SWAP: Smart WAter Protocol for the Irrigation of Urban Gardens in Smart Cities

[EN] The implementation of Smart City projects has experimented a surge in the recent years with examples such as Smart Santander or Barcelona Smart City. Among the different domains that comprise the Smart City, water management has a great importance, more so in areas with water scarcity. Furthermore, water from different sources such as treated sewage water or collected rainwater can be utilized to address water needs where the use of potable water is not necessary. Therefore, the implementation of smart systems for the irrigation of urban gardens and other urban vegetated areas is of great importance to manage both water needs and the available resources. In this paper, a communication protocol for smart irrigation systems designed within the context of the Smart City is presented. The protocol enables the communication among devices with both LoRa and WiFi wireless technologies. Tests were performed with low-cost devices in an urban area. The results demonstrate the good performance of the proposal, obtaining the minimum packet loss by adding a 500 ms delay at the CH node when transmitting messages from WiFi to LoRa and vice versa.This work was supported by the Researchers Supporting Project, King Saud University, Riyadh, Saudi Arabia, under Grant RSP-2021/295.Aldegheishem, A.; Alrajeh, N.; García-García, L.; Lloret, J. (2022). SWAP: Smart WAter Protocol for the Irrigation of Urban Gardens in Smart Cities. IEEE Access. 10:39239-39247. https://doi.org/10.1109/ACCESS.2022.316557939239392471

An energy scaled and expanded vector-based forwarding scheme for industrial underwater acoustic sensor networks with sink mobility

Industrial Underwater Acoustic Sensor Networks (IUASNs) come with intrinsic challenges like long propagation delay, small bandwidth, large energy consumption, three-dimensional deployment, and high deployment and battery replacement cost. Any routing strategy proposed for IUASN must take into account these constraints. The vector based forwarding schemes in literature forward data packets to sink using holding time and location information of the sender, forwarder, and sink nodes. Holding time suppresses data broadcasts; however, it fails to keep energy and delay fairness in the network. To achieve this, we propose an Energy Scaled and Expanded Vector-Based Forwarding (ESEVBF) scheme. ESEVBF uses the residual energy of the node to scale and vector pipeline distance ratio to expand the holding time. Resulting scaled and expanded holding time of all forwarding nodes has a significant difference to avoid multiple forwarding, which reduces energy consumption and energy balancing in the network. If a node has a minimum holding time among its neighbors, it shrinks the holding time and quickly forwards the data packets upstream. The performance of ESEVBF is analyzed through in network scenario with and without node mobility to ensure its effectiveness. Simulation results show that ESEVBF has low energy consumption, reduces forwarded data copies, and less end-to-end delay

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

[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

Driving Assistance System for Ambulances to Minimise the Vibrations in Patient Cabin

[EN] The ambulance service is the main transport for diseased or injured people which suffers the same acceleration forces as regular vehicles. These accelerations, caused by the movement of the vehicle, impact the performance of tasks executed by sanitary personnel, which can affect patient survival or recovery time. In this paper, we have trained, validated, and tested a system to assess driving in ambulance services. The proposed system is composed of a sensor node which measures the vehicle vibrations using an accelerometer. It also includes a GPS sensor, a battery, a display, and a speaker. When two possible routes reach the same destination point, the system compares the two routes based on previously classified data and calculates an index and a score. Thus, the index balances the possible routes in terms of time to reach the destination and the vibrations suffered in the patient cabin to recommend the route that minimises those vibrations. Three datasets are used to train, validate, and test the system. Based on an Artificial Neural network (ANN), the classification model is trained with tagged data classified as low, medium, and high vibrations, and 97% accuracy is achieved. Then, the obtained model is validated using data from three routes of another region. Finally, the system is tested in two new scenarios with two possible routes to reach the destination. The results indicate that the route with less vibration is preferred when there are low time differences (less than 6%) between the two possible routes. Nonetheless, with the current weighting factors, the shortest route is preferred when time differences between routes are higher than 20%, regardless of the higher vibrations in the shortest route.This work was supported by the Researchers Supporting Project number (RSP-2021/295), King Saud University, Riyadh, Saudi Arabia.Aldegheishem, A.; Alrajeh, N.; Parra, L.; Romero Martínez, JO.; Lloret, J. (2022). Driving Assistance System for Ambulances to Minimise the Vibrations in Patient Cabin. Electronics. 11(23):1-19. https://doi.org/10.3390/electronics11233965119112

Heart Disease Prediction Using Stacking Model With Balancing Techniques and Dimensionality Reduction

