Management In Wireless Sensor Networks Research Articles

A framework for wireless sensor networks management for precision viticulture and agriculture based on IEEE 1451 standard

Precision viticulture (PV) and precision agriculture (PA) requires the acquisition and processing of a vast collection of data coming typically from large scale and heterogeneous sensor networks. Unfortunately, sensor integration is far from being simple due to the number of incompatible network specifications and platforms. The adoption of a common, standard communication interface would allow the engineer to abstract the relation between the sensor and the network. This would reduce the development efforts and emerge as an important step towards the adoption of “plug-and-play” technology in PA/PV sensor networks. This paper explores this need and introduces a framework for smart data acquisition in PA/PV that relies on the IEEE 1451 family of standards, which addresses the transducer-to-network interoperability issues. The framework includes a ZigBee end device (sMPWiNodeZ), as an IEEE 1451 WTIM (Wireless Transducer Interface Module), and an IEEE 1451 NCAP (Network Capable Application Processor) that acts as gateway to an information service provider and WSN (Wireless Sensor Network) coordinator. The paper discusses the proposed IEEE 1451 system architecture and its benefits in PA/PV and closes with results/lessons learned from in-field trials towards smarter WSN.

Optimistic Selection of Cluster Heads Based on Facility Location Problem in Cluster-Based Routing Protocols

Cluster-based routing protocols are one of the most favorable approaches for energy management in wireless sensor networks. The selection of the best cluster heads (CHs), as well as the formation of optimal clusters, is an NP-hard problem. The present study proposes an optimal solution for CHs selection to generate a network topology with optimized network performance. The problem is formulated as facility location problem and a linear programming model is used to solve the optimization problem. Results of analysis o the network simulator (NS2) indicate that applying this method in cluster-based routing protocols prolongs 16 % of the network lifetime, increases 15.5 % of data transmission and improves 5.5 % of throughput, as compared to the results of current heuristic methods such as LEACH, DEEC and EDFCM protocols.

A Strategy for Fault Recovery of Wireless Sensor Network Based on v-SVC

Wireless Sensor Network (WSN) is applied widely and large-scale, which is usually used to collect physical parameters in wicked environment. It’s difficult for human to intervene when a WSN has faults. That is to say, fault diagnosis, isolation and recovery which exist automatically in WSN itself can solve this problem. In clustering algorithm, a Cluster Head (CH) is necessary for the whole network. When the CH can’t work, due to the energy, spatial barrier, strength of signal and so on, we have to replace the CH efficiently; when there is no suitable backup CH for one cluster, the cluster should be re-clustering which needs an effective strategy for the nodes joining other clusters. This paper proposed a strategy for the WSN in that situation. Based on the Support Vector Machine (SVM), we suggested a decision-function. In this function, by using support vector we can balance multiple causes of a fault except energy. This light calculation can make fault management of WSN more efficient, practical and easy-deployed to improve the lifetime and robustness of the whole network. The simulation experiment showed that, based on the v-SVC, the CH replacing and re-clustering algorithm are more efficient than the traditional solutions in way of optimizing the training sets and kernel function.

Dynamic Sensor Scheduling for Thermal Management in Biological Wireless Sensor Networks

Biological sensors are a very promising technology that will take healthcare to the next level. However, there are obstacles that must be overcome before the full potential of this technology can be realized. One such obstacle is that the heat generated by biological sensors implanted into a human body might damage the tissues around them. Dynamic sensor scheduling is one way to manage and evenly distribute the generated heat. In this paper, the dynamic sensor scheduling problem is formulated as a Markov decision process (MDP). Unlike previous works, the temperature increase in the tissues caused by the generated heat is incorporated into the model. The solution of the model gives an optimal policy that when executed will result in the maximum possible network lifetime under a constraint on the maximum temperature level tolerable by the patient's body. In order to obtain the optimal policy in a lesser amount of time, two specific types of states are aggregated to produce a considerably smaller MDP model equivalent to the original one. Numerical and simulation results are presented to show the validity of the model and superiority of the optimal policy produced by it when compared with two policies one of which is specifically designed for biological wireless sensor networks.

Qos Management in Wireless Sensor Networks using LFQMS

Wireless sensor networks (WSNs) are emerging rapidly as a new generation of sensor networks. Despite intensive research in wireless sensor networks (WSNs), limited work has been found in the open literature in the field of WSNs. In particular, quality-of-service (QoS) management in WSNs remains an important issue yet to be investigated. As an attempt in this direction, this paper develops a logic for QoS management (LFQMS) scheme for WSNs with constrained resources and in dynamic and unpredictable environments. Taking advantage of the feedback control technology, this scheme deals with the impact of unpredictable changes in traffic load on the QoS of WSNs. It utilizes a logic controller inside each source sensor node to adapt sampling period to the deadline miss ratio associated with data transmission from the sensor to the sensor. The deadline miss ratio is maintained at a pre-determined desired level so that the required QoS can be achieved. The (LFQMS) has the advantages of generality, scalability, and simplicity. Simulation results show that the (LFQMS) can provide WSNs with QoS support.

