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. There are “n” drones, where n ≥ 2 as shown in Fig.1. The drones are categorized into either of the two groups: Sensor Drone (S-Drone) and Gateway Drone (G-Drone). Drones from both the groups are placed in the geographical clusters that collectively make up the mission area. Each of the drones, from both G-Drones and S-Drones, are assigned a unique ID. A cluster has fixed number of drones out of which there must be a G-Drone that is linked to the ground station. A drone has following three layers: physical layer (bottom part), data link layer (middle part) and upper layer (top port). The IEEE 802.15.4 (ZigBee) system is installed on Sensor Drones (S- Drones). Gateway Drones (G-Drones) leverage both the radio technologies i.e. IEEE 802.15.4 (ZigBee) and IEEE 802.11a (Wi-Fi). In this way, the features promised by IEEE 802.11a (high-speed data transmission) and IEEE 802.15.4 (low-power consumption) are utilized by the proposed system. The process of network formation kicks off as soon as a drone lifts off. Here, the drones are, supposedly, fed the information about neighbor’s zone ID, location, altitude and speed etc. Further, the information does include the height sensors, IMU, GPS unit and the flight controller etc. The associated drones are interlinked together using the discovery function, which makes use of the beacon signals. Transmission of data between the S-Drones and G-Drones is accomplished using IEEE 802.15.4 at the frequency of 2.4 GHz. On the other hand, the data is routed between G-Drones and the ground station using IEEE 802.11a at the frequency of 5 GHz. An immediate pay off of the scheme is lower computational cost on the ground station since it only retains the information directed to it.
Fig.1. Network model
Network Model. The network model assumed in this protocol is a wireless network in which a broadcast channel is shared in the network. Due to the broadcast nature of the radio media, a message can be broadcast in the network with only one transmission. Hence, the proposed protocol should take advantage of this feature of the wireless network for better performance. The wireless network can be a multihop ad hoc network, or a WLAN, as long as broadcast messages can be efficiently delivered. If it is a multihop ad hoc network, then we assume that the anonymous routing mechanism is already in place in the network. The source can find a route to an expected destination with such an anonymous routing mechanism, like [19].
Network Model. Figure 2 shows the network environment and its description is as follows:
Network Model. Fig. 1 illustrates the network model where users equipped with wearable devices communicate wirelessly with a remote, trusted cloud server (CS) through their mobile terminals. Additionally, a trusted registration center (RC) is responsible for securely computing and distributing unique secret creden- tials for wearable devices and mobile terminals offline via a secure channel. Notably, RC is considered to provide adequate security features, capable of resisting various known attacks. Moreover, in this network model, data is transmitted between multiple communications to a specific user, the protocol must guarantee user anonymity while ensuring unlinkability and untraceability.
Network Model. Internet TA Group RSU RSU Wireless connection Wire connection
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. The network model is illustrated in Fig. 1, which consists of four primary entities:
Network Model. The targeting network environment is a machine-to-machine IoT environment, which requires a form of data communication that involves three entities that do not require human interaction or intervention in the process of communication. In this paper, we aim at privacy-preserving architecture in IoT. Since IoT uses the internet, then we need to use smart gargets, represented by an IoT device in our protocol. The IoT device will have an active role in initiating communication by sending requests to a service server through a central server. This IoT device must have knowledge of available service providers and the services they provide. The need of privacy necessitates the use of a trusted CS that authenticates all entities in a network. We also use CS in order to save energy as per the research findings in Xxxxxxxxxx et al., they found out that energy consumption of nodes in IoT network is directly proportional to the distance between them, so using CS between IoT device and SS minimizes energy consumption [19]. Communications in IoT are about seeking for services after one undergoes authentication by CS. Therefore, there must be an entity to offer these services. SS represents this entity. The environment consists of an IoT device with sensors and a memory chip (MC), a CS, and a SS as shown in Figure 1. • System setup • IoT device registration • SS registration • Login and authenticated key agreement • Login and authenticated key agreement IoT device with MC Central server (CS) Service server (SS) The roles of each entity are: - IoT device with MC: It consists of software and hardware for generating sensing data, computing meta-information, sending reports, receiving instructions, and acting accordingly. The main role of the IoT device is to collect data and send the data to SS through CS or directly to CS so that SS can take the necessary actions in real time. IoT device comes with some sensors and an MC. The sensors collect environmental data required for the target services. MC is for secure data storage and acting like a smart card in IoT device. - CS: It consists of software and hardware for identification and credentials, it stores unique identification and secret credentials such as keys. This is a fully trusted server responsible for login and AKA between IoT device and SS. It is responsible for system setup, authentication, and key agreement of IoT device and SS. It facilitates communication and data exchanges between the IoT device and SS. - SS: I...
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 XXXx. 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 .