networks’. Proc.
IEEE WCNC, New Orleans, USA,March 2003, vol. 3, p. 1579 – 1584
[21] N. Bulusu, J. Heidemann and D. Estrin, ”GPS-less Low Cost Outdoor Localization For
Very Small Devices”, IEEE Personal Communications, Special Issue on "Smart Spaces and
Environments", Vol. 7, No. 5, pp. 28-34, October 2000.
[22] Radhika Nagpal, ”Organizing a Global Coordinate System from Local Information on an
Amorphous Computer”, MIT AI Memo 1666, August 1999
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