Multi-mode power internet of things (PIoT) combines various communication media to provide spatio-temporal coverage for low-carbon operation in smart park. Edge-end collaboration is feasible to achieve the full utilization of heterogeneous resources and anti-eavesdropping. However, edge-end collaboration-based multi-mode PIoT faces challenges of mutual contradiction in communication and security quality of service (QoS) guarantee, inadaptability of resource management, and multi-mode access conflict. We propose an Adaptive learning based delAy-sensitive and seCure Edge-End Collaboration algorithm ($\text{ACE}^2$) to optimize multi-mode channel selection and split device power into artificial noise (AN) transmission and data transmission for secure data delivery. $\text{ACE}^2$ can achieve multi-attribute QoS guarantee, adaptive resource management and security enhancement, and access conflict elimination with the combined power of deep actor-critic (DAC), “win or learn fast (WoLF)” mechanism, and edge-end collaboration. Simulations demonstrate its superior performance in queuing delay, energy consumption, secrecy capacity, and adaptability to differentiated low-carbon services.

With the rapid developments of Internet of Things (IoT) and proliferation of embedded devices, large volume of personal data are collected, which however, might carry massive private information about attributes that users do not want to share. Many privacy-preserving methods have been proposed to prevent privacy leakage by perturbing raw data or extracting task-oriented features at local devices. Unfortunately, they would suffer from significant privacy leakage and accuracy drop when applied to other tasks as they are designed and optimized for predefined tasks. In this paper, we propose a novel task-free privacy-preserving data collection method via adversarial representation learning, called TF-ARL, to protect private attributes specified by users while maintaining data utility for unknown downstream tasks. To this end, we first propose a privacy adversarial learning mechanism (PAL) to protect private attributes by optimizing the feature extractor to maximize the adversary's prediction uncertainty on private attributes, and then design a conditional decoding mechanism (ConDec) to maintain data utility for downstream tasks by minimizing the conditional reconstruction error from the sanitized features. With the joint learning of PAL and ConDec, we can learn a privacy-aware feature extractor where the sanitized features maintain the discriminative information except privacy. Extensive experimental results on real-world datasets demonstrate the effectiveness of TF-ARL.

Platoon assisted vehicular edge computing has been envisioned as a promising paradigm of implementing offloading services through platoon cooperation. In a platoon, a vehicle could play as a requester that employs another vehicles as performers for workload processing. An incentive mechanism is necessitated to stimulate the performers and enable decentralized decision making, which avoids the information collection from the performers and preserves their privacy. We model the interactions among the requester (leader) and multiple performers (followers) as a Stackelberg game. The requester incentivizes the performers to accept the workloads. We derive the Stackelberg equilibrium under complete information. Furthermore, deep reinforcement learning is proposed to tackle the incentive problem while keeping the performers' information private. Each game player becomes an agent that learns the optimal strategy by referring to the historical strategies of the others. Finally, numerical results are provided to demonstrate the effectiveness and efficiency of our scheme.

In this work, we employ the cache-enabled UAV to provide context information delivery to end devices that make timely and intelligent decisions.Different from the traditional network traffic, context information varies with time and brings in the age-constrained requirement. The cached content items should be refreshed timely based on the age status to guarantee the freshness of user-received contents, which however consumes additional transmission resources. The traditional cache methods separate the caching and the transmitting, which are not suitable for the dynamic context information.We jointly design the cache replacing and content delivery based on both the user requests and the content dynamics to maximize the offloaded traffic from the ground network. The problem is formulated based on the Markov Decision Process (MDP). A sufficient condition of cache replacing is found in closed form, whereby a dynamic cache replacing and content delivery scheme is proposed based on the Deep Q-Network (DQN).Extensive simulations have been conducted. Compared with the conventional popularity-based and the modified Least Frequently Used (i.e., LFU-dynamic) schemes, the UAV can offload around 30% traffic from the ground network by utilizing the proposed scheme in the urban scenario, according to the simulation results.

In this paper, we propose a novel fuzzy matching data sharing scheme named FADS for cloud-edge communications. FADS allows users to specify their access policies, and enables receivers to obtain the data transmitted by the senders if and only if the two sides meet their defined certain policies simultaneously. Specifically, we first formalize the definition and security models of fuzzy matching data sharing in cloud-edge environments. Then, we construct a concrete instantiation by pairing-based cryptosystem and the privacy-preserving set intersection on attribute sets from both sides to construct a concurrent matching over the policies. If the matching succeeds, the data can be decrypted. Otherwise, nothing will be revealed. In addition, FADS allows users to dynamically specify the policy for each time, which is an urgent demand in practice. A thorough security analysis demonstrates that FADS is of provable security under indistinguishable chosen ciphertext attack (IND-CCA) in random oracle model against probabilistic polynomial-time (PPT) adversary, and the desirable security properties of privacy and authenticity are achieved. Extensive experiments provide evidence that FADS is with acceptable efficiency.

Peer-to-peer computation offloading has been a promising approach that enables resource-limited Internet of Things (IoT) devices to offload their computation-intensive tasks to idle peer devices in proximity.Different from dedicated servers, the spare computation resources offered by peer devices are random and intermittent, which affects the offloading performance. The mutual interference caused by multiple simultaneous offloading requestors that share the same wireless channel further complicates the offloading decisions. In this work, we investigate the opportunistic peer-to-peer task offloading problem by jointly considering the stochastic task arrivals, dynamic inter-user interference, and opportunistic availability of peer devices. Each requestor makes decisions on both local computation frequency and offloading transmission power to minimize its own expected long-term cost on tasks completion, which takes into consideration its energy consumption, task delay, and task loss due to buffer overflow. The dynamic decision process among multiple requestors is formulated as a stochastic game. By constructing the post-decision states, a decentralized online offloading algorithm is proposed, where each requestor as an independent learning agent learns to approach the optimal strategies with its local observations. Simulation results under different system parameter configurations demonstrate the proposed online algorithm achieves a better performance compared with some existing algorithms, especially in the scenarios with large task arrival probability or small helper availability probability.

