Number Of Access Points Research Articles

Optimized Received Signal Strength-Based Radio Map Interpolation for Indoor Positioning Systems

The most viable strategy to establish a dependable indoor positioning system is by employing the received signal strength (RSS) based fingerprinting technique, which encompasses both the offline and online phases. The offline phase involves constructing a radio map, which can be arduous in vast indoor environments. To tackle this, radio map interpolation is often used to interpolate RSS by utilizing the RSS recorded at a coarser level of known reference points (RPs). This paper proposes a novel RSS-based radio map interpolation to enhance the existing inverse distance weighting (IDW) interpolation technique. The method divides the deployment area into zones and optimizes the density of known RPs in each zone based on the number of access points (APs) with average RSS exceeding the threshold. It allocates higher RP density for the zones with poor AP coverage and reduces it for well-covered zones. Results demonstrates that the proposed method achieves substantial improvements over the baseline IDW scheme in average positioning error of up to 6.58% at the floor level and 3.77% overall.

Wi-Fi Fingerprint Indoor Localization by Semi-Supervised Generative Adversarial Network.

Wi-Fi fingerprint indoor localization uses Wi-Fi signal strength measurements obtained from a number of access points. This method needs manual data collection across a positioning area and an annotation process to label locations to the measurement sets. To reduce the cost and effort, this paper proposes a Wi-Fi Semi-Supervised Generative Adversarial Network (SSGAN), which produces artificial but realistic trainable fingerprint data. The Wi-Fi SSGAN is based on a deep learning, which is extended from GAN in a semi-supervised learning manner. It is designed to create location-labeled Wi-Fi fingerprint data, which is different to unlabeled data generation by a normal GAN. Also, the proposed Wi-Fi SSGAN network includes a positioning model, so it does not need a external positioning method. When the Wi-Fi SSGAN is applied to a multi-story landmark localization, the experimental results demonstrate a 35% more accurate performance in comparison to a standard supervised deep neural network.

An Analytical Reliability Performance Assessment of Cell-Free Massive MIMO Systems for 5G and Beyond Cellular Networks

In today‟s realm, the 5G cellular networks offer a new era of fully digital environment by connecting people anywhere anytime. This era requires inspiring demands to the networks in terms of spectral and energy efficiencies, low-latency and ultra-reliable communications. A major development to deal with these unparalleled demands is the introduction of Cell- Free Massive MIMO (CF mMIMO) systems to the cellular network. In recent years, mathematical foundations have been laid for a CF mMIMO communication system to evaluate the spectral and energy efficiencies given the number of access points. Various existing literatures have adopted mathematical models to formulate closed-form expressions for these efficiencies using uncorrelated Rayleigh fading for simple linear processing detectors. In this paper, we try to address a compelling and interesting question which has not received much attention lately. The question is how to evaluate the reliability of a connected user terminal in both a CF mMIMO and conventional mMIMO system? We answer this question by first analyzing the signal models for both systems assuming exact channel estimates and consider an uncorrelated Rayleigh fading channel model. The exact Bit Error Rate, as a reliability assessment, in closed-form are then derived based on strong mathematical foundations, which are simple to numerically evaluate in Matlab R2022, for MPSK and MQAM modulation schemes. The mathematical models presented in this work can be used in 5G and Beyond 5G networks to evaluate the distributions of error rates for a user given the number of Access points or Base Station antennas and modulation scheme. Machine Learning algorithms can be applied to the analytical expressions with a view to predict the error performance of a user terminal given the fading characteristic of the propagation environmen

Enhancing Cloud Storage Security in Big Data: A Comprehensive Data Sharing Protocol

A cloud based system gives advantage of a storage facility provided by a cloud service provider for exchange of data with authorised users. cloud providers store shared data in huge data centres outside the data owner's trust zone, which may raise the issue of data confidentiality. Number of parties, devices, app in cloud increases so number of access point also increases and making perfect access access control is challenging, so cloud data subject to being deleted or can be modified by cloud service provider. This article offers a secret sharing group key management protocol (SSGK) to prevent unwanted access in the communication process and shared data. in SSGK, group key is utilised to encrypt the shared data, and a secret sharing mechanism is employed to distribute the group key. security and performance evaluations show that our approach significantly reduces the security and privacy concerns of data sharing in cloud storage. Keywords : Big data, Security and Privacy, Cloud Storage, Data Sharing, Encryption, Decryption.

