It has become an urgent problem to be solved that how to make electric vehicle (EV) give full play to shift the power grid peak load through the regulation of demand response (DR). Game theory is often used to provide a new solution for the optimal decision-making among multi-stakeholders. As a dynamic game, differential game can describe the dynamic changes of time-sharing electricity price (TOU price) about power grid and charging power about EV in real time. Considering the problem of "peak on top of peak" caused by a large number of electric vehicles’ disordered charging, this paper makes the dispatching strategy of EVs entering the grid based on the TOU price, and establishes a dynamic differential game model for the power grid and EV decision makers. The model is solved by taking the TOU price of power grid and the charging power of EV as the strategy, and smoothing the peak valley difference of power grid and minimizes the charging cost of EV as the goal. In the end, DR for optimizing power grid load and reducing user’s low cost is adopted to simulate the proposed model. The simulation results show that the peak-valley difference rate of the optimized power grid is reduced by 6.93%, and the cost of EV users is reduced by 71.52%. The simulation results verify the peak load regulation effect and the economic benefit of the differential game model on the power grid side.

Mobile crowdsensing (MCS) has heated up and has become a new paradigm of data collection. In the process of the task allocation of MCS, users are often required to provide their own location information with the server to conveniently dispatch some suitable tasks to them. However, it is possible for malicious servers to infer some sensitive information based on the user’s location such as the user’s home address or the user’s trajectory and so on which will cause serious privacy issues. Recently, differential privacy (DP) has become a promising privacy protection scheme. However, the existing DP schemes in location privacy protection for MCS do not pay attention to the accuracy of task allocation as equally as the effects of privacy protection, which often results in the task allocation to be inaccurate and inefficient. In order to overcome the shortcoming, we propose a novel MCS task allocation scheme integrating the mapping accuracy of task-worker with the privacy-preserving effect on the user’s location. Besides edge devices, its implementation system consists of a task allocation server and a third party, both of which are semitrusted, that is, both of them only know about the user’s rough location information but cannot obtain the user’s exact location. At first, to improve the response speed of MCS task requests and the location privacy protection of MCS users, our scheme can exploit more than one edge-computing node nearby a user to cooperatively participate in MCS task allocation by aggregating him/her and the near users into groups. Then, our scheme can further enhance the location privacy protection effect based on the Johnson–Lindenstrauss (JL) transformation, which can achieve accurate task allocation and hold the merits of DP. Finally, we verify the feasibility by some experiments based on two data sets. The performance is compared with that of $t$ he typical DP. The results show that our scheme not only provides strict privacy guarantees but also has higher performance.

With electric vehicle's charging information, the utilities can increase the efficiency and reliability of Vehicle-to-Grid (V2G) while the electric vehicle consumers can better manage their energy consumption and costs. However, since charging information are commonly transmitted via public network or wireless link, their communications lack trusted third party for identity authentication and key distribution, and are constantly exposed to traffic analysis attacks. In this article, we propose a secure and anonymous communication scheme for delivering charging information in V2G. Different from previous works, the scheme in this article creatively incorporates identity authentication into key distribution without trusted third party, which improves the security of communication in public networks. Moreover, the proposed scheme is more resistant to traffic analysis since it preserves the anonymity of charging information by splitting and forwarding them pseudo-randomly. As the performance analysis reveals, our scheme is able to provide better anonymity without the support of trusted third party, and what's more, it can achieve high cryptography capability as traditional communication schemes do. Therefore, it is more practical in real-world V2G.

Optofluidics is an exciting new area of study resulting from the fusion of microfluidics and photonics. It broadens the application and extends the functionality of microfluidics and has been extensively investigated in biocontrol, molecular diagnosis, material synthesis, and drug delivery. When light interacts with a microfluidic system, optical force and/or photothermal effects may occur due to the strong interaction between light and liquid. Such opto-physical effects can be used for optical manipulation and sensing due to their unique advantages over conventional microfluidics and photonics, including their simple fabrication process, flexible manipulation capability, compact configuration, and low cost. In this review, we summarize the latest progress in fiber optofluidic (FOF) technology based on optical force and photothermal effects in manipulation and sensing applications. Optical force can be used for optofluidic manipulation and sensing in two categories: stable single optical traps and stable combined optical traps. The photothermal effect can be applied to optofluidics based on two major structures: optical microfibers and optical fiber tips. The advantages and disadvantages of each FOF technology are also discussed.

The Road Side Unit (RSU) is an important part of the Internet of Vehicles (IoV), which collects a large number of user's privacy information and connects vehicles to the Internet. The current RSU is running with static configurations, and the adversary can get enough time to analyze the vulnerability of the RSU and launch attacks, which poses a huge threat to the privacy security of the IoV. Under this asymmetrical condition, the attackers occupy a predominant position, and it is difficult to fully defend. To solve the above asymmetry offense-defense problem, we adopt the moving target defense (MTD) to dynamically change the IP address of the RSU, which increases the difficulty of the attacker's attack. However, due to the unreasonable selection of the RSU's IP hopping frequency, it is difficult for the RSU's IP hopping to exert its defense effect or it may introduce excessive defense overheads. Given the above issue, we propose an intelligent offense and defense mechanism based on a differential game approach, which can adaptively adjust the RSU's IP hopping frequency and maximize its defense benefits. First, based on the offense and defense relationship, we model the RSU's IP hopping frequency intelligent arrangement as a differential game (i.e., IP hopping game) in which the attacker attempts to maximize his potential benefits, and the RSU manages to maximize its defense benefits. On this basis, we calculate a potential Nash equilibrium of the IP hopping game. Finally, simulation results show our proposed mechanism can adaptively adjust the RSU's IP hopping frequency according to the attack frequency and effectively defend attacks. Compared with the mechanisms with a fixed IP hopping frequency, our proposed mechanism can make up for the ineffectiveness of the fixed low-frequency IP hopping defense and avoid the excessive defense overheads of the fixed high-frequency IP hopping defense.

Distributed Denial of Service (DDoS) attack has become one of the major threats to the Internet. Traditional brute-force, high-rate DDoS attacks expose many obvious anomaly features to defense systems, so that they can be easily detected and mitigated. In this paper we propose a new type of low-rate TCP-targeted DoS attack, called NewShrew, which exploits the deficiencies in TCP's timeout mechanism and slow start mechanism. This attack could significantly degrade TCP throughput, while evading the supervision of DoS prevention systems by inconspicuously consuming a small part of network capacity. We use theoretical analysis and numerical simulations to demonstrate the effectiveness of this attack for different RTT heterogeneity, TCP variant, and network environment. We reveal the interactions among the attack parameters, and the trade-offs between throughput degradation and attack cost. Moreover, we empirically show that NewShrew outperforms the classical Shrew DoS attack in terms of lower average attack rate (averagely 47.82%), higher attack efficiency (the ratio between throughput degradation inflicted by an attack and the average attack rate of the attack) with an average of 45.79%, and higher throughput degradation (averagely 11.54%) after deploying a typical defense mechanism (namely, RTO randomization). Our work innovatively exposes TCP slow start mechanism as a possible vulnerability to adversarial attacks, hence it opens new avenue to improving the resilience of TCP.