A Research on Key Management in Wireless Sensor Networks

With the constant development of wireless sensor networks technology, the expanding application scope and the increasing processing power of sensor nodes, there are more and more data collected and processed through the sensor, thus ensuring the security of information transmission between nodes is in urgent needs. The secure application of wireless sensor networks has become a hot technology. This paper mainly expounds the key management schemes of wireless sensor networks, and analyzes the mainstream key management schemes.

Harvesting-Aware Energy Management for Time-Critical Wireless Sensor Networks With Joint Voltage and Modulation Scaling

As Cyber-Physical-Systems (CPSs) evolve they will be increasingly relied on to support time-critical and performance-intensive monitoring and control activities. Further, many CPSs that utilize Wireless Sensor Networking (WSN) technologies will require the use of energy harvesting methods to extend their lifetimes. For this application class, there are currently few algorithmic techniques that combine performance sensitive processing and communication with efficient management techniques for energy harvesting. Our paper addresses this problem. We first propose a general purpose, multihop WSN architecture capable of supporting time-critical CPS systems using energy harvesting. We then present a set of Harvesting Aware Speed Selection (HASS) algorithms. Our technique maximizes the minimum energy reserve for all the nodes in the network, thus ensuring highly resilient performance under emergency or fault-driven situations. We present an optimal centralized solution, along with an efficient, distributed solution. We propose a CPS-specific experimental methodology, enabling us to evaluate our approach. Our experiments show that our algorithms yield significantly higher energy reserves than baseline methods.

A Clustering Algorithm for Wireless Sensor Network Management

In typical clustering algorithms for wireless sensor network, the remained energy of the elected cluster heads is not considered. Moreover, the service failure ratio that the cluster heads provided for nodes in clusters is not considered either. So a new clustering algorithm-MSCBRE (Maximum Succeed Communication Based on Remainder Energy) is proposed for wireless sensor network management. In this algorithm the service failure ratio is set to the ration of the remained energy to the initial energy of nodes. Based on this the elected cluster heads can provide multiple coverage to key nodes to ensure reliable communication. The experiment results show that the new algorithm is correct and effective.

Bloom Filter Supporting Distributed Policy-Based Management in Wireless Sensor Networks

Wireless Sensor Networks (WSN) are particularly special due to many characteristics, such as a working environment that makes maintenance and support a challenge; and hardware resources, particularly memory, processing and battery power, that make it capable of handling only limited software. Consequently, the administration of WSN is becoming a challenge. To overcome these limitations we proposed Distributed Policy-Based Management (DBPM) framework. Our proposed framework is expected to conceal the complexity of administrating policies operations from the users by simplifying the deployment processes. Bloom Filter is an elegant data structure that answers the membership inquiry with no false negative and manageable false positive. In this paper we propose utilizing Bloom Filter in Distributed Policy-Based Management (DPBM) environment in WSN to confirm the existence of any policy within the WSN, which will reduce the traffic within the network as well as preserve the sensor node energy.

Multipath Routing Network Management Protocol for Resilient and Energy Efficient Wireless Sensor Networks

This paper proposes an optimization model for network management in multihop Wireless Sensor Networks (WSNs). Here, we develop a distributed, braided multipath algorithm to deliver the information from the information sources (targets) to the Base stations (sinks) giving the network the ability to adapt to changes or failures. The Base Stations are robust nodes with capabilities for positioning themselves and communicating outside the network, which grants them the benefit of knowing other Base Stations’ position in the area of interest. Targets are nodes that generate information and need a fixed amount of bandwidth to convey this information to a Base Station. Every element in the network is static. This paper shows the mathematical optimization model for the network's energy minimization and resilience maximization. To increase network's resilience, the devices within the network will try to create multiple paths from the beginning trying to reach at least one Base Station. The model is solved through a heuristic algorithm based on the nearest neighbor and minimum hop concepts and it is implemented in Java. Results show that the proposed algorithm finds multiple braided paths for most of the instances proposed and some backup nodes for nodes in the paths that can help the network to extend its lifetime operation and adapt to failures.

Topology Management in Wireless Sensor Networks: Multi-State Algorithms

In order to maximize the network’s lifetime and ensure the connectivity among the nodes, most topology management practices use a subgroup of nodes for routing. This paper provides an in-depth look at existing topology management control algorithms in Multi-state structure. We suggest a new algorithm based on Geographical Adaptive Fidelity (GAF) and Adaptive Self-Configuring Sensor Networks Topology (ASCENT). The new proposed algorithm outperforms both GAF and ASCENT algorithms.

