This research aims at analysing how the usage of Facebook differs on mobile phones and computers from the view of an end user. 10 people were consulted in semistructured interviews about their individual usage. Topics of the interviews included the amount of time they spend on Facebook on their mobile phones and how often, why they are using it on this device and when they rather use a computer. The results of the interviews indicate that people mostly use their mobile phone on their every-day journeys, esp. while they use public transport and they are bored waiting for or beeing on the means of transport. Often they just check the Newsfeed to see what their friends are doing. They mainly want to be informed about the activities of their friends, therefore, produce less content themselves on Facebook Mobile rather than on their computer. The questioned users do not have the feeling they need to log in on Facebook, instead they do it subconsciously all the time. The small screen and keypad on the mobile phone are still the main reasons why the usage of Facebook on the computer is more convenient. Nevertheless, because Facebook on the mobile phone is limited in its features it also offers a good overview. The non-available features on mobile devices are simply not missed and for this reason not even used on the Facebook computer version. However, if a function should be executed, which does not exist on the mobile phone the users are waiting until they can access a computer but possibly they forget about it by then and the importance of the accomplishment disappears.

This research paper aims to investigate the role of enterprise social network to address these concerns such as knowledge sharing and collaboration. The authors have considered a case study based research approach as it is the best method to answer the research question. The leading multinational Bus and truck manufacture Scania was considered for our case study. The study has been done by researching the impact of enterprise social network tools such as Yammer and Lync and their attempt to answer these challenges in the organization. The paper concludes with the analysis based on the empirical findings and theoretical frame of reference. Suggestions and recommendations for further research in this area have also been mentioned in the concluding part of the thesis.

As the role of the Internet has been steadily gaining in importance, overlays are increasingly being used to provide new services and to deploy older ones. Some of the services for which overlays have been proposed include multicast, quality of service (QoS), search, and resilient networks. The use of overlays, in turn, has led to more interest in improving their performance. The performance of an overlay network depends significantly on how the network is structured, i.e., the placement of the nodes in the underlying network topology, the links between the overlay nodes and the access links of these nodes. This thesis focuses on algorithms for improving the performance of

The amount of distributed generation (DG) is increasing worldwide, and it is located in distribution networks close to consumers or even in the consumers¡¦ side of the meter. Therefore, the net demand to be supplied through transmission and distribution networks may decrease, allowing to postpone reinforcement of existing networks. This thesis presents a methodology for assessing the potential benefits of using non--constructional reinforcement strategies to relieve distribution network congestion and increase the utilization of the network assets. Due to the randomness of involved variables (load demand patterns, DG hourly production, DG availability, etc.), a simulation approach is used to model the uncertainties. The benefits of DG, energy storage (ES), and demand response (DR) on congestion relief and investment deferment are evaluated. The analyzed items include: the distribution network investment avoided cost, levelized annual cost, hourly overload probability, and hourly overload risk. Simulation results indicate the potential benefits of non--traditional strategies in increasing the distribution network utilization and relieving network congestion.

The technology is changing fast in current business environment, today¡¦s success maybe will be gone by tomorrow. Therefore, the organization needs to keep learning and innovating to survive. To become a willing sharing and creation organization, the firm needs to gather all the knowledge in department, group and each employee. The key success of knowledge sharning is the employee¡¦s willingness of sharning knowledge. The knowledge is created through people¡¦s interation and the knowledge sharing is built on people¡¦s relation. The purpose of this rearch is to explor the employee¡¦s knowledge sharing willingness through social network approach. In the other hand, the psychological ownership could bring positive work attitude. Therefore the moderating effect of psychological ownership to social network and knowledge sharing willingness will be discussed as well. The target groups of this study are production or technology related departments of high technology companies. The samples are engineers, deputy engineers and supervisors. The total valid samples are 7 companies, 13 departments and 159 employees. Research method includes social network analysis software Ucinet and Hierarchical Regression Analysis. The result shows psychological ownership could raise the engineers¡¦ knowledge sharing willingness and moderate the relationship between in-degree centrality of advice network and knowledge sharing willingness. The findings are as follows: 1. Network degree centrality of internal advice network does not affect knowledge sharing willingness. 2. Psychological ownership positively and significantly affected knowledge sharing willingness. 3. Psychological ownership as the moderator positively and significantly affected the network degree centrality of internal advice network and knowledge sharing willingness partially.

