Wireless Sensor Networks (WSNs) with limited battery, central processing units (CPUs), and memory resources are a widely implemented technology for early warning detection systems. The main advantage of WSNs is their ability to be deployed in areas that are difficult to access by humans. In such areas, regular maintenance may be impossible; therefore, WSN devices must utilize their limited resources to operate for as long as possible, but longer operations require maintenance. One method of maintenance is to apply a resource adaptation policy when a system reaches a critical threshold. This study discusses the application of a security level adaptation model, such as an ARSy Framework, for using resources more efficiently. A single node comprising a Raspberry Pi 3 Model B and a DS18B20 temperature sensor were tested in a laboratory under normal and stressful conditions. The result shows that under normal conditions, the system operates approximately three times longer than under stressful conditions. Maintaining the stability of the resources also enables the security level of a network’s data output to stay at a high or medium level.

View of wireless sensor network (WSN) devices is small but have exceptional functionality. Each node of a WSN must have the ability to compute and process data and to transmit and receive data. However, WSN nodes have limited resources in terms of battery capacity, CPU, memory, bandwidth, and data security. Memory limitations mean that WSN devices cannot store a lot of information, while CPU limitations make them operate slowly and limited battery capacity makes them operate for shorter periods of time. Moreover, the data gathered and processed by the network face real security threats. This article presents an Adaptable Resource and Security Framework (ARSy) that is able to adapt to the workload, security requirements, and available resources in a wireless sensor network. The workload adaptation is intended to preserve the resource availability of the WSN, while the security adaptation balances the level of security with the resource utilization. This solution makes resources available on the basis of the workload of the system and adjusts the level of security for resource savings and makes the WSN devices work more efficiently.

Reliability device Wireless Sensor Network (WSN) can be measured through the effective utilization of energy in the form of battery, memory and CPU. The source energy became a major part of the WSN so that the required energy efficiency techniques to maximize the performance. In the process, implemented energy efficiency carried out by maximizing the process of selection of data to be processed and stored as raw data by applying the concept data mining of existing data. The implementation done by applying an algorithm that is resource-aware framework with Light Weight Classification (LWClass), Light Weight Frequent Item (LWF) and Light Weight Clustering (LWCluster). From the three forms of efficiency of the algorithm is obtained with a value efesiensi pada LWClass, LWF, and algorithms LWCluster each have an efficiency of 14.32%, 15.88% and 17.71 %. Then usability of Resource Aware (RA) is proven to improve the efficiency and lifetime of a network of WSNs, reaching 14-17% and 10-11 hours.

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