Computation In Wireless Sensor Networks Research Articles

Intelligent Sensing and Computing in Wireless Sensor Networks for Multiple Target Tracking

With sixth generation (6G) communication technologies, target sensing can be finished in milliseconds. The mobile tracking-oriented Internet of Things (MTT-IoT) as a kind of emerging application network can detect sensor nodes and track targets within their sensing ranges cooperatively. Nevertheless, huge data processing and low latency demands put tremendous pressure on the conventional architecture where sensing data is executed in the remote cloud and the short transmission distance of 6G channels presents new challenges into the design of network topology. To cope with the above difficulties, this paper proposes a new resource allocation scheme to perform delicate node scheduling and accurate tracking in multitarget tracking mobile networks. The dynamic tracking problem is formulated as an infinite horizon Markov Decision Process (MDP), where the state space that considers energy consumption, system responding delay, and target important degree is extended. A model-free reinforcement learning is applied to obtain satisfied tracking actions by frequent iterations, in which smart agents interact with the complicated environment directly. The performance of each episode is evaluated by the action-value function in search of the optimal reward. Simulation results demonstrate that the proposed scheme shows excellent tracking performance in terms of energy cost and tracking delay.

Secure, Resilient and Green Computing in Wireless Sensor Networks

Secure, Resilient and Green Computing in Wireless Sensor Networks

A multiagent-based hexagon shape approach for shortest path computation in wireless sensors network

The recent advances in sensor technology have resulted in increased utilization of sensors in real time applications such as battlefield surveillance, disaster management, health care and ecosystem management. Wireless Sensor Networks (WSNs) are constrained by limited sensor energy, latency, low network bandwidth size but the real time applications, such as above, demand delay sensitive, reliable, limited sensor node size and energy efficient routing protocols in WSNs. We have proposed a hexagon shape-based approach for shortest path computation in WSN to distribute the load efficiently using multi-agent-based scheme. This shortest path computation scheme to balance the load uses software agents to conserve the energy by reducing the transmission of data and control messages. In this method direct and alternate path are computed for reliable data transmission. The performance of the proposed scheme is evaluated in terms of performance parameters, such as packet delivery ratio, network lifetime and hexagon construction time.

Estimation of energy consumption through parallel computing in wireless sensor networks

The lifetime of a wireless sensor network is the most important design parameter to take into account. Given the autonomous nature of the sensor nodes, this period is mainly related to their energy consumption. Hence, the high interest to evaluate through accurate and rapid simulations the energy consumption for this kind of networks. However, in the case of a network with several thousand nodes, the simulation can be very slow and even impossible. In this paper, we present a new model for computing the energy consumption in wireless sensor networks in parallel. The model uses discrete event simulation implemented on a massively parallel GPU architecture. The results show that the proposed model provides simulation times significantly shorter than those obtained with the sequential model for large networks and for long simulations. This improvement is even more significant if the processing on each node is very time consuming. Finally, the proposed model has been fully integrated and validated on the CupCarbon simulator.

Split Distributed Computing in Wireless Sensor Networks

Split Distributed Computing in Wireless Sensor Networks

Distributed Computation in Wireless Sensor Networks

Wireless Sensor Networks are used to perform distributed sensing in various fields like health, military, home etc where the sensor nodes communicate among themselves and do distributed computation over the sensed values to identify the occurrence of an event. The architecture for distributed computation of primitive recursive functions and median is presented in this paper. This paper assumes the no memory computational model of sensor nodes; in the architecture for primary recursive functions i.e. the sensor nodes only have two registers. This assumption is not made for the computation of median. This paper also explores the applications of wireless sensor networks in building a smart, hassle free transportation system. In purview of emerging technologies like Internet of things and Vehicular Ad Hoc networks, the transport system can be made user friendly by including itinerary planning, dynamic speed boards etc. Already research is moving in the direction of making transport system efficient and user-friendly. This paper serves as a one more step in the process of achieving it.

Minimum Cost Data Aggregation for Wireless Sensor Networks Computing Functions of Sensed Data

We consider a problem of minimum cost (energy) data aggregation in wireless sensor networks computing certain functions of sensed data. We use in-network aggregation such that data can be combined at the intermediate nodes en route to the sink. We consider two types of functions: firstly the summation-type which includessum,mean, andweighted sum, and secondly the extreme-type which includesmaxandmin. However for both types of functions the problem turns out to be NP-hard. We first show that, forsumandmean, there exist algorithms which can approximate the optimal cost by a factor logarithmic in the number of sources. Forweighted sumwe obtain a similar result for Gaussian sources. Next we reveal that the problem for extreme-type functions is intrinsically different from that for summation-type functions. We then propose a novel algorithm based on the crucial tradeoff in reducing costs between local aggregation of flows and finding a low cost path to the sink: the algorithm is shown to empirically find the best tradeoff point. We argue that the algorithm is applicable to many other similar types of problems. Simulation results show that significant cost savings can be achieved by the proposed algorithm.

