Network Model Sample Clauses

Network Model. We assume that a single IoT operator is coordinating the communication between low power IoT devices using UNB transmissions. We consider a single access point (AP) serving a wide area network of IoT devices. The AP reserves TF blocks in the available whitespace in existing licensed spectra for a fixed duration T in the future. Let nt denote the number of available channels of equal bandwidth β at time

Network Model. We consider a large-scale stationary sensor network de- ployed in outdoor environments. Sensors are able to position themselves through any of the techniques proposed in liter- ature (e.g. [8], [18]), and they communicate with each other following a geographic routing protocol (e.g. [13]). We assume homogeneous sensors densely deployed in a given region. Sensors are preloaded with several system pa- rameters, and differentiate themselves as either worker sensors or service sensors after deployment. Worker sensors are in charge of sensing and reporting data, and are expected to operate for years. Service sensors take charge of key space construction and keying information distribution. They may die after their duty is complete.

Network Model. Figure 2 shows the network environment and its description is as follows:

Network Model. ‌ Fig 3.1 illustrates the network model. We consider a single cell two-tier HetNet that consists of a macrocell and multiple N dense femtocells. In our system, we consider a single MUE and multiple FUE. The main purpose of this work is to achieve the required QoS by managing the interference in the downlink of dense two-tier HetNets. High transmission power triggers significant interference to the UE in a BS vicinity, while low transmit power results in the UE not receiving the desired signal. It is assumed that the spectrum of all transmitted signals to be the same; narrowband signaling or single subcarriers of wideband multicarrier signals [74]. In the downlink, the interference is caused by the MBSs and the FAPs. Concretely, there are mainly three interference scenarios: • FAP interferes with neighboring ▇▇▇▇. Although the FAP transmit power being significantly lower than the MBS, the MUE is prone to interference from the FAP if nearly located, leading to a QoS degradation. Consequently, to avoid significant interference to the neighboring MUE, FAPs transmit power should be as low as possible. • MBS interferes with FUE. MBS high transmit power may initiate interference to the FUE, so the FAP transmit power must ensure the FUE’s communication requirements. Fig. 3.1 Two-tier Femtocell HetNet • The interference from the FAP to the other FUE. Since the FAPs are basically deployed indoors, the associated FUEs will be prone to interference when the neighboring FAP select channels of the same frequency. However, because the transmitted power of FAP is inconspicuous, interference only exists between nearby femtocells. The Signal to Interference and Noise Ratio (SINR) of MUE and FUE can be calculated as follows. SINRMUE = Pmhm,MUE (3.1) ∑ i=1 Pih fi,MUE + σ 2 Similarly, SINRFUE = Pih fi,FUEi (3.2) i Pmhm,FUE + ∑N Pjhf ,FUE + σ 2 i j=1, j i j i Pm and Pi denote MBS and FAP transmit powers, respectively. The fading coefficient between an MBS m and the typical UE is denoted by hm. Comparably, the fading coefficient between a FAP f and the typical user UE is denoted by h fi, j .

Network Model. S × S × ··· × S S ⊂ S 1) ni is a positive integer drawn from a subspace i, for i = 1, 2,... , k; 2) Any two subspaces have no intersection, i.e., Si Sj = φ, for i, j = 1, 2,... ,k and i ƒ= j; 3) The cardinality |Si| = Ni, for i = 1, 2,... , k. N = Hence the maximum number of nodes in the network can be and KTC is worse than non-interactive schemes. Our scheme tries to achieve a trade-off between the interactive approach and the non-interactive approach, thus the memory cost per node can be reduced.

Network Model. Internet TA Group RSU RSU Wireless connection Wire connection

Network Model. The network model for the proposed scheme (BioKA-ASVN) is provided in Fig. 1. In this network architecture, we consider the communication entities as a) user (Ui), b) drone (DRj), and c) a ground server (also considered as an authentication server) (GS). GS has a responsibility to register other entities in the network and is assumed to be a fully trusted registration authority. A user Ui can register with the GS by providing minimal information securely, and at the end of the registration process, GS gives some secret credentials for future communication and authentication. The GS registers a drone with unique and distinct credentials for each DRj. Once the registration is over, the entities are deployed into their respective working areas, and GS is placed under a physical locking system. DRj detects information from a drone’s airspace and sends it to the associated GS, which is forwarded to an attached peer-to-peer (P2P) cloud server (CS) network, also known as a blockchain center. The data is finally stored in a blockchain for secure storage.‌

Network Model where ni = 0, 1,..., Ni − 1 for i = 1, 2,..., k. Thus, the k credentials are drawn from different zones in that c1 ∈ [1, N1] and ci [N1 + + Ni−1 + 1, N1 + + Ni] for i = 2,... k, which guarantee they are positive and pairwise different (Fig. 1). For a node (n1, n2,..., nk), a polynomial share Σ We assume each node is identified by an index-tuple 1 (n ,n ,...,n ), where n = 0, 1,...,N − 1,i ∈ fk+1(xk+1) = f (c1, c2,... , ck, xk+1) = ik+1=0 bik+1 ik+1 k+1

Network Model. In the general architecture of vehicular networks, the communication of vehicles among the other vehicles or with the road side units (RSUs) is based on dedicated short-range communication [20], where the vehicle-to-Infrastructure (V2I) communication is the external network among the vehicles and RSUs. Vehicular Cloud Trusted Authority