@article{MRL_AFM_Gossip_Protocal_Internet_App,

title = {Context-Aware Gossip-Based Protocol for Internet of Things Applications},

author = {Lina Altoaimy and Arwa Alromih and Shiroq Al-Megren and Ghada Al-Hudhud and Heba Kurdi and Kamal Youcef-Toumi},

url = {https://www.mdpi.com/1424-8220/18/7/2233},

doi = {10.3390/s18072233},

issn = {1424-8220},

year  = {2018},

date = {2018-07-11},

journal = {Sensors},

volume = {18},

number = {7},

publisher = {MDPI},

abstract = {This paper proposes a gossip-based protocol that utilises a multi-factor weighting function (MFWF) that takes several parameters into account: residual energy, Chebyshev distances to neighbouring nodes and the sink node, node density, and message priority. The effects of these parameters were examined to guide the customization of the weight function to effectively disseminate data to three types of IoT applications: critical, bandwidth-intensive, and energy-efficient applications. The performances of the three resulting MFWFs were assessed in comparison with the performances of the traditional gossiping protocol and the Fair Efficient Location-based Gossiping (FELGossiping) protocol in terms of end-to-end delay, network lifetime, rebroadcast nodes, and saved rebroadcasts. The experimental results demonstrated the proposed protocol’s ability to achieve a much shorter delay for critical IoT applications. For bandwidth-intensive IoT application, the proposed protocol was able to achieve a smaller percentage of rebroadcast nodes and an increased percentage of saved rebroadcasts, i.e., better bandwidth utilisation. The adapted MFWF for energy-efficient IoT application was able to improve the network lifetime compared to that of gossiping and FELGossiping. These results demonstrate the high level of flexibility of the proposed protocol with respect to network context and message priority.},

keywords = {Algorithms, Computational Intelligence, Data-driven learning for intelligent machine maintenance, intelligent systems},

pubstate = {published},

tppubtype = {article}

}

@article{MRL_AFM_MultiCuckoo,

title = {MultiCuckoo: Multi-Cloud Service Composition Using a Cuckoo-Inspired Algorithm for the Internet of Things Applications},

author = {H Kurdi and F Ezzat and L Altoaimy and S H Ahmed and K Youcef-Toumi},

url = {https://ieeexplore.ieee.org/document/8476294?denied=},

doi = {10.1109/ACCESS.2018.2872744},

issn = {2169-3536},

year  = {2018},

date = {2018-01-01},

journal = {IEEE Access},

volume = {6},

pages = {56737-56749},

publisher = {IEEE},

abstract = {Internet of things (IoT) applications aim to provide access to widespread interconnected networks of smart devices, services, and information. This can be achieved by integrating IoT and cloud computing (CC). By using cloud computing service composition (SC), multiple services from various providers can be combined to meet users' requirements. However, SC is known for its complexity and is classified as an NP-hard problem; such problems are usually approached using heuristics, such as bioinspired algorithms. This paper aims at developing a bio-inspired algorithm that mimics the behavior of cuckoo birds (which examine the nests of other birds to find eggs similar to their own) to find a composite service that fulfills a user's request in a multi-cloud environment (MCE). Previous work on cuckoo-inspired algorithms has generally utilized metaheuristics, which try to fit a “good”solution to a general optimization problem. In contrast, we propose a problem-dependent heuristic that considers the SC problem and its particularities in MCE. The proposed algorithm, MultiCuckoo, was thoroughly evaluated based on a well-controlled experimental framework that benchmarks the performance of the new algorithm to other outstanding SC algorithms, including the all clouds combination algorithm, base cloud combination algorithm, and combinatorial optimization algorithm for multiple cloud service Composition. The results show that our algorithm is more efficient in terms of decreasing the number of examined services, the composed clouds, and the running time in comparison to the benchmark algorithms.},

keywords = {Algorithms, Computational Intelligence, Data-driven learning for intelligent machine maintenance, intelligent systems, Mechatronic Design, Simulation},

