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Background and Objectives: Routing and data aggregation are two important techniques for reducing communication cost of wireless sensor networks (WSNs). To minimize communication cost, routing methods can be merged with data aggregation techniques. Compressive sensing (CS) is one of the effective techniques for aggregating network data, which can reduce the cost of communication by reducing the amount of routed data to the sink. Spatiotemporal CS (STCS), with the use of spatial and temporal correlation of sensor readings, can increase the compression rate in WSNs, thereby reducing the cost of communication. Methods: In this paper, a new method of STCS technique based on the geographic adaptive fidelity (GAF) protocol is proposed which can effectively reduce the communication cost and energy consumption in WSNs. In the proposed method, temporal data is obtained from random selection of temporal readings of cluster head (CH) sensors located in virtual cells in the clustered sensors' area and spatial data will be formed from the data readings of CHs located on the routes. Accordingly, a new structure of sensing matrix will be created. Results: The results of proposed method show that the proposed method as compared to the method proposed in ‎[29], which is the most similar method in the literature, reduces energy consumption in the range of 22% to 43% in various scenarios which were implemented based on the number of required measurements at the sink (M) and the number of measurements in the routes (mr). Conclusion: In the proposed method, based on spatio-temporal CS (STCS), a new structure of sensing matrix is created that can increase the compression rate, thereby reducing the communication cost in the WSNs.======================================================================================================Copyrights©2018 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.======================================================================================================

In this paper, a new combination of Minimum Description Length (MDL) or Eigenvalue Gradient Method (EGM), Joint Approximate Diagonalization of Eigenmatrices (JADE) and Modified Forward-Backward Linear Prediction (MFBLP) algorithms is proposed which determines the number of non-coherent source groups and estimates the Direction Of Arrivals (DOAs) of coherent signals in each group. First, the MDL/EGM algorithm determines the number of non-coherent signal groups, and then the JADE algorithm estimates the generalized steering vectors considering white/colored Gaussian noise. Finally, the MFBLP algorithm is applied to estimate DOAs of coherent signals in each group. A new Normalized Root Mean Square Error (NRMSE) is also proposed that introduces a more realistic metric to compare the performance of DOA estimation methods. Simulation results show that the proposed algorithm can resolve sources regardless of QAM modulation size. In addition, simulations in white/colored Gaussian noises show that the proposed algorithm outperforms the JADE-MUSIC algorithm in a wide range of Signal to Noise Ratios (SNRs).

In this paper, in order to modify and improve the handoff process, which contains initiation and execution phases, three new prioritization schemes have been investigated. These new schemes prioritize handoff requests considering both traffic and propagation conditions. According to suggested schemes, priority of different candidate base-stations is based on three measurements in a joint state. These measurements are received power at mobile unit, power variation rates, and the number of free traffic channels of adjacent base-stations. Simulation results show that considering similar conditions, i.e. equal number of handoffs, lower call dropping (outage) and smaller delay distance will be achieved by new prioritization schemes rather than previous works. It means access to lower forced termination probability and interference and hence better QoS (Quality of Service). Finally, due to the best trade offs between propagation and traffic criteria, simulation results introduce SPBPS3 as optimum scheme.

Remote sensing using small spacecraft arising from multi-objective economic activity problems is getting more and more developed. These satellites require very accurate pointing to specific locations of interest, with high reliability and small latency. The space borne imaging systems always attempted to achieve the highest ground resolution possible with the available technology at the given time. Also mass, volume and power consumption of the spacecrafts and instruments followed the trend to miniaturization. But the most promising prospects for high resolution imaging with remote sensing satellites are connected with passive optical systems, especially push broom systems. In this paper optical system design process is described and different parameters of this process such as MTF, SNR, FOV, aperture diameter, stability and pointing, scanning schemes, detector selection, and target radiance are simulated and analyzed.