Amr Tolba

A greedy model with small world for improving the robustness of heterogeneous Internet of Things

Robustness is an important and challenging issue in the Internet of Things (IoT), which contains multiple types of heterogeneous networks. Improving the robustness of topological structure, i.e., withstanding a certain amount of node failures, is of great significance especially for the energy-limited lightweight networks. Meanwhile, a high-performance topology is also necessary. The small world model has been proven to be a feasible way to optimize the network topology. In this paper, we propose a Greedy Model with Small World properties (GMSW) for heterogeneous sensor networks in IoT. We first present the two greedy criteria used in GMSW to distinguish the importance of different network nodes, based on which we define the concept of local importance of nodes. Then, we present our algorithm that transforms a network to possess small world properties by adding shortcuts between certain nodes according to their local importance. Our performance evaluations demonstrate that, by only adding a small number of shortcuts, GMSW can quickly enable a network to exhibit the small world properties. We also compare GMSW with a latest related work, the Directed Angulation toward the Sink Node Model (DASM), showing that GMSW outperforms DASM in terms of small world characteristics and network latency.

ERGID: An efficient routing protocol for emergency response Internet of Things

Abstract In recent years, Internet of Things (IoT) has been applied to many different fields such as smart home, environmental monitoring and industrial control system, etc. Under the pressure of the continuous expansion of network scale, how to ensure the real-time emergency response ability during data transmission has become a challenging problem for researchers. In this paper, we propose a routing protocol for Emergency Response IoT based on Global Information Decision (ERGID) to improve the performances of reliable data transmission and efficient emergency response in IoT. Specifically, we design and realize a mechanism called Delay Iterative Method (DIM), which is based on delay estimation, to solve the problem of ignoring valid paths. Moreover, a forwarding strategy called Residual Energy Probability Choice (REPC) is proposed to balance the load of network by focusing on the residual energy of node. Simulation results and analysis show that ERGID outperforms EA-SPEED and SPEED in terms of end to end (E2E) delay, packet loss and energy consumption. Additionally, we also carry out some practical experiments with STM32W108 sensor nodes, and observe that ERGID can improve the real-time response ability of network.

Exploiting Publication Contents and Collaboration Networks for Collaborator Recommendation

Thanks to the proliferation of online social networks, it has become conventional for researchers to communicate and collaborate with each other. Meanwhile, one critical challenge arises, that is, how to find the most relevant and potential collaborators for each researcher? In this work, we propose a novel collaborator recommendation model called CCRec, which combines the information on researchers’ publications and collaboration network to generate better recommendation. In order to effectively identify the most potential collaborators for researchers, we adopt a topic clustering model to identify the academic domains, as well as a random walk model to compute researchers’ feature vectors. Using DBLP datasets, we conduct benchmarking experiments to examine the performance of CCRec. The experimental results show that CCRec outperforms other state-of-the-art methods in terms of precision, recall and F1 score.

Geo-Social Distance-Based Data Dissemination for Socially Aware Networking

The Internet of Things (IoT), which could connect everything in the world, is promising for the realization of smart cities. However, vehicles connected in the IoT challenges data collection and transmission. Socially aware networking is an emerging paradigm for high-efficiency data dissemination. Existing protocols take advantage of mobile nodes social characteristics (e.g., user interest) to improve dissemination performance. However, they have not exploited enough of what relations are valuable between user interests and how these relations can affect the dissemination of social IoTs. This paper takes advantage of interest inclusion and intersection to solve the dissemination problem in a conference scenario. By constructing the structure of the Interest Tree to solely represent the relations of interest inclusion and interest intersection, we integrate vehicles social factors into their geographical information, and introduce the concept of geo-social distance (GSD) as the basis of the proposed strategy. Simulation results demonstrate the superiority of our presented method by comparing with two other existing protocols in terms of four objective metrics, which include delivery ratio, overhead, hop-count, and average latency.

