Urban networks aim at facilitating users for better experience and services through smart platforms such as the Intelligent Transportation System (ITS). ITS focuses on information acquisition, sensing, contrivance control, data processing and forwarding to ground devices via user-specific application-interfaces. The utility of ITS is further improved via the Internet of Things (IoT), which supports “Connectivity to All”. One of the key applications of IoT-ITS is urban surveillance. Current surveillance in IoT-ITS is performed via fixed infrastructure-based sensing applications which consume an excessive amount of energy leading to several overheads and failures in the network. Such issues can be overcome by the utilization of on-demand nodes, such as drones, etc. However, drones-assisted surveillance requires efficient communication setup as drones are battery operated and any extemporaneous maneuver during monitoring may result in loss of drone or complete failure of the network. The novelty in terms of network layout can be procured by the utilization of drones with LoRaWAN, which is the protocol designated for Low-Power Wide Area Networks (LPWAN). However, even this architectural novelty alone cannot ascertain the formation of fail-safe, highly resilient, low-overhead, and non-redundant network, which is additionally the problem considered in this paper. To resolve such problem, this paper uses drones as LoRaWAN gateway and proposes a communication strategy based on the area stress, resilient factor, and energy consumption that avail in the efficient localization, improved coverage and energy-efficient surveillance with lower overheads, lower redundancy, and almost zero-isolations. The proposed approach is numerically simulated and the results show that the proposed approach can conserve a maximum of 39.2% and a minimum of 12.6% of the total network energy along with an improvement in the area stress between 89.7% and 53.0% for varying number of drones over a fixed area.

Internet of things (IoT) aims at bringing together large business enterprise solutions and architectures for handling the huge amount of data generated by millions of devices. For this aim, IoT is necessary to connect various devices and provide a common platform for storage and retrieval of information without fail. However, the success of IoT depends on the novelty of network and its capability in sustaining the increasing demand by users. In this paper, a self-aware communication architecture (SACA) is proposed for sustainable networking over IoT devices. The proposed approach employs the concept of mobile fog servers which make relay using the train and unmanned aerial vehicle (UAV) networks. The problem is presented based on Wald’s maximum model, which is resolved by the application of a distributed node management (DNM) system and state dependency formulations. The proposed approach is capable of providing prolonged connectivity by increasing the network reliability and sustainability even in the case of failures. The effectiveness of the proposed approach is demonstrated through numerical and network simulations in terms of significant gains attained with lesser delay and fewer packet losses. The proposed approach is also evaluated against Sybil, wormhole, and DDoS attacks for analyzing its sustainability and probability of connectivity in unfavorable conditions.

Due to the openness of the Android-based open market, the distribution of malicious applications developed by attackers is increasing rapidly. In order to reduce the damage caused by the malicious applications, the mechanism that allows more accurate way to determine normal apps and malicious apps for common mobile devices should be developed. In this paper, the normal system call event patterns were analyzed from the most highly used game app in the Android open market, and the malicious system call event patterns were also analyzed from the malicious game apps extracted from 1260 malware samples distributed by Android MalGenome Project. Using the Strace tool, system call events are aggregated from normal and malicious apps. And analysis of relevance to each event set was performed. Through this process of analyzing the system call events, we can extract a similarity to determine whether any given app is malicious or not.

No abstract

No abstract