Department of Computer Science

The main motivation of the research paper is to analyze different performance parameters of three well known Ad-hoc network routing protocols (AODV, DSDV, DSR) with varying node density and velocity. As in real world the movements of nodes are almost always random, therefore we selected the random way point mobility model. Ad hoc network is an active research area now-a-days. Plenty of literature is available in this field. The researcher has developed many Routing Algorithms for effective routing in MANETs. Similarly researchers also have analyzed these protocols in different scenarios. The Research area which needs more work & is still demanding the researcher attention is to work out efficiency of such networks in reliable ( suitable for delay tolerant and error sensitive data) & unreliable (for delay sensitive and error tolerant information such voice and video streaming) transport layer protocols. In this study we evaluate the behavior of different Ad -hoc network routing protocols (AODV, DSDV, DSR) under reliable TCP and unreliable UDP transport layer protocols with variable node density and velocity. From simulation results we observed that each protocol perform in a different way with different node density and velocity.

DDoS attacks are launched through sending a large quantity of packets to a target machine, using instantaneous teamwork of multiple hosts which are distributed throughout the Grid computing environment. Today DDoS attacks on the Internet in general and especially in Grid Computing environment has become a visible issue in computer networks. DDoS attacks are easy to generate but their detection is a very difficult task and therefore, an attractive weapon for hackers. DDoS streams do not have familiar characteristics, therefore currently available IDS cannot detect these attacks perfectly. Similarly, there implementation is a challenging task. In practice, Gossip based DDoS attacks detection mechanism is used to detect such types of attacks in network, by exchanging traffic over line. Gossip based techniques results in network congestion and have overhead of extra packets. Keeping the above drawbacks in mind, we are going to propose a DDoS detection and prevention mechanism, that has the beauty of being easy to adapt and more reliable than existing counterparts. We are going to introduce entropy based detection mechanism for DDoS attack detection. Our proposed solution has no overhead of extra packets, hence resulting in good QoS. Once DDoS is detected, any prevention technique can be used to prevent DDoS in Grid environment. Index Terms-Normalized entropy (NE), denial of service (DoS), grid simulator (GridSim).

A real-time system must respond fast enough that it can serve the task in a particular time interval. The key constraints in real-time systems are to meet timing bounds and for these systems it is necessary to complete all of their tasks in time. Due to increasing complexity of real-time applications, powerful processors are needed to accommodate computational starving applications. Multi-core processor is a solution to such applications. However, multi-core processor is still in its immaturity stage and there is need to address the multi-core partitioning issues with perspective that all cores are equally utilized. More powerful processors are required to execute such applications. Single core processor are not enough capable to meet the increasing complexity of real-time applications. Multiple single core processors in any system require more power consumption which is not tolerable. Multi-core processor provides the solution to complex and computational starving real-time applications. Multi-core processors can provide higher computational power at lower power consumption. Distribution of tasks deals with partitioning the given workload on all processing cores in such a way that all tasks must meet their deadlines. Advantage of multi-core processors can only be fully realized if all cores have equal workload. No workload partitioning technique has been proposed so far that ensures the distribution of workload on all cores equally. Recently, multi-core systems have presented a research challenge to real-time system designers form scheduling perspectives and a lot of attention is being devoted to this research area, however, to the best of our knowledge, no solution addresses real-time systems issues associated with scheduling. The aim of this research is to answer the scheduling problem for multi-core processors and an efficient workload partitioning technique that can fully utilize all the processing cores in multi-core system.

A vehicular Ad-hoc network is composed of moving cars as nodes without any infrastructure. Nodes selforganize to form a network over radio links. Security issues are commonly observed in vehicular ad hoc networks; like authentication and authorization issues. Secure Clustering plays a significant role in VANETs. In recent years, various secure clustering techniques with distinguishing feature have been newly proposed. In order to provide a comprehensive understanding of these techniques are designed for VANETs and pave the way for the further research, a survey of the secure clustering techniques is discussed in detail in this paper. Qualitatively, as a result of highlighting various techniques of secure clustering certain conclusions are drawn which will enhance the availability and security of vehicular ad hoc networks. Nodes present in the clusters will work more efficiently and the message passing within the nodes will also get more authenticated from the cluster heads.

Named Data Networking (NDN) is one of the future envisioned networking paradigm used to provide fast and efficient content dissemination with interest-based content retrieval, name-based routing and in-network content caching. On the one hand, this new breed of future Internet architecture is becoming a key technology for data dissemination in the IoT networks; on the other hand, NDN suffers from new challenges in terms of data security. Among them, a content poisoning attack is the most common data security challenge. The aim of this attack is to inject poisoned content with an invalid signature to the network. Therefore, to prevent NDN against possible content poisoning attack, a signature of the contents is appended to each data packet for verifications. In this paper, we propose an identity-based signature scheme for IoT-based NDN networks, with a special emphasis on content integrity and authenticity. The proposed scheme is based on the concept of the Hyperelliptic curves, which provide the same level of security as Rivest-Shamir-Adleman (RSA), Bilinear pairing and Elliptic Curve Cryptosystems (ECC) with lower-key size. The proposed scheme is subject to both formal and informal security analysis in order to show the feasibility of our scheme. Finally, the performance of the proposed scheme is analyzed via comparison with the relevant existing schemes that authenticates the superiority of our scheme in terms of security and efficiency. INDEX TERMS Content poisoning attack, named data networking (NDN), Internet of Things, identity-based signature.

