We study the multicast capacity for hybrid wireless networks consisting of ordinary ad hoc nodes and base stations under Gaussian Channel model, which generalizes both the unicast and broadcast capacities for hybrid wireless networks. Assume that all ordinary ad hoc nodes transmit at a constant power P, and the power decays along the path, with attenuation exponent α >; 2. The data rate of a transmission is determined by the Signal to Interference plus Noise Ratio (SINR) at the receiver as Blog(1 + SINR). The ordinary ad hoc nodes are placed in the square region A(α) of area a according to a Poisson point process of intensity n/a. Then, m additional base stations (BSs) acting as the relaying communication gateways are placed regularly in the region A(a), and are connected by a high-band width wired network. Let a = n and a = 1, we construct the hybrid extended network (HEN) and hybrid dense network (HON), respectively. We choose randomly and independently ns ordinary ad hoc nodes to be the sources of multicast sessions. We assume that each multicast session has nd randomly chosen terminals. Three broad categories of multicast strategies are proposed. The first one is the hybrid strategy, i.e., the multihop scheme with BS-supported, which further consists of two types of strategies called connectivity strategy and percolation strategy, respectively. The second one is the ordinary ad hoc strategy, i.e., the multihop scheme without any BS-supported. The third one is the classical BS-based strategy under which any communication between two ordinary ad hoc nodes is relayed by some specific BSs. According to the different scenarios in terms of m, n, and nd, we select the optimal scheme from the three categories of strategies, and derive the achievable multicast throughput based on the optimal decision.

/pdf/multicast-throughput-for-hybrid-wireless-networks-under-54insswk39.pdf

Multicast Throughput for Hybrid Wireless Networks under Gaussian Channel Model

This article is concerned with the secure event-triggered (ET) control issue for interval type-2 (IT-2) fuzzy networked systems under stochastic communication (SC) protocol and random false data injection (FDI) attacks. On account of the realistic physical sensor constraint, a saturation function is utilized in the measured output. In an effort to mitigate the network burden and reduce the incidence of data collision, a novel dynamic event-triggered stochastic communication (DETSC) protocol is proposed by integrating with the major merits of the dynamic event-triggered (DET) strategy and SC protocol. Moreover, the fuzzy observer-based controller in the presence of different membership functions are constructed. Subsequently, some sufficient design conditions are put forward for satisfying the asymptotically stable performance of the augmented closedloop IT-2 fuzzy model. In addition, the gain parameters of fuzzy observer and controller can be obtained in virtue of solvable sufficient criteria without nonlinear terms. Eventually, the effectiveness of the theoretical secure fuzzy control scheme is verified by a numerical example. 

Secure Event-Triggered Control for IT-2 Fuzzy Networked Systems With Stochastic Communication Protocol and FDI Attacks

Wireless sensor network consists of densely deployed sensor nodes which have limited resources like energy, node lifetime. Data aggregation is an effective scheme to reduce redundancy of sampled data generated by sensor nodes. Homogeneous sensor networks easily adapt the data aggregation scheme because of easy synchronization of data samples; but heterogeneous sensor network have difficulty to handle data aggregation due to synchronization of different data packets produced by different sensor nodes. In order to perform efficient data aggregation in heterogeneous sensor networks our proposed method Attribute based Spanning Tree (AST) introduced the method of attribute based spanning tree construction over heterogeneous networks. Based on the characteristics of sensor nodes, logical separation of nodes formed then each group constructs Minimum Spanning Tree (MST), aggregation follows this MST to reach sink node. By adapting Kruskal's algorithm into our proposed method MST is constructed in sensor nodes. Our simulation results shows that AST is more spatially convergent and it uses shortest path cost for aggregation leads to increase node lifetime and saves energy.

Attribute Based Spanning Tree Construction for Data Aggregation in Heterogeneous Wireless Sensor Networks

A Distributed Three-Hop Routing Protocol to Increase the Capacity of Hybrid Wireless Networks

An approximate minimum mean-square error estimator for linear discrete time-varying systems: Handling Try-Once-Discard protocol

In local differential privacy (LDP), a challenging problem is the ability to generate high-dimensional data while efficiently capturing the correlation between attributes in a dataset. Existing solutions for low-dimensional data synthesis, which partition the privacy budget among all attributes, cease to be effective in high-dimensional scenarios due to the large-scale noise and communication cost caused by the high dimension. In fact, the high-dimensional characteristics not only bring challenges but also make it possible to apply some technologies to break this bottleneck. This paper presents SamPrivSyn for high-dimensional data synthesis under LDP, which is composed of a marginal sampling module and a data generation module. The marginal sampling module is used to sample from the original data to obtain two-way marginals. The sampling process is based on mutual information, which is updated iteratively to retain, as much as possible, the correlation between attributes. The data generation module is used to reconstruct the synthetic dataset from the sampled two-way marginals. Furthermore, this study conducted comparison experiments on the real-world datasets to demonstrate the effectiveness and efficiency of the proposed method, with results proving that SamPrivSyn can not only protect privacy but also retain the correlation information between the attributes. 

https://link.springer.com/content/pdf/10.1007/s11432-022-3583-x.pdf

Locally differentially private high-dimensional data synthesis

In this work, for a wireless sensor network (WSN) of n randomly placed sensors with node density \lambda \in [1,n], we study the tradeoffs between the aggregation throughput and gathering efficiency. The gathering efficiency refers to the ratio of the number of the sensors whose data have been gathered to the total number of sensors. Specifically, we design two efficient aggregation schemes, called single-hop-length (SHL) scheme and multiple-hop-length (MHL) scheme. By novelly integrating these two schemes, we theoretically prove that our protocol achieves the optimal tradeoffs, and derive the optimal aggregation throughput depending on a given threshold value (lower bound) on gathering efficiency. Particularly, we show that under the MHL scheme, for a practically important set of symmetric functions called divisible perfectly compressible (DPC) functions, including the mean, max, and various kinds of indicator functions, etc., the data from \Theta (n) sensors can be aggregated to the sink at the throughput of a constant order \Theta (1), implying that, our MHL scheme is indeed scalable.

SelectCast: Scalable Data Aggregation Scheme in Wireless Sensor Networks

The issues of both system security and safety can be dissected integrally from the perspective of behavioral \emph{appropriateness}. That is, a system is secure or safe can be judged by whether the behavior of certain agent(s) is \emph{appropriate} or not. Specifically, a so-called \emph{appropriate behavior} involves the right agent performing the right actions at the right time under certain conditions. Then, according to different levels of appropriateness and degrees of custodies, behavioral authentication can be graded into three levels, i.e., the authentication of behavioral \emph{Identity}, \emph{Conformity}, and \emph{Benignity}. In a broad sense, for the security and safety issue, behavioral authentication is not only an innovative and promising method due to its inherent advantages but also a critical and fundamental problem due to the ubiquity of behavior generation and the necessity of behavior regulation in any system. By this classification, this review provides a comprehensive examination of the background and preliminaries of behavioral authentication. It further summarizes existing research based on their respective focus areas and characteristics. The challenges confronted by current behavioral authentication methods are analyzed, and potential research directions are discussed to promote the diversified and integrated development of behavioral authentication.

https://sands.edpsciences.org/articles/sands/pdf/forth/sands20230028.pdf

Behavioral Authentication for Security and Safety

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