Search

The promise of automated-driving cars cause the automotive and consumer electronics (CE) sector to rethink what it means to drive, but the relationship between the car and the consumer. Recent trend in Internet of Vehicle Things (IoVT) promotes robust interactions in between humans and vehicles which altimetry points to enhance human abilities such as hearing or emotion awareness as a part of safety concern. The voice-based interactions (speech recognition, stress monitoring) will improve in-time awareness of the vehicle status. Unfortunately, the existing modulation domain speech enhancement techniques achieve low satisfactory performance in detecting humans stress emotions where the environmental noise is inevitable and varies with every passing location of vehicle. In this direction, we propose frontend processing framework, in particular to stress emotion detection cases in different non-stationary noisy environments. This study encompasses three Inter-related issues: (i) analysis, modification, and synthesis of noisy speech emotion in modulation domain in realtime background noise, (ii) extracting set of Mel-frequency cepstral coefficients (MFCC) features from noisy speech stimuli for speech emotion recognition, and (iii) evaluation of overall system performance by means of objective parameters, and confusion matrix in adverse environments using speech emotion database Interactive Emotional Dyadic Motion Capture (IEMOCAP)

With the exponential increase of interconnected communicating devices which make up the Internet of Things (IoT), securing the network transmission and the fifth generation (5G) systems which is the bedrock for IoT concept actualization is becoming more and more challenging. One of the major attacks which poses a great risk to data transmission is the eavesdropper (Eve) attack which occurs in both single input, single output (SISO), multiple input and multiple output (MIMO) systems. Thus, in this study, our focus is to establish a secured connection in a multiple-antenna transmission when the channel state information (CSI) of Eve is unknown to the network users. Our model comprises a secure wireless communication standard where Eve performs either optimal matched filtering (OMF) or a basic matched filtering (BMF) while the transmitting IoT node employs smart jamming strategy in order to compromise the activities of Eve. With respect to this and in attempt to realize maximum privacy, we examined the design of optimal jamming parameters. In the end, the numerical analysis of our investigation indicates that a substantial privacy advantage is achievable while utilizing only full-duplex jamming against using artificial noise from the transmitter only. However, a joint performance of both results shows a higher privacy improvement.

Nature inspired algorithm plays a very vibrant role in solving the different optimization problems these days. The fundamental attitude of naturalistic approaches is to boost the competence, improvement, proficiency, success in the task except from it to help in underrating the energy use, cost, size. Several computing techniques are taking the benefits from nature inspired algorithms for solving their problems related to load balancing, scheduling and many others. These algorithms have come up with lots of improvements in the results. The aim of this analysis is to make efforts in the betterment in different areas of computing and help in solving various problems related to load balancing, scheduling and energy efficiency. The structure of the paper includes an introduction, contribution to the work, background study, which includes the role of nature inspired techniques in a different computing environment, research challenges and its applications. The sustainable goal and objective of the article is to perform the energy efficiency, load balancing and scheduling on different computing systems which include grid, cloud, distributed, fog and edge computing by using various nature inspired algorithms. This comprehensive study gives the awareness and valuable provision for the researchers in this area by providing a thorough study of different computing techniques in different research fields.

Android smartphones are being utilized by a vast majority of users for everyday planning, data exchanges, correspondences, social interaction, business execution, bank transactions, and almost in each walk of everyday lives. With the expansion of human reliance on smartphone technology, cyberattacks against these devices have surged exponentially. Smartphone applications use permissions to utilize various functionalities of the smartphone that can be maneuvered to launch an attack or inject malware by hackers. Existing studies present various approaches to detect Android malware but lack early detection and identification. Accordingly, there is a dire need to craft an efficient mechanism for malicious applications’ detection before they exploit the data. In this paper, a novel approach DeepAMD to defend against real-world Android malware using deep Artificial Neural Network (ANN) has been adopted including an efficiency comparison of DeepAMD with conventional machine learning classifiers and state-of-the-art studies based on performance measures such as accuracy, recall, f-score, and precision. As per the experimental analysis, DeepAMD outperforms other approaches in detecting and identifying malware attacks on both Static as well as Dynamic layers. On the Static layer, DeepAMD achieves the highest accuracy of 93.4% for malware classification, 92.5% for malware category classification, and 90% for malware family classification. On the Dynamic layer, DeepAMD achieves the highest accuracy of 80.3% for malware category classification and 59% for malware family classification in comparison with the state-of-the-art techniques.

