Nodes in Wireless Sensor Networks (WSNs) are usually deployed in an unattended even hostile environment. What is worse, these nodes are equipped with limited battery, storage, computation and communication resources. Therefore, it is challenging to ensure the security of a WSN without decreasing its network performance. Data Aggregation (DA) combined with security mechanism can provide a good scheme for solving the aforementioned problems. This paper presents a comprehensive review of Secure Data Aggregation (SDA) in WSNs, including its security goals together with existing problems. The traditional network topologies as well as new emerging ones are discussed and compared in order to indicate the application scenes and security levels of different topologies. Meanwhile, the contrastive analyses of security strategies are presented which divides SDA protocols into five categories according to different security mechanisms, security goals and network topologies. Besides, the discussion points out some open issues which may be valuable topics of SDA in the future.

The ultra-reliable and low-latency communication (uRLLC) plays a vital role in road safety and traffic management of intelligent transportation systems (ITS). Integrating unmanned aerial vehicles (UAVs) and non-orthogonal multiple access (NOMA) has become a prospective strategy for uRLLC by separately establishing line-of-sight (LoS) link and simultaneous transmissions. The confidentiality and security of information transmission in the context of uRLLC faces serious challenges, due to the openness nature of the wireless medium. Different from traditional encryption methods, featured by high computing complexity and serious processing delay, physical layer security (PLS) exploits the inherent properties of the wireless medium to prevent transmitted messages from being eavesdropped. In addition, NOMA-UAV systems pose unique challenges for secure transmission due to potential stronger LoS channels between the source and the eavesdroppers. Therefore, in this paper, we investigate the confidentiality and security of a NOMA-UAV uRLLC system, under which an UAV serves two types of users with different requirements, i.e., secret users (both security and reliability) and public users (only reliability). Based on the tools of stochastic geometry, we address the challenges of secure communications caused by the mobility of UAV, and derive the closed-form expression of secrecy rate maximization, a key metric for observing the key system parameters on the security and reliability. Furthermore, joint design of UAV trajectory and transmit power is proposed to enhance the system security. Finally, compared with popular PLS scheme empowered by UAV trajectory design, the proposed scheme is evaluated by numerical simulations for theoretical analyses and effectiveness in practice.

This paper investigates the potential of movable antenna (MA)-enabled micro-mobility to replace UAV-enabled macro-mobility for enhancing physical layer security (PLS) in air-to-ground communications. While UAV trajectory optimization offers high flexibility and Line-of-Sight (LoS) advantages, it suffers from significant energy consumption, latency, and complex trajectory optimization. Conversely, MA technology provides fine-grained spatial reconfiguration (antenna positioning within a confined area) with ultra-low energy overhead and millisecond-scale response, enabling real-time channel manipulation and covert beam steering. To systematically compare these paradigms, we establish a dual-scale mobility framework where a UAV-mounted uniform linear array (ULA) serves as a base station transmitting confidential information to a legitimate user (Bob) in the presence of an eavesdropper (Eve). We formulate non-convex average secrecy rate (ASR) maximization problems for both schemes: 1) MA-based micro-mobility: Jointly optimizing antenna positions and beamforming (BF) vectors under positioning constraints; 2) UAV-based macro-mobility: Jointly optimizing the UAV’s trajectory and BF vectors under kinematic constraints. Extensive simulations reveal distinct operational regimes: MA micro-mobility demonstrates significant ASR advantages in low-transmit-power scenarios or under antenna constraints due to its energy-efficient spatial control. Conversely, UAV macro-mobility excels under resource-sufficient conditions (higher power, larger antenna arrays) by leveraging global mobility for optimal positioning. The findings highlight the complementary strengths of both approaches, suggesting hybrid micro-macro mobility as a promising direction for balancing security, energy efficiency, and deployment complexity in future wireless networks.

Task offloading technology plays a crucial role in the Internet of Vehicles (IoV) by minimizing processing delays through the joint optimization of heterogeneous computing resources supported by vehicles, roadside units (RSUs), and macro base stations (MBSs). Previous works have often ignored the wireless interference during the exchange and sharing of task data. Additionally, the potential for vehicles with similar driving behaviors to form vehicle platooning (VEH-PLA) and effectively integrate individual vehicle resources has not been adequately addressed. Furthermore, as a novel resource management paradigm, VEH-PLA should consider task categorization since vehicles within a VEH-PLA may have identical task offloading requestsan aspect that has also received insufficient attention. In this paper, considering wireless interference, vehicle mobility, VEH-PLA, and task categorization, we propose four task offloading models aimed at minimizing processing delays. By utilizing centralized training and decentralized execution (CTDE) based on multi-agent deep reinforcement learning (MADRL), we present a task offloading decision-making method to find the global optimal offloading decision. This results in significant enhancements in resource load balancing and reductions in processing delays. Finally, simulations validate that the proposed method significantly outperforms traditional task offloading approaches in terms of minimizing processing delays while maintaining balanced resource utilization.

Wireless sensor networks (WSNs) have become one of the most vigorous techniques in the network domain. However, the sensor nodes of WSNs tend to become the target of attackers due to the broadcast communication mode and the unattended deployment nature. Although it can prevent the sensitive data from being compromised, Slice-Mix-AggRegaTe (SMART) needs to exchange messages frequently in a network, which put tremendous overhead on the sensor nodes with limited resources. Faced with these issues, this paper proposes an energy-efficient privacy-preserving data aggregation protocol based on slicing (EPPA) where a novel slicing mode is adopted to reduce the numbers of slices, which can significantly prevent the data from being compromised and decrease the communication overhead. Meanwhile, an enhanced scheme based on EPPA, called multi-function privacy-preserving data aggregation protocol (MPPA), is presented and it supports multiple functions in the process of data aggregation, such as max/min, count, and mean. The theoretical analysis and the simulation evaluation show that the proposed aggregation protocols demonstrate a better performance in the privacy preserving and the communication efficiency.