Due to wireless communication technologies, positioning technologies, and mobile computing develop quickly, mobile services are becoming practical and important on big spatiotemporal databases management. Mobile service users move only inside a spatial network, e.g. a road network. They often issue the K Nearest Neighbor (KNN) query to obtain data objects reachable through the road network. The challenge problem of mobile services is how to efficiently answer the data objects which user interest to the corresponding mobile users. Therefore, how to effectively modeling road networks, effectively indexing, and querying on the road networks have become a popular topic. Lu et. al. have proposed a road network model that captures the real-life road networks better than previous models. Then, based on their model, they have proposed a RNG (Road Network Grid) index for speeding up the KNN query on real-life road networks. The RNG index structure is a quad-tree structure and a point-based structure. However, in their model, they divide the double track road which U-turn is allowed at some parts. This modeling does not capture the real-life road networks accurately. Since they divide the road, this makes the number of points of the graph increase. The number of times of partitioning the graph increases. It increases the execution time of constructing the index structure. The format of the leaf node of the RNG index makes the search time increase. Moreover, the query processing on the RNG index structure has to visit the root repeatedly. This condition makes the search time increase. Therefore, in this thesis, we propose a network model that captures the real-life road networks. We do not have to divide the real-life roads when we map the real-life roads into graph. We map the real-life road networks into graph directly. Then, based on our network model, we propose an EBNA (Edge-Based Nine-Area tree) index structure to make the search time of obtaining the interest edge information quickly. The EBNA index structure is an edge-based index structure. We store all of the edge information on the leaf node. We can obtain the edge information directly. Each edge information entry has a pointer point to link edges. Links of each edge entry consist a graph. This graph makes the KNN query processing visit the root only one time. From our simulation result, we show that the performance of constructing the EBNA index is better than constructing the RNG index and the performance of the KNN query processing by using EBNA index is better than the KNN query processing by using RNG index.

In this thesis, we consider cognitive radio networks (CRN) combined with cooperative transmission, and investigate relay selection and power allocation strategies to maximize network lifetime (NLT). Cognitive radio network enhances spectrum efficiency resource by exploiting capabilities of cognition, learning and coordination against insufficient spectrum resource. In underlay cognitive radio network, however, transmitted energy of secondary user is constrained by interference level observed at primary user (PU). Though cooperation among secondary users (SU), multiple relays from virtual antenna array to improve transmission rate and reliability by exploiting spatial diversity. Most existing works assume that cooperative secondary users are plugged and with infinite energy device. In this thesis, we consider secondary cooperative systems where relays are battery-powered and with finite energy. We will investigate relay-selection schemes to reduce energy consumption of secondary relays and prolong network lifetime under the premises that secondary user¡¦s transmission rate is guaranteed and interference constraint of primary user is met. Our major difference between this work and previous works is the definition of network lifetime, which is defined by the maximum duration that the probability of secondary user¡¦s achievable rate below the guaranteed value, i.e. outage probability, is lower than a predetermined threshold. We proposed four relay-selection methods which take channel state information (CSI) and residual energy information (REI) into considerations to prolong network lifetime. Since the selection metrics of the proposed strategies requires CSI and REI of each individual relay, so the relay-selection can be accomplished in distributed manner through opportunistic sensing. No additional overhead is demanded for information exchange.

Many congestion control mechanisms have been proposed to solve the problems of a high loss rate and inefficient utilization of network resources in the present Internet. This problem is caused by competition between traffic flows while the network is congested. Differentiated Services (DiffServ) architecture permits the allocation of various levels of traffic resource requirements needed for Quality of Service (QoS). Random Early Detection (RED) is an efficient mechanism to pre-drop packets before actual congestion occurs, and it is capable of introducing a random early packet dropping scheme, and based on the queue length in reaching a certain degree of fairness for resource utilization. However, it still suffers from a lack of robustness among light traffic load, or in heavy traffic load using fixed RED parameters. In this dissertation, we modified the RED scheme and proposed a novel adaptive RED model, which we named the OURED model, to enhance the robustness of resource utilization so that it could be utilized in the DiffServ edge router. The OURED model introduces two additional packet dropping traces, one is Over Random Early Detection (ORED), which is used to speed up the dropping of packets when the actual rate is higher than the target rate, and the other one is the Under Random Early Detection (URED), used to slow down the packet dropping rate in the reverse situation. The simulation results show that OURED is not only more robust than MRED in resource utilization, but that it also can be implement efficiently in the DiffServ edge router. Another model proposed in this dissertation is the Age-Based packet discarding Traffic Conditioner. For the reason that the file sizes of on going flows are fairly disparate on the current network, we propose an ¡§Age-Based¡¨ packet discard scheme in the Traffic Conditioner of a gateway, to improve the performance of file transmission. The on going flows will be grouped to three classes of priority according to their ¡§age¡¨ as network congestion occurs and the simulation results show that the proposed model can work efficiently in most of the congestion conditions.

Internet-worms are a major threat to the security of today¡¦s Internet and cause significant worldwide disruptions, a huge number of infected hosts generating overwhelming traffic will impact the performance of the Internet. Network managers have the duty to mitigate this issue . In this paper we propose an automated method for detecting Internet-worm in large network based on NetFlow. We also implement a prototype system ¡V FloWorM which can help network managers to monitor suspect Internet-worms activities and identify their species in their managed networks. Our evaluation of the prototype system on real large and campus networks validates that it achieves pretty low false positive rate and good detecting rate.

Hundred and thousands of wireless sensor node comprise wireless sensor network (WSN), WSN can be applied in many situations, because a wireless sensor node have small size and wireless transmission advantages, the battery provide sensor node power, but the battery¡¦s power is limit, therefore, energy efficiency is a critical issue, routing protocol can make better energy consumption and loading balance, Clustering is useful routing protocol in WSN and provides the direction of energy efficiency. The ARC (Adaptive Re-Clustering) is based on clustering; ARC can reduce the whole WSN power consumption and protect less residue power cluster-head. The experiments prove that ARC can improve network reliability and extend network lifetime.