Healthcare Informatics is a phenomenon being talked about from the early 21st century in the era in which we are living. With evolution of new computing technologies huge amount of data in healthcare is produced opening several research areas. Managing the massiveness of this data is required while extracting knowledge for decision making is the main concern of today. For this task researchers are doing explorations in big data analytics, deep learning (advanced form of machine learning known as deep neural nets), predictive analytics and various other algorithms to bring innovation in healthcare. Through all these innovations happening it is not wrong to establish that disease prediction with anticipation of its cure is no longer unrealistic. First, Dengue Fever (DF) and then Covid-19 likewise are new outbreak in infectious lethal diseases and diagnosing at all stages is crucial to decrease mortality rate. In case of Diabetes, clinicians and experts are finding challenging the timely diagnosis and analyzing the chances of developing underlying diseases. In this paper, Louvain Mani-Hierarchical Fold Learning healthcare analytics, a hybrid deep learning technique is proposed for medical diagnostics and is tested and validated using real-time dataset of 104 instances of patients with dengue fever made available by Holy Family Hospital, Pakistan and 810 instances found for infectious diseases including prognosis of; Covid-19, SARS, ARDS, Pneumocystis, Streptococcus, Chlamydophila, Klebsiella, Legionella, Lipoid, etc. on GitHub. Louvain Mani-Hierarchical Fold Learning healthcare analytics showed maximum 0.952 correlations between two clusters with Spearman when applied on 240 instances extracted from comorbidities diagnostic data model derived from 15696 endocrine records of multiple visits of 100 patients identified by a unique ID. Accuracy for induced rules is evaluated by Laplace (Fig. 8) as 0.727, 0.701 and 0.203 for 41, 18 and 24 rules, respectively. Endocrine diagnostic data is made available by Shifa International Hospital, Islamabad, Pakistan. Our results show that in future this algorithm may be tested for diagnostics on healthcare big data.

Industrial Internet of Things (IIoT) is a convincing stage by interfacing different sensors around us to the Internet, giving incredible chances for the acknowledgment of brilliant living. It is a fast growing technology in the present scenario. IIoT has its effect on almost every advanced field in the society. It has impact not only on work, but also on the living style of individual and organization. Due to high availability of internet, the connecting cost is decreasing and more advanced systems has been developed with Wi-Fi capabilities. The concept of connecting any device with internet is “IIoT”, which is becoming new rule for the future. This manuscript discusses about the applications of Internet of Things in different areas like — automotive industries, embedded devices, environment monitoring, agriculture, construction, smart grid, health care, etc. A regressive review of the existing systems of the automotive industry, emergency response, and chain management on IIoT has been carried out, and it is observed that IIoT found its place almost in every field of technology.

Software as a Service (SaaS) is a paradigm that provides end users or clients easy and seamless access to various applications, using the Internet without requiring any infrastructure or related software. To virtualize the access to applications and functionalities, SaaS providers use the cloud-computing environment to rent resources, thereby reducing both the capital and operational expenditure. SaaS uses the cloud-computing infrastructure to distribute applications to many users, irrespective of their location or infrastructure capacity. This one-to-many model with centralized control has the potential to transform the behavior of traditional IT architecture, its pricing, partnering, and management. With the emergence of SaaS as a delivery system, the whole software environment is moving toward a producer-consumer system, where both are distributed globally. We point out the motivation for accepting the new technologies, such as to reduce the expense, energy consumption, maintenance, etc. We also point out the challenges and risks associated with the paradigm shift. The present study categorically presents state-of-the-art research on the security issues of collaborative SaaS cloud computing and integrating service-level-agreement and quality-of-service-related issues of SaaS in cloud-computing environments.

