A global generic architecture for the future Internet of Things

The envisioned 6A Connectivity of the future IoT aims to allow people and objects to be connected anytime, anyplace, with anything and anyone, using any path/network and any service. Because of heterogeneous resources, incompatible standards and communication patterns, the current IoT is constrained to specific devices, platforms, networks and domains. As the standards have been accepted worldwide, most existing IoT platforms use Web Services to integrate heterogeneous devices. Human-readable protocols of Web Services cause non-negligible overhead for object-to-object communication. Other issues, such as: lack of applications and modularized services, high cost of devices and software development also hinder the common use of the IoT. In this paper, a global generic architecture for the future IoT (GGIoT) is proposed to meet the envisioned 6A Connectivity of the future IoT. GGIoT is independent of particular devices, platforms, networks, domains and applications, and it minimizes transmission message size to fit devices with minimal capabilities, such as passive RFID tags. Thus, lower physical size and cost are possible, and network overhead can be reduced. The proposed GGIoT is evaluated via performance analysis and proof-of-concept case studies

An Elastic Hybrid Sensing Platform: Architecture and Research Challenges

© 2016 Published by Elsevier B.V. The dynamic provisioning of hybrid sensing services that integrates both WSN and MPS is a promising, yet challenging concept. It does not only widen the spatial sensing coverage, but it also enables different types of sensing nodes to collaboratively perform sensing tasks and complement each other. Furthermore, it allows for the provisioning of a new category of services that was not possible to implement in pure WSN or MPS networks. Offering a hybrid sensing platform as a service results in several benefits including, but no limited to, efficient sharing and dynamic management of sensing nodes, diversification and reuse of sensing services, as well as combination of many sensing paradigms to enable data to be collected from different sources. However, many challenges need to be resolved before such architecture can be feasible. Currently, the deployment of sensing applications and services is a costly and complex process, which also lacks automation. This paper motivates the need for hybrid sensing, sketches an early architecture, and identifies the research issues with few hints on how to solve them. We argue that a sensing platform that reuses the virtualization and cloud computing concepts will help in addressing many of these challenges, and overcome the limitations of today\u27s deployment practices

Energy-Efficient Softwarized Networks: A Survey

With the dynamic demands and stringent requirements of various applications, networks need to be high-performance, scalable, and adaptive to changes. Researchers and industries view network softwarization as the best enabler for the evolution of networking to tackle current and prospective challenges. Network softwarization must provide programmability and flexibility to network infrastructures and allow agile management, along with higher control for operators. While satisfying the demands and requirements of network services, energy cannot be overlooked, considering the effects on the sustainability of the environment and business. This paper discusses energy efficiency in modern and future networks with three network softwarization technologies: SDN, NFV, and NS, introduced in an energy-oriented context. With that framework in mind, we review the literature based on network scenarios, control/MANO layers, and energy-efficiency strategies. Following that, we compare the references regarding approach, evaluation method, criterion, and metric attributes to demonstrate the state-of-the-art. Last, we analyze the classified literature, summarize lessons learned, and present ten essential concerns to open discussions about future research opportunities on energy-efficient softwarized networks.Comment: Accepted draft for publication in TNSM with minor updates and editin

Wireless Sensor Network Virtualization: A Survey

Wireless Sensor Networks (WSNs) are the key components of the emerging Internet-of-Things (IoT) paradigm. They are now ubiquitous and used in a plurality of application domains. WSNs are still domain specific and usually deployed to support a specific application. However, as WSN nodes are becoming more and more powerful, it is getting more and more pertinent to research how multiple applications could share a very same WSN infrastructure. Virtualization is a technology that can potentially enable this sharing. This paper is a survey on WSN virtualization. It provides a comprehensive review of the state-of-the-art and an in-depth discussion of the research issues. We introduce the basics of WSN virtualization and motivate its pertinence with carefully selected scenarios. Existing works are presented in detail and critically evaluated using a set of requirements derived from the scenarios. The pertinent research projects are also reviewed. Several research issues are also discussed with hints on how they could be tackled.Comment: Accepted for publication on 3rd March 2015 in forthcoming issue of IEEE Communication Surveys and Tutorials. This version has NOT been proof-read and may have some some inconsistencies. Please refer to final version published in IEEE Xplor

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig