Amar Prakash Azad

Mobile base stations placement and energy aware routing in wireless sensor networks

Increasing network lifetime is important in wireless sensor/ad-hoc networks. In this paper, we are concerned with algorithms to increase network lifetime and amount of data delivered during the lifetime by deploying multiple mobile base stations in the sensor network field. Specifically, we allow multiple mobile base stations to be deployed along the periphery of the sensor network field and develop algorithms to dynamically choose the locations of these base stations so as to improve network lifetime. We propose energy efficient low-complexity algorithms to determine the locations of the base stations; they include i) top-Kmax algorithm, ii) maximizing the minimum residual energy (max-min-RE) algorithm, and iii) minimizing the residual energy difference (MinDiff-RE) algorithm. We show that the proposed base stations placement algorithms provide increased network lifetimes and amount of data delivered during the network lifetime compared to single base station scenario as well as multiple static base stations scenario, and close to those obtained by solving an integer linear program (ILP) to determine the locations of the mobile base stations. We also investigate the lifetime gain when an energy aware routing protocol is employed along with multiple base stations

Optimal Activation and Transmission Control in Delay Tolerant Networks

Much research has been devoted to maximize the life time of mobile ad-hoc networks. Life time has often been defined as the time elapsed until the first node is out of battery power. In the context of static networks, this could lead to disconnectivity. In contrast, Delay Tolerant Networks (DTNs) leverage the mobility of relay nodes to compensate for lack of permanent connectivity, and thus enable communication even after some nodes deplete their stored energy. One can thus consider the lifetimes of nodes as some additional parameters that can be controlled to optimize the performance of a DTN. In this paper, we consider two ways in which the energy state of a mobile can be controlled. Both listening and transmission require energy, besides each of these has a different type of effect on the network performance. Therefore we consider a joint optimization problem consisting of: i) activation, which determines when a mobile will turn on in order to receive packets, and ii) transmission control, which regulates the beaconing. The optimal solutions are shown to be of the threshold type. The findings are validated through extensive simulations.

Analysis of an M/G/1 Queue with Repeated Inhomogeneous Vacations with Application to IEEE 802.16e Power Saving Mechanism

The goal of this paper is to establish a general approach for analyzing queueing models with repeated in homogeneous vacations. At the end of a vacation, the server goes on another vacation, possibly with a different probability distribution, if during the previous vacation there have been no arrivals. In case there have been one or more arrivals during a vacation then a busy period starts after a warm-up time. In order to get an insight on the influence of parameters on the performance, we choose to study a simple M/G/1 queue (Poisson arrivals and general independent service times) which has the advantage of being tractable analytically. The theoretical model is applied to the problem of power saving for mobile devices in which the sleep durations of a device correspond to the vacations of the server. Various system performance metrics such as the frame response time and the economy of energy are derived. A constrained optimization problem is formulated to maximize the economy of energy achieved in power save mode, with constraints as QoS conditions to be met. An illustration of the proposed methods is shown with a WiMAX system scenario to obtain design parameters for better performance. Our analysis allows us not only to optimize the system parameters for a given traffic intensity but also to propose parameters that provide the best performance under worst case conditions.

M/G/1 queue with repeated inhomogeneous vacations applied to ieee 802.16e power saving

We shall analyze and optimize the parameters of the following two types of power saving of IEEE 802.16e in presence of downlink traffic: (i) Type I classes: Under the sleep mode operation, sleep and listen windows are interleaved as long as there is no downlink traffic destined to the node. During listen windows, the node checks with the base station whether there is any buffered downlink traffic destined to it in which case it leaves the sleep mode. Each sleep window is twice the size of the previous one but it is not greater than a specified final value. The initial sleep window is Tmin, the multiplicative factor is a (a = 2 in the standard) and the final value aTmin depends on the exponent l. (ii) Type II classes: All sleep windows are of the same size as the initial window (i.e. a = 1). Sleep and listen windows are interleaved as in type I classes. To model these classes, we analyze an M/G/1 queue in which the server begins a vacation of random length each time that the system becomes empty. If the server returns from a vacation to find an empty queue, a new random vacation initiates; otherwise, the server works until the system empties (exhaustive service regime). Request arrivals are assumed to form a Poisson process with parameter λ. Let σ denote a generic random variable having the same (general) distribution as the queue service times. The queue regenerates each time it empties and the cycles are i.i.d. Each regeneration cycle consists of (see Fig. 1):

Combined optimal control of activation and transmission in delay-tolerant networks

Performance of a delay-tolerant network has strong dependence on the nodes participating in data transportation. Such networks often face several resource constraints especially related to energy. Energy is consumed not only in data transmission, but also in listening and in several signaling activities. On one hand these activities enhance the systems performance while on the other hand, they consume a significant amount of energy even when they do not involve actual node transmission. Accordingly, in order to use energy efficiently, one may have to limit not only the amount of transmissions, but also the amount of nodes that are active at each time. Therefore, we study two coupled problems: 1) the activation problem that determines when a mobile will turn on in order to receive packets; and 2) the problem of regulating the beaconing. We derive optimal energy management strategies by formulating the problem as an optimal control one, which we then explicitly solve. We also validate our findings through extensive simulations that are based on contact traces.