This paper addresses traffic engineering (TE) issues in software-defined vehicular networking (SDVN). A brief analysis of the features of SDVN, which improves the efficiency of TE in SDVN, is presented. The feasibility of using multi-path routing with TE is substantiated. A procedure and an example of the formation of multiple routes based on a modified wave routing algorithm are given. Considering the features of the SDVN technology, a modified TE method is proposed, which reduces both the time complexity of forming multiple paths and the path reconfiguration time. The dynamic path reconfiguration algorithm is presented.

Optical switching networks (OSN) rely on both hardware components and signals for performing efficient switching operations achieving service level requirements of the end-user. In this manuscript, an incremental learning-based wavelength assignment is introduced to minimize asynchronous channel selection and dispersion-free switch-over in OSN. This method accounts on the wavelength dispersion characteristics of the light path for establishing and reconnecting communications between end-to-end devices. The learning process segregates favorable and conflict channels based on blocking probability and channel capacity assigned to the optical users. A conventional wavelength division multiplexing technique is employed for improving data transmission rates. This method is appropriate for evading blocking rates in optical networks to improve throughput rate and to leverage OSN performance. Machine learning (ML) paradigm is designed for OSN leverages the communication rate influenced by channel conflicts and utilization capacity. Therefore, the number of transmissions likely requires optimal switch-over with the knowledge of channel constraints to improve the performance of OSN.

Internet of Things (IoT) has revolutionized the digital world by connecting billions of electronic devices over the internet. IoT devices play an essential role in the modern era when conventional devices become more autonomous and smart. On the one hand, high-speed data transfer is a major issue where the 5G-enabled environment plays an important role. On the other hand, these IoT devices transfer the data by using protocols based on centralized architecture and may cause several security issues for the data. Merging artificial intelligence to 5G wireless systems solves several issues such as autonomous robots, self-driving vehicles, virtual reality, and engender security problems. Building trust among the network users without trusting third party authorities is the system's primary concern. Blockchain emerged as a key technology based on a distributed ledger to maintain the network's event logs. Blockchain provides a secure, decentralized, and trustless environment for IoT devices. However, integrating IoT and blockchain also has several challenges; for example, major challenge is low throughput. Currently, the ethereum blockchain network can process approximately 12 to 15 transactions per second, while IoT devices require relatively higher throughput. Therefore, blockchains are incapable of providing functionality for a 5G-enabled IoT based network. The limiting factor of throughput in the blockchain is their network. The slow propagation of transactions and blocks in the P2P network does not allow miners and verifiers to fastly mine and verify new blocks, respectively. Therefore, network scalability is the major issue of IoT based blockchains. In this work, we solved the network scalability issue using blockchain distributed network while to increase the throughput of blockchain, this article uses the Raft consensus algorithm. Another most important issue with IoT networks is privacy. Unfortunately, the blockchain distributed ledgers are public and sensitive information is available on the network for everyone are private, but in such cases, third party editing is not possible without revealing the original contents. To solve privacy issues, we used zkLedger as a solution that is based on zero knowledge-based cryptography.

