Sardar Kashif Ashraf Khan

Lobby Influence: Opportunistic forwarding algorithm based on human social relationship patterns

In opportunistic networks, nodes have no prior knowledge of the routes to forward the information to its intended destination. Message transfer without generating excessive traffic poses a challenge in these networks. Improvements in message routing/forwarding in opportunistic networks can be achieved by exploiting nodes that exhibit a high level of popularity or influence within the network. Based on these observations, this paper presents a novel algorithm called Lobby Influence. The new algorithm is tested against two previously proposed algorithms and proves to be the better algorithm in terms of message delivery and delay. Moreover, unlike other social based algorithms, which have a tendency to concentrate traffic through their identified routing nodes, our algorithm provides a fairer distribution, thus alleviating the tendency to saturate individual nodes.

Interest-Based Self Organization in Group-Structured P2P Networks

Peer-to-Peer (P2P) Networks are becoming very popular these days. The scattered environment in P2P networks makes it different from the traditional networks. Each member in P2P network can act as a client as well as a server, which is different from its traditional counterpart such as internet, where a central server is required to control the network. Finding resources in P2P systems is a major issue. Extensive search may be needed to resolve queries in the network. In traditional P2P search algorithms such as in Gnutella protocol V0.4 search is via query flooding with significant exploration cost. In this paper, a group-structured P2P network is proposed. The group-based search provides well-organized searching technique by targeting particular group or community. We propose a new protocol for building and repairing of overlay topologies based on the formation of interest-based superpeers. An interest-based superpeer algorithm creates groups or societies that have common interests. Simulations indicate that the proposed protocol is highly capable and powerful even in the dynamic nature of P2P networks, where nodes are continually joining and leaving the network and the protocol supports restoration of the network even after the catastrophic failure of interest-based superpeers.

Tiered approach to infer the behaviour of low entropy mobile people

Being able to understand human behaviour and monitoring daily life activities is seen as a significant approach for alleviating functional decline among elderly people. The aim of the research work presented in this paper is to investigate a mechanism that can recognise high level activities and behaviour of low entropy people in order to help them improve their health related daily life activities by using wireless proximity data (e.g. Bluetooth, Wi-Fi). A number of scenarios and experiments are designed to prove the validity of the proposed methodology. Using wireless proximity data for activity recognition enhances the intrusion into personal privacy and helps exploiting important structures in human behaviour.

Propagation Analysis of Malware Families in Mobile P2P Networks

Viral propagation modelling acts as sandbox for testing intensity of malware, understand patterns adopted for malware propagation and consequently help device strategies for malware detection. Success of P2P networks has encouraged mobile vendors to offer P2P services on mobile networks. Handheld mobile devices though constrained in memory, power and processing resources are capable of using communication technologies like Bluetooth, MMS, SMS, Infrared and WLAN services. Such versatility has however exposed mobile devices to threats like mobile P2P malware. With the number of mobile phone malware escalating to an alarming figure of more than one thousand, it has become ever more important to analyze the affects of propagation of such malware in the wild that could subsequently act as the baseline for protection against such malware. This paper initially presents propagation analysis of generic mobile P2P malware categories and then provides a detailed analysis of propagation of real-world malware from three malware families accommodating around 100 well known mobile P2P malware. Paper is aimed at providing a much needed insight into propagation characteristics of mobile P2P malware like their propagation speed and battery depletion affect.

Behavioural analysis of low entropy mobile people using contextual information

The profusion of wireless enabled mobile devices in daily life routine and advancement in pervasive computing has opened new horizons to analyse and model the contextual information. This contextual information (for example, proximity data and location information) can be very helpful in analysing the human behaviours. Wireless proximity data can provide important information about the behaviour and daily life routines of an individual. In this paper, we used Bluetooth proximity data to validate this concept by detecting repeated activity patterns and behaviour of low entropy mobile people by using n-gram and correlative matrix techniques. Primary purpose is to find out whether contextual information obtained from Bluetooth proximity data is useful for activities and behaviour detection of individuals. Repeated patterns found in Bluetooth proximity data can also show the long term routines such as, monthly or yearly patterns in an individuals daily life that can further help to analyse more complex and abnormal routines of human behaviour.

LOC algorithm: location-aware opportunistic forwarding by using node’s approximate location

Purpose – This paper aims to propose an algorithm, location-aware opportunistic content forwarding (LOC), to improve message directivity using direction vectors in opportunistic networks. The LOC is based on the assumption that if approximate location of the destination node is known, then overall message delivery and cost can be improved. Efficient message delivery with low communication cost is a major challenge in current opportunistic networks. In these networks, nodes do not have prior knowledge of their recipients, and message forwarding can be achieved by selecting suitable forwarder based on some forwarding criteria, as compared to its ancestor mobile ad hoc networks. Design/methodology/approach – In this paper, the authors tested LOC in two sets of mobility models, synthetic movement model and real mobility data sets. In the first set, working day movement is used as synthetic movement model, where proposed algorithm is compared against Lobby Influence (LI) and Epidemic algorithms. In the second set of experiments, the new algorithm is tested in three mobility data sets, namely, Cambridge, Reality and Sassy, and results compared against LI algorithm. The reason of using various movement models is to establish strengths and weaknesses of the proposed algorithm in different scenarios. Findings – The experimental results show that the new algorithm performed extremely well in different scenarios, not only in terms of overall message delivery but also successfully managed to reduce the communication cost. Originality/value – The new contribution increases the overall energy and storage efficiency of nodes by targeting relevant forwarding nodes in the network.

LOC: Location-aware opportunistic content forwarding using direction vectors

In opportunistic networks, nodes do not have prior knowledge of the routes to their intended destinations. The major challenge is to keep the communication cost minimum during the transfer of information towards intended destinations. An improvement can be realised if nodes are aware of their current position in the network through some technological means such as GPS, GSM or WLAN access points. With the knowledge of nodes position in the network, high message directivity can be achieved by using simple concepts of direction vectors. Based on this observation, this paper presents a new algorithm named as Location-aware opportunistic content forwarding (LOC), which is compared against two other algorithms previously proposed. The experimental results have shown that the new algorithm performed extremely well, not only in terms of overall message delivery ratio but also successfully manages to reduce the communication cost with minimum delay.

Recognising the Activities of Low Entropy Mobile People Using Wireless Proximity Data

To improve the functional decline among elderly people, understanding the human behaviour is a substantial approach. Human behaviour is complex in nature and a challenging task to understand by monitoring the daily life activities. A tiered approach is adopted and investigated in this research work to recognise the high level activities and behaviour of the target users in order to alleviate their health related activities. An algorithm is devised to detect low level tasks by using the contextual information (e.g. Bluetooth and Wi-Fi proximity data). These tasks are further utilized for recognition of high level activities. Different scenarios and experiments are conducted to investigate the problem in hand thoroughly. Using wireless proximity data for activity and behaviour detection also improves the understanding of important structures in social relationships.