Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item En-Route Filtering Techniques in Wireless Sensor Networks: A Survey(Springer New York LLC barbara.b.bertram@gsk.com, 2017) Kumar, A.; Pais, A.R.Majority of wireless sensor networks (WSNs) are deployed in unattended environments and thus sensor nodes can be compromised easily. A compromised sensor node can be used to send fake sensing reports to the sink. If undetected these reports can raise false alarms. To deal with the problem of fake report generation, a number of en-route filtering schemes have been proposed. Each of these schemes uses different cryptographic methods to check the authenticity of reports while they are being forwarded hop by hop toward base station. However, majority of these techniques can handle only limited compromised nodes or they either need node localization or statically configured routes for sending reports. Furthermore, majority of en-route filtering techniques are vulnerable to various denial of service attacks. Our main aims in this survey are: (a) to describe the major en-route filtering techniques, (b) to analyze these techniques on various parameters including security and (c) to outline main unresolved research challenges in en-route filtering in WSNs. © 2017, Springer Science+Business Media New York.Item Effective integration of reliable routing mechanism and energy efficient node placement technique for low power IoT networks(IGI Global cust@igi-global.com, 2017) Sarwesh, P.; Shet, N.S.V.; Chandrasekaran, K.Internet of Things (IoT) is the emerging technology that links physical devices (sensor devices) with cyber systems and allows global sharing of information. In IoT applications, devices are operated by battery power and low power radio links, which are constrained by energy. In this paper, node placement technique and routing mechanism are effectively integrated in single network architecture to prolong the lifetime of IoT network. In proposed network architecture, sensor node and relay node are deployed, sensor nodes are responsible for collecting the environmental data and relay nodes are responsible for data aggregation and path computation. In node placement technique, densities of relay nodes are varied based on traffic area, to prevent energy hole problem. In routing technique, energy efficient and reliable path computation is done to reduce number of re transmissions. To adopt IoT scenario, we included IEEE 802.15.4 PHY/MAC radio and IPv6 packet structure in proposed network architecture. Proposed work result shows, proposed architecture prolongs network lifetime. © © 2017, IGI Global.Item ETRT – Cross layer model for optimizing transmission range of nodes in low power wireless networks – An Internet of Things Perspective(Elsevier B.V., 2018) Sarwesh, P.; Shet, N.S.V.; Chandrasekaran, K.Internet of Things network is managed by battery operated devices and low power radio links since they are referred to low power networks. In present communication era, many research works are concentrating on low power wireless network. Cross layer design is one of the acclaimed technique that decidedly improves the network performance. In this article, we come up with the cross-layer model that satisfies distinct network requirements and prolongs network lifetime. It integrates physical layer, data link layer (Media Access Control) and network layer in the protocol stack. In our model, a threshold value called ETRT (Expected Transmission Range Threshold) is introduced, which is computed with the help of routing information. Later, MAC based power control technique utilizes the ETRT value and assigns optimum transmission range for every node. The idea at the heels of proposed cross layer model is estimating the capability (ETRT value) of the particular node and assigning the suitable transmission power for every node, based on its capability (ETRT value). Hence, assigning optimum transmission power based on ETRT information prolongs the network lifetime with better reliability and Quality of Service(QoS). From our results, it is noticed that the ETRT based cross layer model performs twice better than the standard model. © 2018 Elsevier B.V.Item A novel key exchange algorithm for security in internet of things(Institute of Advanced Engineering and Science info@iaesjournal.com, 2019) Koduru, K.; Reddy, P.V.G.D.P.; Preethi, P.Today Internet of things (IoT) interconnects any object possessing sensing and computing capabilities to the internet. In this era, increasing number of electronic devices and applications in Internet of Things (IoT) requires secured communication with low power consumption capabilities. As security is a major challenge in internet of things, it is important to design a key management solution that considers resource constrained nodes and hence key management in public key cryptography is a crucial issue. In this paper, a novel key exchange algorithm was developed and implemented on a low powered “Raspberry pi machine” to realize the overall impact it creates on the device. The performance of the proposed algorithm had shown a great improvement over the popular Diffie Hellman key exchange algorithm and a two-level security for data exchange between the parties is implemented. © © 2019 Institute of Advanced Engineering and Science. All rights reserved.Item Fuzzy optimised routing metric with mobility support for RPL(Institution of Engineering and Technology JBristow@theiet.org, 2019) Sanshi, S.; Jaidhar, C.D.Recently, many Internet of Things (IoT) applications have emerged with mobility as a fundamental requirement. The presence of a mobile node that changes location around the application domain affects the performance of the Routing Protocol for Low Power Lossy Network (RPL) designed for IoT, leading to repeated disruptions that cause data loss and more power dissipation. In this study, a fuzzy optimised routing metric with mobility support (FL-RPL) has been proposed to enhance the performance of the RPL. The fuzzy inference system considers various routing metrics to pick a suitable candidate parent as the preferred parent node to forward the data to the sink node. Further, timer functions have been added to maintain consistent neighbours to support mobility and seamless connectivity. The FL-RPL has been implemented and tested with different parameter settings for a practical scenario. The obtained simulation results clearly demonstrated that the proposed solution increased packet delivery ratio by approximately 12%, and reduced power consumption by 20% compared with the standard RPL. © 2019 The Institution of Engineering and Technology.Item Design and development of advanced smart energy management system integrated with IoT framework in smart grid environment(Elsevier Ltd, 2019) Pawar, P.; Vittal K, P.The day-to-day increased usage of power appliance by consumers is a growing concern in the energy sector, which creates an imbalance in the ratio of demand and supply. Demand-side energy management is an imperative tool to avoid significant deficiency from the supply end and improve energy efficiency. The trend in energy management lays focus on reducing the overall cost of electricity without limiting the consumption counterpart by instead choosing to reduce the power consumption during peak hours. The above issue seeks for design and development of