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Unmanned and aerial systems as interactors among different system components for communications, have opened up great opportunities for truth data discovery in Mobile Crowd Sensing (MCS) which has not been properly solved in the literature. In this paper, an Unmanned Aerial Vehicles-supported Intelligent Truth Discovery (UAV-ITD) scheme is proposed to obtain truth data at low-cost communications for MCS. The main innovations of the UAV-ITD scheme are as follows: (1) UAV-ITD scheme takes the first step in employing UAV joint Deep Matrix Factorization (DMF) to discover truth data based on the trust mechanism for an Information Elicitation Without Verification (IEWV) problem in MCS. (2) This paper introduces a truth data discovery scheme for the first time that only needs to collect a part of n data samples to infer the data of the entire network with high accuracy, which saves more communication costs than most previous data collection schemes, where they collect n or kn data samples. Finally, we conducted extensive experiments to evaluate the UAV-ITD scheme. The results show that compared with previous schemes, our scheme can reduce estimated truth error by 52.25%–96.09%, increase the accuracy of workers’ trust evaluation by 0.68–61.82 times, and save recruitment costs by 24.08%–54.15% in truth data discovery.

Due to their simple hardware, sensor nodes in IoT are vulnerable to attack, leading to data routing blockages or malicious tampering, which significantly disrupts secure data collection. An Intelligent Active Probing and Trace-back Scheme for IoT Anomaly Detection (APTAD) is proposed to collect integrated IoT data by recruiting Mobile Edge Users (MEUs). (a) An intelligent unsupervised learning approach is used to identify anomalous data from the collected data by MEUs and help to identify anomalous nodes. (b) Recruit MEUs to trace back and propose a series of trust calculation methods to determine the trust of nodes. (c) The last, the number of active detection packets and detection paths are designed, so as to accurately identify the trust of nodes in IoT at the minimum cost of the network. A large number of experimental results show that the recruiting cost and average anomaly detection time are reduced by 6.5 times and 34.33% respectively, while the accuracy of trust identification is improved by 20%.

More and more sensor nodes which are deployed in smart cities for data collection and analysis form the ubiquitous urban Internet of Things. Energy saving and rapid data collection play a key role in environmental monitoring, military search, medical care, and general city management in smart cities. Therefore, it is a very meaningful and challenging work to design an energy efficient and fast routing protocol to collect data. Although the widely used duty cycle mechanism can effectively save energy, it brings greater delay to routing. Using wake-up radio enable nodes can effectively reduce the delay, but it will bring a large network deployment cost. So how to reduce the deployment cost and quickly route data is a challenge issue for sensor-based system. In this paper, Pre-Awake for Wake-up Radio Enable Sensor based system (PA-WuRES) routing protocol is proposed to reduce delay and deployment cost. The main contribution of our work are as follows: (a) A novel PA-WuRES protocol is proposed which requires much less WuR(WuR,Wake-up Radio) enable nodes, so its deployment cost is lower. (b) Using WuR enable nodes will increase a certain amount of energy consumption. Therefore, this paper proposes a differentiated service data routing mechanism to minimize energy consumption. (c) Full theoretical analysis and experimental results show that the proposed PA-WuRES protocol reduces the deployed WuR enable nodes by 80%, but sensitive-data routing delay is reduced by 48.4%.

This article presents a power system joint optimization generation regulation method based on the improved balance optimizer, which takes the five factors of power system network loss, voltage offset, generation cost, fuel cost, and comprehensive pollution emission as the objective function and takes the internal power balance of the system, each generator set, generation capacity, generation flow, and up and down climbing as the constraints. Fully considering the current energy-saving development objectives and the impact of economic dispatching, taking stable and safe operation as the core, the power generation dispatching model is established by improving the balance optimizer. The model realizes the maximum power generation with the lowest energy consumption parameters and transitions from the original power generation energy consumption of the power system to the best energy-saving power generation energy consumption so that the power value of the system reaches the target balance and completes efficient dispatching. Simulation experiments show that the proposed method can ensure the most reasonable power load in both summer and winter. The average load in summer and winter is reduced from 254.78/mw to 205.36/mw, down about 19.39%, which can ensure the power generation stability of the power system. The average power generation cost after dispatching is 129,920 $/h, which is significantly improved by comparing with 131,225 $/h before dispatching and can realize certain environmental benefits.

The electricity sector has encountered several economic challenges in recent years. Increasing the expense of fossil fuels and environmental legislation such as the Kyoto Protocol and the Low Carbon Transition Plan have compelled governments to use renewable energy sources (RESs) more widely. In the proposed research, the dynamic economic load dispatch problem has been solved using improved chimp optimizer algorithm. The test systems consisting of 6, 7 and 10-unit generators has been taken into consideration along with significant contribution of renewable energy sources for effective research studies. The test systems has been evaluated for different cases considering renewable energy sources and electric vehicles using proposed algorithms. Experimentally, it has been observed that proposed optimizer yields better results as compared to other recently proposed optimizers.

With the continuous growth of the human population, the demand for fiber is also rising sharply. As one of the main fiber plants available globally, cotton fiber yield (Gossypium hirsutum) is affected by boll abscission, which is related to the formation of the abscission layer. Therefore, we explored the formation of the abscission layer in cotton. The formation of the abscission layer in the cotton boll stalk was promoted by exogenous ethylene. It was found that both the number of the Golgi apparatus and the number of stacking layers increased in the dissociated cells. The GhArfGAP gene family in cotton was screened by the bioinformatics method, and the species and evolutionary relationship of the GhArfGAP gene family were analyzed. qRT-PCR showed that GhArfGAP13, GhArfGAP15, GhArfGAP25, and GhArfGAP34 in cotton had spatiotemporal-specific expression patterns. Subcellular localization suggested that GhArfGAP25 played a role in the Golgi apparatus. The expression of GhArfGAP25 in transgenic Arabidopsis thaliana is increased in the roots, stems, and leaves. Finally, we found that ethylene could induce the formation of the abscission layer in cotton. GhArfGAP13, GhArfGAP15, GhArfGAP25, and GhArfGAP34 might regulate the changes in the Golgi apparatus in the abscission layer. Taken together, the findings provide new ideas for the study of the formation of cotton abscission.

Artificial intelligence-empowered path selection plays an important role in wireless sensor networks (WSNs), because it can exceed the cognitive performance of humans and determine multiple aspects of the network performance. Ant colony optimization (ACO) is an effective intelligence algorithm which succeeds in addressing several issues of WSNs, including data transmission, node deployment, etc. There exist several ACO-based transmission strategies for WSNs, but the summary and comparison of such works are very limited. This paper provides a comprehensive overview of ACO-based transmission strategies for static and mobile WSNs. First, we provide a classification of existing ACO-based transmission methods, which distinguishes itself from other works in network types. Second, the highly typical ACO-based transmission strategies for WSNs are illustrated and discussed. Finally, we summarize the paper and present several open issues concerning the design of such networks. This survey contributes to system design guidance and network performance improvement.