Enhancing lifetime of wireless sensor networks using multiple data sinks

In this paper, we address the fundamental question concerning the limits on the network lifetime in sensor networks when multiple base stations (BSs) are deployed as data sinks. Specifically, we derive upper bounds on the network lifetime when multiple BSs are employed, and obtain optimum locations of the base stations that maximise these lifetime bounds. For the case of two BSs, we jointly optimise the BS locations by maximising the lifetime bound using genetic algorithm. Joint optimisation for more number of BSs becomes prohibitively complex. Further, we propose a suboptimal approach for higher number of BSs, Individually Optimum method, where we optimise the next BS location using optimum location of previous BSs. Individually Optimum method has advantage of being attractive for solving the problem with more number of BSs at the cost of little compromised accuracy. We show that accuracy degradation is quite small for the case of three BSs.

WLC12-2: Bounds on the Lifetime of Wireless Sensor Networks Employing Multiple Data Sinks

Employing multiple base stations is an attractive approach to enhance the lifetime of wireless sensor networks. In this paper, we address the fundamental question concerning the limits on the network lifetime in sensor networks when multiple base stations are deployed as data sinks. Specifically, we derive upper bounds on the network lifetime when multiple base stations are employed, and obtain optimum locations of the base stations (BSs) that maximize these lifetime bounds. For the case of two BSs, we jointly optimize the BS locations by maximizing the lifetime bound using a genetic algorithm based optimization. Joint optimization for more number of BSs is complex. Hence, for the case of three BSs, we optimize the third BS location using the previously obtained optimum locations of the first two BSs. We also provide simulation results that validate the lifetime bounds and the optimum locations of the BSs.

Optimal sampling for state change detection with application to the control of sleep mode

This work considers systems with inactivity periods of unknown duration. We study the question of scheduling “waking up” instants in which a server can check whether the inactivity period is over. There is a cost proportional to the delay from the moment the inactivity period ends until the server discovers it, a (small) running cost while the server is away and also a cost for waking up. As an application to the problem, we consider the energy management in WiMax where inactive mobiles reduce their energy consumption by entering a sleep mode. Various standards exist which impose specific waking-up scheduling policies at wireless devices. We check these and identify optimal policies under various statistical assumptions. We show that periodic fixed vacation durations are optimal and derive the optimal period. We show that this structure does not hold for other inactivity distributions but manage to obtain some suboptimal solutions which perform strictly better than the periodic ones. We finally obtain structural properties for optimal policies for the case of arbitrary distribution of inactivity periods.

Efficient Scheduling of Sensor Activity for Information Coverage in Wireless Sensor Networks

In this paper, we are concerned with algorithms for scheduling the sensing activity of sensor nodes that are deployed to sense/measure point-targets in wireless sensor networks using information coverage. Defining a set of sensors which collectively can sense a target accurately as an information cover, we propose an algorithm to obtain disjoint set of information covers (DSIC), which achieves longer network life compared to the set of covers obtained using an exhaustive-greedy-equalized heuristic (EGEH) algorithm proposed in the literature. We also present a detailed complexity comparison between the DSIC and EGEH algorithms.

Vacation policy optimization with application to IEEE 802.16e power saving mechanism

Much research has been devoted to optimizing the power saving mechanism in wireless mobile devices. Recent advances in wireless radio technology facilitate the implementation of various possible sleep policies. One basic question that arises is: which policy performs best under a certain condition? Furthermore, what are the optimal parameters for a given policy? To answer these questions, we formulate an optimization problem, which entails cost minimization for a given parameterized policy and selection of the best policy among a class. We propose a cost function which captures the inherent tradeoff of delay and energy saving. This takes into account the cost of response time due to the extra sleep, the energy saving during the sleep, and the cost for periodic waking up (for listening). As an application, we consider IEEE 802.16es power saving mechanism. We study various practical policies and check their performance. We show that the constant duration policy is optimal for Poisson inactivity periods, but not for hyper-exponentially distributed inactivity periods. In the policy where vacations are i.i.d. exponential random variables, we derive analytically the optimal control as a function of the expected inactivity period. This result holds for general inactivity periods. Our framework allows us to compare the performance of several optimal and suboptimal practical policies with that of the IEEE 802.16e standard.