Energy-Efficient Multi-Level and Distance-Aware Clustering Mechanism for WSNs

[EN] Most sensor networks are deployed at hostile environments to sense and gather specific information. As sensor nodes have battery constraints, therefore, the research community is trying to propose energyefficient solutions for wireless sensor networks (WSNs) to prolong the lifetime of the network. In this paper, we propose an energy-efficient multi-level and distance-aware clustering (EEMDC) mechanism for WSNs. In this mechanism, the area of the network is divided into three logical layers, which depends upon the hop-count-based distance from the base station. The simulation outcomes show that EEMDC is more energy efficient than other existing conventional approaches.This work has been partially supported by the 'Ministerio de Ciencia e Innovacion', through the 'Plan Nacional de I+D+i 2008-2011' in the 'Subprograma de Proyectos de Investigacion Fundamental', project TEC2011-27516, and by the Polytechnic University of Valencia, through the PAID-15-11 multidisciplinary projectsMehmood, A.; Khan, S.; Shams, B.; Lloret, J. (2015). Energy-Efficient Multi-Level and Distance-Aware Clustering Mechanism for WSNs. International Journal of Communication Systems. 28(5):972-989. https://doi.org/10.1002/dac.2720S972989285Sendra, S., Lloret, J., Garcia, M., & Toledo, J. F. (2011). Power Saving and Energy Optimization Techniques for Wireless Sensor Neworks (Invited Paper). Journal of Communications, 6(6). doi:10.4304/jcm.6.6.439-459Bri D Garcia M Lloret J Dini P Real deployments of wireless sensor networks Third International Conference on Sensor Technologies and Applications (SENSORCOMM 2009) 2009 8 23GUI, L., VAL, T., & WEI, A. (2011). A Novel Two-Class Localization Algorithm in Wireless Sensor Networks. Network Protocols and Algorithms, 3(3). doi:10.5296/npa.v3i3.863Rajeswari, A., & P.T, K. (2011). A Novel Energy Efficient Routing Protocols for Wireless Sensor Networks Using Spatial Correlation Based Collaborative Medium Access Control Combined with Hybrid MAC. Network Protocols and Algorithms, 3(4). doi:10.5296/npa.v3i4.1296Lloret, J., Garcia, M., Tomás, J., & Boronat, F. (2008). GBP-WAHSN: A Group-Based Protocol for Large Wireless Ad Hoc and Sensor Networks. Journal of Computer Science and Technology, 23(3), 461-480. doi:10.1007/s11390-008-9147-6Lloret, J., Garcia, M., Bri, D., & Diaz, J. (2009). A Cluster-Based Architecture to Structure the Topology of Parallel Wireless Sensor Networks. Sensors, 9(12), 10513-10544. doi:10.3390/s91210513LEHSAINI, M., GUYENNET, H., & FEHAM, M. (2010). Cluster-based Energy-efficient k-Coverage for Wireless Sensor Networks. Network Protocols and Algorithms, 2(2). doi:10.5296/npa.v2i2.325Liu, G., Xu, B., & Chen, H. (2011). Decentralized estimation over noisy channels in cluster-based wireless sensor networks. International Journal of Communication Systems, 25(10), 1313-1329. doi:10.1002/dac.1308Cheng, L., Chen, C., Ma, J., & Shu, L. (2011). Contention-based geographic forwarding in asynchronous duty-cycled wireless sensor networks. International Journal of Communication Systems, 25(12), 1585-1602. doi:10.1002/dac.1325Wang, X., & Qian, H. (2011). Hierarchical and low-power IPv6 address configuration for wireless sensor networks. International Journal of Communication Systems, 25(12), 1513-1529. doi:10.1002/dac.1318Zhang, D., Yang, Z., Raychoudhury, V., Chen, Z., & Lloret, J. (2013). An Energy-Efficient Routing Protocol Using Movement Trends in Vehicular Ad hoc Networks. The Computer Journal, 56(8), 938-946. doi:10.1093/comjnl/bxt028Chen, J.-S., Hong, Z.-W., Wang, N.-C., & Jhuang, S.-H. (2010). Efficient Cluster Head Selection Methods for Wireless Sensor Networks. Journal of Networks, 5(8). doi:10.4304/jnw.5.8.964-970Peiravi, A., Mashhadi, H. R., & Hamed Javadi, S. (2011). An optimal energy-efficient clustering method in wireless sensor networks using multi-objective genetic algorithm. International Journal of Communication Systems, 26(1), 114-126. doi:10.1002/dac.1336Zeynali, M., Mollanejad, A., & Khanli, L. M. (2011). Novel hierarchical routing protocol in wireless sensor network. 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COCA: Constructing optimal clustering architecture to maximize sensor network lifetime. Computer Communications, 36(3), 256-268. doi:10.1016/j.comcom.2012.10.006Aslam N Phillips W Robertson W Sivakumar S A multi-criterion optimization technique for energy efficient cluster formation in wireless sensor networks 4th IEEE Consumer Communications and Networking Conference, (CCNC 2007) 2007 650 654Yi, S., Heo, J., Cho, Y., & Hong, J. (2007). PEACH: Power-efficient and adaptive clustering hierarchy protocol for wireless sensor networks. Computer Communications, 30(14-15), 2842-2852. doi:10.1016/j.comcom.2007.05.034Yong, Z., & Pei, Q. (2012). A Energy-Efficient Clustering Routing Algorithm Based on Distance and Residual Energy for Wireless Sensor Networks. Procedia Engineering, 29, 1882-1888. doi:10.1016/j.proeng.2012.01.231Chuan-Chi W A minimum transmission energy consumption routing protocol for user-centric wireless networks 2011 1143 1148Kumar, D., Aseri, T. C., & Patel, R. B. (2009). 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Modeling Security and Resource Allocation for Mobile Multi-hop Wireless Neworks Using Game Theory

