ABSTRACT With the rapid development of mobile cloud computing, the computational capability of edge devices has gradually attracted attention. Computation offloading is a strategy to improve computational efficiency and resource utilization by transferring computational tasks from the main device to edge devices or cloud nodes. Traditional computation offloading methods may have limitations when facing large‐scale and high‐real‐time tasks. To solve these problems, this article proposes a computation offloading method based on an improved whale optimization learning algorithm. First, the computational requirements of each device are planned using the topological sorting method, and the transmission time, energy consumption, and other parameters are determined. Then, a fitness function that considers both computation time and energy consumption is proposed, and the nonconvexity of this function is proved. Finally, the improved whale optimization learning algorithm is applied to the entire device group to solve the optimal offloading ratio. Experimental results show that our proposed strategy has good performance in reducing latency and energy consumption.

The mobile Internet of Things (IoT) has gained popularity due to the quick advancement of mobile communication and intelligent terminal technologies.Focusing on some computationally demanding activities and latency-sensitive services (like health IoT) in smart healthcare that cloud computing (CC) cannot process and respond to rapidly. This research examines the fog computing (FC) and deep reinforcement learning (DRL) strategy-based edge computing resource allocation technique. This methodology generates computational tasks for mobile users at random throughout time. The mobile user has the option to load these tasks to the fog node at the edge or carry out local activities on additional mobile devices (MDs). A proximal policy optimization approach based on DRL is proposed for allocating computational resources to achieve low latency and low system energy consumption, thereby maximizing system revenue while concurrently enhancing the system’s overall quality of service. Its core idea is to combine the advantages of FC’s low-latency processing at the edge with the adaptive decision-making capability of DRL for dynamic resource states. This is achieved by considering the processing location of the computational tasks and the interaction between the device that generates the tasks and other MDs or FC nodes (FNs). According to experimental data, this approach has successfully decreased energy usage and network latency, compared with other algorithms. When the number of candidate nodes is 3, it achieves the lowest average latency and a notably reduced average energy consumption. Moreover, as the number of MDs increases, it maintains the optimal total system overhead and yields the highest average revenue.

Mobile edge computing (MEC) provides reliable solutions for networked vehicles, mobile phones, and other mobile devices to complete intensive computing and delay-sensitive tasks. Most of the existing studies design a series of methods to achieve the expected goals based on assuming that users are absolutely rational. However, due to the subjectivity of users, the actual results will deviate from the real situation. Using the prospect theory (PT) framework, this article studies the problem of computing task offloading in real situations. Aiming at the task offloading problem in the small cellular network scenario, the artificial fish swarm algorithm is used to optimize the system energy with limited delay. Finally, the experimental tests verify the impact of user behavior on the system energy optimization and the effectiveness of the task offloading method proposed in this article.

Edge computing technology can be used to the Internet of Vehicles (IOV) to solve the mobile characteristics of vehicles and the limited communication range between roadside units and vehicles. New method of edge computing-based data adaptive return in IOV is proposed in this article. The transmission strategy can be determined by adaptive estimating the vehicle movement, the amount of data returned, the maximum transmission delay, and effective life of link. And the factors, such as speed, direction, and position of the vehicles, are comprehensively considered and these factors can be measured by the stability effect value when adaptive designing the auxiliary transmission strategy. At the same time, greedy selection method is used when constructing the data return link, and the neighbor node as the relay node with the maximum stability and efficiency value is chosen. Our experimental results show our method in terms of performance on transmission delay and packet delivery rate is better than other ones.

Mobile edge computing provides the possibility for efficient use of mobile devices, but the disclosure of user privacy is still a huge hidden danger. In order to solve the problem of user locking caused by device usage pattern, a privacy aware computing offloading method based on privacy entropy is proposed. By quantifying privacy as privacy entropy, the problem is modeled as maximizing privacy entropy and minimizing computing offloading resource consumption. The Gaussian-Cauchy operator is proposed to improve the Harris hawks optimization algorithm, to expand the search scope of the algorithm and enhance the ability to jump out of the local optimal. Experimental results show that this method can not only ensure the confusion of user information, but also minimize resource consumption and effectively solve the problem of privacy disclosure of behavior mode.

