A queue-based prolong lifetime methods for wireless sensor node

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.

Internet Protocol version 6 (IPv6) over Low-power Personal Area Network (6LoWPAN) is proposed by the Internet Engineering Task Force (IETF) working group to accomplish the concept of IP-based Wireless Sensor Network (WSN). A new layer is incorporated between IPv6 network layer and 802.15.4 MAC layer, which is entitled adaptation layer. Header compression, packet fragmentation, and layer two forwarding are the main tasks of the adaptation layer. An IPv6 packet is too big regarding to the Maximum Transmission Unit (MTU) size of 802.15.4 standard thus it has to be fragmented. Breaking up a big packet to number of small fragments and attaching new headers to each fragment may affect the energy level. In this paper, we place an attention on the impact of the adaptation layer on the energy consumption of a 6LoWPAN sensor node. From our analysis, we found that the adaptation layer, in particular the fragmentation process may increase the energy consumption of a sensor node by 5 to 10 percent.

The rapid development of the agricultural Internet of Things (IoT) is inseparable from the support of wireless sensor networks (WSNs) in recent years. To further facilitate the adaptation of WSNs to agricultural applications, reducing the energy consumption of sensor nodes in agricultural environments has become a crucial problem. Sleep scheduling in randomly deployed WSNs is an effective method to reduce the energy consumption of sensor nodes, which can extend the network lifetime while ensuring network coverage. However, most of the existing sleep scheduling algorithms require frequent information exchange (such as broadcasting to find neighboring nodes and collecting nodes’ residual energy), which will inevitably lead to massive energy consumption. To address this problem, in this article, an unmanned aerial vehicle (UAV)-assisted sleep scheduling algorithm (UAVSS) is proposed, which avoids excessive information exchange among nodes and ensures sufficient network coverage with the least number of sensor nodes. The UAV traverses all the sensor nodes along the shortest path to help information exchange and gather sensing data. Furthermore, the UAV path is planned by using the latest monarch butterfly optimization (MBO) algorithm. Simulation results indicate that the proposed UAVSS can prolong the network lifetime while ensuring the required area coverage.

Sensor networks are composed of many sensors usually far from the region is available. Routing method in wireless sensor networks to transfer information from sensor nodes to base station is especially important. Optimum consumption of energy is important requirements in wireless sensor networks because sensor systems typically use battery power. In this paper, we have presented a routing algorithm to determine the optimal path of energy consumption view point to transfer information from sensor nodes to base station with the data transmission by multi skip. In this algorithm,performance region is divided to some sectors and there are several relay nodes in the each sector. Relay nodes gather data from sensor nodes around them and data transfer to the base station based on the shortest path and minimum skip possible in two-dimensional coordinates (x, y) to the base station. At the end, we have compared the presented algorithm with EESR (Energy Efficient Sensor Routing) algorithm.Simulation results show the optimal performance of presented algorithm in energy consumption, delay and number of live 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.

Abstract. Motivated by the application of industry 4.0 and Internet of Things (IoT) technologies, the development of cyber physical systems (CPS) is gaining momentum. As CPS require multiple measurement technologies to drive the intended function, e.g., condition monitoring and in situ measurement, the integration of measurement systems into industrial processes or individual products becomes a critical activity within the development process. Development methods like the V-Model support developers with methodological guidelines, but the related methods and models do not provide sufficient information regarding the energy supply of embedded systems. If the measurement system, consisting of sensor, calculation and communication unit is integrated inside an either sealed or moveable system, e.g., in a gearbox, ensuring a reliable communication and energy supply is a challenging task. This contribution therefore focuses on the energy supply, in particular the electric power consumption of autarchic measurement systems, referred to as sensor nodes. Based on a literature review of existing physical principles determining the energy consumption of semiconductors, a simple estimation model is derived. Estimation models in the current literature mainly focus on the effects of source code or software in general without analyzing a possible impact of operation strategies, such as generic data processing logics in practical applications. The model presented in this contribution is therefore used to identify the energy consumption of sensor nodes influenced by ambient and operating conditions of sensor nodes. Strategies are examined experimentally using an exemplary sensor node, a climatic chamber and a sensor-integrated gearbox as the system to be observed. An analysis of the conducted experiments leads to a more precise model, which is evaluated regarding its significance for predicting the energy consumption and the underlying simplifications. Finally, general relations influencing the energy consumption are presented and necessary research suggested.