This dissertation presents novel approaches to modeling and analyzing security and resource allocation in mobile ad hoc networks (MANETs). The research involves the design, implementation and simulation of different models resulting in resource sharing and security’s strengthening of the network among mobile devices. Because of the mobility, the network topology may change quickly and unpredictably over time. Moreover, data-information sent from a source to a designated destination node, which is not nearby, has to route its information with the need of intermediary mobile nodes. However, not all intermediary nodes in the network are willing to participate in data-packet transfer of other nodes. The unwillingness to participate in data forwarding is because a node is built on limited resources such as energy-power and data. Due to their limited resource, nodes may not want to participate in the overall network objectives by forwarding data-packets of others in fear of depleting their energy power. To enforce cooperation among autonomous nodes, we design, implement and simulate new incentive mechanisms that used game theoretic concepts to analyze and model the strategic interactions among rationale nodes with conflicting interests. Since there is no central authority and the network is decentralized, to address the concerns of mobility of selfish nodes in MANETs, a model of security and trust relationship was designed and implemented to improve the impact of investment into trust mechanisms. A series of simulations was carried out that showed the strengthening of security in a network with selfish and malicious nodes. Our research involves bargaining for resources in a highly dynamic ad-hoc network. The design of a new arbitration mechanism for MANETs utilizes the Dirichlet distribution for fairness in allocating resources. Then, we investigated the problem of collusion nodes in mobile ad-hoc networks with an arbitrator. We model the collusion by having a group of nodes disrupting the bargaining process by not cooperating with the arbitrator. Finally, we investigated the resource allocation for a system between agility and recovery using the concept of Markov decision process. Simulation results showed that the proposed solutions may be helpful to decision-makers when allocating resources between separated teams

Energy-efficient node selection algorithms with correlation optimization in wireless sensor networks

The sensing data of nodes is generally correlated in dense wireless sensor networks, and the active node selection problem aims at selecting a minimum number of nodes to provide required data services within error threshold so as to efficiently extend the network lifetime. In this paper, we firstly propose a new Cover Sets Balance (CSB) algorithm to choose a set of active nodes with the partially ordered tuple (data coverage range, residual energy). Then, we introduce a new Correlated Node Set Computing (CNSC) algorithm to find the correlated node set for a given node. Finally, we propose a High Residual Energy First (HREF) node selection algorithm to further reduce the number of active nodes. Extensive experiments demonstrate that HREF significantly reduces the number of active nodes, and CSB and HREF effectively increase the lifetime of wireless sensor networks compared with related works.This work is supported by the National Science Foundation of China under Grand nos. 61370210 and 61103175, Fujian Provincial Natural Science Foundation of China under Grant nos. 2011J01345, 2013J01232, and 2013J01229, and the Development Foundation of Educational Committee of Fujian Province under Grand no. 2012JA12027. It has also been partially supported by the "Ministerio de Ciencia e Innovacion," through the "Plan Nacional de I+D+i 2008-2011" in the "Subprograma de Proyectos de Investigacion Fundamental," Project TEC2011-27516, and by the Polytechnic University of Valencia, though the PAID-15-11 multidisciplinary Projects.Cheng, H.; Su, Z.; Zhang, D.; Lloret, J.; Yu, Z. (2014). Energy-efficient node selection algorithms with correlation optimization in wireless sensor networks. 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Improving Energy Efficiency in a Wireless Sensor Network by Combining Cooperative MIMO With Data Aggregation. IEEE Transactions on Vehicular Technology, 59(8), 3956-3965. doi:10.1109/tvt.2010.2063719Wei, G., Ling, Y., Guo, B., Xiao, B., & Vasilakos, A. V. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter. Computer Communications, 34(6), 793-802. doi:10.1016/j.comcom.2010.10.003Xiang, L., Luo, J., & Vasilakos, A. (2011). Compressed data aggregation for energy efficient wireless sensor networks. 2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks. doi:10.1109/sahcn.2011.5984932Xu, Y., & Choi, J. (2012). Spatial prediction with mobile sensor networks using Gaussian processes with built-in Gaussian Markov random fields. Automatica, 48(8), 1735-1740. doi:10.1016/j.automatica.2012.05.029Min, J.-K., & Chung, C.-W. (2010). EDGES: Efficient data gathering in sensor networks using temporal and spatial correlations. Journal of Systems and Software, 83(2), 271-282. doi:10.1016/j.jss.2009.08.004Jianzhong Li, & Siyao Cheng. (2012). (ε, δ)-Approximate Aggregation Algorithms in Dynamic Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 23(3), 385-396. doi:10.1109/tpds.2011.193Hung, C.-C., Peng, W.-C., & Lee, W.-C. (2012). Energy-Aware Set-Covering Approaches for Approximate Data Collection in Wireless Sensor Networks. IEEE Transactions on Knowledge and Data Engineering, 24(11), 1993-2007. doi:10.1109/tkde.2011.224Liu, C., Wu, K., & Pei, J. (2007). An Energy-Efficient Data Collection Framework for Wireless Sensor Networks by Exploiting Spatiotemporal Correlation. IEEE Transactions on Parallel and Distributed Systems, 18(7), 1010-1023. doi:10.1109/tpds.2007.1046Xiaobo Zhang, Heping Wang, Nait-Abdesselam, F., & Khokhar, A. A. (2009). Distortion Analysis for Real-Time Data Collection of Spatially Temporally Correlated Data Fields in Wireless Sensor Networks. IEEE Transactions on Vehicular Technology, 58(3), 1583-1594. doi:10.1109/tvt.2008.928906Karasabun, E., Korpeoglu, I., & Aykanat, C. (2013). Active node determination for correlated data gathering in wireless sensor networks. Computer Networks, 57(5), 1124-1138. doi:10.1016/j.comnet.2012.11.018Gupta, H., Navda, V., Das, S., & Chowdhary, V. (2008). Efficient gathering of correlated data in sensor networks. ACM Transactions on Sensor Networks, 4(1), 1-31. doi:10.1145/1325651.1325655Campobello, G., Leonardi, A., & Palazzo, S. (2012). Improving Energy Saving and Reliability in Wireless Sensor Networks Using a Simple CRT-Based Packet-Forwarding Solution. IEEE/ACM Transactions on Networking, 20(1), 191-205. doi:10.1109/tnet.2011.2158442Tseng, L.-C., Chien, F.-T., Zhang, D., Chang, R. Y., Chung, W.-H., & Huang, C. (2013). 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Web spider defense technique in wireless sensor networks

