In this paper, we explore the optimal use of network coding in multi-source multi-hop Internet of things (IoT) networks, focusing on minimizing the average age of information (AoI) under various system parameters. Specifically, we determine the optimal number of packets for performing network coding at intermediate nodes (servers) to optimize the average AoI performance. Through extensive simulations, we demonstrate how frequency of network coding impacts data freshness across a range of scenarios, including different numbers of sources, transmission success probabilities, and computational capacities. Simulation results provide practical insights into the deployment of network coding in real-world IoT applications, shedding light on the design process of how to implement network coding and highlighting its potential to significantly improve timeliness in data delivery.

In recent years, the increasing demand to see the status of objects over the Internet leads to an increase in the number of Internet of Things (IoT) applications. The unique nature of IoT, which involves potentially millions of interconnected devices with different data rate, power, bandwidth, and range specifications, requires different performance metrics than those conventionally employed in other communication applications. In conventional wireless communication systems such as cellular networks, performance indicators, including data rate and spectral efficiency, have become decisive, whereas in energy-constrained real-time IoT applications which require low data rate, the freshness of information has become a more prominent characteristic. Age of Information (AoI), which is the elapsed time after the last received packet update was created at the source, has emerged as a fundamental metric for determining the freshness of information and has attracted substantial research interest. In this regard, this article is dedicated to provide an overview of the current state of the art on the use of AoI for the design and optimization of a large variety IoT applications. After a brief introduction of the IoT and AoI fundamentals, this article presents a survey of the research works on common design issues, such as AoI-based optimization, scheduling for IoT networks, application of learning methods in large-scale IoT systems, real-life applications, and experimental results together with a synopsis of potential future applications and research challenges.

We investigate the timeliness in delivering updates within a multisource multihop Internet of Things (IoT) network via multicast transmissions with or without employing network coding, using a completely probabilistic model. Age of Information (AoI) is adopted to quantify the timeliness of packets. Extensive simulation results, which corroborate the theoretical findings, demonstrate that in scenarios where the number of sources is high, the number of intermediate nodes relaying to monitors is low, there are multiple monitors, the transmission success probability is low, and computational resources are sufficient, the utilization of network coding has a great potential to improve the data-freshness in multisource multihop IoT networks which closely represent the spine of the real-life scenarios.

We consider the timeliness in delivering an update consisting of multiple message packets to multiple users via multicast transmissions with or without employing network coding where Age of Information (AoI) is adopted to quantify the timeliness of packets. The expressions for peak and average expected AoIs are analytically derived for both uncoded and network coded transmissions for both 2-user and generalized$k$-users scenarios where the computational burden stemming from network coding is taken into account. The behavioral analyses of a number of network parameters are investigated, and the effect of data rate and computational capacity of nodes is analyzed. Simulations are performed for various Internet of Things (IoT) deployments, and the analyses suggest that the use of network coding for multicast transmissions can result in substantial AoI improvements, with the exception of scenarios in which sensors have extremely limited computational capabilities.

Network anomaly detection is an important and rapidly growing area. In this paper, we propose a new network anomaly detection method based on the probability distributions of header information. The distances between the distributions of packet headers are calculated to reflect the main characteristics of the network. These are calculated using the Greedy algorithm which eliminates some requirements associated with Kullback-Leibler divergence such as having the same rank of the probability distributions. Then, Support Vector Machine classifier is used in the detection phase to reduce false alarm rates and to make the system adaptive for different networks. This algorithm is tested on the real data collected from Boğaziçi University network and MIT Darpa 2000 dataset.

Despite the evolving practical approaches to avoid DDoS attacks, it still poses a thread against information services. This paper aims to aid researchers in DDoS detection field to choose better statistical methods by examining structured probability distribution functions among frequently used traffic features in DDoS detection. Ten different continuous probability distributions are fitted using three different measures to attack and normal states of traffic feature vectors. Best probability distributions are chosen to perform binary hypothesis testing and likelihood ratio test.

