Southwest University

About: Southwest University is a education organization based out in Chongqing, China. It is known for research contribution in the topics: Gene & Population. The organization has 29772 authors who have published 27755 publications receiving 409441 citations. The organization is also known as: Southwest University in Chongqing & SWU.

Secure and Efficient Data Communication Protocol for Wireless Body Area Networks

Abstract: Wireless Body Area Networks (WBANs) are expected to play a major role in the field of patient-health monitoring in the near future, which gains tremendous attention amongst researchers in recent years. One of the challenges is to establish a secure communication architecture between sensors and users, whilst addressing the prevalent security and privacy concerns. In this paper, we propose a communication architecture for BANs, and design a scheme to secure the data communications between implanted/wearable sensors and the data sink/data consumers (doctors or nurse) by employing Ciphertext-Policy Attribute Based Encryption (CP_ABE) [1] and signature to store the data in ciphertext format at the data sink, hence ensuring data security. Our scheme achieves a role-based access control by employing an access control tree defined by the attributes of the data. We also design two protocols to securely retrieve the sensitive data from a BAN and instruct the sensors in a BAN. We analyze the proposed scheme, and argue that it provides message authenticity and collusion resistance, and is efficient and feasible. We also evaluate its performance in terms of energy consumption and communication/computation overhead.

Event-Triggered Communication and Data Rate Constraint for Distributed Optimization of Multiagent Systems

Abstract: This paper is concerned with solving a large category of convex optimization problems using a group of agents, each only being accessible to its individual convex cost function. The optimization problems are modeled as minimizing the sum of all the agents’ cost functions. The communication process between agents is described by a sequence of time-varying yet balanced directed graphs which are assumed to be uniformly strongly connected. Taking into account the fact that the communication channel bandwidth is limited, for each agent we introduce a vector-valued quantizer with finite quantization levels to preprocess the information to be exchanged. We exploit an event-triggered broadcasting technique to guide information exchange, further reducing the communication cost of the network. By jointly designing the dynamic event-triggered encoding–decoding schemes and the event-triggered sampling rules (to analytically determine the sampling time instant sequence for each agent), a distributed subgradient descent algorithm with constrained information exchange is proposed. By selecting the appropriate quantization levels, all the agents’ states asymptotically converge to a consensus value which is also the optimal solution to the optimization problem, without committing saturation of all the quantizers. We find that one bit of information exchange across each connected channel can guarantee that the optimiztion problem can be exactly solved. Theoretical analysis shows that the event-triggered subgradient descent algorithm with constrained data rate of networks converges at the rate of ${O}( {\ln t/{\sqrt {t}}})$ . We supply a numerical simulation experiment to demonstrate the effectiveness of the proposed algorithm and to validate the correctness of theoretical results.