Internet of Things (IoTs) is a big world of connected objects, including the small and low-resources devices, like sensors, as well as the full-functional computing devices, such as servers and routers in the core network. With the emerging of new IoT-based applications, such as smart transportation, smart agriculture, healthcare, and others, there is a need for making great efforts to achieve a balance in using the IoT resources, including Computing, Communication, and Caching. This paper provides an overview of the convergence of Computing, Communication, and Caching (CCC) by covering the IoT technology trends. At first, we give a snapshot of technology trends in communication, computing, and caching. As well, we describe the convergence in sensors, devices, and gateways. Addressing the aspect of convergence, we discuss the relationship between CCC technologies in collecting, indexing, processing, and storing data in IoT. Also, we introduce the three dimensions of the IoTs based on CCC. We explore different existing technologies that help to solve bottlenecks caused by a large number of physical devices in IoT. Finally, we propose future research directions and open problems in the convergence of communication, computing, and cashing with sensing and actuating devices.

Fog computing is a new computing paradigm for meeting ubiquitous massive access and latency-critical applications by moving the processing capability closer to end users. The geographical distribution/floating features with potential autonomy requirements introduce new challenges to the traditional methodology of network access control. In this paper, a blockchain-enabled fog resource access and granting solution is proposed to tackle the unique requirements brought by fog computing. The smart contract concept is introduced to enable dynamic, and automatic credential generation and delivery for an independent offer of fog resources. A per-transaction negotiation mechanism supports the fog resource provider to dynamically publish an offer and facilitates the choice of the preferred resource by the end user. Decentralized authentication and authorization relieve the processing pressure brought by massive access and single-point failure. Our solution can be extended and used in multi-access and especially multi-carrier scenarios in which centralized authorities are absent.

Energy source and circuit cost are two critical challenges for the future development of the Internet of Things (IoT). Backscatter communications offer a potential solution to conveniently obtain power and reduce cost for sensors in IoT, and researchers are paying close attention to the technology. Backscatter technology originated from the Second World War and has been widely applied in the logistics domain. Recently, both the academic and industrial worlds are proposing a series of new types of backscatter technologies for communications and IoT. In this paper, we review the history of both IoT and backscatter, describe the new types of backscatter, demonstrate their applications, and discuss the open challenges.

Fifth-generation (5G) systems have brought about new challenges toward ensuring Quality of Service (QoS) in differentiated services. This includes low latency applications, scalable machine-to-machine communication, and enhanced mobile broadband connectivity. In order to satisfy these requirements, the concept of network slicing has been introduced to generate slices of the network with specific characteristics. In order to meet the requirements of network slices, routers and switches must be effectively configured to provide priority queue provisioning, resource contention management and adaptation. Configuring routers from vendors, such as Ericsson, Cisco, and Juniper, have traditionally been an expert-driven process with static rules for individual flows, which are prone to sub optimal configurations with varying traffic conditions. In this paper, we model the internal ingress and egress queues within routers via a queuing model. The effects of changing queue configuration with respect to priority, weights, flow limits, and packet drops are studied in detail. This is used to train a model-based Reinforcement Learning (RL) algorithm to generate optimal policies for flow prioritization, fairness, and congestion control. The efficacy of the RL policy output is demonstrated over scenarios involving ingress queue traffic policing, egress queue traffic shaping, and one-hop router coordinated traffic conditioning. This is evaluated over a real application use case, wherein a statically configured router proved sub optimal toward desired QoS requirements. Such automated configuration of routers and switches will be critical for multiple 5G deployments with varying flow requirements and traffic patterns.

Underwater Wireless Sensor Networks (UWSNs) are widely used in many fields, such as regular marine monitoring and disaster warning. However, UWSNs are still subject to various limitations and challenges: ocean interferences and noises are high, bandwidths are narrow, and propagation delays are high. Sensor batteries have limited energy and are difficult to be replaced or recharged. Accordingly, the design of routing protocols is one of the solutions to these problems. Aiming at reducing and balancing network energy consumption and effectively extending the life cycle of UWSNs, this paper proposes a Hierarchical Adaptive Energy-efficient Clustering Routing (HAECR) strategy. First, this strategy divides hierarchical regions based on the depth of the sensor node in a three-dimensional (3D) space. Second, sensor nodes form different competition radii based on their own relevant attributes and remaining energy. Nodes in the same layer compete freely to form clusters of different sizes. Finally, the transmission path between clusters is determined according to comprehensive factors, such as link quality, and then the optimal route is planned. The simulation experiment is conducted in the monitoring range of the 3D space. The simulation results prove that the HAECR clustering strategy is superior to LEACH and UCUBB in terms of balancing and reducing energy consumption, extending the network lifetime, and increasing the number of data transmissions.

