Internet of Things (IoT) is an emerging networking technology that connects both living and non-living objects globally. In an era where IoT is increasingly integrated into various industries, including healthcare, it plays a pivotal role in simplifying the process of monitoring and identifying diseases for patients and healthcare professionals. In IoT-based systems, safeguarding healthcare data is of the utmost importance, to prevent unauthorized access and intermediary assaults. The motivation for this research lies in addressing the growing security concerns within healthcare IoT. In this proposed paper, we combine the Multi-Step Deep Q Learning Network (MSDQN) with the Deep Learning Network (DLN) to enhance the privacy and security of healthcare data. The DLN is employed in the authentication process to identify authenticated IoT devices and prevent intermediate attacks between them. The MSDQN, on the other hand, is harnessed to detect and counteract malware attacks and Distributed Denial of Service (DDoS) attacks during data transmission between various locations. Our proposed method’s performance is assessed based on such parameters as energy consumption, throughput, lifetime, accuracy, and Mean Square Error (MSE). Further, we have compared the effectiveness of our approach with an existing method, specifically, Learning-based Deep Q Network (LDQN).

Internet of things (IOT) is a layered network and generally heterogeneous as communication between small things like digital cameras and big things like servers has to happen. The communication speeds of various devices connected on to IOT are in great variance affecting the performance of the entire network. The small devices connected on to IOT are fragile in the sense that they tend to fail quite often affecting the performance of the network. The small devices are low in energy depletion of which is rather fast. The small devices also have heavy latency. Many such issues exists that effect the performance of an IOT network. In this paper, various issues/bottlenecks that hamper the performance of an IOT network are and a mitigation strategy for improving the performance of the IOT networks is discussed.

Thermal imaging is a cutting-edge technology which has the capability to detect objects in any environmental conditions, such as smoke, fog, smog, etc. This technology finds its importance mainly during nighttime since it does not require light to detect the objects. Applications of this technology span into various sectors, most importantly in border security to detect any incoming hazards. Object detection and classification are generally difficult with thermal imaging. In this paper, a one-stage deep convolution network-based object detection and classification called retina net is introduced. Existing surveys are based on object detection using infrared information obtained from the objects. This research is focused on detecting and identifying objects from thermal images and surveillance data.

Now a days we all are busy in our regular works, holding the burden of works on our shoulder so, with the help of smart home appliances we can get some secure and an easy way to control the home appliances and we can also know the location information of that particular device. These days lot of researchers are focusing their interest on IoT (Internet of Things). The most vital application where IoT is being mostly used is home automation where all the electronic gadgets are connected to the internet via various layers viz, device level, controller level, restful services level, gateway level and finally the cloud layer. Moreover, gigantic information is being produced by the IoT devices. This information has to be processed and stored in a very effective manner so as to control the home appliances from anywhere in the world. Here we control the home appliances through our mobile by the use of IoT. So, we can control our home appliances very easy and fast compared to the manual operation such as on and off of any device. There might be some relief in our daily life that not to bother about whether we switch off the home appliances or not, instead of it we can check the mobile and solve the situations in short period of time.

Development of surveillance and monitoring systems are quite difficult and challenging task at times. The design of a system depends on the environment to be monitored. Such surveillance systems need to have dynamic features, for e.g., cameras used for monitoring may be mobiles, web cams etc. installed to the system. Such systems are used in various large buildings like shopping malls where it could incur high cost for installing cameras in each level of buildings. Even for people like security officers it could be huge task to cover an entire building. Other examples for dynamic surveillance system could be detecting poisonous gases in an area, explosives and any fire risk elements. Another case is that it can reach where the area is not accessed by humans. In view of these challenges we propose a Remote monitoring system where a Robotic Car is installed with camera, Ultrasonic sensor, DHT11, PIR sensors according to the environment involved. The instructions are given to the robotic car using a third party app called Blynk as user interface. Here the raspberry pi is used as a microcontroller which is connected to WIFI acts as the communication medium to connect the server provided by Blynk. The Blynk app which acts as a user interface is interacted with the car using Wi-Fi and its server.