In this paper, a novel risk-based, two-objective (technical and economical) optimal reactive Power dispatch method in a wind-integrated Power system is proposed which is more consistent with operational criteria.  The technical objective includes the minimization of the new voltage instability risk index. The economical objective includes cost minimization of reactive Power generation and active Power loss. The proposed voltage instability risk employs a hybrid possibilistic (Delphi-Fuzzy)-probabilistic approach that takes into consideration the operator’s experience, the wind speed and demand forecast uncertainties when quantifying the risk index. The decision variables are the reactive Power resources of the system. To solve the problem, the modified multi-objective particle swarm optimization algorithm with sine and cosine acceleration coefficients is utilized. The method is implemented on the modified IEEE 30-bus system. The proposed method is compared with those in the previously published literature, and the results confirm that the proposed risk index is better at estimating the voltage instability risk of the system, especially in cases with severe impact and low probability. In addition, according to the simulation results compared to typical security-based planning, the proposed risk-based planning may increase the security and economy of the system due to better utilization of system resources.

In this paper, a new hybrid rout ing protocol is presented for low Power Wireless Sensor Networks (WSNs). The new system uses an integrated piezoelectric energy harvester to increase the network lifet ime. Power dissipation is one of the most important factors affecting the lifetime of a WSN. An innovative cluster head selection technique using Cuckoo optimization algorithm has been used in the designed protocol. The residual energy of the nodes and the distances to the sink were used in the threshold calculat ions, besides taking advantage of the relay node for communication. A hybrid method using the optimized routing protocol and the integrated energy harvester results in 100% increase in the network lifetime compared to recent clustering-based protocols. The simulations results using MATLAB indicate that energy consumpt ion has been decreased by more than 40%.

Designing wireless Sensor networks should meet appropriate parameters such as quality of service (QoS) defined by different users. The variable physical conditions of the environment, processing and transmission Power limitations and limited communication capabilities are the most important obstacles that influence QoS parameters such as throughput, delay, reliability and network lifetime. The most important problems that directly have destructive effects on these parameters are hidden and exposed node problems. These problems extremely decrease throughput and increase delay and Power consumption in the network. This paper proposes a new and efficient method to decrease the mentioned problems that relies on clustering, grouping and subgrouping strategy. Then different transmission Powers are allocated to different nodes depending on their hierarchy level to communicate in their active time slots to decrease Power consumption. This mechanism has been simulated by OPNET on IEEE802.15.4/Zigbee.

In this paper, we introduce a new linear received signal strength-based estimator for unknown node localization which its accuracy at low Signal-to-noise ratio (SNR) is better than many linear estimators and can compete with estimators based on the convex optimization, but it is much lighter than convex optimization-based estimators. The main ingredients in our proposed linear position estimator are to reformulate the localization problem in terms of Tikhonov-regularization and introduce a biased noise variable. The way that we apply for this reformulation avoids any possible linear approximation in which target position variables are involved, thus saving fair amount of information. The proposed algorithm is also indifferent to the transmit Power and thus, applicable to either known or unknown transmit Power scenarios. Simulation results show the efficacy of the proposed algorithm in comparison to the other methods for both typical RSS-based measurement data model and the modified model for indoor application.

The problem of Power allocation for distributed detection in a Power constrained wireless Sensor network is considered. The Sensors are assumed to have independent observations and send their local decisions to a fusion center over multiple access channels. The Jensen-Shannon divergence between the distributions of the detection statistic under different hypotheses is used as a performance criterion. It is demonstrated that by applying the proposed measure Power allocation is more efficient comparing to other criteria like mean square error or Jeffry divergence.

The wireless Sensor network (WSN) is typically comprised many tiny nodes equipped with processors, sender/receiver antenna and limited battery in which it is impossible or not economic to recharge. Meanwhile, network lifespan is one of the most critical issues because of limited and not renewal used battery in WSN. Several mechanisms have been proposed to prolong network lifespan such as LEACH, HEED and CHEF, but in all of them nodes consume energy continuously. One of the promising techniques is to apply dynamic sleep/wake up scheduling. In this paper, a novel sleep/wake up scheduling algorithm is proposed so-called FT-ECCKN. Each node executes sleep/wake up scheduling right after sending/receiving data where a node changes its status to sleep mode if it has at least k neighbors awake in its radius neighborhood with more residual energy in comparison with the node executing scheduler. Whenever the number of nodes is more than 2k, fuzzy TOPSIS method is used to rank nodes based on residual energy and coverage distance to select k out of number of nodes in ranking list. To evaluate the proposed algorithm, 25 scenarios are conducted in the experimental field 800X600 between 100 through 500 nodes increasing with 100 numbers and k belongs to {1, 2, …5}. Totally, our proposed algorithm outperforms 23.27 percent in term of network life time in comparison with EC-CKN method for overall scenarios. Remarkable results show that the proposed algorithm is beneficial for large scale fields with dense nodes along with smallest k.