Internet of things for disaster management: state-of-the-art and prospects

Disastrous events are cordially involved with the momentum of nature. As such mishaps have been showing off own mastery, situations have gone beyond the control of human resistive mechanisms far ago. Fortunately, several technologies are in service to gain affirmative knowledge and analysis of a disaster's occurrence. Recently, Internet of Things (IoT) paradigm has opened a promising door toward catering of multitude problems related to agriculture, industry, security, and medicine due to its attractive features, such as heterogeneity, interoperability, light-weight, and flexibility. This paper surveys existing approaches to encounter the relevant issues with disasters, such as early warning, notification, data analytics, knowledge aggregation, remote monitoring, real-time analytics, and victim localization. Simultaneous interventions with IoT are also given utmost importance while presenting these facts. A comprehensive discussion on the state-of-the-art scenarios to handle disastrous events is presented. Furthermore, IoT-supported protocols and market-ready deployable products are summarized to address these issues. Finally, this survey highlights open challenges and research trends in IoT-enabled disaster management systems. © 2013 IEEE

Malicious node detection using machine learning and distributed data storage using blockchain in WSNs

In the proposed work, blockchain is implemented on the Base Stations (BSs) and Cluster Heads (CHs) to register the nodes using their credentials and also to tackle various security issues. Moreover, a Machine Learning (ML) classifier, termed as Histogram Gradient Boost (HGB), is employed on the BSs to classify the nodes as malicious or legitimate. In case, the node is found to be malicious, its registration is revoked from the network. Whereas, if a node is found to be legitimate, then its data is stored in an Interplanetary File System (IPFS). IPFS stores the data in the form of chunks and generates hash for the data, which is then stored in blockchain. In addition, Verifiable Byzantine Fault Tolerance (VBFT) is used instead of Proof of Work (PoW) to perform consensus and validate transactions. Also, extensive simulations are performed using the Wireless Sensor Network (WSN) dataset, referred as WSN-DS. The proposed model is evaluated both on the original dataset and the balanced dataset. Furthermore, HGB is compared with other existing classifiers, Adaptive Boost (AdaBoost), Gradient Boost (GB), Linear Discriminant Analysis (LDA), Extreme Gradient Boost (XGB) and ridge, using different performance metrics like accuracy, precision, recall, micro-F1 score and macro-F1 score. The performance evaluation of HGB shows that it outperforms GB, AdaBoost, LDA, XGB and Ridge by 2-4%, 8-10%, 12-14%, 3-5% and 14-16%, respectively. Moreover, the results with balanced dataset are better than those with original dataset. Also, VBFT performs 20-30% better than PoW. Overall, the proposed model performs efficiently in terms of malicious node detection and secure data storage. © 2013 IEEE

Secure location-aware communications in energy-constrained wireless networks

Wireless ad hoc network has enabled a variety of exciting civilian, industrial and military applications over the past few years. Among the many types of wireless ad hoc networks, Wireless Sensor Networks (WSNs) has gained popularity because of the technology development for manufacturing low-cost, low-power, multi-functional motes. Compared with traditional wireless network, location-aware communication is a very common communication pattern and is required by many applications in WSNs. For instance, in the geographical routing protocol, a sensor needs to know its own and its neighbors\u27 locations to forward a packet properly to the next hop. The application-aware communications are vulnerable to many malicious attacks, ranging from passive eavesdropping to active spoofing, jamming, replaying, etc. Although research efforts have been devoted to secure communications in general, the properties of energy-constrained networks pose new technical challenges: First, the communicating nodes in the network are always unattended for long periods without physical maintenance, which makes their energy a premier resource. Second, the wireless devices usually have very limited hardware resources such as memory, computation capacity and communication range. Third, the number of nodes can be potentially of very high magnitude. Therefore, it is infeasible to utilize existing secure algorithms designed for conventional wireless networks, and innovative mechanisms should be designed in a way that can conserve power consumption, use inexpensive hardware and lightweight protocols, and accommodate with the scalability of the network. In this research, we aim at constructing a secure location-aware communication system for energy-constrained wireless network, and we take wireless sensor network as a concrete research scenario. Particularly, we identify three important problems as our research targets: (1) providing correct location estimations for sensors in presence of wormhole attacks and pollution attacks, (2) detecting location anomalies according to the application-specific requirements of the verification accuracy, and (3) preventing information leakage to eavesdroppers when using network coding for multicasting location information. Our contributions of the research are as follows: First, we propose two schemes to improve the availability and accuracy of location information of nodes. Then, we study monitoring and detection techniques and propose three lightweight schemes to detect location anomalies. Finally, we propose two network coding schemes which can effectively prevent information leakage to eavesdroppers. Simulation results demonstrate the effectiveness of our schemes in enhancing security of the system. Compared to previous works, our schemes are more lightweight in terms of hardware cost, computation overhead and communication consumptions, and thus are suitable for energy-constrained wireless networks

Secure Cloud Controlled Software Defined Radio Network For Bandwidth Allocation

The purpose of this research is to investigate the impact of mobility of wireless devices for opportunistic spectrum access and communications using National Instrument Universal Software Radio Peripherals devices. The overall system utilizes software defined radio networks for frequency allocation, cloud connectivity to maintain up-to-date information, and moving target defense as a security mechanism. Each USRP device sends its geolocation to query the spectrum database for idle channels. The cloud cluster was designed for complex data storage and allocation using a smart load balancer to offer ultra-security to users. This project also explores the advantages of data protection and security through moving target defense. To achieve this, the system would use an array of antennas to split the data into different parts and transmit them across separate antennas. This research provides the design to each of the mentioned projects for the implementation of a fully developed system