Downlink Learning-aided Precoding for Cell-Free networks Specially D esigned for beyond 5G Communication System

Objectives: With increase in number of access points in Cell-Free Massive Multiple Input Multiple Output (CFMM) system, spatial complexity of calculating precoding vector increases with traditional precoding schemes. So, to reduce computational complexity, network cost, and run time, a new deep learning -aided precoder is specially designed for CFMM system. The performance of downlink (DL) Cell-Free Massive Multiple Input Multiple Output (CFMM) system operating under Rayleigh fading channel model is analyze using new deep learning-based precoding scheme. Methods: We introduce new deep learning-aided precoder and an improved version of basic scalable pilot assignment algorithm which enhances system performance. We derive closed- form expression for average DL spectral efficiency (SE) for the proposed scheme, which is then compared with Minimum Mean Square Error Successive Interference Cancellation (MMSESIC), Regularized Zero Forcing (RZF), Least Square (LS) and Maximum Ratio (MR) combining techniques. We analyze the proposed scheme with perfect channel state information (CSI), instantaneous CSI, coherent transmission, non-coherent transmission, different pilot configuration. Findings: The spectral efficiency obtained with CFMM-MMSESIC and the proposed scheme is 3 bits/s/Hz, and 2.5bits/s/Hz respectively, which clearly indicates the difference of 20 % only. So, the proposed scheme gives near optimal results, and we can use simple linear combining techniques instead of complex non-linear combining techniques. Also, the proposed scheme with coherent data transmission gives performance curve, which is very close to MMSESIC scheme as desired. The performance of the proposed system is improved by reducing run time and computational complexity as compared to conventional linear precoding schemes. Novelty: The proposed precoder improves performance by reducing run time and computational complexity. Advanced pilot assignment algorithm enhances performance by reducing pilot contamination. Keywords: Cell-Free Massive Multiple Input Multiple Output, Pilot Contamination, Precoding, Minimum Mean Square Error Successive Interference Cancellation, Maximum Ratio, Regularized Zero Forcing (RZF), Least Square (LS)

Deep Learning-aided Channel Estimation Combined with Advanced Pilot Assignment Algorithm to Mitigate Pilot Contamination for Cell-Free Networks

Objectives: The performance of Cell-Free Massive Multiple Input Multiple Output (CFMM) is analyzed in this paper for its two bottlenecks i.e., Pilot Contamination (PC) and Channel Estimation Error (CEE). Methods: The CFMM network is strongly affected by PC which is one of the bottlenecks due to which quality of service and accuracy of channel estimation gets impacted. Therefore, we address this problem by presenting advanced pilot assignment algorithm to mitigate PC and deep learning aided channel estimation for reducing CEE for the CFMM systems to maximize spectral efficiency (SE). We derive achievable uplink and downlink SE expressions for the proposed system, and compare with Minimum Mean Square Error and Maximum Ratio combining techniques. As well, the performance is evaluated for different antenna configurations. The advanced pilot assignment algorithm is compared with greedy pilot assignment and random pilot assignment methods. The performance of cellular massive multiple input multiple output (MIMO) is derived for comparison. The performance of CFMM system is evaluated using MATLAB software. Findings: The UL and DL performance of the proposed system in terms of SE is 3.2 times higher than the conventional CFMM with MMSE and MR combining techniques. Average sum spectral efficiency of the proposed system increases with increase in number of access points (APs). Comparison with different antenna configurations reveals that, with 400 APs equipped with single antenna, only UE with good channel condition shows performance enhancement, but when each AP is equipped with 4 antennas, the UE with unfavourable channel condition also give better performance. Advanced pilot assignment scheme proves to be better than greedy and random pilot assignment techniques. For the same cellular set up, the proposed CFMM system achieves higher SE than the cellular massive MIMO. Novelty: Due to the advanced pilot assignment algorithm used in the proposed CFMM system, at a time, only one AP is selected and the selected AP with its full received power serves the desired UE, which suppresses interference resulting in improved SE performance. The serving AP is selected considering the distance between UE and AP, rather than using large scale fading coefficient which is the unique feature of pilot assignment algorithm. The proposed deep learning-aided channel estimation method, minimizes the mean square error (MSE) between the actual channel and the channel estimates obtained from the MMSE estimation resulting in reduction in channel estimation error. Thus, the use of the proposed advanced pilot assignment algorithm and deep learning-aided channel estimation method increase the SE performance of the CFMM system. Keywords: Cell­Free Massive Multiple Input Multiple Output, Pilot Contamination, Channel Estimation Error, Minimum Mean Square Error, Maximum Ratio

