A Framework for Battery-Aware Sensor Management (original) (raw)
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Improving wireless sensor network lifetime through power aware organization
Wireless Networks, 2005
A critical aspect of applications with wireless sensor networks is network lifetime. Battery-powered sensors are usable as long as they can communicate captured data to a processing node. Sensing and communications consume energy, therefore judicious power management and scheduling can effectively extend operational time. To monitor a set of targets with known locations when ground access in the monitored area is prohibited, one solution is to deploy the sensors remotely, from an aircraft. The loss of precise sensor placement would then be compensated by a large sensor population density in the drop zone, that would improve the probability of target coverage. The data collected from the sensors is sent to a central node for processing. In this paper we propose an efficient method to extend the sensor network operational time by organizing the sensors into a maximal number of disjoint set covers that are activated successively. Only the sensors from the current active set are responsible for monitoring all targets and for transmitting the collected data, while nodes from all other sets are in a low-energy sleep mode. In this paper we address the maximum disjoint set covers problem and we design a heuristic that computes the sets. Theoretical analysis and performance evaluation results are presented to verify our approach.
Efficient energy management in sensor networks
2005
Optimizing the energy consumption in wireless sensor networks has recently become the most important performance objective. We assume the sensor network model in which sensors can interchange idle and active modes. Given monitoring regions, battery life and energy consumption rate for each of n sensors, we formulate the problem of maximizing sensor network lifetime, i.e., time during which the monitored area is (partially or fully) covered. We give the first provably good algorithm for finding monitoring schedule with the approximation ratio 1 + logn. Our contributions also include (1) an efficient data structure to represent the monitored area with at most n 2 points guaranteeing the full coverage which is superior to the previously used approach based on grid points, (2) efficient provably good centralized algorithms for sensor monitoring schedule maximizing the total lifetime for the case when a q-portion of the monitored area is required to cover, e.g., for the 90% area coverage our schedule guarantees to be at most 3.3 times shorter than the best full coverage lifetime, (3) efficient provably good approximation algorithm for sensor network lifetime problem which takes in account the (partial) monitoring and communication cost in case when the communication range is at least twice larger than monitoring range, (4) a family of efficient distributed protocols with trade-off between communication and monitoring power consumption, (5) extensive experimental study of the proposed algorithms showing significant advantage in quality, scalability and flexibility.
Efficient battery management for sensor lifetime
… and Applications Workshops, 2007, 7
It is challenging to design a sensor network if sensors are battery powered. Efficient scheduling and budgeting battery power in sensor networks has become a critical issue in network design. We investigate how energy ratio and the battery ratio, the ratio of initial battery capacities for sensors and cluster heads, affects sensor network lifetime. These results allow the network designer to specify required battery capacities which optimizing energy usage, and therefore leads to reduced total costs for the network which is extremely important in wireless sensor networks.
Coverage Aware Battery Regression Curve Node Scheduling in Wireless Sensor Networks
2016
In energy-limited wireless sensor networks (WSN’s), optimized node scheduling is an important technique for maximizing coverage and network lifetime. Existing coverage protocols present periodical, random and conditional node scheduling based on some coverage metrics. However, these scheduling techniques causes the frequent and un-necessary wake-ups of sleeping nodes which would increase the energy consumption and reduced network lifetime. In this paper, we propose Coverage Aware Battery Regression Node Scheduling (CABR) algorithm using battery discharge curve. In CABR, the coverage computation test determines that there are adequate numbers of sensing nodes in the network field while battery curve regression decides an optimal wakeup rate of sleeping nodes. The coverage computation test ensures minimum coverage redundancy within the network and optimal backoff sleep time derived from regression fit to the battery curve avoids unnecessary, random and frequent wake-ups of sleeping no...
Energy constraints are quite a general issue in maintenance of any sensor network. As the lifetime of battery is quite limited, it is difficult to continue or sustain sensors for a long period of time. In several papers using the limited energy constraint PEAS [1], SPAN [2], [3], [4],[5] several issues have been discussed so far. PEAS extends the networks' lifetime by maintaining a set of working nodes and turning off the redundant ones [1]. SPAN uses a power saving technique to reduce the consumption of energy in the network; it is distributed, randomized algorithm actually where nodes initiates decisions whether to sleep or to join a forwarding backbone as a coordinator[2]. Prime architectural question in the design of network sensors is whether microcontrollers should be used to manage I/O devices or not [3]. Here, in this paper we will overview on conserving of energy of sensors using some protocols used so far in different papers. Mainly networks sensors' characteristics should be like-small physical size and low power consumption, concurrency-intensive operation, limited physical parallelism and controller, diversity in design and usage, robust operation [3]. In [4] it is investigated that the lifetime/density tradeoff under the hypothesis of nodes being distributed uniformly at random in a given region, and being of the traffic evenly distributed across the network. Here also analyzed cell-based strategies can significantly extend network lifetime. In [5] it is highlighted that a MAC protocol (S-MAC) different from traditional wireless MACs such as IEEE 802.11, is created whose goal is energy conservation and self-configuration. In this paper, we want to look at glance or highlight the general issues used to save energy or battery lifetime and at the same time we will also discuss on some constraints of using battery power and protocols for successfully using the power to sustain at least to an application level in a network. If a sensor wants to continue working to a definite period, we shall analyze how optimally it can go on using neighbors assistances.
