Wired/Wireless Internet Communications

As Wireless Sensor Networks (WSN) gain momentum in what concerns applications and deployment, monitoring is becoming crucial in order to guarantee that anomalies are promptly detected. Unfortunately, current WSN monitoring solutions have several limitations, such as being tailored for specific applications, requiring dedicated or specific hardware, consuming precious energy and/or processing resources, or relying on manual or offline intervention. In this paper we propose an approach to anomaly detection in WSNs that addresses these limitations. The approach is based on two very simple metrics, a logging tool, and a data-mining algorithm, thus leading to the following key characteristics: very low resource consumption, application independency, very good potential for multi-WSN monitoring, and automation and simplification of the detection process. The proposed approach was validated by implementation, which showed that it is quite effective in detecting several typical anomalies.

[1]  Kwang-Cheng Chen,et al.  Cognitive Radio Resource Management for QoS Guarantees in Autonomous Femtocell Networks , 2010, 2010 IEEE International Conference on Communications.

[2]  Takanori Hayashi,et al.  Parametric Packet-Layer Model for Monitoring Video Quality of IPTV Services , 2008, 2008 IEEE International Conference on Communications.

[3]  Walid Saad,et al.  Spectrum Leasing as an Incentive Towards Uplink Macrocell and Femtocell Cooperation , 2011, IEEE Journal on Selected Areas in Communications.

[4]  Jean-Yves Le Boudec,et al.  Rate adaptation, Congestion Control and Fairness: A Tutorial , 2000 .

[5]  Mérouane Debbah,et al.  Pricing and bandwidth allocation problems in wireless multi-tier networks , 2011, 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[6]  David J. Goodman,et al.  Single Carrier FDMA: A New Air Interface for Long Term Evolution , 2008 .

[7]  Paul J. Kühn,et al.  Hybrid optical network architectures: bringing packets and circuits together , 2006, IEEE Communications Magazine.

[8]  Jeffrey G. Andrews,et al.  Coverage in multi-antenna two-tier networks , 2009, IEEE Transactions on Wireless Communications.

[9]  Ecole Doctorale Edite.  Automated RRM Optimization of LTE networks using Statistical Learning , 2010 .

[10]  Zhi-Li Zhang,et al.  A first look at inter-data center traffic characteristics via Yahoo! datasets , 2011, 2011 Proceedings IEEE INFOCOM.

[11]  Mohammed Nafie,et al.  Optimized spectrum sensing algorithms for cognitive LTE femtocells , 2012, EURASIP J. Wirel. Commun. Netw..

[12]  Jeffrey G. Andrews,et al.  Femtocells: Past, Present, and Future , 2012, IEEE Journal on Selected Areas in Communications.

[13]  David K. Smith.  Network Flows: Theory, Algorithms, and Applications , 1994 .

[14]  Kavé Salamatian.  Toward a polymorphic future internet: a networking science approach , 2011, IEEE Communications Magazine.

[15]  Hervé Dubreil.  Méthodes d'optimisation de contrôleurs de logique floue pour le paramétrage automatique des réseaux mobiles UMTS , 2005 .

[16]  Xiaoxin Qiu,et al.  On the performance of adaptive modulation in cellular systems , 1999, IEEE Trans. Commun..

[17]  Tae-Jin Lee,et al.  Resource Allocation Method Using Channel Sensing and Resource Reuse for Cognitive Femtocells , 2013 .

[18]  Idelfonso Tafur Monroy,et al.  Polymorphic optical networks: a solution for service differentiation at the optical layer , 2003 .

[19]  Douglas N. Knisely,et al.  Standardization of femtocells in 3GPP , 2009, IEEE Communications Magazine.

[20]  Jeffrey G. Andrews,et al.  Femtocell networks: a survey , 2008, IEEE Communications Magazine.

[21]  Geoffrey Ye Li,et al.  Cross-layer optimization for OFDM wireless networks-part I: theoretical framework , 2005, IEEE Trans. Wirel. Commun..

[22]  Lei Chen,et al.  OPHMR: An Optimized Polymorphic Hybrid Multicast Routing Protocol for MANET , 2007, IEEE Transactions on Mobile Computing.