Why star topology is fastest?

Star networks

In a star network, each instrument and actuator is connected directly to the supervisory computer by its own signal cable. One apparent advantage of a star network is that data can be transferred if necessary using a simple serial communication protocol such as RS232. This is an industry standard protocol and so compatibility problems do not arise, but it represents old technology in which data transfer is slow. Because of this speed problem, parallel communication is usually preferred even for star networks.

Although star networks are simple in structure, the central supervisory computer node is a critical point in the system and failure of this means total failure of the whole system. When any device in the network needs to communicate with another device, a request has to be made to the central supervisory computer and all data transferred are routed through this central node. If the central node is out of operation for any reason, data communication in the network is stopped.

16.2.2 Network Topologies

There are different types of network topologies to facilitate data exchange among partners. A few basic types are discussed in the following sections. Some common network topologies are star, multidrop/bus, ring, and mesh, as shown in Fig. 16.1.

Figure 16.1. Network topologies.

Media used in a LAN is either an unshielded twisted pair or a shielded twisted pair, whereas media for a WAN is generally a shared wideband network (coaxial cable, optical fiber, microwave, etc.). Fig. 16.2 illustrates the logical schematics of LAN and WAN networks.

Figure 16.2. Local area and wide area networks.

3.6.4 RANDOM ACCESS OF A SHARED FIBER SYSTEM VIA CODE-DIVISION MULTIPLEXING

When a transport medium is used as a shared broadcast medium, such as a star network, the benefit is that every user can hear the same information. The drawback is that every user signal acts as interference to other users signals. There are several standard techniques employed to work around the interference problem. Time-division and frequency-division techniques are commonly used. Code-division multiplexing, first used in defense communications and more recently in cellular communications, is a potential candidate for lightwave networks. In this scheme [3.20], each user encodes his or her messages using a unique signature code and broadcasts the resulting signal into the medium. The receiver uses a decoder to sort out the intended user signal, treating all other user signals as noise. This random access scheme is particularly attractive when time synchronization is difficult, as in the case of a sizable all-optical network. Generally, there is a significant bandwidth expansion of the message rate (as much as the number of users sharing the medium), to accommodate many users in the network; thus, this method is less attractive for high-rate lightwave systems except when it is being used in the low-rate signaling channel for network management and diagnostics. The ability to work without time synchronization is an attractive feature for network management because of its ease of operation, particularly during network cold starts.

20.7.1 Architecture

The analyzed system is typical of an early 1990s upgrade. The downstream bandwidth extends to 550 MHz, whereas the return bandwidth is limited to 30 MHz. Although it is logically a simple single star network, several nodes are served from each large fiber-optic cable leaving the headend, and the analysis accounts for this shared risk. Figure 20.3 is a simplified diagram of a typical node. As with Figure 20.1, the tap configuration is not shown though each of the dashed lines contains taps (and sometimes splitters and/or directional couplers). Even though not shown in the Figure, the taps and branching are included in the analysis. The distribution system extending from each node passes approximately 2,000 homes, with a basic penetration rate of 70%. The total number of homes served from the headend is 150,000, split among 75 similar nodes.

Figure 20.3. Simplified schematic diagram of analyzed system.

The coaxial amplifier cascade beyond the node is limited to 4, and the entire node distribution system contains 53 amplifiers. Three power supplies are required to power all the active devices, with a maximum power supply cascade of two. The total number of series-connected taps in any one distribution leg is about 20.

The initial analysis is based on the use of a generator, but no uninterruptible power supply (UPS), at the headend and field standby power supplies with 2-hour battery capacity. It is assumed that this results in a 30-second headend outage every time the commercial power fails (three times per year) until the generator kicks in, and that the field standby power supplies have the effect of reducing the field failure rates to 50% per year in each location, with 1 hour of unprotected outage when the batteries do run down (based on dispatching a crew with portable generators as a result of customer-reported outages). It is assumed that there is no status monitoring of power supplies that would have allowed crews with portable generators to back-power supplies before the batteries expire.

