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. 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. Ui: A user who receives a smart card from GWN and uses it to access multiple servers. After a successful authentication process with Sj, the user is given access to mobile services. Furthermore, the user’s smart card is not tamper-resistant and can be lost or stolen by an adversary. • Sj: A sensor node that collects information and provides services to users who successfully complete the authentication process. Sensors are not equipped with tamper-resistant hardware due to cost constraints, thus an adversary will know all of the keying materials stored in that sensor’s memory. • GWN: A trusted third-party that generates system parameters. It provides smart cards to users and pre-shared keys to sensors. GWN is assumed to be trustworthy and never compromised by an adversary.
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. 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. 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. 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. 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 .
Network Model. As illustrated in Fig 4.1, we consider two-tier HetNet consisting of macrocell and cognitive femtocells. There are three types of FAPs, namely Open, Hybrid and Closed Access. Rayleigh Fading Channel is assumed in the model due to its tractability and simplicity. The MBSs are spatially distributed according to the homogeneous PPP Φb = bi; i = 1, 2, 3, .. with intensity λm where bi is the location of ith MBS. The FAPs are spatially distributed according to an independent homogeneous PPP Φa = ai; i = 1, 2, 3, ... with intensity λf where ai denotes the location of the ith FAP. The UEs are spatially distributed according to an independent PPP Φu = ui; i = 1, 2, 3, ... with intensity λu [28]. In this paper, all the analyses consider Open Access mode with unlicensed UEs where particular UE can be associated with MBS or FAP by measuring the received power of the serving BSs. [88].
Network Model. There are two parts in our network model, namely KDC and General Node (GN). In MEC, the KDC can be regarded as the SCM, and the GN can be regarded as the SCC. All GNs are equal, and there is no hierarchy or subordinate relationship. In addition, all GNs usually have certain computing and storage resources, and they can join or leave a group at any time. All KDCs are wire connected, and each KDC can manage one or more GNs. The network model used in our protocol is shown in Figure 1. In our protocol, multiple KDCs form a blockchain network. In order to improve the efficiency of new block generation, we consider using a more efficient Proof-of-Stake (PoS) [27] or Delegated Proof-of-Stake (DPoS) [28] consensus mechanism, such as ouroboros, a provably secure PoS protocol [27], instead of using a Proof-of-Work (PoW) mechanism [29]. According to this consensus mechanism, at regular intervals, all KDCs will regenerate new blocks including groups whose GNs have changed during this period. In each block, in addition to the hash value of the previous block, the timestamp, and Xxxxxx tree root, it also contains the identifier of these groups, the identity list of all GNs in these group, and the related parameters of all GNs in these group. All GNs only have the permission to read information from the blockchain. In addition, there may be multiple different groups, so after the GN enters the network, it first needs to select a group to join.
Figure 1. The network model used by our protocol. Before the GN joins the network, it can submit its identity to a KDC closest to it. The KDC will calculate a pair of keys based on the identity and distribute it to the GN. After that, all KDCs will generate a new block containing the identity and related information of the newly added GN through the consensus mechanism. The detailed operation of KDCs is described in the next section. Note that not all KDCs participate in group key agreement.
