Summary The main objective of this study is to develop real‐time, energy‐efficient embedded computing for machine learning based target detection. Convolutional neural network (CNN) model based detection, a machine learning technique, can provide higher detection accuracy than constant false alarm rate (CFAR) detection techniques even if it results in higher processing costs. In this study, we achieve three significant improvements for real‐time radar target detection by considering computational cost. The first improvement is to reduce the computational cost of the CNN model. The second achievement is the design of heterogeneous computing optimizations. The third of them is to support energy‐efficient computing solutions for mobile sensors. Compared to the initial CNN model, layer improvements decreased the number of operations by 8.5x. Real‐time operations are satisfied by hardware‐specific improvements like vectorization and parallelization. The embedded NVIDIA Jetson GPU and Intel MYRIAD VPU, which have power consumption of 15 Watts and 1 Watt, respectively, have been used to execute the energy‐efficient target detection. The most energy‐efficient solution is achieved by using Jetson AGX Xavier GPU with 32‐bit single precision and 15 Watts of power consumption.

We investigate k-connectivity in secure wireless sensor networks under the random pairwise key predistribution scheme with unreliable links. When wireless communication links are modeled as independent on-off channels, this amounts to analyzing a random graph model formed by intersecting a random K-out graph and an Erdös-Rényi graph. We present conditions on how to scale the parameters of this intersection model so that the resulting graph is k-connected with probability approaching to one (resp. zero) as the number of nodes gets large. The resulting zero-one law is shown to improve and sharpen the previous result on the 1-connectivity of the same model. We also provide numerical results to support our analysis.

We investigate k-connectivity in secure wireless sensor networks under the random pairwise key predistribution scheme with unreliable links; a network is said to be k-connected if it remains connected despite the failure of any of its (k - 1) nodes or links. With wireless communication links modeled as independent on-off channels, this amounts to analyzing a random graph model formed by intersecting a random K-out graph and an Erdös-Rényi graph. We present conditions on how to scale the parameters of this intersection model so that the resulting graph is k-connected with probability approaching to one (resp. zero) as the number of nodes gets large. The resulting zero-one law is shown to improve and sharpen the previous result on the 1-connectivity of the same model. We also provide numerical results to support our analysis and show that even in the finite node regime, our results can provide useful guidelines for designing sensor networks that are secure and reliable.

We study the secure and reliable connectivity of wireless sensor networks. Security is assumed to be ensured by the random pairwise key predistribution scheme of Chan, Perrig, and Song, and unreliable wireless links are represented by independent ON/OFF channels. Modeling the network by an intersection of a random K-out graph and an Erdos-Rényi graph, we present scaling conditions (on the number of nodes n, the scheme parameter K, and the probability p of a wireless channel being on), such that the resulting graph contains no nodes with a degree less than k with high probability. Results are given in the form of zero-one laws with n getting large, and are shown to improve the previous results by Yagan and Makowski on the absence of isolated nodes (i.e., absence of nodes with degree zero) in the same model. Through simulations, the established zero-one laws are also shown to hold for the property of k-connectivity, i.e., the property that graph remains connected despite the deletion of any k - 1 nodes or edges.

We study the secure and reliable connectivity of wireless sensor networks. Security is assumed to be ensured by the random pairwise key predistribution scheme of Chan, Perrig, and Song, and unreliable wireless links are represented by independent on/off channels. Modeling the network by an intersection of a random K-out graph and an Erdös-Rényi graph, we present scaling conditions (on the number of nodes, the scheme parameter K, and the probability of a wireless channel being on) such that the resulting graph contains no node with degree less than k with high probability, when the number of nodes gets large. Results are given in the form of a zero-one law and are shown to improve the previous results by Yağan and Makowski on the absence of isolated nodes (i.e., absence of nodes with degree zero). Via simulations, the established zero-one laws are shown to hold also for the property of k-connectivity; i.e., the property that graph remains connected despite the deletion of any k - 1 nodes or edges.