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Energy harvesting (EH) nodes, which harvest energy from the environment in order to communicate over a wireless link, promise perpetual operation of wireless networks. The primary focus of the communication system design shifts from being as energy conservative as possible to judiciously handling the randomness in the energy harvesting process in order to enhance the system performance. This engenders a significant redesign of the physical and multiple access layers of communication. In this thesis, we address the problem of maximizing the throughput of a system that consists of rate-adaptive EH nodes that transmit data to a common sink node. We consider the practical case of discrete rate adaptation in which a node selects its transmission power from a set of finitely many rates and adjusts its transmit power to meet a bit error rate (BER) constraint. When there is only one EH node in the network, the problem involves determining the rate and power at which the node should transmit as a function of its channel gain and battery state. For the system with multiple EH nodes, which node should be selected also needs to be determined. We first prove that the energy neutrality constraint, which governs the operation of an EH node, is tighter than the average power constraint. We then propose a simple rate and power adaptation scheme for a system with a single EH node and prove that its throughput approaches the optimal throughput arbitrarily closely. We then arrive at the optimal selection and rate adaptation rules for a multi-EH node system that opportunistically selects at most one node to transmit at any time. The optimal scheme is shown to significantly outperform other ad hoc selection and transmission schemes. The effect of energy overheads, such as battery storage inefficiencies and the energy required for sensing and processing, on the transmission scheme and its overall throughput is also analytically characterized. Further, we show how the time and energy overheads incurred by the opportunistic selection process itself affect the adaptation and selection rules and the overall system throughput. Insights into the scaling behavior of the average system throughput in the asymptotic regime, in which the number of nodes tend to infinity, are also obtained. We also optimize the maximum time allotted for selection, so as to maximize the overall system throughput. For systems with EH nodes or non-EH nodes, which are subject to an average power constraint, the optimal rate and power adaptation depends on a power control parameter, which hitherto has been calculated numerically. We derive novel asymptotically tight bounds and approximations for the same, when the average rate of energy harvesting is large. These new expressions are analytically insightful, computationally useful, and are also quite accurate even in the non-asymptotic regime when average rate of energy harvesting is relatively small. In summary, this work develops several useful insights into the design of selection and transmission schemes for a wireless network with rate-adaptive EH nodes.

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This thesis looks at hardware algorithms that help reduce dynamic power dissipation in video encoder applications. Computational complexity of motion estimation and the data traffic between external memory and the video processing engine are two main reasons for large power dissipation in video encoders. While motion estimation may consume 50% to 70% of total video encoder power, the power dissipated in external memory such as the DDR SDRAM can be of the order of 40% of the total system power. Reducing power dissipation in video encoders is important in order to improve battery life of mobile devices such as the smart phones and digital camcorders. We propose hardware algorithms which extract only the important features in the video data to reduce the complexity of computations, communications and storage, thereby reducing average power dissipation. We apply this concept to design hardware algorithms for optimizing motion estimation matching complexity, and reference frame storage and access from the external memory. In addition, we also develop techniques to reduce searching complexity of motion estimation. First, we explore a set of adaptive algorithms that reduce average power dissipated due to motion estimation. We propose that by taking into account the macro-block level features in the video data, the average matching complexity of motion estimation in terms of number of computations in real-time hardwired video encoders can be significantly reduced when compared against traditional hardwired implementations, that are designed to handle most demanding data sets. Current macro-block features such as pixel variance and Hadamard transform coefficients are analyzed, and are used to adapt the matching complexity. The macro-block is partitioned based on these features to obtain sub-block sums, which are used for matching operations. Thus, simple macro-blocks, without many features can be matched with much less computations compared to the macro-blocks with complex features, leading to reduction in average power dissipation. Apart from optimizing the matching operation, optimizing the search operation is a powerful way to reduce motion estimation complexity. We propose novel search optimization techniques including (1) a center-biased search order and (2) skipping unlikely search positions, both applied in the context of real time hardware implementation. The proposed search optimization techniques take into account and are compatible with the reference data access pattern from the memory as required by the hardware algorithm. We demonstrate that the matching and searching optimization techniques together achieve nearly 65% reduction in power dissipation due to motion estimation, without any significant degradation in motion estimation quality. A key to low power dissipation in video encoders is minimizing the data traffic between the external memory devices such as DDR SDRAM and the video processor. External memory power can be as high as 50% of the total power budget in a multimedia system. Other than the power dissipation in external memory, the amount of data traffic is an important parameter that has significant impact on the system cost. Large memory traffic necessitates high speed external memories, high speed on-chip interconnect, and more parallel I/Os to increase the memory throughput. This leads to higher system cost. We explore a lossy, scalar quantization based reference frame compression technique that can be used to reduce the amount of reference data traffic from external memory devices significantly. In this scheme, the quantization is adapted based on the pixel range within each block being compressed. We show that the error introduced by the scalar quantization is bounded and can be represented by smaller number of bits compared to the original pixel. The proposed reference frame compression scheme uses this property to minimize the motion compensation related traffic, thereby improving the compression scheme efficiency. The scheme maintains a fixed compression ratio, and the size of the quantization error is also kept constant. This enables easy storage and retrieval of reference data. The impact of using lossy reference on the motion estimation quality is negligible. As a result of reduction in DDR traffic, the DDR power is reduced significantly. The power dissipation due to additional hardware required for reference frame compression is very small compared to the reduction in DDR power. 24% reduction in peak DDR bandwidth and 23% net reduction in average DDR power is achieved. For video sequences with larger motion, the amount of bandwidth reduction is even higher (close to 40%) and reduction in power is close to 30%.

We address several problems that arise in a multihop wireless mesh network. First, we study the problem of joint congestion control, routing and MAC layer scheduling. We formulate the problem as an aggregate utility maximization problem and apply duality theory to decompose the problem into two sub-problems, namely, network layer congestion control and routing problem, and MAC layer scheduling problem. Given the link “prices", the source adjusts its rate based on the cost of the least-cost path to the destination, and sends traffic to the destination along the least-cost path, while link scheduling is carried out based on link prices. Optimal link scheduling for a wireless network is known to be NP-hard. We explore the use of a known centralized greedy heuristic, and develop a distributed algorithm that can schedule independent links based on local information. While the link scheduling algorithm above is for a given set of link prices, the solution to our problem depends on the sequence of price vectors generated by the price update algorithm. This leads us to study convergence issues related to the price update algorithm. Next, we develop a practical protocol which maximizes aggregate utility in a wireless mesh network. We simulate our protocol using Qualnet 4.5 and compare the result with a baseline protocol that uses IEEE 802.11 for link scheduling and AODV for routing. Our proposed protocol requires the durations of slots and subslots to be defined. We develop an approach in which given a single cell wireless mesh network using IEEE 802.11 as a reliable message delivery mechanism, one can find upper and lower bounds on the durations of slots. We employ stochastic ordering to compare distributions of random variables and using some properties of stochastic ordering along with the central limit theorem, we devise a way to compute the above mentioned bounds on the durations. In the second part, we shift our focus to model delays incurred by application packets sent over a WLAN. In this section we model the WLAN as a Random Polling System. The packet arrival process at each node i is assumed to be a stationary and independent increment random process with mean ai and second moment a(2)i . The packet lengths at node i are assumed to be i.i.d random variables Pi with finite mean and second moment. Utilizing available results, we obtain expressions for mean packet delay. Extensive simulations are conducted to verify the analytical results.