Unlike commercial networks, the tactical networks drive in a critical environment and without a backbone infrastructure. These networks involve mission-critical operations that are dependent on the rapid and reliable transfer of delay-sensitive data to conduct command and control (C2). Owing to the decentralized and dynamic nature, tactical networks need to survive by maintaining seamless and simultaneous time-sensitive communication among software-defined radios (SDRs). Under mobility and dynamic network topology, link status continuously changes that cause substantial packet loss, and degrade network performance. It is challenging to maintain the connectivity between communicating nodes and find a suitable time for sending control messages (e.g., packet forwarding and route discovery), known as control phase time (CPT). Given a maximum transmission range for narrowband (NB) and wideband (WB) communication, the knowledge of link duration between communicating radios are of major concern, particularly for low latency and reliable communication requirements. Many existing techniques focus on topology control by exchanging mobility parameters in control transmissions, increasing the delay in data transmission. Therefore, it is a non-trivial task to calculate the expected time for the control transmissions due to the confrontation of speed and random movement of nodes. This paper presents a novel methodology to estimate a suitable time to execute the control phase based on the lifetime of communication links between SDRs in tactical MANETs. It uses stochastic distribution to make a network capable of effectively figuring out operative connectivity. The proposed methodology evaluates the maximum network connectivity based on the distances between communicating radios and radio transmission ranges for different quality-of-service (QoS) requirements. The simulation results validate that the proposed methodology's CPT estimations are more appropriate for the timely link-formations in tactical radio MANETs. The proposed technique is generic and can be applied to any MANET environment using different mobility models. INDEX TERMS Mobile ad hoc networks, tactical network, time-critical data, software-defined radio.

With population growth, the demand for vehicles has increased tremendously, which has created an alarming situation in terms of traffic hazards and road accidents. The road accidents percentage is growing exponentially and so are the fatalities caused due to accidents. However, the primary cause of the increased rate of fatalities is due to the delay in emergency services. Many lives could be saved with efficient rescue services. The delay happens due to traffic congestion or unstable communication to the medical units. The implementation of automatic road accident detection systems to provide timely aid is crucial. Many solutions have been proposed in the literature for automatic accident detection. The techniques include crash prediction using smartphones, vehicular ad-hoc networks, GPS/GSM based systems, and various machine learning techniques. With such high rates of deaths associated with road accidents, road safety is the most critical sector that demands significant exploration. In this paper, we present a critical analysis of various existing methodologies used for predicting and preventing road accidents, highlighting their strengths, limitations, and challenges that need to be addressed to ensure road safety and save valuable lives.

Transactions related to vehicles include manufacturing, buying, selling, paying insurance(takaful), obtaining regular inspection, leasing a vehicle from banks, getting in an accident, engaging in a traffic violation, calculating price predictions and renting a vehicle. Many people perform transactions related to vehicles in their daily life; transportation authorities also perform vehicle transactions as part of managing vehicle fleets. But tracking these transactions is a challenging task. There are countrywide solutions that uses centralized systems. However, these solutions have problems with trust management, transparency, and access control. Therefore, we believe there is still room for integrated automation of various vehicle-related transactions. In this paper, we present a blockchain-based framework for vehicle tracking that incorporates the mentioned features. Moreover, blockchain is customized to enable usage control for additional transactions, such as inspection, renting and islamic insurance. The usage control model is integrated with IoT devices to continuously monitor the vehicles for certain conditions and remotely revoke access if needed. The complete transaction set is recorded over an immutable ledger that provides trust, transparency and a complete history of record. In this paper, we also presents a prototype implementation of a permissioned blockchain, which will be made available under the GNUv3 General Public License. Performance analysis is performed on the newly proposed framework implementation over the permissioned blockchain to measure its adoption and suitability. INDEX TERMS Blockchain, decentralized applications, smart contracts, vehicle life cycle tracking.

With the exponential rise in government and private health-supported schemes, the number of fraudulent billing cases is also increasing. Detection of fraudulent transactions in healthcare systems is an exigent task due to intricate relationships among dynamic elements, including doctors, patients, and services. Hence, to introduce transparency in health support programs, there is a need to develop intelligent fraud detection models for tracing the loopholes in existing procedures, so that the fraudulent medical billing cases can be accurately identified. Moreover, there is also a need to optimize both the cost burden for the service provider and medical benefits for the client. This paper presents a novel process-based fraud detection methodology to detect insurance claim-related frauds in the healthcare system using sequence mining concepts. Recent literature focuses on the amount-based analysis or medication versus disease sequential analysis rather than detecting frauds using sequence generation of services within each specialty. The proposed methodology generates frequent sequences with different pattern lengths. The confidence values and confidence level are computed for each sequence. The sequence rule engine generates frequent sequences along with confidence values for each hospital's specialty and compares them with the actual patient values. This identifies anomalies as both sequences would not be compliant with the rule engine's sequences. The process-based fraud detection methodology is validated using last five years of a local hospital's transactional data that includes many reported cases of fraudulent activities.

