Peer-to-Peer Networking and Applications

Due to the random deployment of Wireless Sensor Network (WSN) nodes in dangerous and inaccessible environments and their sensing radius limitations, complete coverage of the monitoring area cannot be achieved. Clustering techniques, applying the mobile nodes, and adjusting the sensing radius of sensor nodes are regarded as practical techniques to improve the coverage and reduce the energy consumption. The challenges including, how to grouping the nodes, selecting the cluster heads, managing the mobility of mobile nodes, and scheduling sleep intervals should be considered in the hybrid techniques to improve the coverage with minimum overlapping. In this paper, we propose a DEHCIC (Distributed Energy-aware Hexagon based Clustering algorithm to Improve Coverage) algorithm that considers energy and topological features such as the number of mobile neighbor nodes and number of neighbor nodes for electing the cluster heads. In addition, DEHCIC attempts to cover the holes as much as possible by the static sensor nodes so that if it is not possible, the closest mobile will cover the coverage holes. Moreover, the DEHCIC algorithm retains the sensor nodes in the active mode that cover the interest points; also, it puts others into the low-powered sleep mode. The simulation results show that the proposed algorithm reduces dependency on the movement of mobile nodes and with the minimum number of active nodes can prolong the coverage lifetime and improve the network coverage effectively.

With the advent of new generation of mobile access devices such as smartphone and tablet PC, there is coming a need for ubiquitous collaboration which allows people to access information systems with their disparate access devices and to communicate with others in anytime and in anywhere. As the number of collaborators with a large number of disparate access devices increases in ubiquitous collaboration environment, the difficulties for protecting secured resources from unauthorized users as well as unsecured access devices will increase since the resources can be compromised by inadequately secured human and devices. Therfore, authentication mechanism for access of legitimate participants is essential in ubiquitous collaboration environment. In this paper we present an efficient authentication mechanism in ubiquitous collaboration environment. We show that proposed scheme is secure through security analysis and is efficient through the experimental results obtained from the practical evaluation of the scheme in ubiquitous collaboration environment.

Although cloud computing has made significant achievement, it still faces many challenges, such as bad interactive performance and unsatisfying user experience over a long-haul wide-area or wireless network. To address these challenges, we proposed a software-defined stream-based code scheduling framework according to the concept of transparent computing. This framework uses the idea of code streaming to decouple the computation and storage of software codes; this idea also leverages the input/output virtualization technique to support legacy operating systems and application software in a feasible and effective way. The software-defined code scheduling framework allows the computation or storage to be adaptively carried out at appropriate machines with the assistance of performance and capacity monitoring facilities. Thus, the framework can improve application performance and user experiences by executing software codes on a nearer or better machine. We developed a pilot system to investigate the advantages of the proposed framework. Preliminary experimental results show that our approach can achieve better performance than current cloud computing-based systems.

With the rapid advancement of emerging technologies, a huge amount of data is created and processed in daily life. Nowadays, Cloud Computing (CC) technology is one of the frequently adopted technologies to access and store data over the internet. CC mainly provides on-demand and pay-per-use services to users. However, access control and security are two major issues that users face in a cloud environment. During data access from a cloud server, the searching time of a Data Owner (DO) and the data accessing time are high. Therefore, users utilize more cloud services and pay more. High system overhead is another issue of a cloud environment. In this paper, a novel access control model has been proposed to overcome all these concerns. Here, the Cloud Service Provider (CSP) maintains a temporary table based on the data type and popularity value of the DO for fast and efficient data accessing. The cloud service provider can easily search the data owner by using the table and the data accessing time is remarkably reduced. Experimental results and theoretical analysis of the proposed scheme prove its efficiency over the existing schemes.

Wireless communication technology is used in various applications and therefore the availability of wireless spectrum is a serious concern. The number of cellular users is increasing rapidly. The 5G network will be able to cater to the requirements of the increasing users. However, the spectrum efficiency needs to be improved. Cooperative spectrum sensing is being widely used by cognitive radios for utilizing the available spectrum in an efficient manner. Evolutionary Algorithm based optimization methods are used in various applications and have proved to be very efficient. These algorithms can also be used for optimizing the cooperative spectrum sensing in cognitive radios. In this paper, two methods are proposed for optimal Cooperative Spectrum Sensing for 5G cognitive networks. The optimization algorithms are designed using whale optimization algorithm (WOA) and Particle Swarm Optimization (PSO). The objective is to increase the probability of detection by optimizing the ‘weighting vector’. In the first method, WOA is used for cooperative spectrum sensing optimization in cognitive radios. In the second method, WOA method is improved using the PSO algorithm. A hybridized WOA-PSO algorithm is proposed to further improve the probability of detection. The results obtained are compared with other existing algorithms. The proposed methods perform better than the existing methods.

