EOTS: An Energy-consumption Optimization-oriented Task Scheduling Algorithm for Wireless Sensor Networks

The nature of multi-hop data transmission in wireless sensor network will cause serious load unbalance which will produce great restrains in related applications considering the limited energy resource. Relative load balance algorithms are usually performed inside the clusters without considering about the energy consumption of the whole network. A cluster-based balanced energy consumption algorithm (BECA) is proposed by introducing in multiple inter-cluster links to distribute the load, so as to achieve global load balance. Moreover, an efficient data collecting mechanism is proposed based on BECA to improve the traffic balance further. Simulating results based on NS2 show that BECA can obtain better balance properties and prolong the network lifetime effectively.

SOHCL-RDT: A self-organized hybrid cross-layer design for reliable data transmission in wireless network

Classification of sensory data is a major research problem in wireless sensor networks and it can be widely used in reducing the data transmission in wireless sensor networks effectively and also in process monitoring. In order to examine the huge size of data set in stream model generated by sensor network, it will be analyzed different sensor's output signal, topology of sensors network, number of sensor parameters and number of acquisition data. In our wind energy monitoring, sensor node monitors six attributes: speed, direction, temperature, pressure, humidity, and battery voltage. Every attribute value is set as four measures: average, instantaneous, minimum, and maximum. This paper presents several data mining techniques applied on the wireless sensor network's data considered: Naïve Bayes, k-nearest neighbor, decision trees, IF-THEN rules, and neural networks. Before classification, the data was clustered in order to be labeled. A similarity based algorithm, k-means, was selected in the clustering process for its simplicity and efficiency. A conclusion that decision trees are a suitable method to classify the large amount of data considered is made finally according to the mining result and its reasonable explanation.

Automatic localization is one of the major issues in Wireless Sensor Networks (WSN). DV-hop algorithm is a well-known localization algorithm in WSN but with limited localization accuracy. In this paper, an improved DV-hop localization algorithm in hybrid optical wireless sensor networks is proposed based on the optimization of the parameters in WSN. Various factors that affect the localization accuracy of the DV-hop algorithm in WSN are investigated, including the communication radius of the node, the number of beacon nodes and the number of the total nodes. As the DV-hop algorithm is applied into hybrid optical sensor and WSNs (O-WSN) with rectangular topology, different parameters have to be optimized accordingly. Simulation results show that the square topology outperforms the rectangle topology more than 45 % under the same network parameters using the improved DV-hop algorithm. Therefore another improved DV-hop called Sub-Square Weighted DV-hop (SSW DV-hop) is proposed for the rectangle topology. Both simulation and experiment results demonstrate that applying the SSW DV-hop algorithmin O-WSNs could significantly improve the localization accuracy.

Sensor nodes are small and low-cost portable devices that are connected wirelessly to form a wireless sensor network. Sensor nodes are typically powered by a chemical battery source that has a load-dependent finite lifetime. Most applications require wireless sensor networks to operate reliably for an extended period of time beginning with their deployment. To ensure both the longevity of a wireless sensor network via power management techniques and development of a self-powered sensor node, it is important to understand a sensor node’s operational energy consumption. It is well understood that wireless sensor network lifetime has a strong functional dependence on sensor node lifetime. This paper presents the development of a stochastic model to capture the expected energy consumption of a schedule-driven sensor node per cycle of operation by utilizing renewal theory, random stopping criteria, and Wald’s inequality. The model captures the expected energy consumed due to generic operation of a schedule-driven sensor node when an external event occurs (i.e., node level activities) and energy consumed due to carrier sense multiple access/collision avoidance medium access control protocol (i.e., retransmission attempts, channel listening, and other network level activities). The stochastic energy model is used to compute the expected lifetime and cycle lifetime of the sensor node. This paper also computes the deterministic energy bounds corresponding to the minimum and maximum energy consumption of a sensor node per cycle of operation. Analytical and numerical model validations are performed and presented.