Wireless sensor networks (WSNs) are currently widely used in many environments. Some of them gather many critical data, which should be protected from intruders. Generally, when an intruder is detected in the WSN, its connection is immediately stopped. But this way does not let the network administrator gather information about the attacker and/or its purposes. In this paper, we present a bioinspired system that uses the procedure taken by the web spider when it wants to catch its prey. We will explain how all steps performed by the web spider are included in our system and we will detail the algorithm and protocol procedure. A real test bench has been implemented in order to validate our system. It shows the performance for different response times, the CPU and RAM consumption, and the average and maximum values for ping and tracert time responses using constant delay and exponential jitter.This work has been partially supported by the "Ministerio de Ciencia e Innovacion", through the "Plan Nacional de I+D+i 2008-2011" in the "Subprograma de Proyectos de Investigacion Fundamental", Project TEC2011-27516.Cánovas Solbes, A.; Lloret, J.; Macias Lopez, EM.; Suarez Sarmiento, A. (2014). Web spider defense technique in wireless sensor networks. International Journal of Distributed Sensor Networks. 2014:1-7. https://doi.org/10.1155/2014/348606S172014Bri, D., Garcia, M., Lloret, J., & Dini, P. (2009). Real Deployments of Wireless Sensor Networks. 2009 Third International Conference on Sensor Technologies and Applications. doi:10.1109/sensorcomm.2009.69Sendra, S., Lloret, J., Garcia, M., & Toledo, J. F. (2011). Power Saving and Energy Optimization Techniques for Wireless Sensor Neworks (Invited Paper). Journal of Communications, 6(6). doi:10.4304/jcm.6.6.439-459Xie, M., Han, S., Tian, B., & Parvin, S. (2011). Anomaly detection in wireless sensor networks: A survey. Journal of Network and Computer Applications, 34(4), 1302-1325. doi:10.1016/j.jnca.2011.03.004Yu, Y., Li, K., Zhou, W., & Li, P. (2012). Trust mechanisms in wireless sensor networks: Attack analysis and countermeasures. Journal of Network and Computer Applications, 35(3), 867-880. doi:10.1016/j.jnca.2011.03.005Zhu, W. T., Zhou, J., Deng, R. H., & Bao, F. (2012). Detecting node replication attacks in wireless sensor networks: A survey. Journal of Network and Computer Applications, 35(3), 1022-1034. doi:10.1016/j.jnca.2012.01.002Maleh, Y., & Ezzati, A. (2013). A Review of Security Attacks and Intrusion Detection Schemes in Wireless Sensor Network. International Journal of Wireless & Mobile Networks, 5(6), 79-90. doi:10.5121/ijwmn.2013.5606Alrajeh, N. A., Khan, S., & Shams, B. (2013). Intrusion Detection Systems in Wireless Sensor Networks: A Review. International Journal of Distributed Sensor Networks, 9(5), 167575. doi:10.1155/2013/167575Sun, B., Osborne, L., Xiao, Y., & Guizani, S. (2007). Intrusion detection techniques in mobile ad hoc and wireless sensor networks. IEEE Wireless Communications, 14(5), 56-63. doi:10.1109/mwc.2007.4396943Fatema, N., & Brad, R. (2013). Attacks and Counterattacks on Wireless Sensor Networks. International Journal of Ad hoc, Sensor & Ubiquitous Computing, 4(6), 1-15. doi:10.5121/ijasuc.2013.4601Ankala, R. P., Kavitha, D., & Haritha, D. (2011). MOBILE AGENT BASED ROUTING in MANETS –ATTACKS & DEFENCES. Network Protocols and Algorithms, 3(4). doi:10.5296/npa.v3i4.1351Hylsberg Jacobsen, R., Zhang, Q., & Skjødeberg Toftegaard, T. (2011). Bioinspired Principles for Large-Scale Networked Sensor Systems: An Overview. Sensors, 11(4), 4137-4151. doi:10.3390/s110404137Kofahi, N. (2013). An Empirical Study to Compare the Performance of some Symmetric and Asymmetric Ciphers. International Journal of Security and Its Applications, 7(5), 1-16. doi:10.14257/ijsia.2013.7.5.01Sisodia, M. S., & Raghuwanshi, V. (2011). Anomaly Base Network Intrusion Detection by Using Random Decision Tree and Random Projection: A Fast Network Intrusion Detection Technique. Network Protocols and Algorithms, 3(4). doi:10.5296/npa.v3i4.1342Zhijie, H., & Ruchuang, W. (2012). Intrusion Detection for Wireless Sensor Network Based on Traffic Prediction Model. Physics Procedia, 25, 2072-2080. doi:10.1016/j.phpro.2012.03.352Al-Gharabally, N., El-Sayed, N., Al-Mulla, S., & Ahmad, I. (2009). Wireless honeypots. Proceedings of the 2009 conference on Information Science, Technology and Applications - ISTA ’09. doi:10.1145/1551950.1551969Gopinath V.Success analysis of deception in wireless sensor networks [M.S. thesis]2010Oklahoma State UniversityZhongshan Zhang, Keping Long, Jianping Wang, & Dressler, F. (2014). On Swarm Intelligence Inspired Self-Organized Networking: Its Bionic Mechanisms, Designing Principles and Optimization Approaches. IEEE Communications Surveys & Tutorials, 16(1), 513-537. doi:10.1109/surv.2013.062613.00014Rathore, H., & Jha, S. (2013). Bio-inspired machine learning based Wireless Sensor Network security. 2013 World Congress on Nature and Biologically Inspired Computing. doi:10.1109/nabic.2013.6617852Alrajeh, N. A., & Lloret, J. (2013). Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 9(10), 351047. doi:10.1155/2013/351047Amirkolaei M. K.Enhancing bio-inspired intrusion response in Ad-hoc networks [Ph.D. thesis]August 2013Edinburgh, UKEdinburgh Napier Universityhttp://researchrepository.napier.ac.uk/6533/Muraleedharan, R., & Osadciw, L. A. (2009). An intrusion detection framework for Sensor Networks using Honeypot and Swarm Intelligence. Proceedings of the 6th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. doi:10.4108/icst.mobiquitous2009.7084Hortos, W. S. (2012). Bio-inspired, cross-layer protocol design for intrusion detection and identification in wireless sensor networks. 37th Annual IEEE Conference on Local Computer Networks -- Workshops. doi:10.1109/lcnw.2012.6424040Benahmed, K., Merabti, M., & Haffaf, H. (2012). Inspired Social Spider Behavior for Secure Wireless Sensor Networks. International Journal of Mobile Computing and Multimedia Communications, 4(4), 1-10. doi:10.4018/jmcmc.2012100101Herberstein, M. E. (Ed.). (2009). Spider Behaviour. doi:10.1017/cbo9780511974496Ficco, M. (2010). Achieving Security by Intrusion-Tolerance Based on Event Correlation. Network Protocols and Algorithms, 2(3). doi:10.5296/npa.v2i3.42

