Much has been written about theory and practice in the law, and the tension between practitioners and theorists. Judges do not cite theoretical articles often; they rarely "apply" theories to particular cases. These arguments are not revisited. Instead the Essay explores the working and interaction of theory and practice, practitioners and theorists. The Essay starts with a story about solving a legal issue using our intellectual tools - theory, practice, and their progenies: experience and "gut." Next the Essay elaborates on the nature of theory, practice, experience and "gut." The third part of the Essay discusses theories that are helpful to practitioners and those that are less helpful. The Essay concludes that practitioners theorize, and theorists practice. They use these intellectual tools differently because the goals and orientations of theorists and practitioners, and the constraints under which they act, differ. Theory, practice, experience and "gut" help us think, remember, decide and create. They complement each other like the two sides of the same coin: distinct but inseparable.

Of Theory and Practice

Radio access technologies for cellular mobile communications are typically characterized by multiple access schemes, e.g., frequency division multiple access (FDMA), time division multiple access (TDMA), code division multiple access (CDMA), and OFDMA. In the 4th generation (4G) mobile communication systems such as Long-Term Evolution (LTE) (Au et al., Uplink contention based SCMA for 5G radio access. Globecom Workshops (GC Wkshps), 2014. doi:10.1109/GLOCOMW.2014.7063547) and LTE-Advanced (Baracca et al., IEEE Trans. Commun., 2011. doi:10.1109/TCOMM.2011.121410.090252; Barry et al., Digital Communication, Kluwer, Dordrecht, 2004), standardized by the 3rd Generation Partnership Project (3GPP), orthogonal multiple access based on OFDMA or single carrier (SC)-FDMA is adopted. Orthogonal multiple access was a reasonable choice for achieving good system-level throughput performance with simple single-user detection. However, considering the trend in 5G, achieving significant gains in capacity and system throughput performance is a high priority requirement in view of the recent exponential increase in the volume of mobile traffic. In addition the proposed system should be able to support enhanced delay-sensitive high-volume services such as video streaming and cloud computing. Another high-level target of 5G is reduced cost, higher energy efficiency and robustness against emergencies.

Non-Orthogonal Multiple Access (NOMA) for cellular future radio access

With the rapid development of Internet of things devices and network infrastructure, there have been a lot of sensors adopted in the industrial productions, resulting in a large size of data. One of the most popular examples is the manufacture inspection, which is to detect the defects of the products. In order to implement a robust inspection system with higher accuracy, we propose a deep learning based classification model in this paper, which can find the possible defective products. As there may be many assembly lines in one factory, one huge problem in this scenario is how to process such big data in real time. Therefore, we design our system with the concept of fog computing. By offloading the computation burden from the central server to the fog nodes, the system obtains the ability to deal with extremely large data. There are two obvious advantages in our system. The first one is that we adapt the convolutional neural network model to the fog computing environment, which significantly improves its computing efficiency. The other one is that we work out an inspection model, which can simultaneously indicate the defect type and its degree. The experiments well prove that the proposed method is robust and efficient.

Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing

Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas.

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A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

The autonomous vehicle, as an emerging and rapidly growing field, has received extensive attention for its futuristic driving experiences. Although the fast developing depth sensors and machine learning methods have given a huge boost to self-driving research, existing autonomous driving vehicles do meet with several avoidable accidents during their road testings. The major cause is the misunderstanding between self-driving systems and human drivers. To solve this problem, we propose a humanlike driving system in this paper to give autonomous vehicles the ability to make decisions like a human. In our method, a convolutional neural network model is used to detect, recognize, and abstract the information in the input road scene, which is captured by the on-board sensors. And then a decision-making system calculates the specific commands to control the vehicles based on the abstractions. The biggest advantage of our work is that we implement a decision-making system which can well adapt to real-life road conditions, in which a massive number of human drivers exist. In addition, we build our perception system with only the depth information, rather than the unstable RGB data. The experimental results give a good demonstration of the efficiency and robustness of the proposed method.

Humanlike Driving: Empirical Decision-Making System for Autonomous Vehicles

Crowd sensing networks play a critical role in big data generation where a large number of mobile devices collect various kinds of data with large-volume features. Although which information should be collected is essential for the success of crowd-sensing applications, few research efforts have been made so far. On the other hand, an efficient incentive mechanism is required to encourage all crowd-sensing participants, including data collectors, service providers, and service consumers, to join the networks. In this article, we propose a new incentive mechanism called QUOIN, which simultaneously ensures Quality and Usability Of INformation for crowd-sensing application requirements. We apply a Stackelberg game model to the proposed mechanism to guarantee each participant achieves a satisfactory level of profits. Performance of QUOIN is evaluated with a case study, and experimental results demonstrate that it is efficient and effective in collecting valuable information for crowd-sensing applications.

