A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

A Time-driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing

Compared to traditional distributed computing environments such as grids, cloud computing provides a more cost-effective way to deploy scientific workflows. Each task of a scientific workflow requires several large datasets that are located in different datacenters from the cloud computing environment, resulting in serious data transmission delays. Edge computing reduces the data transmission delays and supports the fixed storing manner for scientific workflow private datasets, but there is a bottleneck in its storage capacity. It is a challenge to combine the advantages of both edge computing and cloud computing to rationalize the data placement of scientific workflow, and optimize the data transmission time across different datacenters. Traditional data placement strategies maintain load balancing with a given number of datacenters, which results in a large data transmission time. In this study, a self-adaptive discrete particle swarm optimization algorithm with genetic algorithm operators (GA-DPSO) was proposed to optimize the data transmission time when placing data for a scientific workflow. This approach considered the characteristics of data placement combining edge computing and cloud computing. In addition, it considered the impact factors impacting transmission delay, such as the band-width between datacenters, the number of edge datacenters, and the storage capacity of edge datacenters. The crossover operator and mutation operator of the genetic algorithm were adopted to avoid the premature convergence of the traditional particle swarm optimization algorithm, which enhanced the diversity of population evolution and effectively reduced the data transmission time. The experimental results show that the data placement strategy based on GA-DPSO can effectively reduce the data transmission time during workflow execution combining edge computing and cloud computing

A gap analysis of Internet-of-Things platforms

We are experiencing an abundance of Internet-of-Things (IoT) middleware solutions that provide connectivity for sensors and actuators to the Internet. To gain a widespread adoption, these middleware solutions, referred to as platforms, have to meet the expectations of different players in the IoT ecosystem, including device providers, application developers, and end-users, among others. In this article, we evaluate a representative sample of these platforms, both proprietary and open-source, on the basis of their ability to meet the expectations of different IoT users. The evaluation is thus more focused on how ready and usable these platforms are for IoT ecosystem players, rather than on the peculiarities of the underlying technological layers. The evaluation is carried out as a gap analysis of the current IoT landscape with respect to (i) the support for heterogeneous sensing and actuating technologies, (ii) the data ownership and its implications for security and privacy, (iii) data processing and data sharing capabilities, (iv) the support offered to application developers, (v) the completeness of an IoT ecosystem, and (vi) the availability of dedicated IoT marketplaces. The gap analysis aims to highlight the deficiencies of today's solutions to improve their integration to tomorrow's ecosystems. In order to strengthen the finding of our analysis, we conducted a survey among the partners of the Finnish IoT program, counting over 350 experts, to evaluate the most critical issues for the development of future IoT platforms. Based on the results of our analysis and our survey, we conclude this article with a list of recommendations for extending these IoT platforms in order to fill in the gaps.Comment: 15 pages, 4 figures, 3 tables, Accepted for publication in Computer Communications, special issue on the Internet of Things: Research challenges and solution

