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Abstract:In this paper, a dynamic cooperative MAC protocol (DDC-MAC) based on cluster network topology is proposed, which has the capability of differentiated service mechanisms and long-range communication. In DDC-MAC, heterogeneous communications are classified according to service types and quality of service (QoS) requirements, i.e., periodic communication mode (PC mode) is extracted with a QoS guarantee for high-frequency periodic information exchange based on adapt-TDMA mechanisms, while other services are classified as being in on-demand communication mode (OC mode), which includes channel contention and access mechanisms based on a multiple priority algorithm. OC mode is embedded into the adapt-TDMA process adaptively, and the two communication modes can work in parallel. Furthermore, adaptive array hybrid antenna systems and cooperative communication with optimal relay are presented, to exploit the opportunity for long-range transmission, while an adaptive channel back-off sequence is deduced, to mitigate packet collision and network congestion. Moreover, we developed an analytical framework to quantify the performance of the DDC-MAC protocol and conducted extensive simulation. Simulation results show that the proposed DDC-MAC protocol enhances network performance in diverse scenarios, and significantly improves network throughput and reduces average delay compared with other MAC protocols.

Abstract:Clustering Algorithm is a kind of key technique used to reduce energy consumption, which can increase network scalability and lifetime. The main goal of this research is concerning protocols to contain in-motion sensor nodes in large-scale topologies, and constitute a fleet wireless sensor network which can assistant in-flight localization and alignment for carrier-based aircraft. In this paper, we abstract network model according to specific conditions of F-WSN, and select performance indicators to evaluate it. A distributed multiple-weight data gathering and aggregation protocol (DMDG) is proposed, which contains all-sided active clustering scheme and realizes long-range realtime communication by tacical data link under a TDMA/CSMA channel sharing mechanism. An analytical paradigm facilitating the most appropriate choice of the next relay is proposed. Experimental results have shown that DMDG scheme can balance the energy consumption and extend the network lifetime notably, outperform LEACH, PEACH, and DEEC in terms of network lifetime and coverage rate, especially in sparse node density or anisotropic topologies.

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The paper deals with the problem of state estimation for a class of navigation-oriented carriers within navigation carrier ad-hoc networks (NC-NET): the state evolves according to a linear discretetime model subject to communication delay and packet loss. A decentralized estimation scheme is designed mainly based on the receding horizon estimation concept. Hereinto, the communication delay and packet loss are modeled in a uniform Markov process. Also, the approach through the reorganization of measurements is comprised to preprocess the delay-affect observations. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed scheme.

In this paper, the navigational state estimation problem is investigated for a class of networked spatial-navigation systems with quantization effects, mixed time-delays, and network-based observations (i.e. complementary measurements and regional estimations). A decentralized moving horizon estimation approach, featuring complementary reorganization and recursive procedure, is proposed to tackle this problem. First, through the proposed reorganized scheme, a random delayed system with complementary observations is reconstructed into an equivalent delay-free one without dimensional augment. Second, with this equivalent system, a robust moving horizon estimation scheme is presented as a uniform estimator for the navigational states. Third, for the demand of real-time estimate, the recursive form of decentralized moving horizon estimation approach is developed. Furthermore, a collective estimation is obtained through the weighted fusion of two parts, i.e. complementary measurements based estimation, and regional estimations directly from the neighbors. The convergence properties of the proposed estimator are also studied. The obtained stability condition implicitly establishes a relation between the upper bound of the estimation error and two parameters, i.e. quantization density and delay occur probability. Finally, an application example to networked unmanned aerial vehicles is presented and comparative simulations demonstrate the main features of the proposed method.

In this paper, a spatial-temporal correlation aware data collection mechanism is proposed for a event-driven sensor network in terms of the realistic requirements such as real-time data sensing and dynamic network topology. Firstly, in order to reduce the path congestion and the data transmission delay, the perceived data states are classified based on binary representation. Secondly, a low cost manner is studied to aggregate the perceived data at the representative nodes and aggregation nodes respectively based on the spatial-temporal correlation. Furthermore, the best data collection path is obtained by carrying out a particle swarm optimization (PSO). Simulation results validate that the proposed algorithm can effectively reduce the amount of data transmissions in the network event area. Besides, the proposed mechanism also has advantages in reducing the delay and energy consumption. INDEX TERMS Event-driven sensor network, spatio-temporal correlation, data fusion, data collection.