International Journal of Wireless & Mobile Networks (IJWMN)

ISSN:0975-3834 [Online]; 0975-4679 [Print]


Mostafa Hefnawi

Royal Military College of Canada,Canada.


In this paper, we consider the optimization of wireless capacity-limited backhaul links in future heterogeneous networks (HetNets). We assume that the HetNet is formed with one macro-cell base station (MBS), which is associated with multiple small-cell base stations (SBSs). It is also assumed both the MBS and the SBSs are equipped with massive arrays, while all mobiles users (macro-cell and small-cell users) have single antenna. For the backhaul links, we propose to use a capacity-aware beamforming scheme at the SBSs and MRC at the MBS. Using particle swarm optimization (PSO), each SBS seeks the optimal transmit weight vectors that maximize the backhaul uplink capacity and the access uplinks signal-tointerference plus noise ratio (SINR). The performance evaluation in terms of the symbol error rate (SER) and the ergodic system capacity shows that the proposed capacity-aware backhaul link scheme achieves similar or better performance than traditional wireless backhaul links and requires considerably less computational complexity.


HetNets, wireless backhaul, cognitive radio, Massive MIMO, multiuser MIMO, PSO.

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[1] U. Siddique, H. Tabassum, E. Hossain, and D. I. Kim, “Wireless backhauling of 5G small cells: Challenges and solution approaches,” IEEE Wireless Communications, Special Issue on “Smart Backhauling and Fronthauling for 5G Networks”, vol. 22, no. 5, Oct. 2015, pp. 22-31.

[2] Zhen Gao, Linglong Dai, De Mi, Zhaocheng Wang, Muhammad Ali Imran, and Muhammad Zeeshan Shakir, “MmWave Massive MIMO Based Wireless Backhaul for 5G Ultra-Dense Network,” IEEE Wireless Communications, vol. 22, no. 5, pp. 13-21, Oct. 2015.

[3] H. Tabassum, A. Hamdi Sakr, and E. Hossain, “Analysis of massive MIMO-enabled downlink wireless backhauling for full-duplex small cells,” IEEE Transactions on Communications, vol. 64, no. 6, June 2016, pp. 2354-2369.

[4] Mehrdad Shariat, Emmanouil Pateromichelakis, Atta ul Quddus, and Rahim Tafazolli, “Joint TDD Backhaul and Access Optimization in Dense Small-Cell Networks,” IEEE Transactions on Vehicular Technology, vol. 64, no. 11, November 2015, pp. 5288-5299.

[5] H. ElSawy, E. Hossain, and D. I. Kim, “HetNets with cognitive small cells: User offloading and Distributed channel allocation techniques,” IEEE Communications Magazine, Special Issue on “Heterogeneous and Small Cell Networks (HetSNets)”, vol. 51, no. 6, June 2013.

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Dr. Hefnawi is currently a professor and the Chair of Graduate Studies Committee in the Department of Electrical and Computer Engineering at the Royal Military College of Canada. Dr. Hefnawi is a licensed professional engineer in the province of Ontario. He is a contributing author of several refereed journal, book chapters, and proceeding papers in the areas of wireless communications. His research interest includes cognitive radio, wireless sensor network, massive MIMO, cooperative MIMO, multiuser MIMO, and smart grid communications.


Vianney Kengne Tchendji1,Jean Frederic Myoupo2, Pauline Laure Fotso3 and Ulrich Kenfack Zeukeng1,

1University of Dschang, Cameroon, 2University of Picardie Jules Verne, France and 3University of Yaounde 1, Cameroon


This paper proposes a virtual architecture for three-dimensional (3D) wireless sensor networks (WSNs), a dynamic coordinate system, and a scalable energy-efficient training protocol for collections of nodes deployed in the space that are initially anonymous, asynchronous, and unaware of their initial location. The 3D WSNs considered comprise massively deployed tiny energy-constrained commodity sensors and one or more sink nodes that provide an interface to the outside world. The proposed architecture is a generalization of a two-dimensional virtual architecture previously proposed in the literature, in which a flexible and intuitive coordinate system is imposed onto the deployment area and the anonymous nodes are partitioned into clusters where data can be gathered from the environment and synthesized under local control. The architecture solves the hidden sensors problem that occurs because of irregularities in rugged deployment areas or environments containing buildings by training the network of nodes arbitrarily dispersed in the 3D space. In addition, we derive two simple and energy-efficient routing protocols, respectively for dense and sparse networks, based on the proposed dynamic coordinate system. They are used to minimize the power expended in collecting and routing data to the sink node, thus increasing the lifetime of the network.


Wireless Sensor Network, Self-organization, Training protocol, Energy-efficient Routing Protocol.

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[1] Akyildiz, I. F., Su, W., Y. Sankarasubramaniam, Y. and E. Cayirci, E. (2002) ‘Wireless sensor networks: A survey, Comput. Networks’, 38(4), 393-422.

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[13] Li, C., Wang, Z. and Yang, C. (2011) ‘Secure Routing for Wireless Mesh Networks’, International Journal of Network Security, 13(2), 109-120.  International Journal of Wireless & Mobile Networks (IJWMN) Vol. 9, No. 5, October 2017  86

[14] Saroit, I. A., El-Zoghdy, S. F. and Matar, M. (2011) ‘A scalable and distributed security protocol for multicast communications’, International Journal of Network Security, 12(2), 61-74.