Fault tolerant mechanism for multimedia flows in wireless ad hoc networks based on fast switching paths

Multimedia traffic can be forwarded through a wireless ad hoc network using the available resources of the nodes. Several models and protocols have been designed in order to organize and arrange the nodes to improve transmissions along the network. We use a cluster-based framework, called MWAHCA architecture, which optimizes multimedia transmissions over a wireless ad hoc network. It was proposed by us in a previous research work. This architecture is focused on decreasing quality of service (QoS) parameters like latency, jitter, and packet loss, but other network features were not developed, like load balance or fault tolerance. In this paper, we propose a new fault tolerance mechanism, using as a base the MWAHCA architecture, in order to recover any multimedia flow crossing the wireless ad hoc network when there is a node failure. The algorithm can run independently for each multimedia flow. The main objective is to keep the QoS parameters as low as possible. To achieve this goal, the convergence time must be controlled and reduced. This paper provides the designed protocol, the analytical model of the algorithm, and a software application developed to test its performance in a real laboratory.This work has been partially supported by Instituto de Telecomunicacoes, Next Generation Networks and Applications Group (NetGNA), Covilha Delegation, and by National Funding from the FCT-Fundacao para a Ciencia e a Tecnologia through the Pest-OE/EEI/LA0008/2013 Project.Díaz Santos, JR.; Lloret, J.; Jimenez, JM.; Sendra, S.; Rodrigues, JJPC. (2014). Fault tolerant mechanism for multimedia flows in wireless ad hoc networks based on fast switching paths. Mathematical Problems in Engineering. 2014:1-12. doi:10.1155/2014/361543S1122014Sendra, S., Lloret, J., Garcia, M., & Toledo, J. F. (2011). Power Saving and Energy Optimization Techniques for Wireless Sensor Neworks (Invited Paper). Journal of Communications, 6(6). doi:10.4304/jcm.6.6.439-459Lloret, J., Garcia, M., Bri, D., & Sendra, S. (2009). A Wireless Sensor Network Deployment for Rural and Forest Fire Detection and Verification. Sensors, 9(11), 8722-8747. doi:10.3390/s91108722Lloret, J., Bosch, I., Sendra, S., & Serrano, A. (2011). A Wireless Sensor Network for Vineyard Monitoring That Uses Image Processing. Sensors, 11(6), 6165-6196. doi:10.3390/s110606165Akyildiz, I., Melodia, T., & Chowdury, K. (2007). Wireless multimedia sensor networks: A survey. IEEE Wireless Communications, 14(6), 32-39. doi:10.1109/mwc.2007.4407225Lloret, J., Garcia, M., Bri, D., & Diaz, J. (2009). A Cluster-Based Architecture to Structure the Topology of Parallel Wireless Sensor Networks. Sensors, 9(12), 10513-10544. doi:10.3390/s91210513Diaz, J. R., Lloret, J., Jimenez, J. M., & Rodrigues, J. J. P. C. (2014). A QoS-Based Wireless Multimedia Sensor Cluster Protocol. International Journal of Distributed Sensor Networks, 10(5), 480372. doi:10.1155/2014/480372Diaz, J. R., Lloret, J., Jimenez, J. M., & Sendra, S. (2014). MWAHCA: A Multimedia Wireless Ad Hoc Cluster Architecture. The Scientific World Journal, 2014, 1-14. doi:10.1155/2014/913046Sadiq, A. S., Bakar, K. A., Ghafoor, K. Z., Lloret, J., & Khokhar, R. (2013). An Intelligent Vertical Handover Scheme for Audio and Video Streaming in Heterogeneous Vehicular Networks. Mobile Networks and Applications, 18(6), 879-895. doi:10.1007/s11036-013-0465-8Diaz, J. R., Lloret, J., Jiménez, J. M., & Hammoumi, M. (2014). A new multimedia-oriented architecture and protocol for wireless ad hoc networks. International Journal of Ad Hoc and Ubiquitous Computing, 16(1), 14. doi:10.1504/ijahuc.2014.062486Pagani, E., & Rossi, G. P. (1999). Mobile Networks and Applications, 4(3), 175-192. doi:10.1023/a:1019198815518Xue, Y., & Nahrstedt, K. (2004). Providing Fault-Tolerant Ad hoc Routing Service in Adversarial Environments. Wireless Personal Communications, 29(3/4), 367-388. doi:10.1023/b:wire.0000047071.75971.cdBoukerche, A., Werner Nelem Pazzi, R., & Borges Araujo, R. (2006). Fault-tolerant wireless sensor network routing protocols for the supervision of context-aware physical environments. Journal of Parallel and Distributed Computing, 66(4), 586-599. doi:10.1016/j.jpdc.2005.12.007Bheemarjuna Reddy, T., Sriram, S., Manoj, B. S., & Siva Ram Murthy, C. (2006). MuSeQoR: Multi-path failure-tolerant security-aware QoS routing in Ad hoc wireless networks. Computer Networks, 50(9), 1349-1383. doi:10.1016/j.comnet.2005.05.035Chao, H. L., & Chang, C. L. (2008). A fault-tolerant routing protocol in wireless sensor networks. International Journal of Sensor Networks, 3(1), 66. doi:10.1504/ijsnet.2008.016463Melamed, R., Keidar, I., & Barel, Y. (2007). Octopus: A fault-tolerant and efficient ad-hoc routing protocol. Wireless Networks, 14(6), 777-793. doi:10.1007/s11276-006-0013-6Lopes, P., Salvador, P., & Nogueira, A. (2013). Methodologies for Network Topology Discovery and Detection of MAC and IP Spoofing Attacks. Network Protocols and Algorithms, 5(3), 153. doi:10.5296/npa.v5i3.431

Optimised Green IoT Network Architectures

