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Campos, Nídia G. S., Danielo G. Gomes, Flávia C. Delicato, Augusto J. V. Neto, Luci Pirmez, and José Neuman de Souza. "Autonomic Context-Aware Wireless Sensor Networks." Journal of Sensors 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/621326.
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Autonomic Computing allows systems like wireless sensor networks (WSN) to self-manage computing resources in order to extend their autonomy as much as possible. In addition, contextualization tasks can fuse two or more different sensor data into a more meaningful information. Since these tasks usually run in a single centralized context server (e.g., sink node), the massive volume of data generated by the wireless sensors can lead to a huge information overload in such server. Here we propose DAIM, a distributed autonomic inference machine distributed which allows the sensor nodes to do self-management and contextualization tasks based on fuzzy logic. We have evaluated DAIM in a real sensor network taking into account other inference machines. Experimental results illustrate that DAIM is an energy-efficient contextualization method for WSN, reducing 48.8% of the number of messages sent to the context servers while saving 19.5% of the total amount of energy spent in the network.
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Bhajantri, Lokesh B. "Context Aware Resource Allocation in Distributed Sensor Networks." International Journal of Wireless & Mobile Networks 4, no. 2 (2012): 35–51. http://dx.doi.org/10.5121/ijwmn.2012.4203.
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Gellman, Michael. "Oscillations in self-aware networks." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 464, no. 2096 (2008): 2169–85. http://dx.doi.org/10.1098/rspa.2007.0328.
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Adaptive routing is once again of interest owing to the possibility to couple online probing in networks with real-time dynamic and distributed control of paths and flows. Wireless networks, with their rapidly changing network conditions also create a need to revisit this issue. This paper uses measurements in a wired adaptive network test bed, the cognitive packet network (CPN), to investigate the pros and cons of adaptive routing. CPN routes packet flows through a store and forward network according to their quality of service (QoS) needs through an online distributed reinforcement learning mechanism. This paper investigates routing oscillations that occur due to the interaction of multiple flows and studies their effect on QoS in the context of CPN. Our results indicate that routing oscillations can be easily controlled by randomizing the route switching, and that from an overall QoS viewpoint increased switching can also lead to improved performance.
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Aarab, Zineb, Asmae El Ghazi, Rajaa Saidi, and Moulay Driss Rahmani. "Towards a context-aware Wireless Sensor Networks." International Journal of Engineering & Technology 7, no. 3 (2018): 1869. http://dx.doi.org/10.14419/ijet.v7i3.15667.
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Recently, the development of wireless sensor networks (WSNs) is spreading rapidly. WSNs are highly distributed self-organized systems which comprise a large number of resource constrained sensor nodes. Developers of WSNs face many challenges from communication, memory, limited energy… Also, mobility has become a major concern for WSN researchers. Indeed, Mobile WSNs (MWSN) consist of mobile sensor nodes that can move on their own and also interact with the physical environment. Developing applications for MWSN is a complicated process because of the wide variety of WSN applications and low-level implementation details. Integrating context-awareness can improve MWSN applications results. In this paper, some research issues and challenges involved in the design of WSNs are presented. Model-Driven Engineering offers an effective solution to WSN application developers by hiding the details of lower layers and raising the level of abstraction. In this sense, we propose a context-aware WSN architecture and WSN metamodel to ease the work for developers in this field.
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Ruiz, J. L., J. C. Duenas, and F. Cuadrado. "Model-based context-aware deployment of distributed systems." IEEE Communications Magazine 47, no. 6 (2009): 164–71. http://dx.doi.org/10.1109/mcom.2009.5116815.
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Macedo, Daniel, Aldri Dos Santos, Jose S. Nogueira, and Guy Pujolle. "A distributed information repository for autonomic context-aware MANETs." IEEE Transactions on Network and Service Management 6, no. 1 (2009): 45–55. http://dx.doi.org/10.1109/tnsm.2009.090304.
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Verstichel, Stijn, Bruno Volckaert, Bart Dhoedt, Piet Demeester, and Filip De Turck. "Context-Aware Scheduling of Distributed DL-Reasoning Tasks in Wireless Sensor Networks." International Journal of Distributed Sensor Networks 7, no. 1 (2011): 521810. http://dx.doi.org/10.1155/2011/521810.
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Nikookar, Homayoun. "Signal Design for Context Aware Distributed Radar Sensing Networks Based on Wavelets." IEEE Journal of Selected Topics in Signal Processing 9, no. 2 (2015): 204–15. http://dx.doi.org/10.1109/jstsp.2014.2370953.
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Matos, Ricardo, Carlos Marques, and Susana Sargento. "Context-aware control of user-centric virtual networks: Centralized vs distributed approaches." Computer Networks 74 (December 2014): 4–21. http://dx.doi.org/10.1016/j.comnet.2014.06.022.
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Takatsuka, Hiroki, Sachio Saiki, Shinsuke Matsumoto, and Masahide Namamura. "RuCAS." International Journal of Software Innovation 3, no. 3 (2015): 57–68. http://dx.doi.org/10.4018/ijsi.2015070105.
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Machine-to-Machine (M2M) systems and cloud services provide various kinds of data via distributed Web services. A context-aware service recognizes real-world contexts from such data and behaves autonomously. However, it has been challenging to manage contexts and services defined on the heterogeneous and distributed Web services. In this paper, the authors propose a framework, called RuCAS, which systematically creates and manages context-aware service using various Web services. RuCAS describes every context-aware service by an ECA (Event-Condition-Action) rule. For this, an event is a context triggering the service, a condition is a set of contexts to be satisfied for execution, and the action is a set of Web services to be executed by the service. Thus, every context-aware service is managed in a uniform manner. Since RuCAS is published as a Web service, created contexts and services are reusable. As a case study, RuCAS is applied to a real home network system.
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Vamvakas, Panagiotis, Eirini Eleni Tsiropoulou, and Symeon Papavassiliou. "Risk-Aware Resource Management in Public Safety Networks." Sensors 19, no. 18 (2019): 3853. http://dx.doi.org/10.3390/s19183853.
