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A joint optimization scheme of both time allocation and mode selection is proposed for defending smart jammer in internet of things (IoT). Specifically, we employ multiple modes to deal with the potential eavesdropping and jamming attacks in wireless sensor networks (WSN), respectively as wireless-powered relaying (WPR), ambient backscatter relaying (ABR), friendly jamming (FJ) modes. Assuming a specific frame structure, time allocation between energy harvesting and transmission duration is firstly optimized for three modes based on transmission and security data maximization. In addition, considering that a single mode may fail to guarantee the performance demand due to the uncertainty of network conditions and adversary attacks, mode selection is carried out for adapting characteristics of different modes. Moreover, according to the available knowledge of jamming detection, for enhancing the robustness in secure transmission, the process of mode selection is further divided into two phases. Specifically, game theory is also utilized to achieve the Nash equilibrium for mode selection. Finally, numerical results verify the effects and exhibit the advantages of the proposed scheme.

In this paper, the adoption of an intelligent reflecting surface (IRS) for multiple single-antenna source terminal (ST)-DT pairs in two-hop networks is investigated. Different from the previous studies on IRS that merely focused on tuning the reflection coefficient of all the reflection elements at IRS, in this paper, we consider the true reflection resource management. Specifically, the true reflection resource management can be realized via trigger module selection based on our proposed IRS architecture that all the reflection elements are partially controlled by multiple parallel switches of controller. As the number of reflection elements increases, the true reflection resource management will become urgently needed in this context, which is due to the non-ignorable energy consumption. Moreover, the proposed modular architecture of IRS is designed to make the reflection elements part independent and controllable. As such, our goal is to maximize the minimum signal-to-interference-plus-noise ratio (SINR) at DTs via a joint trigger module subset selection, transmit power allocation of STs, and the corresponding passive beamforming of the trigger modules, subject to per ST power budgets and module size constraint. Whereas this problem is NP-hard due to the module size constraint, to deal with it, we transform the hard module size constraint into the group sparse constraint by introducing the mixed row block norm, which yields a suitable semidefinite relaxation. Additionally, the parallel alternating direction method of multipliers (PADMM) is proposed to identify the trigger module subset, and then subsequently the transmit power allocation and passive beamforming can be obtained by solving the original minimum SINR maximization problem without the group sparse constraint via partial linearization for generalized fractional programs., Comment: Please feel free to contact us for questions or remarks

A novel social-tie motivated relay selection scheme is proposed for dynamic device-to-device (D2D) communications overlaying cellular networks. Using the non-edge cellular users to forward data, the proposed relay selection scheme can improve the transmission performance of the cell-edge users as an explicit benefit of D2D relays. Meanwhile, the effects of both the physical layer and social layer on the relay selection are jointly considered, where social ties are regarded as not only the motivation of relay services, but also the metric of security performance. Moreover, a generalized satisfaction index is introduced for designing a uniform quality of experience (QoE) criterion that can map different quality of service (QoS) metrics such as rate, throughput, delay, into a unified metric, and hence, is beneficial for the tradeoff between QoE and resource efficiency of relay selection. Furthermore, a dynamic optimization process is constructed for analyzing the effects of both the mobility of users and the randomness of channel on the relay selection, with the aid of the Lyapunov framework and drift-plus-penalty (DPP) algorithm. Finally, numerical results validate the effects of the proposed relay selection scheme.

