Abstract — In multi-hop wireless sensor networks that are characterized by many-to-one traffic patterns, problems related to energy imbalance among sensors often appear. When each node has a fixed transmission range, the amount of traffic that sensor nodes are required to forward increases dramatically as the distance to the data sink becomes smaller. Thus, sensors closest to the data sink tend to die early, leaving areas of the network completely unmonitored and causing network partitions. Alternatively, if all sensors transmit directly to the data sink, the furthest nodes from the data sink will die much more quickly than those close to the s ink. While it may seem that network lifetime could be improved by use of a more intelligent transmission power control policy that balances the energy used in each node by requiring nodes further from the data sink to transmit over longer distances (although not directly to the data sink), such a policy can only have a limited effect. In fact, this energy balancing can be achieved only at the expense of gross energy inefficiencies. In this paper, we investigate the transmission range distribution optimization problem and show where these inefficiencies exist when trying to maximize the lifetime of many-to-one wireless sensor networks. I.

We address the challenge of how to share the limited wireless channel capacity for the exchange of safety-related information in a fully deployed vehicular ad hoc network (VANET). In particular, we study the situation that arises when the number of nodes sending periodic safety messages is too high in a specific area. In order to achieve a good performance of safety-related protocols, we propose to limit the load sent to the channel using a strict fairness criterion among the nodes. A formal definition of this problem is presented in terms of a max-min optimization problem with an extra condition of per-node maximality. Furthermore, we propose FPAV, a power control algorithm which finds the optimum transmission range of every node, and formally prove its validity under idealistic conditions. Simulations are performed to visualize the result of FPAV in a couple of road situations. Finally, we discuss the issues that must be taken into account when implementing FPAV. 1

In multi-hop wireless sensor networks that are characterized by many-to-one (converge-cast) traffic patterns, problems related to energy imbalance among sensors often appear. When the transmission range is fixed for nodes throughout the network, the amount of traffic that sensors are required to forward increases dramatically as the distance to the data sink becomes smaller. Thus, sensors closest to the data sink tend to die early. Network lifetime can be improved to a limited extent by the use of a more intelligent transmission power control policy that balances the energy used in each node by requiring nodes further from the data sink to transmit over longer distances (although not directly to the data sink). Alternatively, policies such as data aggregation allow the network to operate in a more energy efficient manner. Since the deployment of an aggregator node may be significantly more expensive than the deployment of an ordinary microsensor node, there is a cost tradeoff involved in this approach. This paper provides an analysis of these policies for mitigating the sensor network hot spot problem, considering energy efficiency as well as cost efficiency. 1

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As wireless sensor networks utilize battery-operated nodes, energy efficiency is of paramount importance at all levels of system design. In order to save energy in the transfer of data from the sensor nodes to one or more sinks, the data may be routed through other nodes rather than transmitting it directly to the sink(s). In this paper, we investigate the problem of energy-efficient transmission of data over a noisy channel, focusing on the setting of physical layer parameters. We derive a metric called the energy per successfully received bit, which specifies the expected energy required to transmit a bit successfully over a particular distance given a channel noise model. By minimizing this metric, we can find, for different modulation schemes, the energyoptimal relay distance and the optimal transmit energy as a function of channel noise level and path loss exponent. These results enable network designers to select the hop distance, transmit power and/or modulation scheme that maximize network lifetime.

Optimizing physical layer parameters for wireless sensor networks

Energy management remains a critical problem in ad hoc networks since battery technology cannot keep up with rising expectations in wireless communications. Current approaches to energy conservation focus on reducing the energy consumption of the wireless interface either for a given communication task or during idling. However, these communication-time and idle-time approaches are not necessarily complementary. Therefore, we explore the interactions between the two approaches and their impact on the design of a complete solution to energy conservation. Essentially, a complete solution requires minimizing the energy spent in communication (i.e., for data and control overhead) and in idling while satisfying communication needs. This problem can be expressed as an energy-efficient network design problem, which is, not surprisingly, NP-hard. Therefore, we study several heuristic approaches. Our study shows that minimizing energy consumed in data transmissions as a primary goal does not save energy. Furthermore, jointly reducing energy consumed for both data and in idling becomes cost-prohibitive when the energy spent in control overhead is considered. Hence, we propose a two-stage approach that prioritizes idling energy consumption over energy spent for data transmissions. Due to its low control overhead, this two-stage approach provides an effective way to meet the challenge of operating the network with low energy cost. 1

Mobility management is a leading factor in personal communications services networks. Thus, it is important to verify that the mobile unit receives all services whenever moving from one place to another. This paper deals with the study and analysis of the optimal transmission power at a specific zone through a GSM cellular network. The definition of coverage area is constructed by a proper analysis of signal strength measurement. Some problematic tasks appear through the variation of geographical terrain. An accurate coverage area obtained due to an effective cellular positioning method that not requires any significant changes to the network or mobile device. 1.

hoc and sensor networks, signal processing, and information theory. iii Acknowledgements I would like to begin by thanking Professor Wendi Heinzelman, my thesis advisor and mentor for the past 6 years. Her support, encouragement, and enthusiasm motivated me to believe in myself towards my doctorate degree. It has been a privilege to work with her as a graduate student at the University of Rochester. I thank her for the all the energy and time she has spent for me, discussing everything from research to career choices, reading my papers, and guiding my research through the obstacles and setbacks. Her professional yet caring approach towards the people she works with and her passion for living the life to the fullest have truly inspired me. I owe my thanks to the members of my thesis committee, Gaurav Sharma, Azadeh Vosoughi, and Daniel Stefankovic, for their valuable feedback. My special thanks go to Bulent Tavli, who collaborated on the work in this dissertation and has been a great mentor to me over the years. I would like to thank all my colleagues at the University of Rochester in the Wireless Communications and Networking Group for their help. Specifically, I would like to thank Mark Perillo, Chris Merlin, Stanislava Soro, Lei

The energy consumption of a wireless device is modeled by including not only the energy emitted while transmitting, but also energy consumed by supporting circuitry. In particular also receiver energy consumption is taken into account. It is shown that under this model, compared to traditional routing, the energy reduction offered by network coding is significantly different from results reported in the literature based on an energy consumption model that includes the energy emitted while transmitting only. Moreover, it is illustrated that energy can be saved by increasing the transmission power. Whereas this causes individual transmissions to consume more energy, overall energy consumption can be reduced since more coding opportunities arise. 1