QUOIN: Incentive Mechanisms for Crowd Sensing Networks

The future wireless networks need to improve spectrum and energy efficiency to satisfy the increasing demand for high data rate. Device-to device (D2D) communications have the ability to address this problem. This paper focuses on the underlay D2D relay function to improve cellular coverage quality. Although there are a few relevant works in this aspect, there is room for further improvement. For example, there is the constraint on the number of relays in a transmission path, which hardly meets the requirement of the cell-edge devices to fully improve their cellular throughput. Also, there is little energy constraint for underlay D2D relay selection, which is difficult to guarantee the service life of underlay D2D relaying links. Furthermore, without careful regulation of transmission power in terms of cellular coverage improvement, it is not conducive to the improvement of spectrum and energy efficiency in this aspect. Therefore, this paper proposes the improved scheme to deal with these problems, which can: 1) improve spectrum efficiency by using underlay spectrum sharing mode and alleviating its weakness (e.g., co-channel interference); 2) enhance comprehensive performance of underlay D2D relaying links by jointly considering multiple Quality-of-Service (QoS) metrics; 3) reduce overhead of relay selection by proposing a greedy algorithm based on a distributed local search; and 4) improve both energy efficiency and convergence time by designing a new power adjustment scheme based on the improved potential game decision algorithm. The theoretical analysis proves the existence of Nash equilibrium, and the simulation results show that the proposed game decision algorithm accelerates convergence and the proposed whole scheme improves cellular coverage quality.

Multi-Hop Relay-Aided Underlay D2D Communications for Improving Cellular Coverage Quality

Geographic routing is a research hotspot of the Internet of Vehicles (IoV) and intelligent traffic system (ITS). In practice, the vehicle movement is not only affected by its characteristics and the relationship between the vehicle and position but also affected by some implicit factors. Pointing to this problem, we combine the vehicle moving position probability matrix, the vehicle position association matrix, and the implicit factors to study the influence of vehicle position potential features and vehicle association potential features and propose a routing algorithm based on vehicle position (RAVP) analysis, which can obtain the more accurate vehicle prediction trajectory. Then, the vehicle distance is obtained based on the vehicle prediction trajectory. By the normalization of vehicle distance and cache, the vehicle data forwarding capability is obtained and the transmission decision is made. Simulation results show that the proposed algorithm outperforms the other three routing algorithms in terms of packet delivery ratio, average end-to-end delay, and routing overhead ratio.

Routing Algorithm Based on Vehicle Position Analysis for Internet of Vehicles

Topology Control is one of the most important techniques used in wireless multi-hop networks to obtain the desired network property. The most conventional MST-based topology control schemes both achieve a very good performance in terms of transmission power and implicitly consider network capacity, but they hardly have flexibility in determining whether more importance is placed on network capacity or not. Therefore, we jointly consider network capacity and energy efficiency in this paper. A new localized topology control scheme is proposed to meet the above two competing objectives by finding a balance between them through using Get_min-cost_for_link_(). The simulation results show that, when compared with some typical MST-based schemes, the proposed scheme can achieve the desired performance in terms of network capacity under the appropriate combination of weight values though it has a slightly worse performance in terms of transmission power. Moreover, the proposed scheme achieves the most balanced distribution with respect to path capacity among the compared schemes, especially when both k-hop neighborhood size and network node density increase. More importantly, the proposed scheme can flexibly determine that either network capacity or energy consumption should be given more attention according to the demand of the network application.

Flexible Adjustments Between Energy and Capacity for Topology Control in Heterogeneous Wireless Multi-hop Networks

The bi-directional communication system is an indispensable component in smart grid (SG) for monitoring and exchanging essential information among the electrical devices. IEEE 802.11s based wireless mesh networks recently have been proposed as an important networking technology to deploy in SG for data collection and remote control purposes, as the cost of networking equipments decreases and performance increases. In this paper, we focus on analyzing the MAC layer performance for IEEE 802.11s wireless mesh networks in the smart grid based on Markov model, taking into account the impact of hidden nodes and different QoS requirements of smart grid applications. We first develop a new Markov chain model to analyze the back-off process for different applications with hidden nodes problem. Then based on the analytical model, we derive a few MAC layer performance metrics such as MAC layer packet dropping probability, the mean throughput and the mean packet delay which contains the queuing delay. Finally, the proposed analytical model is validated via comparing the analytical results with simulation results by ns-3 in NAN scenarios with various applications. We observe a good match between the analytical model and simulations which confirms the veracity of our model.

Jinsong Gui

Papers

QUOIN: Incentive Mechanisms for Crowd Sensing Networks

Multi-Hop Relay-Aided Underlay D2D Communications for Improving Cellular Coverage Quality

Routing Algorithm Based on Vehicle Position Analysis for Internet of Vehicles

Flexible Adjustments Between Energy and Capacity for Topology Control in Heterogeneous Wireless Multi-hop Networks

Performance Analysis for IEEE 802.11s Wireless Mesh Network in Smart Grid