Wireless Mesh Network (WMN) is a technology, which has gained popularity due to its cost effective design, robustness, and reliable service coverage. Despite the advantages, WMNs are considered vulnerable to security breaches. Thereby, it is important to consider security in the early design phase in WMNs. Identifying security threats helps the system designer in developing rational security requirements. In this paper we propose threat modeling as a systematic approach to pinpoint the security threats for WMNs as basis for developing security requirements. We identify assets, value them and categorize possible attacks that target the assets in a layer-wise manner. We further elucidate our threat model by use of Attack Trees to clearly define vulnerabilities in the system during early design phase. We take the example of Schools' WMN in a district of Kabul City in Afghanistan as our scenario. We briefly discuss how to assess the risks that are associated with the specified WMN based on the information that is derived from the threat model.Wireless Mesh Network (WMN) is a technology which has evolved in recent years and fits well in today's technological needs. However, due to the wireless nature of WMNs and their deployment in heterogeneous and large scale areas, wireless links often face significant quality fluctuations and performance degradation or weak connectivity. Therefore, failure detection and recovery plays crucial role in performance of WMN. This paper presents a study report on comparison of recent research and techniques developed for the issue of fault tolerance in WMNs. In this survey we present the existing techniques for fault tolerance in WMNs in categories; node failure approach, communication failure approach, routing schemes, fault tolerance techniques, and autonomous reconfiguration systems. The paper also provides an outline of areas which need further research and studies.

The development of virtual personal assistants requires situation awareness. For this purpose, lightweight approaches for the processing of sensor data to derive situation information from available sensor data (e.g., mobile phone data) are required. In this paper, we propose a spatiotemporal approach to derive situational information about social interactions only based on location and time, using data collected with off-the-shelf smartphones. We examine the approach, using location traces of 163 users collected over four weeks. The proposed spatiotemporal approach shows an average social situation recognition result of 45.8 +/- 23.2% F1-measure across the data set using Random Forest classifiers.The aim of role discovery is to identify users who exhibit similar behavior within their communities. In this paper, we present a simplified approach to role discovery that helps domain experts identify roles in their domains starting from simple observations. The approach starts with two normalization steps to give more weight to characteristic features of each user. We apply our approach on log files from a collaborative content management system and present the initial results of our data analysis.