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Design and Managing of Mac Protocol Using Wireless Sensor Network

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We design a unified MAC and routing framework to exploit the temporal and frequency resources to significantly improve the throughput of wireless sensor networks. There are two things mainly concentrate of Network infrastructure and the MAC scheme must be establish the communication link between the sensor nodes and share the communication medium fairly and efficiently. First consider time scheduling on a single frequency channel with the aim of minimizing the number of time slots required (schedule length) to complete a converge cast. Next, we combine scheduling with transmission power control to mitigate the effects of interference, and show that while power control helps in reducing the schedule length under a single frequency, scheduling transmissions using multiple frequencies is more efficient. An efficient MAC protocol for low-traffic delaytolerant wireless sensor networks. We defined a new routing metric that considers the difference in interface speeds, the delay due to retransmission, the impact of interface constraint, and the delay due to node competition for a limited number of channels. Simulations in NS2 (network simulator). In this paper, we discuss about the Energy Efficiency of the MAC (EEMAC) and improvising the MAC protocol (IMAC), based on simulation results we show that IMAC has smaller energy and delay compared to EEMAC.
Wireless sensor networks are collection of sensor nodes connected via wireless LAN links. The information gathered at sensor node is propagates in the form of radio signal to control room via multi hop communication. In the networks, many sensors where lying in same channel to pass message, so as well as minimize the power efficient and delay for sensor networks. An Efficient Medium Access (MAC) protocol is critical for the performance of a Wireless Sensor Networks (WSN), especially in terms of energy consumption. IMAC is a Time Division Multiple Access (TDMA) scheme that extents the common single hop TDMA to a multi hop sensor network, Using a high-powered base station to synchronize the nodes and to schedule their transmission and receptions. The protocol first enables the base station together with topology (connectivity) information. A scheduling algorithm, then determines when each node should receive data and the access point announces the transmission schedule to the other nodes. The performance of EEMAC is compared to existing protocols based on simulations in NS2 (network simulator). In this paper, we discuss about the Energy Efficiency of the MAC (EEMAC) and improvising the MAC protocol (IMAC), based on simulation results we show that IMAC has smaller energy and delay compared to EEMAC
The proposed power aware multicast identifies the characteristics of the proposed routing algorithm. It evaluates its performance under various network conditions. Each plot presented illustrates the average of 10 independent runs that are initiated with different random seeds. For the optimization algorithm, the link cost function is selected, and defined. In all simulations, the period of link state measurements is selected as one second. As a consequence, source nodes can update their rates at best approximately every two seconds since it require two measurements for estimating the gradient vector according to the modified power algorithm. For simplicity set the rate of redundancy due to source coding, to zero. The optimal values suggest that the complexity of having smart routers that are able to forward packets onto each branch at a different rate offers only a marginal benefit in this scenario. However, it is hard to draw any further conclusions as this result may depend on the specific topology and sourcedestination pair selections. Also, our algorithm does better than tradition power algorithm as a consequence of the availability of multiple trees to distribute the traffic load. However, while under network topology model the algorithm is able to minimize the cost to a certain level, it cannot eliminate the packet losses and has a much higher overall cost compared to traditional ones. The reason behind this result is the lack of multicast functionality. Since we cannot create multicast trees, the only savings due to multicasting occurs between the sources and overlay nodes. Once multicast packets reach the overlays, overlay nodes need to create independent unicast sessions for each destination ignoring the multicast nature of the traffic, and this creates a high level of link stress as multiple copies of the same packets are generated. One important observation is that the algorithm is able to converge faster in network model NMIIb than all other models. This is due to the fact that, it is only need to optimize the overlay rates instead of individual receiver rates. Hence, the number of parameters to be calculated is much smaller than the other two cases.


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