Adapting sampling interval of sensor networks using on-line reinforcement learning

Limited energy supply (battery-powered) is a crucial problem in wireless sensor networks (WSNs). Sensor node placement schemes and routing protocols are mostly proposed to address this problem. In this paper, we first present how to place sensor nodes by use of a minimal number of them to maximize the coverage area when the communication radius of the sensor node is different from the sensing radius, which results in the application of regular topology to WSNs deployment. With nodes placed at an equal distance and equipped with an equal power supply, the problem of unbalanced energy consumption in 2-D regular topologies becomes more severe and much more difficult to tackle than that in 1-D chains, though the latter is known as an already quite hard problem. We address this problem and propose an adaptive data collection scheme by employing different communication radii for nodes in different locations to balance the energy consumption in WSNs. In order to achieve the ultimate goal of maximizing network lifetime in grid-based WSNs, we give a mathematical formulation, which shows the problem of maximizing network lifetime is a nonlinear programming problem and NP-hard even in the 1-D case. We discuss several heuristic solutions and show that the halving shift data collection scheme is the best solution among them. We also generalize the maximizing network lifetime problem to the randomly-deployed WSNs, which shows the significance of our mathematical formulation for this crucial problem in WSNs.

Generally, routing techniques are essential for Wireless Sensor Networks (WSNs) to deliver data packets to their destinations (also known as sinks). For a practical application, sensors are deployed to monitor environmental changes and events. Most WSNs detect specific events in their specific environments. But, different WSNs may monitor different events with different sensors in the same area. For example, a smart-home WSN deploys thermal sensors/meters to measure indoor and outdoor temperatures, body-temperature sensors to detect thief intrusion, etc. Also, in the underlying house, there is a health WSN which utilizes physiological sensors to monitor patients’ health condition. In other words, several WSNs of different purposes co-exist in the same geographical area. Currently, each WSN’s data delivery is independent from others’. Basically, if all sensors in such a multi-WSNs environment can share their routing paths/nodes and relay event packets for other WSNs, the delivery efficiency can be enhanced since many more sensors can be found there for packet relay. Consequently, the transmission energy can be reduced since energy consumed for wireless transmission is proportional to d2 where d is the transmission distance between sender and receiver. Therefore, in this study, we propose an energy saving routing mechanism, named Energy-Efficient Cooperative Routing Scheme for Heterogeneous Wireless Sensor Networks (EERH for short), in which several WSNs deployed in the same geographical environment form a heterogeneous sensor network and sensors relay packets for its own WSN and also for other WSNs. Routing paths are dynamically established according to the transmission directions of event packets and the residual energy of the underlying sensors and their neighbors. In addition, the packets routed to the same direction by the same sensors are aggregated to save delivering energy. Moreover, the network parameters of the EERH, like propagation delay of an event packet and the transmission distance of a sensor, are adjustable so as to satisfy the practical environment needs. Simulation results show that the EERH efficiently extends the lifetime of a heterogeneous WSN.

The future of our society, the connected world of tomorrow, will be one of better integration with, and more awareness of our surroundings. Wireless sensor networks will breathe life into objects and allow us to extend our sensory perception to every square of land and water. Before large amounts of information regarding our private lives and surroundings start being sent around the world,privacy issues have to be addressed. At the moment there is no transparent or holistic security solution for wireless sensor networks. Because sensor networks have a wide range of applications with very di erent requirements, and because they have constrained hardware, security solutions can't be static but need to be con gured, tailored, for every single applications, to make effi cient use of the available resources while providing the required security level. Furthermore, the control over the security con guration needs to be placed in the hands of the sensor network's user, because only the user knows the details of the application and can control its parameters. This thesis presents a framework for the security con guration of wireless sensor networks (WSNs), which provides complete and transparent communication security to any data stream, at any network layer, of a sensor network application, without requiring changes to the application code. The design of the framework takes into account the WSN life cycle and its actors, developer and user, as well as their knowledge domains. It is assumed that neither actors have security expertise so all the security decisions are made within the framework. The main contributions of the framework are a security con guration methodology and a modular security architecture. The configuration methodology determines resource-e cient security services and protocols based on application parameters like data sampling rate, network lifetime or power supply, thereby putting the control of security con guration into the user's hands. The security architecture enables the decoupling of application and security code and allows security components to be installed on the application without changing its code. This is achieved through policy-based security of messages intercepted between the layers of the network stack. The security con guration methodology is validated against a scenario and the modular security architecture is tested and compared with hard-coded security. Considering the reduction in complexity and the increased exibility, the performance overhead of the framework is acceptable. The framework is independent of security services and protocols and can easily be extended to support new types. By encouraging reuse of services and protocols it can increase their strength and resilience, through repeated verification of the code. It is hoped that the transparency and ease of use of this framework will provide the necessary con dence in wireless sensor networks as a technology of the future.