Distributed Denial of Service (DDoS) attacks have become one of the most significant problems that affects the user satisfaction by degrading the availability of on-line services. Although intrusion detection systems provide effective mechanism for discriminating various DDoS attacks, they become impotent of detection when bogus packets similar to normal ones are dispatched by the attacker. One idea is to model the normal behavior of the network traffic using time series representation of that traffic together with advanced statistical analysis techniques such as frequency domain analysis for detecting the occurrence frequency (energy) of each basic element in time series. However, frequency domain analysis may become inadequate if the original frequency features are used for the detection anomalies. Therefore, in this work, we propose a hybrid approach that employs frequency domain analysis with sparse representation model to find discriminative characteristics for anomaly-based DDoS detection. The proposed algorithm distinguish abnormal traffic from the normal one based on the energy of time series for the number of packets feature, which is extracted from the time series data by using the sparse representation model. Experimental results show that performance of the proposed algorithm provides better DDoS detection results than the state-of-the-art time-series based approaches in the literature.

With the pervasive use of communications technologies, security of multiparty communication systems becomes crucial more than ever. However, providing a secure communication in distributed and dynamic networks is a challenging issue. Dynamic group key agreement protocols are one of the best candidates to overcome this issue. In dynamic group key agreement protocols, each participant in a group involves into the key computation. Moreover, dynamic group key agreement protocols provide auxiliary dynamic group operations for updating the group key when the set of participants is updated. In this paper, a comparative study on the scalability of dynamic group key agreement protocols is presented to show the best possible group key agreement protocol for specific-sized networks. Furthermore, we present simulations for scalability analysis of dynamic group key agreement protocols. Finally, we analyze and compare the performance of protocols regarding computational and communications costs.

Abstract For commitments on secrets, statistical hiding is a must when we are dealing with a long‐term secret or when the secret domain is small enough for a brute‐force attack by a powerful adversary. Unfortunately, all the Chinese Remainder Theorem‐based verifiable secret sharing schemes in the literature are either insecure or suffer from the vulnerability of computationally hiding commitments. To the best of our knowledge, there exist five such studies where two of them were already proven to be insecure. In this work, we first show that two of the remaining schemes are also insecure, that is, the schemes reveal information on the secret even when the adversary is passive. In addition, the remaining one is only secure against a computationally bounded adversary which can be a problem for secret sharing schemes requiring long‐term secret obscurity or using small secret domain. We propose a modification for the latter scheme and prove that the modified scheme is a secure verifiable secret sharing scheme against an unbounded adversary. Lastly, as an application, we show how to use the new scheme for joint random secret sharing and analyze the practicality and efficiency of the proposed schemes. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract The pervasive usage of the Internet has made secure group communications a significant issue. Conference‐key agreement protocols provide secure group communications with lower computational cost. Providing key agreements and updates of dynamic groups in an efficient manner is a significant challenge for conference‐key agreement protocols. Auxiliary key agreement operations are needed to solve the challenge. In this paper, we propose an improved conference‐key agreement protocol, called Dynamic Conference‐Key Agreement Protocol, that consists of Initial Conference‐Key Agreement Protocol and Auxiliary Conference‐Key Agreement operations. Dynamic Conference‐Key Agreement Protocol has operations to handle dynamic groups. The proposed protocol has better fault correction and provides the same security level with the existing ones. Copyright © 2014 John Wiley & Sons, Ltd.

We formulate the alignment problem of multiple and partially overlapping audio sequences in a probabilistic framework. We define and compare five generative models for several time varying features extracted from audio clips that are recorded independently and asynchronously. For each model, we derive the associated scoring function that evaluates the quality of an alignment. The matching is then achieved with a sequential algorithm. The derived score functions are also able to identify the cases where the sequences do not overlap and handle multiple sequences where no sequence is covering the entire timeline. The simulation results on real data suggest that the approach is able to handle difficult, ambiguous scenarios and partial matchings where simple baseline methods such as correlation fail.