Joint online 3D trajectory optimisation and power allocation of UAVs as mobile access points in a downlink distributed multi-user MIMO system

Unmanned aerial vehicles (UAVs) are introduced as one of the key-enablers for 5G beyond networks due to their low cost and flexible deployment, and mobility degrees-of-freedom. Moreover, low sensitivity to blockage and uncorrelated channels in distributed multiple-input multiple-output (D-MIMO) systems, and their extension which is called cell-free massive MIMO, have made them an attractive research field. The location and number of access points (APs) in D-MIMO systems highly influence the system performance in terms of energy and spectral efficiency that can be handled by UAVs mobility and flexible deployment. In this paper, joint power allocation, and 3D trajectory design in a D-MIMO system for a downlink scenario where multiple UAVs serve as mobile APs (MbAPs) is investigated. Contrary to the existing works in multi-mobile base stations, we introduce space-division multiple access (SDMA) for the online trajectory design as a multi-access technology which does not suffer from the non-casual channel property in traditional trajectory designs. In the proposed design, the smallest user’s ergodic rate lower bound is maximised by jointly optimising MbAPs’ 3D trajectory and transmit power. The block coordinate descent (BCD) algorithm is deployed to break the main problem into two sub-problems and iteratively optimise over the two. Considering the non-convex nature of both of sub-problems, successive convex approximation is also exploited. For performance analysis, complexity and convergence property of the proposed algorithm are investigated. Finally, numerical results validate the improved performance of the proposed system in a practical scenario, where users can connect and disconnect to and from the network.

Robust Resource Control Based on AP Selection in 6G-Enabled IoT Networks.

The diverse application vertices of internet-of-things (IoT) including internet of vehicles (IoV), industrial IoT (IIoT) and internet of drones things (IoDT) involve intelligent communication between the massive number of objects around us. This digital transformation strives for seamless data flow, uninterrupted communication capabilities, low latency and ultra-high reliability. The limited capabilities of fifth generation (5G) technology have given way to sixth generation (6G) wireless technology. This paper presents a dynamic cell-free framework for a 6G-enabled IoT network. A number of access points (APs) are distributed over a given geographical area to serve a large number of user nodes. A pilot-based AP selection (PBAS) algorithm is proposed, which offers robust resource control through AP selection based on pilots. Selecting a subset of APs against all APs for each user node results in improved performance. In this paper, the performance of the proposed transmission model is evaluated for the achieved data rate and spectral efficiency using the proposed algorithm. It is shown that the proposed PBAS algorithm improves the spectral efficiency by 22% at the cell-edge and 1.5% at the cell-center. A comparison of the different combining techniques used at different user locations is also provided, along with the mathematical formulations. Finally, the proposed model is compared with two other transmission models for performance evaluation. It is observed that the spectral efficiency achieved by an edge node with the proposed scheme is 5.3676 bits/s/Hz, compared to 0.756 bits/s/Hz and 1.0501 bits/s/Hz, attained with transmission schemes 1 and 2, respectively.

Gaussian Message Passing Detection With Constant Front-Haul Signaling for Cell-Free Massive MIMO

The cell-free concept has garnered great attention in the industry and academia and is envisaged to be a promising technology for beyond fifth generation (5 G) networks. However, the extremely high front-haul and computational overhead of the original cell-free network with centralized or star topologies limits its practical adoption. In this work, we propose a novel non-linear sequential detection method based on the Gaussian message passing algorithm (GMP) for the sequential topology, i.e., radio stripe network, which requires <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">constant</i> front-haul signaling as the number of access points (APs) increases. Specifically, each AP iteratively calculates means and variances locally and fuses them with the received messages from the preceding AP based on the product principle of the multivariate Gaussian probability density function. The fused messages are forwarded to the next AP to calculate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> probability with constant front-haul signaling, which is independent of the number of APs. Simulation results demonstrate that the proposed sequential GMP detector achieves an attractive trade-off between bit error rate (BER) performance, computational complexity, and front-haul requirements.

