In this paper, a family of ant colony algorithms called DAACA for data aggregation has been presented which contains three phases: the initialization, packet transmission and operations on pheromones. After initialization, each node estimates the remaining energy and the amount of pheromones to compute the probabilities used for dynamically selecting the next hop. After certain rounds of transmissions, the pheromones adjustment is performed periodically, which combines the advantages of both global and local pheromones adjustment for evaporating or depositing pheromones. Four different pheromones adjustment strategies are designed to achieve the global optimal network lifetime, namely Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data aggregation algorithms, DAACA shows higher superiority on average degree of nodes, energy efficiency, prolonging the network lifetime, computation complexity and success ratio of one hop transmission. At last we analyze the characteristic of DAACA in the aspects of robustness, fault tolerance and scalability.
In @cite_32 , a localized, self organizing, robust and energy efficient data aggregation algorithm named L-PEDAP is proposed which combines LMST with RNG @cite_35 . Although it is proved to have the capability of prolonging the network lifetime, its topology construction procedure is nearly identical with that of LMST, hence, it cannot be considered as an energy-efficient algorithm.
@cite_35 @cite_32
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We propose localized, self organizing, robust, and energy-efficient data aggregation tree approaches for sensor networks, which we call Localized Power-Efficient Data Aggregation Protocols (L-PEDAPs). They are based on topologies, such as LMST and RNG, that can approximate minimum spanning tree and can be efficiently computed using only position or distance information of one-hop neighbors. The actual routing tree is constructed over these topologies. We also consider different parent selection strategies while constructing a routing tree. We compare each topology and parent selection strategy and conclude that the best among them is the shortest path strategy over LMST structure. Our solution also involves route maintenance procedures that will be executed when a sensor node fails or a new node is added to the network. The proposed solution is also adapted to consider the remaining power levels of nodes in order to increase the network lifetime. Our simulation results show that by using our power-aware localized approach, we can almost have the same performance of a centralized solution in terms of network lifetime, and close to 90 percent of an upper bound derived here. In this paper, we address the problem of energy efficient localized routing in wireless ad hoc networks. Numerous energy aware routing protocols were proposed to seek the power efficiency of routes. Among them, several geographical localized routing protocols were proposed to help making smarter routing decision using only local information and reduce the routing overhead. However, most of the proposed localized routing methods cannot theoretically guarantee the power efficiency of their routes. In this paper, we give the first localized routing algorithm, called localized energy aware restricted neighborhood routing (LEARN), which can guarantee the power efficiency of its route asymptotically almost sure. Given destination node t, an intermediate node v will only select a certain neighbor v such that pi alpha, our LEARN routing protocol will find the route for any pair of nodes asymptotically almost sure. When the transmission range rn = radicbet alnl pin for some beta < pi alpha, the LEARN routing protocol will not be able to find the route for any pair of nodes asymptotically almost sure. We also conducted simulations to study the performance of LEARN and compare it with a typical localized routing protocol (GPSR) and a global ad hoc routing protocol (DSR) We proposed a new self-organizing net based on the principle of Least Mean Square Error Reconstruction (LMSER) of an input pattern. With this principle, a local learning rule called LMSER is naturally obtained for training nets consisting of either one or several layers. We proved that for one layer with n"1 linear units, the LMSER rule lets their weights converge to rotations of the data's first n"1 principal components. These converged points are stable and corresponding to the global minimum in the Mean Square Error (MSE) landscape, which has many saddles but no local minimum. The results indirectly provided a picture about LMSER's global convergence, which is also suitable for Oja rule since we proved that the evolution direction of the Oja rule has a positive projection on that of LMSER. We have also revealed an interesting fact that slight modifications of the LMSER rule (also the Oja rule) can perform the true Principal Component Analysis (PCA) without externally designing for building asymmetrical circuits required by previous studies. 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.