High-Capacity Robust Behavioral Steganography Method Based on Timestamp Modulation across Social Internet of Things

Social Internet of Things (SIoT) is an emerging field that combines IoT and Internet, which can provide many novel and convenient application scenarios but still faces challenges in data privacy protection. In this paper, we propose a robust behavioral steganography method with high embedding capacity across social networks based on timestamp modulation. Firstly, the IoT devices on the sending end modulate the secret message to be embedded into a timestamp by using the common property on social networks. Secondly, the accounts of multiple social networks are used as the vertices, and the timestamp mapping relationship generated by the interaction behaviors between them is used as the edges to construct a directed secret message graph across social networks. Then, the frequency of interaction behaviors generated by users of mainstream social networks is analyzed; the corresponding timestamps and social networks are used to implement interaction behaviors based on the secret message graph and the frequency of interaction behaviors. Next, we analyze the frequency of interaction behaviors generated by users in mainstream social networks, implement the interaction behaviors according to the secret message graph and the frequency of interaction behaviors in the corresponding timestamps and social networks, and combine the redundant mapping control to complete the embedding of secret message. Finally, the receiver constructs the timestamp mapping relationship through the shared account, key, and other parameters to achieve the extraction of secret message. The algorithm is robust and does not have the problem that existing multimedia-based steganography methods are difficult to extract the embedded messages completely. Compared with existing graph theory-based social network steganography methods, using timestamps and behaviors frequencies to hide message in multiple social networks increases the cost of detecting covert communication and improves concealment of steganography. At the same time, the algorithm uses a directed secret message graph to increase the number of bits carried by each behavior and improves the embedding capacity. A large number of tests have been conducted on mainstream social networks such as Facebook, Twitter, and Weibo. The results show that the proposed method successfully distributes secret message to multiple social networks and achieves complete extraction of embedded message at the receiving end. The embedding capacity is increased by 1.98–4.89 times compared with the existing methods SSN, NGTASS, and SGSIR.

Carpooling in Smart City using SIoT: Vision, Challenges and Future Scope

Carpooling or Ridesharing is the need of the hour owing to increase in transportation cost, increased traffic congestion due to huge number of vehicles on road etc. Several attempts at proposing efficient ridesharing methods are being made by the researchers and Government authorities of several nations. Each method has its own advantage as well as disadvantage. How to group people who would car-pool is a problem of concern. Their source and destination would play a pivotal role. Knowing their preferred route in advance can be helpful. Now, if we consider the Carpooling problem as a part of a Smart City where any physical object is connected to the network through the Internet of Things (IoT) the problem can be dealt with a different perspective. A lot of work is already there where connected vehicles can communicate with each other through Wireless Sensor Network(WSN) or IOT(Internet of Things). Social Internet of Things (SIOT) deals with the concept that in addition to being smart, things are also social. It is a comparatively novel concept; some interesting works are being done regarding this field. Our article’s primary focus is to provide a review of the recent work regarding ridesharing and SIOT which will help us analyse these concepts and pave a path for future developments in this field.