Xiaonan Wang

The Internet of Things (IoT) aims to achieve rapid content transmission for real-time surveillance. The in-network caching and aggregation of the information-centric model can help enhance efficiency of content transmission, so it is integrated with IoT, called information-centric IoT (ICIoT), to transmit content. NDN is not originally designed for IoT, so ICIoT faces the following challenges when achieving in-network caching and aggregation: 1) mobility and resource insufficiency of IoT devices lead to frequent reverse-path disruptions; 2) the strategy of caching everything everywhere (CEE) is inefficient in IoT with limited storage resources; and 3) NDN does not maintain forwarding information toward caching providers. Taking these challenges into account, we propose an ICIoT-based routing and real-time content transmission for ICIoT, and aim to leverage in-network caching and aggregation to reduce content transmission delays, costs, and failures. The experiment results verify the superiority of the proposal.

The Internet of Underwater robotic Things (IoUT) makes intelligent marine environmental exploration possible. Compared to the traditional IoT, IoUT is a three-dimension marine space characterized by water depth, so distances between consumers on the water surface and underwater contents in deep water grow substantially, resulting in considerable content access delays and costs. To achieve rapid and cost-efficient access to marine contents, we propose an information-centric Internet of Underwater robotic Things based content caching and transmission approach, and aim to leverage in-network caching and aggregation in the information-centric networking to access marine contents in deep water. The main ideas of this approach are threefold: (1) Exploit content attributes and node attributes to perform in-network caching, shortening distances between consumers on water surface and contents in deep water; and (2) Learning automata is leveraged to learn real-time forwarding information on caching robots in shallow water; and (3) Aggregation is utilized to share contents in deep water from optimal caching robots in shallow water. The experiment results demonstrate the feasibility and superiority of the proposal.

With the rapid increase in the number of smart devices connected to the Internet of Things (IoT), network traffic has imposed serious overload on backhaul networks and led to network congestion. Data sharing among IoT devices through multi-hop communication between smart devices is expected to ease increasing pressure of backhaul traffic. In this paper, we propose a personalized preference and social attribute based data sharing framework for information-centric IoT, aiming to improve success rates of data sharing among IoT devices and reduce data sharing delays. This framework proposes personalized preferences and social attributes to reduce data response time and avoid data delivery failures caused by obsolete FIB and broken reverse paths. The experiment results justify the advantages of the proposed framework in terms of data sharing success rates and delays.

Road-safety content routing and dissemination through connected vehicles significantly enhance autonomous driving safety and efficiency. This paper proposes a hybrid emergency content routing and dissemination framework for autonomous driving, aiming to alleviate fresh content dissemination latency and improve success rates. The main ideas behind the framework are twofold: 1) Leverage content attributes to ensure freshness of cached contents, and 2) Exploit inter-vehicle attributes to enhance the stability of dissemination paths where fresh contents are pushed and pulled, achieving rapid access to contents. According to the experimental results, the fresh content access latency and cost are reduced by nearly 21.41% and 26.18%, and the success rate is improved by nearly 7.9%.

The underwater Internet of Things (UIoT) is becoming a significant means to explore marine areas, and compared to IoT, UIoT is deployed in a three-dimension marine space characterized by water depth. In this article, we propose a reliable data forwarding framework for UIoT, and aim to leverage the named data networking to reduce delays and costs of underwater data forwarding. The main idea behind this framework is to integrate the push-based data forwarding mode with the pull-based mode to realize in-networking caching and aggregation. That is, the push-based mode implements in-networking caching to shorten distances between users and marine data while the pull-based mode exploits aggregation to share data among users from the closest provider through one data forwarding process. The experimental results show that the framework achieves the above objectives.

The Internet of Things (IoT) employs end-to-end communication to deliver content between a pair of source and destination. To overcome the limitations of end-to-end communication, the Named Data Networking (NDN) is introduced to IoT, called INDN, to improve the efficiency of content forwarding. In INDN, IoT nodes work as providers and content routers, but node mobility and limited resources make it difficult for INDN to fully exploit the advantages of NDN. NDN relies on Forwarding Information Base (FIB) to make forwarding decisions, but frequent FIB updates caused by in-network caching and provider mobility are costly and time-consuming. Moreover, frequent interruptions of reverse paths caused by node mobility cannot enable aggregation and also lead to frequent content forwarding failures. Taking these issues into account, in this paper, we propose a learning automata based content forwarding solution for INDN, and aim to leverage learning automata to support in-network caching and provider mobility and establish continuous reverse paths to enable aggregation, ultimately achieving the goals of alleviating content forwarding latency, costs and failures. The experiment results have demonstrated the feasibility and strengths of the proposal.

The Vehicular Cloud (VC) is a new vehicular system and aims to achieve cost-efficient data retrieval with high success rates. The Named Data Networking (NDN) presents a new data-centric mechanism and its advantages may help achieve VC. Therefore, we are motivated to exploit NDN to achieve VC. However, the intrinsic features of vehicles such as high mobility and the different architectures between NDN and vehicular networks make it challenging to deploy NDN in vehicular scenarios. For example, NDN employs limited broadcast and reverse paths to achieve data retrieval, which might lead to high data retrieval costs and frequent failures in vehicular environments. Furthermore, the lack of mobility support in NDN deteriorates the situation. Taking these issues into account, we propose a cost-efficient and VC-based data retrieval solution and aims to achieve VC by exploiting the advantages of NDN. In this solution, vehicles employ unicast and aggregation to acquire data from the nearest VC member via one data retrieval process, so efficient-cost and low-latency data retrieval is achieved. Moreover, this solution supports vehicle mobility and enhances stability of reverse paths, so data retrieval success rates are effectively improved. The proposal is evaluated, and the experimental results show that the proposal effectively reduces data retrieval costs and improve data retrieval success rates.

Named Data Networking (NDN) might potentially help improve data acquisition efficiency in vehicular environments, so it is introduced into vehicular networks, namely vehicular NDN (VNDN). In the existing VNDN solutions, a large number of vehicles with high mobility are involved in forwarding messages, so it is hard to achieve request aggregation that is the main advantage of NDN and can help reduce the data acquisition delay. Moreover, the solutions employ flooding to achieve data retrieval without consumer mobility support, which increases the data acquisition cost and reduces the data acquisition success rate. In this paper, we propose a novel VNDN framework and aim to reduce the data acquisition delay and cost and improve the data acquisition success rate. In this framework, the cluster-chain vehicular backbone is constructed to enhance the stability of the backbone topology. Based on cluster chains, consumers can employ request aggregation and unicast to acquire data from the nearest provider. Moreover, consumer mobility is supported to guarantee successful retrieval of data in spite of high mobility of vehicles. This framework is quantitatively evaluated. According to the experimental results, compared with the VNDN approaches, our framework reduces the data acquisition cost and delay by nearly 54.6% and 12.3%, respectively, and improves the data retrieval success rate by nearly 7.9%.