Nowadays, wireless sensor networks (WSNs) are becoming increasingly popular due to the wide variety of applications. The network can be utilized to collect and transmit numerous types of messages to a data sink in a many-to-one fashion. The WSNs usually contain sensors with low communication ability and limited battery power, and the battery replacement is difficult in WSNs for large amount embedded nodes, which indicates a balanced routing strategy is essential to be developed for an extensive operation lifecycle. To realize the goal, the research challenges require not only to minimize the energy consumption in each node but also to balance the whole WSNs traffic load. In this article, a Shortest Path Tree with Energy Balance Routing strategy (SPT-EBR) based on a forward awareness factor is proposed. In SPT-EBR, Two methods are presented including the power consumption and the energy harvesting schemes to select the forwarding node according to the awareness factors of link weight. First, the packet forwarding rate factor is considered in the power consumption scheme to update the link weight for the sensors with higher power consumption and mitigate the traffic load of hotspot nodes to achieve the energy balance network. With the assistance of the power consumption scheme, hotspot nodes can be transferred from the irregular location to the same intra-layer from the sink. Based on this feature, the energy harvesting scheme combines both the packet forwarding rate and the power charging rate factors together to update the link weight with a new battery charging rate factor for hotspot nodes. Finally, simulation results validate that both power consumption and energy harvesting schemes in SPT-EBR achieve better energy balance performance and save more charging power than the conventional shortest path algorithm and thus improve the overall network lifecycle.
Abstract:In a wireless sensor network (WSN), many applications have limited energy resources for data transmission. In order to accomplish a better green communication for WSN, a hybrid energy scheme can supply a more reliable energy source. In this article, hybrid energy utilization-which consists of constant energy source and solar harvested energy-is considered for WSN. To minimize constant energy usage from the hybrid source, a Markov decision process (MDP) is designed to find the optimal transmission policy. With a finite packet buffer and a finite battery size, an MDP model is presented to define the states, actions, state transition probabilities, and the cost function including the cost values for all actions. A weighted sum of constant energy source consumption and a packet dropping probability (PDP) are adopted as the cost value, enabling us to find the optimal solution for balancing the minimization of the constant energy source utilization and the PDP using a value iteration algorithm. As shown in the simulation results, the performance of optimal solution using MDP achieves a significant improvement compared to solution without its use.
In the fifth generation (5G) communication system, maximizing energy utilization is one of the key challenges especially with limited green energy sources. In particular, it is important to determine the optimal transmission policy for the hybrid model with grid and green energy in small cell networks. The optimal transmission policy should minimize both grid power consumption and wastage of green harvested energy at the same time; while satisfying the quality of service (QoS) requirements such as minimum packet drop probability. In contrast to existing policies with a single objective for the hybrid powered small cell networks, the proposed transmission policy adopts a multi-objective model checking for Markov decision process (MOMC-MDP) with a linear temporal logic (LTL) scheme. The MOMC-MDP firstly determines all feasible decisions for packet transmissions in every time slot, and then chooses one of the best feasible decisions to obtain the optimal transmission policy. Three dimension Markov decision process (3D-MDP) that includes packet buffer, battery capacity, and channel state, are used with the age of information (AoI) of the packet arrivals to improve the overall performance of the proposed MOMC-MDP scheme. Numerical results validate that the proposed MOMC-MDP with AoI can provide higher green energy utilization compared to the conventional MDP-based schemes. INDEX TERMS Model checking (MC), Linear temporal logic (LTL), Green design, Three dimension Markov decision process (3D-MDP), Stochastic processes, Age of information (AoI).
In this paper, hybrid energy utilization was studied for the base station in a 5G network. To minimize AC power usage from the hybrid energy system and minimize solar energy waste, a Markov decision process (MDP) model was proposed for packet transmission in two practical scenarios. In this model, one buffer is transmitted with a given number of data packets over finite transmission time intervals. In the two scenarios of packet transmission, solar energy harvesting (SEH) is stored in the first scene, while the other scene uses the energy immediately. The MDP model determines the best actions and decisions for both scenarios. When considering a finite battery size and finite packet buffer, the MDP model defines the actions, states, state transition probabilities, and cost value for each action. In the first scenario, the received solar energy harvester will not be used if it is not enough to transmit the packets in the buffer. In the second scenario, the received solar energy harvester will be used immediately. In both scenarios, the cost value is the weighted sum of AC power and SEH wastage. The simulation results showed the optimum buffer sizes could be determined for the balance between AC power consumption and SEH wastage based on the cost value of the proposed model. Finally, the numerical results indicated that the proposed MDP model could reduce AC power usage by up to 50%.
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