Low-Cost and Confidentiality-Preserving Data Acquisition for Internet of Multimedia Things

Secure Wireless Communications Based on Compressive Sensing: A Survey

“…It faces two challenges including low-cost sampling and confidentiality preservation. Accordingly, a mechanism presented in [90] can overcome these two challenges based on chaotic CS. The encryption algorithm is comprised by several key steps as follows.…”

“…CS-Based Secrecy [68] Build up a secure wireless wiretap channel by leveraging the channel asymmetry Secrecy Capacity [69] Quantitatively investigate the lower and upper bounds of the secrecy capacity The Secrecy Based on Distributed CS [126] Design an amplify-forward scheme to cater the distribute nature of wireless sensor network The Secrecy Based on MIMO Precoding [71] Simultaneously maximize the secrecy and the signal-to-noise ratio The Secrecy Based on Circulant Matrix [72] Guarantee the wireless indistinguishability security with some conditions Multicarrier System [73]- [75] Induce artificial noise/channel randomization/particular measurement matrix for the security Cooperative Networks [76] Own the superiority of energy harvesting and high secrecy capacity Establishing Secure Measurement Matrix [77], [78] Design measurement matrix with reciprocal quantization/channel measurements Integrity-Protected CS [79] AES for the encryption of measurements and hash algorithm for integrity checking Capturing Medical Data [80] Capture data firstly and then encrypt them Data Gathering [81]- [84] Combine pseudorandom permutations and symmetric/additively homomorphic encryption Compressed Detection [85], [86] Perform collaborative compressed detection at distributed nodes Adaptive CS for Smart Objects [87] Utilize the information of smart objects to adapt the CS measurement condition Frequency Selection for Static Environment [88], [89] Enlarge the entropy of measured channel and accelerate the rate of generating keys Chaotic CS for Internet of Multimedia Things [90] Realize low-cost sampling and confidentiality preservation Secure Interaction with Cloud [91] Random compressed encryption for the raw data Crowdsensing [92]- [94] Maximize the geographic map coverage and protect the participants' trace privacy Smart Grid [95]- [97] Construct a secret measurement matrix for joint encryption, sampling, and compression Wireless Body Area Networks [98]- [100] Exploit chaotic CS for energy saving and data security a long delay off over fading channels…”

Joint utility optimization for wireless sensor networks with energy harvesting and cooperation

“…This enables wireless sensor networks (WSNs) to become a promising system framework, where many interconnected sensor nodes work together to obtain and fuse data originated from heterogeneous sources [1]. Thus, WSNs can be widely applied to diverse areas including multimedia monitoring networks, real-time tracking of objects, traffic management systems, and disaster management [2][3][4]. However, WSNs also face some challenges, such as limited bandwidth, data storage, battery power consumption and limited processing capabilities.…”

A robust image encryption algorithm based on Chua's circuit and compressive sensing

“…The flourishing development of the information industry has made the security of multimedia information become a major challenge [1]- [5], especially, image security is one of the most crucial problems in many application fields such as military, medical and commercial areas. Therefore, the design of image compression-encryption scheme (CES) has aroused more attention to effectively protect the image data during transmission and storage [6]- [10].…”

“…(4) The robustness of the scheme is improved to be capable to defend the shear and noise attacks. (5) The performance simulation and security analysis of this system are displayed.…”