The COVID-19 pandemic has caused hundreds of thousands of deaths, millions of infections worldwide, and the loss of trillions of dollars for many large economies. It poses a grave threat to the human population with an excessive number of patients constituting an unprecedented challenge with which health systems have to cope. Researchers from many domains have devised diverse approaches for the timely diagnosis of COVID-19 to facilitate medical responses. In the same vein, a wide variety of research studies have investigated underlying medical conditions for indicators suggesting the severity and mortality of, and role of age groups and gender on, the probability of COVID-19 infection. This study aimed to review, analyze, and critically appraise published works that report on various factors to explain their relationship with COVID-19. Such studies span a wide range, including descriptive analyses, ratio analyses, cohort, prospective and retrospective studies. Various studies that describe indicators to determine the probability of infection among the general population, as well as the risk factors associated with severe illness and mortality, are critically analyzed and these findings are discussed in detail. A comprehensive analysis was conducted on research studies that investigated the perceived differences in vulnerability of different age groups and genders to severe outcomes of COVID-19. Studies incorporating important demographic, health, and socioeconomic characteristics are highlighted to emphasize their importance. Predominantly, the lack of an appropriated dataset that contains demographic, personal health, and socioeconomic information implicates the efficacy and efficiency of the discussed methods. Results are overstated on the part of both exclusion of quarantined and patients with mild symptoms and inclusion of the data from hospitals where the majority of the cases are potentially ill.

Blockchain technology can solve current interoperability challenges in health information systems and provide the technical standard which ensures a secure sharing of electronic health data between individuals, healthcare providers, medical care institutions, and medical experts. In healthcare management, IoMT devices can deliver real-time sensory data from patients to be analyzed and processed. In IoMT devices within a range of product areas, the confidentiality of patients’ health data transmission and privacy is still a big problem. In this paper, the Multi-Modal Secure Data Dissemination Framework (MMSDDF) has been proposed based on blockchain in IoMT for secure patient data access and control. To meet the optimized security and privacy requirements of healthcare data management in IoMT devices, the proposed framework is effectively employed. Blockchain’s key has been used to a healthcare application network where the patient’s health data can create warnings that are significant to authenticated healthcare providers securely. The simulation outcomes show that the suggested MMSDDF method achieves a high accuracy ratio of 95.8%, a prediction ratio of 92.3%, less delay of 0.48 s, latency range of 0.5, and response time of 1.5% to other existing methods.

The global demand for electricity has visualized high growth with the rapid growth in population and economy. It thus becomes necessary to efficiently distribute electricity to households and industries in order to reduce power loss. Smart Grids (SG) have the potential to reduce such power losses during power distribution. Machine learning and artificial intelligence techniques have been successfully implemented on SGs to achieve enhanced accuracy in customer demand prediction. There exists a dire need to analyze and evaluate the various machine learning algorithms, thereby identify the most suitable one to be applied to SGs. In the present work, several state-of-the-art machine learning algorithms, namely Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Logistic Regression, Naive Bayes, Neural Networks, and Decision Tree classifier, have been deployed for predicting the stability of the SG. The SG dataset used in the study is publicly available collected from UC Irvine (UCI) machine learning repository. The experimentation results highlighted the superiority of the Decision Tree classification algorithm, which outperformed the other state of the art algorithms yielding 100% precision, 99.9% recall, 100% F1 score, and 99.96% accuracy.

In developed nations, the advent of distributed ledger technology is emerging as a new instrument for improving the traditional system in developing nations. Indeed, adopting blockchain technology is a necessary condition for the coming future of organizations. The distributed ledger technology provides better transparency and visibility. This study investigated the features that may influence the behavioral intention of energy experts to implement the distributed ledger technology for the energy management of developing countries. The proposed model is based on the Technology Acceptance Model construct and the diffusion of the innovation construct. Based on a survey of 178 experts working in the energy sector, the proposed model was tested using structural equation modeling. The findings showed that perceived ease of use, perceived usefulness, attitude, and cost saving had a positive and significant impact during the blockchain technology adoption. However, innovativeness showed a positive effect on the perceived ease of use whereas an insignificant impact on the perceived usefulness. The present study offers a holistic model for the implementation of innovative technologies. For the developers, it suggest rising disruptive technology solutions.