The work in this thesis proposes a number of energy efficient architectures of IoT networks. These proposed architectures are edge computing, Passive Optical Network (PON) and Peer to Peer (P2P) based architectures. A framework was introduced for virtualising edge computing assisted IoT. Two mixed integer linear programming (MILP) models and heuristics were developed to minimise the power consumption and to maximise the number of served IoT processing tasks. Further consideration was also given to the limited IoT processing capabilities and hence the potential of processing task blockage. Two placement scenarios were studied revealing that the optimal distribution of cloudlets achieved 38% power saving compared to placing the cloudlet in the gateway while gateway placement can save up to 47% of the power compared to the optimal placement but blocked 50% of the total IoT object requests. The thesis also investigated the impact of PON deployment on the energy efficiency of IoT networks. A MILP model and a heuristic were developed to optimally minimise the power consumption of the proposed network. The results of this investigation showed that packing most of the VMs in OLT at a low traffic reduction percentage and placing them in relays at high traffic reduction rate saved power Also, the results revealed that utilising energy efficient PONs and serving heterogeneous VMs can save up to 19% of the total power. Finally, the thesis investigated a peer-to-peer (P2P) based architecture for IoT networks with fairness and incentives. It considered three VM placement scenarios and developed MILP models and heuristics to maximise the number of processing tasks served by VMs and to minimise the total power consumption of the proposed network. The results showed that the highest service rate was achieved by the hybrid scenario which consumes the highest amount of power compared to other scenarios

A framework for obesity control using a wireless body sensor network

Low-cost low-power consumption small wireless sensor devices have empowered the development of wireless body area networks (WBANs). In WBANs many sensors are attached to human body for sensing particular health related information to improve healthcare and quality of life. Obesity is one of the most common problems all over the world, which is amongst main causes of cardiovascular diseases. In this research, we explore hardware and software architecture of WBAN for obesity monitoring. The proposed framework consists of few sensor nodes that monitor body motion, calories calculator, and a personal server running on a personal smart phone or a personal computer. The focus of this research is to make obesity patients easier to get rid of this disease.The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University for funding this Research Group no. RG-1435-037.Alrajeh, NA.; Lloret, J.; Cánovas Solbes, A. (2014). A framework for obesity control using a wireless body sensor network. International Journal of Distributed Sensor Networks. 2014:1-6. https://doi.org/10.1155/2014/534760S162014Schmidt, R., Norgall, T., Mörsdorf, J., Bernhard, J., & von der Grün, T. (2002). Body Area Network BAN – a Key Infrastructure Element for Patient-Centered Medical Applications. Biomedizinische Technik/Biomedical Engineering, 47(s1a), 365-368. doi:10.1515/bmte.2002.47.s1a.365Garcia, M., Catala, A., Lloret, J., & Rodrigues, J. J. P. C. (2011). A wireless sensor network for soccer team monitoring. 2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS). doi:10.1109/dcoss.2011.5982204Sun, G., Qiao, G., & Xu, B. (2012). Link Characteristics Measuring in 2.4 GHz Body Area Sensor Networks. International Journal of Distributed Sensor Networks, 8(10), 519792. doi:10.1155/2012/519792Tomas, J., Lloret, J., Bri, D., & Sendra, S. (2011). Sensors and their Application for Disabled and Elderly People. Handbook of Research on Personal Autonomy Technologies and Disability Informatics, 311-330. doi:10.4018/978-1-60566-206-0.ch020Latré, B., Braem, B., Moerman, I., Blondia, C., & Demeester, P. (2010). A survey on wireless body area networks. Wireless Networks, 17(1), 1-18. doi:10.1007/s11276-010-0252-4Zasowski, T., Meyer, G., Althaus, F., & Wittneben, A. (2006). UWB signal propagation at the human head. IEEE Transactions on Microwave Theory and Techniques, 54(4), 1836-1845. doi:10.1109/tmtt.2006.871989Bri, D., Lloret, J., Turro, C., & Garcia, M. (s. f.). Measuring Specific Absorption Rate by using Standard Communications Equipment. Telemedicine and E-Health Services, Policies, and Applications, 81-111. doi:10.4018/978-1-4666-0888-7.ch004Di Renzo, M., Buehrer, R. M., & Torres, J. (2007). Pulse Shape Distortion and Ranging Accuracy in UWB-Based Body Area Networks for Full-Body Motion Capture and Gait Analysis. IEEE GLOBECOM 2007-2007 IEEE Global Telecommunications Conference. doi:10.1109/glocom.2007.717Neirynck D.Channel characterisation and physical layer analysis for body and personal area network development [Ph.D. thesis]2006Bristol, UKUniversity of BristolSendra, S., Lloret, J., Garcia, M., & Toledo, J. F. (2011). Power Saving and Energy Optimization Techniques for Wireless Sensor Neworks (Invited Paper). Journal of Communications, 6(6). doi:10.4304/jcm.6.6.439-459Ranjit, J. S., & Shin, S. (2013). A Modified IEEE 802.15.4 Superframe Structure for Guaranteed Emergency Handling in Wireless Body Area Network. Network Protocols and Algorithms, 5(2), 1. doi:10.5296/npa.v5i2.3375Tang, Q., Tummala, N., Gupta, S. K. S., & Schwiebert, L. (2005). Communication Scheduling to Minimize Thermal Effects of Implanted Biosensor Networks in Homogeneous Tissue. IEEE Transactions on Biomedical Engineering, 52(7), 1285-1294. doi:10.1109/tbme.2005.847527Bag, A., & Bassiouni, M. (2006). Energy Efficient Thermal Aware Routing Algorithms for Embedded Biomedical Sensor Networks. 2006 IEEE International Conference on Mobile Ad Hoc and Sensor Sysetems. doi:10.1109/mobhoc.2006.278619Quwaider, M., & Biswas, S. (2012). Delay Tolerant Routing Protocol Modeling for Low Power Wearable Wireless Sensor Networks. Network Protocols and Algorithms, 4(3). doi:10.5296/npa.v4i3.2054Machado, T. M. F., Lopes, I. M., Silva, B. M., Rodrigues, J. J. P. C., & Lloret, J. (2012). Performance evaluation of cooperation mechanisms for m-health applications. 