Clustering is an effective approach to hierarchically organizing network topology and to prolong the lifetime of a wireless sensor network. Current clustering protocols usually utilize two techniques: selecting cluster heads with more residual energy and rotating cluster heads periodically to distribute the energy consumption among nodes in each cluster. Most of the researches in this field has focused on energy-efficient solutions, but caring less about the communication between Cluster Head (CH) nodes and Base Station (BS). When the sensor field is far away from the base station, the cluster heads are burdened with heavier relay traffic and tend to die much faster. To mitigate the problem, we propose an Energy-Efficient Hierarchical Routing Protocol (EEHRP) for long range transmission in the wireless sensor networks. It uses a number of gateway nodes, which do not engage in clustering, to connect the CHs and base station. They are responsible for transmitting packets received from the CHs to the base station, thus the CHs can preserve some energy in data forwarding and the gateway nodes can ease their burden by not participating in clustering. Simulation results show that EEHRP obviously increases the network lifetime and well balances the energy consumption among the sensor nodes.

Sensors are capable of performing different tasks such as receiving various environmental information and have generated the idea of creating and expanding the traditional networks to Wireless Sensor Networks (WSN). A WSN include various sensor nodes which are distributed in the environment to gather data. The sensor node’s location is not necessarily defined and not specified. Such a feature allows us to release them in hazardous or inaccessible places. On the other hand, this means that the WSN protocols and algorithms must have the ability to self-deny. Other unique features of WSNs are the ability of sensor nodes to collaborate and coordinate with each other. A processor is assigned to each sensor node which initially performs a series of acquired simple information processing to transmits the semi-processed data instead of sending all raw data to the center, Base Station (BS) or the sink node. Fault detections in WSN are of particular importance due to unique technical and conceptual challenges. In this paper, due to the specific limitations and operating conditions of wireless sensor networks, different fault detection algorithms were examined. The best way to improve the ability of fault detection is to reduce energy consumption in sensor nodes. In such techniques, using the residue number system in the wireless sensor network structure improves repair and fault detection. Moreover, modules are attached whenever we require distinguishing and correcting the information security in the number system. Although there are various algorithms yet the Residue Number System algorithm has higher performance.

Wireless sensor networks (WSNs) are being used in a wide variety of critical applications such as military and health‐care applications. Such networks, which are composed of sensor nodes with limited memory capacity, limited processing capabilities, and most importantly limited energy supply, require routing protocols that take into consideration these constraints. The aim of this paper is to provide an efficient power aware routing algorithm for WSNs that guarantees QOS and at the same time minimizes energy consumption by calculating the remaining battery capacity of nodes and taking advantage of the battery recovery process. We present an online‐battery aware geographic routing algorithm. To show the effectiveness of our approach, we simulated our algorithm in ns2 and compared it with greedy perimeter stateless routing for wireless networks and battery‐aware routing for streaming data transmissions in WSNs. Copyright © 2009 John Wiley & Sons, Ltd.

Wireless sensor networks (WSNs) have become an important part of our lives as they can be used in vast application areas from disaster relief to health care. As a consequence, the life span and the energy consumption of a WSN has become a challenging research area. According to the existing studies, instead of using direct transmission or multi-hop routing, clustering can significantly reduce the energy consumption of sensor nodes and can prolong the lifetime of a WSN. In this paper, we propose a low energy fixed clustering algorithm (LEFCA) for WSNs. With LEFCA, the clusters are constructed during the set-up phase. A sensor node which becomes a member of a cluster stays in the same cluster throughout the life span of the network. LEFCA not only improves the lifetime of the network, but also decreases the energy dissipation and increases the throughput significantly.

An Quality of Service(QOS) secure routing algorithm based on ant colony optimization is put forward to solve a variety of security problems in wireless sensor networks. The algorithm combines the ant colony optimization algorithm with the credit evaluation mechanism, and introduces the node 's credit as the control factor to obtain better security performance. The algorithm first cuts the network topology according to the QOS requirements, kicks out the nodes that do not meet the QOS requirements, and then quickly converges the route to the optimal route according to the ant colony optimization method. Finally, the security analysis of the algorithm is carried out from various aspects of network layer attack. The experimental results show that the algorithm has very good performance in wireless sensor networks. As a result, it is concluded that it can be widely applied in wireless sensor networks.

This paper presents an approach of noise-immune data transmission in wireless sensor networks using Frequency-Hopping Spread Spectrum. Redundancy of Residue Number System codes is explored, and adaptive errors correction system is proposed without code algorithm changing by reduction of information modules number or increasing of reference modules number.