Data are an important parts of building applications and providing services to users. With the development of modern technology, the requirements for data quantity, quality, and processing capabilities have become more stringent. Sufficient high-quality data are the foundation for most mobile applications. It is important to collect enough data to effectively build your application. In addition to ensure the amount of data collected, the quality of the data collected is also important. Otherwise, a large amount of data redundancy will occur, which will cause trouble for data processing. At the same time, large amounts of data also mean the need for data processing capabilities. Traditional processing methods cannot meet the growing demand for data processing. Mobile computing is a promising solution to meet the growing computing needs of various mobile applications. The advancement and application of mobile computing require more intelligent ways to sense and collect data, which constantly poses new challenges to researchers.

Abstract The Internet of Things (IoT) is the latest Internet development, with billions of Internet-connected devices and a wide range of industrial applications. Wireless sensor networks are an important part of the Internet of Things. It has received extensive attention from researchers due to its large-scale, self-organizing, and dynamic characteristics and has been widely used in industry, traffic information, military, environmental monitoring, and so on. With the development of microprocessor technology, sensor nodes are becoming more and more powerful, which enables the same wireless sensor networks (WSNs) platform to meet the different quality of service (QoS) requirements of many applications. Applications for industrial wireless sensor networks range from lower physical layers to higher application layers. The same wireless sensor network sometimes needs to process information from different layers. Traditional protocols lack differentiated services and cannot make full use of network resources. In this paper, an Adaptive Retransmit Mechanism for Delay Differentiated Services (ARM-DDS) scheme is proposed to meet different levels of delays of applications. Firstly, we analyze the impact of different retransmit mechanisms and parameter optimization on delays and energy consumption. Based on the results of the analysis, in ARM-DDS scheme, for routes with transmission delay tolerance, energy-saving retransmission mechanisms are used, and low-latency retransmission mechanisms are used for latency-sensitive routes. In this way, the data routing delays of different applications are guaranteed within bound and the energy consumption of the network is reduced. What is more, ARM-DDS scheme makes full use of the residual energy of the network and uses a small delay routing retransmit mechanism in the far-sink area to reduce end-to-end delay. Both theoretical analysis and simulation experiments show that under the premise of the same reliability requirements, ARM-DDS scheme reduces data transmission delay 12.1% and improves network energy utilization 28%. Given that the reliability requirements of the data stream are different, the scheme can also extend the network lifetime.

Abstract 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%.

Due to the ubiquitous utilization of GPS devices, traffic cameras, and sensing devices, data are collected more readily in a smart city. If all the cabs of this city are used as data carriers, data generated by sensing devices will be collected to data centers efficiently and economically. Therefore, vehicular networks joint sensing devices provide more perspectives for a variety of vehicle-based applications. In this paper, three data centers deployment optimization schemes are proposed to optimize data centers' deployment, which can considerably enhance the performance of data collection and code dissemination tasks. Each proposed scheme uses different criteria to optimize the deployment of data centers: 1) locations of sensing devices; 2) locations that have high traffic flow; and 3) locations that have high valid flow, and these schemes are called Scheme 1, Scheme 2, and Scheme 3, respectively. In addition, another scheme that produces the deployment of data centers randomly is called Scheme 4, and it is used as a contrast in experiments. After performing extensive experiments and simulations based on two real-world datasets of cabs' GPS coordinates, the experiment results demonstrate that Scheme 3 noticeably outperformed remaining schemes under various circumstances. The results of February 3 were taken (Dataset 1) for instance. In comparison with Scheme 4, Scheme 3 enhances the total number of collected data packets by 57.71% when the number of the data center is ten, the speed of code dissemination and the coverage of cabs are ameliorated by 23.92% and 12.93%, respectively. Compared with Scheme 1, the total number of Scheme 3's collected packets is 65.00% higher than that of Scheme 1 when there are ten data centers deployed. The figures for Scheme 3's code dissemination and the coverage of sensing devices exceed that of Scheme 1 by 18.98% and 10.21%, respectively.

The recent developments in the areas of the Internet of Things (IoTs) have provided a rapid growth in the epoch of the novel information-centric collections (IC-IoTs). In the IC-IoTs, expanding the ranges of information collections and reducing the costs are important issues for the information required platform. In the previous scenarios, the information is collected in a random manner, which leads to lower coverages and higher costs. Thus, a “optimizing the coverage via the unmanned aerial vehicles (UAVs) with lower costs for information-centric Internet of Things” (OCLC-IoTs) approach is established, which targets to improve the coverage ratio and to reduce the costs of the information-required platform via the cooperation of the information collectors and the UAVs. First, to improve the coverage ratio, an intensive strategy is proposed to inspire the information collectors to bid for the tasks published by the platform and an improved rolling horizon strategy (IRHS) strategy is designed to plan the flying routes of the UAVs to reach more coverage ranges. Then, to reduce the costs factor, the IRHS strategy is designed to shorten the flying routes of the UAVs under the prerequisite of guaranteeing coverage ratio to achieve fewers costs. Finally, a comprehensive theoretical analysis and experiments are provided, which indicates the advancements of the OCLC-IoTs scheme. Compared with the previous studies, the OCLC-IoTs scheme can improve the coverage ratio of information by 21.42% approximately and can reduce the cost ratio by 13.335% to 34.32%.

Recently, the vehicle-to-everything (V2X) paradigm is attracting more attention from both academia and industry. In the smart city, there are a huge number of roadside smart devices (RSDs) undertaking various sensing and monitoring tasks, and they collect information among V2X devices for various applications. With the software-defined technology applying into RSDs, one of the challenging issues is how to update the software of RSDs in a fast and low-cost way. We argue that recruiting a large number of vehicles to disseminate the update code for such RSDs through vehicle-to-sensing devices communications technology is an effective method. A cost-efficient greedy code mules selection scheme (CGCSS) is proposed to disseminate code to a huge number of RSDs in the smart city. In CGCSS, a task is defined as the process of transmitting the update code from the mobile code station (MCS) to RSD. So, the goal of CGCSS is to recruit an appropriate number of vehicles to finish the tasks with low cost and high coverage. A measure function is proposed to take the historical trajectories and cost into account. Therefore, the vehicles with high task completion possibility and low price will be selected as code mules (CMs). Then, a high-performance MCS deploy scheme (HMDS) is proposed to select the optimized MCS positions according to the movement and frequency of the CMs to optimize the performance of the system. Finally, extensive experiments using the real trajectory dataset have been done. The results show the better performance of CGCSS than the basic greedy code mule select scheme and the random code mules select scheme in terms of completion rate, average price, and cost-performance ratio, and the results confirm the validity of the proposed HMDS as well.

Due to battery-powered wireless video sensor networks (WVSNs) takes on a high video data volume, it becomes a crucial issue to reduce energy consumption to maximize lifetime. At the same time, WVSNs are often applied to important situations for real-time and semi-real-time monitoring, thus ensuring the rapid transmission of these perceived data to sink is another critical issue. In static random-access memory (SRAM) communication buffer-based WVSN node, the radio is turned off during the data buffer-filling period as well as idle period. Because each radio transition contains the additional energy consumed by invalid packet header information and communication connection establishment, and the radio ON/OFF transition incurs extra circuit energy consumption. Therefore, from the perspective of saving energy, we need to reduce the ON/OFF transition frequency, which requires a large-sized buffer. However, the large size of SRAM buffer results in more energy consumption because SRAM energy consumption is proportional to the memory size. More importantly, the large ON/OFF transition frequency will lead to a large data transmission delay, which is harmful to applications. In this paper, an adaption resizing communication buffer (ARCB) scheme is proposed to maximize lifetime and reduce delay for WVSNs. In the ARCB scheme, the buffer size takes the minimum energy consumption optimization value in hotspot areas to maximize lifetime and takes a value smaller than the optimized value in non-hotspot areas to reduce delay. Although this consumes more energy for communication, since nodes in the non-hotspot areas have energy surpluses, it will not affect the lifetime. As a result, ARCB scheme can simultaneously increase the lifetime and reduce the delay in WVSNs. The effectiveness of the ARCB scheme is verified by theoretical analysis. The results show that the proposed ARCB scheme reduces the delay by 27.9%, and increases the effective utilization of energy by 24.1% under the condition that its lifetime is no less than previous schemes.