A Localization Algorithm of Nodes Based on Hypersphere Granular Computing in Wireless Sensor Networks

This paper proposes a node localization algorithm based on super chondritic calculation, super chondritic calculation scanning of a training set is able to complete the training process, the training has characteristics of fast, the problem of node localization for wireless sensor networks. Firstly, according to the related parameter information for the location of wireless sensor nodes, a multi input, multi output problem of training set, and then through the methods of grid division, location area, the original training set into the classification of multi input, single output training set, the super chondritic algorithm by scanning training, in order to get the relevant parameters, and the estimation of the unknown node location. Simulation results show that the proposed algorithm has better positioning accuracy.

Privacy-preserving self-helped medical diagnosis scheme based on secure two-party computation in wireless sensor networks.

With the continuing growth of wireless sensor networks in pervasive medical care, people pay more and more attention to privacy in medical monitoring, diagnosis, treatment, and patient care. On one hand, we expect the public health institutions to provide us with better service. On the other hand, we would not like to leak our personal health information to them. In order to balance this contradiction, in this paper we design a privacy-preserving self-helped medical diagnosis scheme based on secure two-party computation in wireless sensor networks so that patients can privately diagnose themselves by inputting a health card into a self-helped medical diagnosis ATM to obtain a diagnostic report just like drawing money from a bank ATM without revealing patients' health information and doctors' diagnostic skill. It makes secure self-helped disease diagnosis feasible and greatly benefits patients as well as relieving the heavy pressure of public health institutions.

Extension to HiRLoc Algorithm for Localization Error Computation in Wireless Sensor Networks

Wireless sensor networks (WSNs) have gained importance in recent years as this support a large spectrum of applications such as automotive, health, military, environmental, home and office. Various algorithms have been proposed for making this technology more adaptive the existing algorithms address issues such as safety, security, power consumption, lifetime and localization. This paper presents an extension to HiRLoc algorithm and highlights its benefits. Extended HiRLoc significantly reduce the average localization error by suggesting a new method directional antenna based scheme.

Survey of data aggregation techniques using soft computing in wireless sensor networks

In wireless sensor networks (WSN), data aggregation using soft computing methods is a challenging issue because of the security factors. When a node is compromised, it is easy for an adversary to inject false data and mislead the aggregator to accept false readings. Therefore there is a need for secure data aggregation. Although sufficient works on the survey of data aggregation in WSNs are done, it seems less satisfactory in terms of maintaining a secured data aggregation, and measuring accurate values. This study presents an up to date survey of major contributions to the security solutions in data aggregation which mainly use soft computing techniques. Here, classification of protocols is done according to the soft computing technique as: fuzzy logic, swarm intelligence, genetic algorithm and neural networks. Accuracy, energy consumption, cost reduction and security measures are the metrics used for the classification. Finally, the authors provide a comparative study of all aggregation techniques.

In-network Distributed Event Boundary Computation in Wireless Sensor Networks: Challenges, State of the Art and Future Directions

Wireless sensor network (WSN) is a promising technology for monitoring physical phenomena at fine-grained spatial and temporal resolution. However, the typical approach of sending each sensed measurement out of the network for detailed spatial analysis of transient physical phenomena may not be an efficient or scalable solution. This paper focuses on in-network physical phenomena detection schemes, particularly the distributed computation of the boundary of physical phenomena (i.e. event), to support energy efficient spatial analysis in wireless sensor networks. In-network processing approach reduces the amount of network traffic and thus achieves network scalability and lifetime longevity. This study investigates the recent advances in distributed event detection based on in-network processing and includes a concise comparison of various existing schemes. These boundary detection schemes identify not only those sensor nodes that lie on the boundary of the physical phenomena but also the interior nodes. This constitutes an event geometry which is a basic building block of many spatial queries. In this paper, we introduce the challenges and opportunities for research in the field of in-network distributed event geometry boundary detection as well as illustrate the current status of research in this field. We also present new areas where the event geometry boundary detection can be of significant importance.

Temperature‐aware methodology for time synchronisation protocols in wireless sensor networks

To exploit distributed computation in wireless sensor networks (WSNs) time synchronisation protocols are required. These protocols support advanced distributed and synchronous tasks between several nodes. Temperature changes have a strong influence in oscillators and may vary the clock of the nodes. The flooding time synchronisation protocol (FTSP) is a common time-sync protocol in WSNs. It cannot deal with rapid temperature changes and needs several long periods to readjust the clock drift. Proposed is an innovative correction factor, implemented in a module called advanced adjustment temperature (A2T), that minimises the impact of temperature in the clock skew. This module may be placed on top of any clock-skew based time synchronisation protocol with minimum coding modifications. For this work, the module has been linked to the FTSP, producing A2T-FTSP. Experiments show that A2T-FTSP reduces noticeably the average synchronisation errors compared to FTSP for the same varying temperature conditions.

