Intelligent Transportation Systems (ITS) have become an important pillar in modern "smart city" framework which demands intelligent involvement of machines. Traffic load recognition can be categorized as an important and challenging issue for such systems. Recently, Convolutional Neural Network (CNN) models have drawn considerable amount of interest in many areas such as weather classification, human rights violation detection through images, due to its accurate prediction capabilities. This work tackles real-life traffic load recognition problem on System-On-a-Programmable-Chip (SOPC) platform and coin it as MAT-CNN-SOPC, which uses an intelligent retraining mechanism of the CNN with known environments. The proposed methodology is capable of enhancing the efficacy of the approach by 2.44x in comparison to the state-of-art and proven through experimental analysis. We have also introduced a mathematical equation, which is capable of quantifying the suitability of using different CNN models over the other for a particular application based implementation.Index Terms-Convolutional neural network (CNN), traffic analysis, traffic density, transfer learning, system-on-aprogrammable-chip (SOPC).

Food safety is an important issue in today’s world. The traditional agri-food production system does not offer easy traceability of the produce at any point of the supply chain, and hence, during a food-borne outbreak, it is very difficult to sift through food production data to track produce and the origin of the outbreak. In recent years, the blockchain based food production system has resolved this challenge; however, none of the proposed methodologies makes the food production data easily accessible, traceable and verifiable by consumers or producers using mobile/edge devices. In this paper, we propose FoodSQRBlock (Food Safety Quick Response Block), a blockchain technology based framework that digitises the food production information and makes it easily accessible, traceable and verifiable by the consumers and producers by using QR codes. We also propose a large-scale integration of FoodSQRBlock in the cloud to show the feasibility and scalability of the framework, as well as give an experimental evaluation to prove this.

Recently we could see several institutions coming together to create consortium based blockchain networks such as Hyperledger. Although for applications of blockchain such as Bitcoin, Litcoin, etc. the majority-attack might not be a great threat but for consortium based blockchain networks where we could see several institutions such as public, private, government, etc. are collaborating, the majority-attack might just prove to be a prevalent threat if collusion among these institutions takes place. This paper proposes a methodology where we can use intelligent software agents to monitor the activity of stakeholders in the blockchain networks to detect anomaly such as collusion, using supervised machine learning algorithm and algorithmic game theory and stop the majority-attack from taking place.

Due to tremendous growth in communication technology, now it is a real problem / challenge to send some confidential data / information through communication network. For this reason, Nath et al. developed several information security systems, combining cryptography and steganography together, and the present method, ASA_QR, is also one of them. In the present paper, the authors present a new steganography algorithm to hide any small encrypted secret message inside QR Code TM , which is then randomized and then, finally embed that randomized QR Code inside some common image. Quick Response Codes (or QR Codes) are a type of two-dimensional matrix barcodes used for encoding information. It has become very popular recently for its high storage capacity. The present method is ASA_QR is a combination of strong encryption algorithm and data hiding in two stages to make the entire process extremely hard to break. Here, the secret message is encrypted first and hide it in a QR Code TM and then again that QR Code TM is embed in a cover file (picture file) in random manner, using the standard method of steganography. In this way the data, which is secured, is almost impossible to be retrieved without knowing the cryptography key, steganography password and the exact unhide method. For encrypting data The authors used a method developed by Nath et al i.e. TTJSA, which is based on generalized modified Vernam Cipher, MSA and NJJSA method; and from the cryptanalysis it is seen that TTJSA is free from any standard cryptographic attacks, like differential attack, plain-text attack or any brute force attack. After encrypting the data using TTJSA,the authors have used standard steganographic method To hide data inside some host file. The present method may be used for sharing secret key, password, digital signature etc.

Thermal cycling as well as temperature gradient in time and space affects the lifetime reliability and performance of heterogeneous multiprocessor systems-on-chips (MPSoCs). Conventional temperature management techniques are not intelligent enough to cater for performance, energy efficiency as well as operating temperature of the system. In this paper we propose a lightweight novel thermal management mechanism in the form of intelligent software agent, which monitors and regulates the operating temperature of the CPU cores to improve reliability of the system. We validated our methodology on the Odroid-XU4 SoC and it has been successful to reduce the operating temperature by 6.32% while improving performance by 7.96% and reducing power consumption by 9.45% than the state-of-the-art. Index Terms-Lifetime reliability, multiprocessor systems-on-chip (MPSoCs), thermal management, thermal cycling, DVFS This work is supported by the UK Engineering and Physical Sciences Research Council EPSRC [EP/R02572X/1 and EP/P017487/1]. 1 DVFS helps to reduce the energy consumption by executing the workload over extra time at a lower voltage and frequency, which could be accounted for reduced power consumption.

Multi-core mobile platforms are on rise as they enable efficient parallel processing to meet ever-increasing performance requirements. However, since these platforms need to cater for increasingly dynamic workloads, efficient dynamic resource management is desired mainly to enhance the energy and thermal efficiency for better user experience with increased operational time and lifetime of mobile devices. This article provides a survey of dynamic energy and thermal management approaches for multi-core mobile platforms. These approaches do either proactive or reactive management. The upcoming trends and open challenges are also discussed.

Heterogeneous Multiprocessor System-on-Chip (MPSoC) are progressively becoming predominant in most modern mobile devices. These devices are required to perform processing of applications within thermal, energy and performance constraints. However, most stock power and thermal management mechanisms either neglect some of these constraints or rely on frequency scaling to achieve energy-efficiency and temperature reduction on the device. Although this inefficient technique can reduce temporal thermal gradient, but at the same time hurts the performance of the executing task. In this paper, we propose a thermal and energy management mechanism which achieves reduction in thermal gradient as well as energy-efficiency through resource mapping and thread-partitioning of applications with online optimization in heterogeneous MPSoCs. The efficacy of the proposed approach is experimentally appraised using different applications from Polybench benchmark suite on Odroid-XU4 developmental platform. Results show 28% performance improvement, 28.32% energy saving and reduced thermal variance of over 76% when compared to the existing approaches. Additionally, the method is able to free more than 90% in memory storage on the MPSoC, which would have been previously utilized to store several task-to-thread mapping configurations.

Mobile user's usage behaviour changes throughout the day and the desirable Quality of Service (QoS) could thus change for each session. In this paper, we propose a QoS aware agent to monitor mobile user's usage behaviour to find the target frame rate, which satisfies the desired user's QoS, and applies reinforcement learning based DVFS on a CPU-GPU MPSoC to satisfy the frame rate requirement. Experimental study on a real Exynos hardware platform shows that our proposed agent is able to achieve a maximum of 50% power saving and 29% reduction in peak temperature compared to stock Android's power saving scheme. It also outperforms the existing state-of-the-art power and thermal management scheme by 41% and 19%, respectively.