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Vianney Kengne Tchendji is a Senior Lecturer of Computer Science at the University of Dschang, Dschang, Cameroon. He received his PhD in Computer Science from the University of Picardie-Jules Verne, Amiens, France, in 2014. His current research interests include network virtualization, parallel algorithms and architectures, scheduling, wireless communication, ad hoc and sensor networking.


Alexander Dikarev, Stanislav Dmitriev, Vitaliy Kubkin and Arthur Abelentsev,

Underwater Communication and Navigation Laboratory, Russia


This paper presents the new protocol for the organization of dynamic addressing nodes in the underwater wireless networks. In view of the extremely limited frequency band and significant delays inherent in the underwater acoustic data transmission channel, as well as the nonlinearity of the propagation paths of the acoustic signal in water, an approach is proposed for constructing a dynamic addressing protocol based on avoiding collisions at the receiving point. The protocol takes into account the peculiarities of the physical layer of the data transmission and is designed for servicing the network of autonomous non-synchronized nodes.


Network Protocols, Underwater Wireless Network,underwater acoustic communication.

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[1] Akyildiz, I.F. and D. Pompili, 2005. Underwater Acoustic sensor Networks: Research Challenges A survey, pp: 257-279

[2] Heidemann J, Li Yuan, Syed Affan, Wills Jack, Ye Wei, 2005. Underwater Sensor Networking: Research Challenges and Potential Applications, USC/ISI Technical Report ISI-TR-2005-603

[3] Manjula R.B., Sunilkumar S. Manvi, 2011. Issues in Underwater Acoustic Sensor Networks, International Journal of Computer and Electrical Engineering, Vol.3, No. 1, February, 2011, pp.101- 102

[4] Jun Liu, Zhong Zhou, Zheng Peng, Jun-Hong Cui, Mobi-Synch: Efficient Time Synchronization for Mobile Underwater Sensor Networks, 2010, UCONN CSE Technical Report: UbiNet-TR10-01, University of Connecticut, Storrs, CT 06269

[5] Ying Guo, Yutao Liu. Time synchronization for Mobile Underwater Sensor Networks. Journal of networks, vol. 8, NO. 1, January 2013

[6] Dhammannagari Deepthi, Shankar Thalla Underwater Sensor System Effective Time Synchronization in Networks of Mobile, International Journal of Computer Trends and Technology (IJCTT) – volume 13 number 4 – Jul 2014

[7] Dikarev, A., Dmitriev, S., Kubkin, V., Kulikov, P., Litvinenko, S., Acoustic communication and positioning system for divers, 1st Underwater Acoustics Conference and Exhibition. Proceedings., 2013., pp 1363-1367

[8] Dikarev A., Griffiths A., Watson S., Lennox B., Green P. R., Combined multiuser acoustic communication and localization system for µAUVs operating in confined underwater environments, 2015. IFAC Workshop on Navigation, Guidance and Control. Girona, Spain

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[10] Ravindra S.,Jagadeesha S. N., Time of arrival based localisation in wireless sensor networks: a linear approach. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.4, August 2013

[11] Long Cheng, Chengdong Wu, Yunzhou Zhang, Hao Wu,MengxinLi,CarstenMaple.,

[12]A Survey of Localization in Wireless Sensor Network. Hindawi Publishing Corporation International Journal of Distributed Sensor Networks Volume 2012, Article ID 962523, 12 pages doi:10.1155/2012/962523


Alexander Dikarev received his M.Eng in Launching equipment of rockets and cosmic apparatus from Volgograd Technical State University, Russia. He has 10 years experience in underwater acoustic communication and navigation system design and development: in Research Insitute of Hydroacoustic Communications (Volgograd, Russia), The University Of Manchester (UK), now he is R&D Director in Underwater communication & Navigation laboratory (Moscow, Russia)

Stanislav Dmitriev received his M.Sc in Radiophysics in Volgograd State University, Russia. He has 10 years of experience in Underwater Acoustic communication & navigation system design & development: in Research Insitute of Hydroacoustic Communications (Volgograd, Russia), now he is Engineering Director in Underwater Communication & Navigation laboratory (Moscow, Russia).

Vitaly Kubkin received his M.Eng in Volgograd Technical State University, Russia. He has 10 years of experience in Underwater Acoustic communication & navigation system design & development: in Research Insitute of Hydroacoustic Communications (Volgograd, Russia), now he is Senior Researcher in Underwater Communication & Navigation laboratory (Moscow, Russia).

Artur Abelentsev received his M.Ec in Management in Kuban State Technological University, Russia. He has more than 15 years of experience in IT, embedded system architect and research management.  He is a CEO in Underwater Communication & Navigation laboratory (Moscow, Russia).


JungSub Ahn and TaeHo Cho,

Sungkyunkwan University, Republic of Korea


Recently, the applications scope of Wireless Sensor Networks (WSNs) has been broadened. WSN communication security is important because sensor nodes are vulnerable to various security attacks when deployed in an open environment. An adversary could exploit this vulnerability to inject false reports into the network. En-route filtering techniques have been researched to block false reports. The CFFS scheme filters the false report by collaboratively validating the report by clustering the nodes. However, CFFS is not considered effective against repetitive attacks. Repeated attacks have a significant impact on network lifetime. In this paper, we propose a method to detect repetitive attacks with cluster-based false data filtering and to identify the compromised nodes and quickly block them. The proposed scheme uses fuzzy logic to determine the distribution of additional keys according to the network conditions, thereby improving energy efficiency.


WSN Security, Fabricated Report Verification, WSN Lifetime Extension, Enhanced CFFS

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[1] Akyildiz, Ian F., et al. “A survey on sensor networks.” IEEE communications magazine 40.8 (2002): 102-114.

