Sixth-generation (6G) mobile networks will have to cope with diverse threats on a space-air-ground integrated network environment, novel technologies, and an accessible user information explosion. However, for now, security and privacy issues for 6G remain largely in concept. This survey provides a systematic overview of security and privacy issues based on prospective technologies for 6G in the physical, connection, and service layers, as well as through lessons learned from the failures of existing security architectures and state-of-the-art defenses. Two key lessons learned are as follows. First, other than inheriting vulnerabilities from the previous generations, 6G has new threat vectors from new radio technologies, such as the exposed location of radio stripes in ultra-massive MIMO systems at Terahertz bands and attacks against pervasive intelligence. Second, physical layer protection, deep network slicing, quantum-safe communications, artificial intelligence (AI) security, platform-agnostic security, real-time adaptive security, and novel data protection mechanisms such as distributed ledgers and differential privacy are the top promising techniques to mitigate the attack magnitude and personal data breaches substantially.

Network lifetime predictability is an essential system requirement for the type of wireless sensor network (WSN) used in safety-critical and highly-reliable applications. All sensor nodes in these time-critical WSNs should meet the lifetime constraint at any time instance, else it may cause severe consequences that involve economic losses, or even fatalities. In the literature, clustering sensors into groups is a popular strategy to maximize the network lifetime, but none of the clustering algorithms address the predictability issue for time-critical WSNs. In this paper, the High Energy First (HEF) clustering algorithm is chosen as a design reference model, which is proved in this paper to be an optimal clustering policy under certain ideal conditions. To address network lifetime predictability in practice, the network lifetime bounds and feasibility test for the HEF are developed via the worst case energy consumption analysis. The network simulator 2 (NS2) is used to verify the proposed network lifetime predictability model, and the results show that the derived bounds of the predictability provide accurate estimations of the system lifetime.