ARES: Reliable and Sustainable Edge Provisioning for Wireless Sensor Networks

Wireless sensor networks have wide applications in monitoring applications. However, sensors’ energy and processing power constraints, as well as the limited network bandwidth, constitute significant obstacles to near-real-time requirements of modern IoT applications. Offloading sensor data on an edge computing infrastructure instead of in-cloud or in-network processing is a promising solution to these issues. Nevertheless, due to (1) geographical dispersion, (2) ad-hoc deployment, and (3) rudimentary support systems compared to cloud data centers, reliability is a critical issue. This forces edge service providers to deploy a huge amount of edge nodes over an urban area, with catastrophic effects on environmental sustainability. In this work, we propose ARES, a two-stage optimization algorithm for sustainable and reliable deployment of edge nodes in an urban area. Initially, ARES applies multi-objective optimization to identify a set of Pareto-optimal solutions for transmission time and energy; then it augments these candidates in the second stage to identify a solution that guarantees the desired level of reliability using a dynamic Bayesian network based reliability model. ARES is evaluated through simulations using data from the urban area of Vienna. Results demonstrate that it can achieve a better trade-off between transmission time, energy-efficiency, and reliability than the state-of-the-art solutions.

[1]  Antonio J. Nebro,et al.  jMetal: A Java framework for multi-objective optimization , 2011, Adv. Eng. Softw..

[2]  Ivona Brandic,et al.  Multi-Objective Mobile Edge Provisioning in Small Cell Clouds , 2019, ICPE.

[3]  Soumaya Ben Letaifa.  How to strategize smart cities: Revealing the SMART model ☆ , 2015 .

[4]  A. Kandasamy,et al.  An Energy-Efficient Clustering Algorithm for Edge-Based Wireless Sensor Networks , 2016 .

[5]  Vincenzo Grassi,et al.  On QoS-aware scheduling of data stream applications over fog computing infrastructures , 2015, 2015 IEEE Symposium on Computers and Communication (ISCC).

[6]  Abhishek Chandra,et al.  Nebula: Distributed Edge Cloud for Data Intensive Computing , 2014, 2014 IEEE International Conference on Cloud Engineering.

[7]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[8]  Adnan Al-Anbuky,et al.  LED-WSN: Light weight edge computed dynamic wireless sensor network routing protocol , 2017, 2017 27th International Telecommunication Networks and Applications Conference (ITNAC).

[9]  Mohamed Medhat Gaber,et al.  Learning from Data Streams: Processing Techniques in Sensor Networks , 2007 .

[10]  Chamil Kulatunga,et al.  Using Edge Analytics to Improve Data Collection in Precision Dairy Farming , 2016, 2016 IEEE 41st Conference on Local Computer Networks Workshops (LCN Workshops).

[11]  Antonio Brogi,et al.  How to Best Deploy Your Fog Applications, Probably , 2017, 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC).

[12]  Hui Li,et al.  Natural Disaster Monitoring with Wireless Sensor Networks: A Case Study of Data-intensive Applications upon Low-Cost Scalable Systems , 2013, Mob. Networks Appl..

[13]  Radu Prodan,et al.  A Two-Stage Multi-objective Optimization of Erasure Coding in Overlay Networks , 2017, 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID).

[14]  Cheng-Zhong Xu,et al.  Exploring event correlation for failure prediction in coalitions of clusters , 2007, Proceedings of the 2007 ACM/IEEE Conference on Supercomputing (SC '07).

[15]  José Francisco Aldana Montes,et al.  Comparing multi-objective metaheuristics for solving a three-objective formulation of multiple sequence alignment , 2017, Progress in Artificial Intelligence.

[16]  Raja Lavanya,et al.  Fog Computing and Its Role in the Internet of Things , 2019, Advances in Computer and Electrical Engineering.

[17]  Ivona Brandic,et al.  Dependency Mining for Service Resilience at the Edge , 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC).

[18]  Xiaowei Yang,et al.  CloudCmp: comparing public cloud providers , 2010, IMC '10.

[19]  Alessandro Sorniotti,et al.  Secure and Trusted in-network Data Processing in Wireless Sensor Networks: a Survey , 2007 .