Mobile network traffic is expected to increase dramatically with the proliferation of smart and diverse wireless devices. Moreover, pervasive and complex services such as mobile cloud, device-to-device communications and mobile multimedia consumption will require higher bandwidth and better network coverage. Hence, more spectrum and capacity with small cells and self-organization of heterogeneous networks are posed as vital solutions to address this burgeoning demand. In this paper, we propose a novel distributed self-organization algorithm for single frequency reuse scheme of OFDMA based heterogeneous networks for improving network capacity with self-configuration and self-optimization. Our approach, namely Spectrum-Aware Dynamic Frequency Reuse (SADFR), enables deployment of single frequency network by mitigating interference while increasing capacity in OFDMA HetNets. Moreover, we provide a comparative performance evaluation of our algorithm and elaborate on interference management issues in these systems.

In our previous work, we formulated multiple audio sequence alignment in a probabilistic framework [1]. Here, we extend the model for multi resolution alignment and focus on pairwise cases. We defined a similarity based approach for binary feature sequences and integrate it into a new generative model. We modify themodel formulti resolution case and the matching is achieved with a SequentialMonte Carlo Sampler (SMCS) which uses low resolution models as bridge distributions. The simulation results on real data sets suggest that our method is very robust and efficient under very noisy conditions with proper choices of model parameters.

The pervasive usage of the Internet has made secure group communications a significant issue. Conference key agreement protocols provide secure group communications against some attacks with lower computational cost in the Internet. However, forward secrecy is a challenging issue in the existing protocols, where it is preserved either the long-term key of a participant is compromised. In this study, we propose an improved conference key agreement protocol with forward secrecy. Besides providing forward secrecy, the proposed protocol preserves the same security level with existing ones.

SUMMARY In 2008, a scalable radio frequency identification (RFID) authentication protocol was proposed by Yanfei Liu to provide security and privacy for RFID tags. This protocol only needs O(1) time complexity to find out the identifier of the RFID tag irrespective of the total number of the tags in the system. Based on our analysis, however, a security flaw, which has gone unnoticed in the design of the protocol, makes the scheme vulnerable to tracking attack, tag impersonation attack, and desynchronization attack, if the attacker has the possibility to tamper with only one RFID tag. Because low‐cost devices are not tamper‐resistant, such an attack could be feasible, and we can apply the resulting attacks on authentication, untraceability, and desynchronization resistance of the protocol. To counteract such flaws, we revise the scheme with a stateful variant and also show that the proposed model requires less tag and server‐side computation. Copyright © 2011 John Wiley & Sons, Ltd.

ABSTRACT Computational constraints mostly driven by the cost concerns of radio frequency identification tags only permit to have security schemes that are simple combinations of elementary operations rather than the bullet proven yet computationally intensive cryptographic primitives. As ultra‐lightweight authentication protocols use only basic bitwise and arithmetic operations such as XOR, OR, and addition modulo powers of 2, they are considered as a class of these schemes. Because most of the early proposed ultra‐lightweight authentication protocols are easily broken, it turned out that designing such protocols is a much deeper task than presumed. This study analyzes a modified version of the stable lightweight mutual authentication protocol denoted by SLMAP*. After exploiting an unnoticed flaw in its design rationale, it is shown that some of the freshly produced variables can be assigned to different values for the reader and the tag that presumably causes a desynchronization vulnerability. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, we analyze storage awareness RFID authentication protocol based on sparse tree structure (SAPA), which provides backward untraceability and reduces the space for storing key sequence. We discover that SAPA does not provide location and information privacy between successful authentication sessions and does not resist denial of service attacks, forward traceability, and server impersonation. We analyze the weaknesses of SAPA and propose a new RFID authentication protocol. The proposed protocol provides better protection against privacy and security threats than those before. Our proposed protocol provides tag information privacy and tag location privacy, and resists replay attacks, denial of service attacks, backward traceability, forward traceability (under an assumption), and server impersonation with an efficient keylookup. Furthermore, our protocol has the least computation and communication load on both the tag and the server side compared to other tree based protocols.

