02532nas a2200265   4500000000100000000000100001008004100002260001200043653001100055653002300066653001900089653001200108653002800120100001600148700002700164700002600191700001600217700002800233245016700261856010000428300001200528490000600540520170600546022001402252       2020                            d  c03/202010aEnergy10aInternet of things10aJob Scheduling10aK-means10aAnt Colony Optimization1 aSumit Kumar1 aVijender Kumar-Solanki1 aSaket Kumar Choudhary1 aAli Selamat1 aRubén González-Crespo00aComparative Study on Ant Colony Optimization (ACO) and K-Means Clustering Approaches for Jobs Scheduling and Energy Optimization Model in Internet of Things (IoT)  uhttps://www.ijimai.org/journal/sites/default/files/files/2020/01/ijimai20206_1_12_pdf_15248.pdf  a107-1160 v63 aThe concept of Internet of Things (IoT) was proposed by Professor Kevin Ashton of the Massachusetts Institute of Technology (MIT) in 1999. IoT is an environment that people understand in many different ways depending on their requirement, point of view and purpose. When transmitting data in IoT environment, distribution of network traffic fluctuates frequently. If links of the network or nodes fail randomly, then automatically new nodes get added frequently. Heavy network traffic affects the response time of all system and it consumes more energy continuously. Minimization the network traffic/ by finding the shortest path from source to destination minimizes the response time of all system and also reduces the energy consumption cost. The ant colony optimization (ACO) and K-Means clustering algorithms characteristics conform to the auto-activator and optimistic response mechanism of the shortest route searching from source to destination. In this article, ACO and K-Means clustering algorithms are studied to search the shortest route path from source to destination by optimizing the Quality of Service (QoS) constraints. Resources are assumed in the active and varied IoT network atmosphere for these two algorithms. This work includes the study and comparison between ant colony optimization (ACO) and K-Means algorithms to plan a response time aware scheduling model for IoT. It is proposed to divide the IoT environment into various areas and a various number of clusters depending on the types of networks. It is noticed that this model is more efficient for the suggested routing algorithm in terms of response time, point-to-point delay, throughput and overhead of control bits.   a1989-1660