With the development of intelligent transportation, various computation intensive and delay sensitive applications are emerging in the Internet of Vehicles (IoV). The B5G/6G (Beyond 5th generation mobile communication technology/6th generation mobile communication technology) network has the characteristics of ultralow latency and ultra many connections. The deployment of the network in boxes (NIBs) supporting B5G/6G network in the vehicle can realize the real-time communication with the edge server (ES) and offload the task to the ES. However, the current multiaccess edge computing (MEC) lacks research on cooperative processing among multiple ESs, and the efficiency of data-intensive computation tasks is still insufficient. In this article, we investigate the cooperative offloading of multitype tasks among ESs in B5G/6G networks under a dynamic environment. In order to minimize the delay of task execution, we regard cooperative offloading as a Markov decision process (MDP), and improve the convergence speed and stability of traditional soft actor-critic (SAC) algorithm by the adaptive weight sampling mechanism. Finally, an offline centralized training distributed execution framework based on improved soft actor critical (OCTDE-ISAC) is proposed to optimize the cooperative offloading strategy. The experimental results show that the proposed algorithm is better than the existing algorithm in terms of latency.

The internet of vehicles (IoVs) provides delay-sensitive services, but high-latency communication with roadside units causes service failures and high costs. Mobile edge computing (EC) is migrating cloud computing platforms from the core network to the edge of mobile networks, giving vehicles local access to numerous computing resources. The classic greedy boundary stateless routing (GPSR) method is widely used to meet the communication requirements of vehicle self-organizing networks. In order to solve the problems of GPSR’s neighbor node position acquisition lag and single routing criterion, a new routing method based on the fuzzy logic system [Geographic Routing method based on Velocity, Angle, and Density (GRVAD)] is proposed. This method gets the relative velocity between the nodes and the angle between the current node, the neighbor node and the target node, and the node density of the neighbor node as the input of fuzzy logic, and the unscented Kalman filter is used to predict the location of the neighbor node to obtain more accurate location information of neighbor nodes. Simulation results show that the routing method compensates for some of the shortcomings of the GPSR method and reasonably considers the delivery rate and end-to-end delay of data packets and is more in line with the communication requirements of the vehicle ad hoc network.

Along with the development of Internet of Vehicles (IoV) and wireless technology, the usage of applications that require low latency, such as autonomous driving and intelligent navigation, is increasing rapidly, and the demand for content is increasing greatly. This paper proposes an edge caching approach for the IoV based on multi-agent deep reinforcement learning (ECSMADRL) so as to resolve the problem of excessive response delay due to the large increase of data traffic in the IoV. The approach jointly considers content distribution and caching in dynamic environments. In other words, each moving vehicle in the IoV can be seen as an agent, and it can make decisions about content caching and content access adaptively according to the changing environment to minimize the delay in the process of content distribution. It is proved by experiments that compared with other methods, the proposed edge caching (EC) approach has better performance in reducing content distribution delay, improving content hit rate and success rate.

Research on machine deep learning with fuzzy neural network (FNN) is one hot topic in the Artificial Intelligent (AI) domain. In order to support the application of the IoT (Internet of Things) and make use of these image data to get perfect image reasonably and efficiently, it is necessary to fuse these sensed data, therefore the multiple-sensors’ image aggregation becomes a key technology. In this paper, novel FNN-based machine deep learning approach for image aggregation in application of the IoT is proposed. When this approach is done, dynamic learning from eigenvalue transition example can improve traditional learning approach based on static eigenvalue of example. And the neural network is used to be demonstrated its unique superiority of image understanding. FNN-based machine deep learning approach can learn from dynamic eigenvalues, the change of data can be learned and the varieties of the eigenvalue can be understood and remembered. The relative experiments have shown the designed approach for image aggregation is fast and effective, and it can be adapted for the many image applications of the IoT.

The privacy protection of perceptual user and their enthusiasm improvement for participating in perceptual tasks are two important problems in MCS (Mobile Crowdsensing) network. A mechanism of local differential privacy protection of attribute correlation can generate perceptual results with higher precision of attribute correlation and protect perceptual users’ privacy data. A flow compensation incentive model for perceptual users’ privacy data protection based on opportunity cooperation transmission can reduce the flow compensation expenditure of MCS and improve perceptual users’ enthusiasm. Experiments show that our approach improves the perceptual result precision, reduces MCS overhead, and reduces flow compensation cost compared with the related approaches.