[2] Wang, Yong, Garhan Attebury, and Byrav Ramamurthy. “A survey of security issues in wireless sensor networks.” (2006).

[3] Akkaya, Kemal, and Mohamed Younis. “A survey on routing protocols for wireless sensor networks.” Ad hoc networks 3.3 (2005): 325-349.

[4] Levis, Philip, et al. “TinyOS: An operating system for sensor networks.” Ambient intelligence. Springer, Berlin, Heidelberg, 2005. 115-148.

[5] Akyildiz, Ian F., et al. “Wireless sensor networks: a survey.” Computer networks 38.4 (2002): 393- 422.

[6] Al-Karaki, Jamal N., and Ahmed E. Kamal. “Routing techniques in wireless sensor networks: a survey.” IEEE wireless communications 11.6 (2004): 6-28.

[7] Karlof, Chris, and David Wagner. “Secure routing in wireless sensor networks: Attacks and

countermeasures.” Sensor Network Protocols and Applications, 2003. Proceedings of the First IEEE. 2003 IEEE International Workshop on. IEEE, 2003.

[8] Kavitha, T., and D. Sridharan. “Security vulnerabilities in wireless sensor networks: A survey.” Journal of information Assurance and Security 5.1 (2010): 31-44.

[9] Mohammadi, Shahriar, and Hossein Jadidoleslamy. “A comparison of physical attacks on wireless sensor networks.” International Journal of Peer to Peer Networks 2.2 (2011): 24-42.


[11] Kumar, Alok, and Alwyn Roshan Pais. “En-route filtering techniques in wireless sensor networks: a survey.” Wireless Personal Communications 96.1 (2017): 697-739.

[12] Ye, Fan, et al. “Statistical en-route filtering of injected false data in sensor networks.” IEEE Journal on Selected Areas in Communications 23.4 (2005): 839-850.

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[16] Liu, Zhixiong, et al. “A Cluster-Based False Data Filtering Scheme in Wireless Sensor Networks.” Adhoc & Sensor Wireless Networks 23 (2014).  International Journal of Wireless & Mobile Networks (IJWMN) Vol. 10, No. 4, August 2018

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[23] Nam, Su Man, and Tae Ho Cho. “Context-aware architecture for probabilistic voting-based filtering scheme in sensor networks.” IEEE Transactions on Mobile Computing 16.10 (2017): 2751-2763.


Jung Sub Ahn received the B.S. degree in computer engineering from Kyunil University in 2016 and now doing Ph.D. degree in Department of Electrical and Computer Engineering from Sungkyunkwan University. His research interests include wireless sensor network security, modelling & simulation, IoT security

Tea Ho Cho received a Ph.D. degree in Electrical and Computer Engineering from the University of Arizona, USA, in 1993, and B.S. and M.S. degrees in Electrical and Computer Engineering from Sungkyunkwan University, Repulic of Korea, and the University of Alabama, USA, respectively. He is currently a Professor in the College of Software at Sungkyunkwan University, Korea.


Nabeel I. Sulieman and Richard D. Gitlin,

University of South Florida, USA.


Enhanced Diversity and Network Coding (eDC-NC), the synergistic combination of Diversity and modified Triangular Network Coding, was introduced recently to provide efficient and ultra-reliable networking with near-instantaneous fault recovery. In this paper it is shown that eDC-NC technology can efficiently and securely broadcast messages in 5G wireless fog-computing-based Radio Access Networks (F-RAN). In particular, this work is directed towards demonstrating the ability of eDC-NC technology to more efficiently provide secure messages broadcasting than standardized methods such as Secure Multicasting using Secret (Shared) Key Cryptography, such that the adversary has no ability to acquire information even if they wiretap the entire F-RAN network (except of course the source and destination nodes). Our results show that using secure eDC-NC technology in F-RAN fronthaul network will enhance secure broadcasting and provide ultra-reliability networking, near-instantaneous fault recovery, and retain the throughput benefits of Network Coding.


5G, Diversity Coding, Triangular Network Coding, F-RAN, Secure Multicasting

For More Details :

Volume Link :


[1] N. Cai and W. Yeung, “Secure Network Coding”, IEEE International Symposium on Information Theory (ISIT), Lausanne, Switzerland, July 2002, pp. 323.

[2] L. Czap, C. Fragouli, V. M. Prabhakaran, and S. Diggavi, “Secure Network Coding with Erasures and Feedback”, 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, USA, Oct. 2013, pp. 1517-1524.

[3] C. Fargouli and E. Soljanin, “(Secure) Linear network coding multicast,” Springer International Journal of Designs, Codes, and Cryptography, vol. 78, issue. 1, pp. 269-310, Jan. 2016.

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[6] N. I. Sulieman, E. Balevi, and R. D. Gitlin, “Near-instant link failure recovery in 5G wireless Fog-basefronthaul networks,” IEEE Wireless Telecommunication Symposium, Phoenix, AZ, April 2018.

[7] N. I. Sulieman and R. D. Gitlin, “Ultra-reliable and energy efficient wireless sensor networks,” IEEE Wireless and Microwave Technology Conference, Sand Key, FL, April 2018.

[8] E. Ayanoglu, C.-L. I, R. D. Gitlin, and J. E. Mazo, “Diversity Coding for transparent self-healing and fault-tolerant communication networks,” IEEE Trans. Commun., vol. 41, pp. 1677–1686, Nov. 1993.