In order to optimize the energy-balanced problem and lengthen the network lifetime for Wireless sensor network(WSN) monitoring, a routing protocol that based on monitoring area partition clustering for energy-balanced(EBPC) was proposed. We divided monitoring area into several virtual partitions, controlled the number of common nodes and clusters by data fusion rate, and forwarded cluster heads data to the next partitions with minimum path selection coefficient. Through performance analysis and simulation of EBPC reaches a conclusion that EBPC routing protocol has a superior balance of energy consumption than LEACH and EEUC, when data fusion rate is 0.5, network lifetime lengthen rate well be 1.3, and the effect of EEUC is more obvious when the data fusion rate have a lower value.

Division of the entire wireless sensor network geographic area into number of equal sized regions by applying virtual grid and strategy to appoint head node of the cluster in each region improves stability, consistency, and lifetime of the wireless sensor network. Applying the same size of grid to the different size of wireless sensor networks will not give consistent performance. In this paper, we are presenting a protocol which will dynamically determine the size of the virtual grid, which will give optimal performance of any geographic sized wireless sensor network. The protocol also allows to place sink node to any location and not at some fixed specific location. Protocol discussed in this paper can also support multiple sink nodes. Protocol dynamically takes decision to transmit the data directly to the sink node or by using multi-hop transmission by considering distances between sensor nodes internally and sink node(s), which will optimize energy efficiency of the sensor nodes and increases the lifetime of wireless sensor network.

One of the most important topics in the field of wireless sensor networks is the development of approaches to improve network lifetime. In this paper, an energy-efficient control and routing protocol for wireless sensor networks is presented. This algorithm is based on reinforcement learning for energy management in the network. This protocol seeks to optimize routing policies to maximize the long-term reward received by each node, using reinforcement learning, which is a machine learning approach. In order to improve the lifetime of wireless sensor network, three energy management approaches have been proposed. The first approach is to navigate correctly using reinforcement learning to reduce the length of the routes and to improve energy consumption. The second approach is to exploit a sleep scheduling technique to improve node energy consumption. The last approach is used to restrict data transmission of each node based on the received data change rate. Simulation results show that in terms of network lifespan, the proposed method significantly outperforms previous reported methods.

Wireless healthcare monitoring sensor networks are multi-hop Zigbee-based systems which usually use broadcast or multicast to reliably deliver vital diagnostics information or event detection to the sink. However, such schemes typically lead to high network traffic and large path search delay. This paper presents an efficient, adaptive, distributed routing mechanism, called anycast Q-routing to route information to the nearest sink in multiple sink wireless sensor networks (WSNs). The proposed scheme requires only local information at each node, modest computational, storage, and learning overhead. Simulation results show that the anycast Q-routing scheme can discover paths using low communication overhead and short path search latency over existing AODV-based broadcast, multicast and anycast schemes used in WSNs for healthcare monitoring.

We analyze the impact of the structure of the Data Gathering (DG) trees on node lifetime (round of first node failure) and network lifetime (minimum number of rounds by which the network gets either disconnected due to node failures or the fraction of coverage loss reaches a threshold) in wireless sensor networks through extensive simulations. The two categories of DG trees studied are: the Bottleneck Node Weight-Based (BNW-DG) trees and Bottleneck Link Weight-Based (BLW-DG) trees. The BNW-DG trees incur a smaller diameter and a significantly larger fraction of nodes as leaf nodes: thus, protecting a majority of the nodes in the network from simultaneously being exhausted of the energy resources (contributing to a significantly larger network lifetime); nevertheless the nodes that serve as intermediate nodes in the first few instances of the BNW-DG trees are bound to lose their energy more quickly than the other nodes, leading to a smaller node lifetime compared to that of the BLW-DG trees (that incur a larger diameter and a relatively lower fraction of nodes as leaf nodes).

Wireless sensor network is an emerging technology capable of having low-cost and low-power and battery-powered sensor node. At this point the protocol concentric clustered power-efficient gathering in sensor information systems and clustered general self organized tree based energy-balance routing protocol energy efficient protocol are used to increase the energy and to extend network lifetime. For these achievement of clustering techniques, optimization technique particle swarm optimization is used for data transmission through base station. If the base station is inaccessible region from the cluster-head, energy consumption will be augmented and it can shrink the lifetime of wireless sensor network. To conquer these, multi-hop gateway node is implemented with both protocol to achieve maximum lifetime and energy efficiency of wireless sensor network. To improve the quality-of-service (QoS) placing more number of gateways is required. So we propose a new algorithm nearer optimal algorithm which recursively computes least possible adequate dominating sets, at the time constantly preserving QoS requirements across iterations. In this paper comparison is done in terms of throughput, packets delivery ratio and number of nodes alive.

In this paper, crowdsourcing-based industrial sensing intelligence (CISI) is proposed as a collaborative approach for large-scale monitoring in modern industrial plants, targeting at improved productivity and increased workplace safety. The proposed approach focuses on middleware, which considers both application and industry-grade requirements. Through embedding crowdsourcing knowledge at different levels and supporting QoS services, systems based on CISI can perform effective work assignment and flexible configuration of wireless sensor networks (WSNs). This paper presents a middleware that addresses these characteristics, which is an extension of GSN, our earlier work on middleware for rapid deployment and integration of heterogeneous sensor networks. Wireless sensor devices and wearable equipment are employed as modeling tools for the middleware implementation.