Internet of Things (IoT) as a prospective platform to develop mobile applications, is facing with significant challenges posed by the tension between resource-constrained mobile smart devices and low-latency demanding applications. Recently, mobile edge computing (MEC) is emerging as a cornerstone technology to address such challenges in IoT. In this paper, by leveraging social ties in human social networks, we investigate the optimal dynamic computation offloading mode selection to jointly minimize the total tasks’ execution latency and the mobile smart devices’ energy consumption in MEC-aided low-latency IoT. Different from the previous studies, which mostly focus on how to exploit social tie structure among mobile smart device users to construct the permutation of all the feasible modes, we consider dynamic computation offloading mode selection with social awareness-aided network resource assignment, involving both the computing resources and transmit power from heterogeneous mobile smart devices. On the one hand, we formulate the dynamic computation offloading mode selection into the infinite-horizon time-average renewal-reward problems subject to time average latency constraints on a collection of penalty processes. On the other hand, an efficient solution is also developed, which elaborates on a Lyapunov optimization-based approach, i.e., drift-plus-penalty (DPP) algorithm. Numerical simulations are provided to validate the theoretical analysis and assess the performance of the proposed dynamic social-aware computation offloading mode selection method considering different configurations of the IoT network parameters.

In this paper, we investigate the optimal dynamic social-aware peer selection with spectrum-power trading to maximize the average sum energy efficiency (EE) of cellular users (CUs) for uplink transmission for an orthogonal frequency division multiple access cellular network with device-to-device (D2D) communications. Different from the previous studies, which mostly focus on how to exploit social ties in human social networks to construct the permutation of all the feasible peers, we consider dynamic peer selection with social awareness-aided spectrum-power trading in D2D overlaying communications. Specifically, the amount of transmit power from the D2D transmitters to relay the CUs for uplink transmission is determined by their social trust levels. Likewise, the D2D transmitters can gain the corresponding amount of spectrum from the CUs for D2D pair link communications, which can be regarded as the compensation of the power consumption for relaying CUs. We formulate the dynamic peer selection problems with social awareness-aided spectrum-power trading in cooperative D2D communications into the infinite-horizon time-average renewal-reward problems subject to time average constraints on a collection of penalty processes. And the Lyapunov optimization concepts-based drift-plus-penalty algorithms are proposed to solve them. The simulation results demonstrate the effectiveness of the proposed dynamic peer selection algorithms. And further performance comparison indicates that the proposed dynamic peer selection algorithms not only maximize the average EE of CUs but also guarantee higher privacy protection.

We investigate the energy-efficient power allocation for downlink transmission in orthogonal frequency division multiple access-based long term evolution systems. Aiming at realizing on-demand power allocation in cellular networks, we explore the available coordination between the base station and multiple users, and propose a new performance merit, namely, the demand side coordination energy efficiency (DSC-EE), which captures the system normalized EE and the demand side information. The proposed DSC-EE is designed to exploit individual disparities from both the entire system and the individual own expected utility perspectives. Our goal is to maximize the DSC-EE of the system via power allocation with a constraint on the maximum transmit power. Specifically, the objective function of DSC-EE maximization problem in a fractional form can be transformed into a subtractive form that is more tractable based on the fractional programming theory. The convergence property of the proposed algorithms and the meaningfulness of the proposed performance merit related to the EE are demonstrated by simulations. The comparison of four EE metrics, the EE and the rate fairness, global-EE, Sum-EE, and DSC-EE, shows that the DSC-EE maximization tends to achieve high implementation level of on-demand power allocation while ensuring the EE of the system. In addition, when the minimum rate replaces the expected rate in the DSC-EE, further performance comparison indicates the necessity and impact of the expected rate in the tolerable quality of service bias function.

In frequency hopping (FH) wireless communications, finding an effective transmission strategy to properly mitigate jamming has been recently considered as a critical issue, due to the inherent broadcast nature of wireless communications. Recently, game theory has been proposed as a powerful tool for dealing with the jamming problem, which can be considered as a player (jammer) playing against a user (transmitter). Different from existing results, in this paper, a bimatrix game framework is developed for modeling the interaction process between the transmitter and the jammer, and the sufficient and necessary conditions for Nash equilibrium (NE) strategy of the game are obtained under the linear constraints. Furthermore, the relationship between the NE solution and the global optimal solution of the corresponding quadratic programming is presented. In addition, a special analysis case is developed based on the continuous game framework in which each player has a continuum of strategies. Finally, we show that the performance can be improved based on our game theoretic framework, which is verified by numerical investigations.