The nodes in wireless sensor networks (WSNs) are responsible for communicating data which is primarily of three types viz. video, audio and text. In literature, a large number of energy aware and shortest path based route selection approaches have been proposed to enhance the routing efficiency in WSNs. Most of these route selection approaches are designed by assuming fixed type and size of data packets and results in the same path selection for all types of data. This path selection, does not consider data size and data priority, results in high contention losses, topology failures and non-uniformity in energy depletion of nodes. In this paper, a route selection approach which is based on the preservation of network connectivity for improving overall network lifetime has been proposed for applications where sensor nodes are required to transmit different types of data. The proposed approach preserve highly connected edges at the initial rounds of data communication in an energy efficient manner, such that network connectivity would be maintained even at later rounds of data communications. The proposed route selection approach, VDR, (Variable Data rate Routing) has been simulated with both proactive and reactive route maintenance strategies. The simulation results show that the proposed algorithm with both proactive and reactive route maintenance strategies result in better network lifetime, energy consumption, overall network connectivity, packet delivery ratio as compared to the existing State-of-the-Art energy efficient route selection approaches with proactive and reactive route maintenance strategies. The results also show that the proposed algorithm is scalable and performs better than the compared algorithms both at low node density and high node density.

BIM2RT: BWAS-immune mechanism based multipath reliable transmission with fault tolerance in wireless sensor networks

Wireless Sensor Networks becomes robust while the nodes possessing the self-configuring capability by which they can be either keeping themselves as a part of the network or they can also simply leave. The transmission of Data is achieved through the inter communication among the nodes participated in the network. For most participative nodes, there is a possibility of energy depletion and thus the network dies despite the other nodes having optimal energy for being operative. Bring decentralized, another classical challenge for WSNs is the threat of intrusion detection which may arise DoS attacks that depleted the energy as well. Major works in the literature focuses either attempts to address the Intrusion detection to fix DoS attacks or keep the optimal energy level by introducing energy preserving strategy and dynamic clustering strategy so that the network lifetime could be extended. This work deals with the hybrid model in which the nodes are clustered together to form the Connected Dominating Set so that the data transmission could be augmented. The objective of the system is to include only nodes with adequate energy that can ensure the validity of the network by marking them as CDS node. The CDS probability of a node depends on foresight of the energy of the participant nodes till the transmission. Packet distribution is done based on the CDS of the node and thus the inappropriate network breakdown that keeps same node waking so often could be avoided. Thereby, retransmission has been avoided due to packet drops, which also conserved the energy of the network. From the simulation results, it is understood that the technique decreases the loss of packets and deferment. The simulation results demonstrated that the proposed model has improved performance metrics such as goodput, throughput, packet dropping ratio. Also, it is seen that the network lifetime has been significantly increased to 50% meantime packet dropping ratio has been reduced to 50% with the energy consumption little lower than 3% when compared with the preliminary enhanced energy-dependent constraint DoS (denial-of-service) detection model and thus the network seems fault-proof and extended reliable.

Wireless sensor networks (WSNs) are crucial for several applications. WSN nodes frequently function with constrained battery capacity, rendering energy efficiency a critical issue for clustering and routing. Moreover, a principal challenge of WSNs is ensuring the dependability and security of transmitted data in susceptible contexts to avert hostile node attacks. This study seeks to establish a secure and energy-efficient routing system for fault data prediction to improve the longevity and dependability of WSNs. This paper presents a sophisticated framework for intelligent fault prediction and energy-efficient data transmission in WSN, utilising bio-inspired optimisation and deep learning methodologies. The model initiates data fault prediction with Multi-Term Fourier Graph Neural Networks (MTFGNN), which examine temporal and spatial relationships to detect anomalies and defective nodes prior to clustering. Faultless nodes are subsequently categorised by Fuzzy C-Means (FCM) clustering, facilitating adaptive and efficient cluster creation. Quokka Swarm Optimisation (QSO) is utilised to improve energy efficiency by selecting ideal cluster heads (CH), thereby balancing energy usage and reducing intra-cluster communication expenses. A trust-based routing technique employs Proximal Policy Optimisation (PPO), a reinforcement learning method that dynamically identifies secure and energy-efficient pathways for data transfer, while reducing the influence of unreliable nodes. The experimental results indicate that it surpasses the rival methods across multiple performance parameters. The performance outcomes of quality of service (QoS) metrics are delineated as follows: energy consumption (0.204), throughput (0.701), packet delivery rate (94.24%), network lifetime (1310 rounds), and fault prediction accuracy (99.78%), precision (98.69%), recall (97.52%) and F1 score (97.83).