An Anti-Gray Hole Attack Scheme in Mobile Ad Hoc Network

International Journal of Wireless Information Networks - Dynamic variation of network topology in mobile ad hoc networks (MANET) forces network nodes to work together and rely on each other for...     

An improved cluster formation process in wireless sensor network to decrease energy consumption

Wireless sensor network has special features and many applications, which have attracted attention of many scientists. High energy consumption of these networks, as a drawback, can be reduced by a hierarchical routing algorithm. The proposed algorithm is based on the Low Energy Adaptive Clustering Hierarchy (LEACH) and Quadrant Cluster based LEACH (Q-LEACH) protocols. To reduce energy consumption and provide a more appropriate coverage, the network was divided into several regions and clusters were formed within each region. In selecting the cluster head (CH) in each round, the amount of residual energy and the distance from the center of each node were calculated by the base station (including the location and residual energy of each node) for all living nodes in each region. In this regard, the node with the largest value had the highest priority to be selected as the CH in each network region. The base station calculates the CH due to the lack of energy constraints and is also responsible for informing it throughout the network, which reduces the load consumption and tasks of nodes in the network. The information transfer steps in this protocol are similar to the LEACH protocol stages. To better evaluate the results, the proposed method was implemented with LEACH LEACH-SWDN, and Q-LEACH protocols using MATLAB software. The results showed better performance of the proposed method in network lifetime, first node death time, and the last node death time.

     

A Convex Programming for Range Assignment to Optimize Lifetime in Network-Coding-Based-Wireless-Sensor Networks

Wireless sensor networks (WSNs) are known to be highly energy-constrained and  consequently lifetime is a critical metric in their design and implementation. Range assignment by adjusting the transmission powers of nodes create a energy-efficient topology for such networks while preserving other network issues, however, it may effect on the performance of other techniques such as network coding. This paper addresses the problem of lifetime optimization for WSNs where the network employs both range assignment and network-coding-based multicast. We formulate the problem and then reformulated it as convex optimization that offer a numerous theoretical or conceptual advantages. The proposed programming leads to efficient or distributed algorithms for solving the problem. Simulation results show that the proposed optimized mechanism decreases end-to-end delay and improve lifetime as compared by other conventional ones.     

RTDGPS Accuracy Improvement Using PSO-LSWSVM and Low-Cost GPS Receivers

Wireless Personal Communications - The position of mobile devices is determined by Real Time Differential Global Positioning System (RTDGPS). This system is composed of fixed and mobile station....     

Deep learning methods for fingerprint-based indoor positioning: a review

ABSTRACT

Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. This paper provides a comprehensive review of deep learning methods in indoor positioning. First, the advantages and disadvantages of various fingerprint types for indoor positioning are discussed. The solutions proposed in the literature are then analyzed, categorized, and compared against various performance evaluation metrics. Since data is key in fingerprinting, a detailed review of publicly available indoor positioning datasets is presented. While incorporating deep learning into fingerprinting has resulted in significant improvements, doing so, has also introduced new challenges. These challenges along with the common implementation pitfalls are discussed. Finally, the paper is concluded with some remarks as well as future research trends.     

Security and Privacy Analysis of Song–Mitchell RFID Authentication Protocol

Many applications, such as e-passport, e-health, credit cards, and personal devices that utilize Radio frequency Identification (RFID) devices for authentication require strict security and privacy. However, RFID tags suffer from some inherent weaknesses due to restricted hardware capabilities and are vulnerable to eavesdropping, interception, or modification. The synchronization and untraceability characteristics are the major determinants of RFID authentication protocols. They are strongly related to privacy of tags and availability, respectively. In this paper, we analyze a new lightweight RFID authentication protocol, Song and Mitchell, in terms of privacy and security. We prove that not only is the scheme vulnerable to desynchronization attack, but it suffers from traceability and backward traceability as well. Finally, our improved scheme is proposed which can prevent aforementioned attacks.     

Advanced Bioresponsive Multitasking Hydrogels in the New Era of Biomedicine

Advanced forms of hydrogels have many inherently desirable properties and can be designed with different structures and functions. In particular, bioresponsive multifunctional hydrogels can carry out sophisticated biological functions. These include in situ single-cell approaches, capturing, analysis, and release of living cells, biomimetics of cell, tissue, and tumor-specific niches. They can allow in vivo cell manipulation and act as novel drug delivery systems, allowing diagnostic, therapeutic, vaccination, and immunotherapy methods. In the present review of multitasking hydrogels, new approaches and devices classified into point-of-care testing (POCT), microarrays, single-cell/rare cell approaches, artificial membranes, biomimetic modeling systems, nanodoctors, and microneedle patches are summarized. The potentials and application of each format are critically discussed, and some limitations are highlighted. Finally, how hydrogels can enable an “all-in-one platform” to play a key role in cancer therapy, regenerative medicine, and the treatment of inflammatory, degenerative, genetic, and metabolic diseases is being looked forward to.     

Frequency selective millimeter‐wave channel estimation based on subspace enhancement algorithms

In millimeter wave (mmW) communication systems, hybrid architecture, including the analog‐digital precoder and combiner matrices, is employed to take advantage of the multistream transceiver. In practice, mmW channel is assumed to be frequency‐selective, since the signal bandwidth is larger than the coherence bandwidth. Hence, orthogonal frequency‐division multiplexing signaling can be remedial. So far, most of the previous works on the frequency‐selective channel estimation have focused on the single measurement vector (SMV) form, whereas finding and exploiting the proper multimeasurement vector (MMV) model can improve upon the estimation procedure based on compressive sensing (CS) concepts. In fact, the estimation procedure based on the MMV model has a faster convergence speed than the SMV method specially, when the training frames are small. In this paper, we first extract the MMV model of the channel. In this model, the rank‐deficiency occurs as the number of training frames is less or equal to the sparsity level. Thus, the conventional estimation methods fail to provide the desirable performance. To overcome this issue, we propose two rank‐aware algorithms based on the enhancement of the observed signal subspace. The first algorithm assumes to know the sparsity level, while the second faces to the lack of knowledge about the sparsity level. The simulation results corroborate the fact that the proposed methods outperform the conventional CS algorithms such as Simultaneous Orthogonal Matching Pursuit.