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Modern Public Safety Networks (PSNs) are assisted by Unmanned Aerial Vehicles (UAVs) to provide a resilient communication paradigm during catastrophic events. In this context, we propose a distributed user-centric risk-aware resource management framework in UAV-assisted PSNs supported by both a static UAV and a mobile UAV. The mobile UAV is entitled to a larger portion of the available spectrum due to its capability and flexibility to re-position itself, and therefore establish better communication channel conditions to the users, compared to the static UAV. However, the potential over-exploitation of the mobile UAV-based communication by the users may lead to the mobile UAV’s failure to serve the users due to the increased levels of interference, consequently introducing risk in the user decisions. To capture this uncertainty, we follow the principles of Prospect Theory and design a user’s prospect-theoretic utility function that reflects user’s risk-aware behavior regarding its transmission power investment to the static and/or mobile UAV-based communication option. A non-cooperative game among the users is formulated, where each user determines its power investment strategy to the two available communication choices in order to maximize its expected prospect-theoretic utility. The existence and uniqueness of a Pure Nash Equilibrium (PNE) is proven and the convergence of the users’ strategies to it is shown. An iterative distributed and low-complexity algorithm is introduced to determine the PNE. The performance of the proposed user-centric risk-aware resource management framework in terms of users’ achievable data rate and spectrum utilization, is achieved via modeling and simulation. Furthermore, its superiority and benefits are demonstrated, by comparing its performance against other existing approaches with regards to UAV selection and spectrum utilization.
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PERTSELAKIS, MINAS, CHRISTOS FERLES, KOSTAS TSIOLIS, and ANDREAS STAFYLOPATIS. "WIRELESS DISTRIBUTED IMPLEMENTATION OF A FUZZY NEURAL CLASSIFICATION SYSTEM." International Journal on Artificial Intelligence Tools 14, no. 04 (2005): 661–82. http://dx.doi.org/10.1142/s0218213005002314.
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Recent years have seen a surge of interest in the field of pervasive context-aware computing. In this framework we propose a novel real implementation of an adaptive self-configurable system, applied within the scope of wireless ad-hoc networks. WiDFuNC is an integrated system that consists of an intelligent unit implemented on a real PDA, a number of sensors and a remote server device to form an efficient prototype system that can be applied in various domains. This implementation of WiDFuNC focuses on pure classification problems with satisfactory experimental results, presenting great adaptability and context-awareness.
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Konstantinou, Nikolaos, Emmanuel Solidakis, Anastasios Zafeiropoulos, Panagiotis Stathopoulos, and Nikolas Mitrou. "A context-aware middleware for real-time semantic enrichment of distributed multimedia metadata." Multimedia Tools and Applications 46, no. 2-3 (2009): 425–61. http://dx.doi.org/10.1007/s11042-009-0361-1.
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Kumar, Neeraj, Naveen Chilamkurti, and Jong-Hyouk Lee. "Distributed context aware collaborative filtering approach for P2P service selection and recovery in wireless mesh networks." Peer-to-Peer Networking and Applications 5, no. 4 (2012): 350–62. http://dx.doi.org/10.1007/s12083-012-0156-4.
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Al-Kanj, Lina, Zaher Dawy, Walid Saad, and Erhan Kutanoglu. "Energy-Aware Cooperative Content Distribution Over Wireless Networks: Optimized and Distributed Approaches." IEEE Transactions on Vehicular Technology 62, no. 8 (2013): 3828–47. http://dx.doi.org/10.1109/tvt.2013.2263158.
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Yang, Soo Mi. "Efficient Ontology Integration Model for Better Inference in Context Aware Computing." Advanced Materials Research 268-270 (July 2011): 841–46. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.841.
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In this paper, we describe efficient ontology integration model for better context inference based on distributed ontology framework. Context aware computing with inference based on ontology is widely used in distributed surveillance environment. In such a distributed surveillance environment, surveillance devices such as smart cameras may carry heterogeneous video data with different transmission ranges, latency, and formats. However even smart devices, they generally have small memory and power which can manage only part of ontology data. In our efficient ontology integration model, each of agents built in such devices get services not only from a region server, but also peer servers. For such a collaborative network, an effective cache framework that can handle heterogeneous devices is required for the efficient ontology integration. In this paper, we propose a efficient ontology integration model which is adaptive to the actual device demands and that of its neighbors. Our scheme shows the efficiency of model resulted in better context inference.
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Aliouat, Zibouda, and Saad Harous. "Energy efficient clustering for wireless sensor networks." International Journal of Pervasive Computing and Communications 10, no. 4 (2014): 469–80. http://dx.doi.org/10.1108/ijpcc-05-2014-0033.
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Purpose – The purpose of this paper is to design a hierarchical routing protocol. Wireless sensor network (WSN) consists of a set of miniature sensor nodes powered by a low-capacity energy battery. This limitation requires that energy is used in an efficient way and kept as long as possible to allow the WSN to accomplish its mission. Thus, energy conservation is a very important problem facing researchers in this context. Because sending and receiving messages is the activity that consumes the most energy in a WSN, so when designing routing protocols, this problem is targeted specifically. The aim of this paper is to propose a solution to this problem by designing a hierarchical routing protocol. Design/methodology/approach – The authors started by designing a protocol called efficient energy-aware distributed clustering (EEADC). Simulation result showed EEADC might generate clusters with very small or very large size. To solve this problem, the authors designed a new algorithm called fixed efficient energy-aware distributed clustering (FEEADC). They concluded from the simulation result that cluster-heads (CHs) far away from the base station die faster than the ones closer to it. To remedy this problem, they propose multi-hop fixed efficient energy-aware distributed clustering (M-FEEADC). It is based on a new fixed clustering mechanism, which aims to create a balanced distribution of CHs. It uses data aggregation and sleep/wakeup techniques. Findings – The simulation results show a significant improvement in terms of energy consumption and network lifetime over the well-known low-energy adaptive clustering hierarchy and threshold-sensitive energy-efficient protocols. Originality/value – The authors propose M-FEEADC. It is based on a new fixed clustering mechanism, which aims to create a balanced distribution of CHs. It uses data aggregation and sleep/wakeup techniques.
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Gu, Bo, Yapeng Chen, Haijun Liao, Zhenyu Zhou, and Di Zhang. "A Distributed and Context-Aware Task Assignment Mechanism for Collaborative Mobile Edge Computing." Sensors 18, no. 8 (2018): 2423. http://dx.doi.org/10.3390/s18082423.
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Mobile edge computing (MEC) is an emerging technology that leverages computing, storage, and network resources deployed at the proximity of users to offload their delay-sensitive tasks. Various existing facilities including mobile devices with idle resources, vehicles, and MEC servers deployed at base stations or road side units, could act as edges in the network. Since task offloading incurs extra transmission energy consumption and transmission latency, two key questions to be addressed in such an environment are (i) should the workload be offloaded to the edge or computed in terminals? (ii) Which edge, among the available ones, should the task be offloaded to? In this paper, we formulate the task assignment problem as a one-to-many matching game which is a powerful tool for studying the formation of a mutual beneficial relationship between two sets of agents. The main goal of our task assignment mechanism design is to reduce overall energy consumption, while satisfying task owners’ heterogeneous delay requirements and supporting good scalability. An intensive simulation is conducted to evaluate the efficiency of our proposed mechanism.