pubstate = {published},

tppubtype = {article}

}

@article{MRL_AFM_Environmental_Dust,

title = {Characterization of Environmental Dust in the Dammam Area and Mud After-Effects on Bisphenol-A Polycarbonate Sheets},

author = {Bekir Yilbas and Haider Ali and Naseer Al-Aqeeli and Mazen Khaled and Syed Said and Abu Dheir N and Necar Merah and Kamal Youcef-Toumi and Kripa Varanasi},

doi = {10.1038/srep24308},

issn = {24308},

year  = {2016},

date = {2016-04-16},

journal = {Scientific Reports},

volume = {6},

pages = {24308},

publisher = {Nature},

abstract = {Owing to recent climate changes, dust storms are increasingly common, particularly in the Middle East region. Dust accumulation and subsequent mud formation on solid surfaces in humid environments typically have adverse effects on surface properties such as optical transmittance, surface texture and microhardness. This is usually because the mud, which contains alkaline and ionic species, adheres strongly to the surface, often through chemical bonds and is therefore difficult to remove. In this study, environmental dust and the after-effects of mud formed on a polycarbonate sheet, which is commonly used as a protective glass in photovoltaic cells. Ionic compounds (OH−) are shown to significantly affect the optical, mechanical and textural characteristics of the polycarbonate surface and to increase the adhesion work required to remove the dry mud from the polycarbonate surface upon drying. Such ability to modify characteristics of the polycarbonate surface could address the dust/mud-related limitations of superhydrophobic surfaces.},

keywords = {Data-driven learning for intelligent machine maintenance, Mechatronic Design, Physical System Modeling, Robotics & Automation, Simulation, Visualization},

pubstate = {published},

tppubtype = {article}

}

@article{MRL_AFM_Economic_Energy_Water_Nexus,

title = {Real-time economic dispatch for the supply side of the energy-water nexus},

author = {Apoorva Santhosh and Amro Farid and Kamal Youcef-Toumi},

doi = {10.1016/j.apenergy.2014.01.062},

year  = {2014},

date = {2014-06-01},

journal = {Applied Energy},

volume = {122},

pages = {42–52},

publisher = {researchgate},

abstract = {Clean energy and water are two essential resources that any society must securely deliver. Their usage raises sustainability issues and questions of nations’ resilience in face of global changes and mega-trends such as population growth, global climate change, and economic growth. Traditionally, the infrastructure systems that deliver these precious commodities, the water distribution and power transmission networks are thought of as separate, uncoupled systems. However, in reality, they are very much coupled in what is commonly known as the energy-water nexus. Although this subject has recently caught the attention of numerous policy and regulatory agencies, rarely is it holistically addressed in terms of an integrated engineering system for its management, planning, and regulation as an interdisciplinary concern. This work specifically fills this gap by addressing the supply side of this integrated engineering system. Specifically, it develops the multi-plant real-time simultaneous economic dispatch of power and water. While significant background literature has addressed traditional power dispatch, and the emerging co-dispatch of power and heat, as of now there does not exist a parameterized model for the optimized dispatch of power and water for multiple power, water, and coproduction facilities. The work presents such a model where production costs are minimized subject to capacity, demand and process constraints. It is demonstrated on an illustrative example of modest size. Interesting results were observed suggesting that the coproduction minimum capacity limits and process constraints can lead to scenarios where cheaper single product plants can be crowded out of the dispatch. The program provides a systematic method of achieving optimal results and can serve as a basis for set-points upon which individual plants can implement their optimal control. In so doing, it makes a supply-side contribution to the ongoing grand-challenge of improving the sustainability of the energy-water nexus.},

keywords = {Algorithms, Computational Intelligence, Control Theory, Data-driven learning for intelligent machine maintenance, intelligent systems, Simulation},

pubstate = {published},

tppubtype = {article}

}