Energy limitation is the main concern of any wireless sensor network application. The communication between nodes is the greedy factor for the energy consumption. One important mechanism to reduce energy consumption is the in-network data aggregation. In-network data aggregation removes redundancy as well as unnecessary data forwarding, and hence cuts on the energy used in communications. Recently a new kind of applications are proposed which consider, in addition to energy efficiency, data latency and accuracy as important factors. Reducing data latency helps increasing the network throughput and early events detection. Before performing the aggregation process, each node should wait for a predefined time called WT (waiting time) to receive data from other nodes. Data latency (resp., accuracy) is decreased (resp., increased), if network nodes are well scheduled through optimal distribution of WT over the nodes. Many solutions have been proposed to schedule network nodes in order to make the data aggregation process more efficient. In this paper, we propose a taxonomy and classification of existing data aggregation scheduling solutions. We survey main solutions in the literature and illustrate their operations through examples. Furthermore, we discuss each solution and analyze it against performance criteria such as data latency and accuracy, energy consumption and collision avoidance. Finally, we shed some light on future research directions and open issues after deep analysis of existing solutions.

Data Aggregation Scheduling Algorithms in Wireless Sensor Networks: Solutions and Challenges

Wireless sensor network (WSN) is widely applied in many fields since its emergence. However, the limited resources of a sensor, especially limited battery life, limited bandwidth and limited processing power, are the main challenges for deploying and operating WSNs. This paper proposes a novel architecture based on cloud computing for wireless sensor network, which can improve the performance of WSN. Based on this architecture, a cloud acts as a virtual sink with many sink points that collect sensing data from sensors. Each sink point is responsible for collecting data from the sensors within a zone. Sensing data are stored and processed in distributed manner in cloud. Our simulation results show that the proposed architecture improves the performance of WSN, e.g., reduced packet transmission error rate, decreased number of end-to-end hops, and improved efficiency of energy consumption.

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A Novel Architecture Based on Cloud Computing for Wireless Sensor Network

Rapid advances in Wireless Sensor Networks (WSNs) indicate that they are becoming increasingly complex. Consequently users and applications are becoming more demanding. Due to unique characteristics of WSNs, like small dimensions and limited resources and capabilities, Quality of Service (QoS) is imposed as one of the key factors of WSNs. In this paper, we surveyed two main approaches for QoS provisioning in WSNs: layered and cross-layer approach. QoS provisioning with layered approach is surveyed in three WSN layers: MAC, network and transport layer. Current developments show that they can be efficiently used for QoS provisioning. However, they consider QoS only as layer specific isolated set of problems and they are highly dependent on the performance of other layers. Cross-layer approach does not have the restrictions as layered approach and hence can dispose with information from all layers of the communication protocol stack. Although it has huge potential to become the most efficient solution for QoS provisioning in WSNs, current development indicate that there are still many issues and challenges that need to be overcome. Since the concept of the QoS is relatively new in WSNs, there are not a large number of patents currently dealing with this issue, however but in coming years a large increase in the number of such patents is expected. Available patents in this domain are described in the paper.

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Quality of Service in Wireless Sensor Networks: A Survey and Related Patents

To improve the health care facilities for the people a tiny, automated, and intelligent medical device and technology namely wireless body area network (WBAN) evolved. The derivation of this technology is wireless sensor network (WSN) consists of sensors, actuators, radio systems, and transmission systems which should be situated in, on, and around the human body to measure, store, and transmitted patients’ vital signs and syndromes for further investigation, treatment, decisions, and awareness. Researchers in all around the world now have very enthusiastic interest to advance this technology especially on certain standardizations like its architecture, cost effectiveness, energy consumption or power efficiency, security and privacy topics. Academicians are trying to improve the QoS factors in sensor networks by suggesting new protocols. But quality of services (QoS) factors concerning WBAN technology still remains insignificant whereas QoS issues are also a major concern. WBAN needs diverse functioning requirements vary from application to application which are very perceptive hence entail more attention. Research personnel concerning QoS issues in WBAN should handle real time data transmission system, data rate, end-to-end data transmission, data transmission accuracy rate, latency, delay time, jitter, low power data transmission, data availability, minimum data loss, data security, network coverage, frequency, bandwidth, throughput, and reliability matters very seriously and an effective way. In this paper we tried to focus on the overall limitation, scopes, and challenges issues concerning QoS factors in WBAN. We have also given our effort to identify, analyze and present some of the recent protocols and technologies developed towards updating QoS issues in WBAN. This research article will help the researchers, academicians and practitioners to further study, modification, and develop more efficient QoS-based-WBAN. At the end of this research paper a number of open research issues are also discussed.

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QoS Taxonomy towards Wireless Body Area Network Solutions

To preserve the privacy of multimedia big data and achieve the efficient data aggregation in wireless multimedia sensor networks (WMSNs), a distributed compressed sensing--based privacy-preserving data aggregation (DCSPDA) approach is proposed in this article. First, in this approach, the original multimedia sensor data are compressed and measured by distributed compressed sensing (DCS) and the compressed data measurements are uploaded to the sink, by which the inherent characteristics between sensor data can be obtained. Second, the original multimedia data are jointly recovered and the common and innovation sparse components are obtained through solving the optimization problem and linear equations at the sink. Third, through least squares support vector machine (LSSVM) learning of the sparse components, the sparse position configuration can be determined and disseminated for each node to conduct the privacy-preserving data configuration. After receiving the configuration message, original multimedia sensor data are accordingly customized, compressed, and measured by the common measurement matrix, aggregated at the cluster heads, and transmitted to the sink. Finally, the aggregated multimedia sensor data are recovered by the sink according to the data configuration to achieve the privacy-preserving data aggregation and transmission. Our comparative simulation results validate the efficiency and scalability of DCSPDA and demonstrate that the proposed approach can effectively reduce the communication overheads and provide reliable privacy-preserving with low computational complexity for WMSNs.

Privacy-Preserving Multimedia Big Data Aggregation in Large-Scale Wireless Sensor Networks

The advance in CMOS camera and wireless sensor network (WSN) has promoted the development of wireless multimedia sensor network (WMSN), which can collect more abundant video data and image data. One important enabling technology for WMSN is data aggregation, which is essential for WMSN to be reusable and cost-efficient. The data aggregation technologies can be divided into three parts, namely, data acquisition, data transmission and data processing. This paper presents the state of the art in data aggregation of WMSN according to this category.

Cheng Chen

Papers

Data aggregation technologies of wireless multimedia sensor networks: A survey