The wireless sensor network (WSN) has been among the fast-growing areas in recent years in different fields, like health applications, military applications, the operations of disaster relief, etc. In an unattended, even hostile environment, nodes in WSNs are commonly deployed. What is worse, these nodes are fitted with minimal resources for communication, computation, storage and battery. It is also impossible to guarantee the lifespan of WSNs without reducing their network performance. A massive volume of application-specific data is produced by WSNs. Such data must be processed and sent to the base station by sensor nodes, which is an expensive matter. As the resources of sensor nodes are constrained, the key challenges facing WSNs are effective data processing and energy conservation. To tackle those challenges, data aggregation can be used, which is a popular approach that filters redundant data in-network and speeds up the extraction of knowledge. In this paper, we propose an energy-saving data aggregation method for WSNs based on the extraction of extrema points (DAEP) to greatly minimize the energy consumption of each node and boost the higher lifespan of WSN. The efficiency of the proposed method was measured by performing multiple simulation experiments based on real sensor readings collected in Intel Berkeley laboratories and comparing the results achieved with some work in the literature. The results show the ability of the proposed method to reduce the load on the sensor nodes in terms of reducing the amount of transmitted data by up to 69-80%, energy consumed by 73-77% while maintaining an acceptable level of accuracy compared to some existing works.

In wireless sensor network, the storage amount of information is high, and the transmission and scheduling of control information is reasonable. The node communication model, network structure model, and energy consumption model are constructed. On this basis, the high-density data in wireless sensor network are scheduled to optimize the time for nodes to perform tasks. The nodes in the network are fully scheduled to control the generation time of packets in the network and the generation time of packets in the network. Experimental results show that in different iterations, the proposed method has lower node delay and node energy consumption, with values less than 0.2 and 2, respectively, and the maximum data fusion quality can reach 98, with high fusion benefits, so as to improve the transmission and scheduling efficiency and quality of high-density data in wireless sensor network.

In this paper, we propose an energy-efficient data query method for wireless sensor networks (WSNs). Our method is flexible and adaptive. To solve the cluster head coverage problem in our method, we propose virtual grid clustering (VGC) protocol in which the clusters are constructed only in the query area when they are needed. The clusters are destroyed after the query ends. Theoretical analysis and simulations show that our method can save significant amount of energy comparing to routing tree protocol and LEACH.

With recent technological advance, wireless sensor networks are often used in data collection and surveillance. One of the objectives of research on routing methods in wireless sensor networks is maximizing the energy life of sensor nodes that have limited energy. Among basic routing methods, the method using location information is efficient because it requires less information for calculation in route setting than flat routing and hierarchical routing. Because it utilizes distance, however, sensor nodes’ energy utility may go down. In this study, we tried to even energy use in a wireless sensor network by giving a weight to the transition probability of ACS(Ant Colony System), which is commonly used to find the optimal path, based on the amount of energy in a sensor and the distance of the sensor from the sink. The proposed method showed improvement by 46.80% on the average in energy utility in comparison with representative routing method GPSR (Greedy Perimeter Stateless Routing), and its residual energy after operation for a specific length of time was 6.7% more on the average than that in ACSKeywordsSensor NetworkRoutingEnergyHeuristic

In recent years, the growth of Wireless Sensor Networks (WSN) has been extended to various applications. Coverage is one of the major issues in the field to know about the patient’s activity. Sensors are used to monitor and track the patients in the hospitals. Wireless body sensors are placed in the body to obtain the values of the targeted area like electroencephalography (EEG), blood pressure sugar level and electrocardiography (ECG). Sensor nodes require energy to perform the tasks like collecting the targeted patient’s data, processing and broadcasting the data based on the application. The power consumption in the sensor nodes is considerably high which limits the lifetime of the surveillance systems. One of the prime factors to increase the lifetime of WSN is by splitting the deployed sensors into covers which are the group of sensors that monitors all the targets. Once sensor power drains from one cover, another cover gets activated. The proposed NeuroGenetic Algorithm (NGA) minimizes the energy consumption of sensor nodes by improvising the number of covers. It is achieved by minimizing the number of sensors which has been utilized at a particular time. Simulation is done in Matlab and the max number of covers obtained and analyzed with the existing algorithms in the literature. The comparison results shown in NGA are more efficient than the existing algorithm by discovering the number of covers which increases the lifetime of WSN.