Intelligent communication technology can enhance user experience and improve people's lifestyle. However, due to the limited resources of wireless sensor networks (WSNs), the congestion occurs when the node's traffic load exceeds it's the available capacity. Congestion may cause serious problems such as high packet loss and low throughput, which is an extremely deleterious impact on the performance of WSNs. To tackle this issue, a differentiated rate control data collection (DRCDC) scheme is proposed to avoid or alleviate the network congestion. Different from the previous scheme, the DRCDC scheme can mitigate the congestion by intelligently reducing the data that contains less information, and maintain the distortion rate of information collected by the network is small. The DRCDC mainly avoids or mitigates congestion on the spatial and temporal through information entropy theory, and at the same time makes the information collected by the network has the lowest distortion rate. 1) Congestion mitigation in spatial. Since there is a spatial correlation between sensing data, so in the case of collecting a certain amount of data, some missing data in space can be recovered from another spatially acquired data by matrix completion theory. Therefore, in the DRCDC scheme, when congestion occurs, the amount of data transmitted (forwarded) for nodes are congested will be reduced by an intelligent approach, thereby being able to mitigate congestion. 2) Congestion mitigation in temporal. when congestion occurs, reducing the data collection of time slots containing small information to reduce the flow rate that needs to be transmitted, thereby avoiding congestion while minimizing data distortion rate of the network. The experimental results of the DRCDC scheme in a planar network show better performance than the traditional data collection schemes and reducing the packet loss 2.8%-6.8% while reduce the maximum delay by 34.2%-76.3%.

Due to the huge number of objects/things connected to the Internet of Things (IoT) which are embedded with electronics, software, and sensors, the IoT creates many exciting applications such as smart grids, smart homes, and smart cities. In the IoT, the sensing and control of objects/things can be abstracted as a task, in which many sensing devices sense and collect data. However, the substantial case studies show that by simply connecting them without further collaboration among the objects/things will lead to the bad performance of the system. With the number of sensing devices connected to the IoT increases, the collaboration for completing the task is becoming more and more urgent. In this paper, a Collaborative Multi-Tasks Data Collection Scheme (CMDCS) is proposed to solve the problem by constructing a collaborative platform for task publisher and data reporter. The main contribution of CMDCS includes the following two aspects: (1) a Task Unit Bid-based task selection strategy is proposed to select the task which can bring higher profits to the system, in which the Task Unit Bid is the ratio of task bid to the amount of data which are needed to collect sensing tasks; and (2) a greedy contributed density-based data collector set selection method is proposed to reduce the cost of data collection so as to maximize system profit, in which the contribution density is used to measure the contribution of a single data collector to a specific sensing task. A large number of experiments have been carried out to verify the effectiveness of our proposed strategy. The experiments show that compared to the traditional data collection strategy Random Task selection with Coverage First Reporter selection, in which the Task Unit Bid and Contribution Density are not used, the profit of the system is improved by 92.08%.

Fast and energy efficient transmission is an important and challenging issue for wireless sensor networks (WSNs). Energy efficiency is also an important concern of green communication in WSNs. Optimizing the duty cycle (DC) of nodes and the size of forwarding node-set (SFNS) can effectively reduce the delay and improve energy efficiency. In this paper, we conclude that an optimal DC value can minimize the delay when the number of SFNS is constant. The reason why the delay is larger when the DC is smaller than the optimal value is that when the sender sends data, it needs to wait for the forwarding node to wake up which causes a large delay. When DC is larger than the optimal value, the delay is also larger because SFNS has more than one node in the awake state, which causes conflicts and increases the delay. For energy consumption, the sender with smaller DC has fewer collisions, so the energy consumption is less. Similarly, when DC is constant, SFNS also has an optimal value, which minimizes the delay. Based on the theoretical analysis, an Optimization Duty-Cycle and Size of Forwarding Node Set (ODC-SFNS) scheme is proposed to reduce delay and improve energy efficiency. The main optimization methods adopted are as follows: (a) For dense and high-DC networks, one hop delay and the number of hops needed for the packet to reach sink can be reduced by appropriately limiting the SFNS, thus the end-to-end delay can be effectively reduced. (b) For sparse and low-DC networks, increasing the DC in the far sink region can reduce delay while not reducing the network life, and improve energy efficiency. Compared with the traditional scheme, the ODC-SFNS scheme can reduce the end-to-end delay by 20.52%-79.96%, and it has dramatically increased energy efficiency. Moreover, its scope of application is broader than previous strategies.

In services-oriented computing networks, packets in the process of routing to a data center must wait for a sufficient amount of data before service aggregation to reduce the network transmission load. However, packets must be uploaded to the data center as soon as possible to reduce delay. With the exponential growth in the number of IoT connected devices, the wait time for packets is longer at routers due to massive amounts of data, which causes a large queuing delay. If this queuing time can be utilized for service aggregation in a service-oriented computing network, the network performance will be substantially improved. Therefore, a queuing delay utilization scheme for on-path service aggregation (SAQD) is proposed in this paper. This scheme has the following innovations: 1) SAQD fully utilizes the queuing delay of packets for service aggregation, which can effectively reduce the transmission volume and communication overhead. Based on the proposed service aggregation algorithm, packets are divided into forwarding packets and aggregating packets, and the service aggregation of aggregating packets is completed by utilizing the transmission time of forwarding packets to ensure that the transmission volume and communication overhead are effectively reduced without additional latency. 2) SAQD can effectively alleviate the traffic pressure of the data center and balance the workload of routers. By the service aggregation and intranet cache of routers, some requests for the data center can be handled by routers, which reduces the traffic pressure of the data center, especially in the peak period. Compared with conventional schemes, the experimental results demonstrate that SAQD reduces the workload of the data center by 55.8%-66.26% and provides users with a better quality of experience by reducing the request response delay by 31.33%~51.41%.

The Internet of Things (IoT) is an important part of the new generation of information technology. It utilizes many sensor nodes to collect data and monitor the environment and can be applied in various fields. However, because the energy of sensor nodes is limited and the batteries cannot be replaced, the lifetimes of the sensor nodes are limited by the energy of their batteries. Only a reasonable method for saving energy can reduce the energy loss of the nodes in the communication process. Many studies have found that batteries have the property of battery-friendliness; that is, if they continue working, their energy drops rapidly. However, if they rest after working for a period of time, the batteries' energy can be partially restored. In this paper, an effective relay selection scheme for prolonging the lifetimes of nodes is designed based on the battery-friendly nature of the battery. In the proposed battery-friendly relay selection (BFRS) scheme, the relay node is selected based on the weighted synthesis value of the recovered energy, its distance to the sink and the residual energy. A forwarding node that has recovered more energy and is closer to the sink is more likely to be selected as the relay node because of its larger weight. Hence, the BFRS-based routing scheme can improve the lifetime by recovering more energy. Through extensive experiments, we demonstrate that our BFRS scheme outperforms previously proposed schemes. In a network with 500 nodes, compared with the shortest routing algorithm, the network lifetime is increased by 51.26%, and the amount of transferred data is increased by 42.93%.

