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Source location privacy (SLP) protection is an emerging research topic in wireless sensor networks. Because the source location represents the valuable information of the target being monitored and tracked, it is of great practical significance to achieve a high degree of privacy of the source location. Although many studies based on phantom nodes have alleviates the protection of SLP to some extent. It is urgent to solve the problems, such as complicate the ac path between nodes, improve the centralized distribution of phantom nodes near the source nodes and reduce the network communication overhead. In this paper, protection scheme based on sector phantom routing (PSSPR) routing is proposed as a visible approach to address SLP issues. We use the coordinates of the center node V to divide sector domain, which act an important role in generating a new phantom node. The phantom nodes perform specified routing policies to ensure that they can choose various locations. In addition, the directed random route can ensure that data packets avoid the visible range when they move to the sink node hop by hop. Thus, the source location is protected. Theoretical analysis and simulation experiments show that this protocol achieves higher security of source node location with less communication overhead. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has attracted extensive attention in the information field. Its rapid development has promoted several monitoring application domains. However, the resource constraint of sensor nodes and the security of data transmission have emerged as significant issues. In this paper, an image communication system for IoT monitoring applications is exploited to solve the above-mentioned problems simultaneously. The proposed system can satisfy the requirements of sensor nodes for low computational complexity, low-energy consumption, and low storage overhead. We also present a new compressed sensing (CS) model, as well as the corresponding parallel reconstruction algorithm, which help to reduce the image encryption/decryption time. Based on chaotic systems, we integrate the quantization and diffusion operations into the system to further enhance the transmission security. The simulations are executed to demonstrate the feasibility and the effectiveness of the proposed method. Compared with the traditional CS, our numerical results indicate that the proposed model reduces $\text{413}\,{\text{ms}}$ computation time and $\text{3.13} \times {\text{10}^\text{6}}$ elements stored for large-scale images. Besides, we verify the flexibility and the diversity of choosing two submatrices for different-sized images. Experimental results also show the proposed system performs well in terms of security performance. Particularly the key space reaches ${2^{253}}$. [ABSTRACT FROM AUTHOR]

Secret sharing is an important aspect of key management in wireless ad hoc and sensor networks. In this paper, we define a new secure model of secret sharing, use the Lagrange interpolation and the bilinear cyclic groups to construct an efficient publicly verifiable secret sharing scheme on the basis of this model, and show that this scheme is provably secure against adaptively chosen secret attacks (CSAs) based on the decisional bilinear Diffie-Hellman (DBDH) problem. We find that this scheme has the following properties: (a) point-to-point secure channels are not required in both the secret distribution phase and the secret reconstruction phase; (b) it is a noninteractive secret sharing system in that the participants need not communicate with each other during subshadow verification; and (c) each participant is able to share many secrets with other participants despite holding only one shadow. [ABSTRACT FROM AUTHOR]

Visual signal detection and data transmitting are key issues in the fields of security monitoring. In this work, aiming at recognizing the object in real time, cameras are employed for image detection. Based on WiFi transmission principle, the communication path of data is therefore built up. Visual signals are delivered to the host computer and processed automatically. In order to facilitate the processing, analysis for identifying the surveillance from current collection are carried out, considering the properties of targets and bounding boxes. Specifically, to improve the working performance, detection model is developed by revising the state-of-the-art algorithm SSD (Single Shot Detector). Experimental resulting representation satisfies the detection accuracy in a quantitative form. In addition, the deploying configuration of the system is illustrated, allowing a straightforward construction of the security monitoring system. [ABSTRACT FROM AUTHOR]

Mission-critical target detection imposes stringent performance requirements for wireless sensor networks, such as high detection probabilities and low false alarm rates. Data fusion has been shown as an effective technique for improving system detection performance by enabling efficient collaboration among sensors with limited sensing capability. Due to the high cost of network deployment, it is desirable to place sensors at optimal locations to achieve maximum detection performance. However, for sensor networks employing data fusion, optimal sensor placement is a nonlinear and nonconvex optimization problem with prohibitively high computational complexity. In this paper, we present fast sensor placement algorithms based on a probabilistic data fusion model. Simulation results show that our algorithms can meet the desired detection performance with a small number of sensors while achieving up to seven-fold speedup over the optimal algorithm. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present a novel local-world model of wireless sensor networks (WSN) with two kinds of nodes: sensor nodes and sink nodes, which is different from other models with identical nodes and links. The model balances energy consumption by limiting the connectivity of sink nodes to prolong the life of the network. How the proportion of sink nodes, different energy distribution and the local-world scale would affect the topological structure and network performance are investigated. We find that, using mean-field theory, the degree distribution is obtained as an integral with respect to the proportion of sink nodes and energy distribution. We also show that, the model exhibits a mixed connectivity correlation which is greatly distinct from general networks. Moreover, from the perspective of the efficiency and the average hops for data processing, we find some suitable range of the proportion of sink nodes would make the network model have optimal performance for data processing. [ABSTRACT FROM AUTHOR]