Resilient IoT Architectures over Dynamic Sensor Networks with Adaptive Components

Abstract:

As competing industries delve into the Internet of Things (IoT), a growing challenge of interoperability and redundant deployments is magnified. Specifically, as we augment more “things” in the IoT fabric, how will these components interact across their heterogeneity, let alone collaborate. In this paper we address the core issue of component interaction and operation under the IoT umbrella. We present our contribution in the framework of Wireless Sensor Networks (WSNs), as a founding block in the IoT. More importantly, we present a novel paradigm in the design of WSNs, to build a resilient architecture that decouples operational mandates from the nodes. We abstract IoT things as wirelessly interfaced components, which introduce functionality physically decoupled from their devices; boosting resilience, dynamicity and resource utilization. This approach dissects the study of any IoT nodal capacity to its “connected” components, and empowers dynamic associativity between things to serve varying functional requirements and levels. It also enables re-introducing only the components required to suffice for network operation, or only those needed to meet a new requirement. More importantly, critical resources in the network will be shared within their neighborhoods. Thus network lifetime will relate to functional cliques of dynamic IoT nodes, rather than individual networks. We evaluate the cost effectiveness and resilience of our paradigm via simulations.

 

Existing System:

 

 We address the challenge of IoT proliferation by leveraging the resilience and coordination of interaction between “things”. Specifically, as WSNs form the founding block of IoT, we will elaborate on a novel component-based design, which enables resource sharing and resilient operation between WSN components. This work targets a foundational block in IoT proliferation, as we present a framework for adaptive association between functional components (things) in the grand scheme of building sensing applications. This component-based framework will encompass sensing, communication and control components that realize the foundation of a scalable and truly synergetic view of IoT. We hereafter label this paradigm as a Dynamic WSN (D-WSN) framework. In all fairness, WSNs were application specific from their inception, and the idea of a generic platform/protocol was clearly eliminated early on in literature due to various tradeoffs. For example, prolonging network lifetime came at the price of time-latency constraints for sensing and communication [1][2]. Then, as load balancing was researched to marginalize the tradeoff, control-overhead became a discriminatory metric [3].

 

Proposed System:

 

The D-WSN paradigm introduced in this paper presents a three-fold contribution, namely (1) assigning network functionality to individual components that dynamically associate with active sensor nodes, to augment their capabilities as needed, (2) re-engineering WSN operation in the IoT to accommodate for dynamic architectures that evolve over time to boost resilience and lifetime, based on individual components rather than static WSN nodes, and 3) present a deterministic model for WSN functional lifetime in the IoT, tightly coupled with functional capacity rather than individual nodes. Previous efforts in literature have presented the notion of platforms with multiple components [5], and others focused mainly on multiple transceivers/antennas for boosting communication and evaluated their performance [3]. Other studies investigated the possibility of having multi-level duty cycles, to allow a node to operate in different states based on available resources [4] [6]. Nevertheless, these notions are static by nature and are pre-designed to cater for fixed application requirements.

 

Conclusion:

 

We argued for a dynamic paradigm that integrates IoT things in a real-time association model. With a growing abundance of wireless technologies that enable sensing and communication, and interact over multiple access mediums, it is imperative to re-assess our view of what a WSN is, and how its interplay with IoT should manifest. In the near future, most of the sensing applications, especially in urban settings, will not rely on dedicated and overpriced WSNs. In fact, sensing systems provided by smart vehicles and smartphone are already changing our view of WSN capabilities. However, a major hindrance in the latter technologies is their isolated operation. We presented a paradigm that enables dynamic nodes to change their functional mandates post deployment, enabling a WSN that can change its application span over time. We contend that the presented DCNs will be easily replaceable with smartphones, and WDCs will evolve from resources offered by a myriad of wirelessly enabled devices. We realize a true opportunity for synergy in the IoT, and hence presented this paradigm to shift the operation of WSNs from its isolated progression to a ubiquitous IoT enabler.

 

References:

 

[1] S. Oteafy and H. Hassanein, “Dynamic Wireless Sensor Networks”, Wiley-ISTE, ISBN: 978-1-84821-531-3, June 2014.

 

[2] I. Dietrich, and F. Dressler. “On the lifetime of wireless sensor networks.” ACM Transactions on Sensor Networks (TOSN), Vol 5, Iss.1, 2009.

 

[3] A. AlIslam, M. Hossain, V. Raghunathan, Y. Hu, “Backpacking: Energy- Efficient Deployment of Heterogeneous Radios in Multi-Radio High- Data-Rate Wireless Sensor Networks,” IEEE Access, Vol. 2, pp.1281- 1306, 2014.

 

[4] Y. Gu, L. Cheng, J. Niu, T. He, and D. Du, “Achieving asymmetric sensing coverage for duty cycled wireless sensor networks”, IEEE Transactions on Parallel and Distributed Systems, Vol. 25, No. 12, pp.3076-3087, 2014

 

[5] K. Mikhaylov, J. Petäjäjärvi, M. Mäkeläinen, A. Paatelma, and T. Hänninen. “Demo: Modular Multi-radio Wireless Sensor Platform for IoT Trials with Plug&Play Module Connection.” ACM International Conference on Mobile Computing and Networking, pp. 188-189, 2015.

 

[6] S. Oteafy, H. Aboelfotoh, H. Hassanein, “Decentralized Multi-Level Duty Cycling in Sensor Networks,” Global Telecommunications Conference, IEEE, pp.1-5, 2008

 

[7] S. Oteafy and H. Hassanein, “Re-Usable Resources in Wireless Sensor Networks: A Linear Optimization for a Novel Application Overlay Paradigm over Multiple Networks.” Global Telecommunications Conference, pp. 1-5, 2011.

 

[8] G. Degirmenci, J. Kharoufeh, and O. Prokopyev. “Maximizing the Lifetime of Query-Based Wireless Sensor Networks”. ACM Transactions on Sensor Networks (TOSN), Vol. 10, No. 4, Article 56, pp.1-24, 2014

 

[9] S. Lin, G. Zhou, M. Al-Hami, K. Whitehouse, Y. Wu, J. Stankovic, T. He, X. Wu, and H. Liu, “Toward Stable Network Performance in Wireless Sensor Networks: A Multilevel Perspective.” ACM Transactions on Sensor Networks (TOSN) , Vol. 11, No. 3, Article 42, pp.1-26, 2015.

 

[10] A. Munir, A. Gordon-Ross, and S. Ranka. “Multi-core embedded wireless sensor networks: Architecture and applications.” IEEE Transactions on Parallel and Distributed Systems, Vol. 25, No. 6, pp. 1553-1562, 2014.