Search

This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where the sensing data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute with abnormal data, which makes the existing cooperative sensing schemes ineffective. To tackle these challenges, we first propose a generalized modeling approach for sensing data with an arbitrary abnormal component. Under this model, we then analyze the impact of general abnormal data on the performance of the cooperative sensing, by deriving closed-form expressions of the probabilities of global false alarm and global detection. To improve sensing data quality and enhance cooperative sensing performance, we further formulate an optimization problem as stable principal component pursuit, and develop a data cleansing-based robust spectrum sensing algorithm to solve it, where the under-utilization of licensed spectrum bands and the sparsity of nonzero abnormal data are jointly exploited to robustly cleanse out the potential nonzero abnormal data component from the original corrupted sensing data. Extensive simulation results demonstrate that the proposed robust sensing scheme performs well under various abnormal data parameter configurations. [ABSTRACT FROM PUBLISHER]

Fifth generation wireless systems (5G) must achieve high user Quality of Experience (QoE) in order to compete for market share. Each candidate 5G wireless radio frequency (RF) band offers advantages such as longer range or higher data rate than 2G, 3G, and 4G, but no single band or air interface standard by itself fully achieves ubiquitous levels of QoE for the complete range of wireless access devices. Spectrum clearing cannot keep pace with user demand, so public-private spectrum sharing is emerging as an affordable, near-term method of increasing radio access network (RAN) capacities for content delivery. This paper presents a fresh look at QoE; spectrum scarcity; public uses that underutilize candidate 5G radio frequency (RF) spectrum; and emerging public-private radio interference management frameworks that enable near-term spectrum sharing, with positive consequences for 5G price, performance, and total user QoE. [ABSTRACT FROM PUBLISHER]

The article discusses an integrated mobile surveillance and wireless sensor system called the iMouse. The iMouse is a security system that allows real-time monitoring and uses a wireless sensor network of mobile sensors with webcams to capture images of events. The architecture of the iMouse system also incorporates static sensors and an external server. The static sensors feature sensing boards that can collect data on light, sound, and temperature. Computer simulations indicate how mobile sensors can be deployed to visit emergency sites in a manner that conserves battery power. INSET: Related Work in Wireless Surveillance.