The spatial structure of transmitters in wireless networks plays a key role in evaluating the mutual interference and hence the performance. Although the Poisson point process (PPP) has been widely used to model the spatial configuration of wireless networks, it is not suitable for networks with repulsion.The Ginibre point process (GPP) is one of the main examples of determinantal point processes that can be used to model random phenomena where repulsion is observed. Considering the accuracy, tractability and practicability tradeoffs, we introduce and promote the β-GPP, an intermediate class between the PPP and the GPP, as a model for wireless networks when the nodes exhibit repulsion. To show that the model leads to analytically tractable results in several cases of interest, we derive the mean and variance of the interference using two different approaches: the Palm measure approach and the reduced second moment approach, and then provide approximations of the interference distribution by three known probability density functions. Besides, to show that the model is relevant for cellular systems, we derive the coverage probability of the typical user and also find that the fitted β-GPP can closely model the deployment of actual base stations in terms of the coverage probability and other statistics.. In other words, the family of β-GPPs generalizes the GPP and also includes the PPP as a limiting case. Intuitively, we can always find a β of the β-GPP to model those real network deployments with repulsion accurately as long as they are not more regular than the GPP. In view of this, we fit the β-GPPs to the base station (BS) locations in real cellular networks obtained from a public database. Another attractive feature of the GPP is that it has some critical properties (in particular, the form of its moment densities and its Palm distribution) that other soft-core processes do not share, which enables us to obtain expressions or bounds of important performance metrics in wireless networks. B. ContributionsThe main objective of this paper is to introduce and promote the GPP as a model for wireless networks where nodes exhibit repulsion. The GPP not only captures the geometric characteristics of real networks but also is fairly tractable analytically, in contrast to other repulsive point process models.Specifically, we first derive some classical statistics, such as the K function, the L function and the J function, the distribution of each point's modulus and the Palm measure for the β-GPPs.And then, to show that the model leads to tractable results in several cases of interest, we analyze the mean and variance of the interference using two different approaches: one is with the aid of the Palm measure and the other is to use the reduced second moment measure. We also provide comparisons of the mean interference between the β-GPPs and other common point processes.Further, based on the mean and variance of the interference, we provide approximations of the interference distribution using three known probability densi...

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Sensor networks are handicapped by limited resources in the form of energy, processing, and memory. This paper proposes a new multi-hop energy efficient protocol, namely a routing algorithm using the ring-zone (RARZ) model. The protocol is lightweight, takes routing decisions based on the remaining energy of nodes, and performs location-based routing without the need for the nodes to know their respective positions. The protocol partitions the network into concentric rings around the base station. Each node assigns itself to a particular ring, known by a ringID. Multi-hop routing is performed and nodes within inner rings carry data for the outer rings towards the base station. Simulation results show that RARZ outperforms the address-light integrated MAC routing protocol (AIMRP), ad hoc on-demand distance vector (AODV) and Flooding in terms of end-to-end delay, average hop count, and energy consumption.

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