In wireless sensor networks, a few sensor nodes end up being vulnerable to potentially rapid depletion of the battery reserves due to either their central location or just the traffic patterns generated by the application. Traditional energy management strategies, such as those which use topology control algorithms, reduce the energy consumed at each node to the minimum necessary. In this paper, we use a different approach that balances the energy consumption at each of the nodes, thus increasing the functional lifetime of the network. We propose a new distributed dynamic topology control algorithm called Energy Balanced Topology Control (EBTC) which considers the actual energy consumed for each transmission and reception to achieve the goal of an increased functional lifetime. We analyze the algorithm's computational and communication complexity and show that it is equivalent or lower in complexity to other dynamic topology control algorithms. Using an empirical model of energy consumption, we show that the EBTC algorithm increases the lifetime of a wireless sensor network by over 40 compared to the best of previously known algorithms.
Traditional topology control algorithms use an energy management strategy in which each sensor node reduces its transmission power from its maximum value to a relatively small quantity while maintaining the connectivity of the network. Algorithms of this class include Directed Relative Neighborhood Graph (DRNG) @cite_14 , Directed Local Spanning Subgraph (DLSS) @cite_14 , @cite_11 and @cite_2 . The topology of the network is determined at the very beginning of the network's life, and remains the same throughout the network lifetime.
@cite_14 @cite_2 @cite_11
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Most existing topology control algorithms assume homogeneous wireless networks with uniform maximal transmission power, and cannot be directly applied to heterogeneous wireless networks where the maximal transmission power of each node may be different. We present two localized topology control algorithms for heterogeneous networks: Directed Relative Neighborhood Graph (DRNG) and Directed Local Spanning Subgraph (DLSS). In both algorithms, each node independently builds its neighbor set by adjusting the transmission power, and defines the network topology by using only local information. We prove that: 1) both DRNG and DLSS can preserve network connectivity; 2) the out-degree of any node in the resulting topology generated by DRNG or DLSS is bounded by a constant; and 3) DRNG and DLSS can preserve network bi-directionality. Simulation results indicate that DRNG and DLSS significantly outperform existing topology control algorithms for heterogeneous networks in several aspects. In a large and complex distributed sensor network, the individual sensor directly communicates only with its neighboring sensors. Despite being restricted to such local communications, the network itself should be self-organizing to maintain certain desirable properties, such as network connectivity and lifetime maximization. Topology control is a technique to determine the optimal transmit parameters for each sensor, such that the network topology has the best possible network performance. In this paper, we investigate a dynamic topology control scheme to improve the network lifetime for wireless sensor networks in the presence of selfish sensors. A non-cooperative game aided topology control approach is developed for designing energy-efficient and energy balanced network topologies dynamically. Each sensor in the topology control game tries to minimize its unwillingness for constructing a connected network according to its residual energy and transmit power. We prove the existence of Nash equilibrium (NE) and demonstrate that the NE is Pareto optimal as well. Specifically, a fully distributed algorithm-topology control with lifetime extension (TCLE)-is proposed to construct dynamic network topologies. The topologies derived by TCLE algorithm can improve the network lifetime significantly as compared with existing algorithms. Simulations demonstrate the efficiency of our TCLE algorithm. In this paper a new energy-aware weighted dynamic topology control (WDTC) algorithm is proposed to extend the lifetime of wireless network and balance the nodes’ energy consumption. The idea is that each node builds its local minimum spanning tree (MST) based on the energy-aware weighted graph and the network topology is adjusted accordingly. It was proved theoretically that the topology under WDTC algorithm could preserve the network connectivity and a sufficient condition for the degree of no more than 6 was also given. Simulation shows that WDTC algorithm can effectively prolong the network lifetime and has good topological features. Large-scale, self-organizing wireless sensor and mesh network deployments are being driven by recent technological developments such as The Internet of Things (IoT), Smart Grids and Smart Environment applications. Efficient use of the limited energy resources of wireless sensor network (WSN) nodes is critically important to support these advances, and application of topology control methods will have a profound impact on energy efficiency and hence battery lifetime. In this survey, we focus on the energy efficiency issue and present a comprehensive study of topology control techniques for extending the lifetime of battery powered WSNs. First, we review the significant topology control algorithms to provide insights into how energy efficiency is achieved by design. Further, these algorithms are classified according to the energy conservation approach they adopt, and evaluated by the trade-offs they offer to aid designers in selecting a technique that best suits their applications. Since the concept of "network lifetime" is widely used for assessing the algorithms' performance, we highlight various definitions of the term and discuss their merits and drawbacks. Recently, there has been growing interest in algorithms for non-planar topologies such as deployments in underwater environments or multi-level buildings. For this reason, we also include a detailed discussion of topology control algorithms that work efficiently in three dimensions. Based on the outcomes of our review, we identify a number of open research issues for achieving energy efficiency through topology control.