a flexible and portable system to cover a wide variety of consumers for balancing the overall system. The design of smart energy management system is intended to replace the scenario of a complete power outage in a region with partial load shedding in a controlled manner as per the consumer's preference. Demonstration of experimental work is carried out assuming demand response event and also, considering the maximum demand limit constraint with different cases and changing the order of priority assigned to an appliance. Cost optimization algorithms based on time of usage and user comfort level with sensory information features are embedded within SEMS. Reliable ZigBee communication for home area network is established and also, an IoT environment is developed for data storage and analytics. © 2019 Elsevier LtdItem CoCoA++: Delay gradient based congestion control for Internet of Things(Elsevier B.V., 2019) Rathod, V.; Jeppu, N.; Sastry, S.; Singala, S.; Tahiliani, M.P.In this paper, we propose a new congestion control algorithm called CoCoA++ to address the issue of network congestion in Internet of Things (IoT). Unlike the existing congestion control mechanisms that operate on instantaneous Round Trip Time (RTT) measurements in IoT, we use delay gradients to get a better measure of network congestion, and implement a probabilistic backoff to deal with congestion. We integrate the delay gradients and the probability backoff factor with Constrained Application Protocol (CoAP). The proposed algorithm is implemented and evaluated using the Cooja network simulator provided by Contiki OS. Subsequently, it is deployed and evaluated in a real testbed by using the FIT/IoT-LAB. We observe that delay gradients give a more accurate measure of congestion and the Retransmission Time Out (RTO) is reduced significantly, thereby leading to less delays and high packet sending rates. CoCoA++ being a minor improvement over the existing algorithm is easy to deploy. © 2019 Elsevier B.V.Item A trust model based batch verification of digital signatures in IoT(Springer, 2020) Kittur, A.S.; Pais, A.R.In the modern day world, the Internet of things (IoT) is not a new concept. IoT is getting deployed in various applications and fields. Hence with this fast-growing trend, it is essential to maintain the security in the IoT network. Digital Signature is one of the important ways to authenticate an electronic document or a message during communication. Multiple digital signatures are verified at once through the concept of batch verification. Batch verification of multiple digital signatures reduces the computation load and time. Hence this concept is beneficial in IoT environment where nodes have low computation power and operate in a real-time environment. In this paper, we have developed a Trust Model for IoT which helps the Gateway node to identify the trusted sensor nodes which perform batch verification. The sensor nodes receive a batch of signatures from the Gateway node and verify signatures through batch verification and accordingly send back the results. The trust model that we have developed in this paper significantly reduces the probability of selecting unreliable nodes for verification and also reduces the computation load at Gateway node. We have implemented our trust model and presented the results for batch verification of digital signatures. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.Item Low power digital on-chip implementation of Fibonacci non-magnetic switching converter for IoT applications(Inderscience Publishers editor@inderscience.com 29, route de Pre-Bois Case Postale 856, CH-1215 Geneva 15 CH-1215, 2020) Subburaj, V.; Jena, D.; Parthiban, P.Power management (PM) block is one of the important block in system on chip (SoC). Non-magnetic converters (NMCs) are more reliable for SoC since it is small in size and having low power level. Recently SoC is applicable to all internet of things (IoTs). NMCs are having the capability to share more transformation voltage levels to different part of the ICs in order to obtain high efficiency. Compare to all NMCs, the Fibonacci converter generates different voltage level (i.e.) 21 voltage levels in single PM block. Power frequency modulation (PFM) controller is used to select and control different voltage levels. In this paper fully integrated digital controller (Verilog-HDL) based converter design is implemented using 180 nm technology for load current which is less than 1 µA and consumes 10 µW total power with an area of 166 µm × 205 µm. © © 2020 Inderscience Enterprises Ltd.Item Real-time big data analytics framework with data blending approach for multiple data sources in smart city applications(West University of Timisoara, 2020) Manjunatha, S.; Annappa, A.Advancement in Information Communication Technology (ICT) and the Internet of Things (IoT) has to lead to the continuous generation of a large amount of data. Smart city projects are being implemented in various parts of the world where analysis of public data helps in providing a better quality of life. Data analytics plays a vital role in many such data-driven applications. Real-time analytics for finding valuable insights at the right time using smart city data is crucial in making appropriate decisions for city administration. It is essential to use multiple data sources as input for the analysis to achieve better and more accurate data-driven solutions. It helps in finding more accurate solutions and making appropriate decisions. Public safety is one of the major concerns in any smart city project in which real-time analytics is much useful in the early detection of valuable data patterns. It is crucial to find early predictions of crime-related incidents and generating emergency alerts for making appropriate decisions to provide security to the people and safety of the city infrastructure. This paper discusses the proposed real-time big data analytics framework with data blending approach using multiple data sources for smart city applications. Analytics using multiple data sources for a specific data-driven solution helps in finding more data patterns, which in turn increases the accuracy of analytics results. The data preprocessing phase is a challenging task in data analytics when data being ingested continuously in real-time into the analytics system. The proposed system helps in the preprocessing of real-time data with data blending of multiple data sources used in the analytics. The proposed framework is beneficial when data from multiple sources are ingested in real-time as input data and is also flexible to use any additional data source of interest. The experimental work carried out with the proposed framework using multiple data sources to find the crime-related insights in real-time helps the public safety solutions in the smart city. The experimental outcome shows that there is a significant increase in the number of identified useful data patterns as the number of data sources increases. A real-time based emergency alert system to help the public safety solution is implemented using a machine learning-based classification algorithm with the proposed framework. The experiment is carried out with different classification algorithms, and the results show that Naive Bayes classification performs better in generating emergency alerts. © 2020 SCPE.