Heart disease is a serious worldwide health issue with wide-reaching effects. Since heart disease is one of the leading causes of mortality worldwide, early detection is crucial. Emerging technologies like Machine Learning (ML) are currently being actively used by the biomedical, healthcare, and health prediction industries. PaRSEL, a new stacking model is proposed in this research, that combines four classifiers, Passive Aggressive Classifier (PAC), Ridge Classifier (RC), Stochastic Gradient Descent Classifier (SGDC), and eXtreme Gradient Boosting (XGBoost), at the base layer, and LogitBoost is deployed for the final predictions at the meta layer. The imbalanced and irrelevant features in the data increase the complexity of the classification models. The dimensionality reduction and data balancing approaches are considered very important for lowering costs and increasing the accuracy of the model. In PaRSEL, three dimensionality reduction techniques, Recursive Feature Elimination (RFE), Linear Discriminant Analysis (LDA), and Factor Analysis (FA), are used to reduce the dimensionality and select the most relevant features for the diagnosis of heart disease. Furthermore, eight balancing techniques, Proximity Weighted Random Affine Shadowsampling (ProWRAS), Localized Randomized Affine Shadowsampling (LoRAS), Random Over Sampling (ROS), Adaptive Synthetic (ADASYN), Synthetic Minority Oversampling Technique (SMOTE), Borderline SMOTE (B-SMOTE), Majority Weighted Minority Oversampling Technique (MWMOTE) and Random Walk Oversampling (RWOS), are used to deal with the imbalanced nature of the dataset. The performance of PaRSEL is compared with the other standalone classifiers using different performance measures like accuracy, F1-score, precision, recall and AUC-ROC score. Our proposed model achieves 97% accuracy, 80% F1-score, precision is greater than 90%, 67% recall, and 98% AUC-ROC score. This shows that PaRSEL outperforms other standalone classifiers in terms of heart disease prediction. Additionally, we deploy SHapley Additive exPlanations (SHAP) on our proposed model. It helps to understand the internal working of the model. It illustrates how much influence a classifier has on the final prediction outcome

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

A Modified Feature Selection and Artificial Neural Network-Based Day-Ahead Load Forecasting Model for a Smart Grid

In the operation of a smart grid (SG), day-ahead load forecasting (DLF) is an important task. The SG can enhance the management of its conventional and renewable resources with a more accurate DLF model. However, DLF model development is highly challenging due to the non-linear characteristics of load time series in SGs. In the literature, DLF models do exist; however, these models trade off between execution time and forecast accuracy. The newly-proposed DLF model will be able to accurately predict the load of the next day with a fair enough execution time. Our proposed model consists of three modules; the data preparation module, feature selection and the forecast module. The first module makes the historical load curve compatible with the feature selection module. The second module removes redundant and irrelevant features from the input data. The third module, which consists of an artificial neural network (ANN), predicts future load on the basis of selected features. Moreover, the forecast module uses a sigmoid function for activation and a multi-variate auto-regressive model for weight updating during the training process. Simulations are conducted in MATLAB to validate the performance of our newly-proposed DLF model in terms of accuracy and execution time. Results show that our proposed modified feature selection and modified ANN (m(FS + ANN))-based model for SGs is able to capture the non-linearity(ies) in the history load curve with 97 . 11 % accuracy. Moreover, this accuracy is achieved at the cost of a fair enough execution time, i.e., we have decreased the average execution time of the existing FS + ANN-based model by 38 . 50 %

Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks

[EN] Video streaming has become extremely widespread, especially with the growing number of users and the spread of mobile devices, along with the increase in the availability and diversity of multimedia applications and communication technologies. Real-time video communication requires awareness of the quality of experience (QoE) to provide customers with a satisfactory service, for example, in smart cities that use video surveillance systems. The quality of service (QoS) is dependent on network performance, which directly affects the QoE. However, reliance on traditional network infrastructure and routing protocols cannot assure QoS. The emergence of software defined networks (SDN) may eliminate current network limitations. Due to SDN's global view and programmability characteristics, such capabilities could help in providing an automated QoS control and management. This paper introduces video streaming adaptive QoS-based routing and resource reservation (VQoSRR), which gives SDN networks the ability to meet video demands and enhance user experience over best effort networks, such as the ones required for video surveillance in smart cities. In order to implement QoS-based routing (QBR), we developed algorithms for calculating routing, installing routing paths in the forwarding devices, and shifting traffic to an alternative path when QoE is violated. As well, we used queuing mechanisms to allocate resources based on the QoE requirements of video streaming. Our results indicate that resource reservation mechanisms combined with QoS-based routing enable effective control over routes and resources. Our framework guarantees the video quality as well. This technique of using video streaming would improve the tools and applications used for smart cities such as surveillance systems for hospitals and civil defense organizations.Researchers Supporting Project number (RSP-2021/295), King Saud University, Riyadh, Saudi Arabia.Elbasheer, MO.; Aldegheishem, A.; Alrajeh, N.; Lloret, J. (2022). Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks. Electronics. 11(8):1-26. https://doi.org/10.3390/electronics1108125212611