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Various IoT systems are composed of low power wireless embedded devices, which are installed outdoor and indoor for harvesting energy by using energy harvesting techniques and increasing the lifetime of IoT system (Haight et al., 2016). An IoT devices consist of various components like processor, Ram, Flash memory, microcontroller, set of sensors and actuators, SD card slot and so on. Each IoT nodes have the ability to monitor the environmental phenomenon and locally store and process the monitored data. Whenever node detect occurrence of any event then they send the monitored data to the gateway which transmit this data through internet to the remote users. The biggest problem with an IoT node is the finite-capacity batteries which are required by most of the IoT nodes for performing their operation. Total energy consumption of IoT node is calculated as the sum of the energy consumed by each component of the IoT node at each state. The lifetime of IoT node is limited because when the battery charge drops down the threshold value then IoT node stops working and remain in dead state until battery is recharged or replaced. The cost of replacing and maintaining millions of batteries is very high. Nowadays this problem is removed with the help of energy harvesting systems.
Energy harvesting systems harvests the energy from the available natural resources (like sun, wind, water, radio frequencies and so on) and the energy released by the human body (like walking, cycling, breathing and so on) for powering the IoT nodes (Raghunathan et al., 2005). Most commonly used energy harvesting architectures are, 1) Harvest and Use: harvested energy is directly used and 2) Harvest-Store-Use: harvested energy is directly used and extra energy is saved for further operations (Kansal et al., 2007). The amount of energy harvested by the energy harvesting systems depends on environmental phenomenon, and the harvested energy is uncontrollable and is not continuous. Therefore, to avoid unavailability of IoT node, batteries are required. IoT nodes use the rechargeable batteries that is recharged when energy harvesting system extract more than required amount of energy. When energy harvesting system extracts less than required energy, then these batteries are used by the IoT node for performing their operation. However, in the case when battery capacity is less than the threshold value and no energy is harvested by energy harvesting system, then system remains in the inactive state until required amount of energy is harvested. In such type of systems, an efficient energy management approach is necessary to infinitely prolong the lifetime of the IoT nodes while performing the operations of the IoT nodes.
In this work, author considers an internet of thing network comprises of IoT nodes for an outdoor application. Author propose an energy harvesting system namely SWEH which harvests energy from multiple sources that are wind speed and sunlight, and energy production model for estimating maximum amount of energy harvested by SWEH system. In this work, aim of the author is to make IoT system energy neutral, that is, within the given time slot, the amount of energy consumed by IoT node should be greater than or equal to the energy produced by SWEH in such a way that IoT node will have the same amount of battery after given time frame as it had at the beginning of the time frame. Several operations performed by IoT nodes are modeled as tasks; for example, task can be defined as communicating, monitoring, processing, sensing, and transmitting. Some cost is associated with each task, defined as energy required per unit of time. Each IoT node can execute number of tasks with certain cost. When the energy harvested by harvesting system is insufficient to sustain the IoT node operation, then IoT node perform no operation that’s mean IoT node remain in idle state. To maximize the lifetime, IoT nodes should select the most appropriate task from set of tasks for execution such that IoT nodes work continuously. Based on this model, author proposed a scheduling strategy which is dedicated to find out the most appropriate scheduling plan for IoT nodes along a time frame, for instance, one hour, such that it prolongs the lifetime of IoT nodes and leave the system in energy neutral state.