Internet of Mobile Things (IoMTs) refers to the interconnection of mobile devices, for example, mobile phones, vehicles, robots, etc. For mobile data, strong extra processing resources are normally required due to the limited physical resources of the mobile devices in IoMTs. Due to latency or bandwidth limitations, it may be infeasible to transfer a large amounts of mobile data to remote server for processing. Thus, distributed computing is one of the potential solutions to overcome these limitations. We consider the device mobility in IoMTs. Two situations of the movement position of the mobile devices, i.e., unpredictable and predictable, are considered. In addition, three possible relative positions between the two server sets which respectively correspond to the positions of a mobile device for computation tasks offloading and for output results receiving, i.e., within the same server sets, with two different server sets and with two adjacent server sets, are studied. Coded schemes with high flexibility and low complexity are proposed based on Fountain codes to reduce the total processing time and latency of the distributed fog computing process in IoMTs for the above different situations. The latency related performance, i.e., the computation, the communication and the transmission loads, is analyzed. We also compare of the Fountain code-based and the uncoded schemes and numerical results demonstrate that shorter total processing time and lower latency can be achieved by the Fountain code-based schemes.

Machine learning is an effective technique for big data analytics. We focus on the study of big data analytics with decentralized learning in large-scale networks. Fountain codes are applied to the decentralized learning process to reduce communication load for exchanging intermediate learning parameters among fog nodes. Two scenarios, i.e., disjoint datasets and overlapping datasets, are analyzed. Comparison results show that communication load can be reduced significantly by the Fountain-based scheme for large-scale networks, especially when the quality of communication links is relatively bad and/or the number of fog nodes is large.

In Industrial Internet of Things (IIoTs), data generated during manufacturing are collected by sensors and need be processed timely. The direction of data transmissions from sensors to processing centers (fog nodes) is often called uplink transmission. In this paper, the cases with single and multiple distributed fog nodes, which are also referred to as centralized and distributed models, are studied. Two distributed schemes based on batched sparse (BATS) codes are proposed separately for the uplink of these two models. The expected rank and the recovery probability of the information from sensors at fog node(s) are derived. Comparison results show that by using the proposed BATS-based schemes, improved transmission reliability can be achieved compared to the XOR-based network coding (NC) scheme.

In this paper, we study the characteristics of dominant interference power with directional reception in a random network modelled by a Poisson Point Process. Additionally, the Laplace functional of cumulative interference excluding the n dominant interferers is also derived, which turns out to be a generalization of omni-directional reception and complete accumulative interference. As an application of these results, we study the impact of directional receivers in random networks in terms of outage probability and error probability with queue length constraint.

A social network is a social structure made up of a set of social actors and a set of dyadic ties between these actors. The actors form a number of communities. In communities, some actors want to transmit their information to all other actors. Each actor corresponds to a user equipment (UE). The UE of the actor which has information to be transmitted is also called the source, and the UEs of all other actors are called destinations. The information is transmitted from the source to destinations with the assistance of helpers, which can be small cell base stations (SCBSs). A novel information dissemination approach, namely expanded network coding with a finite buffer size (ENCFB), is proposed for the case when the buffer size of helpers is limited. The performance comparison of the uncoded information dissemination approach, the network coded approach and the ENCFB approach is conducted. Comparison results show that the ENCFB approach can significantly improve the performance of information dissemination when the buffer size is limited.

Content caching at small base stations has been considered as an efficient way to alleviate the use of expensive backhauling. In this paper, we study the caching content update for a cache-enabled heterogeneous network. Through analyzing a benchmark content update policy, the problem of content consistency, which is caused by the distinct update time at each caching entity, is firstly revealed. As well, the close form representation of the consistency probability in a mobile environment is derived. Furthermore, a deterministic content update strategy is investigated, where the trade-off between consistency probability and storage cost is characterized. Detailed simulations are provided to support our analysis, as well as present the impacts of moving speed of mobile terminals and transmission bandwidth for content delivery on the consistency performance.

In an industrial automation system, one of the most important parts is control loop. Fog computing is a potential solution for industrial control in time-critical applications as it provides distributed computing services closer to the connected devices. However, a huge amount of data exchanging among fog nodes causes high communication load, which constrains the overall response time from fog nodes to actuators. In this paper, we consider the erasure environment, batched sparse (BATS) codes are applied to the Map and the Data Shuffling stages of distributed fog computing process to reduce both the communication and the computation loads. The communication loads of the uncoded, the coded, and the proposed BATS-based schemes over erasure channels are calculated, respectively. Numerical results show that the BATS-based scheme can reduce the communication and the computation loads simultaneously, and furthermore reduce the overall response time from fog nodes to the actuators.