In the task of data routing in Internet of Things enabled volatile underwater environments, providing better transmission and maximizing network communication performance are always challenging. Many network issues such as void holes and network isolation occur because of long routing distances between nodes. Void holes usually occur around the sink because nodes die early due to the high energy consumed to forward packets sent and received from other nodes. These void holes are a major challenge for I-UWSANs and cause high end-to-end delay, data packet loss, and energy consumption. They also affect the data delivery ratio. Hence, this paper presents an energy efficient watchman based flooding algorithm to address void holes. First, the proposed technique is formally verified by the Z-Eves toolbox to ensure its validity and correctness. Second, simulation is used to evaluate the energy consumption, packet loss, packet delivery ratio, and throughput of the network. The results are compared with well-known algorithms like energy-aware scalable reliable and void-hole mitigation routing and angle based flooding. The extensive results show that the proposed algorithm performs better than the benchmark techniques.

Gender inclusion and female participation in STEM courses have always been challenging in a patriarchal country like Pakistan. Women are often underrepresented in engineering fields, as engineering fields are usually considered manly. Female students confront many challenges, be it workplace or educational institutes. Some of the self-perceived and determined challenges are discussed in this study. As determined through this research study, factors affecting female participation are low STEM confidence, Gender disparity, Social and Academic Support, and Learning Environment. Empirical Evidence is collected using a questionnaire with 25 items based on the Likert scale. The dependent variables are STEM Confidence and Learning Environment, whereas the independent variables are Social and Academic Support. Participants included 170 female UG and PG engineering students from 4 universities in Pakistan. Data were collected through the Questionnaire, which included an online survey. Correl...

Fog computing is a technology that brings computing and storage resources near to the end user. Being in its infancy, fog computing lacks standardization in terms of architectures and simulation platforms. There are a number of fog simulators available today, among which a few are open-source, whereas rest are commercially available. The existing fog simulators mainly focus on a number of devices that can be simulated. Generally, the existing simulators are more inclined toward sensors' configurations, where sensors generate raw data and fog nodes are used to intelligently process the data before sending to back-end cloud or other nodes. Therefore, these simulators lack network properties and assume reliable and error-free delivery on every service request. Moreover, no simulator allows researchers to incorporate their own fog nodes management algorithms, such as scheduling. In existing work, device handover is also not supported. In this paper, we propose a new fog simulator called FogNetSim++ 1 that provides users with detailed configuration options to simulate a large fog network. It enables researchers to incorporate customized mobility models and fog node scheduling algorithms, and manage handover mechanisms. In our evaluation setup, a traffic management system is evaluated to demonstrate the scalability and effectiveness of proposed simulator in terms of CPU and memory usage. We have also benchmarked the network parameters, such as execution delay, packet error rate, handovers, and latency.

Streaming large amounts of data to cloud data centers cause network congestion resulting in high network and energy consumption. The concept of fog computing is introduced to reduce workload from backbone networks and support delay-sensitive Internet of Things (IoT) applications. The concept places compute, storage, and network services closer to the source of the requests. In general fog-based simulators are used for better understanding and optimum fog resource allocation. Unfortunately, most of the simulators lack core features like network delay, latency, packet error rate, energy consumption, and distributed fog node management. In this paper, we propose a fog simulation framework termed as xFogSim to support latency-sensitive applications at the fog layer with multi-objective optimization to trade-off cost, availability, and performance among the fog federation. Moreover, during peak load, the framework provides locality-aware distributed broker node management that enables borrowing resources from nearby fog locations to meet service and energy requirements. The results show that the framework is lightweight, configurable, and scalable, capable of handling a large number of user requests using dynamic resource provisioning across the fog federation.

The main motivation of the research paper is to analyze different performance parameters of three well known Ad-hoc network routing protocols (AODV, DSDV, DSR) with varying node density and velocity. As in real world the movements of nodes are almost always random, therefore we selected the random way point mobility model. Ad hoc network is an active research area now-a-days. Plenty of literature is available in this field. The researcher has developed many Routing Algorithms for effective routing in MANETs. Similarly researchers also have analyzed these protocols in different scenarios. The Research area which needs more work & is still demanding the researcher attention is to work out efficiency of such networks in reliable ( suitable for delay tolerant and error sensitive data) & unreliable (for delay sensitive and error tolerant information such voice and video streaming) transport layer protocols. In this study we evaluate the behavior of different Ad -hoc network routing protocols (AODV, DSDV, DSR) under reliable TCP and unreliable UDP transport layer protocols with variable node density and velocity. From simulation results we observed that each protocol perform in a different way with different node density and velocity.