A wireless data collection network (DCN) is the key constituent of the IoT. It is used in many applications such as transport, logistics, security and monitoring. Despite the continuous development of DCN, communication between nodes in such network presents several challenges. The major issue is the deployment of connected objects and, more precisely, how numerous nodes are appropriately positioned to attain full coverage. The current work presents a hybrid technique, named DTABC, combining a geometric deployment method, called Delaunay Triangulation diagram DT, and an optimization algorithm named the Artificial Bee Colony (ABC) algorithm. In the centralized approach, this hybrid method is executed on a single node while, in a distributed approach, it is executed in parallel on different nodes deployed in a wireless data collection network. This study aims at enhancing the coverage rate in data collection networks utilizing less sensor nodes. The Delaunay Triangulation diagram is utilized to produce solutions showing the first locations of the IoT objects. Then, the Artificial Bee Colony algorithm is used to improve the node deployment coverage rate. The developed DTABC approach performance is assessed experimentally by prototyping M5StickC nodes on a real testbed. The obtained results reveal that the coverage rate, the number of the objects’ neighbors, the RSSI and the lifetime of the distributed approach are better than those of the algorithms introduced in previous research works.

As an extension of cloud computing, the edge computing has become an important pattern to deal with novel service scenarios of Internet of Everything (IoE) under 5G, especially for the delay sensitive computing tasks generated from edge equipment. The edge computing provides the key support to meet the characteristics of delay sensitivity by deploying servers near network edges. However, a great many uneven distributed computing tasks in different network edges usually lead to task processing delay bottleneck for single Edge Computing Server (ECS). Tasks assignment is mainly based on the local ECS status without the global network view considered, which also easily leads to unbalanced task loads among multiple ECSs. In this paper, the novel networking idea of Software Defined Network (SDN) is introduced into the edge computing pattern. The logically highly centralized control plane consists of multiple physically distributed ECSs, so as to collaboratively assign computing tasks in a global view. In order to optimize the task assignment and minimize the task processing delay, three schemes are proposed in this paper. The scheme of assessing the ECS’s task computing features is firstly proposed, then the scheme of predicting the ECS’s future unit task processing time is presented. Thus, different types of computing tasks can be assigned to appropriate ECSs that are better at dealing with them with processing delay minimized. Furthermore, the scheme of optimizing the delay of task processing time estimation is devised, so as to further improve task assignment efficiency. Experimental results show that the proposed mechanism is able to optimize the task assignment and minimize the task processing delay more efficiently than the state of the art. Specifically, our mechanism is capable of improving the average unit task processing delay and the ECS load balancing degree by about 14% and 23% respectively, compared with corresponding work.

Instant Messaging (IM) has been an effective, popular and important mode for immediate communication in the digital age. Then analysis of daily user-online movement in IM is increasingly important for its significant effect on information propagation, commercial operation, resource allocation and maintenance management. In this study, the so-called elliptic orbit model is proposed to describe daily user-online movement in IM by mapping its time-series into the polar coordinates to establish the elliptic orbit model, in which each 24-hour movement is depicted as one elliptic orbit. Experiments in the most popular IM service in China called the Tencent QQ and result analysis indicate potentiality of the proposed method. It is shown that the daily 24-hour user-online movement in IM is well described by the elliptic orbit model, which provides a vivid approach for modeling and predicting daily user-online movement in IM in a concise and intuitive way.

Improving the travel efficiency of citizens and the operation efficiency of urban has always been the goal of Intelligent Transportation System. Due to the neglect of strong traffic demand and driving behaviour preferences, coupled with the insufficient of communication and computing power, the existing measures based on the centralized control of vehicles or mandatory traffic restrictions lead to the traffic efficiency dramatically deviates from the system optimum. Which puts forward an urgent demand for multi-vehicle collaborative apportionment, but also brings challenges. In this paper, a double-layer collaborative apportionment method for connected vehicles, 2L-CoV for short, is proposed under the assistance of Space-air-ground integrated networks. 2L-CoV includes the traffic flow scheduling in global-layer and the vehicle routing planning in local-layer. Firstly, a distributed collaborative framework based SAGIN is presented to make a large-scale of virtual vehicles can interact with each other. Then, at the global-layer, traffic flow is guided speedily by an improved back-pressure algorithm to complete traffic flow scheduling; at the local-layer, considering the driving behaviour preferences, a game evolution online learning approach based on dominant strategy is proposed to plan the vehicle routing. Finally, the simulation results show that 2L-CoV can effectively balance the traffic network, improves the network throughput, and reduces the total travel time.