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Alonso, Ricardo S., Dante I. Tapia, and Juan M. Corchado. "SYLPH." International Journal of Ambient Computing and Intelligence 3, no. 2 (2011): 1–15. http://dx.doi.org/10.4018/jaci.2011040101.
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The significance that Ambient Intelligence (AmI) has acquired in recent years requires the development of innovative solutions. In this sense, the development of AmI-based systems requires the creation of increasingly complex and flexible applications. The use of context-aware technologies is an essential aspect in these developments in order to perceive stimuli from the context and react upon it autonomously. This paper presents SYLPH, a novel platform that defines a method for integrating dynamic and self-adaptable heterogeneous Wireless Sensor Networks (WSN). This approach facilitates the inclusion of context-aware capabilities when developing intelligent ubiquitous systems, where functionalities can communicate in a distributed way. A WSN infrastructure has been deployed for testing and evaluating this platform. Preliminary results and conclusions are presented in this paper.
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Amato, Alba, Salvatore Venticinque, and Beniamino Di Martino. "A Distributed and Scalable Solution for Applying Semantic Techniques to Big Data." International Journal of Mobile Computing and Multimedia Communications 6, no. 2 (2014): 50–67. http://dx.doi.org/10.4018/ijmcmc.2014040105.
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The digital revolution changes the way culture and places could be lived. It allows users to interact with the environment creating an immense availability of data, which can be used to better understand the behavior of visitors, as well as to learn about their thoughts on what the visit creates excitement or disappointment. In this context, Big Data becomes immensely important, making possible to turn this amount of data in information, knowledge, and, ultimately, wisdom. This paper aims at modeling and designing a scalable solution that integrates semantic techniques with Cloud and Big Data technologies to deliver context aware services in the application domain of the cultural heritage. The authors started from a baseline framework that originally was not conceived to scale when huge workloads, related to big data, must be processed. They provide an original formulation of the problem and an original software architecture that fulfills both functional and not-functional requirements. The authors present the technological stack and the implementation of a proof of concept.
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Mcheick, Hamid, and John Sayegh. "A Self-Adaptive and Efficient Context-Aware Healthcare Model for COPD Diseases." Informatics 8, no. 3 (2021): 41. http://dx.doi.org/10.3390/informatics8030041.
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The emergence of pervasive computing technology has revolutionized all aspects of life and facilitated many everyday tasks. As the world fights the coronavirus pandemic, it is necessary to find new ways to use technology to fight diseases and reduce their economic burden. Distributed systems have demonstrated efficiency in the healthcare domain, not only by organizing and managing patient data but also by helping doctors and other medical experts to diagnose diseases and take measures to prevent the development of serious conditions. In the case of chronic diseases, telemonitoring systems provide a way to monitor patients’ states and biomarkers in the course of their everyday routines. We developed a Chronical Obstructive Pulmonary Disease (COPD) healthcare system to protect patients against risk factors. However, each change in the patient context initiated the execution of the system’s entire rule base, which diminished performance. In this article, we use separation of concerns to reduce the impact of contextual changes by dividing the context, rules and services into software modules (units). We combine healthcare telemonitoring with context awareness and self-adaptation to create an adaptive architecture model for COPD patients. The model’s performance is validated using COPD data, demonstrating the efficiency of the separation of concerns and adaptation techniques in context-aware systems.
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L*, Anusha, and Nagaraja G S. "Mobile Cloud Computing Provides Service Availability using A Context Aware Architecture." International Journal of Recent Technology and Engineering 10, no. 1 (2021): 252–55. http://dx.doi.org/10.35940/ijrte.a5924.0510121.
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Mobile systems are becoming increasingly important, and new promising paradigms such as Mobile Cloud Computing. Mobile Cloud Computing is an application that allows data to be stored and processed outside of the mobile node. There is a lot of interest in using the resources that can be accessed by transparently using distributed resource pooling offered by nearby mobile nodes. This type of device is used in emergency, education, and tourism. Systems basically use dynamic network topologies in which network partitions and disconnection occurs frequently, so the availability of the services has been compromised. In this paper proposes the context aware architecture to provide availability of the services deployed in mobile and dynamic network environments which provides better response time, the services need not be migrated at real time, so the bandwidth and energy used has been more efficient.
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Kumar, Kamal, and Jyoti Thaman. "Opportunistic Two Virtual Machines Placements in Distributed Cloud Environment." International Journal of Grid and High Performance Computing 12, no. 4 (2020): 13–34. http://dx.doi.org/10.4018/ijghpc.2020100102.
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Cloud computing is a potentially tremendous platform and its presence is experienced in day to day life. Most infrastructure and technology enterprises have migrated to a cloud-based infrastructure and storage. With so much dependence on the cloud as a distributed and reliable platform, but a few issues remain as a challenge and provide food for the ever-active research entity. Considering a very basic aspect of VM migration followed by VM placement, one VM at a time is a prominent approach. This article presents a novel idea of placing two VMs at a time. This proposal is a draft of solution for the Two VM Placement problem. The experimental validation was done against a well-known placement algorithm, the power aware best fit decreasing (PABFD). PABFD and TVMP were applied on a given context and results were obtained for three important parameters, which include the number of VM migrations, reallocation means, and energy efficiency. Improvements on these parameters may prove beneficial.
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Zhao, Zhongyuan, Weiguang Sheng, Jinchao Li, Pengfei Ye, Qin Wang, and Zhigang Mao. "Similarity-Aware Architecture/Compiler Co-Designed Context-Reduction Framework for Modulo-Scheduled CGRA." Electronics 10, no. 18 (2021): 2210. http://dx.doi.org/10.3390/electronics10182210.
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Modulo-scheduled coarse-grained reconfigurable array (CGRA) processors have shown their potential for exploiting loop-level parallelism at high energy efficiency. However, these CGRAs need frequent reconfiguration during their execution, which makes them suffer from large area and power overhead for context memory and context-fetching. To tackle this challenge, this paper uses an architecture/compiler co-designed method for context reduction. From an architecture perspective, we carefully partition the context into several subsections and only fetch the subsections that are different to the former context word whenever fetching the new context. We package each different subsection with an opcode and index value to formulate a context-fetching primitive (CFP) and explore the hardware design space by providing the centralized and distributed CFP-fetching CGRA to support this CFP-based context-fetching scheme. From the software side, we develop a similarity-aware tuning algorithm and integrate it into state-of-the-art modulo scheduling and memory access conflict optimization algorithms. The whole compilation flow can efficiently improve the similarities between contexts in each PE for the purpose of reducing both context-fetching latency and context footprint. Experimental results show that our HW/SW co-designed framework can improve the area efficiency and energy efficiency to at most 34% and 21% higher with only 2% performance overhead.