Wireless Sensor Networks (WSN) concentrate on the essential problem of increasing the network lifetime of WSN, which are bordered by low capacity batteries1,3 One of the fundamental egresses in WSN is maximizing the network lifetime by mitigating the Hotspot problem. It is the problem of isolation of sink node due to energy diminution of nodes most closelipped to sink1. In WSN nodes which are most adjacent to the Base Station (BS) need to relay many packets while compare with nodes far away from the sink. The outcome of this approach is these nodes deplete their energy very quickly. It results in a network partitioning and reduces the network lifetime. This paper proposes and evaluates a technique for maximizing the lifetime of WSN by preventing sink isolation due to hotspot problem. The clustering mechanism can be used to reduce the energy depletion of node near to the sink node. The cluster is formed based on distance attribute. This mechanism provides the scalability and avoids the redundancy through data collection operation. This paper will analyze the energy consumption of proposed technique using various distance measures. *Author for correspondence Indian Journal of Science and Technology, Vol 7(S4), 92–98, April 2014 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 Increase the Lifetime of WSN by Preventing Sink Isolation using Supercluster Formation R. Devika1 , B. Santhi2 and T. Sivasubramanian3 1,2School of Computing, SASTRA University, Thanjavur, India; srdevika.cse@sastra.edu, b.shanthi@sastra.edu 3 Kings College of Engineering, Punalkulam, Pudhukkotai District, India; shivamiru@gmail.com Keywords: Clustering Mechanism, Data Aggregation Operation, Hotspot Problem, Network Lifetime, Sink

The application of wireless sensor network for process monitoring of laser cutting process is a promising solution to address the problems in achieving good cutting performance. Wireless sensor network provide real time data monitoring flexibility, maintenance convenient, and easy to relocation for laser cutting process application. This paper presents the development of wireless sensor network for monitoring temperature at cutting surfaces during laser cutting process. The system consists of thermocouple sensors, microcontroller programmable system on chip platform, wireless radio frequency and software data logger. The workability and performance of the system was tested by using gas cutting.

Nowadays wireless sensor networks (WSNs) have attracted a great deal of study due to their low cost and wide-range applications. Most of the sensors used so far are point sensors which have a disc-shaped sensing region. In this paper, we introduce a new type of sensor: the fiber optic cable sensor. Unlike a traditional point sensor, this type of sensor has a rectangular sensing region with a processor installed on it to do processing and communication. Like wireless sensor networks with point sensors, energy-efficient communication is still an important issue in wireless sensor networks with cable sensors because of the need to efficiently use limited resources. To address the issue, we propose a Cable Mode Transition (CMT) algorithm, which determines the minimal number of active sensors to maintain $K$-coverage of a terrain as well as $K$-connectivity of the network. Specifically, it allocates periods of inactivity for cable sensors without affecting the coverage and connectivity requirements of the network based only on local information. Before presenting CMT, we first show the relationship between coverage and connectivity, then the eligibility algorithm permitting a cable sensor to decide whether to stay active. CMT calls the eligibility algorithm to schedule cables. Simulation results show that our scheme is efficient in saving energy and thus can prolong the lifetime of wireless cable sensor networks.

Recent advances in Compact and wireless embedded Systems coupled with the technological innovations in Micro Electro Mechanical Systems(MEMS), Wireless Communications and networks paved the way for the development of small, resource constrained devices called Sensor Nodes (SNs). Performance of Sensor Node is highly dependent on the effective and efficient usage of the available limited resources with it, leading to maximum lifetime of Wireless Sensor Network (WSN) which is a primary concern of WSNs. The depletion of a SN energy leads to change in topology of WSNs often over a period of time. Therefore, protocols which consider the dynamic nature of WSNs are crucial for improving the network lifetime. In this paper, we propose bio inspired Ant Colony optimization based Self healing routing scheme inspired by swarm Intelligence technique to improve lifetime of a WSN.

Wireless sensor networks are mainly designed for environment monitoring. The lifetime of wireless sensor networks depends on the durability of the battery resource sensors, so it is crucial for sensors to efficiently use their limited battery resources. The energy expends by sensors in data communication makes up a significant quantum of their total amount of energy consumption. In the existing research, most of the power-saving medium access control protocols reduce energy consumption by putting periodically sensors to sleep mode. Such a regular active/sleep schedule fails to adjust individual sensor sleep duration according to per-sensor communication traffic-load, which causes unnecessary idle-listening problem and low power-efficiency. In this paper, we present a new power-saving medium access control protocol for saving power in a wireless sensor networks. This proposed protocol dynamically adjusts the active/sleep time duration of each sensor according to per-sensor communication traffic-load. Meanwhile, it can lead to much greater power-efficiency by prolonging the sensors sleeping duration, when in a sensor network at per-sensor communication traffic-load is low. The basic idea of this proposed protocol is an analytical model for estimating the accurate per-sensor communication traffic-load in a wireless sensor network and a grid-based quorum concept for regular active/sleep schedule.