This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols, and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. This paper starts by providing a horizontal overview of the IoT. Then, we give an overview of some technical details that pertain to the IoT enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols and application issues to enable researchers and application developers to get up to speed quickly on how the different protocols fit together to deliver desired functionalities without having to go through RFCs and the standards specifications. We also provide an overview of some of the key IoT challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore the relation between the IoT and other emerging technologies including big data analytics and cloud and fog computing. We also present the need for better horizontal integration among IoT services. Finally, we present detailed service use-cases to illustrate how the different protocols presented in the paper fit together to deliver desired IoT services.

Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications

Fog/edge computing has been proposed to be integrated with Internet of Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user’s experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between cyber-physical systems and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications.

A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications

IoT security: Review, blockchain solutions, and open challenges

This document provides updates to IEEE Std 802.16's MIB for the MAC, PHY and asso- ciated management procedures in order to accommodate recent extensions to the standard.

IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems Draft Amendment: Management Information Base Extensions

Mobile-edge computing (MEC) is an emerging paradigm to meet the ever-increasing computation demands from mobile applications. By offloading the computationally intensive workloads to the MEC server, the quality of computation experience, e.g., the execution latency, could be greatly improved. Nevertheless, as the on-device battery capacities are limited, computation would be interrupted when the battery energy runs out. To provide satisfactory computation performance as well as achieving green computing, it is of significant importance to seek renewable energy sources to power mobile devices via energy harvesting (EH) technologies. In this paper, we will investigate a green MEC system with EH devices and develop an effective computation offloading strategy. The execution cost , which addresses both the execution latency and task failure, is adopted as the performance metric. A low-complexity online algorithm is proposed, namely, the Lyapunov optimization-based dynamic computation offloading algorithm, which jointly decides the offloading decision, the CPU-cycle frequencies for mobile execution, and the transmit power for computation offloading. A unique advantage of this algorithm is that the decisions depend only on the current system state without requiring distribution information of the computation task request, wireless channel, and EH processes. The implementation of the algorithm only requires to solve a deterministic problem in each time slot, for which the optimal solution can be obtained either in closed form or by bisection search. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Sample simulation results shall be presented to corroborate the theoretical analysis as well as validate the effectiveness of the proposed algorithm.

Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices

In-network processing and data-aggregation are key features of Wireless Sensor Networks. In fact, they allow the data to be processed and aggregated as they go through the network. In this work, we propose an innovative congestion control algorithm for wireless sensor networks based on data aggregation, which will be referred to as Data-Aggregation Congestion Control (DACC). The algorithm has been designed by exploiting linear discrete time control theory. It can be applied in a fully distributed way. Using our approach, each node periodically evaluates the amount of data to aggregate in order to control the level of its transmission queue. The aggregation process involves only linear operations and allows the sink node to estimate the confidence level of received data. The effectiveness of DACC has been evaluated with reference to a temperature monitoring problem in a fire detection scenario, using the Castalia simulator. Results have shown that DACC is able to significantly improve the estimation accuracy.

Congestion control based on data-aggregation for Wireless Sensor Networks

The latest advances in mobile devices and the widespread diffusion of networked objects are driving the evolution of traditional Mobile Cloud Computing (MCC) systems toward a new framework where storage, computing, sensing, and other device capabilities are offered as a service at the network edge. This visionary scenario, encompassing heterogeneous resources generated, shared, and consumed everywhere in the network, requires innovative architectural and protocol design. In this context, can the approaches recently formulated in the Future Internet research arena (e.g., middleware-based virtualization, Information Centric Networking, and Software-Defined Networking/Network Function Virtualization) support the evolution of mobile cloud systems? This paper provides an affirmative answer by proposing Future-MCC, a novel architecture that capitalizes on such promising approaches and re-thinks (when needed) their philosophy to better fit the evolution of MCC systems. The performance of Future-MCC has been investigated in a representative heterogeneous and dynamic Smart City scenario. Computer simulation results clearly demonstrate that the proposed solution ensures (i) a reduction of the bandwidth requirements spanning from 66 to 91 percent and (ii) an average energy saving equal to 99 percent with respect to a conventional cloud computing platform.

Gazing into the Crystal Ball: When the Future Internet Meets the Mobile Clouds

Packet sampling techniques introduce measurement errors that should be carefully handled in order to correctly characterize the network behavior. In the literature several works have studied the statistical properties of packet sampling and the way it should be inverted to recover the original network measurements. Here we take the new direction of studying the spectral properties of packet sampled traffic. A novel technique to model the impact of packet sampling is proposed based on a theoretical analysis of network traffic in the frequency domain. Moreover, a real-time algorithm is also presented to detect the spectrum portion of the network traffic that can be restored once packet sampling has been applied. Preliminary experimental results are reported to validate the proposed approach.

/pdf/an-analysis-of-packet-sampling-in-the-frequency-domain-2opb5mnkas.pdf

An analysis of packet sampling in the frequency domain

Software-Defined Systems are offering great opportunities for deploying programmable networks, as well as large-scale services distributed across clouds. Starting from the baseline principles of both Software-Defined Networking and Network Function Virtualization, they embrace a number of novel enabling technologies (like containers, container orchestrators, and many other supporting tools) that significantly simplify the integration and the management of virtual components, while promising high level of flexibility, isolation, and performance. The major tech giants are drastically building their business on these technologies. However, many other companies are struggling in the selection of suitable platforms, tools, and any other software instruments allowing them to move forwards in this direction. Based on these premises, this paper provides a three-folded contribution. First, it explores the state of the art of container engines and container orchestrators. Second, it analyzes the main supporting tools that offer advanced and additional features to the resulting container networking. Third, it defines a set of qualitative Key Performance Indicators to carry out a preliminary comparison of the reviewed technologies. The proposed study aims at providing high-level guidelines and constructive comments to foster the widespread usage of Software-Defined Systems.

A Qualitative Cross-Comparison of Emerging Technologies for Software-Defined Systems

This document defines a Scheduling Function called "Scheduling
Function Zero" (SF0) in its Experimental version. SF0 dynamically
adapts the number of scheduled cells between neighbor nodes, based on
the amount of currently allocated cells and the neighbor nodes'
cell requirements. Neighbor nodes negotiate in a distributed neighbor-
to- neighbor basis the number of cell(s) to be added/deleted. SF0 uses
the 6P signaling messages to add/delete cells in the schedule. This
function selects the candidate cells from the schedule, defines which
cells will be added/deleted and triggers the allocation/deallocation
process.

Luigi Alfredo Grieco

Papers

Congestion control based on data-aggregation for Wireless Sensor Networks

Gazing into the Crystal Ball: When the Future Internet Meets the Mobile Clouds

An analysis of packet sampling in the frequency domain

A Qualitative Cross-Comparison of Emerging Technologies for Software-Defined Systems

6TiSCH 6top Scheduling Function Zero / Experimental (SFX)