Security is one of the main issues to adopt RFID technology in daily use. Due to resource constraints of RFID systems, it is very restricted to design a private authentication protocol based on existing cryptographic functions. In this paper, we propose a new RFID authentication protocol. The proposed protocol provides better protection against privacy and security threats than those before. Our proposed protocol is resistant to server impersonation attack introduced in [17]. Former proposal assumes that the adversary should miss any reader-to-tag communication flows and claims that their protocol is secure against forward traceability only in such communication environment. We show that even under such an assumption, the former proposed protocol is not secure. Our proposed protocol is secure against forward traceability, if the adversary misses any reader-to-tag communication flows. Our protocol also has low computational load on both the tag and the server side.

In 2005, Laih, Ding and Huang proposed a password-based key establishment protocol such that a user and a server can authenticate each other and generate a strong session key by their shared weak password within a symmetric cipher in an insecure channel. In this protocol, a special function which is a combination of a picture function and a distortion function e.g. CAPTCHA, is combined to authenticate the user and protect the password from the dictionary attacks that are major threats for most of the weak password-based protocols. They claim that the proposed protocol is secure against some well known attacks. However Tang and Mitchell show that the protocol suffers from an offline dictionary attack requiring a machine-based search of size 223which takes only about 2.3 hours. So designing such a protocol with providing practical security against offline attack is still an open problem. In this study, we introduce two password-based authenticated key establishment protocols that provide practical security against offline dictionary attacks by only using symmetric key cryptography.

In this paper, the bit-search type irregular decimation algorithms, that are used within linear-feedback shift register (LFSR)-based stream ciphers, are investigated. In particular, bit-search generator (BSG) and and its variant ABSG are concentrated on and two different setups are considered for the analysis. In the first case, the input is assumed to be an$m$-sequence; it is shown that all possible output sequences can be classified into two sets, each of which is characterized by the equivalence of their elements up to shifts. Furthermore, it is proved that the cardinality of each of these sets is equal to the period of one of its elements and subsequently the (upper and lower) bounds on the expected output period (assuming that no subperiods exist) are derived. In the second setup, we work in a probabilistic framework and assume that the input sequence is evenly distributed (i.e., independent and identically distributed (i.i.d.) Bernoulli process with probability$1/2$). Under these assumptions, closed-form expressions are derived for the distribution of the output length and the output rate, which is shown to be asymptotically Gaussian-distributed and concentrated around the mean with exponential tightness.

In this paper, we propose a new N-tier sAtellite Multicast sEcurity Protocol based on multi-recipient Signcryption schemes (NAMEPS). Our protocol is especially designed for very large and highly dynamic satellite multicast systems which require high security and reliability. Our N-tier architecture significantly reduces workload of the satellite layers especially for bandwidth consumption, computation resources and storage requirements. N-tier approach localizes effects of the rekeying operation (forward-backward security) and provides significant performance gain. Moreover, batch keying and ticketing mechanisms are used which additionally reduce workload of the satellite and terrestrial layers. Also, as a novel approach for cryptographic method, our protocol uses multi-recipient signcryption scheme, which provides confidentiality, authentication, unforgeability and non-repudiation together, more efficiently than classical sign-then-encrypt approaches. As a result, NAMEPS has many advantages for very large, dynamic and security critic satellite multicast systems.

A recent work explicitly models the discontinuous motion estimation problem in the frequency domain where the motion parameters are estimated using a harmonic retrieval approach. The vertical and horizontal components of the motion are independently estimated from the locations of the peaks of respective periodogram analyses and they are paired to obtain the motion vectors using a procedure proposed. In this paper, we present a more efficient method that replaces the motion component pairing task and hence eliminates the problems of the pairing method described. The method described in this paper uses the fuzzy c-planes (FCP) clustering approach to fit planes to three-dimensional (3-D) frequency domain data obtained from the peaks of the periodograms. Experimental results are provided to demonstrate the effectiveness of the proposed method.

In this study, we address the problem of prefiltering before segmentation. Prefiltering is quite important since it simplifies the data, producing flat zones without computing the contour information (Salembier and Pardas, 1994). Morphological filters with reconstruction restore the contour of the components. However, covered and uncovered areas in moving objects may be connected during the reconstruction process. In this case, open-close by partial reconstruction filters remove those artifacts in the background. After that, the contours of the regions can be located by the watershed algorithm. Although the number of resulting regions at the end of watershed with open-close by partial reconstruction is more than local averaging, applying a premerging by comparing the difference of mean gray values to these regions results in less number and greater-sized regions. Therefore, open-close filtering makes merging of more regions possible.

