in Mohammed Ahmed

Mobile Multimedia Recommendation in Smart Communities: A Survey

Due to the rapid growth of Internet broadband access and proliferation of modern mobile devices, various types of multimedia (e.g., text, images, audios, and videos) have become ubiquitously available anytime. Mobile device users usually store and use multimedia contents based on their personal interests and preferences. Mobile device challenges such as storage limitation have, however, introduced the problem of mobile multimedia overload to users. To tackle this problem, researchers have developed various techniques that recommend multimedia for mobile users. In this paper, we examine the importance of mobile multimedia recommendation systems from the perspective of three smart communities, namely mobile social learning, mobile event guide, and context-aware services. A cautious analysis of existing research reveals that the implementation of proactive, sensor-based and hybrid recommender systems can improve mobile multimedia recommendations. Nevertheless, there are still challenges and open issues such as the incorporation of context and social properties, which need to be tackled to generate accurate and trustworthy mobile multimedia recommendations.

Exploiting Social Relationship to Enable Efficient Replica Allocation in Ad-hoc Social Networks

Replication is an important mechanism in modern wireless networks and has attracted significant efforts to improve its performance with different metrics including read cost, consistency and relocation cost. Traditionally, different ideal approaches are widely used to facilitate data availability. However, the quality of wireless links would be affected by many factors like mobility and overhead. The accessibility and reliability of Ad-hoc Social Network (ASNET) services can be assured by replication approaches. It is used to increase data availability by replicating data items locally or nearby. In ASNETs, replication helps to avoid data losses in case of an unpredictable group mobility that causes community partition and also aids in reducing the number of hops when a data is transmitted from source to destination. A new data replication method called ComPAS (community-partition aware replica allocation method) is proposed in this paper. This method can significantly improve ASNETs performance by exploiting social relationship while replicating in the community to achieve better efficiency and consistency while keeping the replica relocation cost as low as possible. This type of replica allocation method will increase the availability of different data items in a partitioned social community. Evaluation results verify the effectiveness of the method.

Event-Based Mobile Social Networks: Services, Technologies, and Applications

Event-based mobile social networks (MSNs) are a special type of MSN that has an immanently temporal common feature, which allows any smart phone user to create events to share group messaging, locations, photos, and insights among participants. The emergence of Internet of Things and event-based social applications integrated with context-awareness ability can be helpful in planning and organizing social events like meetings, conferences, and tradeshows. This paper first provides review of the event-based social networks and the basic principles and architecture of event-based MSNs. Next, event-based MSNs with smartphone contained technology elements, such as context-aware mobility and multimedia sharing, are presented. By combining the feature of context-aware mobility with multimedia sharing in event-based MSNs, event organizers, and planners with the service providers optimize their capability to recognize value for the multimedia services they deliver. The unique features of the current event-based MSNs give rise to the major technology trends to watch for designing applications. These mobile applications and their main features are described. At the end, discussions on the evaluation of the event-based mobile applications based on their main features are presented. Some open research issues and challenges in this important area of research are also outlined.

Community-Based Event Dissemination with Optimal Load Balancing

Distributed publish/subscribe systems are poised with challenges of performance degradation and poor scalability. This is typically caused by an uneven load distribution of real-world applications and the susceptibility of link failure in networks. Partitioning and replication techniques have been implemented by exploring community-based load balancing to cope with such issues. The novel approach herein exploits offloading at the inter-community level as well as filter replication at the intra-community level. This results in the dynamic distribution and forwarding of publication and subscription services among brokers during run time. The proposed method, Co-Lab (COmmunity-based LoAd Balancing), seeks to improve the network performance by clustering brokers in a community by taking into consideration interest similarity and filter replication. It attempts to effectively achieve a more consistent and uniform load distribution among brokers and to circumvent the occurrence of highly overloaded brokers. Performance evaluations indicate that Co-Lab has promising advantages by achieving relatively better load balance, reduced overall load and robustness against failures.

Social-Similarity-Aware TCP With Collision Avoidance in Ad Hoc Social Networks

An ad hoc social network (ASNET), which explores social connectivity between users of mobile devices, is becoming one of the most important forms of todays Internet. In this context, maximum bandwidth utilization of intermediate nodes in resource scarce environments is one of the challenging tasks. The traditional Transport Control Protocol (TCP) uses the round-trip time mechanism for sharing bandwidth resources between users. However, it does not explore socially aware properties between nodes and cannot differentiate effectively between various types of packet losses in wireless networks. In this paper, a socially aware congestion avoidance protocol, namely, TIBIAS, which takes advantage of similarity-matching social properties among intermediate nodes, is proposed to improve the resource efficiency of ASNETs. TIBIAS performs efficient data transfer over TCP. During the course of bandwidth resource allocation, it gives high priority for maximally matched interest similarity between different TCP connections on ASNET links. TIBIAS does not require any modification at lower layers or on receiver nodes. Experimental results show that TIBIAS performs better as compared with existing protocols, in terms of link utilization, unnecessary reduction of the congestion window, throughput, and retransmission ratio.

User popularity-based packet scheduling for congestion control in ad-hoc social networks

Traditional ad-hoc network packet scheduling schemes cannot fulfill the requirements of proximity-based ad-hoc social networks (ASNETs) and they do not behave properly in congested environments. To address this issue, we propose a user popularity-based packet scheduling scheme for congestion control in ASNETs called Pop-aware. The proposed algorithm exploits social popularity of sender nodes to prioritize all incoming flows. Pop-aware also provides fairness of service received by each flow. We evaluate the performance of Pop-aware through a series of simulations. In comparison with some existing scheduling algorithms, Pop-aware performs better in terms of control overhead, total overhead, average throughput, packet loss rate, packet delivery rate and average delay. User popularity-based packet scheduling is explored for ad-hoc social networks.The scheme can avoid the dropping of the most popular nodes data.It helps effectively control congestion and fully utilize available bandwidth.It can maintain fairness among flows and nodes.Simulations have been conducted for performance evaluation and comparison.

BoDMaS: Bio-inspired Selfishness Detection and Mitigation in Data Management for Ad-hoc Social Networks

Abstract Existing data management protocols for socially-aware networks assume that users are cooperative when participating in operations such as data forwarding. However, selfishness as a non-cooperative act of misbehavior can seriously degrade network performance and fairness, particularly in Ad-hoc Social Networks (ASNETs). Therefore, detecting and counteracting selfishness on performance of cooperative users are crucial to the success of ASNETs. In this paper, we propose BoDMaS, a biologically inspired method, to detect and mitigate the impact of node selfishness on data management performance and efficiency of ASNETs. In design of BoDMaS, we consider social willingness (which depends on depth of social relationship among users) as a social behavior and bacteria chemical products as a counter to achieve optimal ASNETs performance. Counter is a parameter attached to individual user counting successful data operations performed in relation with others. Using social willingness and counter, BoDMaS assesses and classifies users, and counteracts their selfishness. BoDMaS is evaluated from different aspects demonstrating its ability to accurately detect and counteract selfishness in replication operations for ASNET environments.

Bio-inspired packet dropping for ad-hoc social networks

SUMMARY Ad-hoc social networks (ASNETs) explore social properties of nodes in communications. The usage of various social applications in a resource-scarce environment and the dynamic nature of the network create unnecessary congestion that might degrade the quality of service dramatically. Traditional approaches use drop-tail or random-early discard techniques to drop data packets from the intermediate node queue. Nonetheless, because of the unavailability of the social properties, these techniques are not suitable for ASNETs. In this paper, we propose a Bio-inspired packet dropping (BPD) algorithm for ASNETS. BPD imitates the matching procedure of receptors and epitopes in immune systems to detect congestions. The drop probability settings depend on the selection of data packets, which is based on node popularity level. BPD selects the most prioritized node through social properties, which is inspired by the B-cell stimulation in immune systems. To fairly prioritize data packets, two social properties are used: (1) similarity and (2) closeness centrality between nodes. Extensive simulations are carried out to evaluate and compare BPD to other existing schemes in terms of mean goodput, mean loss rate, throughput, delay, attained bandwidth, and overhead ratio. The results show that the proposed scheme outperforms these existing schemes. Copyright

Data dissemination using interest-tree in socially aware networking

Interrelation between interests and its impact on data dissemination is explored.A layered structure is presented to model various relationships between interests.An Interest-Tree based data dissemination scheme is proposed to improve efficiency.Extensive simulations verify performance and effectiveness of the proposed scheme. Socially aware networking (SAN) exploits social characteristics of mobile users to streamline data dissemination protocols in opportunistic environments. Existing protocols in this area utilized various social features such as user interests, social similarity, and community structure to improve the performance of data dissemination. However, the interrelationship between user interests and its impact on the efficiency of data dissemination has not been explored sufficiently. In this paper, we analyze various kinds of relationships between user interests and model them using a layer-based structure in order to form social communities in SAN paradigm. We propose Int-Tree, an Interest-Tree based scheme which uses the relationship between user interests to improve the performance of data dissemination. The core of Int-Tree is the interest-tree, a tree-based community structure that combines two social features, i.e., density of a community and social tie, to support data dissemination. The simulation results show that Int-Tree achieves higher delivery ratio, lower overhead, in comparison to two benchmark protocols, PROPHET and Epidemic routing. In addition, Int-Tree can perform with 1.36 hop counts in average, and tolerable latency in terms of buffer size, time to live (TTL) and simulation duration. Finally, Int-Tree keeps stable performance with various parameters.

Social Community-Partition Aware Replica Allocation in Ad-Hoc Social Networks

Ad-hoc social network (ASNET) services deal with the dynamic nature and resource constraints of mobile nodes to support various applications. The availability, accessibility and reliability of these services can be assured by data management approaches such as replica allocation methods. Data replication is used to increase data availability by replicating data items locally or nearby. In ASNETs, replication helps to avoid data losses in case of an unpredictable community or network partition and also aids in reducing the number of hops when a data is transmitted from source to destination. A new data replication method called ComPAS (community-partition aware replica allocation method for ASNETs) is proposed in this paper. This method can significantly improve a social networks efficiency by taking into account social relationships and properties of its data while replicating in the community to achieve better load-balance. This type of replica allocation method will increase the availability of different data items in a partitioned social community. Evaluation results verify the effectiveness of the method.