It is known to all that mobile ad hoc network (MANET) is more vulnerable to all sorts of malicious attacks which affects the reliability of data transmission because the network has the characteristics of wireless, multi-hop, etc. We put forward novel approach of distributed & adaptive trust metrics for MANET in this paper. Firstly, the method calculates the communication trust by using the number of data packets between nodes, and predicts the trust based on the trend of this value, and calculates the comprehensive trust by considering the history trust with the predict value; then calculates the energy trust based on the residual energy of nodes and the direct trust based on the communication trust and energy trust. Secondly, the method calculates the recommendation trust based on the recommendation reliability and the recommendation familiarity; adopts the adaptive weighting, and calculates the integrate direct trust by considering the direct trust with recommendation trust. Thirdly, according to the integrate direct trust, considering the factor of trust propagation distance, the indirect trust between nodes is calculated. The feature of the proposed method is its ability to discover malicious nodes which can partition the network by falsely reporting other nodes as misbehaving and then proceeds to protect the network. Simulation experiments and tests of the practical applications of MANET show that the proposed approach can effectively avoid the attacks of malicious nodes, besides, the calculated direct trust and indirect trust about normal nodes are more conformable to the actual situation.

Novel approach of distributed & adaptive trust metrics for MANET

A new method of data missing estimation with FNN-based tensor heterogeneous ensemble learning for internet of vehicle

During mobile edge computing, due to the movement of nodes and the exhaustion of node energy, link failure occurs thus reducing the network lifetime in the mobile ad-hoc network. When the route fails, because the single-path protocols need to restart the route discovery process, the delay of the network is greatly increased. Therefore, the multi-path routing protocol is proposed, saving the cost of route discovery. In this paper, we propose an ad hoc on-demand multi-path distance vector (AOMDV) routing protocol based on link lifetime and energy consumption prediction (named LLECP-AOMDV) for mobile edge computing. In the route discovery phase, the energy grading strategy is adopted. When the node energy is lower than the threshold, it no longer participates in the route discovery. In the routing selected phase, the path is selected based on the lifetime of the route link and the minimum energy consumption of the route. According to energy consumption, packet delivery rate, end-to-end delay performance indicators, we evaluate the comparison results. The result shows that under most network performance indicators and parameters, the proposed LLECP-AOMDV is superior to the other three protocols, which improves the network lifetime, reduces the node's energy consumption and the average end-to-end delay. The protocol is very useful for mobile edge computing.

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A Multi-Path Routing Protocol Based on Link Lifetime and Energy Consumption Prediction for Mobile Edge Computing

In general, the mobile ad hoc networks (MANETs) are built on the basis of the random distribution of the nodes, while the nodes of real networks usually have the characteristics of location preference choice. The evolving network model based on local-area choice is proposed for MANET based on the complex network theory. The proposed model of the topology not only considers the scale-free nature of the network and the actual mobility of MANET but also considers the consumption of the node energy. Random failures often occur in MANET. Most of the existing research focuses on the changes in network topology caused by random node failures. In order to describe the impact of random edge failures on the topology of MANET, we focus on the average shortest path length (ASPL) which is an important feature of the network topology and propose the formula for calculating the ASPL of the MANET after the random edge failure. The experimental simulation analyzes the change of the ASPL of MANET in the random failure scenario (after random edge deletion). By comparing with the actual scene results, the proposed estimation formula which describes this change more accurately is proved. The formula proposed in this paper provides a general framework for studying the shortest path of MANET.

Dynamic Analysis for the Average Shortest Path Length of Mobile Ad Hoc Networks Under Random Failure Scenarios

Due to the limited computing resources and energy of IoT devices, complex computing tasks are offloaded to sufficient computing servers, such as Cloud Center. However, offloading may increase the latency and congestion of the IoT network. Mobile Edge Computing (MEC) is a promising approach, which can decrease the delay and energy consumption of IoT devices significantly. In this paper, we investigate the problem of multi-user computation offloading under dynamic environment. Considering the channel interference when multiple IoT devices offload computing tasks via wireless channels at the same time, we formulate the computation offloading as an evolutionary game model. We use the replicator dynamics to analyze the evolutionary process of IoT devices and prove that multi-user computation offloading exists unique Evolutionary Stability Strategy (ESS). Finally, we design an evolutionary game algorithm based on reinforcement learning in practical application scenarios. Experiments can verify the convergence and performance of the proposed algorithm in multi-user scenarios.

Novel method of mobile edge computation offloading based on evolutionary game strategy for IoT devices

Since there is a competition between subcarriers because FBMC (Filter Bank Multicarrier) modulation technology does not need subcarriers to be orthogonal to each other, we consider the evolutionary game method to optimize subcarrier allocation. Because the adjacent subcarriers do not need to be orthogonal to each other in FBMC, there is conflict and competition, thus the evolutionary game theory is used to optimize the subcarrier allocation problem. We innovatively introduced the channel state matrix to show the quality of subcarriers. 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. The utility function for each secondary user is established when the evolutionary game operator is designed. When the utility function becomes optimal, the evolutionary game reaches Nash equilibrium, and the strategy combination is considered to be the energy efficient resource allocation scheme. 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.

New Method of Energy Efficient Subcarrier Allocation Based on Evolutionary Game Theory

Wireless mesh networks (WMNs) are a kind of wireless network technology that can transmit multi-hop information, and have been regarded as one of the key technologies for configuring wireless machines. In WMNs, wireless routers can provide multi-hop wireless connections between nodes in the network and access the Internet through gateway devices. Multicast is a communication technology that uses best effort to send information from a source node to multiple destinations. In this paper, we study the problems of channel interference and time slot multi-user collisions caused by radio during WMNs information transmission. Through innovative use of edge computing technology to construct a node data cache model and step-by-step calculation of node channel separation, a multi-strategy channel allocation algorithm for edge computing is proposed. The mechanism of this algorithm is to use edge computing technology to pre-store the data in the nodes in the multicast tree and calculate the channel separation between the nodes, and then select the transmission channel number of the node with the least interference to avoid mutual interference between node information transmissions. Through experimental tests and comparisons, the MSCA (Multi-Strategy Channel Allocation algorithm for edge computing) algorithm presented in this paper can minimize channel interference and overall network energy consumption while satisfying throughput and end-to-end delay.

New algorithm of multi-strategy channel allocation for edge computing

The invention discloses a QoS constraint routing method for edge computing node energy optimization, and belongs to the field of Internet of Things. The invention provides the method taking three QoSconstraint conditions of end-to-end delay, transmission reliability and energy consumption into consideration and innovatively applying related technologies in edge calculation and machine learning. According to the algorithm, network energy consumption is reduced by adopting an automaton and environment interaction mode to accelerate algorithm convergence, control the dormant activation state ofnodes and the like. Through experiments, the MQEN algorithm is compared with a QMCRA-DP algorithm, a CMQRA algorithm and an LQOR algorithm, and compared with other three algorithms, the MQEN algorithmcan meet the requirements of multi-QoS constraint on end-to-end delay and reliability services while remarkably reducing the network energy consumption.

Chen Chen

Papers

New Method of Energy Efficient Subcarrier Allocation Based on Evolutionary Game Theory

New algorithm of multi-strategy channel allocation for edge computing

QoS constraint routing method for edge computing node energy optimization