Fusion Techniques for Reliable Information: A Survey <s> Figure 1. A Fusion System consists of Three Layers <s> Abstract This paper presents a comprehensive discussion and systematic classification of the sensor management (SM) with respect to its roles, concepts, architectures and techniques. The SM is put in perspective with the discussion of the sensing system framework. The role of SM was presented based on the different level of its functionality. The architectures discussed in this paper include centralized, decentralized and hierarchical methods. SM techniques such as scheduling and decision-making techniques are discussed. A generic SM framework is also presented. <s> BIB001 </s> Fusion Techniques for Reliable Information: A Survey <s> Figure 1. A Fusion System consists of Three Layers <s> We have previously introduced Learn ++ , an en- semble of classifiers based algorithm capable of incre- mental learning from additional data, and pointed to its fea- sibility in data fusion applications. In this contribution, we provide additional details, updated results and insight on how such a system can be used in integrating complemen- tary knowledge provided by different data sources obtained from different sensors. Essentially, the algorithm generates an ensemble of classifiers using data from each source, and combines these classifiers using a weighted voting proce- dure. The weights are determined based on the individual classifier's training performance as well as the observed or predicted reliability of each data source. <s> BIB002 </s> Fusion Techniques for Reliable Information: A Survey <s> Figure 1. A Fusion System consists of Three Layers <s> Broad classes of statistical classification algorithms have been developed and applied successfully to a wide range of real-world domains. In general, ensuring that the particular classification algorithm matches the properties of the data is crucial in providing results that meet the needs of the particular application domain. One way in which the impact of this algorithm/application match can be alleviated is by using ensembles of classifiers, where a variety of classifiers (either different types of classifiers or different instantiations of the same classifier) are pooled before a final classification decision is made. Intuitively, classifier ensembles allow the different needs of a difficult problem to be handled by classifiers suited to those particular needs. Mathematically, classifier ensembles provide an extra degree of freedom in the classical bias/variance tradeoff, allowing solutions that would be difficult (if not impossible) to reach with only a single classifier. Because of these advantages, classifier ensembles have been applied to many difficult real-world problems. In this paper, we survey select applications of ensemble methods to problems that have historically been most representative of the difficulties in classification. In particular, we survey applications of ensemble methods to remote sensing, person recognition, one vs. all recognition, and medicine. <s> BIB003 </s> Fusion Techniques for Reliable Information: A Survey <s> Figure 1. A Fusion System consists of Three Layers <s> Information fusion can assist in the development of sensor network applications by merging capabilities, raw data and decisions from multiple sensors through distributed and collaborative integration algorithms. In this paper, we introduce a multi-layered, middleware-driven, multi-agent, interoperable architecture for distributed sensor networks that bridges the gap between the programmable application layer consisting of software agents and the physical layer consisting of sensor nodes. We adopt an energy-efficient, fault-tolerant approach for collaborative information processing among multiple sensor nodes using a mobile-agent-based computing model. In this model the sink/base-station deploys mobile agents that migrate from node to node following a certain itinerary, either pre-determined or determined on-the-fly, and fuse the information/data locally at each node. This way, the intelligence is distributed throughout the network edge and communication cost is reduced to make the sensor network energy-efficient. We evaluate the performance of our mobile-agent-based approach as well as that of the traditional client/server-based computing model, vis-a-vis energy consumption and execution time, through both analytical study and simulation. We draw important conclusions based on our findings. Finally, we consider a collaborative target classification application, supported by our architectural framework, to illustrate the efficacy of the mobile-agent-based computing model. <s> BIB004 </s> Fusion Techniques for Reliable Information: A Survey <s> Figure 1. A Fusion System consists of Three Layers <s> Data processing applications for sensor streams have to deal with multiple continuous data streams with inputs arriving at highly variable and unpredictable rates from various sources. These applications perform various operations (e.g. filter, aggregate, join, etc.) on incoming data streams in real-time according to predefined queries or rules. Since the data rate and data distribution fluctuate over time, an appropriate join tree for processing join queries must be adaptively maintained in response to dynamic changes to prevent rapid degradation of the system performance. In this paper, we address the problem of finding an optimal join tree that maximizes throughput for sliding window based multi-join queries over continuous data streams and prove its NP-Hardness. We present a dynamic programming algorithm, OptDP, which produces the optimal tree but runs in an exponential time in the number of input streams. We then present a polynomial time greedy algorithm, XGreedyJoin. We tested these algorithms in ARES, an adaptively re-optimizing engine for stream queries, which we developed by extending Jess (Jess is a popular RETE-based, forward chaining rule engine written in java). For almost all instances, trees from XGreedyJoin perform close to the optimal trees from OptDP, and significantly better than common heuristics-based XJoin algorithms. <s> BIB005
Therefore, in this survey, first we review the related fusion concepts: 1) terminologies, 2) objectives, and 3) advantages and limitations. Second, we compare wellknown models, algorithms, systems, and applications based on different approaches to discuss uncertainty in estimation. Finally, we introduce related current issues and research directions that support to obtain reliable contextual information by adapting different methods. For instance, heterogeneous sensor management BIB001 , a multiple classifier BIB002 , an ensemble method BIB003 , and an adaptive multi-level fusion BIB004 , BIB005 are adapted to pervasive and ubiquitous computing areas. The remainder of the paper is organized as follows. In section 2, concepts about some fusion techniques are introduced. Fusion models are discussed in section 3. In section 4, we compare famous fusion algorithms and theories that are applied to the estimation or inference methods. We discuss related systems for specific areas in section 5. Section 6 deals with issues and suggests research directions for fusion techniques. In section 7, we then conclude the survey.