Typical mobile agent networks, such as multi-unmanned aerial vehicle (UAV) systems, are constrained by limited resources: energy, computing power, memory and communication bandwidth. In particular, limited energy affects system performance directly, such as system lifetime. Moreover, it has been demonstrated experimentally in the wireless sensor network literature that the total energy consumption is often dominated by the communication cost, i.e., the computational and the sensing energy are small compared to the communication energy consumption. For this reason, the lifetime of the network can be extended significantly by minimizing the communication distance as well as the amount of communication data, at the expense of increasing computational cost. In this paper, we aim at attaining an optimal tradeoff between the communication and the computational energy. Specifically, we propose a mixed-integer optimization formulation for a multihop hierarchical clustering-based self-organizing UAV network incorporating data aggregation, to obtain an energy-efficient information routing scheme. The proposed framework is tested on two applications, namely target tracking and area mapping. Based on simulation results, our method can significantly save energy compared to a baseline strategy, where there is no data aggregation and clustering scheme.
Similar to our Multi-UAV information gathering problem, the goal of a Wireless Sensor Network (WSN) is to maximize network lifetime while delivering raw data to the sink (base station) @cite_7 . In order to maximize the lifetime of a network, data aggregation techniques have been proposed for WSNs where some computations are performed within the node to reduce the communication cost. It has been shown that by using a sensor node as a communication relay aggregator, an energy-efficient communication strategy can be obtained @cite_6 @cite_34 . Data correlations between different sensor nodes can be exploited to minimize the number of sensors sending the data to the base station @cite_19 . A compressed sensing technique to reduce the data volume to be transmitted was proposed in @cite_8 .
@cite_7 @cite_8 @cite_6 @cite_19 @cite_34
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We derive a general formula for the lifetime-of wireless sensor networks which holds independently of the underlying network model including network architecture and protocol, data collection initiation, lifetime definition, channel fading characteristics, and energy consumption model. This formula identifies two key parameters at the physical layer that affect the network lifetime: the channel state and the residual energy of sensors. As a result, it provides not only a gauge for performance evaluation of sensor networks but also a guideline for the design of network protocols. Based on this formula, we propose a medium access control protocol that exploits both the channel state information and the residual energy information of individual sensors. Referred to as the max-min approach, this protocol maximizes the minimum residual energy across the network in each data collection. A key challenge in ad-hoc, data-gathering, wireless sensor networks is achieving a lifetime of several years using nodes that carry merely hundreds of joules of stored energy. We explore the fundamental limits of energy-efficient collaborative data-gathering by deriving upper bounds on the lifetime of increasingly sophisticated sensor networks. In this article, we design techniques that exploit data correlations in sensor data to minimize communication costs (and hence, energy costs) incurred during data gathering in a sensor network. Our proposed approach is to select a small subset of sensor nodes that may be sufficient to reconstruct data for the entire sensor network. Then, during data gathering only the selected sensors need to be involved in communication. The selected set of sensors must also be connected, since they need to relay data to the data-gathering node. We define the problem of selecting such a set of sensors as the connected correlation-dominating set problem, and formulate it in terms of an appropriately defined correlation structure that captures general data correlations in a sensor network. We develop a set of energy-efficient distributed algorithms and competitive centralized heuristics to select a connected correlation-dominating set of small size. The designed distributed algorithms can be implemented in an asynchronous communication model, and can tolerate message losses. We also design an exponential (but nonexhaustive) centralized approximation algorithm that returns a solution within O(log n) of the optimal size. Based on the approximation algorithm, we design a class of centralized heuristics that are empirically shown to return near-optimal solutions. Simulation results over randomly generated sensor networks with both artificially and naturally generated data sets demonstrate the efficiency of the designed algorithms and the viability of our technique—even in dynamic conditions. The lifetime of a network, dependent upon battery capacity and energy consumption efficiency, plays an important role in wireless sensor networks. In this paper, the reduction of energy consumption is investigated by the considerations of the effects of cluster-based data aggregation routing in conjunction with well-scheduled and dynamic power range. Modeling power consumption as a mixed integer- and nonlinear-programming problem, the objective function provides the basis by which the total energy consumption is minimized. The problem of cluster construction and data aggregation trees were proven NP- complete. Thus, we proposed heuristics for cluster construction (i.e., AvgEnergy and MaxNumS) and data aggregation routing (i.e., CDAR). We also propose a duty cycle scheduling scheme and dynamic radius to ensure the total energy consumption is minimized. The experimental results show that the heuristic proposed above outperforms other modified existing algorithms.