Broadcast in Ad hoc Wireless Networks with Selfish Nodes: A Bayesian Incentive Compatibility Approach

We consider the incentive compatible broadcast (ICB) problem in ad hoc wireless networks with selfish nodes. We design a Bayesian incentive compatible Broadcast (BIC-B) protocol to address this problem. VCG mechanism based schemes have been popularly used in the literature to design dominant strategy incentive compatible (DSIC) protocols for ad hoe wireless networks. VCG based mechanisms have two critical limitations: (i) the network is required to he bi-connected, (ii) the resulting protocol is not budget balanced. Our proposed BIC-B protocol overcomes these difficulties. We also prove the optimality of the proposed scheme.

Stability of scheduled message communication over degraded broadcast channels

We develop a multi-class discrete-time processor-sharing queueing model for scheduled message communication over a discrete memoryless degraded broadcast channel. The framework we consider here models both the random message arrivals and the subsequent reliable communication by suitably combining techniques from queueing theory and information theory. Requests for message transmissions are assumed to arrive according to i.i.d. arrival processes. Then, (i) we derive an outer bound to the stability region of message arrival rate vectors achievable by the class of stationary scheduling policies, (ii) we show for any message arrival rate vector that satisfies the outer bound, that there exists a stationary "state-independent" policy that results in a stable system for the corresponding message arrival processes, and (iii) under an asymptotic regime, we show that the stability region of information arrival rate vectors is the information-theoretic capacity region of a degraded broadcast channel.

Systems Analysis of Tank Irrigation: II. Delayed Start and Water Deficit

Tank irrigation systems in the semiarid regions of India are discussed in this paper. To optimize the grain yield of rice, it is essential to start the agricultural operations in the second week of July so that favorable climatic conditions will prevail during flowering and yield formation stages. Because of low inflow during the initial few weeks of the crop season, often farmers are forced to delay planting until sufficient sowing rain and inflow have occurred or to adopt deficit irrigation during this period. The delayed start affects the grain yield, but will lead to an improved irrigation efficiency. A delayed start of agricultural operations with increased irrigation efficiency leads to the energy resources becoming critical during the peak requirement week, particularly those of female labor and animal power. This necessitates augmenting these resources during weeks of their peak use, either by reorganizing the traditional methods of cultivation or by importing from outside the system.

Constellation Constrained Capacity of Two-user Broadcast Channels

Capacity region for two-user Gaussian Broadcast Channels (GBC) is well known with the optimal input being Gaussian. In this paper we explore the capacity region for GBC when the users' symbols are taken from finite complex alphabets (like M-QAM, M-PSK). When the alphabets for both the users are the same we show that rotation of one of the alphabets enlarges the capacity region. We arrive at an optimal angle of rotation by simulation. The effect of rotation on the capacity region at different SNRs is also studied using simulation results. Using the setup of Fading Broadcast Channel (FBC) given by [Li and Goldsmith, 2001], we study the ergodic capacity region with inputs from finite complex alphabets. It is seen that, using the procedure for optimum power allocation obtained in [Li and Goldsmith, 2001] for Gaussian inputs, to allocate power to symbols from finite complex alphabets, relative rotation between the alphabets does not improve the capacity region. Simulation results for a modified heuristic power allocation procedure for finite-constellation case, show that Constellation Constrained capacity region enlarges with rotation.

Optimal reservoir operation for irrigation of multiple crops using genetic algorithms

This paper presents a genetic algorithm (GA) model for obtaining an optimal operating policy and optimal crop water allocations from an irrigation reservoir. The objective is to maximize the sum of the relative yields from all crops in the irrigated area. The model takes into account reservoir inflow, rainfall on the irrigated area, intraseasonal competition for water among multiple crops, the soil moisture dynamics in each cropped area, the heterogeneous nature of soils. and crop response to the level of irrigation applied. The model is applied to the Malaprabha single-purpose irrigation reservoir in Karnataka State, India. The optimal operating policy obtained using the GA is similar to that obtained by linear programming. This model can be used for optimal utilization of the available water resources of any reservoir system to obtain maximum benefits.

Artificial neural networks and multicriterion analysis for sustainable irrigation planning

The objective of the present paper is to select the best compromise irrigation planning strategy for the case study of Jayakwadi irrigation project, Maharashtra, India. Four-phase methodology is employed. In phase 1, separate linear programming (LP) models are formulated for the three objectives, namely. net economic benefits, agricultural production and labour employment. In phase 2, nondominated (compromise) irrigation planning strategies are generated using the constraint method of multiobjective optimisation. In phase 3, Kohonen neural networks (KNN) based classification algorithm is employed to sort nondominated irrigation planning strategies into smaller groups. In phase 4, multicriterion analysis (MCA) technique, namely, Compromise Programming is applied to rank strategies obtained from phase 3. It is concluded that the above integrated methodology is effective for modeling multiobjective irrigation planning problems and the present approach can be extended to situations where number of irrigation planning strategies are even large in number. (c) 2004 Elsevier Ltd. All rights reserved.

