28,142 research outputs found

    Optimized Cluster-Based Dynamic Energy-Aware Routing Protocol for Wireless Sensor Networks in Agriculture Precision

    Full text link
    [EN] Wireless sensor networks (WSNs) are becoming one of the demanding platforms, where sensor nodes are sensing and monitoring the physical or environmental conditions and transmit the data to the base station via multihop routing. Agriculture sector also adopted these networks to promote innovations for environmental friendly farming methods, lower the management cost, and achieve scientific cultivation. Due to limited capabilities, the sensor nodes have suffered with energy issues and complex routing processes and lead to data transmission failure and delay in the sensor-based agriculture fields. Due to these limitations, the sensor nodes near the base station are always relaying on it and cause extra burden on base station or going into useless state. To address these issues, this study proposes a Gateway Clustering Energy-Efficient Centroid- (GCEEC-) based routing protocol where cluster head is selected from the centroid position and gateway nodes are selected from each cluster. Gateway node reduces the data load from cluster head nodes and forwards the data towards the base station. Simulation has performed to evaluate the proposed protocol with state-of-the-art protocols. The experimental results indicated the better performance of proposed protocol and provide more feasible WSN-based monitoring for temperature, humidity, and illumination in agriculture sector.This work has also been partially supported by the European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR.Qureshi, KN.; Bashir, MU.; Lloret, J.; León Fernández, A. (2020). Optimized Cluster-Based Dynamic Energy-Aware Routing Protocol for Wireless Sensor Networks in Agriculture Precision. Journal of Sensors. 2020:1-19. https://doi.org/10.1155/2020/9040395S1192020Sneha, K., Kamath, R., Balachandra, M., & Prabhu, S. (2019). New Gossiping Protocol for Routing Data in Sensor Networks for Precision Agriculture. Soft Computing and Signal Processing, 139-152. doi:10.1007/978-981-13-3393-4_15Qureshi, K. N., Abdullah, A. H., Bashir, F., Iqbal, S., & Awan, K. M. (2018). Cluster-based data dissemination, cluster head formation under sparse, and dense traffic conditions for vehicular ad hoc networks. International Journal of Communication Systems, 31(8), e3533. doi:10.1002/dac.3533Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, 67, 104-122. doi:10.1016/j.comnet.2014.03.027Feng, X., Zhang, J., Ren, C., & Guan, T. (2018). An Unequal Clustering Algorithm Concerned With Time-Delay for Internet of Things. IEEE Access, 6, 33895-33909. doi:10.1109/access.2018.2847036Savaglio, C., Pace, P., Aloi, G., Liotta, A., & Fortino, G. (2019). Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks. IEEE Access, 7, 29355-29364. doi:10.1109/access.2019.2902371Srbinovska, M., Gavrovski, C., Dimcev, V., Krkoleva, A., & Borozan, V. (2015). Environmental parameters monitoring in precision agriculture using wireless sensor networks. Journal of Cleaner Production, 88, 297-307. doi:10.1016/j.jclepro.2014.04.036Lloret, 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/s91210513Qureshi, K. N., Din, S., Jeon, G., & Piccialli, F. (2020). Link quality and energy utilization based preferable next hop selection routing for wireless body area networks. Computer Communications, 149, 382-392. doi:10.1016/j.comcom.2019.10.030Kumar, S. A., & Ilango, P. (2017). The Impact of Wireless Sensor Network in the Field of Precision Agriculture: A Review. Wireless Personal Communications, 98(1), 685-698. doi:10.1007/s11277-017-4890-zAnisi, M. H., Abdul-Salaam, G., & Abdullah, A. H. (2014). A survey of wireless sensor network approaches and their energy consumption for monitoring farm fields in precision agriculture. Precision Agriculture, 16(2), 216-238. doi:10.1007/s11119-014-9371-8Long, D. S., & McCallum, J. D. (2015). On-combine, multi-sensor data collection for post-harvest assessment of environmental stress in wheat. Precision Agriculture, 16(5), 492-504. doi:10.1007/s11119-015-9391-zFu, X., Fortino, G., Li, W., Pace, P., & Yang, Y. (2019). WSNs-assisted opportunistic network for low-latency message forwarding in sparse settings. Future Generation Computer Systems, 91, 223-237. doi:10.1016/j.future.2018.08.031Mehmood, A., Khan, S., Shams, B., & Lloret, J. (2013). Energy-efficient multi-level and distance-aware clustering mechanism for WSNs. International Journal of Communication Systems, 28(5), 972-989. doi:10.1002/dac.2720Pantazis, N. A., Nikolidakis, S. A., & Vergados, D. D. (2013). Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey. IEEE Communications Surveys & Tutorials, 15(2), 551-591. doi:10.1109/surv.2012.062612.00084De Farias, C. M., Pirmez, L., Fortino, G., & Guerrieri, A. (2019). A multi-sensor data fusion technique using data correlations among multiple applications. Future Generation Computer Systems, 92, 109-118. doi:10.1016/j.future.2018.09.034Rao, P. C. S., Jana, P. K., & Banka, H. (2016). A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless Networks, 23(7), 2005-2020. doi:10.1007/s11276-016-1270-7Fu, X., Fortino, G., Pace, P., Aloi, G., & Li, W. (2020). Environment-fusion multipath routing protocol for wireless sensor networks. Information Fusion, 53, 4-19. doi:10.1016/j.inffus.2019.06.001Liu, X. (2015). Atypical Hierarchical Routing Protocols for Wireless Sensor Networks: A Review. IEEE Sensors Journal, 15(10), 5372-5383. doi:10.1109/jsen.2015.2445796Jan, N., Javaid, N., Javaid, Q., Alrajeh, N., Alam, M., Khan, Z. A., & Niaz, I. A. (2017). A Balanced Energy-Consuming and Hole-Alleviating Algorithm for Wireless Sensor Networks. IEEE Access, 5, 6134-6150. doi:10.1109/access.2017.2676004Gupta, G. P., Misra, M., & Garg, K. (2014). Energy and trust aware mobile agent migration protocol for data aggregation in wireless sensor networks. Journal of Network and Computer Applications, 41, 300-311. doi:10.1016/j.jnca.2014.01.003Safa, H., Karam, M., & Moussa, B. (2014). PHAODV: Power aware heterogeneous routing protocol for MANETs. Journal of Network and Computer Applications, 46, 60-71. doi:10.1016/j.jnca.2014.07.035Liu, X. (2015). An Optimal-Distance-Based Transmission Strategy for Lifetime Maximization of Wireless Sensor Networks. IEEE Sensors Journal, 15(6), 3484-3491. doi:10.1109/jsen.2014.2372340Brar, G. S., Rani, S., Chopra, V., Malhotra, R., Song, H., & Ahmed, S. H. (2016). Energy Efficient Direction-Based PDORP Routing Protocol for WSN. IEEE Access, 4, 3182-3194. doi:10.1109/access.2016.2576475Abo-Zahhad, M., Ahmed, S. M., Sabor, N., & Sasaki, S. (2015). Mobile Sink-Based Adaptive Immune Energy-Efficient Clustering Protocol for Improving the Lifetime and Stability Period of Wireless Sensor Networks. IEEE Sensors Journal, 15(8), 4576-4586. doi:10.1109/jsen.2015.2424296Huynh, T.-T., Dinh-Duc, A.-V., & Tran, C.-H. (2016). Delay-constrained energy-efficient cluster-based multi-hop routing in wireless sensor networks. Journal of Communications and Networks, 18(4), 580-588. doi:10.1109/jcn.2016.000081Shen, J., Wang, A., Wang, C., Hung, P. C. K., & Lai, C.-F. (2017). An Efficient Centroid-Based Routing Protocol for Energy Management in WSN-Assisted IoT. IEEE Access, 5, 18469-18479. doi:10.1109/access.2017.2749606Awan, K. M., Shah, P. A., Iqbal, K., Gillani, S., Ahmad, W., & Nam, Y. (2019). Underwater Wireless Sensor Networks: A Review of Recent Issues and Challenges. Wireless Communications and Mobile Computing, 2019, 1-20. doi:10.1155/2019/6470359Sajwan, M., Gosain, D., & Sharma, A. K. (2018). CAMP: cluster aided multi-path routing protocol for wireless sensor networks. Wireless Networks, 25(5), 2603-2620. doi:10.1007/s11276-018-1689-0Varga, A. (2010). OMNeT++. Modeling and Tools for Network Simulation, 35-59. doi:10.1007/978-3-642-12331-3_3Lartillot, O., Toiviainen, P., & Eerola, T. (2008). A Matlab Toolbox for Music Information Retrieval. Studies in Classification, Data Analysis, and Knowledge Organization, 261-268. doi:10.1007/978-3-540-78246-9_31Mathur, P., Nielsen, R. H., Prasad, N. R., & Prasad, R. (2016). Data collection using miniature aerial vehicles in wireless sensor networks. IET Wireless Sensor Systems, 6(1), 17-25. doi:10.1049/iet-wss.2014.0120Zou, T., Lin, S., Feng, Q., & Chen, Y. (2016). Energy-Efficient Control with Harvesting Predictions for Solar-Powered Wireless Sensor Networks. Sensors, 16(1), 53. doi:10.3390/s16010053Song, Y., Ma, J., Zhang, X., & Feng, Y. (2012). Design of Wireless Sensor Network-Based Greenhouse Environment Monitoring and Automatic Control System. Journal of Networks, 7(5). doi:10.4304/jnw.7.5.838-844Nikolidakis, S., Kandris, D., Vergados, D., & Douligeris, C. (2013). Energy Efficient Routing in Wireless Sensor Networks Through Balanced Clustering. Algorithms, 6(1), 29-42. doi:10.3390/a6010029Ndzi, D. L., Harun, A., Ramli, F. M., Kamarudin, M. L., Zakaria, A., Shakaff, A. Y. M., … Farook, R. S. (2014). Wireless sensor network coverage measurement and planning in mixed crop farming. Computers and Electronics in Agriculture, 105, 83-94. doi:10.1016/j.compag.2014.04.01