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[15] B. Weis, S. Rowles, and T. Hardjono, “The Group Domain of Interpretation,” RFC 6407, IETF, October 2011.


Nabeel I. Sulieman received his B.S. degree in Electrical Engineering from University of Baghdad, Baghdad – Iraq in 1998, he was one of the ten highest ranked students in the Electrical Engineering Department, and he received his M.S. degree with merit in wireless communications systems from Brunel University, London – UK in 2008. From 2002 until 2014, he worked for Iraqi Telecommunication and Post Company as a technical support engineer. In addition, he worked as an instructor for short technical courses in Higher Institute of Telecommunications-Baghdad-Iraq. Currently, he is a Ph.D. Candidate in Electrical Engineering Department at the University of South Florida in the Innovations in Wireless Information Networking Laboratory (iWINLAB) under the supervision of Dr. Richard Gitlin, and his research interests include Diversity Coding, Network Coding, 5G Wireless Fronthaul Networks, Synchronization of Diversity and Network Coding, Software Defined Networking (SDN), and Network Function Virtualization (NFV).

Richard D. Gitlin is a State of Florida 21st Century World Class Scholar, Distinguished University Professor and the Agere Systems Chaired Distinguished Professor of Electrical Engineering at the University of South Florida. He has more than 45 years of leadership in the communications and networking industry. He was at Bell Labs/Lucent Technologies for 32-years performing and leading pioneering research and development in digital communications, broadband networking, and wireless systems. Dr. Gitlin was Senior VP for Communications and Networking Research at Bell Labs and later CTO of Lucent’s Data Networking Business Unit. After retiring from Lucent, he was visiting professor of Electrical Engineering at Columbia University, where he supervised several doctoral students and research projects and then Chief Technology Officer of Hammerhead Systems, a venture funded networking company in Silicon Valley. Since joining USF in 2008, he has focused on the intersection of communications with medicine and created an interdisciplinary team that is focused on wireless networking of in vivo miniature wirelessly controlled devices to advance minimally invasive surgery and other cyber-physical health care systems. Dr. Gitlin has also directed research on advancing wireless local and 4G and 5G cellular systems by increasing their reliability and capacity. Dr. Gitlin is a member of the National Academy of Engineering (NAE), a Fellow of the IEEE, a Bell Laboratories Fellow, a Charter Fellow of the National Academy of Inventors (NAI), and a member of the Florida Inventors Hall of Fame. He is also a co-recipient of the 2005 Thomas Alva Edison Patent Award and the S.O. Rice prize, has co-authored a text, published ~150 papers and holds 65 patents.


Laila Nassef1,2, Remah Elhebshi1 and Linta Jose1,

1King Abdulaziz University, Saudi Arabia and 2Cairo University, Egypt


Wireless Sensor Networks (WSNs) is a strong candidate for smart grid applications, such as advanced metering infrastructure, demand response management, dynamic pricing, load control, electricity fraud detection, fault diagnostics, substation monitoring and control as well as automation of various elements of the power grid. The realization of these applications directly depends on efficiency of communication facilities among power grid elements. However, the harsh power grid environmental conditions with obstacles, noise, interference, and fading pose great challenges to reliability of these facilities to monitor and control the power grid. The purpose of this paper is to evaluate performance of WSNs in different power grid environments such as 500 kv substations, main power control room, and underground network transformer vaults. The power grid environments are modeled using a log-normal shadowing path loss model channel with realistic parameters. The network is simulated and performance is evaluated using packet delivery ratio, communication delay, and energy consumption. The simulation results have revealed that different environments have considerable impacts on performance of WSNs which make it suitable for most applications that need low data rate with low reliability requirements.


Smart Grid; Wireless Sensor Networks; Propagation Models; NS-2

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Volume Link :


[1] S. Bi, C. K. Ho & R. Zhang, (08 April 2015) “Wireless powered communication: opportunities and challenges,” IEEE Communications Magazine, Vol. 53, Issue: 4, pp. 117 – 125, DOI: 10.1109/MCOM.2015.7081084.

[[2] A. Leonardi, K. Mathioudakis, A. Wiesmaier & F. Zeiger, (2014) “Towards the Smart Grid: Substation Automation Architecture and Technologies,” Hindawi Publishing Corporation, Vol. 2014, Article ID 896296,

[[3] M. Erol-Kantarci & H. Mouftah, (17 March 2011) “Wireless Sensor Networks for Cost-Efficient Residential Energy Management in the Smart Grid,” IEEE Transactions on Smart Grid, Vol. 2, Issue: 2, pp. 314 – 325, DOI: 10.1109/TSG.2011.2114678.

[[4] I. Dietrich & F. Dressler, (February 2009) “On the lifetime of wireless sensor networks,” ACM Transactions on Sensor Networks (TOSN), Vol. 5 Issue: 1, pp. 1872 – 1899, DOI: 10.1145/1464420.1464425.

[[5] B. Rashid & H. Rehmani, (January 2016) “Applications of wireless sensor networks for urban areas: A survey,” Journal of Network and Computer Applications, Vol. 60, pp. 192-219,

[[6] C. Greer, D. Wollman, D. Prochaska & P. Boynton, (October 01, 2014) “NIST Framework and Roadmap or Smart Grid interoperability Standards, Release 1.0,” NIST Standards.

[[7] “,” Zigbee Smart Energy Profile. [Online].

[[8] “,” IEEE802. [Online].