An integrated model for optimal reservoir operation for irrigation of multiple crops

An integrated model is developed, based on seasonal inputs of reservoir inflow and rainfall in the irrigated area, to determine the optimal reservoir release policies and irrigation allocations to multiple crops. The model is conceptually made up of two modules, Module 1 is an intraseasonal allocation model to maximize the sum of relative yields of all crops, for a given state of the system, using linear programming (LP). The module takes into account reservoir storage continuity, soil moisture balance, and crop root growth with time. Module 2 is a seasonal allocation model to derive the steady state reservoir operating policy using stochastic dynamic programming (SDP). Reservoir storage, seasonal inflow, and seasonal rainfall are the state variables in the SDP. The objective in SDP is to maximize the expected sum of relative yields of all crops in a year. The results of module 1 and the transition probabilities of seasonal inflow and rainfall form the input for module 2. The use of seasonal inputs coupled with the LP-SDP solution strategy in the present formulation facilitates in relaxing the limitations of an earlier study, while affecting additional improvements. The model is applied to an existing reservoir in Karnataka State, India.

A token-based distributed algorithm for total order atomic broadcast

In this paper, we propose a new token-based distributed algorithm for total order atomic broadcast. We have shown that the proposed algorithm requires lesser number of messages compared to the algorithm where broadcast servers use unicasting to send messages to other broadcast servers. The traditional method of broadcasting requires 3(N - 1) messages to broadcast an application message, where N is the number of broadcast servers present in the system. In this algorithm, the maximum number of token messages required to broadcast an application message is 2N. For a heavily loaded system, the average number of token messages required to broadcast an application message reduces to 2, which is a substantial improvement over the traditional broadcasting approach.

Modeling reservoir irrigation in uncertain hydrologic environment

In a detailed model for reservoir irrigation taking into account the soil moisture dynamics in the root zone of the crops, the data set for reservoir inflow and rainfall in the command will usually be of sufficient length to enable their variations to be described by probability distributions. However, the potential evapotranspiration of the crop itself depends on the characteristics of the crop and the reference evaporation, the quantification of both being associated with a high degree of uncertainty. The main purpose of this paper is to propose a mathematical programming model to determine the annual relative yield of crops and to determine its reliability, for a single reservoir meant for irrigation of multiple crops, incorporating variations in inflow, rainfall in the command area, and crop consumptive use. The inflow to the reservoir and rainfall in the reservoir command area are treated as random variables, whereas potential evapotranspiration is modeled as a fuzzy set. The model's application is illustrated with reference to an existing single-reservoir system in Southern India.

Precoder Designs for MIMO Broadcast Channels with Imperfect CSI

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Integrated Reservoir Operation Modeling for Irrigation Systems - A Multi-Objective Approach

An integrated reservoir operation model is presented for developing effective operational policies for irrigation water management. In arid and semi-arid climates, owing to dynamic changes in the hydroclimatic conditions within a season, the fixed cropping pattern with conventional operating policies, may have considerable impact on the performance of the irrigation system and may affect the economics of the farming community. For optimal allocation of irrigation water in a season, development of effective mathematical models may guide the water managers in proper decision making and consequently help in reducing the adverse effects of water shortage and crop failure problems. This paper presents a multi-objective integrated reservoir operation model for multi-crop irrigation system. To solve the multi-objective model, a recent swarm intelligence technique, namely elitist-mutated multi-objective particle swarm optimisation (EM-MOPSO) has been used and applied to a case study in India. The method evolves effective strategies for irrigation crop planning and operation policies for a reservoir system, and thereby helps farming community in improving crop benefits and water resource usage in the reservoir command area.

Broadcast in Adhoc Wireless Networks with Selfish Nodes: A Bayesian Incentive Compatibility Approach

We consider the incentive compatible broadcast (ICB) problem in ad hoc wireless networks with selfish nodes. We design a Bayesian incentive compatible broadcast (BIC-B) protocol to address this problem. VCG mechanism based schemes have been popularly used in the literature to design dominant strategy incentive compatible (DSIC) protocols for ad hoc wireless networks. VCG based mechanisms have two critical limitations: (i) the network is required to be bi-connected, (ii) the resulting protocol is not budget balanced. Our proposed BIC-B protocol overcomes these difficulties. We also prove the optimality of the proposed scheme.

Analog/RF Built-in-Self-Test Subsystem for a Mobile Broadcast Video Receiver in 65-nm CMOS

A built-in-self-test (BIST) subsystem embedded in a 65-nm mobile broadcast video receiver is described. The subsystem is designed to perform analog and RF measurements at multiple internal nodes of the receiver. It uses a distributed network of CMOS sensors and a low bandwidth, 12-bit A/D converter to perform the measurements with a serial bus interface enabling a digital transfer of measured data to automatic test equipment (ATE). A perturbation/correlation based BIST method is described, which makes pass/fail determination on parts, resulting in significant test time and cost reduction.