| 1. | Rakshit Ramesh; Mukunth Arunachalam; Hari Krishna Atluri; Chetan Kumar; SVR Anand; Paventhan Arumugam; Bharadwaj, Amrutur LoRaWAN for smart cities: experimental study in a campus deployment, Book Chapter in for IoT, Book Chapter LPWAN Technologies; Applications, M2M (Ed.): pp. 327-345, Academic Press, 2020. Abstract | BibTeX | Links:   @inbook{@Rakshit,
title = {LoRaWAN for smart cities: experimental study in a campus deployment,},
author = {Rakshit Ramesh; Mukunth Arunachalam; Hari Krishna Atluri; Chetan Kumar; SVR Anand; Paventhan Arumugam; Bharadwaj, Amrutur},
editor = {Book Chapter in LPWAN Technologies for IoT and M2M Applications},
url = {https://cps.iisc.ac.in/wp-content/uploads/2020/10/LoRaWAN-for-Smart-Cities-Experimental-Study-in-a-Campus-Deployment.pdf},
doi = {10.1016/B978-0-12-818880-4.00016-8},
year = {2020},
date = {2020-08-01},
pages = {327-345},
publisher = {Academic Press},
abstract = {The advent of cheap low-powered long-range radio technologies such as LoRa that operate on an unlicensed band resulted in many real-world deployments of smart city applications. Unlike a rural and semiurban areas where we can expect long radio-frequency (RF) propagation due to strong line-of-sight component, a city that typically comprises diverse terrain environments comprising a mix of buildings, dense wooded areas, parks, and gated communities limits the radio coverage and range. This necessitates the need for proper RF network planning to ensure optimal coverage of the network in such areas with the ability to remotely manage the infrastructure equipment to make deployment and network management hassle-free. On a city scale, there is a need for an interoperable middleware that provides a single point of contact for a developer to operate devices on various network protocols such as LoRa and Zigbee. In this chapter, we describe our experiences in deploying such an interoperable long-range wide-area network and management aspects of it in a campus environment with dense foliage interspersed with buildings and shed light on some deployment insights we have obtained in order to make an optimal network deployment.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
The advent of cheap low-powered long-range radio technologies such as LoRa that operate on an unlicensed band resulted in many real-world deployments of smart city applications. Unlike a rural and semiurban areas where we can expect long radio-frequency (RF) propagation due to strong line-of-sight component, a city that typically comprises diverse terrain environments comprising a mix of buildings, dense wooded areas, parks, and gated communities limits the radio coverage and range. This necessitates the need for proper RF network planning to ensure optimal coverage of the network in such areas with the ability to remotely manage the infrastructure equipment to make deployment and network management hassle-free. On a city scale, there is a need for an interoperable middleware that provides a single point of contact for a developer to operate devices on various network protocols such as LoRa and Zigbee. In this chapter, we describe our experiences in deploying such an interoperable long-range wide-area network and management aspects of it in a campus environment with dense foliage interspersed with buildings and shed light on some deployment insights we have obtained in order to make an optimal network deployment. |
| 2. | Karanjkar, Neha; Tejasvi, Poorna Chandra; Amrutur, Bharadwaj A simpy-based simulation testbed Presentation 18.04.2019. Abstract | BibTeX | Links: @misc{Karanjkar2019,
title = {A simpy-based simulation testbed },
author = {Neha Karanjkar and Poorna Chandra Tejasvi and Bharadwaj Amrutur},
doi = {10.1145/3302505.3312591},
year = {2019},
date = {2019-04-18},
abstract = {A real-time testbed that emulates a large number of IoT end-points generating traffic to the middleware/application layers can be used for debugging and performance evaluation of the smart-city software platforms prior to deployment. We propose an architecture for such a simulation testbed based on Python's SimPy library. The simulated IoT end-points communicate with the middleware in real-time and can also interact directly with each other and with a common shared environment. This makes the testbed particularly suited for modeling system-wide scenarios such as synchronized faults and power outages.},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
A real-time testbed that emulates a large number of IoT end-points generating traffic to the middleware/application layers can be used for debugging and performance evaluation of the smart-city software platforms prior to deployment. We propose an architecture for such a simulation testbed based on Python's SimPy library. The simulated IoT end-points communicate with the middleware in real-time and can also interact directly with each other and with a common shared environment. This makes the testbed particularly suited for modeling system-wide scenarios such as synchronized faults and power outages. |
| 3. | Babu, Arun; Thomas, Jithin Jose Freestyle, a randomized version of ChaCha for resisting offline brute-force and dictionary attacks Journal Article Forthcoming Journal of Information Security and Applications, 49 , Forthcoming. Abstract | BibTeX | Links: @article{Babu2019,
title = {Freestyle, a randomized version of ChaCha for resisting offline brute-force and dictionary attacks},
author = {Arun Babu and Jithin Jose Thomas},
url = {http://10.0.54.4/wp-content/uploads/2019/10/Freestyle-a-randomized-version-of-ChaCha-for-resisting-offline-brute-force-and-dictionary-attacks.pdf},
doi = {10.1016/j.jisa.2019.102396},
year = {2019},
date = {2019-12-01},
journal = {Journal of Information Security and Applications},
volume = {49},