[[9] H. S. Savitha, K. S. Divya, Abhilasha, Chandhini & S. Manasa, (May 2017) “Industrial Wireless Sensor Networks: Challenges,Design Principles, and Technical Approaches,” International Journal of Engineering Research in Electronics and Communication Engineering (IJERECE), pp. 215-221, ISSN (Online) 2394-6849.

[[10] L. L.Nassef, (December 2010) “On the Effects of Fading and Mobility in On Demand Routing Protocols,” Egyptian Informatics Journal, Vol. 11, Issue 2, pp. 67-74,

[[11] P. Sharma & G. Pandove, (May-June 2017) “A Review Article on Wireless Sensor Network in Smart Grid,” International Journal of Advanced Research in Computer Science, Vol. 8, No. 5, pp. 1903- 1907, ISSN No. 0976-5697.

[[12] X. Zhang and W. Li, (2014) “Simulation of the smart grid communications: Challenges, techniques, and future trends,” Computers and Electrical Engineering, Vol. 40, pp. 270–288,

[[13] S. Jabbar, M. A. Habib, A. A. Minhas, M. Ahmad, R. Ashraf, S. Khalid & K. Han, (18 February 2018) “Analysis of Factors Affecting Energy Aware Routing in Wireless Sensor Network,” Wireless Communications and Mobile Computing- Hindawi, Vol. 2018, Article ID 9087269, pp. 396-405,

[[14] S. Jabbar, M. Asif Habib, A. Minhas, M. Ahmad, R. Ashraf, S. Khalid & K. Han, (2018) “Analysis of Factors Affecting Energy Aware Routing in Wireless Sensor Network,” Hindawi, Wireless Communications and Mobile Computing, Vol. 2018, Article ID 9087269,

[[15] B. Kim, H. Park, K. H. Kim, D. Godfrey & K.-I. Kim, (2017) “A Survey on Real-Time Communications in Wireless Sensor Networks,” Hindawi, Wireless Communications and Mobile Computing, Vol. 2017, Article ID 1864847,

[[16] M. Doddavenkatappa, M. Choon Chan & B. Leong, (03 January 2012) “Improving Link Quality by Exploiting Channel Diversity in Wireless Sensor Networks,” in Real-Time Systems Symposium (RTSS), 2011 IEEE 32nd, Vienna, Austria, DOI: 10.1109/RTSS.2011.22.

[[17] F. Alassery, (November 2017) “A virtual MIMO transmission scenarios for high energy efficiency smart wireless sensor networks over Rayleigh flat fading channel,” in Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2017 8th IEEE Annual, Vancouver, BC, Canada, 23 DOI: 10.1109/IEMCON.2017.8117127.

[[18] H. Kim, (07 January 2016) “An energy-efficient load balancing scheme to extend lifetime in wireless sensor networks,” Springer US, Vol. 19, Issue 1, pp. 279–283, 0526-9.

[[19] Y. Dong, M. Hossain & J. Cheng, (11 February 2016) “Performance of Wireless Powered Amplify and Forward Relaying Over Nakagami-m Fading Channels With Nonlinear Energy Harvester,” IEEE Communications Letters, Vol. 20, Issue 4, pp. 672 – 675, DOI: 10.1109/LCOMM.2016.2528260.

[[20] W. Chongburee & O. Musikanon , (2012) “ZigBee Propagations and Performance Analysis in Last Mile Network,” International Journal of Innovation, Management and Technology, Vol. 3, No. 4.

[[21] “,” The Network Simulator, NS-2. [Online].


Abhishek Kumar,

Nurture Education Solutions Private Limited, India.


Localization entails position estimation of sensor nodes by employing different techniques and mathematical computations. Localizable sensors also form an inherent part in the functioning of IoT devices and robotics. In this article, the author extends1 a novel scheme for node localization implemented using a hybrid fuzzy logic system to trace the node locations inside the deployment region, presented by the Abhishek Kumar et. al. The results obtained were then optimized using Gauss Newton Optimization to improve the localization accuracy by 50% to 90% vis-à-vis weighted centroid and other fuzzy based localization algorithms. This article attempts to scale the proposed scheme for large number of sensor nodes to emulate somewhat real world scenario by introducing cooperative localization in previous presented work. The study also analyses the effectiveness of such scaling by comparing the localization accuracy. In next section, the article incorporates security in the proposed cooperative localization approach to detect malicious nodes/anchors by mutual authentication using El Gamel digital Signature scheme. A detailed study of the impact of incorporating security and scaling on average processing time and localization coverage has also been performed. The processing time increased by a factor of 2.5s for 500 nodes (can be attributed to more number of iterations and computations and large deployment area with small radio range of nodes) and coverage remained almost equal, albeit slightly low by a factor of 1% to 2%. Apart from these, the article also discusses the impact of adding extra functionalities in the proposed hybrid fuzzy system based localization scheme on processing time and localization accuracy. Lastly, this study also briefs about how the proposed scalable, cooperative and secure localization scheme tackles the type of attacks that pose threat to localization.


Sugeno Fuzzy Inference Systems, Mamdani Fuzzy Inference Systems, Localization algorithms, Gauss Newton method, Cooperative localization, Mutual authentication, Elgamel Digital Signature

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Volume Link :


[1] Kumar A., Saini B. (2018) A Sugeno-Mamdani Fuzzy System Based Soft Computing Approach Towards Sensor Node Localization with Optimization. In: Bhattacharyya P., Sastry H., Marriboyina V., Sharma R. (eds) Smart and Innovative Trends in Next Generation Computing Technologies. NGCT 2017. Communications in Computer and Information Science, vol 828. pp 40-55, Springer, Singapore.