LoTAD: long-term traffic anomaly detection based on crowdsourced bus trajectory data

As the development of crowdsourcing technique, acquiring amounts of data in urban cities becomes possible and reliable, which makes it possible to mine useful and significant information from data. Traffic anomaly detection is to find the traffic patterns which are not expected and it can be used to explore traffic problems accurately and efficiently. In this paper, we propose LoTAD to explore anomalous regions with long-term poor traffic situations. Specifically, we process crowdsourced bus data into TS-segments (Temporal and Spatial segments) to model the traffic condition. Later, we explore anomalous TS-segments in each bus line by calculating their AI (Anomaly Index). Then, we combine anomalous TS-segments detected in different lines to mine anomalous regions. The information of anomalous regions provides suggestions for future traffic planning. We conduct experiments with real crowdsourced bus trajectory datasets of October in 2014 and March in 2015 in Hangzhou. We analyze the varieties of the results and explain how they are consistent with the real urban traffic planning or social events happened between the time interval of the two datasets. At last we do a contrast experiment with the most ten congested roads in Hangzhou, which verifies the effectiveness of LoTAD.

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.

SEAIoT: Scalable E-Health Architecture based on Internet of Things

In this paper, a scalable e-health architecture based on the Internet of Things (IoT) technology is proposed. In this suggested architecture, the clinic hardware can communicate with other clinics hardware remotely with minimal or no human intervention, which is a key difference between the proposed architecture and the former ones. The idea of our architecture is to use the internet as a communication media between different architecture hardware. Each hardware clinic should be adapted to receive its required information from the internet. Using our system will provide the medical specialists with advantages such as scalability and flexibility. Furthermore, the cost and the effort will be decreased. The processes in our proposed architecture are executed on advanced medical devices in real time mode, which maximize the diagnosis accuracy and minimize the processing time. Finally, a case study, which is related to 2D and 4D ultrasonography devices, is demonstrated to clarify our architecture.

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.

Trust-aware recommendation for improving aggregate diversity

Recommender systems are becoming increasingly important and prevalent because of the ability of solving information overload. In recent years, researchers are paying increasing attention to aggregate diversity as a key metric beyond accuracy, because improving aggregate recommendation diversity may increase long tails and sales diversity. Trust is often used to improve recommendation accuracy. However, how to utilize trust to improve aggregate recommendation diversity is unexplored. In this paper, we focus on solving this problem and propose a novel trust-aware recommendation method by incorporating time factor into similarity computation. The rationale underlying the proposed method is that, trustees with later creation time of trust relation can bring more diverse items to recommend to their trustors than other trustees with earlier creation time of trust relation. Through relevant experiments on publicly available dataset, we demonstrate that the proposed method outperforms the baseline method in terms of aggregate diversity while maintaining almost the same recall.

Soft computing approaches based bookmark selection and clustering techniques for social tagging systems

Big Data analysis is the era of goliath measures of data more than a brief stage like social tagging framework. Social tagging frameworks such as BibSonomy and del.icio.us have turned out to be progressively famous owing to their across-the-board utilization of the web. The social tagging framework could be typical on account of the comments on web 2.0 resources. The social tagging frameworks allow the web clients to clarify distinctive types of web resources with free-form tags. Labels are broadly used to translate and arrange the web 2.0 resources. Tag clustering is characterized as a gathering procedure that implies that the comparable labels are assembled into groups. Tag clustering is exceptionally valuable for sorting out and seeking the web 2.0 resources. Furthermore, it is essential for achieving social tagging frameworks. The objective of feature selection is to decide upon a negligible bookmarked URL subcategory from Web 2.0 data while remembering with reasonably high exactness when speaking to the first bookmarks. In this study, Unsupervised Quick Reduct feature selection calculation is connected so as to locate an arrangement of most frequently tagged bookmarks. Furthermore, clustering techniques such as the Unsupervised Quick Reduct Particle Swarm Optimization (UQRPSO) algorithm are applied for clustering the selected tagged bookmarks, and this algorithm is then compared with k-means clustering (k-means), bat algorithm, and firefly algorithm.