2012 IEEE Global Communications Conference (GLOBECOM). doi:10.1109/glocom.2012.6503353Alrajeh, N. A., Khan, S., Lloret, J., & Loo, J. (2013). Secure Routing Protocol Using Cross-Layer Design and Energy Harvesting in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 9(1), 374796. doi:10.1155/2013/374796Macias, E., Suarez, A., & Lloret, J. (2013). Mobile Sensing Systems. Sensors, 13(12), 17292-17321. doi:10.3390/s131217292Meghanathan, N., & Mumford, P. (2013). Centralized and Distributed Algorithms for Stability-based Data Gathering in Mobile Sensor Networks. Network Protocols and Algorithms, 84. doi:10.5296/npa.v5i4.4208Hanson, M. A., Powell, H. C., Barth, A. T., Ringgenberg, K., Calhoun, B. H., Aylor, J. H., & Lach, J. (2009). Body Area Sensor Networks: Challenges and Opportunities. Computer, 42(1), 58-65. doi:10.1109/mc.2009.5Chen, M., Gonzalez, S., Vasilakos, A., Cao, H., & Leung, V. C. M. (2010). Body Area Networks: A Survey. Mobile Networks and Applications, 16(2), 171-193. doi:10.1007/s11036-010-0260-8Lopes, I. M., Silva, B. M., Rodrigues, J. J. P. C., Lloret, J., & Proenca, M. L. (2011). A mobile health monitoring solution for weight control. 2011 International Conference on Wireless Communications and Signal Processing (WCSP). doi:10.1109/wcsp.2011.6096926Nachman, L., Huang, J., Shahabdeen, J., Adler, R., & Kling, R. (2008). IMOTE2: Serious Computation at the Edge. 2008 International Wireless Communications and Mobile Computing Conference. doi:10.1109/iwcmc.2008.19

Systems and algorithms for wireless sensor networks based on animal and natural behavior

In last decade, there have been many research works about wireless sensor networks (WSNs) focused on improving the network performance as well as increasing the energy efficiency and communications effectiveness. Many of these new mechanisms have been implemented using the behaviors of certain animals, such as ants, bees, or schools of fish.These systems are called bioinspired systems and are used to improve aspects such as handling large-scale networks, provide dynamic nature, and avoid resource constraints, heterogeneity, unattended operation, or robustness, amongmanyothers.Therefore, thispaper aims to studybioinspired mechanisms in the field ofWSN, providing the concepts of these behavior patterns in which these new approaches are based. The paper will explain existing bioinspired systems in WSNs and analyze their impact on WSNs and their evolution. In addition, we will conduct a comprehensive review of recently proposed bioinspired systems, protocols, and mechanisms. Finally, this paper will try to analyze the applications of each bioinspired mechanism as a function of the imitated animal and the deployed application. Although this research area is considered an area with highly theoretical content, we intend to show the great impact that it is generating from the practical perspective.Sendra, S.; Parra Boronat, L.; Lloret, J.; Khan, S. (2015). Systems and algorithms for wireless sensor networks based on animal and natural behavior. International Journal of Distributed Sensor Networks. 2015:1-19. doi:10.1155/2015/625972S1192015Iram, R., Sheikh, M. I., Jabbar, S., & Minhas, A. A. (2011). Computational intelligence based optimization in wireless sensor network. 2011 International Conference on Information and Communication Technologies. doi:10.1109/icict.2011.5983561Lloret, J., Bosch, I., Sendra, S., & Serrano, A. (2011). A Wireless Sensor Network for Vineyard Monitoring That Uses Image Processing. Sensors, 11(6), 6165-6196. doi:10.3390/s110606165Lloret, J., Garcia, M., Bri, D., & Sendra, S. (2009). A Wireless Sensor Network Deployment for Rural and Forest Fire Detection and Verification. Sensors, 9(11), 8722-8747. doi:10.3390/s91108722Dasgupta, P. (2008). A Multiagent Swarming System for Distributed Automatic Target Recognition Using Unmanned Aerial Vehicles. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 38(3), 549-563. doi:10.1109/tsmca.2008.918619Quwaider, M., & Biswas, S. (2012). Delay Tolerant Routing Protocol Modeling for Low Power Wearable Wireless Sensor Networks. Network Protocols and Algorithms, 4(3). doi:10.5296/npa.v4i3.2054Sendra, S., Lloret, J., Garcia, M., & Toledo, J. F. (2011). Power Saving and Energy Optimization Techniques for Wireless Sensor Neworks (Invited Paper). Journal of Communications, 6(6). doi:10.4304/jcm.6.6.439-459Liu, M., & Song, C. (2012). Ant-Based Transmission Range Assignment Scheme for Energy Hole Problem in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 8(12), 290717. doi:10.1155/2012/290717Riva, G., & Finochietto, J. M. (2012). Pheromone-based In-Network Processing for Wireless Sensor Network Monitoring Systems. Network Protocols and Algorithms, 4(4). doi:10.5296/npa.v4i4.2206Garcia, M., Sendra, S., Lloret, J., & Canovas, A. (2011). Saving energy and improving communications using cooperative group-based Wireless Sensor Networks. Telecommunication Systems, 52(4), 2489-2502. doi:10.1007/s11235-011-9568-3Kim, J.-Y., Sharma, T., Kumar, B., Tomar, G. S., Berry, K., & Lee, W.-H. (2014). Intercluster Ant Colony Optimization Algorithm for Wireless Sensor Network in Dense Environment. International Journal of Distributed Sensor Networks, 10(4), 457402. doi:10.1155/2014/457402Dressler, F., & Akan, O. B. (2010). A survey on bio-inspired networking. Computer Networks, 54(6), 881-900. doi:10.1016/j.comnet.2009.10.024Atakan, B., & Akan, O. B. (2006). Immune System Based Distributed Node and Rate Selection in Wireless Sensor Networks. 2006 1st Bio-Inspired Models of Network, Information and Computing Systems. doi:10.1109/bimnics.2006.361806Di Pietro, R., & Verde, N. V. (2011). Introducing epidemic models for data survivability in Unattended Wireless Sensor Networks. 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks. doi:10.1109/wowmom.2011.5986165Marwaha, S., Indulska, J., & Portmann, M. (2009). Biologically Inspired Ant-Based Routing in Mobile Ad hoc Networks (MANET): A Survey. 2009 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing. doi:10.1109/uic-atc.2009.95Jha, V., Khetarpal, K., & Sharma, M. (2011). A survey of nature inspired routing algorithms for MANETs. 