Mobile management is a challenging technology in Mobile Edge Computing (MEC). When the device is moving, computation tasks need to be dynamically migrated between multiple edge servers to maintain service continuity. This paper proposes a migration optimization of distributed task in MEC by deep reinforcement learning solution to optimize delay. In Multi-agent Deep Reinforcement Learning (MADRL), we construct an adaptive weight deep deterministic policy gradient (AWDDPG) algorithm to optimize the migration cost and service delay, and adopt centralized training and distributed execution to solve the high-dimensional problem. Experiments show that our algorithm greatly reduces the service delay compared with the related algorithms.

To solve the incentive problem for MCS (Mobile Crowdsensing) users based on privacy protection, we proposed an incentive method of flow compensation for the privacy protection of users and designed a system model which combined MCS with MEC (Mobile Edge Computing). The EC (Edge Center) uploaded the perception results to the MCS diminishing MCS’s overhead. We also designed an incentive model based on Q-Learning algorithm for privacy protection of user data, which can reduce the incentive expenditure and improve users’ enthusiasm for participation. Compared with the existing incentive method based on privacy protection, our method improves the perceptual result precision, decreases MCS cloud overhead, and declines flow compensation cost.

In mobile edge computing(MEC), application partitioning can split the executions into local and edge server parts. Optimal partitioning will allow mobile devices to obtain the highest benefit from Mobile Edge Computing (MEC). In this paper, a new approach on task offloading scheduling for application of mobile edge computing is proposed. We divide the computing task into several subtasks. Then, we analyze the overhead of the system, and propose a fine-grained strategy for task scheduling and offloading in a multi-user MEC system. For reducing the energy consumption and delay, the computation offloading is considered as a constrained multi-objective optimization problem (CMOP), which can be solved by an improved NSGA-II algorithm. The experimental results show that the proposed algorithm can find a large number of optimal solutions to adjust the corresponding offloading decision according to the real-world situation.

We propose an oriented edge computing nodes energy optimized QoS constrained routing algorithm. The mechanism of this algorithm is to optimize the network energy consumption and extend the network life cycle by creatively combining the edge computing technology to preprocess the original data of the node, accelerating the transmission and processing of effective data, accelerating the algorithm convergence by using the way of automata interacting with the environment and controlling the dormant activation state of the node. Through experimental testing and comparison, this paper gave the Multi-QoS constrained routing algorithm for Edge computing and Node energy optimization algorithm (MQEN), which can meet the requirements of end-to-end delay and reliability service with multi-QoS constraints when significantly reducing network energy consumption.

Secure message dissemination is an important issue in vehicular networks, especially considering the vulnerability of vehicle-to-vehicle message dissemination to malicious attacks. Traditional security mechanisms, largely based on message encryption and key management, can only guarantee secure message exchanges between a known source and destination pairs. In vehicular networks, however, every vehicle may learn its surrounding environment and contributes as a source, while in the meantime, acting as a destination or a relay of information from other vehicles, and message exchanges often occur between “stranger” vehicles. This makes secure message dissemination against malicious tampering much more intricate. For secure message dissemination in vehicular networks against insider attackers, who may tamper the content of the disseminated messages, ensuring the consistency and integrity of the transmitted messages becomes a major concern which the traditional message encryption and key management-based approaches fall short to provide. However, it is challenging for a vehicle to distinguish which message is true when the messages received from multiple nearby vehicles are conflicting. In this paper, by incorporating the underlying network topology information, we propose an optimal decision algorithm that is able to maximize the chance of making a correct decision on the message content, assuming the prior knowledge of the percentage of malicious vehicles in the network. Furthermore, a novel heuristic decision algorithm is proposed that can make decisions without the aforementioned knowledge of the percentage of malicious vehicles. The simulations are conducted to compare the security performance achieved by our proposed decision algorithms with that achieved by the existing ones that do not consider or only partially consider the topological information to verify the effectiveness of the algorithms. Our results show that by incorporating the network topology information, the security performance can be much improved. This paper sheds light on the optimum algorithm design for secure message dissemination.

This paper proposes and designs a new method of the best path selection algorithm with length priority to analyze and solve the optimal path planning problem of intelligent driving vehicles in practical applications. Through the understanding and learning of the reinforcement learning algorithm, we proposed a new method of the best path selection with length priority based on the prior knowledge applied reinforcement learning strategy, and improved the search direction setting of the shortest path in the program, simplified the process of shortest path search. This path optimization method can effectively help different types of intelligent driving vehicles to smoothly select the best path in the traffic network with limited height, width and weight, accident and traffic jam. Through simulation experiments and scene experiments, it is proved that the proposed algorithm has good stability, high efficiency and practicability.