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Analoui, Morteza, Mohsen Sharifi, and Mohammad Hossein Rezvani. "Probabilistic Proximity-aware Resource Location in Peer-to-Peer Networks Using Resource Replication." International Journal of Computers Communications & Control 5, no. 4 (2010): 418. http://dx.doi.org/10.15837/ijccc.2010.4.2492.
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Nowadays, content distribution has received remarkable attention in distributed computing researches and its applications typically allow personal computers, called peers, to cooperate with each other in order to accomplish distributed operations such as query search and acquiring digital contents. In a very large network, it is impossible to perform a query request by visiting all peers. There are some works that try to find the location of resources probabilistically (i.e. non-deterministically). They all have used inefficient protocols for finding the probable location of peers who manage the resources. This paper presents a more efficient protocol that is proximity-aware in the sense that it is able to cache and replicate the popular queries proportional to distance latency. The protocol dictates that the farther the resources are located from the origin of a query, the more should be the probability of their replication in the caches of intermediate peers. We have validated the proposed distributed caching scheme by running it on a simulated peer-to-peer network using the well-known Gnutella system parameters. The simulation results show that the proximity-aware distributed caching can improve the efficiency of peer-to-peer resource location services in terms of the probability of finding objects, overall miss rate of the system, fraction of involved peers in the search process, and the amount of system load.
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Nalepa, Grzegorz, and Szymon Bobek. "Rule-based solution for context-aware reasoning on mobile devices." Computer Science and Information Systems 11, no. 1 (2014): 171–93. http://dx.doi.org/10.2298/csis130209002n.
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With the rapid evolution of mobile devices, the concept of context aware applications has gained a remarkable popularity in recent years. Smartphones and tablets are equipped with a variety of sensors including accelerometers, gyroscopes, and GPS, pressure gauges, light and GPS sensors. Additionally, the devices become computationally powerful which allows real-time processing of data gathered by their sensors. Universal network access viaWiFi hot-spots and GSM network makes mobile devices perfect platforms for ubiquitous computing. Most of existing frameworks for context-aware systems, are usually dedicated to static, centralized, clientserver architectures. However, mobile platforms require from the context modeling language and inference engine to be simple and lightweight. The model should also be powerful enough to allow not only solving simple context identification tasks but more complex reasoning. The original contribution of the paper is a proposal of a new rule-based context reasoning platform tailored to the needs of such intelligent distributed mobile computing devices. It contains a proposal of a learning middleware supporting context acquisition. The platform design is based on a critical review and evaluation of existing solutions given in this paper. A preliminary evaluation of the platform is given along with use cases including a social system supporting crime detection and investigation.
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Moore, Philip T., and Hai V. Pham. "Personalization and rule strategies in data-intensive intelligent context-aware systems." Knowledge Engineering Review 30, no. 2 (2015): 140–56. http://dx.doi.org/10.1017/s0269888914000265.
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AbstractThe concept of personalization in its many forms has gained traction driven by the demands of computer-mediated interactions generally implemented in large-scale distributed systems and ad hoc wireless networks. Personalization requires the identification and selection of entities based on a defined profile (a context); an entity has been defined as a person, place, or physical or computational object. Context employs contextual information that combines to describe an entities current state. Historically, the range of contextual information utilized (in context-aware systems) has been limited to identity, location, and proximate data; there has, however, been advances in the range of data and information addressed. As such, context can be highly dynamic with inherent complexity. In addition, context-aware systems must accommodate constraint satisfaction and preference compliance.This article addresses personalization and context with consideration of the domains and systems to which context has been applied and the nature of the contextual data. The developments in computing and service provision are addressed with consideration of the relationship between the evolving computing landscape and context. There is a discussion around rule strategies and conditional relationships in decision support. Logic systems are addressed with an overview of the open world assumption versus the closed world assumption and the relationship with the Semantic Web. The event-driven rule-based approach, which forms the basis upon which intelligent context processing can be realized, is presented with an evaluation and proof-of-concept. The issues and challenges identified in the research are considered with potential solutions and research directions; alternative approaches to context processing are discussed. The article closes with conclusions and open research questions.
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Bo, Lili, and Shujuan Jiang. "A Constraint-Aware Optimization Method for Concurrency Bug Diagnosis Service in a Distributed Cloud Environment." Security and Communication Networks 2018 (October 9, 2018): 1–11. http://dx.doi.org/10.1155/2018/6241921.
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The advent of cloud computation and big data applications has enabled data access concurrency to be prevalent in the distributed cloud environment. In the meantime, security issue becomes a critical problem for researchers to consider. Concurrency bug diagnosis service is to analyze concurrent software and then reason about concurrency bugs in them. However, frequent context switches in concurrent program execution traces will inevitably impact the service performance. To optimize the service performance, this paper presents a static constraint-aware method to simplify concurrent program buggy traces. First, taking the original buggy trace as the operation object, we calculate the maximal sound dependence relations based on the constraint models. Then, we iteratively check the dependent constraints and move forward current event to extend thread execution intervals. Finally, we obtain the simplified trace that is equivalent to the original buggy trace. To evaluate our approach, we conduct a set of experiments on 12 widely used Java projects. Experimental results show that our approach outperforms other state-of-the-art approaches in terms of execution time.
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Layne, Elliot, Erika N. Dort, Richard Hamelin, Yue Li, and Mathieu Blanchette. "Supervised learning on phylogenetically distributed data." Bioinformatics 36, Supplement_2 (2020): i895—i902. http://dx.doi.org/10.1093/bioinformatics/btaa842.
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Abstract Motivation The ability to develop robust machine-learning (ML) models is considered imperative to the adoption of ML techniques in biology and medicine fields. This challenge is particularly acute when data available for training is not independent and identically distributed (iid), in which case trained models are vulnerable to out-of-distribution generalization problems. Of particular interest are problems where data correspond to observations made on phylogenetically related samples (e.g. antibiotic resistance data). Results We introduce DendroNet, a new approach to train neural networks in the context of evolutionary data. DendroNet explicitly accounts for the relatedness of the training/testing data, while allowing the model to evolve along the branches of the phylogenetic tree, hence accommodating potential changes in the rules that relate genotypes to phenotypes. Using simulated data, we demonstrate that DendroNet produces models that can be significantly better than non-phylogenetically aware approaches. DendroNet also outperforms other approaches at two biological tasks of significant practical importance: antiobiotic resistance prediction in bacteria and trophic level prediction in fungi. Availability and implementation https://github.com/BlanchetteLab/DendroNet.
