The emergence of new data handling technologies and analytics enabled the organization of big data in processes as an innovative aspect in wireless sensor networks (WSNs). Big data paradigm, combined with WSN technology, involves new challenges that are necessary to resolve in parallel. Data aggregation is a rapidly emerging research area. It represents one of the processing challenges of big sensor networks. This paper introduces the big data paradigm, its main dimensions that represent one of the most challenging concepts, and its principle analytic tools which are more and more introduced in the WSNs technology. The paper also presents the big data challenges that must be overcome to efficiently manipulate the voluminous data, and proposes a new classification of these challenges based on the necessities and the challenges of WSNs. As the big data aggregation challenge represents the center of our interest, this paper surveys its proposed strategies in WSNs.

Big Data Challenges and Data Aggregation Strategies in Wireless Sensor Networks

Machine learning for coverage optimization in wireless sensor networks: a comprehensive review

A ubiquitous embedded network, such as wireless sensor network (WSN), is characterised by its capability to carry out common tasks by sharing resources that are placed in-node or in-network domains. One of the most critical properties of mobile agent (MA) based wireless sensor network is how to design the itinerary through the WSN for mobile agent. In addition to that, a huge amount of redundant data is generated by sensors due to node density and placement. This consumes network resources such as bandwidth and energy, thus decreasing the life time of sensor network. Several studies have demonstrated the benefits of using mobile agent technology as an effective technique to overcome these limitations. MA itinerary planning techniques can be classified into three categories depending on the factors that define the route of the MA: static-itinerary, dynamic-itinerary, and hybrid-based. This paper presents a survey of the state-of-the-art MA itinerary planning techniques in WSN. The benefits and shortcomings of different MA itinerary planning approaches are presented as motivation for future work into energy efficient MA itinerary planning mechanism.

Mobile Agent Itinerary Planning Approaches in Wireless Sensor Networks- State of the Art and Current Challenges

Mobile Agent (MA) technology brings many benefits into Wireless Sensor Networks (WSNs), such as saving network bandwidth and enabling energy efficient mechanisms for collecting sensor data. Nowadays, itinerary planning for MAs is one of the most important features of the WSN. However, the way in which all dispatched MAs are routed inside the sensor networks must be intelligently planned to reduce energy consumption and improve information accuracy. There have been many research efforts designing itinerary planning algorithms to deploy multiple MAs in a given sensor network, where routes are generated so that MAs can follow different routes to collect data from sensor nodes efficiently and effectively. This paper proposes a new energy efficient Graph-based Static Mutli-Mobile Agent Itinerary Planning approach (GSMIP). GSMIP applies Directed Acyclic Graph (DAG) related techniques and divide sensor nodes into different groups based on the routes defined by MAs itineraries. MAs follow the predefined routes and only collect data from the groups they are responsible for. The experimental findings demonstrate the effectiveness and superiority of the proposed approach compared to the existing approaches in terms of energy consumption and task delay (time).

An energy efficient multi-mobile agent itinerary planning approach in wireless sensor networks

Wireless sensor networks (WSNs) are extensively used in various fields. However, limited battery power is one of the largest challenges to deploy WSNs application, thus reduce of node energy consumption has become research focus. The common method to save energy is to use sleeping–awaking mechanism for node. Therefore, IST-REC (improved sleep time-based relieved energy consumption) based on optimized sleep time was proposed. IST-REC algorithm was used to improve energy efficiency of node through dynamic set of sleep time for node. IST-REC algorithm was used to firstly establish sensing model for an abnormal event so as to predict occurrence rate of the next abnormal event for establishing cost function according to residual energy factor of node and data risk factor. Conflict between network energy consumption and detection delay of abnormal events could be balanced through cost function. Eventually, cost function could be solved through bisection method so as to obtain the optimal sleep time. The experimental data indicates that proposed IST-REC algorithm can effectively reduce energy consumption and can prolong network lifetime at the time of not increasing detection delay for abnormal events.

A novel WSNs routing algorithm based on game theory and energy consumption balance

Sensor networks are often part of a cyber-physical system. A large-scale sensor network often involves big data collection and data fusion. The agent technology has drawn much attention in wireless sensor networks (WSNs) to perform data fusion and energy balancing. Existing multi-agent itinerary algorithms are either time-consuming or too complicated in practice. In this paper, we design a routing itinerary planning scheme for the multi-agent itinerary problem by constructing the spanning tree of WSN nodes. First, we build a multi-agent-based distributed WSN (DWSN) model and energy consumption model. Second, we present a novel routing itinerary algorithm named DMAIP, which can group all the sensor nodes into multiple itineraries for agents. We also extend DMAIP and design DMAIP-E, which can avoid long-distance transmission in DMAIP. Our evaluation results demonstrate that our algorithms are better in terms of life cycle and energy consumption than the existing DWSN data collecting schemes.

/pdf/disjoint-multi-mobile-agent-itinerary-planning-for-big-data-191vv5bq6z.pdf

Disjoint multi mobile agent itinerary planning for big data analytics

The invention discloses a meta-path-based link prediction method for aligned heterogeneous social networks, mainly solves the problem of link prediction (mainly including friend relationship prediction and position prediction) in the aligned heterogeneous social networks, and relates to related technologies of the aligned heterogeneous social networks, feature selection, link prediction and the like. The method mainly comprises six steps of (a) modeling the aligned heterogeneous social networks; (b) automatically extracting a meta-path; (c) defining a meta-path-based eigenvalue calculation method; (d) dividing data sets according to timestamps; (e) proposing a two-stage gradual forward greedy feature selection algorithm for performing feature selection; and (f) training a decision tree classifier based on a feature selection result to perform the link prediction. Based on the method, the aligned heterogeneous social networks consisting of Foursquare and Twitter are subjected to the link prediction; and the method has practical application values for entity recommendation, accurate marketing, criminal gang discovery and the like in the social networks.

Meta-path-based link prediction method for aligned heterogeneous social networks

The agent technology has been widely used in wireless sensor networks to perform data fusion and energy balancing. Existing multi-agent itinerary algorithms are either time-consuming, or too complicated to realize in reality. In this paper, we design a routing itinerary planning scheme for the multi-agent itinerary problem by constructing the spanning tree of WSN nodes. First, we build a multi-agent based distributed WSN (DWSN) model. Second, we present a novel routing itinerary algorithm named DMAIP, which can group all the sensor nodes into multiple itineraries for agents. We also extend DMAIP and design DMAIP-E, which can avoid long distance transmission in DMAIP. Our evaluation results demonstrate that our algorithms are better in the aspect of life cycle and energy consumption than the existing DWSN data collecting schemes.

On Computing Multi-Agent Itinerary Planning in Distributed Wireless Sensor Networks

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Disjoint multi mobile agent itinerary planning for big data analytics

Meta-path-based link prediction method for aligned heterogeneous social networks

On Computing Multi-Agent Itinerary Planning in Distributed Wireless Sensor Networks