    Comparison of Energy Efficient Clustering Protocols in Wireless Sensor Networks –A Review

    Get PDF
    — Wireless sensor networks with hundreds of sensor nodes have emerged in recent years as important platforms for a wide spectrum of monitoring tasks ranging from environmental to military applications. Its growth is expeditiously increasing and that’s why there is an immense field for research in this area. Sensors depend entirely on the trust of their battery for power, which cannot be revitalized or substituted. So the design of energy aware protocol is essential in respect to enhance the network lifetime. LEACH, LEACH C and HEED are energy-efficient hierarchical based protocols that balances the energy expense, saves the node energy and hence prolongs the lifetime of the network. So this paper presents a detailed review and analysis of these energy efficient protocols. Comparison of various network parameters is done in the form of tables and graphs. In the last of the paper conclusions is drawn

    Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

    Full text link
    The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

    Event-Driven Data Gathering in Pure Asynchronous Multi-Hop Underwater Acoustic Sensor Networks

    Full text link
    [EN] In underwater acoustic modem design, pure asynchrony can contribute to improved wake-up coordination, thus avoiding energy-inefficient synchronization mechanisms. Nodes are designed with a pre-receptor and an acoustically adapted Radio Frequency Identification system, which wakes up the node when it receives an external tone. The facts that no synchronism protocol is necessary and that the time between waking up and packet reception is narrow make pure asynchronism highly efficient for energy saving. However, handshaking in the Medium Control Access layer must be adapted to maintain the premise of pure asynchronism. This paper explores different models to carry out this type of adaptation, comparing them via simulation in ns-3. Moreover, because energy saving is highly important to data gathering driven by underwater vehicles, where nodes can spend long periods without connection, this paper is focused on multi-hop topologies. When a vehicle appears in a 3D scenario, it is expected to gather as much information as possible in the minimum amount of time. Vehicle appearance is the event that triggers the gathering process, not only from the nearest nodes but from every node in the 3D volume. Therefore, this paper assumes, as a requirement, a topology of at least three hops. The results show that classic handshaking will perform better than tone reservation because hidden nodes annulate the positive effect of channel reservation. However, in highly dense networks, a combination model with polling will shorten the gathering time.Blanc Clavero, S. (2020). Event-Driven Data Gathering in Pure Asynchronous Multi-Hop Underwater Acoustic Sensor Networks. Sensors. 