Recent years have disclosed a remarkable proliferation of compute-intensive applications in smart cities. Such applications continuously generate enormous amounts of data which demand strict latency-aware computational processing capabilities. Although edge computing is an appealing technology to compensate for stringent latency-related issues, its deployment engenders new challenges. In this article, we highlight the role of edge computing in realizing the vision of smart cities. First, we analyze the evolution of edge computing paradigms. Subsequently, we critically review the state-of-the-art literature focusing on edge computing applications in smart cities. Later, we categorize and classify the literature by devising a comprehensive and meticulous taxonomy. Furthermore, we identify and discuss key requirements, and enumerate recently reported synergies of edge computing-enabled smart cities. Finally, several indispensable open challenges along with their causes and guidelines are discussed, serving as future research directions.

Edge-Computing-Enabled Smart Cities: A Comprehensive Survey

Recent years have disclosed a remarkable proliferation of compute-intensive applications in smart cities. Such applications continuously generate enormous amounts of data which demand strict latency-aware computational processing capabilities. Although edge computing is an appealing technology to compensate for stringent latency related issues, its deployment engenders new challenges. In this survey, we highlight the role of edge computing in realizing the vision of smart cities. First, we analyze the evolution of edge computing paradigms. Subsequently, we critically review the state-of-the-art literature focusing on edge computing applications in smart cities. Later, we categorize and classify the literature by devising a comprehensive and meticulous taxonomy. Furthermore, we identify and discuss key requirements, and enumerate recently reported synergies of edge computing enabled smart cities. Finally, several indispensable open challenges along with their causes and guidelines are discussed, serving as future research directions.

Monitoring the status of the facilities and detecting any faults are considered an important technology in a smart factory. Although the faults of machine can be analyzed in real time using collected data, it requires a large amount of computing resources to handle the massive data. A cloud server can be used to analyze the collected data, but it is more efficient to adopt the edge computing concept that employs edge devices located close to the facilities. Edge devices can improve data processing and analysis speed and reduce network costs. In this paper, an edge device capable of collecting, processing, storing and analyzing data is constructed by using a single-board computer and a sensor. And, a fault detection model for machine is developed based on the long short-term memory (LSTM) recurrent neural networks. The proposed system called LiReD was implemented for an industrial robot manipulator and the LSTM-based fault detection model showed the best performance among six fault detection models.

LiReD: A Light-Weight Real-Time Fault Detection System for Edge Computing Using LSTM Recurrent Neural Networks

Classification of optimization problems in fog computing

Triad link prediction method based on the evolutionary analysis with IoT in opportunistic social networks

In the social scene of opportunistic networks, message applications find suitable relay nodes or certain transmission destinations from the surrounding neighbors through specific network addresses of users. However, at the dawn of big data and 5G networks, the variational location information of nodes is difficult to be available to mobile devices all the time, and a long wait for the destination may cause severe end-to-end delay. To improve the transmission environment, this study constructs an efficient routing-delivery scheme (Predict and Forward) based on node profile for the opportunistic networks. The node profile effectively characterizes nodes by analyzing and comparing their attributes instead of network addresses, such as physical characteristics, places of residence, workplaces, occupations or hobbies. According to the optimal stopping theory, this algorithm implements the optimal transmission for Prelearn messages by dividing the complex data transmission process into two different phases (Predict and Forward). Through simulations and the comparison of routing algorithms in opportunistic networks, the proposed strategy increases the delivery ratio by 80% with the traditional methods on average, and the average end-to-end delay in this algorithm is the lowest.

Predict and Forward: An Efficient Routing-Delivery Scheme Based on Node Profile in Opportunistic Networks

Due to the rapid popularization of various short distance communication mobile devices, the use scenarios of opportunistic networks are increasing day by day. However, in opportunistic networks, because of the complexity of community structure, many methods lack of symmetry between application and theoretical research. Thus, the connection strength between nodes is different, and the degree of message diffusion is different. If the above factors cannot be accurately estimated and analyzed, and effective data forwarding and scheduling strategies cannot be formulated, the delivery ratio will be low, the delay will be relatively high, and the network overhead will be large. In light of improving symmetry problems in opportunistic networks, this paper establishes the message duplicate adaptive allocation and spray routing strategy (MDASRS) algorithm model, measures the connection strength between nodes through social pressure, and estimates the diffusion of current messages in the network through the probability of messages leaving the current node successfully, so as to develop the self-adaptive control replication transmission mode and achieve the effect of reducing the network burden and network overhead. This is done through experiments and comparison of social network algorithms, comparing the MDASRS with Epidemic, Spray and Wait, and EIMST algorithms. The experiment results showed that this method improves the cache utilization of nodes, reduces data transmission delay, and improves the network’s overall efficiency.