Rate analysis of ZF receiver for uplink cell-free massive MIMO systems with D2D communications

This paper proposes a transmission structure of zero forcing (ZF) receiver for uplink cell-free massive multiple-input multiple-output (MIMO) systems with device-to-device (D2D) communications, followed by a rate analysis. We assumed that D2D users (DUEs) can utilize orthogonal radio resources to improve the efficiency of the scarce utilization or repurpose the time–frequency-spectrum resources currently used by the cell-free users (CFUEs). Assuming that the imperfect channel state information (CSI) is realizable, after that, the use-and-forget bounding technique is then used to respectively obtain the closed-form expressions of the CFUEs and DUEs, which provide the lower bounds on the ergodic approximate realizable rate of both communication links. First, we calculate the minimum-mean-square error (MMSE) estimation for all channels. Then, the derived results of the achievable uplink sum rate provide us with a tool that enables us to explain how some important parameters, such as the number of access points (APs)/CFUEs, each AP/CFUE/antenna, and the density of DUEs, affect system performance, highlighting the significance of cooperation between cell-free massive MIMO and D2D communication.

Mobility Prediction of Mobile Wireless Nodes

Artificial intelligence (AI) is a fundamental part of improving information technology systems. Essential AI techniques have revolutionized communication technology, such as mobility models and machine learning classification. Mobility models use a virtual testing methodology to evaluate new or updated products at a reasonable cost. Classifiers can be used with these models to achieve acceptable predictive accuracy. In this study, we analyzed the behavior of machine learning classification algorithms—more specifically decision tree (DT), logistic regression (LR), k-nearest neighbors (K-NN), latent Dirichlet allocation (LDA), Gaussian naive Bayes (GNB), and support vector machine (SVM)—when using different mobility models, such as random walk, random direction, Gauss–Markov, and recurrent self-similar Gauss–Markov (RSSGM). Subsequently, classifiers were applied in order to detect the most efficient mobility model over wireless nodes. Random mobility models (i.e., random direction and random walk) provided fluctuating accuracy values when machine learning classifiers were applied—resulting values ranged from 39% to 81%. The Gauss–Markov and RSSGM models achieved good prediction accuracy in scenarios using a different number of access points in a defined area. Gauss–Markov reached 89% with the LDA classifier, whereas RSSGM showed the greatest accuracy with all classifiers and through various samples (i.e., 2000, 5000, and 10,000 steps during the whole experiment). Finally, the decision tree classifier obtained better overall results, achieving 98% predictive accuracy for 5000 steps.

Traffic speed prediction techniques in urban environments

The present study developed Multiple Linear Regression (MLR) and machine learning (ML) models, including Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF), to predict the mean free-flow speed (FFS) using several geometric, traffic, and pavement condition variables. The traffic features group includes spot speed, speed limit, average speed, 85th percentile speed, traffic and crossing pedestrian volumes, volume of exiting vehicles, percentage of elderly crossing pedestrians (Elderly%), percentage of heavy vehicles (HV%), and traffic calming measures (TCMs). The geometric characteristics include lateral clearance, number of effective lanes, number of access points (including median openings), road grade, effective lane width, and median width. The pavement condition category includes pavement roughness in the International Roughness Index (IRI). A total of 11 urban arterials were used to develop the MLR model and train the ML models. Test data were collected from two randomly selected roads to evaluate the performance of each model, investigate the differences between conventional linear regression and ML approaches, and determine the best prediction models based on the results of the two techniques. Results showed that the proposed ML algorithms outperformed linear regression models. They are believed to be valuable and strong tools to predict the mean FFS that adapts to sudden changes in traffic flow caused by exogenous conditions on urban arterials and can be employed in determining the most influential factors and building reliable prediction models where spot study is not feasible due to time and resource limitations.

