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We optimize the overall energy consumption of a Narrowband Internet of Things (NB-IoT) application created using a hybrid blockchain framework. We accomplish this by engineering the underlying hash function (SHA-256) that is used in different procedures (Unique ID generation, Device Join, and Device Transaction) of the blockchain-based NB-IoT system. In order to reduce the complexity of hash verification, IoT devices in the NB-IoT application are built to save the hashes of their authorized transactions as a linear hash chain rather than the entire Merkle tree. Furthermore, base station memory is dynamically partitioned to improve memory usage efficiency and scalability. Compared with the state-of-the-art approach, our approach considerably reduces the total energy consumption of the state-of-the-art application. 

Communication is arguably the most important way to enable cooperation among multiple robots. In numerous such settings, robots exchange local sensor measurements to form a global perception of the environment. One example of this setting is adaptive multi-robot informative path planning, where robots’ local measurements are “fused” using probabilistic techniques (e.g., Gaussian process models) for more accurate prediction of the underlying ambient phenomena. In an adversarial setting, in which we assume a malicious entity–-the adversary-–can modify data exchanged during inter-robot communications, these cooperating robots become vulnerable to data integrity attacks. Such attacks on a multi-robot informative path planning system may, for example, replace the original sensor measurements with fake measurements to negatively affect achievable prediction accuracy. In this paper, we study how such an adversary may design data integrity attacks using a Generative Adversarial Network (GAN). Results show the GAN-based techniques learning spatial patterns in training data to produce fake measurements that are relatively undetectable yet significantly degrade prediction accuracy. 

In this work, we propose a blockchain-based solution for securing robot-to-robot communication for a task with a high socioeconomic impact—information gathering. The objective of the robots is to gather maximal information about an unknown ambient phenomenon such as soil humidity distribution in a field. More specifically, we use the proof-of-work (PoW) consensus protocol for the robots to securely coordinate while rejecting tampered data injected by a malicious entity. As the blockchain-based PoW protocol has a large energy footprint, we next employ an algorithmically-engineered energy-efficient version of PoW. Results show that our proposed energy-efficient PoW-based protocol can reduce energy consumption by 14% while easily scaling up to 10 robots.