1. Energy-Efficient Randomized Switching for Maximizing Lifetime in Tree- Based Wireless Sensor Networks Sk Kajal Arefin Imon, Adnan Khan, Mario Di Francesco, and Sajal K. Das

2. Outlines Abstract Introduction Related work Problem formulation The algorithm Analysis Performance evaluation Conclusion

3. Abstract In most wireless sensor network (WSN) applications, data are gathered by sensor nodes and reported to a data collection point called sink. To support such pattern, a tree structure rooted at the sink is defined. The energy consumption of nodes in different paths of the data collection tree may vary largely, thus affecting the overall network lifetime. This paper addresses the problem of lifetime maximization of WSNs based on data collection trees. Specifically, we propose a novel and efficient algorithm, called Randomized Switching for Maximizing Lifetime (RaSMaLai), which randomly switches some sensor nodes from their original paths to other paths with lower load. Under appropriate settings of the operating parameters, RaSMaLai converges with a low time complexity. And its distributed version achieve a longer network lifetime

4. I. Introduction Wireless sensor networks (WSNs) are increasingly being deployed in a wide variety of applications. They operate unattended, may be randomly deployed. Sensed data are reported to a data collection point (called sink) by organizing the WSN into a data collection tree. Due to the limited energy budget of sensor nodes, energy conservation is one of the most important challenges in WSNs. Efficient duty cycling, data aggregation, and load balancing are some of the ways to reduce energy conservation. It has been shown that only a handful of nodes may determine the network lifetime, thus it is important to extend the lifetime of the network by spreading the energy consumption as uniformly as possible.

5. Our work addresses lifetime maximization of data collection trees and considers the network lifetime as the time elapsed until the first node in the network depletes all of its Energy. This definition is application- independent and is suitable for diverse scenarios. This paper specifically addresses the challenges of scalability and efficiency by proposing a novel and efficient randomized approach to load balancing in WSNs.

6. II. Related work A.Lifetime Maximization and Load Balancing Some works defined lifetime in terms of coverage, which is an application-specific characterization. Several works have exploited multipath routing for energy efficient communication in WSNs. One work proposed algorithms to construct degree-constrained trees and capacitated spanning trees in order to reduce the number of bottleneck nodes for scheduling purposes, but its main focus is to reduce the time-division multiple access (TDMA) schedule length.

7. B. Lifetime of Data Collection Trees Some works first build an initial tree, and then optimize its structure iteratively. Some works derive efficient data collection trees directly from the connectivity graph without relying on an initial routing structure. A game-theoretic approach was designed for the scenario where nodes act selfishly to maximize their own local utility.

8. This paper take a network-oriented approach that is independent of a specific application, but general enough to be applied in different scenarios. It allows more flexibility in rerouting the messages toward the sink.

9. III. Problem formulation A. System model and assumptions Assumption: Individual sensor nodes may have different initial energy budgets, and that data might be forwarded without any aggregation. A node may generate data from its own sensing activity and also receive data from other nodes.

10. B. Maximum lifetime revisited LMP is NP-complete

12. Finding an optimal bounded balanced tree is also NP-complete

13. C. Rationale Behind δ-Bounded Balanced Trees It may suffice to find an η-load balanced tree with minimum to maximize the network lifetime. Nevertheless, the value of δ provides a better characterization of how much a given tree is actually balanced. In fact, the sheer value of η only indicates the maximum path load of a tree, but provides no information about how the other path loads are distributed with respect to η.

14. D. Bounded Load-Balanced Tree Problem (B-LBTP)

15. IV. Rasmalai algorithm

18. An example

19. When considering nodes as potential parents, RaSMaLai compares their path loads rather than their individual load values. The node with the minimum load may have a node with much higher load in its path to the sink. Therefore, choosing the node with the minimum load as parent may not help balance the path loads. Since RaSMaLai allows oscillation, the difference between the maximum and the minimum path loads may actually increase from the initial tree to the following ones. However, these intermediate inefficient trees may lead to better results in subsequent iterations.

20. D-RaSMaLai: A Distributed Version

21. V. Analysis

22. VI. Performance evaluation Simulation: on MATLAB

23. It may appear that choosing a very low δ results in more balanced trees. However, this is not necessarily true since such a low value may not be realistic. In fact, it may be topologically impossible to achieve an arbitrarily low δ -bounded balanced tree for a given connectivity graph representing the sensor network. When δ is too low, RaSMaLai generates more switching, thus trying to find a more balanced tree that may not actually exist. Hence, more oscillations occur and diminish the quality of the convergence state (i.e., the lifetime of the final tree). This is not desirable, especially for the distributed version (D-RaSMaLai). d(h) denotes the difference between the maximum and the minimum path load after iterations. It eventually becomes very close to the given threshold.

24. Simulation results Lifetime and energy efficiency

25. Average number of switches and impact of δ

26. Impact of Density on the Number of Switches

27. Average Number of Packets

28. Impact of aggregation

29. Impact of Different Definitions of Lifetime We measure the lifetime of a network as the time until a given percentage of nodes runs out of battery power or becomes disconnected from the sink (due to the lack of neighbors). This definition is still application-independent. As we relax the definition of lifetime by increasing the percentage of nodes allowed to leave the network, the advantage in using D- RaSMaLai becomes more apparent.

30. Testbed Experiments

31. VII. Conclusion In this paper, we have presented RaSMaLai, an efficient randomized switching algorithm that maximizes the lifetime of data collection trees in WSNs by means of load balancing. Based on the concept of bounded balanced trees, our algorithm randomly switches the data forwarding paths of nodes. We have provided a simple yet effective switching strategy that results in a fast convergence. We have also presented a distributed implementation of our scheme that has a low overhead. An extensive study through both simulations and experiments on a real WSN testbed confirmed that our approach can significantly increase the network lifetime with a lower time complexity than the current state of the art in a wide range of operating conditions.

32. Thank you!