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9.2.1.1 Congestion Control and Quality Adaptation The Internet in its rudimentary form provides a transport network that delivers packets from one point to another. It provides a shared environment, and its stability depends on the end systems implementing appropriate congestion control algorithms. The end-to-end congestion control algorithms help to reduce packet loss and delay in the network. Unfortunately, it is not possible for streaming applications to implement end-to-end congestion control algorithms since stored multimedia applications typically have intrinsic transmission rates. Streaming applications are rate-based and typically transmit data with a near-constant rate or loosely adjust their transmission rate on long timescales since the required rate for being well behaved is not compatible with their nature. For streaming applications, congestion control takes the form of rate control that attempts to minimize the possibility of congestion by matching the rate of streaming media to the available network bandwidth. A vast majority of the Internet applications implement TCP-based congestion control that uses the additive increase, multiplicative decrease (AIMD) algorithm. Under this algorithm, the transmission rate is linearly increased until a loss of packet signals congestion and a multiplicative decrease is performed. TCP, as it is, is not appropriate for delay-sensitive applications such as streaming. To ensure fairness and ef cient utilization of network resources, rate control algorithms for streaming applications should be TCP-friendly [7 9]. This means that a streaming application sharing the same path with a TCP ow should obtain the same average throughput during a session. A number of model-based TCP-friendly rate control mechanisms [10] have been proposed for streaming applications. These mechanisms are based on the mathematical models that relate the throughput of a typical TCP connection to the network parameters [7]:

where E [P] is the average frame payload size and Ptr is the probability that at least one station is transmitting with a total of n competing stations. Therefore, Ptr=1 (1 t)n. Also, Ps=nt(1 t)n 1/Ptr is the probability that a transmission is successful given that exactly one station is transmitting with conditional probability that at least one station is transmitting, Ts is the average time the channel is sensed busy because of a successful transmission, Tc is the average time the channel is sensed busy by a station during a collision, and s is the duration of an empty slot time. Results in [3] show that RTS/CTS overhead does decrease network capacity slightly due to the extra overhead at light traf c load. However, when the number of stations increases, the basic DCF suffers severely from throughput degradation. Also, it is clear that without RTS/CTS it is more desirable to use larger window sizes to alleviate the channel contention so that throughput does not decrease signi cantly. However, with RTS/CTS, the CW does not have much effect on the saturation throughput due to its effectiveness on preventing transmission failure of large-size frames. The Markov chain has been widely used for analysis of IEEE 802.11 performance under various scenarios [5, 12, 13, 15, 18, 19]. 4.2.3 Optimal Throughput of IEEE 802.11 WLANs

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2. 3.

Select WPA-PSK. However, if there is an option to select an encryption mode called AES (Advanced Encryption Standard), use it because it offers stronger encryption than WPA. Create an encryption key (a type of password) that contains between 8 and 63 characters. These characters should be a combination of letters, numbers, and symbols (for example, N1o2S0i8D0a5M).You will be required to type it into each wireless computer that wants to access your network, so don t lose or forget the key. Either memorize it or write it down and store it in a secure location like a re safe. Save the changes by clicking either the Apply or Save Settings button located at the bottom of the screen.

Real-Time Requirements. Depending on the application, it may be necessary for nodes within the sensor network to respond quickly to detected events. For example, in an environmental monitoring application, if a re is detected, some sort of corrective action should be initiated as quickly as possible. The data collected by the wireless sensor and actor network must be timely and current when the corrective action is taken. Coordination. In a wireless sensor network, the process of data collection is coordinated by a central entity, such as a base station. In a wireless sensor and actor network, sensor sensor coordination, actor sensor coordination, and actor actor coordination are required. Sensor nodes report detected events to actor nodes, which in turn, take some appropriate action. This may include coordinating response activities with other actor nodes, providing additional instructions to nearby sensor nodes, or processing sensed event information to relay back to a central base station.

In the Script pane, select the line of code on which you want to set or remove a breakpoint. Do one of the following:

You can also set the minimum width of an element (with min-width).

In addition, the systems include geostationary satellites. There are three such satellites for EGNOS (Inmarsat 3 AOR-E, Inmarsat 3 IOR, and Artemis) and four such satellites for WAAS (Inmarsat POR, Inmarsat AOR-W, Telesat Anik F1R, and Panamsat Galaxy 15). A similar SBAS system is also planned for Galileo, with comparable features. The main difference will be that the transmission from satellites to receivers will be based on the Galileo satellites rather than on geostationary satellites. This system is called ERIS (External Regional Integrity Service).

p(x, y) . p(x)p(y)