In this paper, we study capacity scaling laws of the deterministic dissemination (DD) in random wireless networks under the generalized physical model (GphyM). This is truly not a new topic. Our motivation to readdress this issue is two-fold: Firstly, we aim to propose a more general result to unify the network capacity for general homogeneous random models by investigating the impacts of different parameters of the system on the network capacity. Secondly, we target to close the open gaps between the upper and the lower bounds on the network capacity in the literature. We derive the general upper bounds on the capacity for the arbitrary case of (λ, nd, ns) by introducing the Poisson Boolean model of continuum percolation, where λ, nd, and ns are the general node density, the number of destinations for each session, and the number of sessions, respectively. We prove that the derived upper bounds are tight according to the existing general lower bounds constructed in the literature.

In this paper, we mainly investigate capacity scaling laws of the mobile ad hoc social networks (MAHSNs)where social networking applications are implemented over the underlying mobile ad hoc networks. We model the real-world mobility pattern of mobile social users by introducing a clustered model that defines two levels of mobility, i.e., Strong mobility and weak mobility, according to the impacts of mobility on the gain of network capacity. To address the formation of social relationships among mobile social users, we adopt a distance and density aware social model called population-distance-based model that comprehensively and practically takes account of the clustering levels of friendship degree and distribution. Under those models, we derive the capacity scaling laws for social-broadcast sessions in MAHSNs. The results provide the exploratory insights into the impacts of users' mobility patterns and the formation of social relationships on the network capacity of MAHSNs.

In this paper, we focus on the asymptotic capacity and delay, and their tradeoffs in mobile ad hoc networks (MANETs). As we all know, some fixed rate communication models such as the protocol model and the physical model have been studied in the past. However, our work aims to investigate the impact of an adaptive rate communication model on capacity-delay tradeoffs in MANETs under classical mobility models. Specifically, we adopt a well-known adaptive rate model called the generalized physical model (GphyM). The mobility of nodes is characterized by two broad classes of practical mobility models and they are hybrid random walk models and discrete random direction models. The two models generalize many mobility models studied in the literature, including the random walk, i.i.d., Brownian, and random way point models. For each mobility model, we derive the optimal delay for the optimal per-session unicast capacity (that of constant order Θ(1)) under the generalized physical model, depending on the individual parameters of mobility models. In particular, we show that for the i.i.d. model, compared with those under the protocol and physical models, the adaptive feature of link rate under the generalized physical model results in a significant decrease in the optimal delay for the optimal capacity; more precisely, both the optimal capacity and optimal delay can be simultaneously achieved, while there is no improvement for the random way-point model.