In the modern world, there is a huge increase in the usage of various technologies which may produce heterogeneous data. Using the latest security algorithms like Triple Data Encryption Standard (TDES), Rivest Shamir Adleman (RSA), Advanced Encryption Standard (AES) these (heterogeneous) data are transferred over the internet. Despite of such good security mechanisms, there exist vulnerabilities to the user data. In the recent years, researchers started the usage of bio-inspired computing models towards security. In this paper, two such bio-inspired computing models namely splicing system and Deoxyribonucleic acid (DNA) sequencing were used. We proposed a novel cryptosystem using contextual array splicing system for DNA sequenced input data. In this process, we have designed Turing machine(s) that perform(s) the conversion of a binary input to DNA sequence and the cryptosystem operation using contextual array splicing system. Next, the developed novel cryptosystem is applied in the domain of Medical Internet of Things (MIoT). Further, we had investigated the proof of correctness. Next, the proposed cryptosystems are proved to be Turing computable. Finally, the security analysis and evaluation of the proposed cryptosystem has been studied.

In the recent years, important key factor for urban planning is to analyze the sustainability and its functionality towards smart cities. Presently, many researchers employ the conservative machine learning based analysis but those are not appropriate for IoT based health data analysis because of their physical feature extraction and low accuracy. In this paper, we propose remote health monitoring and data analysis by integrating IoT and deep learning concepts. We proposed novel IoT based FoG assisted cloud network architecture that accumulates real-time health care data from patients via several medical IoT sensor networks, these data are analyzed using a deep learning algorithm deployed at Fog based Healthcare Platform. Furthermore, the proposed methodology is applied to the sustainable smart cities to evaluate the process for real-time. The proposed framework not only analyses the healthcare data but also provides immediate relief measures to the patient facing critical conditions and needs immediate consultancy of doctor. Performance is measure in terms of accuracy, precision and sensitivity of the proposed DHNN with task scheduling algorithm and it is obtained 97.6%, 97.9%, and 94.9%. While accuracy, precision and sensitivity for deep CNN is 96.5%, 97.5% and 94% and for Deep auto-encoder is 92%, 91%, and 82.5%.

Management of the post-traumatic open abdomen (OA) using negative pressure wound therapy (NPWT) alone is associated with low rates of primary fascial closure. The abdominal reapproximation anchor (ABRA) system exerts dynamic medial fascial traction and may work synergistically with NPWT to facilitate primary fascial closure. Patients with an OA following trauma laparotomy between 2009 and 2018 were identified from a prospectively maintained institutional database. Patients treated with ABRA in conjunction with NPWT (ABRA) versus NPWT alone (NPWT) were compared in terms of primary fascial closure rate, number of surgeries to closure, tracheostomy duration, length of stay and incidence of entero-atmospheric fistula. Multivariable linear regression was performed to identify predictors of tracheostomy duration. We identified 48 patients [ABRA, 12 and NPWT, 36]. The ABRA group was significantly younger (25 vs. 37 years, p = 0.027) and included a lower proportion of males (58% vs. 89%, p = 0.032). Groups were similar with respect to the incidence of hollow viscus injury, injury severity score and abdominal abbreviated injury score. Compared to the NPWT group, the ABRA group had a significantly higher rate of primary fascial closure (100% vs. 28%, p

Background: Bone regeneration in dehiscence and fenestration defect can be improved with the use of platelet rich fibrin (PRF) that provides a scaffold for new bone regeneration. This study was conducted to assess the effectiveness of PRF as a graft material and membrane in dehiscence and fenestration defects. Materials and Methods: This prospective clinical study included patients who received dental implants that demonstrated peri-implant defects which were augmented using Leukocyte- PRF (L-PRF) or Advanced-PRF (A-PRF). Twenty four weeks postoperatively the defect resolution and the density of regenerated bone were assessed by CBCT and re-entry surgery. The assessment also included measurement of primary and secondary implant stability using Periotest® M, success rate and complication rate of the installed implants. Results: The mean overall intraoperative defect size was 29.44 (± 14.1) mm2, postoperatively it became 2.07 (± 3.6) mm2 with a statistically significant difference (p= < 0.0001). There was no significant difference between L-PRF and A-PRF. Defect resolution ranged from 80% to 100% with a mean of 95.7% (± 6.7%). Defects that showed complete resolution were significantly smaller in size (21.2± 7 mm2) than those that showed partial resolution (44.4± 11 mm2). The overall mean primary stability recorded was 2.9 (± 1.6) Periotest values (PTV) and overall mean secondary stability was -0.22 (±1.4) (P