[2] A. Kumar, N. Chand, V. Kumar, and V. Kumar, “Range Free Localization Schemes for Wireless Sensor Networks,” Int. J. Comput. Networks Commun., vol. 3, no. 6, pp. 115–129, 2011.

[3] A. Kumar and V. Kumar, “Fuzzy Logic Based Improved Range Free Localization for Wireless Sensor Networks,” vol. 177005, no. 5, pp. 534–542, 2013.

[4] M. A. Monfared, “Range Free Localization of Wireless Sensor Networks Based on Sugeno Fuzzy Inference,” no. c, pp. 36–41, 2012.

[5] S. K. Gharghan, R. Nordin, and M. Ismail, “A wireless sensor network with soft computing localization techniques for track cycling applications,” Sensors (Switzerland), vol. 16, no. 8, 2016.

[6] M. Kadkhoda, M. A. Totounchi, S. Member, M. H. Yaghmaee, and Z. Davarzani, “A Probabilistic Fuzzy Approach for Sensor Location Estimation in Wireless Sensor Networks,” 2010.

[7] L. Mallu and R. Ezhilarasie, “Live migration of virtual machines in cloud environment: A survey,” Indian J. Sci. Technol., vol. 8, no. July, pp. 326–332, 2015.

[8] B. Li, N. Wu, H. Wang, P. H. Tseng, and J. Kuang, “Gaussian message passing-based cooperative localization on factor graph in wireless networks,” Signal Processing, vol. 111, no. April, pp. 1–12, 2015.

[9] H. Naseri and V. Koivunen, “A Bayesian algorithm for distributed network localization using distance and direction data,” pp. 1–11.

[10] Tashnim Jabir Shovon Chowdhury, “A Distributed Cooperative Algorithm for Localization in Wireless Sensor Networks Using Gaussian Mixture Modeling,” University of Toledo, 2016.

[11] Kumar A., Prashar D. (2018) A Novel Approach for Node Localization in Wireless Sensor Networks. In: Singh R., Choudhury S., Gehlot A. (eds) Intelligent Communication, Control and Devices. Advances in Intelligent Systems and Computing, vol 624. pp 419-428, Springer, Singapore

[12] X. Liu, R. Yang, and Q. Cui, “An Efficient Secure DV-Hop Localization for Wireless Sensor Network,” System, vol. 9, no. 7, pp. 275–284, 2015.

[13] H. Chen, W. Lou, Z. Wang, J. Wu, Z. Wang, and A. Xi, “Securing DV-Hop localization against wormhole attacks in wireless sensor networks,” Pervasive Mob. Comput., vol. 16, no. PA, pp. 22– 35, 2015.

[14] W. Shi, M. Barbeau, J. P. Corriveau, J. Garcia-Alfaro, and M. Yao, “Secure localization in the presence of colluders in WSNs,” Sensors (Switzerland), vol. 17, no. 8, 2017.

[15] P. Li, X. Yu, H. Xu, J. Qian, L. Dong, and H. Nie, “Research on secure localization model based on trust valuation in wireless sensor networks,” Secur. Commun. Networks, vol. 2017, 2017.

[16] G. Kumar, M. K. Rai, H. J. Kim, and R. Saha, “A Secure Localization Approach Using Mutual Authentication and Insider Node Validation in Wireless Sensor Networks,” Mob. Inf. Syst., vol. 2017, 2017.

[17] L. Lazos and R. Poovendran, “SeRLoc: Secure Range-Independent Localization for Wireless Sensor Networks,” Netw. Secur., pp. 21–30, 2004.


Jean Louis Ebongue Kedieng Fendji1 and Sidoine Djuissi Samo2,

1University Institute of Technology, Ngaoundéré and 2Apple College, Dubai, United Arab Emirates


In this dynamic world, communication is a sine qua non for development. Communication represents sharing of information which can be local or remote. Though local communications may occur face to face between individuals remote communications take place among people over long distances. Mobile ad hoc networks (MANETs) are becoming an interesting part of research due to the increasing growth of wireless devices (laptops, tablets, mobiles etc.) and as well as wireless internet facilities like 4G/Wi-Fi.  A MANET is any infrastructure-less network formed by independent and self-configuring nodes. Each node acts as router. In order to send data, the source node initiates a routing process by using a routing protocol. The nature of the wireless medium is always insecure. So, during routing many attacks can take place. The main objective of an eavesdropper is to grab the confidential information in the network.  This secret information is used by a malicious node to perform further attacks. Here, the entire problem lies in identifying the eavesdropper because the eavesdropper acts a normal node in the network.  In this paper, we analyzed the impact of eavesdropper while executing an Ad hoc On Demand routing (AODV) protocol in MANETs. All the simulations are done using QualNet 5.1 network simulator. From the results, it is found that the network performance degrades in presence of an eavesdropper.


MANETs, AODV, eavesdropper, energy consumption, QualNet

For More Details :

Volume Link :


[1]   D. P. Agrawal and Q-A Zeng. “Introduction to Wireless and Mobile Systems,” Brooks/Cole Publishing, ISBN No. 0534-40851-6, 436 pages, 2003.

[2]   S. Giordano and W. W. Lu, “Challenges in mobile ad hoc networking,” IEEE Communications Magazine, vol. 39, no. 6, pp. 129–181, June 2001.

[3]   J. Broch, D. Maltz, D. Johnson, Y. Hu, and J. Jetcheva. “Multi-Hop Wireless Ad Hoc   Network Routing Protocols.” ACM/IEEE International Conference on Mobile Computing and Networking (MOBICOM’98), pages 85-97, 1998.