2011 3rd International Conference on Electronics Computer Technology. doi:10.1109/icectech.2011.5942042Fernandez-Marquez, J. L., Di Marzo Serugendo, G., Montagna, S., Viroli, M., & Arcos, J. L. (2012). Description and composition of bio-inspired design patterns: a complete overview. Natural Computing, 12(1), 43-67. doi:10.1007/s11047-012-9324-yCamilo, T., Carreto, C., Silva, J. S., & Boavida, F. (2006). An Energy-Efficient Ant-Based Routing Algorithm for Wireless Sensor Networks. Lecture Notes in Computer Science, 49-59. doi:10.1007/11839088_5Selvakennedy, S., Sinnappan, S., & Shang, Y. (2006). T-ANT: A Nature-Inspired Data Gathering Protocol for Wireless Sensor Networks. Journal of Communications, 1(2). doi:10.4304/jcm.1.2.22-29Almshreqi, A. M. S., Ali, B. M., Rasid, M. F. A., Ismail, A., & Varahram, P. (2012). An improved routing mechanism using bio-inspired for energy balancing in wireless sensor networks. The International Conference on Information Network 2012. doi:10.1109/icoin.2012.6164367Chen, G., Guo, T.-D., Yang, W.-G., & Zhao, T. (2006). An improved ant-based routing protocol in Wireless Sensor Networks. 2006 International Conference on Collaborative Computing: Networking, Applications and Worksharing. doi:10.1109/colcom.2006.361893Okdem, S., & Karaboga, D. (2006). Routing in Wireless Sensor Networks Using Ant Colony Optimization. First NASA/ESA Conference on Adaptive Hardware and Systems (AHS’06). doi:10.1109/ahs.2006.63Salehpour, A.-A., Mirmobin, B., Afzali-Kusha, A., & Mohammadi, S. (2008). An energy efficient routing protocol for cluster-based wireless sensor networks using ant colony optimization. 2008 International Conference on Innovations in Information Technology. doi:10.1109/innovations.2008.4781748Wen, Y., Chen, Y., & Pan, M. (2008). Adaptive ant-based routing in wireless sensor networks using Energy*Delay metrics. Journal of Zhejiang University-SCIENCE A, 9(4), 531-538. doi:10.1631/jzus.a071382Liao, W.-H., Kao, Y., & Wu, R.-T. (2011). Ant colony optimization based sensor deployment protocol for wireless sensor networks. Expert Systems with Applications, 38(6), 6599-6605. doi:10.1016/j.eswa.2010.11.079Pavai, K., Sivagami, A., & Sridharan, D. (2009). Study of Routing Protocols in Wireless Sensor Networks. 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies. doi:10.1109/act.2009.133Juan, L., Chen, S., & Chao, Z. (2007). Ant System Based Anycast Routing in Wireless Sensor Networks. 2007 International Conference on Wireless Communications, Networking and Mobile Computing. doi:10.1109/wicom.2007.603Wang, C., & Lin, Q. (2008). Swarm intelligence optimization based routing algorithm for Wireless Sensor Networks. 2008 International Conference on Neural Networks and Signal Processing. doi:10.1109/icnnsp.2008.4590326Jiang, H., Wang, M., Liu, M., & Yan, J. (2012). A quantum-inspired ant-based routing algorithm for WSNs. Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD). doi:10.1109/cscwd.2012.6221881Okazaki, A. M., & Frohlich, A. A. (2011). Ant-based Dynamic Hop Optimization Protocol: A routing algorithm for Mobile Wireless Sensor Networks. 2011 IEEE GLOBECOM Workshops (GC Wkshps). doi:10.1109/glocomw.2011.6162356Hui, X., Zhigang, Z., & Xueguang, Z. (2009). A Novel Routing Protocol in Wireless Sensor Networks Based on Ant Colony Optimization. 2009 International Conference on Environmental Science and Information Application Technology. doi:10.1109/esiat.2009.460AbdelSalam, H. S., & Olariu, S. (2012). BEES: BioinspirEd backbonE Selection in Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 23(1), 44-51. doi:10.1109/tpds.2011.100Da Silva Rego, A., Celestino, J., dos Santos, A., Cerqueira, E. C., Patel, A., & Taghavi, M. (2012). BEE-C: A bio-inspired energy efficient cluster-based algorithm for data continuous dissemination in Wireless Sensor Networks. 2012 18th IEEE International Conference on Networks (ICON). doi:10.1109/icon.2012.6506592Neshat, M., Sepidnam, G., Sargolzaei, M., & Toosi, A. N. (2012). Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artificial Intelligence Review, 42(4), 965-997. doi:10.1007/s10462-012-9342-2Antoniou, P., Pitsillides, A., Blackwell, T., & Engelbrecht, A. (2009). Employing the flocking behavior of birds for controlling congestion in autonomous decentralized networks. 2009 IEEE Congress on Evolutionary Computation. doi:10.1109/cec.2009.4983153Ruihua, Z., Zhiping, J., Xin, L., & Dongxue, H. (2011). Double cluster-heads clustering algorithm for wireless sensor networks using PSO. 2011 6th IEEE Conference on Industrial Electronics and Applications. doi:10.1109/iciea.2011.5975688Kulkarni, R. V., Venayagamoorthy, G. K., & Cheng, M. X. (2009). Bio-inspired node localization in wireless sensor networks. 2009 IEEE International Conference on Systems, Man and Cybernetics. doi:10.1109/icsmc.2009.5346107Kulkarni, R. V., & Venayagamoorthy, G. K. (2010). Bio-inspired Algorithms for Autonomous Deployment and Localization of Sensor Nodes. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 663-675. doi:10.1109/tsmcc.2010.2049649Xin Song, Cuirong Wang, Wang, J., & Bin Zhang. (2010). A hierarchical routing protocol based on AFSO algorithm for WSN. 2010 International Conference On Computer Design and Applications. doi:10.1109/iccda.2010.5541265Gao, X. Z., Wu, Y., Zenger, K., & Huang, X. (2010). A Knowledge-Based Artificial Fish-Swarm Algorithm. 2010 13th IEEE International Conference on Computational Science and Engineering. doi:10.1109/cse.2010.49Wang, L., & Ma, L. (2011). A hybrid artificial fish swarm algorithm for Bin-packing problem. Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology. doi:10.1109/emeit.2011.6022829Yiyue, W., Hongmei, L., & Hengyang, H. (2012). Wireless Sensor Network Deployment Using an Optimized Artificial Fish Swarm Algorithm. 2012 International Conference on Computer Science and Electronics Engineering. doi:10.1109/iccsee.2012.453Yang, X.