20(5):1-16. https://doi.org/10.3390/s20051407S116205Roy, A., & Sarma, N. (2018). Effects of Various Factors on Performance of MAC Protocols for Underwater Wireless Sensor Networks. Materials Today: Proceedings, 5(1), 2263-2274. doi:10.1016/j.matpr.2017.09.228Awan, K. M., Shah, P. A., Iqbal, K., Gillani, S., Ahmad, W., & Nam, Y. (2019). Underwater Wireless Sensor Networks: A Review of Recent Issues and Challenges. Wireless Communications and Mobile Computing, 2019, 1-20. doi:10.1155/2019/6470359Rudnick, D. L., Davis, R. E., Eriksen, C. C., Fratantoni, D. M., & Perry, M. J. (2004). Underwater Gliders for Ocean Research. Marine Technology Society Journal, 38(2), 73-84. doi:10.4031/002533204787522703Petritoli, E., & Leccese, F. (2018). High Accuracy Attitude and Navigation System for an Autonomous Underwater Vehicle (AUV). ACTA IMEKO, 7(2), 3. doi:10.21014/acta_imeko.v7i2.535Nam, H. (2018). Data-Gathering Protocol-Based AUV Path-Planning for Long-Duration Cooperation in Underwater Acoustic Sensor Networks. IEEE Sensors Journal, 18(21), 8902-8912. doi:10.1109/jsen.2018.2866837Sun, J., Hu, F., Jin, W., Wang, J., Wang, X., Luo, Y., … Zhang, A. (2020). Model-Aided Localization and Navigation for Underwater Gliders Using Single-Beacon Travel-Time Differences. Sensors, 20(3), 893. doi:10.3390/s20030893Wahid, A., Lee, S., Kim, D., & Lim, K.-S. (2014). MRP: A Localization-Free Multi-Layered Routing Protocol for Underwater Wireless Sensor Networks. Wireless Personal Communications, 77(4), 2997-3012. doi:10.1007/s11277-014-1690-6Sánchez, A., Blanc, S., Yuste, P., Perles, A., & Serrano, J. J. (2012). An Ultra-Low Power and Flexible Acoustic Modem Design to Develop Energy-Efficient Underwater Sensor Networks. Sensors, 12(6), 6837-6856. doi:10.3390/s120606837Li, S., Qu, W., Liu, C., Qiu, T., & Zhao, Z. (2019). Survey on high reliability wireless communication for underwater sensor networks. Journal of Network and Computer Applications, 148, 102446. doi:10.1016/j.jnca.2019.102446Jiang, S. (2018). State-of-the-Art Medium Access Control (MAC) Protocols for Underwater Acoustic Networks: A Survey Based on a MAC Reference Model. IEEE Communications Surveys & Tutorials, 20(1), 96-131. doi:10.1109/comst.2017.2768802Chirdchoo, N., Soh, W., & Chua, K. C. (2008). RIPT: A Receiver-Initiated Reservation-Based Protocol for Underwater Acoustic Networks. IEEE Journal on Selected Areas in Communications, 26(9), 1744-1753. doi:10.1109/jsac.2008.081213Zenia, N. Z., Aseeri, M., Ahmed, M. R., Chowdhury, Z. I., & Shamim Kaiser, M. (2016). Energy-efficiency and reliability in MAC and routing protocols for underwater wireless sensor network: A survey. Journal of Network and Computer Applications, 71, 72-85. doi:10.1016/j.jnca.2016.06.005Khasawneh, A., Latiff, M. S. B. A., Kaiwartya, O., & Chizari, H. (2017). A reliable energy-efficient pressure-based routing protocol for underwater wireless sensor network. Wireless Networks, 24(6), 2061-2075. doi:10.1007/s11276-017-1461-xSánchez, A., Blanc, S., Yuste, P., Perles, A., & Serrano, J. J. (2015). An Acoustic Modem Featuring a Multi-Receiver and Ultra-Low Power. Circuits and Systems, 06(01), 1-12. doi:10.4236/cs.2015.6100