[20]  William Donnelly,et al.  Leveraging Fog Analytics for Context-Aware Sensing in Cooperative Wireless Sensor Networks , 2019, ACM Trans. Sens. Networks.

[21]  Shusen Yang,et al.  IoT Stream Processing and Analytics in the Fog , 2017, IEEE Communications Magazine.

[22]  H. Madsen,et al.  Reliability in the utility computing era: Towards reliable Fog computing , 2013, 2013 20th International Conference on Systems, Signals and Image Processing (IWSSIP).

[23]  Ratul Mahajan,et al.  Odin: Microsoft's Scalable Fault-Tolerant CDN Measurement System , 2018, NSDI.

[24]  Radu Prodan,et al.  Multi-objective list scheduling of workflow applications in distributed computing infrastructures , 2014, J. Parallel Distributed Comput..

[25]  Peter Langendörfer,et al.  How public key cryptography influences wireless sensor node lifetime , 2006, SASN '06.

[26]  Ivona Brandic,et al.  Quality of Service Channelling for Latency Sensitive Edge Applications , 2017, 2017 IEEE International Conference on Edge Computing (EDGE).

[27]  Atakan Aral,et al.  Learning Spatiotemporal Failure Dependencies for Resilient Edge Computing Services , 2021, IEEE Transactions on Parallel and Distributed Systems.

[28]  Rekha Jain,et al.  Wireless Sensor Network -A Survey , 2013 .

[29]  Radu Prodan,et al.  An Improved Model for Live Migration in Data Centre Simulators , 2016, 2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC).

[30]  Yacine Challal,et al.  Energy efficiency in wireless sensor networks: A top-down survey , 2014, Comput. Networks.

[31]  Ruben Mayer,et al.  FogStore: Toward a distributed data store for Fog computing , 2017, 2017 IEEE Fog World Congress (FWC).

[32]  Vincenzo De Maio,et al.  Multi-objective scheduling of extreme data scientific workflows in Fog , 2020, Future Gener. Comput. Syst..

[33]  Atay Ozgovde,et al.  EdgeCloudSim: An environment for performance evaluation of Edge Computing systems , 2017, 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC).

[34]  Guisheng Fan,et al.  QoS-Aware Task Placement With Fault-Tolerance in the Edge-Cloud , 2020, IEEE Access.

[35]  Rajkumar Buyya,et al.  iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments , 2016, Softw. Pract. Exp..

[36]  Stephen P. Boyd,et al.  ECOS: An SOCP solver for embedded systems , 2013, 2013 European Control Conference (ECC).

[37]  Marat Zhanikeev,et al.  A cloud visitation platform to facilitate cloud federation and fog computing , 2015, Computer.

[38]  Sven Helmer,et al.  A Container-Based Edge Cloud PaaS Architecture Based on Raspberry Pi Clusters , 2016, 2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW).

[39]  Minho Jo,et al.  Recovery for overloaded mobile edge computing , 2017, Future Gener. Comput. Syst..

[40]  Mahadev Satyanarayanan,et al.  The Emergence of Edge Computing , 2017, Computer.

[41]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[42]  Siddhartan Govindasamy,et al.  Uplink Performance of Multi-Antenna Cellular Networks With Co-Operative Base Stations and User-Centric Clustering , 2017, IEEE Transactions on Wireless Communications.

[43]  R. Lyndon While,et al.  A faster algorithm for calculating hypervolume , 2006, IEEE Transactions on Evolutionary Computation.

[44]  Joseph L. Hellerstein,et al.  Obfuscatory obscanturism: Making workload traces of commercially-sensitive systems safe to release , 2012, 2012 IEEE Network Operations and Management Symposium.

[45]  Tolga Ovatman,et al.  A Decentralized Replica Placement Algorithm for Edge Computing , 2018, IEEE Transactions on Network and Service Management.

[46]  Kevin P. Murphy,et al.  Learning the Structure of Dynamic Probabilistic Networks , 1998, UAI.

[47]  Rodrigo Roman,et al.  Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges , 2016, Future Gener. Comput. Syst..