Container deployment strategy for edge networking

Conference code: 156753 Cited By :2 Export Date: 1 February 2021 References: AlertManager, , https://prometheus.io/docs/alerting/alertmanager/, Accessed: 2019-01-30; Docker Swarm Mode Overview, , https://docs.docker.com/engine/swarm/, Accessed: 2019-01-30; Google cAdvisor, , https://github.com/google/cadvisor, Accessed: 2019-01-30; Prometheus - Monitoring System & Time Series Database, , https://prometheus.io, Accessed: 2019-01-30; The Kubernetes Scheduler, , https://kubernetes.io/docs/reference/command-line-tools-reference/kube-scheduler/, Accessed: 2019-01-30; (2018) Ericsson Mobility Report, , https://www.ericsson.com/assets/local/mobility-report/documents/2018/ericsson-mobilityreport-june-2018.pdf, Technical Report; Balan, R., Flinn, J., Satyanarayanan, M., Sinnamohideen, S., Yang, H.-I., The case for cyber foraging (2002) Proceedings of the 10th Workshop on ACM SIGOPS European Workshop (EW 10), pp. 87-92. , https://doi.org/10.1145/1133373.1133390, ACM, New York, NY, USA; Gordon, M.S., Anoushe Jamshidi, D., Mahlke, S., Mao, Z.M., Chen, X., CoMET: Code offload by migrating execution transparently (2012) Proceedings of the 10th USENIX Conference on Operating Systems Design and Implementation (OSDI’12), pp. 93-106. , http://dl.acm.org/citation.cfm?id=2387880.2387890, USENIX Association, Berkeley, CA, USA; Habak, K., Ammar, M., Harras, K.A., Zegura, E., Femto clouds: Leveraging mobile devices to provide cloud service at the edge (2015) 2015 IEEE 8th International Conference on Cloud Computing, pp. 9-16. , https://doi.org/10.1109/CLOUD.2015.12; Hindman, B., Konwinski, A., Zaharia, M., Ghodsi, A., Joseph, A.D., Katz, R., Shenker, S., Stoica, I., Mesos: A Platform for Fine-grained Resource Sharing in the Data Center (2011) Proceedings of the 8th USENIX Conference on Networked Systems Design and Implementation (NSDI’11), pp. 295-308. , http://dl.acm.org/citation.cfm?id=1972457.1972488, USENIX Association, Berkeley, CA, USA; Pahl, C., Lee, B., Containers and clusters for edge cloud architectures – A technology review (2015) 2015 3rd International Conference on Future Internet of Things and Cloud, pp. 379-386. , https://doi.org/10.1109/FiCloud.2015.35; Roughan, M., Simplifying the synthesis of internet traffic matrices (2005) SIGCOMM Comput. Commun. Rev., 35 (5), pp. 93-96. , https://doi.org/10.1145/1096536.1096551, Oct. 2005; Satyanarayanan, M., Bahl, P., Caceres, R., Davies, N., The case for VM-based cloudlets in mobile computing (2009) IEEE Pervasive Computing, 8 (4), pp. 14-23. , https://doi.org/10.1109/MPRV.2009.82, Oct 2009; Saurez, E., Hong, K., Lillethun, D., Ramachandran, U., Ottenwälder, B., Incremental Deployment and Migration of Geo-distributed Situation Awareness Applications in the Fog (2016) Proceedings of the 10th ACM International Conference on Distributed and Event-Based Systems (DEBS’16), pp. 258-269. , https://doi.org/10.1145/2933267.2933317, ACM, New York, NY, USA; Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L., Edge computing: Vision and challenges (2016) IEEE Internet of Things Journal, 3 (5), pp. 637-646. , https://doi.org/10.1109/JIOT.2016.2579198, Oct 2016; Wu, C.-P., Suresh, M.A., Silva, D.D., Container lifecycle management for edge nodes: Poster (2017) Proceedings of the Second ACM/IEEE Symposium on Edge Computing (SEC’17), p. 2; Yi, S., Hao, Z., Qin, Z., Li, Q., Fog computing: Platform and applications (2015) 2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb), pp. 73-78Edge computing paradigm has been proposed to support latency-sensitive applications such as Augmented Reality (AR)/ Virtual Reality(VR) and online gaming, by placing computing resources close to where they are most demanded, at the edge of the network. Many solutions have proposed to deploy virtual resources as close as possible to the consumers using virtual machines and containers. However, the most popular container orchestration tools, e.g., Docker Swarm and Kubernetes, do not take into account the locality aspect during deployment, resulting in poor location choices at the edge of the network. In this paper, we propose an edge deployment strategy to tackle the lack of locality awareness of the container orchestrator. In this strategy, the orchestrator collects information about latency and the real-time resource consumption from the current container deployments, providing a bird’s-eye view of the most demanded locations and the best places for deployment to cover the largest number of clients. We evaluated the proposed model using 16 AWS regions across the globe and compared to the standard deployment strategies. The experimental results show our edge strategy reduces the average latency between serving container to the clients by up to 4 times compared to the standard deployment algorithms. © 2019 Association for Computing Machinery.Peer reviewe