Service Embedding in IoT Networks

• Published in: IEEE access 2020, Vol.8, p.2948-2962
• Main Authors: Al-Shammari, Haider Qays, Lawey, Ahmed Q., El-Gorashi, Taisir E. H., Elmirghani, Jaafar M. H.
• Format: Article
• Language: English
• Subjects: Actuation; Automation; Embedding; Energy efficiency; Industrial plants; Integer programming; Internet of Things; IoT; Linear programming; Links; MILP; Mixed integer; Network latency; Nodes; Optimization; Power consumption; Power demand; Quality of service; queuing; Sensors; service oriented architecture (SOA); Service-oriented architecture; Smart buildings; Topology; Virtual networks; Virtualization; Workflow
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2962271

The Internet of Things (IoT) is the cornerstone of smart applications such as smart buildings, smart factories, home automation, and healthcare automation. These smart applications express their demands in terms of high-level requests. Application requests in service-oriented IoT architectures are translated into a business process (BP) workflow. In this paper, we model such a BP as a virtual network containing a set of virtual nodes and links connected in a specific topology. These virtual nodes represent the requested processing and locations where sensing and/or actuation are needed. The virtual links capture the requested communication requirements between nodes. We introduce a framework, optimized using mixed integer linear programming (MILP), that embeds the BPs from the virtual layer into a lower-level implementation at the IoT physical layer. We formulate the problem of finding the optimal set of IoT nodes and links to embed BPs into the IoT layer considering three objective functions: i) minimizing network and processing power consumption only, ii) minimizing mean traffic latency only, iii) minimizing a weighted combination of power consumption and traffic latency to study the trade-off between minimizing the power consumption and minimizing the traffic latency. We have established, as reference, a scenario where service embedding is performed to meet all the demands with no consideration to power consumption or latency. Compared to this reference scenario, our results indicate that the power savings achieved by our energy efficient embedding scenario is 42% compared with the energy-latency unaware service embedding (ELUSE) reference scenario, while our low latency embedding reduced the traffic latency by an average of 47% compared to the ELUSE scenario. Our combined energy efficient low latency service embedding approach achieved high optimality by jointly realizing 91% of the power and latency reductions obtained under the single objective of minimizing power consumption or latency.