IJERT-Energy Efficient Coverage by Sensor Scheduling in Wireless Sensor Networks
International Journal of Engineering Research and Technology (IJERT), 2015
https://www.ijert.org/energy-efficient-coverage-by-sensor-scheduling-in-wireless-sensor-networks https://www.ijert.org/research/energy-efficient-coverage-by-sensor-scheduling-in-wireless-sensor-networks-IJERTV4IS041184.pdf In WSN, sensors work with batteries. Sensors have only limited amount of energy in batteries. It is infeasible to recharge or replace batteries in case of discharge. It is necessary to schedule the activities of the sensors in WSN for Energy Efficient Coverage (EEC). By scheduling, the optimal sensor subsets are formed at each timeslot, which covers all the points of interest in the targeted area. So, the limited energy of the sensors can be saved, which prolongs the network lifetime in WSN. In this paper, we propose an Ant Colony Based-Sensor Scheduling (ACB-SS) algorithm in order to maximize the network lifetime and the energy coverage of the sensors. The probabilistic sensor detection model is also used. The proposed algorithm is compared with the traditional ACO. Simulation results are performed to verify the effectiveness of the proposed algorithm.
Lifetime-Aware Battery Allocation for Wireless Sensor Network under Cost Constraints
IEICE Transactions on Communications, 2012
Battery-powered wireless sensor networks are prone to premature failures because some nodes deplete their batteries more rapidly than others due to workload variations, the many-to-one traffic pattern, and heterogeneous hardware. Most previous sensor network lifetime enhancement techniques focused on balancing the power distribution, assuming the usage of the identical battery. This paper proposes a novel fine-grained cost-constrained lifetime-aware battery allocation solution for sensor networks with arbitrary topologies and heterogeneous power distributions. Based on an energy-cost battery pack model and optimal node partitioning algorithm, a rapid battery pack selection heuristic is developed and its deviation from optimality is quantified. Furthermore, we investigate the impacts of the power variations on the lifetime extension by battery allocation. We prove a theorem to show that power variations of nodes are more likely to reduce the lifetime than to increase it. Experimental results indicate that the proposed technique achieves network lifetime improvements ranging from 4-13× over the uniform battery allocation, with no more than 10 battery pack levels and 2-5 orders of magnitudes speedup compared with a standard integer nonlinear program solver (INLP).
Power-Aware Sensor Coverage: An Optimal Control Approach
Sensor networks primarily have two competing objectives: they must sense as much as possible, yet last as long as possible when deployed. In this paper, we approach this problem using optimal control. We describe a model that relates each sensor's "footprint" to their power consumption and use this model to derive optimal control laws that maintain the area coverage for a specified operational lifetime. This optimal control approach is then deployed onto different sensor networks and evaluated for its ability to maintain coverage during their desired lifetime.
O.R. Applications Maximizing system lifetime in wireless sensor networks
One of the most critical issues in wireless sensor networks is represented by the limited availability of energy on network nodes; thus, making good use of energy is necessary to increase network lifetime. In this paper, we define network lifetime as the time spanning from the instant when the network starts functioning properly, i.e., satisfying the target level of cov- erage of the area of interest, until the same level of coverage cannot be guaranteed any more due to lack of energy in sen- sors. To maximize system lifetime, we propose to exploit sensor spatial redundancy by defining subsets of sensors active in different time periods, to allow sensors to save energy when inactive. Two approaches are presented to maximize network lifetime: the first one, based on column generation, must run in a centralized way, whereas the second one is based on a heuristic algorithm aiming at a distributed implementation. To assess their performance and provide guidance to network design, the tw...
Maximizing system lifetime in wireless sensor networks
European Journal of Operational Research, 2007
One of the most critical issues in wireless sensor networks is represented by the limited availability of energy on network nodes; thus, making good use of energy is necessary to increase network lifetime. In this paper, we define network lifetime as the time spanning from the instant when the network starts functioning properly, i.e., satisfying the target level of coverage of the area of interest, until the same level of coverage cannot be guaranteed any more due to lack of energy in sensors. To maximize system lifetime, we propose to exploit sensor spatial redundancy by defining subsets of sensors active in different time periods, to allow sensors to save energy when inactive. Two approaches are presented to maximize network lifetime: the first one, based on column generation, must run in a centralized way, whereas the second one is based on a heuristic algorithm aiming at a distributed implementation. To assess their performance and provide guidance to network design, the two approaches are compared by varying several network parameters. The column generation based approach typically yields better solutions, but it may be difficult to implement in practice. Nevertheless it provides both a good benchmark against which heuristics may be compared and a modeling framework which can be extended to deal with additional features, such as reliability.