This article focuses on the service freshness guarantee for cooperative sensing in the Internet of Everything. Specifically, the peak Age of Information (AoI) is first introduced to evaluate the information and service freshness. An analytical model is then constructed to decouple the components of peak AoI into the interarrival time and transmission time. With the knowledge of identical increments of update arrival and transmission process, a close bound of peak AoI violation probability is derived based on martingale theory. Furthermore, the impact of source node parameter configuration on the peak AoI violation probability are analyzed and a task allocation scheme is proposed to guide cooperative sensing. Numerical analysis validates the tightness of the peak AoI violation probability bound and the effectiveness of the proposed scheme in service freshness guarantee.

As an important technology for Internet of Things, wireless sensor networks have been receiving widespread attention in recent years. Traditional wireless sensor network deploys resources for specific applications, which causes the problem of low resource utilization. Additionally, the energy consumption brought by the diversification of application requirements has also raised the burden on sensor nodes. Therefore, based on the introduction of wireless energy transfer technology, this paper proposes a resource allocation strategy for virtualized wireless sensor networks. Specifically, physical resources are pooled by the the wireless sensor network service provider. Then, virtual sensor networks are built through network slicing technology to provide one-to-one services based on the application requirements and the current status of the physical sensor nodes. Furthermore, in order to minimize the overall network energy consumption, a system-friendly resource allocation strategy is proposed to optimize the jointly configurations of sensing frequency, time slot and transmission power. Simulation results validate that the proposed strategy can not only effectively save the network energy, but also meet the diverse personalized needs of applications.

How to support massive access efficiently is one of the challenges in the future Internet of Things (IoT) systems. To address such challenge, this paper proposes an effective preamble collision resolution scheme to sustain massive random access (RA) for an IoT system. Specifically, a new sub-preamble structure is first proposed to reduce the preamble collision probability. To identify different devices that send the same preamble to the gNB on the same physical random access channel (PRACH), a multiple timing advance (TA) capturing scheme is then proposed. Thereafter, an RA scheme is designed to sustain massive access and the performance of the scheme is studied analytically. Finally, the effectiveness of the proposed RA scheme is validated by extensive simulation experiments in terms of preamble detection probability, preamble collision probability, RA success probability, resource efficiency and TA capturing.

How to guarantee the data rate and latency requirement for an application with limited energy is an open issue in wireless virtualized sensor networks. In this paper, we integrate the wireless energy transfer technology into the wireless virtualized sensor network and focus on the stochastic performance guarantee. Firstly, a joint task and resource allocation optimization problem are formulated. In order to characterize the stochastic latency of data transmission, effective capacity theory is resorted to study the relationship between network latency violation probability and the transmission capability of each node. The performance under the FDMA mode and that under the TDMA mode are first proved to be identical. We then propose a bisection search approach to ascertain the optimal task allocation with the objective to minimize the application latency violation probability. Furthermore, a one-dimensional searching scheme is proposed to find out the optimal energy harvesting time in each time block. The effectiveness of the proposed scheme is finally validated by extensive numerical simulations. Particularly, the proposed scheme is able to lower the latency violation probability by 11.6 times and 4600 times while comparing with the proportional task allocation scheme and the equal task allocation scheme, respectively.