Advanced communications and networks greatly enhance the user experience and have a major impact on all aspects of people’s lifestyles. Widely deployed sensor nodes provide support for these services. However, although energy harvesting and transfer technology provides a solution to allow the long-term survival of wireless sensor nodes for wireless sensor networks, the single collection scheme causes a lot of energy waste. Thus, efficient energy utilization and fast data collection are still serious challenges for energy harvesting wireless sensor networks. To overcome these challenges, an adaptive collection scheme based on matrix completion (ACMC) is proposed to reduce delay and to improve the energy utilization of the network. In the ACMC scheme, compared with traditional data collection schemes, the data collection schemes vary with the available energy, collecting large amounts of data when the available energy is sufficient to obtain high-quality data-based applications. Otherwise, adaptive selecting the collected data based on previously collected data, the amount of data collected can be effectively reduced based on the application requirements, thereby improving the energy utilization of the network. The ACMC scheme also proposes a method for reducing the delay by increasing the duty cycle of the nodes that are far from the CC. At the same time, the transmission reliability of these nodes increases due to the increase in the transmission frequency. Thus, the ACMC scheme can also further reduce the delay of the network. The experimental results of the ACMC scheme in planar networks show better performance than the traditional data collection schemes and can improve the energy utilization of the network by 4.26%–6.68% while reducing the maximum delay by 9.4%.

With the development of software-defined network (SDN) technology, sensor nodes can update soft codes to enable themselves to have new functions so as to make wireless sensor network (WSN) full of new vitality. However, how to spread the new code to every node in the network quickly and energy-efficient is a challengeable issue. In this paper, a Two-hop Neighborhood Information joint Double Broadcast Radius (TNI-DBR) scheme is proposed to disseminate the codes in duty cycle-based WSNs in a fast and energy-efficient style. The main innovations of this paper are as follows. The TNI-DBR scheme makes full use of the unbalanced energy consumption of the sensor network in the process of data collection and doubles the broadcast radius in the area with redundant energy so that more nodes can receive the new code in one broadcast and the broadcast range is further, which can reduce the delay of code dissemination effectively. Different from the conventional code dissemination schemes that select broadcasting nodes according to the information of the one-hop neighbors, in the TNI-DBR scheme, an $O(n)$ two-hop neighbor information exchange algorithm is proposed to obtain the duty cycle information of the two-hop neighbors. The TNI-DBR scheme selects the best broadcasting nodes based on information in the range of two-hop neighbors to enlarge the number of active nodes and reduce the time required for code dissemination. Sufficient theoretical analysis and experimental results show that the TNI-DBR scheme can significantly optimize the performance of code dissemination. Compared to previous schemes, the delay of code dissemination can be reduced by 43.09%~78.69%, the number of broadcasts can be reduced by 44.51%~86.18%, and the energy utilization ratio is improved by about 24.5%.

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.

With the rapid developments in edge devices and wireless technologies, the underwater wireless sensor networks (UWSNs) are in the process of vigorous development. In UWSNs, the traditional multi-hop data collection methods have some disadvantages such as high power consumption, severe unbalance in power consumption, and so on. In recent years, mobile edge elements (such as an autonomous underwater vehicle, AUV) are widely used in underwater data collection to solve energy consumption imbalance problems. However, the existing methods do not fully consider the efficient mobile edge computing and the real mobility model of AUV in the underwater environment. In this paper, we propose a data collection scheme based on a mobility model of mobile edge elements under water. In this model, the mobility direction and velocity are fully considered, which are close to the mobility characteristic of AUVs in the stable 3D environment. By using computing, storage, and mobility abilities of AUVs, a target selection algorithm is designed to calculate the mobility path of data collection for AUV. The theoretical analysis and experimental results show that the proposed method improves the efficiency of data collection, reduces the power consumption of nodes, and extends the network lifetime.

The technology of software-defined devices has great prospect in updating the programs of the sensing devices to enable the devices to adapt to the new environments and applications. However, to effectively spread the code to each node in the software-defined-based wireless sensor networks (WSNs) is still a challenging task. In this paper, a proportion to duty cycle length-based broadcast (PDLB) scheme is proposed to disseminate code in duty cycle-based WSNs in a fast and energy-efficient style. In PDLB scheme, the sink first initiates a code broadcast. Then, the nodes that have received code start counting and calculate the priority of broadcasting based on an improved counter-based priority broadcasting approach proposed in this paper. Next, the nodes with the highest priority select k slots to broadcast according to the length of duty cycle so that the code can be spread from the sink to the network periphery. Finally, the nodes that have not obtained the code wait for a period of timeout to broadcast to actively send code request; after receiving code request, the node that obtained the code initiates a code broadcast to cause the code requester to obtain the code. The PDLB scheme has been validated by comprehensive experiments that show the following regularities. The smaller the count threshold value Ch, the larger the number of slots selected for broadcasting that can guarantee a certain coverage ratio, while the code dissemination speed slows down at the same time. The longer the duty cycle is, the more the broadcast time is required, and the larger the transmission delay is. For each slot added in a working cycle, the number of broadcasts is increased by 1.54%-33.33% and the transmission delay is augmented by 2.27%-70.04%. The higher the node density is, the shorter the code spread speed is. As the network density increases, the transmission delay is reduced by 0.83%-65.87%. When the number of broadcasting slots is half of the total number of slots in a work cycle, the energy consumption reaches the lowest value.

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.

Compressed sensing technology is one of the effective techniques to effectively reduce the amount of data transmission in wireless sensor networks. Compressed sensing technology can reduce the amount of data that a node undertakes from n to m, where m ≪ n, but we still hope to further reduce the amount of data that the node bears to improve network lifetime. In this paper, a Compressive Sensing based Clustering Joint Annular Routing Data Gathering (CS-CARDG) scheme is proposed to improve the network life. The key technology adopted by CS-CARDG scheme is: data is collected by cluster. The network first forms a cluster, and each node in the cluster sends the data packet to the cluster head. Each cluster forms m-dimensional data according to the requirements of the compressed sensing technology to ensure that the data can be recovered. When the cluster head node routes the m-dimensional data to the sink, the CS-CARDG scheme adopts a two-stage routing scheme with the same ring routing and shortest path that is completely different from the previous scheme. The same ring routing means that the cluster heads with the same number of sink hops are routed around the ring for one week to route the compressed sensing data of the same ring to a node in the ring. In this way, each sub-dimension data in the same ring is routed to the corresponding node of each ring through the same-loop route, and then the shortest-circuit strategy of the second phase is started. That is, from the outermost ring, the same fractal data is sequentially compressed from the outside to the inside, and is routed to the sink by the shortest path. In this round of data collection, the number of data packets that the nodes in the near-sink one-hop range bears is only m, and the nodes in the near-sink region directly send data to the sink node, thereby reducing the amount of data that the node bears to m/k + 1, where k is the number of nodes within the node's broadcast radius.. In this paper, the compressed sensing strategies proposed in the past are compared by detailed theoretical analysis. The theoretical analysis results show that the CS-CARDG strategy proposed in this paper can effectively reduce the amount of data carried by nodes. This scheme reduces the amount of data in the network from (1+r/l2) λ + (1+r/l3) λ + ··· + m/k+d to m/k+1. Reduced the amount of data by at least 20%. In the network with R=480 m, the energy utilization rate can reach more than 90%.

