Cache-assisted ultradense mobile-edge computing (MEC) networks are a promising solution for meeting the increasing demands of numerous Internet of Things mobile devices (IMDs). To address the complex interferences caused by small base stations (SBSs) deployed densely in such networks, this article exploits the combination of orthogonal frequency-division multiple access (OFDMA), nonorthogonal multiple access (NOMA), and base station (BS) clustering. Additionally, security measures are introduced to protect IMDs’ tasks offloaded to BSs from potential eavesdropping and malicious attacks. Within this network framework, a computation offloading scheme is proposed to minimize IMDs’ energy consumption while considering constraints, such as delay, power, computing resources, and security costs, optimizing channel selections, task execution decisions, device associations, power controls, security service assignments, and computing resource allocations. To solve the formulated problem efficiently, we develop a further improved hierarchical adaptive search (FIHAS) algorithm, providing some insights into its parallel implementation, computation complexity, and convergence. Simulation results demonstrate that the proposed algorithms can achieve lower total energy consumption and delay compared to other algorithms when strict latency and cost constraints are imposed.

With potential advantages over TCP/IP for content delivery, mobility, and security, Named Data Networking (NDN) has become a promising architecture for the next-generation network. However, its poor performance in reliable transmission is still an unsolved problem. Many existing schemes in NDN employ inaccurate retransmission timeouts calculated with RTTs from diverse content sources to detect packet loss, which is lagging and may deteriorate transmission performance. Besides, after identifying the loss, the consumer costly resends the request to recover it, further increasing recovery time. In this paper, we propose an in-network Proactive Loss Recovery (PLR) scheme, which provides an efficient in-network method for timely detection and proactive recovery of lost packets. Deployed on each router, PLR detects the loss by monitoring queue status and sends high-priority explicit feedback to notify consumers of loss events timely. Meanwhile, lost packets are stored in each router's cache and will be retransmitted at an adaptive rate based on the detected remaining bandwidth. The simulation shows that PLR can vastly reduce the number of retransmissions on consumers, and the content completion time can be decreased by up to 21.8% compared with the baseline.

As a promising implementation of Information Centric Networking, Named Data Networking (NDN) can facilitate content distribution with in-network caching and location-independent data access. However, the reliance on caches makes NDN vulnerable to content poisoning attacks, which waste network resources and decrease transmission efficiency. Most mitigating schemes follow the pattern that each content is repeatedly verified individually in each router and all producers have the same status, which wastes computation resources and degrades network performance. In this paper, we propose a Reputation-based Probabilistic Batch Verification (RPBV) scheme to address the issue, in which producers’ reputation is estimated according to verification results to distinguish different producers. We provide an adaptive probabilistic verification method based on reputation to avoid a lot of unnecessary verification operations. At the same time, we adopt an efficient batch verification algorithm to simultaneously verify multiple content, which reduces the overhead greatly. With the above mechanisms implemented only on the edge router to avoid repeated verification, we provide an optional probabilistic verification method on intermediate routers to strengthen the security. The extensive simulations show that RPBV achieves much lower computation overhead and shorter content retrieval time than the traditional schemes.

In fully-connected passive RFID backscatter networks, it is challenging to provide accurate range estimations due to complex channels. Facing this problem, we propose a differential analysis based anti-multipath technique by introducing a reference tag. Through the linear difference between target tag received power measurements with the reference tag activated and not, the power with respect to the reader-reference-target link is separated out from the mixed measurements, achieving the mitigation of multipath and accurately ranging. Under distributed localization schemes, after breaking down the full network into a series of fragments, it is vital but challenging to determine how to assemble them satisfactorily. VIABLE, virtual-actual assembling algorithm, is proposed to achieve distributed and cooperative localization. The virtual assembling phase rectifies the fragments over and over until their errors converge, which enables the mining of a satisfactory and adaptive assembling order. Subsequently, the actual phase assembles the rectified fragments together with that order and accomplishes the overall localization accurately. The theoretical analysis of performance limit is presented via the derivation of Cramér-Rao lower bound approximated by a particle approach. Extensive simulations demonstrate that our proposed framework outperforms existing algorithms for cooperative localization.

The device-free localization (DFL) has promising application prospects in intrusion detection, emergency rescue, and smart homes, because it does not require the target to carry any auxiliary positioning equipment. Radio tomographic imaging (RTI) is one of the most potential DFL techniques and has many advantages over other methods. However, in passive ultrahigh frequency radio frequency identification scenario, there are few researches and many problems to be solved. The difficult but urgent matter is how to identify the locations of multiple targets from many false targets and artifacts. This paper proposes a novel method based on cross-sectional scan (CSS), gray value distribution analysis (GVDA), and naive Bayes classifier to solve this problem. The CSS obtains the gray value distributions of the local maximum pixel in an RTI reconstructed image. Then, the GVDA extracts several characteristic parameters from gray value distributions, such as the size, height, and shape of the peak. Finally, the naive Bayes classifier utilizes these series of characteristics to judge whether local maximum pixels are false targets or real targets. The method can also recognize the number of targets that are very close to each other. Simulation and experimental results show that this method can accurately determine the locations and the number of targets.

Radio frequency identification (RFID) is a promising technology in indoor localization. However, multipath cannot be avoided in indoor passive ultrahigh frequency RFID localization environment, which results in poor localization accuracy. In this paper, a range-based localization scheme has been developed to mitigate the effect of multipath errors and improve the localization accuracy. The proposed localization methods are based on semi-definite programming and second-order cone programming and do not require any statistics of multipath error. First, all the phase information of multifrequency is utilized. Dual-frequency phase difference of all dual-frequency combinations can be obtained to range. The ranging error is assumed to follow the Gaussian distribution. Then, convex relaxation methods with limiting the mean ranging error to one standard deviation of the Gaussian distribution are proposed. Finally, in order to solve the feasibility problem, the constraint of the mean ranging error is relaxed. Simulation results demonstrate that the proposed methods outperform some existing localization algorithms in multipath environment.