[4]   Latiff, L. A. and Fisal, N. 2003. ‘Routing Protocols in Wireless Mobile Ad Hoc Network – A Review’. The 9th Asia-Pasific Conference on Communication (APCC 2003), vol. 2, pp. 600- 604.

[5]   S. Lee, M. Gerla, and C. Chiang. “On-Demand Multicast Routing Protocol.” IEEE Wireless Communications and Networking Conference (WCNC’99), 1999.

[6]   J. Broch, D. Maltz, D. B. Johnson, Yih-Chun Hu, J. Jetcheva. “A Performance Comparison of Multi-Hop Wireless Ad Hoc Network Routing  protocols.”Proceedings of the Fourth Annual ACM/IEEE on Mobile Computing and Networking,  MOBICOM 98, October 1998.

[7]   C.E. Perkins, E.M. Royer & S. Das, Ad Hoc On Demand Distance Vector (AODV) Routing, IETFInternet draft, draft-ietf-manet-aodv-08.txt, March 2001

[8]   C. E. Perkins, and E. M. Royer, “Ad-Hoc On-Demand Distance Vector Routing,” in Proceedings of the 2nd IEEE Workshop on Mobile Computing Systems and Applications, New Orleans, LA, pp. 90-100, February 1999

[9]   QualNet 5.1 Developer Model Library, Scalable Network Technologies, Inc.,

[10] Jiejun Kong, Xiaoyan Hong. AODV: anonymous on demand routing with untraceable routes for mobile adhoc networks. MobiHoc’03, June 1–3, 2003, Annapolis, Maryland, USA

[11] Bing Wu, Jianmin Chen, Jie Wu, Mihaela Cardei, “A Survey on Attacks and Countermeasures in Mobile Ad Hoc Networks”, WIRELESS/MOBILE NETWORK SECURITY Y. Xiao, X. Shen, and D.-Z. Du (Eds.) pp.,2006 Springer

[12] S. Yi and R. Kravets, Composite Key Management for Ad Hoc Networks. Proc. of the 1st Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous’04), pp. 52-61, 2004.

[13] R. Oppliger, Internet and Intranet Security, Artech House, 1998.

[14] Qiu Wang and Hong-Ning Dai and Qinglin Zhao, “Eavesdropping Security in Wireless Ad Hoc Networks with Directional Antennas

[15] Satoshi Kurosawa, Hidehisa Nakayama, Nei Kato, Abbas Jamalipour2, and Yoshiaki Nemoto, “Detecting Blackhole Attack on AODV-based Mobile Ad Hoc Networks by Dynamic Learning

Method”, International Journal of Network Security, Vol.5, No.3, PP.338–346, Nov. 2007

[17] Viren Mahajan, Maitreya Natu, and Adarshpal Sethi, “ANALYSIS OF WORMHOLE INTRUSION ATTACKS IN MANETS”, 2008 IEEE

[18] Neeraj Tantubay,Dinesh Ratnam Gautam and Mukesh Kumar Dharjwal , “A Review of Power Conservation in Wireless Mobile Ad hoc Network (MANET)”,IJCSI Vol.8, Issue 4, No.1 , July,2011.


John R Rankin,

Charles Sturt University Study Group Melbourne, Australia


Battery life is a drawback of Wireless Sensor Networks (WSNs) but careful management of the network can provide optimum performance before batteries need replacing. This paper models and analyses the productivity and energy consumption of a 2-level balanced WSN. By optimizing the energy versus time curve first with respect to quiescent periods we obtain a curve of rest length as a function of the number of level two nodes and then by secondly optimizing this curve with respect to the number of remote nodes per level one sensor node, an overall optimum network management strategy is achieved. Programs were written to display the productivity and longevity curves and determine the optimum point if it exists.


Wireless Sensor Network, Network Architecture, Optimization, Productivity, Longevity

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[1] Hoang AN, Forster A, Puccinelli D, Giordano S, “Sensor Node Lifetime: An Experimental Study”,

[2] Yu-Chee Tseng , You-Chiun Wang , Kai-Yang Cheng “iMouse: An Integrated Mobile Surveillance and Wireless Sensor System” IEEE Computer Volume: 40, Issue: 6, June 2007.

[3] Wen-Tsuen Chen , Po-Yu Chen , Wei-Shun Lee , Chi-Fu Huang , “Design and Implementation of a Real Time Video Surveillance System with Wireless Sensor Networks”, Vehicular Technology Conference, 2008. VTC Spring 2008. IEEE.

[4] Ali Benzerbadj, BouabdellahKechar,AhcéneBounceur, Bernard Pottier “Energy Efficient Approach for Surveillance Applications Based on Self Organized Wireless Sensor Networks”, Procedia Computer Science, (Elzevier), Volume 63, 2015, Pages 165-170.

[5] Mahmood Ali, Annette Böhm and Magnus Jonsson, “Wireless Sensor Networks for Surveillance Applications – A Comparative Survey of MAC Protocols”, In: The fourth international conference on wireless and mobile communications (ICWMC 2008). Piscataway: IEEE; 2008. p. 399-403.

[6] Muhammad Farhan Khan, Emad A. Felemban,; SaadQaisar, Salman Ali, “Performance Analysis on Packet Delivery Ratio and End-to-End Delay of Different Network Topologies in Wireless Sensor Networks (WSNs)”, pp 324-329, 2013 IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks, 2013.

