The Long-Range (LoRa) system for the Internet of Things (IoT) has proved effective in infrastructure, energy consumption, resource reduction, and pollution control. However, in terms of security, LoRa faces several challenges. LoRa has a centralised architecture where all sensor data is transmitted to a fog (edge) server via LoRa gateways and processed at the fog server. This exposes sensitive sensor data to some major security threats, such as data tampering, injection of fake data and data loss. LoRa gateways in such architecture simply forward packets to the server. We present a blockchain-based open-source solution for LoRa Gateways that addresses the security vulnerabilities of this architecture and utilises the computing and storage resources of LoRa Gateways. The main challenges in implementing blockchain at LoRa gateways are limited processing, storage capabilities and battery power. The number of gateway nodes improves security and reduces the workload on the fog server. With the introduction of packet processing at the gateway, unnecessary packets are dropped at the gateway itself. Simulation results depict a reduced CPU usage at the fog server for up to 2 % when 16 end devices are being used. Further, our work reduces the overall packet handling time by up to 17% compared to existing works.

Electric vehicles (EVs) provide a promising solution to global pollution by replacing conventional vehicles. Enough public charging stations must be built to encourage the broad adoption of electric vehicles (EVs). Additionally, efficient scheduling and planning of EV arrivals at charging stations with a variety of power sources is essential for improving overall operating efficiency. This paper presents a framework for optimizing both the profit and throughput of a charging station that incorporates a solar photovoltaic system and an energy storage system. We present an Integer Linear Programming model to find the optimal solution. We propose a heuristic strategy to efficiently find near-optimal solutions. Our experimental results demonstrate that the proposed approach can provide a reasonably good solution and for small inputs, the performance deviation is less than 5%. For larger inputs, a comparison of the heuristic and baseline is presented. In certain scenarios, the heuristic performs well, while the baseline exhibits better performance in others. We can use both approaches and select the one that yields better results.

Real data plays a fundamental role in determining various features related to the collection site, such as monitoring, controlling, predictions, etc. Several systems in the Internet of Things (IoT) environment produce millions of data from the sensing node, and transmitting each of the data is costly in terms of bandwidth requirements, energy consumption, and protecting data from being corrupt or from potential threats such as man-in-the-middle attacks. In many environmental applications such as agriculture, the absolute change in the consecutive data points is usually very small (we call it slow changing environment). So, there is a need for a system that can predict the next data point within a predefined tolerable limit, then transmitting each data point can be avoided. To address this issue, we proposed an analytical prediction algorithm using estimations (APAEs). The algorithm is deployed and runs simultaneously in the three layers of architecture: sensing, fog, and cloud layers. The algorithm predicts the next data sensed by the sensor. If the difference between the actual sensed data point and the predicted data point is beyond the predefined tolerance, then the sensed value is sent to the fog node and further to the cloud; otherwise, the estimated value is accepted. We have implemented the proposed algorithm on a real testbed and also tested it on two data sets. We compare the amount of data points transmitted with the state of the state-of-the-art scheme. We also highlighted the reduction in energy consumption and high accuracy of our algorithm on the two data sets and a real testbed.

Smart agriculture is one of the most promising areas where IoT-enabled technologies have the potential to substantially improve the quality and quantity of the crops and reduce the operational cost. However, building a smart agriculture system presents several challenges, including high latency and bandwidth consumption associated with cloud computing, Internet disconnections in rural areas, and the need to keep costs low for farmers. To address these issues, this paper proposes a fog-based smart agriculture infrastructure with edge computing and LoRa communication. We address the top concern of farmers - animals intruding - by proposing a solution that detects animal intrusion using low-cost PIR sensors, cameras, and computer vision and predicts animal locations using a novel algorithm. Our system can detect animals before the intrusion, identify them, predict their future locations, and alert farmers promptly. The paper proposes three sensor layouts, and experiments confirm the system’s effectiveness and lower cost compared to state-of-the-art systems.

The growth of the Internet of Things (IoT) has led to an increased demand for contactless payment via IoT devices. However, machine-to-machine (M2M) payment in the IoT has been limited by poor centralized transaction management, given the distributed nature of the IoT, which results in massive communication overhead. Blockchain technology provides a promising solution for M2M payments in the IoT, but the current blockchain-based solutions for contactless payment lack security and scalability. To address this, a new customised Committee Member Auction mechanism has been proposed that is both safe and scalable for resource-constrained IoT devices. This mechanism also reduces the block mining time by integrating the BLAKE hashing algorithm, as opposed to SHA-256. The proposed framework was implemented utilizing an ESP32 microcontroller and RC522 RFID reader to record over 100 transactions each for SHA-256 and Blake. The results obtained validate that Blake hashing algorithm along with Bidirectional blockchain outperforms SHA-256 by reducing 30% of mining time. Besides improved mining efficiency, this scheme is also found to be resistant against Long Range Attack, Double Spend Attack and Eclipse attack. The proposed scheme thus offers a more secure and scalable option for contactless payments in IoT systems.

Cloud computing is an emergent paradigm to provide reliable and resilient infrastructure enabling the users (data owners) to store their data and the data consumers (users) can access the data from cloud servers. This paradigm reduces storage and maintenance cost of the data owner. At the same time, the data owner loses the physical control and possession of data which leads to many security risks. Therefore, auditing service to check data integrity in the cloud is essential. This issue has become a challenge as the possession of data needs to be verified while maintaining the privacy. To address these issues this work proposes a secure and efficient privacy preserving provable data possession (SEPDP). Further, we extend SEPDP to support multiple owners, data dynamics and batch verification. The most attractive feature of this scheme is that the auditor can verify the possession of data with low computational overhead.