This work analyzes the performance of a cache-enabled vehicular communication network with platooned vehicular users aided by base stations (BSs) for cellular connectivity. We consider a caching scheme that prioritizes searching for files within the same platoon to minimize latency and resorts to the BS connection if the file is unavailable in the platoon. The file access probability representing the probability of the typical vehicular user acquiring a file from another vehicle in the same platoon or from a BS is presented. We also study the design of optimal caching placement that maximizes the total file access probability subject to memory constraints. Further, design insights are provided with the help of derived expressions and numerical results. Results indicate that based on the system parameters and relative link quality of vehicular and cellular links, it may be optimal to cache more popular files or may not be optimal to cache any file at all.

Vehicular platooning is a promising solution to increase road capacity and ensure a seamless traffic flow. Despite its relevance in the current vehicular networks, its rigorous system-level analysis has not been performed yet. In this work, we develop a comprehensive framework to model and analyze a vehicular communication network with platooned traffic. The network of roads is modeled as a Poisson line process (PLP) and vehicles are placed on each road according to an independent Matérn cluster process (MCP) to capture platooning. The resulting point process formed by the locations of the vehicles is a Cox process driven by a PLP, which we term as the PLP-MCP. We first characterize PLP-MCP and present some of its key properties. Assuming that the cellular BSs are distributed as an independent Poisson point process (PPP), we then derive the load distribution on the typical BS of the network which is an important ingredient in the analysis of many key performance metrics, such as coverage probability and the rate distribution over the network. We then provide several system-design insights, including the impact of platooning on coverage probability.

In this paper, we present a comprehensive analytical framework for the system level analysis of THz cellular networks, which incorporates all key features of THz propagation, including blocking, directionality and scattering. This framework is particularly novel from the perspective of including the effect of scattering that has been largely ignored in such analyses thus far. We model the locations of the THz base-stations (BSs) as a homogeneous Poisson point process (PPP) and users (UEs) as another independent point process (PP). Further, the blockages and scatterers are modeled using a Boolean process and an independent PPP, respectively. The framework also incorporates distinction of line-of-sight (LOS), non-line-of-sight (NLOS) links, a realistic bounded path-loss model with absorption losses, and antenna directivity. Using the proposed framework, we first characterize the interference caused by BSs and scatterers via its Laplace transform (LT). We then derive the SINR (signal to interference plus noise ratio) coverage probability. With the help of a dummy exponential random variable (RV), we also derive the exact mean SINR. Our analysis concretely demonstrates that the scatterers have a significant impact on the coverage probability. Further, our results show that the coverage probability does not always increase with the increasing density of THz BSs.

Vehicular platooning is a promising way to facilitate efficient movement of vehicles with a shared route. Despite its relevance, the interplay of platooning and the communication performance in the resulting vehicular network (VN) is largely unexplored. Inspired by this, we develop a comprehensive approach to statistical modeling and system-level analysis of VNs with platooned traffic. Modeling the network of roads using the by-now well-accepted Poisson line process (PLP), we place vehicles on each road according to an independent Matérn cluster process (MCP) that jointly captures randomness in the locations of platoons on the roads and vehicles within each platoon. The resulting triply-stochastic point process is a PLP-driven-Cox process, which we term the PLP-MCP. We first present this new point process’s distribution and derive several fundamental properties essential for the resulting VN’s analysis. Assuming that the cellular base-stations (BSs) are distributed as a Poisson point process (PPP), we derive the distribution of the loads served by the typical BS and the BS associated with the typical user. In deriving the latter, we also present a new approach to deriving the length distribution of a tagged chord in a Poisson Voronoi tessellation. Using the derived results, we present the rate coverage of the typical user while considering partial loading of the BSs. We also provide a comparative analysis of VNs with and without platooning of traffic.

In this paper, we consider wireless sensor networks (WSNs) with sensor nodes exhibiting clustering in their deployment. We model the coverage region of such WSNs by Boolean Poisson cluster models (BPCM) where sensors nodes' location is according to a Poisson cluster process (PCP) and each sensor has an independent sensing range around it. We consider two variants of PCP, in particular Matérn and Thomas cluster process to form Boolean Matérn and Thomas cluster models. We first derive the capacity functional of these models. Using the derived expressions, we compute the sensing probability of an event and compare it with sensing probability of a WSN modeled by a Boolean Poisson model where sensors are deployed according to a Poisson point process. We also derive the power required for each cluster to collect data from all of its sensors for the three considered WSNs. We show that a BPCM WSN has less power requirement in comparison to the Boolean Poisson WSN, but it suffers from lower coverage, leading to a trade-off between per-cluster power requirement and the sensing performance. A cluster process with desired clustering may provide better coverage while maintaining low power requirements.

Ecosystem of a forest suffers from many adverse events such as wild-fire which can occur randomly anywhere in the forest and grows in size with time. This paper aims to analyze performance of a network of randomly deployed wireless sensors for the early detection of these time-critical and time-evolving events in a forest. We consider that the forest lies in a confined space (e.g. a circular region) and the wireless sensors, with fixed sensing range, are deployed within the boundary of forest itself. The sensing area of the network is modeled as a finite Boolean-Poisson model. In this model, the locations of sensors are modeled as a finite homogeneous Poisson Point Process (PPP) and the sensing area of each sensor is assumed to be a finite set. This paper aims to answer questions about the proximity of a typical sensor from a randomly occurred event and the total sensing area covered by sensors. We first derive the distribution of contact distance of a FHPPP and the expression of the capacity functional of a finite Boolean-Poisson model. Using these, we then derive the probability of sensing the event at time t, termed event-sensing probability.