Abstract 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%.

Abstract Low delay and long lifetime are a very important issue for industrial wireless sensor networks (IWSNs) in which it require long-time monitoring of industrial sites and respond quickly to events that is monitored; therefore, high delay communications can cause serious damage to property and personnel at industrial field. Due to delay, lifetime, and other performance involved to multiple layers, it is difficult to optimize from a single layer. Therefore, a cross-layer design optimal scheme for reducing delay and maximizing lifetime (RDML) scheme is proposed for IWSNs which is from several layers such as transmitted power, duty cycle, and node deployment positions to optimize the network performance of delay and lifetime etc. Firstly, due to the node which sends a packet within a cycle, different duty cycle leads to different selection of the modulation level, resulting in different power consumption efficiency of transmitting data. Through careful analysis, the optimal value of the duty cycle is given which has the lowest energy consumption per bit. In fact, the energy consumption of the node is not balanced. Therefore, an optimization method of changing the duty cycle is proposed. In this paper, larger duty cycle is chosen for nodes with residual energy to improve the reliability of data transmission, reducing the probability of data retransmission, so that the network delay can be reduced in IWSNs. Third, based on the previous analysis, a network optimization deployment algorithm is proposed, which not only maximizes the energy efficiency of a single node but also maximizes the network lifetime and the total network energy efficiency. Both our comprehensive theoretical analysis results indicate that the performance of RDML scheme is better than the previous studies. Relative to equal distance and optimal duty cycle scheme, the RDML scheme can reduce the delay by 19–30% and increase the lifetime by more than 43%.

Cyber-physical network (CPN) as well as cyber-physical social computing and networking (CPSCN), as the next generation of computing and networking, utilize a wide variety of sensing devices with significant sensing capabilities. However, it face huge challenge that data packet is transmitted to destination security. Data packets can be dropped by malicious nodes in CPN/CPSCN, causing serious damage to the network. Thus, ensuring the security of data transmission is a crucial issue for wireless sensor networks which is one of the important component of CPN/CPSCN. In this paper, a trust with abstract information verified (TAIV) routing scheme is proposed to improve the data packet arrival ratio. In the TAIV scheme, backbone routing paths are created by constructing an optimal connected dominating set (CDS). CDS routing ensures communication among all nodes in the network. In addition, as the source node sends the data packet to the destination through the backbone routing path, the abstract information is also transmitted to the destination through the auxiliary routing path to allow the destination node to know whether data has been sent to it. Depending on whether the data packet is transmitted to the destination or dropped by the malicious node and whether the abstract message is transmitted to the destination successfully, the destination will reduce or increase the trust in nodes in this routing path accordingly. After a period of time, the trust information of nodes will be diffused by information exchange. If the trust information is sufficient, the malicious node, whose trust is lower than that of the normal node, will be identified and eliminated from the backbone routing path, reducing the number of malicious nodes in the backbone. Thus, the data packet arrival ratio will be improved. The theoretical analysis shows that compared with the previous scheme, the data packet arrival ratio increases by 4.251%-80.208%, and nearly all malicious nodes are identified while retaining long network lifetime.

With the emergence of Internet of Things, the number of connected devices has been dramatically increasing, causing severe spectrum shortage problem. To fully explore the spectrum resources, big data, and cloud computing can be employed by cognitive radio networks, to make efficient use of various sensing results from different sensing sources. However, the massive growth of sensing data brings tremendous load pressure on the data center, resulting in long service response time and poor Quality of Experience. Edge computing and fog computing deal with these issues by placing computation resources at the network edge. However, compared with the data center, the capabilities at edge servers are limited. Therefore, a services routing-based caching scheme (SRCS) is proposed, which can greatly lighten the load on the data center and maintain the advantages of global intelligent computing of traditional cloud computing. Specifically, SRCS first introduces the concept of transmitting service flow. At the edge layer, data are converted to service flow by network hardware and software, thus achieving the network architecture centered on service computing. Then, SRCS proposes a service routing based on service similarity, transmits similar services through the same path, and service data are fused on the path to minimize transmission load. Moreover, SRCS caches services in content routers (CRs). When the service is requested again, CRs are used as service providers to return data, thus achieving the nearest access to the content. Both theoretical analysis and experiment results demonstrate that comparing existing schemes, SRCS improves service response time by 13.67%–51.15%, reduces transmitting data amount by 23.62%–30.3%, and makes energy consumption more balanced.

Millions of sensors are deployed to monitor the smart grid. They consume huge amounts of energy in the communication infrastructure. Therefore, the establishment of an energy-efficient medium access control (MAC) protocol for sensor nodes is challenging and urgently needed. The Quorum-based MAC protocol independently and adaptively schedules nodes' wake-up times and decreases idle listening and collisions, thereby increasing the network throughput and extending the network lifetime. A novel Quorum time slot adaptive condensing (QTSAC)-based MAC protocol is proposed for achieving delay minimization and energy efficiency for the wireless sensor networks (WSNs). Compared to previous protocols, the QTSAC-based MAC protocol has two main novelties: 1) It selects more Quorum time slots (QTSs) than previous protocols in the area that is far from the sink according to the energy consumption in WSNs to decrease the network latency and 2) It allocates QTSs only when data are transmitted to further decrease the network latency. Theoretical analyses and experimental results indicate that the QTSAS protocol can greatly improve network performance compared with existing Quorum-based MAC protocols. For intermediate-scale wireless sensor networks, the method that is proposed in this paper can enhance the energy efficiency by 24.64%-82.75%, prolong the network lifetime by 58%-27.31%, and lower the network latency by 3.59%-29.23%.

With the explosive growth of the Internet of Things, the gap between the rapidly growing demands of data rates and the existing bandwidth-limited network infrastructures has become increasingly prominent, leading to network congestion, high latency, and energy consumption and deterioration of the user's quality of service. To narrow this gap, an orchestrating data as services-based computing and communication (ODAS-CC) model is proposed to reduce the data rate, latency, and energy consumption for information-centric Internet of Things. The main innovations of the ODAS-CC model that differ from previous strategies are as follows. First, the services-based network architecture proposed in this paper can run on the current network effectively. In the proposed architecture, the data are orchestrated to services when they route to the data center; therefore, the data are transmitted after the service conversion, thereby forming a service-based network, which can greatly reduce the amount of data transmitted in the network, latency, and energy consumption. Second, a service conversion is performed in the process of routing such that the user's service request can be satisfied locally, thereby improving the user's quality of experience. Third, based on the ODAS-CC model, the performance evaluation model for energy consumption, latency, and load is provided in detail, and the performance of the ODAS-CC model is evaluated comprehensively. The theoretical analyses and experimental results show that compared with the previous approach, the ODAS-CC model can reduce the network traffic, the average latency of data upload by 26.3%, the service latency by up to 30.1%, and the data center load, thereby reducing energy consumption from 20.0% to 30.0%.