    An Energy Aware and Secure MAC Protocol for Tackling Denial of Sleep Attacks in Wireless Sensor Networks

    Get PDF
    Wireless sensor networks which form part of the core for the Internet of Things consist of resource constrained sensors that are usually powered by batteries. Therefore, careful energy awareness is essential when working with these devices. Indeed,the introduction of security techniques such as authentication and encryption, to ensure confidentiality and integrity of data, can place higher energy load on the sensors. However, the absence of security protection c ould give room for energy drain attacks such as denial of sleep attacks which have a higher negative impact on the life span ( of the sensors than the presence of security features. This thesis, therefore, focuses on tackling denial of sleep attacks from two perspectives A security perspective and an energy efficiency perspective. The security perspective involves evaluating and ranking a number of security based techniques to curbing denial of sleep attacks. The energy efficiency perspective, on the other hand, involves exploring duty cycling and simulating three Media Access Control ( protocols Sensor MAC, Timeout MAC andTunableMAC under different network sizes and measuring different parameters such as the Received Signal Strength RSSI) and Link Quality Indicator ( Transmit power, throughput and energy efficiency Duty cycling happens to be one of the major techniques for conserving energy in wireless sensor networks and this research aims to answer questions with regards to the effect of duty cycles on the energy efficiency as well as the throughput of three duty cycle protocols Sensor MAC ( Timeout MAC ( and TunableMAC in addition to creating a novel MAC protocol that is also more resilient to denial of sleep a ttacks than existing protocols. The main contributions to knowledge from this thesis are the developed framework used for evaluation of existing denial of sleep attack solutions and the algorithms which fuel the other contribution to knowledge a newly developed protocol tested on the Castalia Simulator on the OMNET++ platform. The new protocol has been compared with existing protocols and has been found to have significant improvement in energy efficiency and also better resilience to denial of sleep at tacks Part of this research has been published Two conference publications in IEEE Explore and one workshop paper