Cloudlet is an important part of providing cloud services in Mobile Edge Computing (MEC) with sustainability. As the number of mobile users grows rapidly in the current era, the load in the cloudlet becomes very high. The cloudlet is considered in the middle layer for providing cloud services with low latency and energy efficiency. Hence the task allocation and scheduling inside of the cloudlet is a challenging job. In the recent past, many research works conducted without considering real-time network parameters. In this work, a heuristic load balancing strategy is designed and analyzed to minimize the task completion time and makespan, which enhances the efficiency of cloud services. The proposed method is considered a dynamic task allocation strategy to the cloudlet concerning network bandwidth, network delay, energy efficiency, and this approach is compared with the queue-based task allocation strategy. The experiment is conducted to compare the proposed method with the recently developed standard algorithms over the synthetic dataset. Experimental results of the proposed work show a significant improvement in task scheduling in terms of energy and execution cost, as well as time, compared to the existing methods. The proposed method outperforms the standard algorithm in most of the observed cases with sustainability.

Wearable wireless sensors, or Wireless Body Sensor Network (WBSN), is one of the most recent fields of research in healthcare applications. The sensors are placed on the human body to detect the critical medical parameters and transmit the collected data to a centralized node. Though the devices work at low energy and power constraints, maintenance of such devices is a crucial aspect. In light of this requirement, an efficient medium access control (MAC) protocol should be utilized to extend the life time of the network. In this paper, we propose a new “Priority based Energy Efficient (PrEE) MAC protocol” incorporated with various priorities in predefined devices based on data sensing and normal devices changing to priority when the value is more than the threshold, by modifying IEEE 802.15.4 header bits. The PrEE MAC protocol is designed based on the IEEE 802.15.4 and its Superframe structure. The simulation results show that the energy efficiency and lifetime of the proposed MAC protocol is more as compared to the existing MAC protocol.

Recently, there is an exponential increase in the multimedia and other data over the Internet of Things (IoT). This data is generally send to the cloud for processing and storage. The fog layer in-between readily bridges communication among the IoT devices and the cloud. It delivers services efficiently by computing and analyzing various multimedia information generated by the IoT devices residing on the sensors. However, provision of effective security and energy are critical challenges. The purpose of this work is to enhance the secure transfer of information like multimedia. This scheme uses Message Queue Telemetry Transport (MQTT) protocol over SSL/TLS. Since MQTT is vulnerable to eavesdropping, the Elliptic curve-ElGamal cryptography algorithm is introduced which lends a homomorphic factor thereby mitigating man-in-the-middle attack. The dynamic key change and proportional offloading of data as proposed in the current research work helps to preserve node energy by selectively transferring data to the cloud and the fog according to the data topic. The results depict that the system security and lifetime can be improved in comparison to the existing protocols.

In this paper, we present an identity (ID) based protocol that secures AODV and TCP so that it can be used in dynamic and attack prone environments of mobile ad hoc networks. The proposed protocol protects AODV using Sequential Aggregate Signatures (SAS) based on RSA. It also generates a session key for each pair of source-destination nodes of a MANET for securing the end-to-end transmitted data. Here each node has an ID which is evaluated from its public key and the messages that are sent are authenticated with a signature/ MAC. The proposed scheme does not allow a node to change its ID throughout the network lifetime. Thus it makes the network secure against attacks that target AODV and TCP in MANET. We present performance analysis to validate our claim.

