The Internet Protocol.

There is a growing interest in using wireless sensor technologies in various Internet of things scenarios. Considering the huge growth of smart objects and their applications, the need to collect and analyze their product data are becoming one of the main challenges. Sensor nodes are powered by batteries, efficient operations in term of energy are critical. Toward that end, it is desirable for a sensor node to eliminate redundancies in the received data from the neighboring nodes before transferring the final data to the central station. Data aggregation is one of the influential techniques in elimination of data redundancy and improvement of energy efficiency; also it increases the lifespan of Wireless Sensor Networks. In addition, the efficient data aggregation protocol can reduce network traffic. When a specific objective takes place in a specific area, it might be detected by more than one sensor. Considering the main challenges and aspects of data aggregation in wireless sensor networks, a review on different types of data aggregation techniques and protocols are presented in this paper. The ultimate objective of this study is to make the basic foundations to develop new advanced designs based on data integration techniques and clustering that have been proposed so far. Major techniques of data integration in wireless sensor networks covering ground, underground and underwater sensor networks are presented in this paper and the applications, advantages and disadvantages of using each technique are described.

A survey on data aggregation techniques in IoT sensor networks

Securing internet of medical things systems: Limitations, issues and recommendations

From digital to sustainable: A scientometric review of smart city literature between 1990 and 2019

Health care service delivery based on the Internet of things: A systematic and comprehensive study

Automatic detection of COVID-19 from chest radiographs using deep learning.

Internet of Things (IoT) has dramatically revolutionized different aspects of living over the past few years. IoT is a huge network of devices that are able to sense and hold sensitive information about their owner/surroundings. Research stands testimony to the fact that these tiny IoT devices have security flaws. Recent ransomware attacks like WannaCry and NotPetya have shattered the illusion that a back-up can protect an organizations digital data from being hacked. If huge organizations were forced to pay ransom, one can only imagine the situation when a single individual is involved. In other words, when the worlds of IoT and ransomware collide and cybercriminals begin to load IoT devices with the malware, a perfect storm of cyber security arms race will be created. IoT offers a huge extortion landscape for the attackers and a ransomware in it can be extremely dangerous for it can affect the entire range of security services i.e. integrity, confidentiality and availability which can result not only in monetary losses but also in sensitive information breach and life risks. This paper discusses both the customary as well as the recent IoT directed ransomwares, explains the methodologies that were taken, and identifies the lessons learnt after the attacks, what precautions should be taken and the possible solutions available.

RansomWare and Internet of Things: A New Security Nightmare

Deep Learning for Internet of Things Data Analytics

Internet of Things (IoT), a paradigm added to the ever-growing technological arena in recent times acts like a bridge between the things in the physical world and their representation within the digital world. The basic “things” in the IoT are sensor devices, which gather as well as monitor all types of data on physical machines and human social life. IoT enables data sending and receiving for each “thing” through the communication network. The purpose of Data Aggregation is to decrease the number of communications/transmissions among the objects/things in the Internet of Things framework. The effectiveness of the data aggregation technique employed is a key factor in the success of IoT systems in terms of data freshness and efficiency. Different data aggregation techniques have been proposed in the recent past, which include – Tree-Based, Cluster-Based and Centralized data aggregation techniques. The paper aims at a detailed study and analysis of data aggregation schemes employed in the Internet of Things in terms of working and time complexity. Lowest Common Ancestor (LCA) aided Tree-Based Data Aggregation algorithm is designed. In addition, the Cluster-Based data aggregation algorithm incorporated with the β-dominating set and Centralized Data Aggregation algorithm incorporated with the SUM() aggregation function are proposed. The algorithms are supported by wellformed flowcharts describing the flow and working of the data aggregation mechanisms designed. The results are obtained on a system consisting of 60 nodes with all the three aggregation algorithms being evaluated against each other. The centralized data aggregation algorithm is better when the number of nodes in the network is lesser. However, as the number of nodes increases, the cluster-based and tree-based algorithms produce better results as compared to the centralized data aggregation algorithm.

Data Aggregation Mechanisms in the Internet of Things: A Study, Qualitative and Quantitative Analysis

The intelligence in the Internet of Things (IoT) can be embedded by analyzing the huge volumes of data generated by it in an ultralow latency environment. The computational latency incurred by the cloud-only solution can be significantly brought down by the fog computing layer, which offers a computing infrastructure to minimize the latency in service delivery and execution. For this purpose, a task scheduling policy based on reinforcement learning (RL) is developed that can achieve the optimal resource utilization as well as minimum time to execute tasks and significantly reduce the communication costs during distributed execution.,To realize this, the authors proposed a two-level neural network (NN)-based task scheduling system, where the first-level NN (feed-forward neural network/convolutional neural network [FFNN/CNN]) determines whether the data stream could be analyzed (executed) in the resource-constrained environment (edge/fog) or be directly forwarded to the cloud. The second-level NN ( RL module) schedules all the tasks sent by level 1 NN to fog layer, among the available fog devices. This real-time task assignment policy is used to minimize the total computational latency (makespan) as well as communication costs.,Experimental results indicated that the RL technique works better than the computationally infeasible greedy approach for task scheduling and the combination of RL and task clustering algorithm reduces the communication costs significantly.,The proposed algorithm fundamentally solves the problem of task scheduling in real-time fog-based IoT with best resource utilization, minimum makespan and minimum communication cost between the tasks.

Mohammad Ahsan Chishti

Papers

Automatic detection of COVID-19 from chest radiographs using deep learning.

RansomWare and Internet of Things: A New Security Nightmare

Deep Learning for Internet of Things Data Analytics

Data Aggregation Mechanisms in the Internet of Things: A Study, Qualitative and Quantitative Analysis

Adaptive task scheduling in IoT using reinforcement learning