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Anghel, Ionut, Tudor Cioara, Ioan Salomie, Mihaela Dinsoreanu, and Anca Rarau. "Middleware for Smart Environments Management." International Journal of Computers Communications & Control 5, no. 2 (2010): 148. http://dx.doi.org/10.15837/ijccc.2010.2.2470.
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This paper introduces a self-configuring middleware that manages the processes of context information acquisition and representation from smart closed environments, targeting the development of context aware applications. The environment context information is modeled using three sets: context resources, context actors and context policies. The context model artifacts are generated and administrated at run time by a management infrastructure based on intelligent software agents. The self-configuring property is enforced by monitoring the closed environment in order to detect variations or conditions for which the context model artifacts must be updated. The middleware was tested and validated within the premises of our Distributed Systems Research Laboratory smart environment.
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Blanco, Bego, Ianire Taboada, Jose Oscar Fajardo, and Fidel Liberal. "A Robust Optimization Based Energy-Aware Virtual Network Function Placement Proposal for Small Cell 5G Networks with Mobile Edge Computing Capabilities." Mobile Information Systems 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/2603410.
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In the context of cloud-enabled 5G radio access networks with network function virtualization capabilities, we focus on the virtual network function placement problem for a multitenant cluster of small cells that provide mobile edge computing services. Under an emerging distributed network architecture and hardware infrastructure, we employ cloud-enabled small cells that integrate microservers for virtualization execution, equipped with additional hardware appliances. We develop an energy-aware placement solution using a robust optimization approach based on service demand uncertainty in order to minimize the power consumption in the system constrained by network service latency requirements and infrastructure terms. Then, we discuss the results of the proposed placement mechanism in 5G scenarios that combine several service flavours and robust protection values. Once the impact of the service flavour and robust protection on the global power consumption of the system is analyzed, numerical results indicate that our proposal succeeds in efficiently placing the virtual network functions that compose the network services in the available hardware infrastructure while fulfilling service constraints.
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Lekova, Anna. "Exploiting Mobile Ad Hoc Networking and Knowledge Generation to Achieve Ambient Intelligence." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–6. http://dx.doi.org/10.1155/2012/262936.
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Ambient Intelligence (AmI) joins together the fields of ubiquitous computing and communications, context awareness, and intelligent user interfaces. Energy, fault-tolerance, and mobility are newly added dimensions of AmI. Within the context of AmI the concept of mobile ad hoc networks (MANETs) for “anytime and anywhere” is likely to play larger roles in the future in which people are surrounded and supported by small context-aware, cooperative, and nonobtrusive devices that will aid our everyday life. The connection between knowledge generation and communication ad hoc networking is symbiotic—knowledge generation utilizes ad hoc networking to perform their communication needs, and MANETs will utilize the knowledge generation to enhance their network services. The contribution of the present study is a distributed evolving fuzzy modeling framework (EFMF) to observe and categorize relationships and activities in the user and application level and based on that social context to take intelligent decisions about MANETs service management. EFMF employs unsupervised online one-pass fuzzy clustering method to recognize nodes' mobility context from social scenario traces and ubiquitously learn “friends” and “strangers” indirectly and anonymously.
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CHO, HYUN SANG, TAKEKAZU KATO, TATSUYA YAMAZAKI, and MINSOO HAHN. "A POWER CONSUMPTION ACTIVITY BASED HETEROGENEOUS SOA FRAMEWORK FOR CONTEXT-AWARE HOME SERVICES." Journal of Circuits, Systems and Computers 20, no. 07 (2011): 1211–30. http://dx.doi.org/10.1142/s0218126611007840.
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The home network is one of the emerging areas from the last century. However, the growth of the home network market is stationary at present. This paper describes the limitations of the home network system and the requirements for overcoming the current limitations. Also described is a new home network service system known as COWS and its easy installation and scalable operation. COWS consists of power consumption monitor and control devices along with a service server that is a complementary combination of Open Service Gateway initiative (OSGi) and web services. A home network system has a dynamic, heterogeneous, distributed, and scalable topology. Service Oriented Architecture (SOA) has been proposed as a solution that satisfies the requirement of a home network, and OSGi and web services are two successful SOA-based frameworks. An included service server has a flexible architecture that consists of a core and extendable service packages. A power consumption monitor and control function provides useful context information for activity-based context-aware services and optimizes the power consumption. The system can be installed easily into existing and new houses to solve the current barrier of the popularization of home network services.
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Yang, Dan, Jing Zhang, Sifeng Wang, and XueDong Zhang. "A Time-Aware CNN-Based Personalized Recommender System." Complexity 2019 (December 18, 2019): 1–11. http://dx.doi.org/10.1155/2019/9476981.
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Recommender system has received tremendous attention and has been studied by scholars in recent years due to its wide applications in different domains. With the in-depth study and application of deep learning algorithms, deep neural network is gradually used in recommender systems. The success of modern recommender system mainly depends on the understanding and application of the context of recommendation requests. However, when leveraging deep learning algorithms for recommendation, the impact of context information such as recommendation time and location is often neglected. In this paper, a time-aware convolutional neural network- (CNN-) based personalized recommender system TC-PR is proposed. TC-PR actively recommends items that meet users’ interests by analyzing users’ features, items’ features, and users’ ratings, as well as users’ time context. Moreover, we use Tensorflow distributed open source framework to implement the proposed time-aware CNN-based recommendation algorithm which can effectively solve the problems of large data volume, large model, and slow speed of recommender system. The experimental results on the MovieLens-1m real dataset show that the proposed TC-PR can effectively solve the cold-start problem and greatly improve the speed of data processing and the accuracy of recommendation.
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Firouzi, Ramin, Rahim Rahmani, and Theo Kanter. "Context-based Reasoning through Fuzzy Logic for Edge Intelligence." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 01 (2021): 17–25. http://dx.doi.org/10.5383/juspn.15.01.003.
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With the advent of edge computing, the Internet of Things (IoT) environment has the ability to process data locally. The complexity of the context reasoning process can be scattered across several edge nodes that are physically placed at the source of the qualitative information by moving the processing and knowledge inference to the edge of the IoT network. This facilitates the real-time processing of a large range of rich data sources that would be less complex and expensive compare to the traditional centralized cloud system. In this paper, we propose a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge.
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Ji, Jiequ, Kun Zhu, Ran Wang, Bing Chen, and Chen Dai. "Energy Efficient Caching in Backhaul-Aware Cellular Networks with Dynamic Content Popularity." Wireless Communications and Mobile Computing 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/7532049.