[7] MeenakshiTripathi, R. B. Battula, M. S. Gaur, V. Laxmi, “Energy Efficient Clustered Routing for Wireless Sensor Network” , pp 324-329, 2013 IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks, 2013.


Dr J Rankin has undertaken research over 45 years starting with General Relativity and Mathematical Physics at the University of Adelaide followed by Computer Graphics, Fuzzy Logic Systems and Games Technology at La Trobe University and most recently Wireless Sensor Networks with Charles Sturt University Study Group Melbourne.


Gholamreza Farahani,

Iranian Research Organization for Science and Technology (IROST), Iran


From one side, sensor manufacturing technology and from other side wireless communication technology improvement has an effect on the growth and deployment of Wireless Network Sensor (WSN). The appropriate performance of WSN has abundant necessity which has dependent on the different parameters such as optimize sensor placement and structure of network sensor. The optimized placement in WSN not only would optimize number of sensors, but also help to reach to the more precise information. Therefore different solutions are proposed to reduce cost and increase life time of sensor networks that most of them are concentrated in the field of routing and information transmission. In this paper, places which they need new sensors placement or sensor movements are determined and then with applying these changes, performance of WSN will calculate. To achieve the optimum placement, the network should evaluate precisely and effective criteria on the performance should extract. Therefore the criteria should be ranked and after weighting with using AHP algorithms, with use of Geographical Information System (GIS), these weighted criteria will combined and in the locations which WSN doesn’t have enough performance, new sensor placement will create. New proposed method, improve 21.11% performance of WSN with sensor placement in the low performance locations. Also the number of added sensor is 26.09% which is lowest number of added sensors in comparison with other methods.


Sensor, Wireless Sensor Network, Geographic Information System, Network performance, Analytical Hierarchy process, Overlap Index

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Volume Link :


[1] Miri S. M. & Afshar A., (2014) “Optimum Layout for Sensors in Water Distribution Networks through Ant Colony Algorithm: A Dual use Vision“, Water and Sewage Quarterly, Vol. 25, No. 3, pp 67-75.

[2] Ashrafee Kh., Ghader S., Esfehanian V. & Motasadi S., (2007) “Air Pollution Measurement Layout in Large Tehran“, Journal of Environmental Study, Vol. 33, No. 44, pp 1-10.

[3] Bahrampour M. & Bemaniam M. R., (2012) “Study on Optimum Location of Disaster Management Sites with use of GIS“, Journal of Emergency Management, Vol. 1, No. 1, pp 51-59.

[4] Osais, Y.E., M. St-Hilaire & R.Y. Fei, (2010), “Directional sensor placement with optimal sensing range, field of view and orientation“, Mobile Networks and Applications, Vol. 15, No. 2, pp 216-225.

[5] Isovitsch, S.L. & J.M. VanBriesen, (2008), “Sensor placement and optimization criteria dependencies in a water distribution system“, Journal of Water Resources Planning and Management, Vol. 134, No. 2, pp 186-196.

[6] Castello, C.C. & Fan J., (2010), “Optimal sensor placement strategy for environmental monitoring using wireless sensor networks“, 42nd Southeastern Symposium on System Theory (SSST), pp 275- 279.

[7] Mini, S., S.K. Udgata & S.L. Sabat, (2014), “Sensor deployment and scheduling for target coverage problem in wireless sensor networks“, IEEE sensors journal, Vol. 14, No. 3, pp 636-644.

[8] Argany, M., (2015), Development of a GIS-Based Method for Sensor Network Deployment and Coverage Optimization, PhD Thesis, Université Laval.

[9] M. Marks, (2010), “A survey of multi-objective deployment in wireless sensor networks“, Journal of Telecommunications and Information Technology, pp 36-41.

[10] C.Zhu, C. Zheng, L. Shu & G. Han, (2012),”A survey on coverage and connectivity issues in wireless sensor networks“, Journal of Network and Computer Applications, Vol. 35, No. 2, pp 619-632.

[11] S.Megerian, F. Koushanfar, M. Potkonjak & M.B. Srivastava, (2005), “Worst and best-case coverage in sensor networks“, IEEE transactions on mobile computing, Vol. 4, No. 1, pp 84-92.

[12] S.R.Angajala, (2012), “Coverage Problems in Sensor Networks”, IJECCE, Vol. 3, No. 1, pp 104-110.

[13] A. Ghosh & S.K. Das, (2006), “Coverage and connectivity issues in wireless sensor networks”, Mobile, wireless, and sensor networks: Technology, applications, and future directions, pp 221-256.

[14] K.Chakraborty, S.S. Iyengar, H. Qi & E. Cho, (2002), “Grid coverage for surveillance and target

location in distributed sensor networks”, IEEE transactions on computers, Vol. 51, Vol. 12, pp 1448- 1453.

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[18] F.Ishmanov , A.S. Malik & S.W. Kim, (2011), “Energy consumption balancing (ECB) issues and mechanisms in wireless sensor networks (WSNs): a comprehensive overview“, European Transactions on Telecommunications, Vol. 22, No. 4, pp 151-167.

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[24] EarthData availabe from:


Gholamreza Farahani received his BSc degree in electrical engineering from Sharif University of Technology, Tehran, Iran, in 1998 and MSc and PhD degrees in electrical engineering from Amirkabir University of Technology (Polytechnic), Tehran, Iran in 2000 and 2006 respectively. Currently, he is an assistant professor in the Institute of Electrical and Information Technology, Iranian Research Organization for Science and Technology (IROST), Iran. His research interest is Wireless Network especially Wireless Sensor network.