Recent years have witnessed billions of new manufactured sensors, equipments and machines being connected to our almost omnipotent Internet. While enjoying the comfort and convenience brought by Internet of Things (IoT), we also have to face tremendous energy consumption and carbon emissions which even cause climate deterioration. Extended from cloud computing, edge/fog computing and caching provide new thoughts on processing big data generated from distributed IoT devices. With the purpose to help deal with the data explosion problem by edge caching, in this paper we apply in-memory storage & processing to reduce energy consumption. We design two kinds of Time to Live (TTL) in four cache replacement policies to cache data at edge. We carry out simulation experiment in a 3-tier heterogeneous network structure using Random Waypoint (RWP) model and test the performance of in-memory caching and traditional method. The analysis results manifest that our in-memory method is able to obtain better energy efficiency in edge caching and has stable & low backhaul rate.

Faster, wider bandwidth and better user experience, 5G is our vision for the future wireless communication. And the Tactile Internet, with ultra low latency, high availability, reliability and security, is going to bring us the unprecedented real-time interactions just like the human sensing. In this paper, we focus on the solving problem of energy efficiency improvement in proactive in-network caching. We design a hybrid edge caching scheme based on four existing methods taking effect in different parts of the network. We also put forward a cache replacement policy to match the hybrid caching scheme considering the popularity of cached files which obeys Zipf distribution. The simulation results show that our proposed methods can reduce latency and achieve better performance in overall energy efficiency than existing ones.

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As one of the most striking research hotspots in both academia and industry, Internet of Things (IoT) has been constantly changing our daily life by joining together nearly all we can imagine. From home furnishings and vehicles to urban facilities, all these smart things need powerful managing and processing capabilities to deal with mass multimedia data in different content forms such as images, audios, videos. Nowadays, since Moore's Law is no longer applicable, conventional thinking may not be adequate in facing the explosive growing amount of data. Hence, in this paper we adopt the idea of in-memory processing to solve the problem of real-time multimedia big data computing in IoT. We apply closed-loop feedback in the scheduling method design to integrate in-memory storages of all devices within a 3-tier network structure. In addition, we consider the respective conditions of different real-time required levels and content forms. The analysis results show that our scheduling method can achieve better workload allocation with less latency in comparison of existing methods.

The current boom in IoT is changing daily life in many ways, from wearable devices to connected vehicles and smart cities. We used to regard fog computing as an extension of cloud computing, but it is now becoming an ideal solution for transmitting and processing large-scale geo-distributed big data. In this paper, we propose a Byzantine fault tolerant networking method and two resource allocation strategies for IoT fog computing. Our aim is to build a secure fog network called SIoTFog to resist Byzantine faults and improve the efficiency of transmitting and processing IoT big data. We consider two cases: a case with a single Byzantine fault and a case with multiple faults to compare their performances when facing different degrees of risk. We chose latency, forwarding hops in the transmission and device use rate as the metrics for analysis of the simulation results. The simulation results show that our strategies can help achieve an efficient and reliable fog network.