Mobile edge computing has emerged as a new paradigm to enhance computing capabilities by offloading complicated tasks to nearby cloud server. To conserve energy as well as maintain quality of service, low time complexity algorithm is proposed to complete task offloading and server allocation. In this paper, a multi-user with multiple tasks and single server scenario is considered for small network, taking full account of factors including data size, bandwidth, channel state information. Furthermore, we consider a multi-server scenario for bigger network, where the influence of task priority is taken into consideration. To jointly minimize delay and energy cost, we propose a distributed unsupervised learning-based offloading framework for task offloading and server allocation. We exploit a memory pool to store input data and corresponding decisions as key-value pairs for model to learn to solve optimization problems. To further reduce time cost and achieve near-optimal performance, we use convolutional neural networks to process mass data based on fully connected networks. Numerical results show that the proposed algorithm performs better than other offloading schemes, which can generate near-optimal offloading decision timely.

In industrial Internet of Things systems, state estimation plays an important role in multi-sensor cooperative sensing. However, the state information received by remote control center experiences random delay, which inevitably affects the state estimation performance. Moreover, the computation and storage burden of remote control center is very huge, due to the large amount of state information from all sensors. To address this issue, we propose a layered network architecture and design the mobile edge computing (MEC) enabled cooperative sensing scheme. In particular, we first characterize the impact of random delay on the error of state estimation. Based on this, the cooperative sensing and resource allocation are optimized to minimize the state estimation error. The formulated constrained minimization problem is a mixed integer programming problem, which is effectively solved with problem decomposition based on the information content of delivered data packets. The improved marine predators algorithm (MPA) is designed to choose the best edge estimator for each sensor to pretreat the sensory information. Finally, the simulation results show the advantage and effectiveness of proposed scheme in terms of estimation accuracy.

Cloud computing provides powerful processing capabilities for large-scale intelligent Internet of things (IoT) terminals. However, the massive real-time data processing requirements challenge the existing cloud computing model. The edge server is closer to the data source. The end-edge-cloud collaboration offloads the cloud computing tasks to the edge environment, which solves the shortcomings of the cloud in resource storage, computing performance, and energy consumption. IoT terminals and sensors have caused security and privacy challenges due to resource constraints and exponential growth. As the key technology of IoT, Radio-Frequency Identification (RFID) authentication protocol tremendously strengthens privacy protection and improves IoT security. However, it inevitably increases system overhead while improving security, which is a major blow to low-cost RFID tags. The existing RFID authentication protocols are difficult to balance overhead and security. This paper designs an ultra-lightweight encryption function and proposes an RFID authentication scheme based on this function for the end-edge-cloud collaborative environment. The BAN logic proof and protocol verification tools AVISPA formally verify the protocol's security. We use VIVADO to implement the encryption function and tag's overhead on the FPGA platform. Performance evaluation indicates that the proposed protocol balances low computing costs and high-security requirements.

The one-time pad (OTP) is an application-layer encryption technique to achieve the information-theoretic security, and the physical-layer secret key generation (SKG) technique is a promising candidate to provide the random keys for OTP. In this paper, we propose a joint SKG and OTP encryption scheme with the aid of a reconfigurable intelligent surface (RIS) to boost secret key rate. To maximize the efficiency of secure communication, we divide the process of secure transmission into two stages: SKG and then encrypted packet transmission. Meanwhile, we design an optimal algorithm for allocating time slots for SKG to maximize SKG efficiency without security risk. Furthermore, we design a key updating protocol based on our SKG scheme for OTP encryption. Simulation results verify that our scheme can generate keys securely and efficiently, and significantly improve the secure communication performance in an intelligent IoT system.

Biometric key is generated from the user's unique biometric features, and can effectively solve the security problems in cryptography. However, the current prevailing biometric key generation techniques such as fingerprint recognition and facial recognition are poor in randomness and can be forged easily. According to the characteristics of Electroencephalographic(EEG) signals such as the randomness, non-linear and non-stationary etc., it can significantly avoid these flaws. This paper proposes a novel method to generate keys based on EEG signals with end-edge-cloud collaboration computing. Using sensors to measure motor imagery EEG data, the key is generated via pre-processing, feature extraction and classification. Experiments show the total time consumption of the key generation process is about 2.45s. Our scheme is practical and feasible, which provides a research route to generate biometric keys using EEG data.

Link flooding attack (LFA) is a fresh distributed denial of service attack (DDoS). Attackers can cut off the critical links, making the services in the target area unavailable. LFA manipulates legal low-speed flow to flood critical links, so traditional technologies are difficult to resist such attack. Meanwhile, LFA is also one of the most important threats to Internet of things (IoT) devices. The introduction of software defined network (SDN) effectively solves the security problem of the IoT. Aiming at the LFA in the software defined Internet of things (SDN-IoT), this paper proposes a new LFA mitigation scheme ReLFA. Renyi entropy is to locate the congested link in the data plane in our scheme, and determines the target links according to the alarm threshold. When LFA is detected on the target links, the control plane uses the method based on deep reinforcement learning (DRL) to carry out traffic engineering. Simulation results show that ReLFA can effectively alleviate the impact of LFA in SDN IoT. In addition, the rerouting time of ReLFA is superior to other latest schemes.