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Caching popular contents at base stations (BSs) has been regarded as an effective approach to alleviate the backhaul load and to improve the quality of service. To meet the explosive data traffic demand and to save energy consumption, energy efficiency (EE) has become an extremely important performance index for the 5th generation (5G) cellular networks. In general, there are two ways for improving the EE for caching, that is, improving the cache-hit rate and optimizing the cache size. In this work, we investigate the energy efficient caching problem in backhaul-aware cellular networks jointly considering these two approaches. Note that most existing works are based on the assumption that the content catalog and popularity are static. However, in practice, content popularity is dynamic. To timely estimate the dynamic content popularity, we propose a method based on shot noise model (SNM). Then we propose a distributed caching policy to improve the cache-hit rate in such a dynamic environment. Furthermore, we analyze the tradeoff between energy efficiency and cache capacity for which an optimization is formulated. We prove its convexity and derive a closed-form optimal cache capacity for maximizing the EE. Simulation results validate the proposed scheme and show that EE can be improved with appropriate choice of cache capacity.
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Ogbuachi, Michael Chima, Anna Reale, Péter Suskovics, and Benedek Kovács. "Context-Aware Kubernetes Scheduler for Edge-native Applications on 5G." Journal of communications software and systems 16, no. 1 (2020): 85–94. http://dx.doi.org/10.24138/jcomss.v16i1.1027.
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This paper is an extension of work originally presented in SoftCOM 2019 [1]. The novelty of this work reside in its focused improvement of our scheduling algorithm towards its usage on a real 5G infrastructure. Industrial IoT applications are often designed to run in a distributed way on the devices and controller computers with strict service requirements for the nodes and the links between them. 5G, especially in concomitance with Edge Computing, will provide the desired level of connectivity for these setups and it will permit to host application run-time components in edge clouds. However, allocation of the edge cloud resources for Industrial IoT (IIoT) applications, is still commonly solved by rudimentary scheduling techniques (i.e. simple strategies based on CPU usage and device readiness, employing very few dynamic information). Orchestrators inherited from the cloud computing, like Kubernetes, are not satisfying to the requirements of the aforementioned applications and are not optimized for the diversity of devices which are often also limited in capacity. This design is especially slow in reacting to the environmental changes. In such circumstances, in order to provide a proper solution using these tools, we propose to take the physical, operational and network parameters (thus the full context of the IIoT application) into consideration, along with the software states and orchestrate the applications dynamically.
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Argany, M., and M. A. Mostafavi. "DEVELOP A GIS-BASED CONTEXT-AWARE SENSOR NETWORK DEPLOYMENT ALGORITHM TO OPTIMIZE SENSOR COVERAGE IN AN URBAN AREA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 11–17. http://dx.doi.org/10.5194/isprs-archives-xlii-1-11-2018.
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<p><strong>Abstract.</strong> Adequate coverage is an important issue in geosensor networks in order to fulfill the sensing applications in urban areas. GIS as well as Optimization methods are widely used to distribute geosensors in the network to achieve the desired level of coverage. Most of the algorithms applied in urban domain suffer from the lack of considering real environmental information. In this paper, the problem of placing sensors to get optimum coverage is studied by investigating the concept of urban contextual information in sensor network. Then, a local GIS-based context-aware framework of sensor network deployment optimization method is introduced. Obtained results of our algorithm under different working conditions and applications show the effectiveness of our approach.</p>
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Halkai, Abhijit, and Sujatha Terdal. "QALPA- An Efficient QoS Aware Hybrid Localization Model Using Particle Swarm Optimization and Ant Colony Optimization for Cognitive Wireless Sensor Networks." International Journal of Intelligent Engineering and Systems 14, no. 1 (2021): 270–80. http://dx.doi.org/10.22266/ijies2021.0228.26.
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A sensor network operates wirelessly and transmits detected information to the base station. The sensor is a small sized device, it is battery-powered with some electrical components, and the protocols should operate efficiently in such least resource availability. Here, we propose a novel improved framework in large scale applications where the huge numbers of sensors are distributed over an area. The designed protocol will address the issues that arise during its communication and give a consistent seamless communication system. The process of reasoning and learning in cognitive sensors guarantees data delivery in the network. Localization in Scarce and dense sensor networks is achieved by efficient cluster head election and route selection which are indeed based on cognition, improved Particle Swarm Optimization, and improved Ant Colony Optimization algorithms. Factors such as mobility, use of sensor buffer, power management, and defects in channels have been identified and solutions are presented in this research to build an accurate path based on the network context. The achieved results in extensive simulation prove that the proposed scheme outperforms ESNA, NETCRP, and GAECH algorithms in terms of Delay, Network lifetime, Energy consumption.
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Jin, Mingshuang, Hongbin Luo, Shuai Gao, and Bohao Feng. "Joint Location-Dependent Pricing and Request Mapping in ICN-Based Telco CDNs For 5G." Future Internet 11, no. 6 (2019): 125. http://dx.doi.org/10.3390/fi11060125.
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Telco content delivery networks (CDNs) have envisioned building highly distributed and cloudified sites to provide a high-quality CDN service in the 5G era. However, there are still two open problems to be addressed. First, telco CDNs are operated upon the underlay network evolving towards information-centric networking (ICN). Different from CDNs that perform on the application layer, ICN enables information-centric forwarding to the network layer. Thus, it is challenging to take advantage of the benefits of both ICN and CDN to provide a high-quality content delivery service in the context of ICN-based telco CDNs. Second, bandwidth pricing and request mapping issues in ICN-based telco CDNs have not been thoroughly studied. In this paper, we first propose an ICN-based telco CDN framework that integrates the information-centric forwarding enabled by ICN and the powerful edge caching enabled by telco CDNs. Then, we propose a location-dependent pricing (LDP) strategy, taking into consideration the congestion level of different sites. Furthermore, on the basis of LDP, we formulate a price-aware request mapping (PARM) problem, which can be solved by existing linear programming solvers. Finally, we conduct extensive simulations to evaluate the effectiveness of our design.
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Symeonaki, Eleni, Konstantinos Arvanitis, and Dimitrios Piromalis. "A Context-Aware Middleware Cloud Approach for Integrating Precision Farming Facilities into the IoT toward Agriculture 4.0." Applied Sciences 10, no. 3 (2020): 813. http://dx.doi.org/10.3390/app10030813.
