M Tech Dissertations

Permanent URI for this collectionhttp://ir.daiict.ac.in/handle/123456789/3

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    Traffic driven topology control for network lifetime maximization
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2012) Agrawal, Rupesh; Srivastava, Sanjay; Muthu, Rahul; Muthu, Rahul
    Energy consumption is a major concern in ad hoc wireless networks. Network life-time can be maximized by minimizing the network power consumption. Topology control is one of the most important mechanisms used for reducing network power consumption. Topology control can be achieved either by transmit power control or sleep scheduling. In transmit power control the optimization problem is to find the transmission power at which the power consumption is minimized while maintaining desired connectivity. In sleep scheduling, nodes which are redundant are switched on and off using a scheduling algorithm. One of the main problem with sleep scheduling is whenever the traffic pattern changes, path going through awake nodes may become longer while there may exist a shorter path considering some of the sleeping nodes. This will increase per packet transmission energy consumption as well as end to end delay. In this thesis we propose a traffic aware topology control protocol to solve this problem. It finds out subset of sleeping nodes to be awakened on the basis of current traffic scenarios so that total traffic weighted network cost can be minimized at global level hence saving the overall energy of the network and maximizes the network life- time. Simulation results show that,our proposed protocol is able to save a signicant amount of energy while maintaining end to end packet delay.
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    Power management of wireless sensor node by dynamic power measurement
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Kapasi, Jay; Ranjan, Prabhat
    Wireless sensor network(WSN) is a collection of spatially distributed autonomous sensor nodes, which cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants. As Wireless sensor node cannot be connected to a large source of power supply, they have to run on portable energy sources such as solar cell and batteries. Thus it is very important to minimize the use of power in each individual node of network as even a single point of power failure may prove to be a great loss of functionality. Even though minimization of power consumption may appear to be a complex problem, due to advances in technology and availability of devices with different low power states, it has become a simplified matter of just determining the power state of each device in system to minimize power consumption. The work presented here shows an implementation of a power aware wireless sensor network. Power is measured dynamically in sensor node as certain events trigger the addition of new loads.
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    Lifetime analysis of wireless sensor nodes using queuing models
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2010) Anand, Guneshwar; Srivastava, Sanjay
    Prolonging the lifetime of wireless sensor networks (WSN) is one of the key issues for wireless sensor network applications. For increasing the lifetime of network, each node should conserve its energy. Sensor nodes consume different power in different operating modes. It also consumes significant amount of power while switching from one mode to another mode. So it is important that how frequently a node is changing its mode. To address this question we have used queuing theory based control policy, which finds the optimal parameter for switching between modes. We have analysed two different control policies namely, N-policy and T-policy and their effect on the lifetime of a sensor node. In N-policy, a sensor node switches its mode only when total number of packets are N. We find an optimal value of N that minimizes the energy consumption per unit time. Similarly, in T-policy whenever system becomes empty it goes on vaccation for a fixed duration T. It changes its mode only again after T unit of time and stays in the same mode as long as there is a packet. In this case also we find the optimal value of T that minimizes the energy consumption per unit time. But this improvement in lifetime comes at the cost of longer delay and larger waiting time. We have given the expression for the latency delay. Depending on the application requirement one can tune the parameters to get the best result between the energy saving and latency delay.