We propose a pure 0-1 formulation for the wireless network design problem, i.e., the problem of configuring a set of transmitters to provide service coverage to a set of receivers. In contrast with classical mixed-integer formulations, where power emissions are represented by continuous variables, we consider only a finite set of power values. This has two major advantages: it better fits the usual practice and eliminates the sources of numerical problems that heavily affect continuous models. A crucial ingredient of our approach is an effective basic formulation for the single knapsack problem representing the coverage condition of a receiver. This formulation is based on the generalized upper bound GUB cover inequalities introduced by Wolsey [Wolsey L 1990 Valid inequalities for 0-1 knapsacks and mips with generalised upper bound constraints. Discrete Appl. Math. 292--3:251--261]; and its core is an extension of the exact formulation of the GUB knapsack polytope with two GUB constraints. This special case corresponds to the very common practical situation where only one major interferer is present. We assess the effectiveness of our formulation by comprehensive computational results over realistic instances of two typical technologies, namely, WiMAX and DVB-T.

This paper was accepted by Dimitris Bertsimas, optimization.

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GUB Covers and Power-Indexed Formulations for Wireless Network Design

Transmission power control has paramount importance in the design of energy-efficient wireless sensor networks (WSNs). In this paper, we systematically explore the effects of various transmission power control strategies on WSN lifetime with an emphasis on discretization of power levels and strategies for transmission power assignment. We investigate the effects of the granularity of power levels on energy dissipation characteristics through a linear programming framework by modifying a well known and heavily utilized continuous transmission power model (HCB model). We also investigate various transmission power assignment strategies by using two sets of experimental data on Mica motes. A novel family of mathematical programming models are developed to analyze the performance of these strategies. Bandwidth requirements of the proposed transmission power assignment strategies are also investigated. Numerical analysis of our models are performed to characterize the effects of various design parameters and to comparethe relative performance of transmission power assignment strategies. Our results show that the granularity of discrete energy consumption has a profound impact on WSN lifetime, furthermore, more fine-grained control of transmission power (i.e., link level control) can extend network lifetime up to 20% in comparison to optimally-assigned network-level single transmission power.

The Impact of Transmission Power Control Strategies on Lifetime of Wireless Sensor Networks

Activity recognition systems are used in rehabilitation centres to monitor activity of daily living in order to assess daily functional status of elderly. A low-cost, non-invasive, and continuous wearable activity monitoring system can be realized by one or multiple wearable sensor nodes to form a self-managing wireless medical body area network. There are several arising challenges essential to be dealt within developing wearable activity recognition systems, namely sensor node lifetime and detection accuracy. This paper investigates existing solutions, which address the key opposing challenges. We propose a feedback controller algorithm to dynamically adapt sampling rate for maintaining the tradeoff between the energy efficiency and accuracy. The Number of samples and transmitted data packets is the main sources of energy consumption that impacts the system accuracy. To validate the accuracy of our proposed algorithm, a public wearable activity recognition data set is constructed. The data set is collected from 20 healthy subjects over 7 activity types excluding the transition states, using up to four accelerometer sensors connected with IEEE 802.15.4 enabled nodes in our setup. Our proposed feedback controller algorithm nearly doubles the activity recognition system lifetime. This, in turn improves the users’ quality of experience by reducing the demand for battery replacements while the accuracy of detection is maintained at the same level.

An Adaptive Algorithm to Improve Energy Efficiency in Wearable Activity Recognition Systems

This paper studies the problem of constructing maximum-lifetime data aggregation trees in wireless sensor networks for collecting sensor readings. This problem is known to be NP-hard. Wireless sensor networks in which transmission power levels of sensors are adjustable and heterogeneous are considered. An approximation algorithm is developed to construct a data aggregation tree whose inverse lifetime is guaranteed to be within a bound from the optimal one. Adjustable transmission power levels of the sensors introduce an additional term in the bound compared with the bound for networks in which transmission power levels of all sensors are fixed. The additional term is proportional to the difference between the maximum and minimum amounts of energy for a sensor to transmit a message using respectively its maximum and minimum transmission power levels. The proposed algorithm is further enhanced to obtain an improved version. Simulation results show that properly adjusting transmission power levels of the sensors yields higher lifetime of the network than keeping their transmission power levels at the maximum level.

An Approximation Algorithm for the Maximum-Lifetime Data Aggregation Tree Problem in Wireless Sensor Networks

Wireless sensor networks (WSNs), which are used in a wide variety of mission-critical applications, are comprised of sensor nodes with limited battery power The feasibility of such applications is highly influenced by the longevity or sustainability of these networks Improvement of energy efficiency and estimation of network lifetime are important issues in these energy constrained networks In this paper, we present a probabilistic model for estimating the network lifetime of a sensor network The traffic generation model is designed for irregular surfaces while combining the Elfes sensing model and event generation model A discrete radio model is considered in this paper for better energy efficiency Two different scenarios, such as, single-hop and multi-hop networks are analyzed with the proposed model The likelihood of achieving the desired network lifetime with the proposed model is examined Extensive simulations are done taking the topology from real-sensor network testbeds We also estimate the packet loss rates and the results also confirm the accuracy and energy efficiency of the proposed model

On Network Lifetime Expectancy With Realistic Sensing and Traffic Generation Model in Wireless Sensor Networks

Research in the wireless sensor network field has been plagued by difficulties in realistic simulations. These difficulties are often the result of non-realistic assumptions which need to be removed from the equation. Recent work in the field has identified realistic radio consumption models, signal strength estimation and that reception cost may be more than the transmission cost. In our work we combine these techniques into a single model for estimating radio power costs. We also investigate the effects of discrete power levels on transmission cost and show that transmission costs do not always increase as the transmission distance increases.

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Discrete Radio Power Level Consumption Model in Wireless Sensor Networks

Wireless sensor networks (WSNs) can be used to record and store audio data at remote and inaccessible places. However, audio data adds an additional concern to the design of the WSN; the motes need a larger amount of memory resources to be able to store the collected data. In this paper, we evaluate the ideas already presented, specifically the EnviroMic, for data collection and storage and apply a realistic power consumption model to estimate the lifetime of an audio sensor network.

/pdf/discrete-model-to-estimate-lifetime-of-a-wireless-sensor-4jqdm06if7.pdf

P. Drane

Papers

Discrete Radio Power Level Consumption Model in Wireless Sensor Networks

Discrete Model to Estimate Lifetime of a Wireless Sensor Network for Audio Storage