Data Transmission and Management Based on Node Communication in Opportunistic Social Networks

Mobile Edge Computing (MEC) has evolved into a promising technology that can relieve computing pressure on wireless devices (WDs) in the Internet of Things (IoT) by offloading computation tasks to the MEC server. Resource management and allocation are challenging because of the unpredictability of task arrival, wireless channel status and energy consumption. To address such a challenge, in this paper, we provide an energy-efficient joint resource management and allocation (ECM-RMA) policy to reduce time-averaged energy consumption in a multi-user multi-task MEC system with hybrid energy harvested WDs. We first formulate the time-averaged energy consumption minimization problem while the MEC system satisfied both the data queue stability constraint and energy queue stability constraint. To solve the stochastic optimization problem, we turn the problem into two deterministic sub-problems, which can be easily solved by convex optimization technique and linear programming technique. Correspondingly, we propose the ECM-RMA algorithm that does not require priori knowledge of stochastic processes such as channel states, data arrivals and green energy harvesting. Most importantly, the proposed algorithm achieves the energy consumption-delay trade-off as [ O ( 1 / V ) , O ( V ) ] . V, as a non-negative weight, which can effectively control the energy consumption-delay performance. Finally, simulation results verify the correctness of the theoretical analysis and the effectiveness of the proposed algorithm.

Energy-Efficient Online Resource Management and Allocation Optimization in Multi-User Multi-Task Mobile-Edge Computing Systems with Hybrid Energy Harvesting.

5G has brought a huge increase in data, and the number of nodes and types of messages are becoming more and more complex. The Internet of things has become a large and complex network. More and more devices can be used as nodes in opportunistic social networks. The attitude of nodes to messages is different and changeable. However, in the previous opportunistic network algorithm and mass data transmission environment, due to the lack of effective information selection and management means, it was easy to lead to transmission delay and high consumption. Therefore, we propose Effective Data Selection and Management (EDSM). EDSM uses the current state of the node as the basis for forwarding messages. When the cache space is insufficient, EDSM will perform cache replacement based on the message cache value and delete the information with the lowest cache value. Simulation results show that the algorithm has good performance in terms of delivery rate and latency.

https://www.mdpi.com/2079-9292/9/8/1271/pdf

Effective Data Selection and Management Method Based on Dynamic Regulation in Opportunistic Social Networks

In steelmaking-continuous casting production processes, the optimizing scheduling of ladles is an effective way to reduce energy consumption and improve productivity. Because the ladle scheduling must satisfy multiple conflicting goals and conflicting constraints, it is difficult to adopt the existing optimal scheduling methods. The intelligent ladle scheduling method was proposed for the ladle scheduling problem of molten steel in Baosteel in China. First, the strategy of ladle scheduling was proposed. Then, the first-order rule learning was used to select the optimal objective, and the least general generalization rule reasoning was used to extract the ladle matching rules. In addition, an optimal scheduling model for ladle was established, and an intelligent scheduling method for ladle was proposed, including the ladle matching, ladle path optimization and crane scheduling. Rule reasoning was employed to select decarbonized ladle or dephosphorization ladle. The multi-priority heuristic method was designed to determine the path from ladle to converter, refining furnace and continuous caster. The heuristic method of avoiding interference strategy was designed to decide the crane for ladle transportation in the air and the start and end time of operation. Finally, a software system for ladle scheduling was developed, and industrial validation was carried out with real data. The results show that the overall production efficiency and ladle turnover rate are improved.

Yutong Xiao

Papers

Predict and Forward: An Efficient Routing-Delivery Scheme Based on Node Profile in Opportunistic Networks

Data Transmission and Management Based on Node Communication in Opportunistic Social Networks

Energy-Efficient Online Resource Management and Allocation Optimization in Multi-User Multi-Task Mobile-Edge Computing Systems with Hybrid Energy Harvesting.

Effective Data Selection and Management Method Based on Dynamic Regulation in Opportunistic Social Networks

Research on a wireless sensor network method in a real-time system