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The adoption of Precision Farming (PF) practices involving ubiquitous computing advancements and conceptual innovations of “smart” agricultural production toward Agriculture 4.0 is a significant factor for the benefit of sustainable growth. In this context, the dynamic integration of PF facility systems into the Internet of Things (IoT) represents an excessive challenge considering the large amount of heterogeneous raw data acquired in agricultural environments by Wireless Sensor and Actuator Networks (WSANs). This paper focuses on the issue of facilitating the management, process, and exchange of the numerous and diverse data points generated in multiple PF environments by introducing a framework of a cloud-based context-aware middleware solution as part of a responsive, adaptive, and service-oriented IoT integrated system. More particularly, the paper presents in detail a layered hierarchical structure according to which all functional elements of the system cope with context, while the context awareness operation is accomplished into a cloud-based distributed middleware component that is the core of the entire system acting as a Decision Support System (DSS). Furthermore, as proof of concept, the functionality of the proposed system is studied in real conditions where some evaluation results regarding its performance are quoted.
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Lee, Kwang-Eog, Joon Goo Park, and Sang-Jo Yoo. "Intelligent Cognitive Radio Ad-Hoc Network: Planning, Learning and Dynamic Configuration." Electronics 10, no. 3 (2021): 254. http://dx.doi.org/10.3390/electronics10030254.
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Cognitive radio (CR) is an adaptive radio technology that can automatically detect available channels in a wireless spectrum and change transmission parameters to improve the radio operating behavior. A CR ad-hoc network (CRAHN) should be able to coexist with primary user (PU) systems and other CR secondary systems without causing harmful interference to licensed PUs as well as dynamically configure autonomous and decentralized networks. Therefore, an intelligent system structure is required for efficient spectrum use. In this paper, we present a learning-based distributed autonomous CRAHN network system model for network planning, learning, and dynamic configuration. Based on the system model, we propose machine learning-based optimization algorithms for spectrum sensing, cluster-based ad-hoc network configuration, and context-aware signal classification. Using the sensing engine and the cognitive engine, the surrounding spectrum usage and the neighbor network operation status can be analyzed. The proposed policy engine can create network operation policies for the dynamically changing surrounding wireless environment, detect policy conflicts, and infer the optimal policy for the current situation. The decision engine finally determines and configures the optimal CRAHN configuration parameters through cooperation with a learning engine, in which we implement the proposed machine-learning algorithms. The simulation results show that the proposed machine-learning CRAHN algorithms can construct CR cluster networks that have a long network lifetime and high spectrum utility. Additionally, with high signal context recognition performance, we can ensure coexistence with neighboring systems.
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Radenkovic, Milena. "Cognitive Privacy for Personal Clouds." Mobile Information Systems 2016 (2016): 1–17. http://dx.doi.org/10.1155/2016/7107103.
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This paper proposes a novel Cognitive Privacy (CogPriv) framework that improves privacy of data sharing between Personal Clouds for different application types and across heterogeneous networks. Depending on the behaviour of neighbouring network nodes, their estimated privacy levels, resource availability, and social network connectivity, each Personal Cloud may decide to use different transmission network for different types of data and privacy requirements. CogPriv is fully distributed, uses complex graph contacts analytics and multiple implicit novel heuristics, and combines these with smart probing to identify presence and behaviour of privacy compromising nodes in the network. Based on sensed local context and through cooperation with remote nodes in the network, CogPriv is able to transparently and on-the-fly change the network in order to avoid transmissions when privacy may be compromised. We show that CogPriv achieves higher end-to-end privacy levels compared to both noncognitive cellular network communication and state-of-the-art strategies based on privacy-aware adaptive social mobile networks routing for a range of experiment scenarios based on real-world user and network traces. CogPriv is able to adapt to varying network connectivity and maintain high quality of service while managing to keep low data exposure for a wide range of privacy leakage levels in the infrastructure.
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Guo, Lei, Yu Han, Haoran Jiang, Xinxin Yang, Xinhua Wang, and Xiyu Liu. "Learning to Make Document Context-Aware Recommendation with Joint Convolutional Matrix Factorization." Complexity 2020 (January 27, 2020): 1–15. http://dx.doi.org/10.1155/2020/1401236.
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Context-aware recommendation (CR) is the task of recommending relevant items by exploring the context information in online systems to alleviate the data sparsity issue of the user-item data. Prior methods mainly studied CR by document-based modeling approaches, that is, making recommendations by additionally utilizing textual data such as reviews, abstracts, or synopses. However, due to the inherent limitation of the bag-of-words model, they cannot effectively utilize contextual information of the documents, which results in a shallow understanding of the documents. Recent works argued that the understanding of document context can be improved by the convolutional neural network (CNN) and proposed the convolutional matrix factorization (ConvMF) to leverage the contextual information of documents to enhance the rating prediction accuracy. However, ConvMF only models the document content context from an item view and assumes users are independent and identically distributed (i.i.d). But in reality, as we often turn to our friends for recommendations, the social relationship and social reviews are two important factors that can change our mind most. Moreover, users are more inclined to interact (buy or click) with the items that they have bought (or clicked). The relationships among items are also important factors that can impact the user’s final decision. Based on the above observations, in this work, we target CR and propose a joint convolutional matrix factorization (JCMF) method to tackle the encountered challenges, which jointly considers the item’s reviews, item’s relationships, user’s social influence, and user’s reviews in a unified framework. More specifically, to explore items’ relationships, we introduce a predefined item relation network into ConvMF by a shared item latent factor and propose a method called convolutional matrix factorization with item relations (CMF-I). To consider user’s social influence, we further integrate the user’s social network into CMF-I by sharing the user latent factor between user’s social network and user-item rating matrix, which can be treated as a regularization term to constrain the recommendation process. Finally, to model the document contextual information of user’s reviews, we exploit another CNN to learn user’s content representations and achieve our final model JCMF. We conduct extensive experiments on the real-world dataset from Yelp. The experimental results demonstrate the superiority of JCMF compared to several state-of-the-art methods in terms of root mean squared error (RMSE) and mean average error (MAE).
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Kailasam, Sriram, Santosh Kumar, and Janakiram Dharanipragada. "Arogyasree: An Enhanced Grid-Based Approach to Mobile Telemedicine." International Journal of Telemedicine and Applications 2010 (2010): 1–11. http://dx.doi.org/10.1155/2010/536237.
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A typical telemedicine system involves a small set of hospitals providing remote healthcare services to a small section of the society using dedicated nodal centers. However, in developing nations like India where majority live in rural areas that lack specialist care, we envision the need for much larger Internet-based telemedicine systems that would enable a large pool of doctors and hospitals to collectively provide healthcare services to entire populations. We propose a scalable, Internet-based P2P architecture for telemedicine integrating multiple hospitals, mobile medical specialists, and rural mobile units. This system, based on the store and forward model, features a distributed context-aware scheduler for providing timely and location-aware telemedicine services. Other features like zone-based overlay structure and persistent object space abstraction make the system efficient and easy to use. Lastly, the system uses the existing internet infrastructure and supports mobility at doctor and patient ends.