In view of the successful application of deep learning, mainly in the field of image recognition, deep learning applications are now being explored in the fields of communication and computer networks. In these fields, systems have been developed by use of proper theoretical calculations and procedures. However, due to the large amount of data to be processed, proper processing takes time and deviations from the theory sometimes occur due to the inclusion of uncertain disturbances. Therefore, deep learning or nonlinear approximation by neural networks may be useful in some cases. We have studied a user datagram protocol (UDP) based rate-control communication system called the simultaneous multipath communication system (SMPC), which measures throughput by a group of packets at the destination node and feeds it back to the source node continuously. By comparing the throughput with the recorded transmission rate, the source node detects congestion on the transmission route and adjusts the packet transmission interval. However, the throughput fluctuates as packets pass through the route, and if it is fed back directly, the transmission rate fluctuates greatly, causing the fluctuation of the throughput to become even larger. In addition, the average throughput becomes even lower. In this study, we tried to stabilize the transmission rate by incorporating prediction and learning performed by a neural network. The prediction is performed using the throughput measured by the destination node, and the result is learned so as to generate a stabilizer. A simple moving average method and a stabilizer using three types of neural networks, namely multilayer perceptrons, recurrent neural networks, and long short-term memory, were built into the transmission controller of the SMPC. The results showed that not only fluctuation reduced but also the average throughput improved. Together, the results demonstrated that deep learning can be used to predict and output stable values from data with complicated time fluctuations that are difficultly analyzed.

Unlimited and seamless coverage as well as ultra-reliable and low-latency communications are vital for connected vehicles, in particular for new use cases like autonomous driving and vehicle platooning. In this paper, we propose a novel Space-Air-Ground integrated vehicular network (SAGiven) architecture to gracefully integrate the multi-dimensional and multi-scale context-information and network resources from satellites, High-Altitude Platform stations (HAPs), low-altitude Unmanned Aerial Vehicles (UAVs), and terrestrial cellular communication systems. One of the key features of the SAGiven is the reconfigurability of heterogeneous network functions as well as network resources. We first give a comprehensive review of the key challenges of this new architecture and then provide some up-to-date solutions on those challenges. Specifically, the solutions will cover the following topics: (1) space-air-ground integrated network reconfiguration under dynamic space resources constraints; (2) multi-dimensional sensing and efficient integration of multi-dimensional context information; (3) real-time, reliable, and secure communications among vehicles and between vehicles and the SAGiven platform; and (4) a holistic integration and demonstration of the SAGiven. Finally, it is concluded that the SAGiven can play a key role in future autonomous driving and Internet-of-Vehicles applications.

As Internet of Things (IoT) applications become more prevalent and grow in their use, a limited number of wireless communication methods may be unable to enable dependable, robust delivery of information. It is necessary to enable adaptive communication and interoperability over a variety of wireless communication media to meet the requirements of large-scale IoT applications. This paper utilizes Named Data Networking (NDN), an up-and-coming Information-Centric Network architecture, to interconnect differing communication links via the network layer, and implements dynamic forwarding strategies and routing mechanisms which aid in the efficient dissemination of information. This work targets the creation of an interface technique to allow NDN to be transported via LoRa. This is acheived via the coupling of LoRa and WiFi using the NDN Forwarding Daemon (NFD) to create a universal ad hoc network. This network has the capacity for high range and multi-hop Device-to-Device (D2D) communication together with compatibility with other network communication media. Testing of the system in a real environment has shown that the newly created ad hoc network is capable of communicating over a several kilometer radius, while making use of the features provided by NDN to capitalize upon various links available to enable the efficient dissemination of data. Furthermore, the newly created network leverages NDN features to enable content-based routing within the LoRa network and utilize content-based routing techniques.

This study focuses on the problem of multitarget tracking. To address the existing problems of current tracking algorithms, as manifested by the time consumption of subgroup separation and the uneven group size of unmanned aerial vehicles (UAVs) for target tracking, a multitarget tracking control algorithm under local information selection interaction is proposed. First, on the basis of location, number, and perceived target information of neighboring UAVs, a temporary leader selection strategy is designed to realize the local follow-up movement of UAVs when the UAVs cannot fully perceive the target. Second, in combination with the basic rules of cluster movement and target information perception factors, distributed control equations are designed to achieve a rapid gathering of UAVs and consistent tracking of multiple targets. Lastly, the simulation experiments are conducted in two- and three-dimensional spaces. Under a certain number of UAVs, clustering speed of the proposed algorithm is less than 3 s, and the equal probability of the UAV subgroup size after group separation is over 78%.