abstract = {This paper introduces Freestyle, a randomized and variable round version of the ChaCha cipher. Freestyle uses the concept of hash based halting condition where a decryption attempt with an incorrect key is likely to take longer time to halt. This makes Freestyle resistant to key-guessing attacks i.e. brute-force and dictionary based attacks. Freestyle demonstrates a novel approach for ciphertext randomization by using random number of rounds for each block, where the exact number of rounds are unknown to the receiver in advance. Freestyle provides the possibility of generating 2^128 different ciphertexts for a given key, nonce, and message; thus resisting key and nonce reuse attacks. Due to its inherent random behavior, Freestyle makes cryptanalysis through known-plaintext, chosen-plaintext, and chosen-ciphertext attacks difficult in practice. On the other hand, Freestyle has costlier cipher initialization process, typically generates 3.125% larger ciphertext, and was found to be 1.6 to 3.2 times slower than ChaCha20. Freestyle is suitable for applications that favor ciphertext randomization and resistance to key-guessing and key reuse attacks over performance and ciphertext size. Freestyle is ideal for applications where ciphertext can be assumed to be in full control of an adversary, and an offline key-guessing attack can be carried out. },
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
This paper introduces Freestyle, a randomized and variable round version of the ChaCha cipher. Freestyle uses the concept of hash based halting condition where a decryption attempt with an incorrect key is likely to take longer time to halt. This makes Freestyle resistant to key-guessing attacks i.e. brute-force and dictionary based attacks. Freestyle demonstrates a novel approach for ciphertext randomization by using random number of rounds for each block, where the exact number of rounds are unknown to the receiver in advance. Freestyle provides the possibility of generating 2^128 different ciphertexts for a given key, nonce, and message; thus resisting key and nonce reuse attacks. Due to its inherent random behavior, Freestyle makes cryptanalysis through known-plaintext, chosen-plaintext, and chosen-ciphertext attacks difficult in practice. On the other hand, Freestyle has costlier cipher initialization process, typically generates 3.125% larger ciphertext, and was found to be 1.6 to 3.2 times slower than ChaCha20. Freestyle is suitable for applications that favor ciphertext randomization and resistance to key-guessing and key reuse attacks over performance and ciphertext size. Freestyle is ideal for applications where ciphertext can be assumed to be in full control of an adversary, and an offline key-guessing attack can be carried out. |
| 4. | Poorna Chandra, Tejasvi User story contest @ RabbitMQ Summit Presentation 04.11.2019. Abstract | BibTeX @misc{@Poorna2019,
title = {User story contest @ RabbitMQ Summit},
author = {Poorna Chandra, Tejasvi },
year = {2019},
date = {2019-11-04},
abstract = {RabbitMQ Summit brings together users and developers from around the world. Learn from speakers and keynotes on what’s happening in and around RabbitMQ, and how top companies utilise RabbitMQ to power their services. },
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
RabbitMQ Summit brings together users and developers from around the world. Learn from speakers and keynotes on what’s happening in and around RabbitMQ, and how top companies utilise RabbitMQ to power their services. |
| 5. | Ghosh, Rajrup; Simmhan, Yogesh Distributed scheduling of event analytics across edge and cloud Journal Article ACM Transactions on Cyber-Physical Systems, 2 (4), pp. 24:1-28, 2018. Abstract | BibTeX | Links: @article{Ghosh2018,
title = {Distributed scheduling of event analytics across edge and cloud},
author = {Rajrup Ghosh and Yogesh Simmhan},
url = {http://www.rbccps.org/wp-content/uploads/2018/12/a24-ghosh.pdf},
doi = {10.1145/3140256},
year = {2018},
date = {2018-10-31},
journal = {ACM Transactions on Cyber-Physical Systems},
volume = {2},
number = {4},
pages = {24:1-28},
abstract = {Internet of Things (IoT) domains generate large volumes of high-velocity event streams from sensors, which need to be analyzed with low latency to drive decisions. Complex Event Processing (CEP) is a Big Data technique to enable such analytics and is traditionally performed on Cloud Virtual Machines (VM). Leveraging captive IoT edge resources in combination with Cloud VMs can offer better performance, flexibility, and monetary costs for CEP. Here, we formulate an optimization problem for energy-aware placement of CEP queries, composed as an analytics dataflow, across a collection of edge and Cloud resources, with the goal of minimizing the end-to-end latency for the dataflow. We propose a Genetic Algorithm (GA) meta-heuristic to solve this problem and compare it against a brute-force optimal algorithm (BF). We perform detailed real-world benchmarks on the compute, network, and energy capacity of edge and Cloud resources. These results are used to define a realistic and comprehensive simulation study that validates the BF and GA solutions for 45 diverse CEP dataflows, LAN and WAN setup, and different edge resource availability. We compare the GA and BF solutions against random and Cloud-only baselines for different configurations for a total of 1,764 simulation runs. Our study shows that GA is within 97% of the optimal BF solution that takes hours, maps dataflows with 4--50 queries in 1--26s, and only fails to offer a feasible solution ≤20% of the time.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Internet of Things (IoT) domains generate large volumes of high-velocity event streams from sensors, which need to be analyzed with low latency to drive decisions. Complex Event Processing (CEP) is a Big Data technique to enable such analytics and is traditionally performed on Cloud Virtual Machines (VM). Leveraging captive IoT edge resources in combination with Cloud VMs can offer better performance, flexibility, and monetary costs for CEP. Here, we formulate an optimization problem for energy-aware placement of CEP queries, composed as an analytics dataflow, across a collection of edge and Cloud resources, with the goal of minimizing the end-to-end latency for the dataflow. We propose a Genetic Algorithm (GA) meta-heuristic to solve this problem and compare it against a brute-force optimal algorithm (BF). We perform detailed real-world benchmarks on the compute, network, and energy capacity of edge and Cloud resources. These results are used to define a realistic and comprehensive simulation study that validates the BF and GA solutions for 45 diverse CEP dataflows, LAN and WAN setup, and different edge resource availability. We compare the GA and BF solutions against random and Cloud-only baselines for different configurations for a total of 1,764 simulation runs. Our study shows that GA is within 97% of the optimal BF solution that takes hours, maps dataflows with 4--50 queries in 1--26s, and only fails to offer a feasible solution ≤20% of the time. |