In this paper, we present a clustering-based energy aware secure routing protocol that computes trustworthy and energy efficient secure routes for wireless ad hoc networks (WANETs). Clustering in ad hoc networking paradigm is very useful for achieving scalability and robustness as it helps to save the bandwidth, to take simpler routing decisions, thus results in decreased energy dissipation of the network. WANETs are often deployed in such places where recharge or replace of energy sources is difficult. Further, unpredicted mobility and quasi-static nature of nodes and dynamic network topology yield a serious need for battery awareness during routing. In light of this, we first provide a definition of network lifetime based on application-specific criteria. Also a mathematical model for calculating residual battery power has been framed to propose an energy aware routing protocol. Trust-based scheme is used to assure secure end-to-end delivery of packets in the network. Extensive simulation has be...

TCP is a reliable transport protocol designed to perform well in wired networks where packet losses are due congestion. However in wireless ad hoc networks, where packet losses are due to channel errors or link failures between mobile nodes, TCP degrades its performance. Further, it lacks certain protection mechanisms from internal and external malicious nodes. In this paper, a security enhanced and adaptive TCP, namely SA-TCP, has been proposed for wireless ad hoc networks. SA-TCP uses network layer information to detect various types of packet losses and adjusts the value of congestion window dynamically according to the conditions of the dynamic network. It works normally to collect the samples of congestion window and calculates the mean from these samples to set the value of future congestion window. SA-TCP also adjusts the value of congestion window limit according to network conditions. In order to make SA-TCP suitable in highly vulnerable wireless ad hoc networks, a less complex identity-based public key cryptography has been integrated with the proposed protocol. The three-way handshaking process of SA-TCP is made secure by generating a secret session key on-fly between source-destination. Simulation results show that SA-TCP gives higher throughput compared to the popular New Reno and ATCP in different wireless ad hoc network scenarios.

This paper proposes a novel quantitative trust model that supports both first-hand (direct) and second-hand (recommendation) trust opinion to calculate the final trust of each node in the network. In order to compute direct trust of a node, the proposed trust model utilizes the theory of ARMA/GARCH to predict the trust parameters and a probabilistic model to combine those parameters. The proposed trust model collects recommendation trusts from common neighbors of the node under review and combines these trusts to get the resultant trust using a weighted combination model. Based on proposed trust model, a routing protocol has been driven to provide trustworthy routes. Simulation results show that the proposed protocol gives significantly higher packet delivery fraction as compared to CBRP even in presence of malicious nodes in the network. In summary, our proposed model is lightweight in terms of computation and powerful in terms of flexibility and accuracy in managing trust in WANET.

In this poster we have proposed a novel network virtualization framework for information centric data center networks that decouples the control plane and data plane, where the fully distributed data plane is controlled and managed by a well-defined and centralized control plane. Exploiting some network virtualization notion, the proposed framework provides an easily and fast deployable solution to information centric data center networks to provide several virtual networks on same physical network and efficiently handle client requests. It also deals with reliable access to virtual networks as well as mapping of virtual and physical network elements. The proposed framework utilizes the efficiency of information centric networking by providing in-network caching and faster access to requested data.

In this paper, we propose a secure and adaptive transmission control protocol (SA-TCP) for wireless ad hoc networks that utilizes network layer information to detect various types of packet losses and adjusts the value of congestion window (CWND) dynamically according to the dynamic network conditions. SA-TCP operates normally to collect the samples of CWND and takes the mean from these samples to set for the future samples. It also adjusts the value of congestion window limit (CWL) according to the dynamic network environment. A low complexity identity-based public key cryptography has been integrated with SA-TCP to make it suitable in highly vulnerable wireless ad hoc networks. A secret session key is generated on-fly between source and destination nodes to secure the three-way handshaking process of SA-TCP. Simulation results are given to show that SA-TCP outperforms the popular New Reno and ATCP in terms of throughput in different network scenarios.