No abstract

 Abstract: Internet of Things (IoT) makes everything in the real world to get connected. The resource constrained characteristics and the different types of technology and protocols tend to the IoT be more vulnerable than the conventional networks. Intrusion Detection System (IDS) is a tool which monitors analyzes and detects the abnormalities in the network activities. Machine Learning techniques are implemented with the Intrusion detection systems to enhance the performance of IDS. Various studies on IoT reveals that Artificial Neural Network (ANN) provides better accuracy and detection rate than other approaches. In this paper, an Artificial Neural Network based IDS (ANNIDS) technique based on Multilayer Perceptron (MLP) is proposed to detect the attacks initiated by the Destination Oriented Direct Acyclic Graph Information Solicitation (DIS) attack and Version attack in IoT environment. Contiki O.S/Cooja Simulator 3.0 is used for the IoT simulation.

Abstract

Abstract: Internet of Things (IoT) is a boon to the technological developments during the past decade. Though the adoption of this technology in agriculture has gone up immensely in recent years, the implementation of the smart irrigation system remains its initial stage in this agricultural setup. The sprinkler or dripper irrigation methods are widely used in the smart irrigation environment. In this paper a hybrid method is proposed to select the irrigation method automatically based on the climate changes and soil moisture level. By enhancing this method using the rapid growing technologies and IoT enabled smart irrigation controllers, the agriculture sector will be improved over the foreseeable future.

Internet of Things (IoT) based smart devices are the core elements for any smart environment. The sensors and actuators make the life easier when they are connected to one another and to the Internet. The Smart city and ‘Swach Bharath Abhiyan’ projects introduced by the Government of India tried to promote clean and hygienic Environment. The constant growth of population, industrialization and urbanization increase the unorganized manner of dumping the solid waste in landfills. Smart waste management is the must in all countries due to the voluminous generation of solid waste. In this paper, a methodology for monitoring the dustbins in smart cities, household or organization is proposed. The dustbins are monitored very often to check the garbage level. Whenever the dustbins reach maximum level, alert will be sent to the corresponding authorities with the bin details to dispose the waste. Additionally, the gas sensors in the dustbins detect the bad smell and alert when it reaches the threshold level though the garbage level will not reach the dustbin’s maximum capacity. The areas which require emptying the dustbins very often are also identified. Large-scale implementation of the system will promote a clean and hygienic environment.