System Description - Concurrent Communicating Process Specification

Figure 4 outlines the process specification of a mobile terminal receiver demodulator to be used in the MSBN satellite communication network [4]. This network allows a bi-directional data and voice communication in a star network consisting of a fixed earth station and multiple mobile stations. Two different data channels, called pilot and traffic channel, are sent over on the same transmission carrier using the CDMA technique, i.e. correlating the channels with orthogonal pseudo-noise codes enabling them to use the same frequency spectrum without interference. The former channel carries network system information (e.g. average channel bit error rate), the latter carries the actual user data. Acquisition and tracking of the transmission carrier is performed on the pilot channel in cooperation with an intelligent antenna.

Figure 4. Concurrent Process Specification of the MSBN demodulator

Triggered by an external interrupt, the read_decorr process reads periodically (at a rate of 3.4 kHz) the memory mapped decorrelator FPGA. This process sends data to the track_pilot&demod and the traffic_demod processes, which perform the tracking of the transmission carrier and the demodulation (i.e. gain, carrier phase and bit phase correction). After a 1:3 rate conversion the demodulated traffic data is formatted by the traffic_manage_data process and via the send_vocoder process transmitted to a second, memory mapped processor. In contrast, the demodulated pilot data will be further processed on the same processor.

The track_pilot&demod process not only delivers its demodulated data to the pilot_manage_data process, it steers the frequency of the NCO (numerical controlled oscillator) in the preceeding analog demodulation part through use of the on-chip serial peripheral. Moreover, together with traffic_demod process it sends information concerning carrier synchronization to the display_LEDs process and write_antenna process. The channel decoding of demodulated pilot data is carried out by the pi1ot_DSP_functions process, which operates on a 1024 element frame basis, so a multi-rate transition is present between the pilot_manage_data and this latter process. The output data of the pilot channel decoding is sent to a PC computer using the on-chip DMA engine. The setup_DMA process is triggered when output data is available from the pilot_DSP_functions process and sets up and starts the DMA process.

Asynchronously with this chain of periodic processes, the read_sys_cmd and read_antenna process control the internal parameters of the demodulation processes. They respectively perform the man-machine interface connected to the system using a memory mapped flag, allowing the user to alter the system operating parameters, and the interface with the antenna controller which is connected via an external interrupt. The former is a sporadic process, since a user will adapt the parameters only once in a while, and is allowed to have a large response time. The latter is a time-critical process: when the antenna controller looses the beam, it will signal this immediately to the demodulator, which must take special re-tracking actions.

1.7 High-Level Architecture Changes

Through a continuing process of industry consolidation, individual cable systems have grown steadily larger. This, combined with decreasing node sizes, has made it simply impractical to serve every node directly from the headend in a simple star network. Not only is the cost of running multiple, dedicated fibers to each node very high, the huge optical cables that would be required in a major metropolitan system would create major single points of failure.

As a result, operators have developed distributed networks, in which at least some of the headend functionality is moved to multiple hubs. Typically, the transport between headend and each hub is entirely optical and redundant, with route-diverse transport for all critical signals. High levels of multiplexing, whether at the baseband digital level, through wavelength division multiplexing, or a combination of both, reduces the required fiber counts between major facilities. In the largest systems, the core headend signal-processing requirements are sometimes duplicated at two locations, so the entire headend is redundant.

Bluetooth (IEEE802.15.1 and .2)

5.4.1 Introduction to Cooperative Localization

This section offers an overview of the main issues for the design of cooperative positioning algorithms.

In centralized solutions, the measures provided by the nodes in known or unknown locations are transmitted toward a unique point that processes all the data and achieves an estimate of nodes deployment in the network. This processing node may result from the network organization (e.g. a star network) or from a particular node technology. The advantage of this solution is its simple organization (e.g. in small networks) and performance if the processing node has a large computational capability. The main drawback is the amount of transmissions to the unique processing point, which is expressed in terms of consumed energy and increased traffic load in the network.

