Billions of devices are connected to the Internet of Things (IoT). These devices generate a large volume of data, which poses an enormous burden on conventional networking infrastructures. As an effective computing model, edge computing is collaborative with cloud computing by moving part intensive computation and storage resources to edge devices, thus optimizing the network latency and energy consumption. Meanwhile, the software-defined networks (SDNs) technology is promising in improving the quality of service (QoS) for complex IoT-driven applications. However, building SDN-based computing platform faces great challenges, making it difficult for the current computing models to meet the low-latency, high-complexity, and high-reliability requirements of emerging applications. Therefore, a cloud-mobile edge computing (MEC) collaborative task offloading scheme with service orchestration (CTOSO) is proposed in this article. First, the CTOSO scheme models the computational consumption, communication consumption, and latency of task offloading and implements differentiated offloading decisions for tasks with different resource demand and delay sensitivity. What is more, the CTOSO scheme introduces orchestrating data as services (ODaS) mechanism based on the SDN technology. The collected metadata are orchestrated as high-quality services by MEC servers, which greatly reduces the network load caused by uploading resources to the cloud on the one hand, and on the other hand, the data processing is completed at the edge layer as much as possible, which achieves the load balancing and also reduces the risk of data leakage. The experimental results demonstrate that compared to the random decision-based task offloading scheme and the maximum cache-based task offloading scheme, the CTOSO scheme reduces delay by approximately 73.82%–74.34% and energy consumption by 10.71%–13.73%.

A Cloud–MEC Collaborative Task Offloading Scheme With Service Orchestration

Trust based energy efficient data collection with unmanned aerial vehicle in edge network

For the cloud computing based on software-defined networks (SDNs), a larger amount of data is collected to cloud for analysis, which will cause the larger amount of redundancy data and longer service response time due to the capacity-limited Internet. To solve this problem, a novel service orchestration and data aggregation framework (SODA) is proposed, which can orchestrate data as services and aggregate data packets to reduce data redundancy and service response delay. In SODA, the network is divided into three layers. 1) Data centers layer (DCL). Data centers (DCs) release software with a specific function to all devices in the network, devices orchestrate data as services and aggregate data packets using software to reduce service response delay. 2) Middle routing layer (MRL). The routing path of data packets in this layer is adjusted according to the correlation of data packets and routing distance. The correlation of data packets is higher and routing distance is short, the probability that data packets are transmitted along the same routing path is higher to reduce redundancy data. 3) Vehicle network layer (VNL). Mobile vehicles are used to transmit data packets and services among devices. A series of experiments and simulation is conducted. The results illustrate that the proposed scheme has better performance compared with the traditional scheme.

A Novel Load Balancing and Low Response Delay Framework for Edge-Cloud Network Based on SDN

A trajectory privacy-preserving scheme based on a dual-K mechanism for continuous location-based services

With the rapid development of software-defined technologies, emerging multimedia applications are booming, which require real-time communication and computation via devices. Meanwhile, the program codes in multimedia applications need to be updated periodically to accommodate changes in the edge networking environment. Therefore, a huge number of smart sensing devices which are deployed in the infrastructures of smart city can play a bigger role than ever before due to their program codes in multimedia applications can be updated by sensing data. However, how to spread program codes to a large amount of devices which are distributed in smart city in a low-cost and fast way is a challenging issue. To solve the issue, in this paper, an Unmanned Aerial Vehicles (UAVs) joint Vehicles as Data Mules for Fast Codes Dissemination (UVDCD) scheme is proposed to spread codes as a fast and low-cost pattern for edge networking in smart city. In UVDCD scheme, first of all, large amount of vehicles in smart city act as mules for code dissemination. Although the cost of the method is low and can quickly cover most networks, there is a very long trailing phenomenon for this approach, i.e. in the early stage of code dissemination, the code dissemination rate increases rapidly over time and have a high efficiency, but after a short time, the increase of code dissemination rate has become very slowly over time. So, in this situation, the unmanned aerial vehicle is used to spread the program codes of intelligent devices where vehicles are hard to spread, thereby eliminating the trailing phenomenon in code dissemination. In order to achieve a high dissemination efficiency for UAV, first, we cluster the device nodes which have not received codes, then we select the optimized UAV flight trajectory based on the cluster so that the total length of the UAV flight path is the shortest, i.e. low-cost, and the number of devices which can receive codes is the largest. Finally, the validity of UVDCD scheme is confirmed based on real vehicle data, and through experiments, it can effectively overcome the trailing phenomenon in code dissemination after using UAV. The coverage ratio and dissemination speed are higher than those of previous strategies and increase 2.11% and 69.44% respectively.

UAVs joint vehicles as data mules for fast codes dissemination for edge networking in Smart City

Sensor nodes equipped with various sensory devices can sense a wide range of information regarding human or things, thereby providing a foundation for Internet of Thing (IoT). Fast and energy-efficient data collection to the control center (CC) is of significance yet very challenging. To deal with this challenge, a low redundancy data collection (LRDC) scheme is proposed to reduce delay as well as energy consumption for monitoring network by using matrix completion technique. Due to the correlation of the location-dependent sensing data, some data without being collected can still be recovered by the matrix completion technology, thereby reducing the data amount for data collection and transmission, reducing the network energy consumption, and accelerating the process of data acquisition. Based on matrix completion technique, LRDC scheme can select only part of the nodes to sense data and transmit less data to CC. By doing so, the data collected by the network can be greatly reduced, which can effectively improve the network lifetime. In addition, LRDC scheme also proposes a method for quickly compensate sample data in cases of packet loss, whereby part of redundant data is sent in advance to the area closer to CC. If the data required for matrix completion is lost, these redundant data can be quickly obtained by CC, so the LRDC scheme has low delay characteristics. Simulation results demonstrate that LRDC scheme can achieve better performance than the traditional strategy, and it can reduce the maximum energy consumption of the network by 27.6–57.9% and reduce the delay by 0.7–17.9%.