With the development of smart equipment and technologies, vehicular networks (VNs) are expected to have wide-range applications in intelligent transportation system services. The software code in these devices often contains bugs that can be remotely exploited by attackers, so gathering software vulnerabilities information and updating program codes timely in those devices are crucial for network security. To improve the security of VN, a Complete Software Update based on Trust and Priority (CSUTP) scheme is proposed. The scheme includes two processes: one is the process of collecting status information of program codes from devices to data centers and another is the process of disseminating program codes from data centers to devices. When collecting status information of program codes, mobile vehicles pick up the status information with higher priority due to the limited storage space. Furthermore, mobile vehicles can also disseminate the status information to data centers though another mobile vehicles which is with higher trust. Thus, the success arrival ratio and the coverage ratio of collecting status information of program codes are improved. In the process of dissemination program codes, data centers select the mobile vehicles with higher trust and higher coverage ratio to disseminate program codes. The comprehensive experimental results indicate that the performance of CSUTP scheme is better than the random program codes diffusion and collection scheme (RPCDC). The CSUTP scheme increases data coverage ratio by 25%-86.67% and success arrival ratio by 52.119%-93.65%. The ratio of data redundancy in the CSUTP scheme to the RPCDC scheme is about 54.07%-62.061%, and the ratio of dissemination delay in the CSUTP scheme to the RPCDC scheme is about 80.854%-91.492%.

Vehicular ad-hoc networks, as one of the major components of Internet of Things in smart cities, have enormous idle transportation capability due to the huge number of vehicles. This paper argues that it is feasible to employ a large number of mobile vehicles in smart city to construct a large-scale low-cost delivery infrastructure using opportunistic routing to improve the city's social welfare. To this end, a low-cost good delivery system (LCGDS) with opportunistic relay is proposed to meet the requirements of good delivery. In LCGDS, an enormous number of mobile vehicles constitute a stable, highly reliable, low-cost infrastructure with large transportation capabilities, called the mobile Internet of mission-critical things (M-IoMCT). The main contributions of the LCGDS are: 1) demonstrate the technological feasibility of M-IoMCT for delivering goods through mobile vehicles in a “ride-sharing” fashion with low costs and 2) in order to increase the reliability of goods transmission, the LCGDS proposes mobile vehicle selection algorithm which effectively selects a vehicle with higher probability of arriving at the destination. Both theoretical and experimental simulation results show that the performance of the LCGDS outperforms those of other solutions. Compared with other solutions, our results demonstrate that the costs in the LCGDS are only about 24% of the costs in the origin goods transmission scheme.

Large numbers of software-defined sensor devices have formed a basic infrastructure for data collection. One of the challenges is how to update the program codes of devices in a low-cost and reliable style. This paper figures that the vehicular wireless networks (VWNs), combined with a base station (BS) platform, can play a significant role in disseminating program codes in a smart city. A code dissemination algorithm that selects vehicles as disseminators is proposed to accomplish the coverage ratio of codes and benefits of vehicular disseminators in a reliable manner and to reduce the costs of the BS. The algorithm targets to make the disseminators to achieve more coverage regions of a code. The benefit distribution method, which targets the improvement of benefits in suburban regions and encourages vehicle owners, is redefined and differs from previous benefit distribution schemes. Through disseminating, the vehicles can obtain more benefits, and the costs of the BS can be reduced. We ultimately simulate the performances of the selection algorithms with GPS datasets to prove the progressiveness of the “Code Disseminations through Opportunistic Communication in Vehicular Wireless Networks”(CD-VWNs) scheme in terms of coverage and benefits. The benefit-ratio performance criterion can be improved by 21.078% over other models. The coverage-ratio performance criterion can result in an improvement of 31.94% over other selection schemes. The evaluation metric Zr performance criterion can result in an improvement of approximately 25.256%. With the CD-VWNs scheme, loss of codes can be decreased, and the dissemination system remains reliable and survivable.

Quickly and efficiently transmitting data to sink via intelligent routing is an important issue in wireless sensor networks. In previous scenarios, there has existed the phenomenon of “energy hole,”which results in difficulties in synchronous optimization of energy and delay. Thus, a smart High-Speed Backbone Path (HSBP) construction approach is proposed in this paper. In the HSBP approach, several High-speed Backbone Paths (HBPs) are established at different locations of the network, and the duty cycles of nodes on the HBPs are increased to 1; therefore, the data are forwarded by HBPs without the existence of sleeping delay, which greatly reduces transmission latency. Furthermore, the HBPs are built in regions with adequate residual energy, and they are switched periodically; thus, more nodes can be utilized to equalize the energy consumption. A comprehensive performance analysis demonstrates that the HSBP approach has obvious advantages in improving network performance compared with previous studies; it reduces transmission delay by 48.10% and improves energy utilization by 38.21% while guaranteeing the same network lifetime.

Telemedicine is expected to play a significant role in previsioning better service and meeting various needs of patients, healthcare providers, and policy makers by exploiting advanced information and communication technologies. Because health data are highly sensitive, the security and privacy of telemedicine should be protected carefully to protect telemedicine from being potentially interrupted and attacked. In telemedicine networks, the sink node is the most vulnerable to attacks because it is where sensitive data will be collected. Thus, it is of great importance to protect the privacy of the sink node in telemedicine networks. To this end, this paper proposes an efficient privacy-preserving protocol for sink node location in telemedicine networks. Compared with the existing work, the scheme can improve the safe time of telemedicine networks by injecting request packets and reduce the delivery time by transmitting along the shortest route. In particular, the safe time can be improved by 28.57% to 52.70%, and the delivery time can be reduced by 22.86% to 27.61%.

The smart device combined with artificial intelligence can act as robot system to perform data collection task. To minimize the data collection cost and to guarantee the quality of service (QoS) of tasks are two vital issue in such mobile robot system. Data collection platform and data reporter often needs to negotiate with each other before start of data collection which will generate a certain cost. Once the platform and the data reporter agree to the cooperation, data reporter will collect and report data for a period. However, in previous researches, it was often considered that data reporters can report data at any time without considering the cost of interaction and negotiation, which is not suitable for the practice. In this paper, we propose an efficiency cost data collection scheme (ECDCS) in which the data reporter is selected according to the contribution that all the data it collects have on the whole system rather than a single data samples. Because there exists correlation in data, matrix completion technology can be adopted to recover the missing data samples with partial data while guarantee the QoS of the task. So, a data reporter selection scheme ECDCS based on the matrix completion technology is proposed in which the selection is in terms of the cooperation effect of the reporters rather than a single data sample. The main goal is to select the reporter set with low cost and high QoS which has the best cooperative effect. By doing so, in the proposed data collection scheme, the missing of partial data can be tolerated which can reduce data collection cost while guarantee the QoS. The extensive experiments results indicate that the proposed scheme can effectively reduce the data cost while maintain the QoS of application.