On the other hand, in distributed solutions the algorithm execution occupies all the nodes, and the coordinate result is achieved locally. Distributed solutions are more attractive than conventional centralized ones, as they avoid forwarding measurements to a central processor, thus reducing the communication energy costs. In distributed approaches, sensors exchange information only with their neighbours, and the location of the unknown nodes is obtained iteratively by successive refinements. Each node exchanges information with its neighbors and refines its estimate based on the neighbors' information, iterating the process till convergence. The advantage of this approach with respect to the centralized one is communication and energy cost saving [96]: Delivering data from each node to the central unit, processing the whole collected data, and disseminating location information back to all nodes may lead to unfeasible energy consumption. This is true especially in large networks in which many hops are needed to reach the central unit. On the other hand, distributed processing is based on (repeated) one-hop only transmissions and thus can provide significant cost reduction (clearly if the number of iterations required to obtain the desired location accuracy is not too large). A possible way to extend conventional localization methods to distributed processing is network multilateration or iterative multilateration as described in Chapter 3 [68, 89, 102].

A promising solution for distributed localization is message-passing algorithms, such as the well-known belief propagation (BP) method [50, 126]. Localization in this case is formulated as an inference problem on a graphical model that can be solved by iterative message passing [31, 101, 103, 123]. The communication network used for collaboration among the nodes is described by a connectivity graph and modeled as a Bayesian network. Each node computes a local belief of its position based on its own measurements and the marginal beliefs provided by the linked nodes. The location is estimated by refining the belief computation through iterated message exchanges. However, efficient representations of the probability densities are needed to avoid huge communication overhead for belief exchange. Neither grid-based nor Gaussian approaches are well suited to solve this problem, since the location space is too large for efficient discretization and the localization model is usually nonlinear non-Gaussian. Analytical approximations and sampled representation of the probability densities have been proposed based on the use of Gaussian mixture [50], particle filters (PFs), or Monte Carlo methods [3]. By PF, the location density can be efficiently represented by a set of nonuniform samples (particles) weighted according to their likelihood.

Distributed mobile positioning is also an open direction for research. In dynamic networks where the nodes to be localized are moving, Bayesian tracking algorithms have been used to reduce false localizations due to multipath and NLOS conditions [82]. Distributed PF algorithms have been proposed in Refs. [21, 52, 109], whereas a factor graph approach is considered in Ref. [123]. The theory of Bayesian filtering and PFs, as well as their use in positioning problems, will be addressed in Chapter 6.

One of the design key points for an effective use of cooperative approaches is the energy budget of the network. This is a crucial aspect for WSN where nodes and battery lives coincide, and it is especially true for the cooperative approach since the final budget will be affected by transmission rate and processing of data among the nodes. Transmission is usually much more expensive than data processing, and this is important in the evaluation of distributed cooperative algorithms if data traffic can be reduced.

So the key advantage of cooperative localization strategies is related to the energy budget. Message exchange among cooperating nodes has to be evaluated with respect to the final performance advantage, and in distributed solutions, local processing and multihop routing of messages could mean less network traffic (especially less long-distance traffic) with respect to centralized solutions.

The CramérRao lower bound (CRB) provides a useful means for the analysis of positioning accuracy also in cooperative networks. A CRB analysis for distributed positioning based on TOA ranging is also given in Ref. [69], for both conventional and cooperative positioning, showing the effects of clock bias on TOA measurements. In Ref. [108], the analysis of node positions error bound in TOA-based cooperative localization shows that anchors and unknown nodes are essentially equivalent in the cooperative approach: Anchors are just special nodes with infinite accuracy in the localization process.

33.5.1 Impedance characteristics

Two-winding technology does not apply. The essence of the procedure for a three-winding unit is that the leakage impedance can be represented by assuming each of the three windings to have an individual resistance and leakage reactance, and mutual impedance effects (other than those that result from these individual values) to be absent. The equivalent circuit can be represented by the star network in Figure 33.21. The leakage impedance values (in per-unit form to a common kilovolt-ampere base) are given in terms of the conventional two-winding impedances: for resistances

Figure 33.21. Equivalent circuit of a three-winding transformer

X being substituted for R to give the leakage reactances. These for the individual arms are then combined to give the effective values between any pair of terminals: e.g. R12 = R1 + R2, X23, = X2 + X3, and so on. (As the equations for the individual arms include a negative term, some particular evaluations may be found to be negative.)