Distributed Tracking with Energy Management in Wireless Sensor Networks

We consider a wireless sensor network (WSN) tasked with tracking a process using a set of distributed nodes. Here multiple remote sensor nodes estimate the physical process (viz., a moving object) and transmit quantized estimates to a fusion center for processing. At the fusion node a BLUE (best linear unbiased estimation) approach is used to combine the sensor estimates and to create a final estimate of the state. In this framework the uncertainty of the overall estimate is derived and shown to depend on the individual sensor transmit energy and quantization levels, as well as the Kalman tracker uncertainty at the node. Since power and bandwidth are critically constrained resources in battery operated sensor nodes, we attempt to quantify the trade-off between the lifetime of the network and the estimation quality over time. Three different convex formulations of the underlying nonconvex mixed integer nonlinear optimization problem are presented. Unlike previous work this effort incorporates the operating state of the nodes into the decisions of the optimum bits and the transmission power levels based on a heuristic. Simulation results for all formulations demonstrate the quality of the state estimate as well as the extended lifetime of the WSN.

Cooperative Hybrid ARQ in Solar Powered Wireless Sensor Networks

Energy harvesters are used in today’s Wireless Sensor Networks (WSNs) to harvest energy from the environment. Although an energy harvester can provide a supply source with a much greater lifetime than a battery, the amount of available energy for an energy harvesting system is a random variable. Furthermore, the proper management of energy harvesters has a considerable impact on reliability. It has been observed that cooperative error control mechanisms like Cooperative Automatic Repeat Request (C-ARQ) and Cooperative Hybrid ARQ (C-HARQ) can be used for improving the energy management and reliability in Energy Harvesting WSNs (EH-WSNs). Recently, the impact of C-ARC mechanism has been considered in an energy harvesting system (GAP4S), however, GAP4S is an EH-WSN powered by microwave with linear recharge rate and unbounded storage capacity. This work evaluates the effect of C-HARC using a new method of relay node selection in EH-WSNs which are powered by the solar energy and have variable recharge rates. Furthermore, a comparative study of C-ARQ and C-HARQ mechanisms, considering the impact of variations in transmission rate and packet error probability on the energy consumption, is provided. The results show that C-HARQ outperforms C-ARQ under the same conditions and the energy consumption of a relay node in C-HARQ is 50% less than the one in C-ARQ.

Distributed resource management in wireless sensor networks using reinforcement learning

In wireless sensor networks (WSNs), resource-constrained nodes are expected to operate in highly dynamic and often unattended environments. Hence, support for intelligent, autonomous, adaptive and distributed resource management is an essential ingredient of a middleware solution for developing scalable and dynamic WSN applications. In this article, we present a resource management framework based on a two-tier reinforcement learning scheme to enable autonomous self-learning and adaptive applications with inherent support for efficient resource management. Our design goal is to build a system with a bottom-up approach where each sensor node is responsible for its resource allocation and task selection. The first learning tier (micro-learning) allows individual sensor nodes to self-schedule their tasks by using only local information, thus enabling a timely adaptation. The second learning tier (macro-learning) governs the micro-learners by tuning their operating parameters so as to guide the system towards a global application-specific optimization goal (e.g., maximizing the network lifetime). The effectiveness of our framework is exemplified by means of a target tracking application built on top of it. Finally, the performance of our scheme is compared against other existing approaches by simulation. We show that our two-tier reinforcement learning scheme is significantly more efficient than traditional approaches to resource management while fulfilling the application requirements.

A reconfigurable component model with semantic type system for dynamic WSN applications

Abstract Runtime reconfigurable component models provide several attractions with regard to the management of wireless sensor network (WSN) applications operating in dynamic environments and under evolving application requirements. One such attraction is the runtime discovery of suitable components for reuse in changing application compositions. Syntactic interface typing, provided by contemporary component models, however only supports exact interface matching. This causes limited reuse of components and complicates management of WSN applications. We argue that more flexibility is required to efficiently manage the complex, large-scale and dynamic WSN deployments of the future. In this paper, we describe the addition of semantic service descriptions to component interfaces to support compatibility and subtype testing. This allows rich discovery and reuse of third-party functionality and reasoning at the level of equivalent service types. We report on the incorporation of these semantic interface definitions in the Loosely Coupled Component Infrastructure (LooCI). Evaluation thereof shows that the scheme imposes minimal computational and memory overhead, while significantly reducing the complexity and cost of reconfiguration.