-S. (2010). A New Metaheuristic Bat-Inspired Algorithm. Studies in Computational Intelligence, 65-74. doi:10.1007/978-3-642-12538-6_6Goyal, S., & Patterh, M. S. (2013). Performance of BAT Algorithm on Localization of Wireless Sensor Network. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 6(3), 351-358. doi:10.24297/ijct.v6i3.4481Krishnanand, K. N., & Ghose, D. (2006). Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multiagent and Grid Systems, 2(3), 209-222. doi:10.3233/mgs-2006-2301Apostolopoulos, T., & Vlachos, A. (2011). Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem. International Journal of Combinatorics, 2011, 1-23. doi:10.1155/2011/523806Liao, W.-H., Kao, Y., & Li, Y.-S. (2011). A sensor deployment approach using glowworm swarm optimization algorithm in wireless sensor networks. Expert Systems with Applications, 38(10), 12180-12188. doi:10.1016/j.eswa.2011.03.053Sun, Y., Jiang, Q., & Zhang, K. (2012). A clustering scheme for Reachback Firefly Synchronicity in wireless sensor networks. 2012 3rd IEEE International Conference on Network Infrastructure and Digital Content. doi:10.1109/icnidc.2012.6418705Zungeru, A. M., Ang, L.-M., & Seng, K. P. (2012). Termite-Hill. International Journal of Swarm Intelligence Research, 3(4), 1-22. doi:10.4018/jsir.2012100101KumarE, S., S. M., K., & Kumar B. P., V. (2014). Clustering Protocol for Wireless Sensor Networks based on Rhesus Macaque (Macaca mulatta) Animal's Social Behavior. International Journal of Computer Applications, 87(8), 20-27. doi:10.5120/15229-3754Breza, M., & McCann, J. A. (2008). Lessons in Implementing Bio-inspired Algorithms on Wireless Sensor Networks. 2008 NASA/ESA Conference on Adaptive Hardware and Systems. doi:10.1109/ahs.2008.72Aziz, N. A. B. A., Mohemmed, A. W., & Sagar, B. S. D. (2007). Particle Swarm Optimization and Voronoi diagram for Wireless Sensor Networks coverage optimization. 2007 International Conference on Intelligent and Advanced Systems. doi:10.1109/icias.2007.4658528Falcon, R., Li, X., Nayak, A., & Stojmenovic, I. (2012). A harmony-seeking firefly swarm to the periodic replacement of damaged sensors by a team of mobile robots. 2012 IEEE International Conference on Communications (ICC). doi:10.1109/icc.2012.6363859Antoniou, P., & Pitsillides, A. (2010). A bio-inspired approach for streaming applications in wireless sensor networks based on the Lotka–Volterra competition model. 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Energy-aware service provisioning in P2P-assisted cloud ecosystems

Cotutela Universitat Politècnica de Catalunya i Instituto Tecnico de LisboaEnergy has been emerged as a first-class computing resource in modern systems. The trend has primarily led to the strong focus on reducing the energy consumption of data centers, coupled with the growing awareness of the adverse impact on the environment due to data centers. This has led to a strong focus on energy management for server class systems. In this work, we intend to address the energy-aware service provisioning in P2P-assisted cloud ecosystems, leveraging economics-inspired mechanisms. Toward this goal, we addressed a number of challenges. To frame an energy aware service provisioning mechanism in the P2P-assisted cloud, first, we need to compare the energy consumption of each individual service in P2P-cloud and data centers. However, in the procedure of decreasing the energy consumption of cloud services, we may be trapped with the performance violation. Therefore, we need to formulate a performance aware energy analysis metric, conceptualized across the service provisioning stack. We leverage this metric to derive energy analysis framework. Then, we sketch a framework to analyze the energy effectiveness in P2P-cloud and data center platforms to choose the right service platform, according to the performance and energy characteristics. This framework maps energy from the hardware oblivious, top level to the particular hardware setting in the bottom layer of the stack. Afterwards, we introduce an economics-inspired mechanism to increase the energy effectiveness in the P2P-assisted cloud platform as well as moving toward a greener ICT for ICT for a greener ecosystem.La energía se ha convertido en un recurso de computación de primera clase en los sistemas modernos. La tendencia ha dado lugar principalmente a un fuerte enfoque hacia la reducción del consumo de energía de los centros de datos, así como una creciente conciencia sobre los efectos ambientales negativos, producidos por los centros de datos. Esto ha llevado a un fuerte enfoque en la gestión de energía de los sistemas de tipo servidor. En este trabajo, se pretende hacer frente a la provisión de servicios de bajo consumo energético en los ecosistemas de la nube asistida por P2P, haciendo uso de mecanismos basados en economía. Con este objetivo, hemos abordado una serie de desafíos. Para instrumentar un mecanismo de servicio de aprovisionamiento de energía consciente en la nube asistida por P2P, en primer lugar, tenemos que comparar el consumo energético de cada servicio en la nube P2P y en los centros de datos. Sin embargo, en el procedimiento de disminuir el consumo de energía de los servicios en la nube, podemos quedar atrapados en el incumplimiento del rendimiento. Por lo tanto, tenemos que formular una métrica, sobre el rendimiento energético, a través de la pila de servicio de aprovisionamiento. Nos aprovechamos de esta métrica para derivar un marco de análisis de energía. Luego, se esboza un marco para analizar la eficacia energética en la nube asistida por P2P y en la plataforma de centros de datos para elegir la plataforma de servicios adecuada, de acuerdo con las características de rendimiento y energía. Este marco mapea la energía desde el alto nivel independiente del hardware a la configuración de hardware particular en la capa inferior de la pila. Posteriormente, se introduce un mecanismo basado en economía para aumentar la eficacia energética en la plataforma en la nube asistida por P2P, así como avanzar hacia unas TIC más verdes, para las TIC en un ecosistema más verde.Postprint (published version