A sensor-based wearable system is a promising platform to monitor the health and movement of human in real time to improve the quality of life. However, it places stringent requirements on communication delay, energy efficiency, and throughput. In this paper, an adaptive beaconing medium access control (AB-MAC) protocol is proposed to reduce communication delay and improve throughput and energy efficiency for duty cycle-based wearable system. AB-MAC protocol belongs to receiver initiated (RI) MAC protocol, where the sender keeps listening the channel to send data while the receiver sends beacons actively for the preparation of receiving data when it becomes awake. AB-MAC protocol exploits the unbalance energy consumption in the network, where the nodes near the sink (or hub) consume more energy than that of nodes far away from the hub. AB-MAC protocol makes efficient use of the remaining energy of the nodes far away from the hub to send more beacons during the sleep time, which can decrease the sleep delay and improves the energy efficiency and throughput. In addition, the number of beacons that can be sent for the nodes is also obtained based on the remaining energy level. The theoretical analysis is conducted for analyzing the performance of AB-MAC protocol. Furthermore, experimental results are provided, which demonstrate that compared with RI-MAC protocol, AB-MAC protocol can reduce the delay by 32%-51%, increase the throughput by 45%-116%, and improve the energy efficiency by 12%-21% without sacrificing the network lifetime.

Device to device (D2D) communication is a key candidate for 5G. Its purpose is to enable direct communication between user devices that are close to each other, thereby reducing the load on the base station. Wireless sensor networks (WSNs) have received a lot of attention as the basis for D2D. The opportunistic routing (OR) scheme is proposed to deal with data transmission problems in loss WSNs. In this paper, a duty cycle adaptive adjustment-based bopportunistic routing (DCAAOR) scheme is proposed to speed up reliable data transmission. According to the wake-up rule of nodes, we propose three different DCAAOR schemes, respectively. In modified opportunistic routing (MOR) scheme, the nodes are random awake/sleep. In active slot uniform distribution (ASUD) scheme, the active slot of relay nodes are evenly distributed by adaptive adjustment and the active slot group (ASG) scheme divides the active slots of the relay nodes into groups. The active slots of each group are the same, and the active slots of different groups are evenly distributed. After a lot of theoretical analysis, the ASUD and ASG scheme proposed in this paper is superior to the simple modified MOR scheme in terms of energy consumption and delay. When the duty length τ = 20, the energy consumption of the nodes in the MOR scheme is reduced by 38.75% compared with the OR scheme, and the other two schemes are reduced by 41.83%.

Abstract The Industrial Internet of Things (IIoTs) is creating a new world which incorporates machine learning, sensor data, and machine-to-machine (M2M) communications. In IIoTs, the length of the transmission delay is one of the pivotal performance because dilatory communication will cause heavy losses to industrial applications. In this paper, a learning-based synchronous (LS) approach from forwarding nodes is proposed to reduce the delay for IIoTs. In an asynchronous Media Access Control protocol, when senders need to send data, they always require to wait for their corresponding receiver to wake up. Thus, the delay here is greater than in the synchronous network. However, the synchronization cost of the whole network is enormous, and it is difficult to maintain. Therefore, LS mechanism uses a partial synchronization approach to reduce synchronization costs while effectively reducing delay. In LS approach, instead of synchronizing the nodes in the entire network, only sender nodes and part of the nodes in their forwarding node set are synchronized by self-learning methods, and accurate synchronization is not required here. Thus, the delay can be effectively reduced under the low cost. Secondly, the nodes near sink maintain the original duty cycle, while the nodes in the regions away from the sink use their remaining energy and perform synchronization operations, so as not to damage the network lifetime. Finally, because the synchronization in this paper is based on different synchronization periods among different nodes, it can improve the network performance by reducing the conflict between simultaneous data transmission. The theoretical analysis results show that compared with the previous approach FFSC, LS approach can reduce the end-to-end delay by 5.13–11.64% and increase the energy efficiency by 14.29–17.53% under the same lifetime with a more balanced energy utilization.

Vehicular social networks, as one type of new future networks, integrate mobile wireless communications and social networks together, and they have attracted a plenty of interest from researchers. This paper argues that vehicular social networks joined with cloud and fog computing platforms can serve as recommenders and play a significant role in social lives. A new application for recommender systems that selects vehicles as recommenders is proposed to support marketers in improving marketing effectiveness. Then, we propose three algorithms to enhance the marketers' marketing effectiveness based on different evaluation standards. The first algorithm selects those vehicles that can obtain maximum benefits for the marketers, in which the vehicles are selected based on passing regions with more benefits. However, this selection method may lose some potential markets because of region limitations. The second algorithm selects those vehicles that can reach the maximum coverage ratio in the city and bring the marketers more marketing effectiveness in the future, although the current benefits are not the best. The third algorithm combines the two prior algorithms, finding a tradeoff between coverage and benefits. We finally evaluate the effectiveness of the proposed algorithm with real-world data to show the effectiveness and efficiency of the "On Selecting Vehicles as Recommenders for Vehicular Social Networks" scheme: for the scheme based on the benefit factor, the performance criteria of the benefit ratio can be improved by 21% over the existing selection methods; for the scheme based on the coverage factor, the performance criteria of the coverage ratio can be improved by 13% over the existing selection methods; and for the third selection algorithm based on the two factors, the performance criteria of the benefit ratio and the metric 9 can be improved by 18.9% and 13.7%, respectively.

Fog computing (FC) is currently receiving a great deal of focused attention. FC can be viewed as an extension of cloud computing that services the edges of networks. A cooperative relationship among applications to collect data in a city is a fundamental research topic in FC. When considering the green cloud, people or vehicles with smart-sensor devices can be viewed as users in FC and can forward sensing data to the data center. In a traditional sensing process, rewards are paid according to the distances between the users and the platform, which can be seen as the existing solution. Because users with smart-sensing devices tend to participate in tasks with high rewards, the number of users in suburban regions is smaller, and data collection is sparse and cannot satisfy the demands of the tasks. However, there are many users in urban regions, which makes data collection costly and of low quality. In this paper, a cooperative-based model for smartphone tasks, named a cooperative-based model for smart-sensing tasks (CMST), is proposed to promote the quality of data collection in FC networks. In the CMST scheme, we develop an allocation method focused on improving the rewards in suburban regions. The rewards to each user with a smart sensor are distributed according to the region density. Moreover, for each task there is a cooperative relationship among the users; they cooperate with one another to reach the volume of data that the platform requires. Extensive experiments show that our scheme improves the overall data-coverage factor by 14.997% to 31.46%, and the platform cost can be reduced by 35.882%.

Wireless sensor networks are the foundation of cyber physical systems. In loss and delay sensitive wireless sensor networks, there is much room for improving the performance of opportunistic routing scheme. In this article, an adaptive duty cycle control–based opportunistic routing scheme was put forward to improve the performance of the opportunistic routing scheme. Our main work is as follows: (a) the proposed duty cycle control–based opportunistic routing scheme implements the opportunistic routing in the duty cycle–based wireless sensor networks. Compared with the opportunistic routing scheme in the non-duty cycle mode, the duty cycle control–based opportunistic routing scheme can effectively reduce the energy consumption by 55.64%, although the performance in the delay is degraded. (b) Next, adaptive duty cycle control–based opportunistic routing scheme is proposed to reduce the delay of nodes in duty cycle control–based opportunistic routing. In adaptive duty cycle control–based opportunistic routing scheme, the node’s duty cycle is increased in the area where the energy has remaining, so that the network delay can be reduced without reducing the network lifetime. Compared with duty cycle control–based opportunistic routing scheme, the maximum end-to-end delay of the node in the network can be reduced by 69.83%.

