Geometric random graph theory [51] | Number of nodes placed in uniformly distributed area. Homogeneous topology control | Asymptotic distribution to analyze the critical transmission range | It can only be used to model dense ad hoc networks, not for sparse ad hoc networks [69] |

Centralized topology control algorithm [54] | Relative distance of all nodes are given as input to a centralized topology control algorithm | Minimize the maximum transmitting range of nodes. Also, reduce the energy consumption and improve the network throughput | No information has been given to select the appropriate number of neighbors for LINT heuristic (Local Information No Topology). Danger of Network partition in LINT. |

Distributed position based network protocol [55] | Every node is equipped with a GPS receiver. Also, The number of iteration needed to determine the enclosure is based on the definition of the initial search engine | Minimizes the energy needed to communicate with a single master node. | The search engine is a critical aspect which affects the energy consumption of the protocol. |

Cone-based distributed topology control algorithm [56] | Each node has some power function which transmits the minimum power needed to establish a communication link to a node far away from that node. | Reduce the power consumption and discuss the modification to deal with mobility. | Initial range assignment for nodes and its step increase has not been discussed. |

Depth first search algorithm [57] | Nodes know their location and periodically update their neighbors with their current locations. | Determine the critical links whose failure cause partitioning of the network and then supporting these links either by modifying the trajectory of the nodes involved in the critical links or bringing an outside node to reinforce them. | Increase delivery rates due to Prolonged network connectivity. Communication overhead due to running DFS (Depth First Search) for detecting critical links was not measured. |

Localized algorithm for testing k-connectivity [59] | Each node makes its own decision based on the information available in its local neighborhood. Each node verifies whether or not itself and each of its p-hop neighbors of a given node is k-connected. All nodes declare themselves locally 1-connected. | Find the critical nodes and links using local topology and location information | Detected critical points may not be global critical points due to existence of alternate routes |

Genetic Algorithm and Fish Swarm Algorithm [62] | Realistic Mobility Model | Improve the node connectivity issue by adding static nodes with consideration to deployment cost | Energy loss while deploying static nodes is not considered |

Linear Programming and Neural Networks [63] | Consider a network of homogeneous and energy constrained sensor nodes that are randomly deployed in a sensor field. | scheme is quite effective to deliver 95% of packets to their destination with increase in network coverage | It cannot guarantee the full sensing coverage of the network. |

Fractal Propagation Model [70] | For every two nodes within the transmission range will be connected with a probability as function of their geometrical distance. | Giant component size can be characterized by a single parameter i.e. average node degree. | Giant component size has been estimated empirically rather than analytically |

Undirected Geometric Random Graph [71] | n nodes are randomly and uniformly distributed on a square and link exist between two nodes if the power received at one node from the other nodes is greater then a given threshold. | It shows an empirical formula relating the giant component size and the average node degree in random geometric graphs. | Giant component size of network has been estimated empirically rather then analytically. |