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Mannweiler, C., A. Klein, J. Schneider, and H. D. Schotten. "Context-awareness for heterogeneous access management." Advances in Radio Science 8 (November 17, 2010): 257–62. http://dx.doi.org/10.5194/ars-8-257-2010.
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Abstract. The increasing availability of both static and dynamic context information has steadily been driving the development of context-aware communication systems. Adapting system behavior according to current context of the network, the user, and the terminal can yield significant end-to-end performance improvements. In this paper, we present a concept for how to use context information, in particular location information and movement prediction, for Heterogeneous Access Management (HAM). In a first step, we outline the functional architecture of a distributed and extensible context management system (CMS) that defines the roles, tasks, and interfaces of all modules within such a system for large-scale context acquisition and dissemination. In a second step, we depict how the available context information can be exploited for optimizing terminal handover decisions to be made in a multi-RAT (radio access technology) environment. In addition, the utilized method for predicting terminal location as well as the objective functions used for evaluating and comparing system performance are described. Finally, we present preliminary simulation results demonstrating that HAM systems that include current and future terminal context information in the handover decision process clearly outperform conventional systems.
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Bertolli, Carlo, Daniele Buono, Gabriele Mencagli, and Marco Vanneschi. "An Approach to Mobile Grid Platforms for the Development and Support of Complex Ubiquitous Applications." International Journal of Advanced Pervasive and Ubiquitous Computing 2, no. 4 (2010): 24–38. http://dx.doi.org/10.4018/ijapuc.2010100102.
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Several complex and time-critical applications require the existence of novel distributed, heterogeneous and dynamic platforms composed of a variety of fixed and mobile processing nodes and networks. Such platforms, that can be called Pervasive Mobile Grids, aim to merge the features of Pervasive Computing and Highperformance Grid Computing onto a new emerging paradigm. In this Chapter we study a methodology for the design and the development of high-performance, adaptive and context-aware applications. We describe a programming model approach, and we compare it with other existing research works in the field of Pervasive Mobile Computing, discussing the rationales of the requirements and the features of a novel programming model for the target platforms and applications. In order to exemplify the proposed methodology we introduce our programming framework ASSISTANT, and we provide some interesting future directions in this research field.
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Polaczyk, Bartosz, Piotr Chołda, and Andrzej Jajszczyk. "Peer-to-Peer Multicasting Inspired by Huffman Coding." Journal of Computer Networks and Communications 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/312376.
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Stringent QoS requirements of video streaming are not addressed by the delay characteristics of highly dynamic peer-to-peer (P2P) networks. To solve this problem, a novel locality-aware method for choosing optimal neighbors in live streaming multicast P2P overlays is presented in this paper. To create the appropriate multicast tree topology, a round-trip-time (RTT) value is used as a parameter distinguishing peers capabilities. The multicast tree construction is based on the Huffman source coding algorithm. First, a centrally managed version is presented, and then an effective use of a distributed paradigm is shown. Performance evaluation results prove that the proposed approach considerably improves the overlay efficiency from the viewpoint of end-users and content providers. Moreover, the proposed technique ensures a high level of resilience against gateway-link failures and adaptively reorganizes the overlay topology in case of dynamic, transient network fluctuations.
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Wu, Liangshun, and Hengjin Cai. "Energy-Efficient Adaptive Sensing Scheduling in Wireless Sensor Networks Using Fibonacci Tree Optimization Algorithm." Sensors 21, no. 15 (2021): 5002. http://dx.doi.org/10.3390/s21155002.
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Wireless sensor networks are appealing, largely because they do not need wired infrastructure, but it is precisely this feature that renders them energy-constrained. The duty cycle scheduling is perceived as a contributor to the energy efficiency of sensing. This paper developed a novel paradigm for modeling wireless sensor networks; in this context, an adaptive sensing scheduling strategy is proposed depending on event occurrence behavior, and the scheduling problem is framed as an optimization problem. The optimization objectives include reducing energy depletion and optimizing detection accuracy. We determine the explicit form of the objective function by numerical fitting and found that the objective function aggregated by the fitting functions is a bivariate multimodal function that favors the Fibonacci tree optimization algorithm. Then, with the optimal parameters optimized by the Fibonacci tree optimization algorithm, the scheduling scheme can be easily deployed, and it behaves consistently in the coming hours. The proposed “Fibonacci Tree Optimization Strategy” (“FTOS”) outperforms lightweight deployment-aware scheduling (LDAS), balanced-energy scheduling (BS), distributed self-spreading algorithm (DSS) and probing environment and collaborating adaptive sleeping (PECAS) in achieving the aforementioned scheduling objectives. The Fibonacci tree optimization algorithm has attained a better optimistic effect than the artificial bee colony (ABC) algorithm, differential evolution (DE) algorithm, genetic algorithm (GA) algorithm, particle swarm optimization (PSO) algorithm, and comprehensive learning particle swarm optimization (CLPSO) algorithm in multiple runs.
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Amadeo, Marica, Giuseppe Ruggeri, Claudia Campolo, Antonella Molinaro, Valeria Loscrí, and Carlos T. Calafate. "Fog Computing in IoT Smart Environments via Named Data Networking: A Study on Service Orchestration Mechanisms." Future Internet 11, no. 11 (2019): 222. http://dx.doi.org/10.3390/fi11110222.
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By offering low-latency and context-aware services, fog computing will have a peculiar role in the deployment of Internet of Things (IoT) applications for smart environments. Unlike the conventional remote cloud, for which consolidated architectures and deployment options exist, many design and implementation aspects remain open when considering the latest fog computing paradigm. In this paper, we focus on the problems of dynamically discovering the processing and storage resources distributed among fog nodes and, accordingly, orchestrating them for the provisioning of IoT services for smart environments. In particular, we show how these functionalities can be effectively supported by the revolutionary Named Data Networking (NDN) paradigm. Originally conceived to support named content delivery, NDN can be extended to request and provide named computation services, with NDN nodes acting as both content routers and in-network service executors. To substantiate our analysis, we present an NDN fog computing framework with focus on a smart campus scenario, where the execution of IoT services is dynamically orchestrated and performed by NDN nodes in a distributed fashion. A simulation campaign in ndnSIM, the reference network simulator of the NDN research community, is also presented to assess the performance of our proposal against state-of-the-art solutions. Results confirm the superiority of the proposal in terms of service provisioning time, paid at the expenses of a slightly higher amount of traffic exchanged among fog nodes.