It is desirable for alarm packets to be forwarded to the sink as quickly as possible in wireless sensor networks. In this paper, we initially analyze the theory of the relationships between network configurations and network lifetime as well as transmission delay. Then, we propose an approximate optimization approach to minimize the end-to-end delay with a reduced complexity of configuration under the condition that the network lifetime remains greater than the specified target value. A local forwarding approach named Fast data collection for nodes Far away from the sink and slow data collection for nodes Close to the Sink (FFSC) is proposed. This approach is energy efficient. Moreover, it can further reduce the end-to-end delay. Both the comprehensive theoretical analysis and the experimental results indicate that the performance of FFSC is better than the methods proposed by previous studies. Relative to the direct forwarding strategy, the FFSC approach can reduce the delay by 7.56%-23.16% and increase the lifetime by more than 25%. It can also increase the energy efficiency as much as 18.99%. Relative to the single fixed threshold strategy, the FFSC approach can reduce the delay by 4.16%-9.79% and increase the energy efficiency by 19.28% while still guaranteeing the same lifetime as those previous methods.

To enable the intelligent management of Smart City and improve overall social welfare, it is desirable for the status of infrastructures detected and reported by intelligent devices embedded in them to be forwarded to the data centers. Using “SCmules” such as taxis, to opportunistically communicate with intelligent devices and collect data from the sparse networks formed by them in the process of moving is an economical and effective way to achieve this goal. In this paper, the social welfare data collection paradigm SWDCP-SCmules data collection framework is proposed to collect data generated by intelligent devices and forward them to data centers, in which “SCmules” are data transmitters picking up data from nearby intelligent devices and then store-carry-forwarding them to nearby data centers via short-range wireless connections in the process of moving. Because of the storage limitations, “SCmules” need to weigh the value of data and select some less valuable data to discard when necessary. To quantify the value of data and find a well-performed selection strategy, the concept of priority is introduced to the SWDCP-SCmules scheme, and then, the simulated annealing for priority assignment SA-PA algorithm is proposed to guide the priority assignment. The SA-PA algorithm is a universal algorithm that can improve the performance of SWDCP-SCmules scheme by finding better priority assignment with respect to various optimization targets, such as maximizing collection rate or minimizing redundancy rate, in which priority assignment problem is converted into an optimization problem and simulated annealing is used to optimize the priority assignment. From the perspective of machine learning, the process of optimization is equal to automatically learn social-aware patterns from past GPS trajectory data. Experiments based on real GPS trajectory data of taxis in Beijing are conducted to show the effectiveness and efficiency of SWDCP-SCmules scheme and SA-PA algorithm.

In existing recommendation systems, there exist the issues of “cold start” and “excessively mature recommendation,” which cause the recommendation systems to have weak effectiveness; thus, a trust-based recommendation model with a small recommendation probability for unknown items (RM-UI) is proposed for social networks. In the RM-UI scheme, the recommendation values of items are primarily derived from the probabilities calculated by a similar mature recommendation system during the initiation stage of the recommendation system. Thus, the “cold start” phenomenon can be overcome. When the recommendation system enters the period of maturation, the recommendation probabilities mainly adopt the recommendation values computed by the self-system. However, in the existing recommendation systems, there remains the problem of “excessively mature recommendation,” which causes the system to lose the opportunity to recommend more optimized items. Thus, in the RM-UI scheme, except for recommending items with higher recommendation probabilities, we recommend items with lower recommendation probabilities with a small probability to enable those items that can bring greater welfare to recommendation systems to be recommended. This breaks the weakness of a confined recommendation that exists in previous recommendation systems, and it enlarges the welfare of the system. After theoretical analysis, it has been proven that the RM-UI scheme can achieve greater welfare than the previous recommendation scheme and gain more vitality. Experiments based on real social network data are conducted to show the effectiveness and efficiency of the RM-UI scheme: in the initiation stage, the performance criteria of precision ratio, recall ratio, and F1-measure are improved by approximately 21.08%, 21.57%, and 21.32%, respectively, over the previous schemes, and in the mature stage, although the three indications are similar to those of the previous schemes, the improvement in the overall revenue of the system is 17.6%.

In this paper, a Per-Hop Acknowledgement (PHACK)-based scheme is proposed for each packet transmission to detect selective forwarding attacks. In our scheme, the sink and each node along the forwarding path generate an acknowledgement (ACK) message for each received packet to confirm the normal packet transmission. The scheme, in which each ACK is returned to the source node along a different routing path, can significantly increase the resilience against attacks because it prevents an attacker from compromising nodes in the return routing path, which can otherwise interrupt the return of nodes’ ACK packets. For this case, the PHACK scheme also has better potential to detect abnormal packet loss and identify suspect nodes as well as better resilience against attacks. Another pivotal issue is the network lifetime of the PHACK scheme, as it generates more acknowledgements than previous ACK-based schemes. We demonstrate that the network lifetime of the PHACK scheme is not lower than that of other ACK-based schemes because the scheme just increases the energy consumption in non-hotspot areas and does not increase the energy consumption in hotspot areas. Moreover, the PHACK scheme greatly simplifies the protocol and is easy to implement. Both theoretical and simulation results are given to demonstrate the effectiveness of the proposed scheme in terms of high detection probability and the ability to identify suspect nodes.

In order to solve the problem of spectrum scarcity in wireless sensor networks, cognitive radio technology can be introduced into wireless sensor networks, giving rising to cognitive radio sensor networks. Delay-sensitive data applications in cognitive radio sensor networks require efficient real-time communication. Opportunistic pipeline routing is a potential technology to reduce the delay, which can use nodes outside the main forwarding path forward data opportunistically when the transmission fails. However, the energy efficiency of cognitive radio sensor networks with opportunistic pipeline routing is low, and the data transmission delay can be further optimized. In view of this situation, we propose the delay optimal opportunistic pipeline routing scheme named Variable Duty Cycle for Opportunistic Pipeline Routing (VDCOPR). In the Variable Duty Cycle for Opportunistic Pipeline Routing scheme, the nodes employ high duty cycle in the area far from the sink, and low duty cycle in the area near to the sink, which can achieve the balance of energy consumption and reduce the data transmission delay while not affecting network lifetime. The theoretical analysis and experimental results show that, compared with previous opportunistic pipeline routing, energy consumption of network is relatively balanced and the data transmission delay can be reduced by 36.6% in the Variable Duty Cycle for Opportunistic Pipeline Routing scheme.

It is a major challenge to transfer target sensing data efficiently to sink in Internet of things. The low-efficiency data transmission can cause low quality of service. To realize the emergent detection and periodic data gathering, the sensed data should be transferred to the sink efficiently and quickly. Recently, there are many related studies. However, there are few researches taking energy efficiency, transport delay, and network reliability into comprehensive consideration. In this article, a novel adaptive green and reliable routing scheme based on a fuzzy logic system is proposed in consideration of energy efficiency, end-to-end transport delay, and network transmission reliability. The key idea of the proposed scheme is to generate different number of renewed packet copies after certain steps according to the fuzzy inference. The fuzzy inference reflects the knowledge that the nodes in the region far to the sink and with more remaining energy initiate and transmit more packet copies concurrently by multiple routing paths to ensure the success rate of data transmission, whereas less. Thus, the high energy efficiency and low latency are obtained for data collection. Our analysis and simulation results show that adaptive green and reliable routing is more superior than the existing scheme.