Joint Communication and Motion Energy Minimization in UGV Backscatter Communication

Abstract

While backscatter communication emerges as a promising solution to reduce power consumption at IoT devices, the transmission range of backscatter communication is short. To this end, this work integrates unmanned ground vehicles (UGVs) into the backscatter system. With such a scheme, the UGV could facilitate the communication by approaching various IoT devices. However, moving also costs energy consumption and a fundamental question is: what is the right balance between spending energy on moving versus on communication? To answer this question, this paper proposes a joint graph mobility and backscatter communication model. With the proposed model, the total energy minimization at UGV is formulated as a mixed integer nonlinear programming (MINLP) problem. Furthermore, an efficient algorithm that achieves a local optimal solution is derived, and it leads to automatic trade-off between spending energy on moving versus on communication. Numerical results are provided to validate the performance of the proposed algorithm.

Secrecy Rate of Resource-Constrained Mobile Relay Model under Two-Way Wiretap Channel

“…Based on the above settings, we simulate the secrecy rate map in a 20 m × 20 m = 400 m 2 square area [14,27], which is a typical size of a smart warehouse. Inside this map, 2 IoT users, 1 eavesdropper, 1 relay with 8 (N = 8) antennas, and M = 15 vertices representing stopping points are randomly scattered, with the eavesdropper being near to the starting point of the relay.…”

“…Alice and Bob exchange data through the mobile relay under the condition of Eve eavesdropping the whole process. The scenario is described as a directed graph (ν, ε) as shown in Figure 2, where ν is the set of M vertices representing the possible stopping points, and ε is the set of directed edges representing the allowed movement paths [14,15]. We let D = (D m,j ), 1 ≤ m ≤ j ≤ M be an asymmetric M × M non-negative real distance matrix associated with ε, and we assume that the following three conditions hold:…”

“…To obtain the local optimal solution of s, we repeat the above process and obtain a sequence of {s (0) , s (1) , s (2) , • • • }. The convergence point is guaranteed to be a local optimal solution to P1 [14]. To summarize the process given above, an algorithm for obtaining the local optimal solution to P3 (equal to P1) is proposed as Algorithm 2.…”

“…In terms of computational complexity, the computing problem (25) requires O((M − 1) 2 • 2 M−1 ) in the worst case [14,25]. Solving P2 by CVX requires a complexity of O((3M + 1) 3.5 ) [14,26] due to the fact that P2 has 3M + 1 variables. Therefore, the complexity order…”