Two new design techniques for adaptive orthogonal block transforms based on vector quantization (VQ) codebooks are presented. Both techniques start from reference vectors that are adapted to the characteristics of the signal to be coded, while using different methods to create orthogonal bases. The resulting transforms represent a signal coding tool that stands between a pure VQ scheme on one extreme and signal-independent, fixed block transformation-like discrete cosine transform (DCT) on the other. The proposed technique has superior compaction performance as compared to DCT both in the rendition of details of the image and in the peak signal-to-noise ratio (PSNR) figures.

An object-oriented codec for video transmission at very low bit rates is proposed. The object motion is modeled by quadratic transform for which the coefficients are estimated by the nonlinear quasi-Newton method. The segmentation problem is put forward as a constrained optimization problem which interacts with the motion estimation process in the course of region growing. A context-based shape coding method which takes into account the image synthesis error as well as the geometric distortion, is also proposed. Quantitative and subjective performance results of the codec on various test sequences are included.

This paper addresses the problem of object based image coding for the purpose of video transmission at very low bit rates (below 32 kbit/s). Each frame of an image sequence is first segmented into temporally unchanged (background) and changed regions via a change detector. Then the changed regions are subdivided into arbitrarily shaped model failure and model compliance regions so as to satisfy the chosen quadratic transform motion model. Our work brings in two original contributions: (1) the use of region growing motion segmentation, and (2) joint use of quadratic transform with optical flow motion estimation.

We extend a method presented previously, which considers the problem of the semicausal autoregressive (AR) parameter identification for images degraded by observation noise only. We propose a new approach to identify both the causal and semicausal AR parameters and blur parameters without a priori knowledge of the observation noise power and the PSF of the degradation. We decompose the image into 1-D independent complex scalar subsystems resulting from the vector state-space model by using the unitary discrete Fourier transform (DFT). Then, by applying the expectation-maximization (EM) algorithm to each subsystem, we identify the AR model and blur parameters of the transformed image. The AR parameters of the original image are then identified by using the least squares (LS) method. The restored image is obtained as a byproduct of the EM algorithm.

In this correspondence, the problem of directional and multiscale edge detection is considered. Orthogonal and linear-phase M-band wavelet transform is used to decompose the image into MxM channels. These channels are then combined such that each combination, which we refer to as decomposition filter, results in zero-crossings at the locations of edges corresponding to different directions and resolutions, and inherently performs regularization against noise. By applying a zero-crossing detector on the outputs of the decomposition filters, edge maps of desired resolution and direction are obtained. In addition, with the application of the Teager's energy operator at the analysis stage, it is possible to obtain a reduction in unwanted zero-crossings. Final edge maps of images are obtained through simple combinations of directional edge maps.

The problem of directional and multiscale edge detection is considered. Orthogonal and linear-phase M-band wavelet transform is used to decompose the image into channels which correspond to different directions and resolution levels. "Meaningful" combinations of the synthesis filter outputs at the selected channels yield edge maps with desired properties. In addition, with the application of energy operators at the analysis stage, it is possible to obtain a reduction in unwanted zero-crossings. Final edge maps of images are obtained through simple combinations of directional edge maps.>

The method presented by T. Katayama and T. Hirai (1990), who considered the problem of semicausal autoregressive (AR) parameter identification for images degraded by observation noise, is extended. In particular, an approach to identifying both the causal and semicausal AR parameters without a priori knowledge of the observation noise power is proposed. The image is decomposed into 1-D independent complex scalar subsystems resulting from the vector state-space model, using the unitary discrete Fourier transform (DFT). Then the expectation-maximization algorithm is applied to each subsystem to identify the AR parameters of the transformed image. The AR parameters of the original image are then identified using the least-square method. The restored image is obtained as a byproduct of the EM algorithm.