No abstract

KEYWORDSLocation-based service, GPS, time and attendance system, sending SMS, android applications.

The problem of moving target tracking in directional sensor networks (DSNs) introduces new research challenges, including optimal selection of sensing and communication sectors of the directional sensor nodes, determination of the precise location of the target and an energy-efficient data collection mechanism. Existing solutions allow individual sensor nodes to detect the target's location through collaboration among neighboring nodes, where most of the sensors are activated and communicate with the sink. Therefore, they incur much overhead, loss of energy and reduced target tracking accuracy. In this paper, we have proposed a clustering algorithm, where distributed cluster heads coordinate their member nodes in optimizing the active sensing and communication directions of the nodes, precisely determining the target location by aggregating reported sensing data from multiple nodes and transferring the resultant location information to the sink. Thus, the proposed target tracking mechanism minimizes the sensing redundancy and maximizes the number of sleeping nodes in the network. We have also investigated the dynamic approach of activating sleeping nodes on-demand so that the moving target tracking accuracy can be enhanced while maximizing the network lifetime. We have carried out our extensive simulations in ns-3, and the results show that the proposed mechanism achieves higher performance compared to the state-of-the-art works.

In this paper, we design a power-aware distributed access point scheduling algorithm, PowerNap, to enhance power conservation of co-existing multiple access points (APs) each having multiple clients in the same wireless vicinity. This consequently addresses low channel utilization, degraded throughput, and unfairness problems of Wi-Fi networks in an energy-efficient way. PowerNap schedules transmission periods of APs according to their traffic loads to ensure fair access of the medium from their respective clients' perspective. It supports dynamic rescheduling of AP transmission periods to aid client mobility and traffic fluctuations. The scheduling also ensures that no two APs, in a shared environment, wake up their clients at exactly the same time, decreasing data packet collisions and thus increasing network throughput and energy-efficiency. PowerNap achieves decentralization by exploiting single-hop neighborhood information (e.g., traffic loads) only, and thus, it is scalable. Performance evaluations, carried out in ns-3, depict that the effectiveness of the proposed PowerNap algorithm surpasses the state-of-the-art approaches in terms of energy consumption, network throughput, and fairness.

Smartphone has implemented several power saving strategies for its Wi-Fi radio as the Wi-fi radio consumes significant amount of energy. Previously, Wi-Fi energy saving mechanisms have been designed in such a way that add unfairness to the Wi-Fi network. Prime reasons of the unfairness are retransmissions due to collisions in the channel, unnecessary waiting in high power mode and hidden terminals in the vicinity.We propose PowerNap an improved distributed energy efficient access point (AP) scheduling algorithm that addresses the above issues. PowerNap schedules the APs to scale down the overlapping among the transmission time of APs in the same vicinity. This trims out the energy consumption of Smartphone and also avoids unfairness. PowerNap schedules the APs in a weighted manner and also supports dynamic rescheduling. The proposed algorithm could be implemented via software installation in APs. Implementation of the PowerNap prototype improves Smartphone battery life upto 49-60%.