    A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

    Get PDF
    In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

    Adaptive Cross-Layer Multipath Routing Protocol for Mobile Ad Hoc Networks

    Full text link
    [EN] Mobile ad hoc networks (MANETs) are generally created for temporary scenarios. In such scenarios, where nodes are in mobility, efficient routing is a challenging task. In this paper, we propose an adaptive and cross-layer multipath routing protocol for such changing scenarios. Our routing mechanisms operate keeping in view the type of applications. For simple applications, the proposed protocol is inspired from traditional on-demand routing protocols by searching shortest routes from source to destination using default parameters. In case of multimedia applications, the proposed mechanism considers such routes which are capable of providing more data rates having less packet loss ratio. For those applications which need security, the proposed mechanism searches such routes which are more secure in nature as compared to others. Cross-layer methodology is used in proposed routing scheme so as to exchange different parameters across the protocol stack for better decision-making at network layer. Our approach is efficient and fault tolerant in a variety of scenarios that we simulated and tested.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. 037-1435-RG.Iqbal, Z.; Khan, S.; Mehmood, A.; Lloret, J.; Alrajeh, NA. (2016). Adaptive Cross-Layer Multipath Routing Protocol for Mobile Ad Hoc Networks. Journal of Sensors. 2016:1-18. https://doi.org/10.1155/2016/5486437S1182016Abusalah, L., Khokhar, A., & Guizani, M. (2008). A survey of secure mobile Ad Hoc routing protocols. IEEE Communications Surveys & Tutorials, 10(4), 78-93. doi:10.1109/surv.2008.080407Murthy, S., & Garcia-Luna-Aceves, J. J. (1996). An efficient routing protocol for wireless networks. Mobile Networks and Applications, 1(2), 183-197. doi:10.1007/bf01193336Toh, C.-K. (1997). Wireless Personal Communications, 4(2), 103-139. doi:10.1023/a:1008812928561Pearlman, M. R., & Haas, Z. J. (1999). Determining the optimal configuration for the zone routing protocol. IEEE Journal on Selected Areas in Communications, 17(8), 1395-1414. doi:10.1109/49.779922ZHEN, Y., WU, M., WU, D., ZHANG, Q., & XU, C. (2010). Toward path reliability by using adaptive multi-path routing mechanism for multimedia service in mobile Ad-hoc network. The Journal of China Universities of Posts and Telecommunications, 17(1), 93-100. doi:10.1016/s1005-8885(09)60431-3Sivakumar, R., Sinha, P., & Bharghavan, V. (1999). CEDAR: a core-extraction distributed ad hoc routing algorithm. IEEE Journal on Selected Areas in Communications, 17(8), 1454-1465. doi:10.1109/49.779926Zapata, M. G. (2002). Secure ad hoc on-demand distance vector routing. ACM SIGMOBILE Mobile Computing and Communications Review, 6(3), 106-107. doi:10.1145/581291.581312Khan, S., & Loo, J. (2010). Cross Layer Secure and Resource-Aware On-Demand Routing Protocol for Hybrid Wireless Mesh Networks. Wireless Personal Communications, 62(1), 201-214. doi:10.1007/s11277-010-0048-ySharma, V., & Alam, B. (2012). Unicaste Routing Protocols in Mobile Ad Hoc Networks: A Survey. International Journal of Computer Applications, 51(14), 9-18. doi:10.5120/8108-1714Tarique, M., Tepe, K. E., Adibi, S., & Erfani, S. (2009). Survey of multipath routing protocols for mobile ad hoc networks. Journal of Network and Computer Applications, 32(6), 1125-1143. doi:10.1016/j.jnca.2009.07.002Shiwen Mao, Shunan Lin, Yao Wang, Panwar, S. S., & Yihan Li. (2005). Multipath video transport over ad hoc networks. IEEE Wireless Communications, 12(4), 42-49. doi:10.1109/mwc.2005.1497857Li, Z., Chen, Q., Zhu, G., Choi, Y., & Sekiya, H. (2015). A Low Latency, Energy Efficient MAC Protocol for Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 11(8), 946587. doi:10.1155/2015/946587Zheng, Z., Liu, A., Cai, L. X., Chen, Z., & Shen, X. (2016). Energy and memory efficient clone detection in wireless sensor networks. IEEE Transactions on Mobile Computing, 15(5), 1130-1143. doi:10.1109/tmc.2015.2449847Dong, M., Ota, K., Liu, A., & Guo, M. (2016). Joint Optimization of Lifetime and Transport Delay under Reliability Constraint Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 27(1), 225-236. doi:10.1109/tpds.2015.2388482Hamrioui, S., Lorenz, P., Lloret, J., & Lalam, M. (2013). A Cross Layer Solution for Better Interactions Between Routing and Transport Protocols in MANET. Journal of Computing and Information Technology, 21(3), 137. doi:10.2498/cit.1002136Sanchez-Iborra, R., & Cano, M.-D. (2014). An approach to a cross layer-based QoE improvement for MANET routing protocols. Network Protocols and Algorithms, 6(3), 18. doi:10.5296/npa.v6i3.5827Cho, J.-H., Swami, A., & Chen, I.-R. (2011). A Survey on Trust Management for Mobile Ad Hoc Networks. IEEE Communications Surveys & Tutorials, 13(4), 562-583. doi:10.1109/surv.2011.092110.0008
    • …
    corecore