Distributed Collaborative Camera Actuation Scheme Based on Sensing-Region Management for Wireless Multimedia Sensor Networks

Considering the high energy consumption of image acquisition, computation, and transmission in wireless multimedia sensor networks (WMSNs), two-tier network structure is usually used to lighten the energy consumption burden on camera sensors. Thus, a camera sensor can only be actuated when an event is detected by scalar sensors within its field of view (FoV). In this paper, we study the event-driven camera actuation problem and propose a distributed collaborative camera actuation scheme based on sensing-region management (DCCA-SM). The basic idea of this scheme is to divide the whole sensing field into many sensing regions which are covered by different sets of camera sensors. During the running of the network, by forming a cluster of the scalar sensors in each sensing region, the events occurring in each sensing region can be managed by the scalar cluster head. Therefore, by hearing from the scalar cluster heads, each camera sensor can know the exact coverage overlaps without changing information with the neighboring camera sensors. Meanwhile, sensing-region management avoids repeatedly event reporting from scalar sensors. In order to show the performance of the DCCA-SM, a simulation has been conducted. The comparative performance evaluations demonstrate effectiveness and energy efficiency of the proposed scheme.

An integrated framework for wireless sensor network management

Wireless sensor networks WSNs have significant potential in many application domains, ranging from precision agriculture and animal welfare to home and office automation. Although sensor network deployments have only begun to appear, the industry still awaits the maturing of this technology to realize its full benefits. The main constraints to large-scale commercial adoption of WSN have been the lack of available network management and control tools, such as for determining the degree of data aggregation prior to transforming it into useful information, localizing the sensors accurately so that timely emergency actions can be taken at an exact location, routing data by reducing sensor energy consumption, and scheduling data packets so that data are sent according to their priority and fairness. Moreover, to the best of our knowledge, no integrated network management solution comprising efficient localization, data scheduling, routing, and data aggregation approaches exists in the literature for a large-scale WSN. Thus, we introduce an integrated network management framework comprising sensor localization, routing, data scheduling, and data aggregation for a large-scale WSN. Experimental results show that the proposed framework outperforms an existing approach that comprises only localization and routing protocols in terms of localization energy consumption, localization error, end-to-end delay, packet loss ratio, and network energy consumption. Moreover, the proposed WSN management framework has potential in building a future Internet of Things. Copyright © 2012 John Wiley & Sons, Ltd.

Selecting key management schemes for WSN applications

Key management in wireless sensor networks (WSN) is an active research topic. Due to the fact that a large number of key management schemes (KMS) have been proposed in the literature, it is not easy for a sensor network designer to know exactly which KMS best fits in a particular WSN application. In this article, we offer a comprehensive review on how the application requirements and the properties of various key management schemes influence each other. Based on this review, we show that the KMS plays a critical role in determining the security performance of a WSN network with given application requirements. We also develop a method that allows the network designers to select the most suitable KMS for a specific WSN network setting. In addition, the article also addresses the issues on the current state-of-the-art research on the KMS for homogeneous (i.e. non-hierarchical) networks to provide solutions for establishing link-layer keys in various WSN applications and scenarios.

(t, n) Secret Sharing Scheme Based on Cylinder Model in Wireless Sensor Networks

Since the existence of characteristics of heterogeneity, limited energy, complexity and so forth, it turns into a research hot spot on the security mechanism of wireless sensor networks (WSN), especially the problems on the key management. On regard of the deficiency of extant secret sharing schemes, we develop a ( t , n ) threshold secret sharing scheme which is based on the cylinder model, including the procedures of master-key reconfiguration and sub-key updating. This scheme enables several nodes to be responsible for the security of key together, and by the mean time, we introduce a monitoring mechanism to improve the capability of anti-capturing. With the security theoretical analysis and the comparison with the performance on B-PCGR, GKD simulation experiment, it reveals that our scheme satisfies the security requirement of key management in wireless sensor networks, and can effectively reduce the cost on communication and computation.

Fuzzy based Optimization for Power Management in Wireless Sensor Networks

Wireless Sensor Networks (WSN), sensor node deployment is essential for maximizing the coverage and detection probabilities. But the existing optimization solution suffers from limited energy storage, node death, increased network traffic etc. To solve these issues, we propose a fuzzy based optimization model for power management in wireless sensor networks. The objectives considered in the paper include maximizing network coverage, connectivity, network lifetime and minimizing traffic load. A fuzzy rules table is constructed with the input parameters such as node degree, link quality, residual energy and traffic rate. Depending upon the outcome of the fuzzy logic, the nodes are categorized into good, normal and bad. After the initial deployment of good nodes, the SLEEP and WAKEUP Procedure is applied to maximize the lifetime of wireless sensor networks. By putting nodes to sleep when there are no events, the energy consumption of the sensor nodes can be significantly reduced. In SLEEP and WAKEUP Procedure, asynchronous type is used; it allows each node in the wireless sensor network to set its own SLEEP and WAKEUP schedule independently in order to save energy. Simulation result shows that the proposed Procedure provides maximization of the lifetime of Wireless Sensor Network (WSN).