Identifying Optimal Locations for Speed Enforcement Cameras

Speed enforcement cameras are used to improve traffic safety. These cameras are usually placed in areas with significant crashes related to speeding. However, in many countries, detailed crash data are not available or are of poor quality. This study aims to propose and explore three simple ranking methods for identifying optimal locations for speed enforcement cameras that do not depend on crash records. The first method is based on the Star Rating Scores and the traffic volume data. The second method relies on developing a data-driven model for predicting speeding violation rate (VR) based on road geometry aspects and comparing it with available road segment crash risk. The third method uses a combination of the first two methods. The proposed methods and prediction models were applied to a case study and successfully identified the optimal location for speed enforcement cameras. The developed prediction models suggest that speed limit, junction spacing, and the number of access points per kilometer are the most common variables to estimate speed VRs for both urban and rural roads. Availability of service roads and the number of lanes were variables that affect the speed VR on urban roads only. The proposed methods are considered valuable simple tools for road safety practitioners to effectively prepare speed enforcement plans for different road networks.

Segment-Level Spatial Heterogeneity of Arterial Crash Frequency Using Locally Weighted Generalized Linear Models

Decades of literature in traffic crash modeling show the popularity of generalized linear models (GLMs). However, because of the failure to accommodate spatial heterogeneity, parameters estimated with these models are inconsistent and inefficient. In light of that, this study aims to investigate the spatial heterogeneity of crashes aggregated at roadway segment levels using geographically weighted Poisson regression (GWPR) and two variants of the geographically weighted negative binomial regression (GWNBR) model. The results indicate: (i) the GWNBR model with global dispersion parameter outperforms conventional Poisson, GWPR, and negative binomial (NB) models; (ii) the performance of the GWNBR model further enhances as the dispersion parameter becomes spatially non-stationary; (iii) tests of spatial heterogeneity and autocorrelation reveal the existence of non-stationarity and less than 1% likelihood of randomness; and (iv) median of parameter estimates reveal a positive association between crashes and posted speed limit, number of lanes, number of three-leg intersections, number of access points, and vehicle miles traveled (VMT). The study concludes that, when spatial heterogeneity is evident, conventional GLMs should be avoided to circumvent the futile estimation of parameters on the arterial segments. The findings are expected to contribute to the small pool of literature on spatial non-stationarity of parameters in segment-based aggregation, identification and selection of segments with similar influence factors in corridor-level studies, and spatial analysis of arterial crashes.

Indoor Positioning and Navigating System Application Using Wi-Fi with Fingerprinting Method and Weighted K-Nearest Neighbor Algorithm

The need for accurate indoor location determination, object tracking, digital maps and indoor travel routes is increasing along with the construction of buildings that have complex and spacious layouts. The current Global Positioning System navigation system is only effective for outdoor use. However, when used indoors it becomes inaccurate due to factors such as signal attenuation and multipath caused by wall obstructions in the building. This study designed an application of Indoor Positioning and Navigating System Using Wi-Fi with Fingerprinting method and Weighted K-Nearest Neighbor algorithm. In the design process, it is necessary to create a fingerprinting database by considering the number of Access points and environmental conditions. Based on the results of the study, the location results of the application show that from floors 1,2, and 3. Floor 1 has a room accuracy result of 89% and a point accuracy of 86% with an average deviation of 1.42 px or 0.9 m, floor 2 has room accuracy results. of 65% and a point accuracy of 70% with an average deviation of 2.43 px or 1.7 m, and the 3rd floor has a room accuracy of 86% and a point accuracy of 68% with an average deviation of 2.27 or 1.5 m. Based on the data above, this application is proven to be able to detect the position of someone in the room with a success percentage on the 1st floor by 90%, the 2nd floor by 55%, and the 3rd floor by 80%.

Local-Partial Signal Combining Schemes for Cell-Free Large-Scale MU-MIMO Systems with Limited Fronthaul Capacity and Spatial Correlation Channels

Cell-free large-scale multi-user MIMO is a promising technology for the 5G-and-beyond mobile communication networks. Scalable signal processing is the key challenge in achieving the benefits of cell-free systems. This study examines a distributed approach for cell-free deployment with user-centric configuration and finite fronthaul capacity. Moreover, the impact of scaling the pilot length, the number of access points (APs), and the number of antennas per AP on the achievable average spectral efficiency are investigated. Using the dynamic cooperative clustering (DCC) technique and large-scale fading decoding process, we derive an approximation of the signal-to-interference-plus-noise ratio in the criteria of two local combining schemes: Local-Partial Regularized Zero Forcing (RZF) and Local Maximum Ratio (MR). The results indicate that distributed approaches in the cell-free system have the advantage of decreasing the fronthaul signaling and the computing complexity. The results also show that the Local-Partial RZF provides the highest average spectral efficiency among all the distributed combining schemes because the computational complexity of the Local-Partial RZF is independent of the UTs. Therefore, it does not grow as the number of user terminals (UTs) increases.

