The rising number of IoT devices is accelerating the research on new solutions that will be able to efficiently deal with unreliable connectivity in highly dynamic computing applications. To improve the overall performance in IoT applications, there are multiple communication solutions available, either proprietary or open source, all of which satisfy different communication requirements. Most commonly, for this kind of communication, developers choose REST HTTP protocol as a result of its ease of use and compatibility with the existing computing infrastructure. In applications where mobility and unreliable connectivity play a significant role, ensuring a reliable exchange of data with the stateless REST HTTP protocol completely depends on the developer itself. This often means resending multiple request messages when the connection fails, constantly trying to access the service until the connection reestablishes. In order to alleviate this problem, in this paper, we combine REST HTTP with random linear network coding (RLNC) to reduce the number of additional retransmissions. We show how using RLNC with REST HTTP requests can decrease the reconnection time by reducing the additional packet retransmissions in unreliable highly dynamic scenarios.
However, even with the improvements gained with fog based system architectures, the issue of intermittent connections in highly dynamic IoT applications and disruptions that come as their consequence has still many open questions. This has led to many different research efforts in improving these solutions. In @cite_1 authors approach the problem by developing a handover mechanism for mobility support in a IoT-fog systems tested in a health monitoring application. The handover procedure has also been optimized for another fog based framework that tackles high dynamic scenario of connected vehicles in @cite_7 . Beside handover optimization, the choice of the application layer protocol has also been a subject of research when tackling consequences of unreliable connections in these kind of solutions. In @cite_13 authors are using a fog based solution and RESTCONF, an HTTP based protocol for smart vehicle related communication and data computations. In @cite_3 authors have presented a disruption-tolerant RESTful support, tested both with HTTP and CoAP. Their main goal was to improve communication in a dynamic scenario where many devices are prone to disconnections while moving. Idea of improving communication with the adaptation of REST can be used, this time by using network coding.
@cite_13 @cite_3 @cite_1 @cite_7
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The rising number of IoT devices is accelerating the research on new solutions that will be able to efficiently deal with unreliable connectivity in highly dynamic computing applications. To improve the overall performance in IoT applications, there are multiple communication solutions available, either proprietary or open source, all of which satisfy different communication requirements. Most commonly, for this kind of communication, developers choose REST HTTP protocol as a result of its ease of use and compatibility with the existing computing infrastructure. In applications where mobility and unreliable connectivity play a significant role, ensuring a reliable exchange of data with the stateless REST HTTP protocol completely depends on the developer itself. This often means resending multiple request messages when the connection fails, constantly trying to access the service until the connection reestablishes. In order to alleviate this problem, in this paper, we combine REST HTTP with random linear network coding (RLNC) to reduce the number of additional retransmissions. We show how using RLNC with REST HTTP requests can decrease the reconnection time by reducing the additional packet retransmissions in unreliable highly dynamic scenarios. There are several use cases that claim the need for a connected car. Among them there is the need for connectivity between vehicles and information sources, or V2V and V2X exchanges for accident prevention. In order to cope with the need for novel applications running on top of an interconnected network, the concept of fog computing appears as a realistic solution for both intra-car and inter-car data processing and decision making. This paper describes the proposed architecture and experimental evaluation of an innovative proof-of-concept (PoC) for a connected car, modeled with YANG, which can be remotely controlled using SDN NFV and fog computing technologies. As an example, the remote control of the car might be based on a service application running on a fog node, which can be located close to a road side unit (RSU). We also propose a fog architecture in order to enable cooperative perception between connected cars. Finally, the performance evaluation uses a RESTCONF server installed in a Raspberry Pi aboard of a small car. This server is responsible for the sensors and actuators of the car and allows for its remote control from a user terminal (e.g., a smartphone, tablet, or laptop) and through the fog node, running a control application as a service. Handover mechanism for mobility support in a remote real-time streaming Internet-of-Things (IoT) system was proposed in this paper. The handover mechanism serves to keep the connection between sensor nodes and a gateway with a low latency. The handover mechanism also attentively considers oscillating nodes which often occur in many streaming IoT systems. By leveraging the strategic position of smart gateways and Fog computing in a real-time streaming IoT system, sensor nodes’ loads were alleviated whereas advanced services, like push notification and local data storage, were provided. The paper discussed and analyzed metrics for the handover mechanism based on Wi-Fi. In addition, a complete remote real-time health monitoring IoT system was implemented for experiments. The results from evaluating our mobility handover mechanism for mobility support shows that the latency of switching from one gateway to another is 10 –50 less than other state-of-the-art mobility support systems. The results show that the proposed handover mechanism is a very promising approach for mobility support in both Fog computing and IoT systems. Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitter issues. Fog computing is an emerging paradigm that aims at distributing small-size self-powered data centers (e.g., Fog nodes) between remote Clouds and VCs, in order to deliver data-dissemination real-time services to the connected VCs. Motivated by these considerations, in this paper, we propose and test an energy-efficient adaptive resource scheduler for Networked Fog Centers (NetFCs). They operate at the edge of the vehicular network and are connected to the served VCs through Infrastructure-to-Vehicular (I2V) TCP IP-based single-hop mobile links. The goal is to exploit the locally measured states of the TCP IP connections, in order to maximize the overall communication-plus-computing energy efficiency, while meeting the application-induced hard QoS requirements on the minimum transmission rates, maximum delays and delay-jitters. The resulting energy-efficient scheduler jointly performs: (i) admission control of the input traffic to be processed by the NetFCs; (ii) minimum-energy dispatching of the admitted traffic; (iii) adaptive reconfiguration and consolidation of the Virtual Machines (VMs) hosted by the NetFCs; and, (iv) adaptive control of the traffic injected into the TCP IP mobile connections. The salient features of the proposed scheduler are that: (i) it is adaptive and admits distributed and scalable implementation; and, (ii) it is capable to provide hard QoS guarantees, in terms of minimum maximum instantaneous rates of the traffic delivered to the vehicular clients, instantaneous rate-jitters and total processing delays. Actual performance of the proposed scheduler in the presence of: (i) client mobility; (ii) wireless fading; and, (iii) reconfiguration and consolidation costs of the underlying NetFCs, is numerically tested and compared against the corresponding ones of some state-of-the-art schedulers, under both synthetically generated and measured real-world workload traces.