Abu Raihan M. Kamal

Failure detection in wireless sensor networks: A sequence-based dynamic approach

Wireless Sensor Network (WSN) technology has recently moved out of controlled laboratory settings to real-world deployments. Many of these deployments experience high rates of failure. Common types of failure include node failure, link failure, and node reboot. Due to the resource constraints of sensor nodes, existing techniques for fault detection in enterprise networks are not applicable. Previously proposed WSN fault detection algorithms either rely on periodic transmission of node status data or inferring node status based on passive information collection. The former approach significantly reduces network lifetime, while the latter achieves poor accuracy in dynamic or large networks. Herein, we propose Sequence-Based Fault Detection (SBFD), a novel framework for network fault detection in WSNs. The framework exploits in-network packet tagging using the Fletcher checksum and server-side network path analysis to efficiently deduce the path of all packets sent to the sink. The sink monitors the extracted packet paths to detect persistent path changes which are indicative of network failures. When a failure is suspected, the sink uses control messages to check the status of the affected nodes. SBFD was implemented in TinyOS on TelosB motes and its performance was assessed in a testbed network and in TOSSIM simulation. The method was found to achieve a fault detection accuracy of 90.7% to 95.0% for networks of 25 to 400 nodes at the cost of 0.164% to 0.239% additional control packets and a 0.5% reduction in node lifetime due to in-network packet tagging. Finally, a comparative study was conducted with existing solutions.

Packet-level attestation (PLA): A framework for in-network sensor data reliability

Wireless sensor networks (WSN) show enormous potential for collection and analysis of physical data in real-time. Many papers have proposed methods for improving the network reliability of WSNs. However, real WSN deployments show that sensor data-faults are very common. Several server-side data reliability techniques have been proposed to detect these faults and impute missing or erroneous data. Typically, these techniques reduce the lifetime of the network due to redundant data transmission, increase latency, and are computation and storage intensive. Herein, we propose Packet-Level Attestation (PLA), a novel framework for sensor data reliability assessment. It exploits the spatial correlation of data sensed at nearby sensors. The method does not incur additional transmission of control message between source and sink; instead, a verifier node sends a validation certificate as part of the regular data packet. PLA was implemented in TinyOS on TelosB motes and its performances was assessed. Simulations were performed to determine its scalability. It incurs only an overhead of 1.45% in terms of packets transmitted. Fault detection precision of the framework varied from 100% to 99.48%. Comparisons with existing methods for data reliability analysis showed a significant reduction in data transmission, prolonging the network lifetime.

Reliable data approximation in wireless sensor network

Wireless Sensor Network (WSN) is highly budgeted by energy since sensor nodes are mostly battery-powered and deployed in hard-to-reach area for prolonged duration. Moreover radio communication is very expensive for WSN. At the same time, a substantial portion WSN applications require periodic data collection. In this paper we investigate this issue in depth and present a solution architecture: 2PDA, that eliminates repeated transmission. The solution is founded upon temporal linear correlation among sensor data. Instead of sending each data packet we model them using method of least square that exploits temporal correlation among sensor data. 2PDA observes sensor data and performs operation parameterized by application-precision. After successful computation only the parameters of the model are sent over the radio to the application-end or sink. 2PDA was implemented in TinyOS. Implementation showed a significant improvement (i.e. 80%) for the nodes life-time. Rigorous numerical analysis was done on various sensor data which indicated its modest efficiency under different scenario. Effects of various parameters such as type of sensory information, time and place of data collection were assessed. Finally a network simulation was carried out to evaluate its scalability.

A security adaptive protocol suite: Ranked Neighbor Discovery (RND) and Security Adaptive AODV (SA-AODV)

With the increasing demand of mobility and ad hoc networking, vulnerability of wireless networks is also becoming a crucial issue. This dissertation sheds light on the security features of wireless communication, and proposes a model with an increased integration of security features. The proposed model, a ranked neighbor discovery (RND), and a security adaptive ad-hoc on-demand distance vector (SA-AODV) routing protocol suite lays out the percept of solution for the security issues, which includes the neighbor discovery, as well as the routing protocol for transmission. The neighbor discovery phase consists of the determination of trusted neighbors, based on distance metrics, leading to a process of trust ranking. The routing protocol, using the fact of the trusted neighbors, and the required security level, then sets up a security adapted route from the source to its destination. The main advantage of this protocol pair would be the achievement to obtain a route with a user-defined level of security for a specific application. The two protocols thus provide the anchor to a package for a total solution for a secured environment for wireless transmission with an increased integration of security features.