Inference of Wi-Fi busy time fraction based on Markov chains

IEEE 802.11 has emerged as a vital wireless network access technology for mobile devices. By providing the potential for high connectivity speeds, this technology has led to a huge rise in the number of access points (APs). In such environments, mobile devices have the choice to join several Wi-Fi networks. Despite its importance to user Quality of Experience (QoE), the AP selection is still trivial since it focuses at best on the received signal strength if not only the user’s history. Crucial metrics that capture the overall dynamics of the AP load condition, such as the network load, are not taken into account. In this paper, we propose to use the Busy Time Fraction (BTF) as a metric to choose the best AP to attach to. The BTF level of a given channel is inferred based on the frame aggregation scheme proposed since the 802.11n standard. In this regard, we build a proof of concept system, FAM (Frame Aggregation based method) that leverages the theoretical frame aggregation levels of a probe traffic returned by two analytical Markovian models and the measured ones in order to estimate not only the BTF but also the nature of the traffic. We validate the accuracy of our proposed approach against ns-3 simulations under several scenarios.

Determining Optimal Locations of Postal Access Points Based on Simulated Annealing

The development of a sustainable postal system depends on the sustainability of its postal network, especially the organization of the postal access points. Postal service providers around the world face great challenges due to changes in the service trends and in consumer behavior. In this paper, the authors argue the importance of the postal network and its significance in rural areas. Methodologies including advanced search algorithms such as brute force and simulated annealing are proposed for best determining the possible locations of postal access points with no or minimal decrease in service accessibility. To the best of our knowledge, these algorithms are scarcely used for determining postal service accessibility. The results show that the decline of the postal office could significantly affect service accessibility. An analysis of the numbers of access points and their impacts on the postal network is provided, as well as the scenario of an additional access point. This methodology could help postal service providers to better plan network organization and resources. For service users, the application of the methodology adds to better service accessibility that considers the distances traveled.

Power Optimization Model for Energy Sustainability in 6G Wireless Networks

Internet-of-Things (IoT) networks are witnessing a rapid proliferation of connected devices and mobile terminals each day. The wireless information flow between these massive battery-powered devices has a huge energy burden and will lead to an energy crisis in the near future; thus, there is an urgent search for sustainable energy networks. To offer a sustainable energy solution in order to meet the energy demands of these massive IoT networks, this paper presents a dynamic practical model that enables the efficient management of power resources. Two user-scheduling algorithms, namely, minimum distance scheduling (MDS) and maximum channel gain scheduling (MCS), are proposed; when these algorithms were used alongside a power optimization, they led to improved network efficiency. Further, the network’s performance was measured with parametric variations in the number of access points (APs); the deployment of APs and AP configuration is carried out for different precoding schemes. The impact of spatial correlation and the access to perfect channel state information (CSI) on the spectral efficiency of the system was also evaluated. In the end, the study compares the performance of different power-allocation methods and suggests that the power allocated to a particular user node by an AP can be controlled using the proposed algorithms. It is observed that, as compared to the MDS algorithm, the MCS algorithm results in better spectral efficiency for all the users with fractional power allocation. In addition, each AP assigns a maximum power of 141.7 mW to a user with strong channel conditions with the AP, and a minimum power of 3.1882 mW to the user with the worst channel conditions using centralized PMMSE precoding.

Untraceable Authentication Protocol for IEEE802.11s Standard

In the current paper, a new handover authentication protocol for IEEE802.11s Wireless mesh networks is presented. The new protocol divides the network into a number of cells, each cell contains a number of access points and based on the concept of ticket authentication, the mesh user takes a new ticket when enters the region of a new cell which decreases the handover latency. Moreover, in the current paper, a new idea for ticket generation is proposed, called Chain Ticket Derivation Function (CTDF), which uses the concept of a chain. Using CTDF in our proposed protocol raises the level of privacy for the users. The security analysis presented in the paper showed more strengths in our proposed scheme. Two formal verification tools, AVISPA and BAN logic are used to test the proposed protocol.