Networked Computing in Wireless Sensor Networks for Structural Health Monitoring

This paper studies the problem of distributed computation over a network of wireless sensors. While this problem applies to many emerging applications, to keep our discussion concrete, we will focus on sensor networks used for structural health monitoring. Within this context, the heaviest computation is to determine the singular value decomposition (SVD) to extract mode shapes (eigenvectors) of a structure. Compared to collecting raw vibration data and performing SVD at a central location, computing SVD within the network can result in significantly lower energy consumption and delay. Using recent results on decomposing SVD, a well-known centralized operation, we seek to determine a near-optimal communication structure that enables the distribution of this computation and the reassembly of the final results, with the objective of minimizing energy consumption subject to a computational delay constraint. We show that this reduces to a generalized clustering problem and establish that it is NP-hard. By relaxing the delay constraint, we derive a lower bound. We then propose an integer linear program (ILP) to solve the constrained problem exactly as well as an approximate algorithm with a proven approximation ratio. We further present a distributed version of the approximate algorithm. We present both simulation and experimentation results to demonstrate the effectiveness of these algorithms .

Horizontal and Vertical Zone Based Location Techniques for Wireless Sensor Networks

Localization is an important feature in Wireless sensor networks (WSNs). Accuracy in node localization with proper synchronization and required localization of sensor nodes, save node energy and enhance the performance of communication network protocols. In this paper we propose distributed localization algorithms and assume position known Cluster Head (CH) and position unknown three beacon nodes for each cluster. Using trilateration technique beacon nodes are located. Additional beacon node is added to confirm the location of beacon nodes and maintain location accuracy. These position localized beacon nodes help to locate other sensor nodes. The proposed two distributed zone based localization algorithms are (i) Horizontal Location Position System (H-LPS), where cluster is divided into Horizontal Zones (HZs) and beacon nodes locate in horizontal direction and (ii) Vertical Location Position System (V-LPS), where cluster is divided into Vertical Zones (VZs) and beacon nodes locate in vertical direction. The main advantage of zone based localization is nodes belonging to a bounded zone (horizontal or vertical) are localized and participate in WSN computing. If a bounded zone is eliminated during localization, then nodes do not participate in localization and thus save WSN computing. We provide zone based simulations for H-LPS and V-LPS in comparison with existing localization algorithms like Ad hoc Positioning System (APS), Recursive Positioning Estimation (RPE) and Directed Positioning Estimation (DPE). Performance evaluation of H-LPS and V-LPS illustrate that for zone based localization, H-LPS and V-LPS perform better that existing localization techniques. Bounded zone performance is optimal for H-LPS and V-LPS compared to existing localization techniques in WSN.

Design of SOA Based Framework for Collaborative Cloud Computing in Wireless Sensor Networks

WSN deployments are growing at a fast rate; however, current WSN architectures and setup do not promote the sharing of data on an inter-WSN basis. Cloud computing has emerged as a promising area to deal with participatory and collaborative data and services, and is envisaged that collaborative cloud computing WSN could be a viable solution for sharing data and services for WSN applications. In this paper, SOA based architecture has been proposed to support collaborating cloud computing in WSN. The architecture consists of layered service stack that has management, information, presentation and communication layers with all required services and repositories. Interactions between WSN, subscribers and other cloud are also presented as sequence diagrams. The proposed framework serves the cloud subscribers with wide range of queries on the data of multiple WSNs through suitable interface to solve large scale problems.

Secure median computation in wireless sensor networks

Wireless sensor networks (WSNs) have proven to be useful in many applications, such as military surveillance and environment monitoring. To meet the severe energy constraints in WSNs, several researchers have proposed to use the in-network data aggregation technique (i.e., combining partial results at intermediate nodes during message routing), which significantly reduces the communication overhead. Given the lack of hardware support for tamper-resistance and the unattended nature of sensor nodes, sensor network protocols need to be designed with security in mind. Recently, researchers proposed algorithms for securely computing a few aggregates, such as Sum (the sum of the sensed values), Count (number of nodes) and Average. However, to the best of our knowledge, there is no prior work which securely computes the Median, although the Median is considered to be an important aggregate. The contribution of this paper is twofold. We first propose a protocol to compute an approximate Median and verify if it has been falsified by an adversary. Then, we design an attack-resilient algorithm to compute the Median even in the presence of a few compromised nodes. We evaluate the performance and cost of our approach via both analysis and simulation. Our results show that our approach is scalable and efficient.

Toward a theory of in-network computation in wireless sensor networks

Sensor networks are not just data networks with sensors being the sources of data. Rather, they are often developed and deployed for a specific application, and the entire network operation is accordingly geared toward satisfying this application. For overall system efficiency, it may be necessary for nodes to perform computations on data, as opposed to simply originating or forwarding data. Thus, the entire network can be viewed as performing an application-specific distributed computation. The topic of this article is to survey some lines of research that may be useful in developing a theory of in-network computation, which aims to elucidate how a wireless sensor network should efficiently perform such distributed computation. We review several existing approaches to computation problems in network settings, with a particular emphasis on the communication aspect of computation. We begin by studying the basic two-party communication complexity model and how to optimally compute functions of distributed inputs in this setting. We proceed to larger multihop networks, and study how block computation and function structure can be exploited to provide greater computational throughput. We then consider distributed computation problems in networks subject to noise. Finally, we review some randomized gossip-based approaches to computing aggregate functions in networks. These are diverse approaches spanning many different research communities, but together may find a role in the development of a more substantial theoretical foundation for sensor networks.