Topology control can reduce interference and energy consumption. Moreover, it can improve network performance, such as network capacity, fault-tolerance, and scalability [
2‐
24]. Typically, topology control algorithms can be categorized by preserving 1-connectivity and
k-connectivity, respectively. For preserving 1-connectivity, current works mainly focused on prolonging network lifetime and increasing network capacity, without considering topology fault-tolerance [
2‐
11,
25]. To achieve fault-tolerance, algorithms that construct
k-connected topologies have been proposed [
12‐
24]. In [
12], the relationship between
k-connectivity and node degree was described. Then, the authors presented an algorithm that can preserve
k-connectivity. However, the minimum node degree was not given. Fukunaga et al. [
22] derived an analytical expression of minimum node degree for constructing
k-connected topology with a high probability. Based on Yao structure (YAO
p
,
k+1), Li et al. proposed an algorithm to sustain
k-connectivity. The key issue is to assume there are
p equal cones around one node and choose
k + 1 closest nodes in each cone. Li et al. [
23] proposed the communications-based train control (CBTC) algorithm. In CBTC, each node needs to link at least one node in every cone of degree
a centered at this node. Meanwhile, they proved that it can preserve
k-connectivity when
a < 2π/3
k. Li et al. [
13] developed centralized FGSS
k
and localized FLSS
k
algorithms, which both guarantee
k-connectivity when a unit disk graph (UDG) is
k-connected. The FGSS
k
and FLSS
k
are min-max optimal. Miyao et al. [
14] gave out a
k-connectivity-preserving algorithm with lower time complexity, called local tree-based reliable topology (LTRT). However, it only guarantees
k-edge connectivity while takes no account of
k-node connectivity. Wang et al. [
15] proposed a
k-connected energy-aware algorithm and proved the total power consumption is minimum. Bagci et al. [
16] presented a distributed fault-tolerant algorithm. Zhao et al. [
17,
18,
24] studied the schemes based on cooperative communication to achieve topology control, and Guo et al. [
19] present a more efficient fault-tolerant topology control with
k-connectivity. By exploiting the advantage of cooperative communications, it can achieve path energy-efficiency and lower power consumption. Ao et al. [
20] consider topology control with opportunistic interference cancelation. Luo et al. [
21] provide the optimization problem of joint topology control and authentication design in mobile ad hoc networks with cooperative communications. Burkhart et al. [
26] revealed that the minimum total power does not lead to minimum interference. Recently, mobile crowd sensing-based method [
27‐
29] is proposed to process social sensing data.