Peer Azmat Shah

Review: Performance comparison of end-to-end mobility management protocols for TCP

Mobility management for TCP connections has been discussed in literature since last several years. Many mobility management solutions have been proposed including end-to-end solutions and infrastructure dependent solutions. These solutions operate at different layers of TCP/IP protocol stack. End-to-end mobility management solutions have the advantage of not requiring the deployment of additional entities in network infrastructure to handle mobility management as compared to the infrastructure dependant solutions. Performance analysis of these end-to-end mobility management protocols seems missing in literature. In this paper, we analyze the performance of different end-to-end mobility management protocols using mathematical modeling. Evaluation has been carried out for evaluating the handover delay, throughput degradation time and protocol overhead. Some security and implementation issues pertaining to these protocols have also been discussed. Results show that protocols that allow simultaneous communication through multiple network interfaces provide throughput stability during vertical handovers, thus facilitating smooth handovers.

MAC Layer Challenges and Proposed Protocols for Vehicular Ad-hoc Networks

Vehicular ad-hoc network (VANET ) is a special form of mobile ad-hoc network (MANET) in which vehicles communicate with each other by creating an ad-hoc network. Before the deployment of VANET, it is necessary to address some important issues of VANET, specially concerning about architecture, routing, mobility, and security. Medium access control (MAC) protocols specify the way in which nodes share the underlying channel. As no standard exists for VANET, the research community has previously used IEEE 802.11a and 802.11b as the MAC layer access technologies. In this paper, we have discussed that the main challenge for VANET safety applications is to design an efficient MAC, so that all safety-related messages can be sent on time and such a protocol should be reliable because human lives are involved in the case of VANET. First, the challenges and requirements of a medium access protocol for VANET are discussed and then a survey of MAC solutions available in the literature to deal with these challenges in VANET is presented.

Grey wolf optimization based clustering algorithm for vehicular ad-hoc networks

Abstract In vehicular ad-hoc network (VANETs), frequent topology changes occur due to fast moving nature of mobile nodes. This random topology creates instability that leads to scalability issues. To overcome this problem, clustering can be performed. Existing approaches for clustering in VANETs generate large number of cluster-heads which utilize the scarce wireless resources resulting in degraded performance. In this article, grey wolf optimization based clustering algorithm for VANETs is proposed, that replicates the social behaviour and hunting mechanism of grey wolfs for creating efficient clusters. The linearly decreasing factor of grey wolf nature enforces to converge earlier, which provides the optimized number of clusters. The proposed method is compared with well- known meta-heuristics from literature and results show that it provides optimal outcomes that lead to a robust routing protocol for clustering of VANETs, which is appropriate for highways and can accomplish quality communication, confirming reliable delivery of information to each vehicle.

Guaranteed QoS for UDP and TCP Flows to Measure Throughput in VANETs

The level of Quality of Service (QoS) guarantees in vehicular ad hoc networks (VANETs) is much more tasking and challenging as result of rapid topology changing and high mobility of mobile hosts. Thus, making multi-hop communication and as well as contention for channel access more difficult. QoS in VANETs is measured in terms of throughput, connection duration and packet loss. In this paper, UDP and TCP protocols are used as traffic to satisfy bandwidth requirement while optimizing network throughput for providing QoS using clustering approach. The results obtained through NCTUns simulator are used for analysis of throughput for both UDP and TCP traffics.

A fluid flow model for SCTP traffic over the Internet

Fluid Flow (FF) model is used to model the behavior of network protocols at the flow level. Protocols that transmit data in bulk form, like TCP and SCTP, can be modeled using this model. These protocols send multiple packets in a single congestion window, making a bulk flow of data in the network, generating events for the FF. We have discussed FF based mathematical model for SCTP, a transport layer protocol. The proposed model has been built at the Network level, Source level and at the Sink level. To test our model, we have performed simulation in NS2 for SCTP. Results obtained through model are similar to simulated results.

An Enhanced Procedure for Mobile IPv6 Route Optimization to Reduce Handover Delay and Signaling Overhead

Mobile IPv6 was proposed to provide mobility support to IPv6 based mobile nodes for continuous communication when they roam across different networks. As mobile nodes are dependent on a central entity home agent, hence due to mobility the path from mobile node to correspondent node via the home agent might be longer as compared to the direct path between them and results in triangular routing. Route Optimization in Mobile IPv6 using Return Routability provides the opportunity to eliminate the inefficient triangular routing between mobile and corresponding nodes and therefore, greatly improves the network performance. However, this Return Routability based route optimization involves the verification of mobile node’s reachability at home address and at care-of address (home test and care-of test) that results in higher handover delays and signalling overhead. This article presents an enhanced procedure for Mobile IPv6 Route Optimization that uses the concepts of shared secret Token and one time password along-with verification of mobile node via direct communication. The new solution was implemented in NS-2 making changes in the implementation of Mobile IPv6. Results show that the new solution provides lower handover delays and signalling overhead in comparison to the Return Routability based Mobile IPv6.

Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO)

Internet of vehicles (IoV) is a branch of the internet of things (IoT) which is used for communication among vehicles. As vehicular nodes are considered always in motion, hence it causes the frequent changes in the topology. These changes cause major issues in IoV like scalability, dynamic topology changes, and shortest path for routing. Clustering is among one of the solutions for such type of issues. In this paper, the stability of IoV topology in a dynamic environment is focused. The proposed metaheuristic dragonfly-based clustering algorithm CAVDO is used for cluster-based packet route optimization to make stable topology, and mobility aware dynamic transmission range algorithm (MA-DTR) is used with CAVDO for transmission range adaptation on the basis of traffic density. The proposed CAVDO with MA-DTR is compared with the progressive baseline techniques ant colony optimization (ACO) and comprehensive learning particle swarm optimization (CLPSO). Numerous experiments were performed keeping in view the transmission dynamics for stable topology. CAVDO performed better in many cases providing minimum number of clusters according to current channel condition. Considerable important parameters involved in clustering process are: number of un-clustered nodes as a re-clustering criterion, clustering time, re-clustering delay, dynamic transmission range, direction, and speed. According to these parameters, results indicate that CAVDO outperformed ACO-based clustering and CLPSO in various network settings. Additionally, to improve the network availability and to incorporate the functionalities of next-generation network infrastructure, 5G-enabled architecture is also utilized.

A Route Optimized Distributed IP-Based Mobility Management Protocol for Seamless Handoff across Wireless Mesh Networks

A Wireless Mesh Network (WMN) can provide Internet connectivity to end users through heterogenous access network technologies. However, the mobility of mobile nodes across these access networks in WMNs results in service disruption. Existing mobility management protocols are designed for single hop networks and are centralized in nature. A Distributed IP-based Mobility Management Protocol (DIMMP) is proposed in this paper that provides seamless mobility with service continuation for mobile nodes when they roam across WMNs. Instead of relying on a centralized mobility anchor, the mobility functionality is distributed at multiple nodes in the WMN, in order to reduce the chances of potential single point of failure. The proposed protocol manages both types of mobilities i.e. intra-WMN and inter-WMN and uses a new enhanced route optimization procedure. Simulation results show that this work has contributed by improving the performance of handoff procedure with respect to handoff latency, packet loss and signalling overhead, as compared to the existing protocols.

Machine learning-assisted signature and heuristic-based detection of malwares in Android devices

Abstract Malware detection is an important factor in the security of the smart devices. However, currently utilized signature-based methods cannot provide accurate detection of zero-day attacks and polymorphic viruses. In this context, an efficient hybrid framework is presented for detection of malware in Android Apps. The proposed framework considers both signature and heuristic-based analysis for Android Apps. We have reverse engineered the Android Apps to extract manifest files, and binaries, and employed state-of-the-art machine learning algorithms to efficiently detect malwares. For this purpose, a rigorous set of experiments are performed using various classifiers such as SVM, Decision Tree, W-J48 and KNN. It has been observed that SVM in case of binaries and KNN in case of manifest.xml files are the most suitable options in robustly detecting the malware in Android devices. The proposed framework is tested on benchmark datasets and results show improved accuracy in malware detection.

Computationally efficient generic adaptive filter (CEGAF)

Enhancement to clean speech from noisy speech has always been a challenging issue for the researcher’s community. Various researchers have used different techniques to resolve this problem. These techniques can be classified into the unsupervised and supervised approaches. Amongst the unsupervised approaches, Spectral Subtraction and Wiener Filter are commonly exploited. However, such approaches do not yield significant enhancement in the speech quality as well as intelligibility. As compared to unsupervised, supervised approaches such as Hidden Markov Model produces enhanced speech signals with better quality. However, supervised approaches need prior knowledge about the type of noise which is considered their major drawback. Moreover, for each noise type, separate models need to be trained. In this paper, a novel hybrid approach for the enhancement of speech is presented to overcome the limitations of both supervised and unsupervised approaches. The filter weights adjustment on the basis of Delta Learning Rule makes it a supervised approach. To address the issue of construction of new model for each noise type, the filter adjusts its weights automatically through minimum mean square error. It is unsupervised as there is no need of estimation of noise power spectral density. Various experiments are performed to test the performance of proposed filter with respect to different parameters. Moreover, the performance of the proposed filter is compared with state-of-the-art approaches using objective and subjective measures. The results indicate that CEGAF outperforms the algorithms such as Wiener Filter, supervised NMF and online NMF.