The key solution to study birds in their natural habitat is the continuous survey using wireless sensors networks (WSN). The final objective of this study is to conceive a system for monitoring threatened bird species using audio sensor nodes. The principal feature for their recognition is their sound. The main limitations encountered with this process are environmental noise and energy consumption in sensor nodes. Over the years, a variety of birdsong classification methods has been introduced, but very few have focused to find an adequate one for WSN. In this paper, a tonal region detector (TRD) using sigmoid function is proposed. This approach for noise power estimation offers flexibility, since the slope and the mean of the sigmoid function can be adapted autonomously for a better trade-off between noise overvaluation and undervaluation. Once the tonal regions in the noisy bird sound are detected, the features gammatone teager energy cepstral coefficients (GTECC) post-processed by quantile-based cepstral normalization were extracted from the above signals for classification using deep neural network classifier. Experimental results for the identification of 36 bird species from Tonga lake (northeast of Algeria) demonstrate that the proposed TRD–GTECC feature is highly effective and performs satisfactorily compared to popular front-ends considered in this study. Moreover, recognition performance, noise immunity and energy consumption are considerably improved after tonal region detection, indicating that it is a very suitable approach for the acoustic bird recognition in complex environments with wireless sensor nodes.

Traditional data acquisition methods for wireless sensor networks (WSNs) require all sensor nodes to transmit at least once to the sink node to obtain a full view of the network. In this paper we present Disagree, a different data acquisition method to retrieve data in WSNs that works in the opposite way as compared with traditional methods. In Disagree, only nodes that do not satisfy an assertion are required to transmit data back to the sink node in order to obtain a complete view of the network. We show that this behavior is the base of an energy-efficient way to gather all data. An important feature of Disagree is that it saves energy at the sensor level by exploiting data correlation. Rather than requesting explicit data readings from all sensor nodes, Disagree estimates the readings from sensor nodes that did not respond to the assertion. As a result of this policy, Disagree can obtain a view of the sensing field with different levels of resolution involving the transmission of only a subset of the sensor nodes by exploiting spatial data redundancy. We implemented Disagree in NS-2 network simulator and results indicate Disagree can significantly reduce the percentage of nodes replying to queries compared with flat and a cluster head based approaches.

This paper focuses on enhancement of lifetime in an event driven wireless sensor network (WSN). The network lifetime is enhanced by optimal deployment of sensor nodes in a given region as premature shortening of life takes place leading to uneven consumption of energy. In the proposed scheme, the senor nodes are linearly deployed over the sub-regions and the inter-node distance between the sensor nodes depends on the statistics of events sensed by the sensor nodes in multi hop mode of communication. The performance is measured with respect to the residual energy at each sensor node and total number of days spent by sensor nodes. The lifetime of the sensor network is defined as the survival of some fraction of nodes. For an approximate Poisson distribution for occurrence of events in different sub-regions, the variation of energy consumption in sensor nodes is negligibly small. Further if some nodes are having finite residual energy even after failure of some nodes, then those nodes can continue their communication in single hop mode and hence the lifetime of the network is further extended.

Wireless sensor networks (WSNs) are limited to resources including computing power, storage capacity, and especially energy supply. Thus, energy consumption of sensor nodes has become a dominant performance index for a WSN. In addition, data transmission between sensor nodes is a main energy consumer of WSNs. This paper presents a method called immune genetic algorithm based multiple-mobile-agent itinerary planning (IGA-M2IP) that addresses issues of energy consumption in large-scale WSNs. The IGA-M2IP preserves a GA’s advantages, and further improves a GA’s efficiency by restraining possible degenerative phenomena during the evolutionary process.