In wireless sensor networks, sensor nodes are capable of not only measuring real world phenomena, but also storing, processing, and transferring these measurements. Many techniques have been proposed for storing and retrieving data in these networks. These methods are classified into three main categories. Data-centric storage is the most important one. The name comes from the fact that data's storage location is determined according to it's event type. However, most of the methods proposed in this category use a fixed event-location mapping or don't take nodes' resources into consideration. Therefore, storage nodes may experience unbalanced resource utilization problems which results into hot spot areas. We address this problem by proposing a dynamic mapping scheme which aims to reach two goals: First, to prolong network lifetime by efficient energy consumption and second, increasing quality of service by increasing data availability. The proposed method comes in two phases: First hot spot detection phase where storage nodes' status information is gathered at sink. Applying the proposed hot spot detection mechanism, sink detects a hot spotted node and alarms the node by sending a warning message to it. The hot spotted storage node receives the warning message and triggers the second phase named hot spot mitigation. In this stage it should transfer the responsibility of storing the event to another node in its' h hop neighborhood. Two metrics are considered in selecting a new storage node: 1) minimizing the total energy consumed for storing and retrieving the event 2) distributing network load evenly. The simulation presented here evaluates the proposed method in terms of network lifetime, data availability and energy consumption. The results show that the proposed method will delay hot spot effect with acceptable overhead.

A distributed data-centric storage method for hot spot mitigation in wireless sensor networks

Multidimensional WSNs are deployed in complex environments to sense and collect data relating to multiple attributes (multidimensional data). Such networks present unique challenges to data dissemination, data storage and in-network query processing (information discovery). In this paper, we investigate efficient strategies for information discovery in large-scale multidimensional WSNs and propose the Adaptive Multi-Dimensional Multi-Resolution Architecture (A-MDMRA) that efficiently combines “push” and “pull” strategies for information discovery and adapts to variations in the frequencies of events and queries in the network to construct optimal routing structures. We present simulation results showing the optimal routing structure depends on the frequency of events and query occurrence in the network. It also balances push and pull operations in large scale networks enabling significant QoS improvements and energy savings.

An adaptive approach to information discovery in multi-dimensional wireless sensor networks

Multidimensional WSNs are deployed in complex environments to sense and collect data relating to multiple attributes (multi-dimensional data). Such networks present unique challenges to data dissemination, data storage and in-network query processing (information discovery). Recent algorithms proposed for such WSNs are aimed at achieving better energy efficiency and minimizing latency. This creates a partitioned network area due to the overuse of certain nodes in areas which are on the shortest or closest or path to the base station or data aggregation points which results in hotspots nodes. In this paper, we propose a time-based multi-dimensional, multi-resolution storage approach for range queries that balances the energy consumption by balancing the traffic load as uniformly as possible. Thus ensuring a maximum network lifetime. We present simulation results to show that the proposed approach to information discovery offers significant improvements on information discovery latency compared with current approaches. In addition, the results prove that the Quality of Service (QoS) improvements reduces hotspots thus resulting in significant network-wide energy saving and an increased network lifetime.

Energy efficient information discovery approach for range queries in multi-dimensional WSNs

Multi-dimensional Wireless sensor grids (WSG)s are deployed in complex environments to sense and collect data relating to multiple attributes (multi-dimensional data). Such networks present unique challenges to data dissemination, data storage of in-network information discovery. However, in order to fully exploit these networks for mission-critical applications, energy-efficient and scalable solutions for information discovery are essential. In this paper, we propose a novel and adaptive method for information discovery for multi-dimensional WSGs that can significantly increase network lifetime and minimize query processing latency, resulting in quality of service improvements that are of immense benefit to mission-critical applications. Further, we investigate efficient strategies for information discovery in large-scale wireless sensor networks and propose the Adaptive Multi-Dimensional Multi-Resolution Architecture (A-MDMRA) that efficiently combines “push” and “pull” strategies for information discovery. The A-MDMRA also adapts to variations in the frequencies of events and queries in the network to construct optimal routing structures. We present simulation results to show that the proposed approach to information discovery offers significant improvements on query resolution latency compared with current approaches. We observe that our proposed methods outperform existing schemes such as double rulings, comb needle and Time-Parameterized Data Centric Storage by up to 14% in terms of query resolution latency and up to 20% in terms of energy-efficiency.

A Novel Approach for Information Discovery in Wireless Sensor Grids

Autonomous Wireless sensor networks(WSNs) have sensors that are usually deployed randomly to monitor one or more phenomena. They are attractive for information discovery in large-scale data rich environments and can add value to mission–critical applications such as battlefield surveillance and emergency response systems. However, in order to fully exploit these networks for such applications, energy-efficient, load balanced and scalable solutions for information discovery are essential. Multi-dimensional autonomous WSNs are deployed in complex environments to sense and collect data relating to multiple attributes (multi-dimensional data). Such networks present unique challenges to data dissemination, data storage of in-network information discovery. In this paper, we propose a novel method for information discovery for multi-dimensional autonomous WSNs which sensors are deployed randomly that can significantly increase network lifetime and minimize query processing latency, resulting in quality of service (QoS) improvements that are of immense benefit to mission–critical applications. We present simulation results to show that the proposed approach to information discovery offers significant improvements on query resolution latency compared with current approaches.

Novel approach for information discovery in autonomous wireless sensor networks

We propose a distributed index based multi-resolution storage architecture (DIMSA) for data storage and retrieval at multiple levels of detail in wireless sensor networks. The proposed scheme could automatically retrieve subsets of the data in correspondence to the user defined level of detail to save energy. In addition, DIMSA deploys attribute allocation scheme to further reduce energy consumption in the condition that some attributes are included in many events. In this condition, traditional data-centric storage schemes consumes more energy because the values for these attributes have to be replicated and stored at different places in the network for each individual event. A distributed index scheme is also proposed to avoid query flooding and extra data transmission. Analysis and simulations are conducted to evaluate the performance of the proposed scheme. The results show that DIMSA outperforms the traditional data-centric storage scheme like GHT.

A Distributed Index Based Multi-Resolution Data Storage Architecture in Wireless Sensor Networks

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A Distributed Index Based Multi-Resolution Data Storage Architecture in Wireless Sensor Networks