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A low redundancy data collection scheme to maximize lifetime using matrix completion technique

Data collection is the basic functions of the Internet of Things (IoT), in which the sensed data are concentrations from sensor nodes to the sink, with a timely style, so the smart response can be done for emergency. The goal of multi-modal sensor data fusion is to obtain simple and accurate data to enhance system reliability and fault tolerance. Energy efficiency and small delay are the most important indicators which govern the performance of IoT. Convergecast is a low-latency data collection strategy based on effective time division multiple access (TDMA), in which each sensor node generates a packet, and m packets can aggregate to a packet. However, in most practical networks, sensor nodes do not necessarily generate packets during each data collection cycle, but instead generate packets from time to time. In the previous convergecast strategy, each node was fixedly allocated a slot, which increased the delay and wasted energy. A delay and energy-efficient data collection (DEEDC) scheme-based matrix filling theory is proposed to collect data in a randomly generated WSNs with minimum delay and energy consumption. The DEEDC scheme uses a clustering approach. For each cluster, the number of slots required for transmission is calculated by matrix filling theory, not the number of nodes that actually generate data. This ensures that data can be collected in a network with randomly generated data (number of slots ≤ number of nodes), thereby avoiding the allocation of slots for each node and the acquisition of redundant data to lead to the wastage of time and energy. Based on the above, a mixed slot scheduling strategy is proposed to construct energy and delay-efficient, collision-free schedule scheme. After extensive theoretical analysis, by using the DEEDC scheme, the delay is reduced by about 50~80%, and the energy consumed is reduced by about 40~57%.

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Delay and energy-efficient data collection scheme-based matrix filling theory for dynamic traffic IoT

Cyber-physical-social (CPS) systems integrate Big Data Collectors (BDCs), Service Organizers (SOs) and users to build a unified data-centric computing framework. In CPS systems, BDCs leverage a vast variety of sensing devices to collect cyber-physical-social data, and report these data to SOs to orchestrate various services provided to users, thus offering a great potential for solving complex network tasks that are far beyond the capabilities of existing networks. However, due to the lack of an economic model to describe such complex data interactions, their applications are limited. So, a game-based economic model is proposed in this paper to make smart price decisions in CPS systems. Specifically, it has the following innovations: (a) The economic model gives a dynamic game income matrix which can accurately describe the revenue changes of BDCs in the game, so as to help BDCs select appropriate game parameters and strategies, and make BDCs competitive in the game. (b) The economic model can help SOs to make optimized data purchase price and service selling price based on data collection cost and competitor price analysis, so that SOs can have a better Quality of Service (QoS) and users attraction, and maximize the profit. Experimental results demonstrate that the proposed model can help BDCs and SOs find the most suitable game strategy and price adjustment principle, which has great significance in applications.

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A Game-Based Economic Model for Price Decision Making in Cyber-Physical-Social Systems

Fast and energy efficient data collection is the most important function of wireless sensor networks (WSNs) which governs the performance of WSNs. Time division multiple access (TDMA) is an effective data collection strategy in which node only schedules the active slot to save energy when transmitting data. However, many previous studies are designed for static traffic pattern such that each node to generate one data packet in a data collection cycle. Moreover, all nodes in the network use the same transmission radius r, so that the network data collection has a larger delay, a lower lifetime, and energy utilization rate. In view of the shortcomings of previous research, this paper proposes a reducing delay and maximizing lifetime (RDML) data collection strategy for WSNs with dynamic traffic patterns. The main innovation point of our work is the data transmission radius is no longer unified as r, while some nodes with energy surplus use a data transmission radius of mr(m > 1). The following network performance can be achieved by adopting the RDML scheme. First, because the nodes in the near sink mr radius area can send data directly to sink, the amount of data carried by hotspots nodes in the original strategy is greatly reduced, and the lifetime is improved. Second, since some nodes can go mr distance to the sink in data transmission, thus accelerating the data routing and reducing the time needed for data collection. Finally, since the remaining energy of nodes is fully utilized to increase the radius of data transmission, the energy utilization rate can be effectively improved. Through the theoretical analysis and experimental results, the RDML scheme can reduce the maximum delay of data collection by 22.32% and improve lifetime as well as the energy utilization rate by 0.86%-84.40%, and 23.33%-88.00%, respectively, compared with previous strategies.

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Guoping Zhang

Papers

A low redundancy data collection scheme to maximize lifetime using matrix completion technique

Delay and energy-efficient data collection scheme-based matrix filling theory for dynamic traffic IoT

A Game-Based Economic Model for Price Decision Making in Cyber-Physical-Social Systems

Reducing Delay and Maximizing Lifetime for Wireless Sensor Networks With Dynamic Traffic Patterns