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Named data networking (NDN) aims to change the traditional content delivery method and caching by router nodes caching and participating in forwarding. NDN-caching can reduce the expected flood of global data traffic by providing cache storage at intermediate nodes for transmitted content objects, making data broadcasting in an efficient way. In this paper, a novel caching strategy based on entropy weighting method and TOPSIS is proposed for efficient content dissemination to improve NDN’s in-network caching performance. Firstly, the consumer’s request process is modeled by the entropy weighting method and TOPSIS to obtain the best cache node for the cache object according to the real-time status of the node. Secondly, two cache replacement algorithms (composed of an active cache replacement algorithm and a passive cache replacement algorithm) are proposed to reduce the cache redundancy on the delivery path and improve the utilization of data packets in the nodes. Finally, an effective cache mechanism and data packet migration scheme are proposed to further improve the cache performance according to the different types of cache nodes. The performance evaluation shows that the proposed scheme performs better in terms of cache hit rate, latency, and link load compared with some existing strategies.

As an innovative networking paradigm in the Internet of Things, information-centric networking (ICN) has improved security than the traditional IP networks. However, it still faces a large number of security threats, especially those threats from internal attacks. The trust management technology is an effective approach for defending against internal attacks. In this paper, our contributions lie in our investigating the requirements of cybersecurity in ICN and analyzing the typical attack behaviors and defense schemes. Then, we propose a fast and efficient trust management scheme (FETMS) for defending against the on-off attack, which is an intelligent internal attack. The simulation results show that FETMS can efficiently detect and remove the malicious node in a short period of time; meanwhile, it can achieve lower latency and add its mobility.

This research proposes a novel wireless information-centric networking (ICN) architecture, namely, Context-Aware Green ICN Model (CAGIM), which can adapt the power consumption of network nodes to optimized values according to the associated link utilization. The power adaption in ICN nodes is conducted through dynamically adjusting the link-rate corresponding to content popularity and traffic load to reduce wasteful energy consumption. Moreover, we propose a smart popularity-based caching strategy, called distinguished caching scheme (DCS), with the introduction of hot and cold-caching partitions of ICN node's cache storage for popular and non-popular content objects, respectively. DCS improves the content diversity of the cache storage by adjusting, for each content, the number of chunks to be cached at ICN nodes based on its type and popularity level. DCS thus can further decrease the network system power consumption, thanks to its improved cache hit that reduces network traffic load. Toward the goal of realizing a context-aware green wireless network system with efficient content delivery, we also design a WiFi Direct based scheme as an alternative approach to minimize power consumption and latency by sharing essential/important content objects via direct communications with power-saving mechanisms in the case that wireless local area network connections are not available. The evaluation results show that CAGIM can improve network efficiency by reducing both hop-count and power consumption considerably compared with existing wireless network systems with different well-known caching schemes. This proposal enables a flexible and efficient content delivery mechanism for future networks with various real-life scenarios, like Green building, Green company, and Green campus content accesses.

The dynamic adaptive streaming technique flexibly adapts the video bit-rate to link fluctuations, which can improve the quality of experience (QoE). In this paper, we present a systematic framework of video streaming in the context of information-centric networking, in order to facilitate the large-scale deployment of the dynamic adaptive streaming technique. Specifically, we design the network as a two-layer coordinating structure, namely, the control layer and the transmission layer. The control layer employs the statistical data recorders to record the variations of the video popularity, link states, and user demands. On the other side, the network forwards user requests and caches data packets in the transmission layer, based on the statistical data which is obtained in the control layer. In addition, the network executes the real-time monitoring of link conditions in the transmission layer, and adjusts the video bit-rate accordingly. Under the above feedback circumstance, we first develop a distributed algorithm of joint dynamic forwarding and caching to theoretically maximize the total user demand rates within the network stability region. Then, we modify the distributed algorithm with a practical caching strategy to make the system applicable to real scenarios. Simulation results show the superior performance of the modified distributed algorithm in terms of low user delay and high QoE performance.