Energy Efficient Hierarchical Clustering (EEHC) is a distributed random clustering algorithm for RWSNs with the goal of maximizing network lifetime. The novel energy-efficient hierarchical clustering approach based on neighbor rotation for edge computing of the Internet of Things (IOT) proposed in this paper determines whether a node can become a cluster head node by comparing the comprehensive weighting of the degree of the sensor node and the distance between the sensor node and the center of the subarea. When the cluster head is replaced, a member node in the set of neighbor nodes of the current cluster head becomes the new cluster head for a random time slice, and the merged information is transmitted to the nearest sink node in a multi-hop manner according to the routing information. Simulation results demonstrate that the proposed method is more effective in saving network energy, improving data transmission efficiency, and maximizing network lifetime.

This paper proposes a unified spatio-temporal model for short-term road traffic prediction. The contributions of this paper are as follows. First, we develop a physically intuitive approach to traffic prediction that captures the time-varying spatio-temporal correlation between traffic at different measurement points. The spatio-temporal correlation is affected by the road network topology, time-varying speed, and time-varying trip distribution. Distinctly different from previous black-box approaches to road traffic modeling and prediction, parameters of the proposed approach have physically intuitive meanings which make them readily amendable to suit changing road and traffic conditions. Second, unlike some existing techniques that capture the variation of spatio-temporal correlation by a complete re-design and calibration of the model, the proposed approach uses a unified model that incorporates the physical factors potentially affecting the variation of spatio-temporal correlation into a series of parameters. These parameters are relatively easy to control and adjust when road and traffic conditions change, thereby greatly reducing the computational complexity. Experiments using two sets of real traffic traces demonstrate that the proposed approach has superior accuracy compared with the widely used space-time autoregressive integrated moving average (STARIMA) and the back propagation neural network approaches, and is only marginally inferior to that obtained by constructing multiple STARIMA models for different times of the day, however, with a much reduced computational and implementation complexity.

As a hierarchical network architecture, the cluster architecture can improve the routing performance greatly for vehicular ad hoc networks (VANETs) by grouping the vehicle nodes. However, the existing clustering algorithms only consider the mobility of a vehicle when selecting the cluster head. The rapid mobility of vehicles makes the link between nodes less reliable in cluster. A slight change in the speed of cluster head nodes has a great influence on the cluster members and even causes the cluster head to switch frequently. These problems make the traditional clustering algorithms perform poorly in the stability and reliability of the VANET. A novel passive multi-hop clustering algorithm (PMC) is proposed to solve these problems in this paper. The PMC algorithm is based on the idea of a multi-hop clustering algorithm that ensures the coverage and stability of cluster. In the cluster head selection phase, a priority-based neighbor-following strategy is proposed to select the optimal neighbor nodes to join the same cluster. This strategy makes the inter-cluster nodes have high reliability and stability. By ensuring the stability of the cluster members and selecting the most stable node as the cluster head in the N-hop range, the stability of the clustering is greatly improved. In the cluster maintenance phase, by introducing the cluster merging mechanism, the reliability and robustness of the cluster are further improved. In order to validate the performance of the PMC algorithm, we do many detailed comparison experiments with the algorithms of N-HOP, VMaSC, and DMCNF in the NS2 environment.

Very recent studies showed that the area spectral efficiency (ASE) of downlink cellular networks will continuously decrease and finally crash to zero as the base station (BS) density increases toward infinity if the absolute height difference between BS antenna and user equipment antenna is larger than zero. Such a phenomenon is referred to as the ASE crash. We revisit this issue by considering optimizing the BS antenna downtilt in cellular networks. It is common to adjust antenna pattern to tune the direction of the vertical beamforming and thus increasing received signal power and/or reducing inter-cell interference power to improve network performance. This paper focuses on investigating the relationship between the BS antenna downtilt and the downlink network performance in terms of the coverage probability and the ASE. Our results reveal an interesting find that there exists an optimal antenna downtilt to achieve the maximum coverage probability for each BS density. Numerically solvable expressions are derived for such optimal antenna downtilt, which is a function of the BS density. Our numerical results show that after applying the optimal antenna downtilt, the network performance can be significantly improved, and hence the ASE crash can be delayed by nearly one order of magnitude in terms of the BS density. Our results also give guidance on setting the optimum downtilt angle to maximize network performance given a fixed BS density.