2PDA: two-phase data approximation in wireless sensor network

Wireless Sensor Network (WSN) is highly budgeted by energy since sensor nodes are mostly battery-powered and deployed in hard-to-reach area for prolonged duration. Moreover radio communication is very expensive for WSN. At the same time, a substantial portion WSN applications require periodic data collection. In this paper we investigate this issue in depth and present a solution architecture: 2PDA, that eliminates repeated transmission. The solution is founded upon temporal linear correlation among sensor data. Instead of sending each data packet we model them using method of least square that exploits temporal correlation among sensor data. 2PDA observes sensor data and performs operation parameterized by application accuracy. After successful computation only the parameters of the model are sent over the radio to the application-end or sink. We have evaluated 2PDA on real-time sensor data indicating substantial energy-savings in overall transmission. We have implemented 2PDA in TinyOS for telosb sensor node that reflects its deployment plausibility in terms of memory requirement. Finally we have proposed a simplified multi-path routing strategy to enhance the reliability of data which is of prime importance in all WSN applications.

A key distribution scheme for secure communication in acoustic sensor networks

Abstract Cryptographic key distribution is always a major problem in large scale wireless communications such as Acoustic Sensor Networks (ASNs) based on wireless sensors in an Internet of Things (IoT) environment. Because of the resource constraints of the nodes in such networks, the traditional cryptographic primitives are not suitable solutions. Our endeavor in this paper is to develop a new deterministic key distribution mechanism for such networks. In particular, we bring in a novel construction methodology from two-dimensional geometry by exploiting the properties of regular Hexagon. One advantage of using the proposed approach, as opposed to randomized distribution techniques, is that, the configurations have a compact and efficient algebraic description. This yields nice algorithm for shared-key discovery, in which very little information (or no information at all) needs to be broadcasted. Furthermore, it is shown that the security strength of the proposed approach outperforms well-known deterministic techniques in terms of resilience. Furthermore, the distribution technique ensures 100% connectivity (i.e., the probability that two nodes share a key is 1) and average key-path length is 1.

A scalable framework for protecting user identity and access pattern in untrusted Web server using forward secrecy, public key encryption and bloom filter: Protecting user identity and access pattern in untrusted web

Securing user identity from data breach in a web server is one of the major concerns for the users of the web applications. Similarly, protecting user access pattern from unauthorized access should be taken seriously, because the potential threats such attacks may pose, are huge. However, these security measures should not be adopted at the expense of user experience and convenience. Nevertheless, any extra overhead in the form of security measures introduced in a distributed system results in significant performance declination. The target of a secured framework for a distributed system like web application should be a reasonable trade‐off between security and user experience. Thus, in this work, we present a framework that ensures security for the user identity along with keeping the online activities of the users anonymous while ensuring scalability of the system. Our framework is designed in a scalable form that can work with other distributed architectures that provide security to user data and identities. To ensure all these measures, our proposal includes the implementation of Forward Secrecy using Diffie‐Hellman Key exchange protocol where the server cannot remember a users history after a session ends. In addition, we present our own mechanism to hide logical data sharing strategies to protect users against selective DoS attacks. Moreover, we implemented a modified version of bloom filter to safeguard user access pattern in a compromised server. Our proposed implementation of bloom filter also ensures that the scalability of distributed system is preserved even with little infrequent overhead in the server because of security measures proposed in this work. Finally, we implemented different modules of our framework using both Web Socket and Long Polling transport protocols and recorded the time required to perform various tasks. Web socket protocol took less time to perform each task than the long polling protocol, which is convincing enough to suggest that web socket performs better than long polling in the given scenarios. Copyright

Congestion mitigation using in-network sensor datasummarization

Congestion can occur in Wireless Sensor Networks due to simultaneous event detection at multiple nodes, link failure or node failure. Most previously proposed congestion mitigation algorithms rely on rate control protocols to reduce network traffic. Typically, these solutions reduce application-level precision since the rate control mechanisms reduce the packet generation rate or force local packet drop without considering the implications of data loss. Herein, we propose a distributed, in-network algorithm for congestion mitigation by exploiting the inherent temporal correlation in sensor data. The proposed algorithm was implemented in TinyOS and deployed in a real-world testbed. Experimental results show that the algorithm provides significant reductions in packet drop ratio, from 25.30% to 1.92% and from 25.65% to 15.43% for temperature and light data, respectively, while incurring low distortion in the sensor data. A comparative study and network simulation were performed to assess its performance.

Depression detection from social network data using machine learning techniques

PurposeSocial networks have been developed as a great point for its users to communicate with their interested friends and share their opinions, photos, and videos reflecting their moods, feelings and sentiments. This creates an opportunity to analyze social network data for user’s feelings and sentiments to investigate their moods and attitudes when they are communicating via these online tools.MethodsAlthough diagnosis of depression using social networks data has picked an established position globally, there are several dimensions that are yet to be detected. In this study, we aim to perform depression analysis on Facebook data collected from an online public source. To investigate the effect of depression detection, we propose machine learning technique as an efficient and scalable method.ResultsWe report an implementation of the proposed method. We have evaluated the efficiency of our proposed method using a set of various psycholinguistic features. We show that our proposed method can significantly improve the accuracy and classification error rate. In addition, the result shows that in different experiments Decision Tree (DT) gives the highest accuracy than other ML approaches to find the depression.ConclusionsMachine learning techniques identify high quality solutions of mental health problems among Facebook users.