In order to effectively predict reliability of the link between two vehicles of VANETs (Vehicular ad hoc networks) and design a reliable routing protocol to satisfy various applications requirements, we analyze the details about motion characteristics of the vehicle and the reasons that cause links disconnect in this paper. We evaluate the reliability of links and introduce it as an important parameter to design a novel routing protocol. The Q-Learning algorithm is used to dynamically adjust the routing path through interaction with the surrounding environment. Based on these, a new reliable self-adaptive routing protocol (RSAR) is proposed. The experimental results show that the performance of RSAR protocol is very good on delivery rate and delay.

Considered the wireless sensor network clustering structure, a new big data collecting method based on compressive sensing is proposed. The collection process is as follows: in the cluster, the sink node sets the corresponding seed vector based on the distribution of network, and then sends it to each cluster head. Cluster head can generate corresponding own random spacing sparse matrix based on its received seed vector, and collect data through compressive sensing technology; Among clusters, clusters forward measurement values to sink node along multi-hop routing tree which we built before. Performance analyzing and comparison of results show that this method is superior to other methods regardless of in a cluster or inter-cluster.

Since there is a competition between subcarriers of Internet of Things (IOT) because FBMC (Filter Bank Multicarrier) modulation technology does not need subcarriers to be orthogonal to each other, we consider the game method to optimize subcarrier allocation. Considering the height of secondary user and base station's antenna, the total data transmission rate limit, total power consumption constraint and power consumption constraint on a single subcarrier, a nonlinear fractional programming problem is established where maximum energy efficiency is the objective function, total data transmission rate limit, total power consumption constraint and power consumption constraint on a single subcarrier are constraint conditions. Through experimental simulation, EESA- EG proposed in this paper gives the most reasonable subcarrier allocation scheme, allocates more subcarriers for the subcarriers with better channel state and the energy efficiency in EESA-EG is optimal.

Moving object detection is an important part in intelligent video surveillance under the banner of Internet of things. The detection of moving target’s shadow is also an important step in moving object detection. On the accuracy of shadow detection will affect the detection results of the object directly. Based on the variety of shadow detection method, we find that only using one feature can’t make the result of detection accurately. Then we present a new method for shadow detection which contains colour information, the invariance of optical and texture feature. Through the comprehensive analysis of the detecting results of three kinds of information, the shadow was effectively determined. It gets ideal effect in the experiment when combining advantages of various methods.

Wireless location is one of the core technologies of Wireless Sensor Network. In many applications, the accuracy of the location is the precondition of the useful of data information the node collected. Under the premise of cost limits, improving the accuracy of wireless sensor node position has crucial significance. After analyzing reasons of the location weakness of Dv-Distance algorithm due to errors caused by different paths, we propose an estimation algorithm that is based on the distance of different paths between node and the anchor node, thereby balancing the error of different paths to locate the node. This is an improved algorithm which is based on circle focal point positioning.

With the development of Internet, various kinds of new applications appear constantly. They all have high requirements to the time delay, throughput, especially strong real-time applications such as mobile monitoring, video calls. This is a new challenge to the existing congestion control method. In order to solve this problem, we propose novel adaptive queue management intelligent algorithm in this paper. New active queue management algorithm adopts a new formula to calculate the discard packet rate. The discard packet rate can be calculated according to the changes of average queue and the nonlinear function. This new algorithm named ASRED (Adaptive Sigmoid RED) is based on the framework of RED (Random Early Detection). ASRED uses a new function to calculate the discarding probability. In addition, the adaptive adjustment of maxp mechanism is added into the algorithm.

One of the key problems that should be solved is the collision between tags which lowers the efficiency of the RFID system. The existed popular anti-collision algorithms are ALOHA-type algorithms and QT. But these methods show good performance when the number of tags to read is small and not dynamic. However, when the number of tags to read is large and dynamic, the efficiency of recognition is very low. A novel approach to mapped correlation of ID for RFID anti-collision has been proposed to solve the problem in this paper. This method can increase the association between tags so that tags can send their own ID under certain trigger conditions, by mapped correlation of ID, querying on multi-tree becomes more efficient. In the case of not too big number of tags, by replacing the actual ID with the temporary ID, the method can greatly reduce the number of times that the reader reads and writes to tag's ID. In the case of dynamic ALOHA-type applications, the reader can determine the locations of the empty slots according to the position of the binary pulse, so it can avoid the decrease in efficiency which is caused by reading empty slots when reading slots. Experiments have shown this method can greatly improve the recognition efficiency of the system.